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Package amt provides a reference implementation of the IPLD AMT (Array Mapped Trie) used in the Filecoin blockchain. The AMT algorithm is similar to a HAMT https://en.wikipedia.org/wiki/Hash_array_mapped_trie but instead presents an array-like interface where the indexes themselves form the mapping to nodes in the trie structure. An AMT is suitable for storing sparse array data as a minimum amount of intermediate nodes are required to address a small number of entries even when their indexes span a large distance. AMT is also a suitable means of storing non-sparse array data as required, with a small amount of storage and algorithmic overhead required to handle mapping that assumes that some elements within any range of data may not be present. The AMT algorithm produces a tree-like graph, with a single root node addressing a collection of child nodes which connect downward toward leaf nodes which store the actual entries. No terminal entries are stored in intermediate elements of the tree, unlike in a HAMT. We can divide up the AMT tree structure into "levels" or "heights", where a height of zero contains the terminal elements, and the maximum height of the tree contains the single root node. Intermediate nodes are used to span across the range of indexes. Any AMT instance uses a fixed "width" that is consistent across the tree's nodes. An AMT's "bitWidth" dictates the width, or maximum-brancing factor (arity) of the AMT's nodes by determining how many bits of the original index are used to determine the index at any given level. A bitWidth of 3 (the default for this implementation) can generate indexes in the range of 0 to (3^2)-1=7, i.e. a "width" of 8. In practice, this means that an AMT with a bitWidth of 3 has a branching factor of _between 1 and 8_ for any node in the structure. Considering the minimal case: a minimal AMT contains a single node which serves as both the root and the leaf node and can hold zero or more elements (an empty AMT is possible, although a special-case, and consists of a zero-length root). This minimal AMT can store array indexes from 0 to width-1 (8 for the default bitWidth of 3) without requiring the addition of additional nodes. Attempts to add additional indexes beyond width-1 will result in additional nodes being added and a tree structure in order to address the new elements. The minimal AMT node is said to have a height of 0. Every node in an AMT has a height that indicates its distance from the leaf nodes. All leaf nodes have a height of 0. The height of the root node dictates the overall height of the entire AMT. In the case of the minimal AMT, this is 0. Elements are stored in a compacted form within nodes, they are "position-mapped" by a bitmap field that is stored with the node. The bitmap is a simple byte array, where each bit represents an element of the data that can be stored in the node. With a width of 8, the bitmap is a single byte and up to 8 elements can be stored in the node. The data array of a node _only stores elements that are present in that node_, so the array is commonly shorter than the maximum width. An empty AMT is a special-case where the single node can have zero elements, therefore a zero-length data array and a bitmap of `0x00`. In all other cases, the data array must have between 1 and width elements. Determining the position of an index within the data array requires counting the number of set bits within the bitmap up to the element we are concerned with. If the bitmap has bits 2, 4 and 6 set, we can see that only 3 of the bits are set so our data array should hold 3 elements. To address index 4, we know that the first element will be index 2 and therefore the second will hold index 4. This format allows us to store only the elements that are set in the node. Overflow beyond the single node AMT by adding an index beyond width-1 requires an increase in height in order to address all elements. If an element in the range of width to (width*2)-1 is added, a single additional height is required which will result in a new root node which is used to address two consecutive leaf nodes. Because we have an arity of up to width at any node, the addition of indexes in the range of 0 to (width^2)-1 will still require only the addition of a single additional height above the leaf nodes, i.e. height 1. From the width of an AMT we can derive the maximum range of indexes that can be contained by an AMT at any given `height` with the formula width^(height+1)-1. e.g. an AMT with a width of 8 and a height of 2 can address indexes 0 to 8^(2+1)-1=511. Incrementing the height doubles the range of indexes that can be contained within that structure. Nodes above height 0 (non-leaf nodes) do not contain terminal elements, but instead, their data array contains links to child nodes. The index compaction using the bitmap is the same as for leaf nodes, so each non-leaf node only stores as many links as it has child nodes. Because additional height is required to address larger indexes, even a single-element AMT will require more than one node where the index is greater than the width of the AMT. For a width of 8, indexes 8 to 63 require a height of 1, indexes 64 to 511 require a height of 2, indexes 512 to 4095 require a height of 3, etc. Retrieving elements from the AMT requires extracting only the portion of the requested index that is required at each height to determine the position in the data array to navigate into. When traversing through the tree, we only need to select from indexes 0 to width-1. To do this, we take log2(width) bits from the index to form a number that is between 0 and width-1. e.g. for a width of 8, we only need 3 bits to form a number between 0 and 7, so we only consume 3 bits per level of the AMT as we traverse. A simple method to calculate this at any height in the AMT (assuming bitWidth of 3, i.e. a width of 8) is: 1. Calculate the maximum number of nodes (not entries) that may be present in an sub-tree rooted at the current height. width^height provides this number. e.g. at height 0, only 1 node can be present, but at height 3, we may have a tree of up to 512 nodes (storing up to 8^(3+1)=4096 entries). 2. Divide the index by this number to find the index for this height. e.g. an index of 3 at height 0 will be 3/1=3, or an index of 20 at height 1 will be 20/8=2. 3. If we are at height 0, the element we want is at the data index, position-mapped via the bitmap. 4. If we are above height 0, we need to navigate to the child element at the index we calculated, position-mapped via the bitmap. When traversing to the child, we discard the upper portion of the index that we no longer need. This can be achieved by a mod operation against the number-of-nodes value. e.g. an index of 20 at height 1 requires navigation to the element at position 2, when moving to that element (which is height 0), we truncate the index with 20%8=4, at height 0 this index will be the index in our data array (position-mapped via the bitmap). In this way, each sub-tree root consumes a small slice, log2(width) bits long, of the original index. Adding new elements to an AMT may require up to 3 steps: 1. Increasing the height to accommodate a new index if the current height is not sufficient to address the new index. Increasing the height requires turning the current root node into an intermediate and adding a new root which links to the old (repeated until the required height is reached). 2. Adding any missing intermediate and leaf nodes that are required to address the new index. Depending on the density of existing indexes, this may require the addition of up to height-1 new nodes to connect the root to the required leaf. Sparse indexes will mean large gaps in the tree that will need filling to address new, equally sparse, indexes. 3. Setting the element at the leaf node in the appropriate position in the data array and setting the appropriate bit in the bitmap. Removing elements requires a reversal of this process. Any empty node (other than the case of a completely empty AMT) must be removed and its parent should have its child link removed. This removal may recurse up the tree to remove many unnecessary intermediate nodes. The root node may also be removed if the current height is no longer necessary to contain the range of indexes still in the AMT. This can be easily determined if _only_ the first bit of the root's bitmap is set, meaning only the left-most is present, which will become the new root node (repeated until the new root has more than the first bit set or height of 0, the single-node case). See https://github.com/ipld/specs/blob/master/data-structures/hashmap.md for a description of a HAMT algorithm. And https://github.com/ipld/specs/blob/master/data-structures/vector.md for a description of a similar algorithm to an AMT that doesn't support internal node compression and therefore doesn't support sparse arrays. Unlike a HAMT, the AMT algorithm doesn't benefit from randomness introduced by a hash algorithm. Therefore an AMT used in cases where user-input can influence indexes, larger-than-necessary tree structures may present risks as well as the challenge imposed by having a strict upper-limit on the indexes addressable by the AMT. A width of 8, using 64-bit integers for indexing, allows for a tree height of up to 64/log2(8)=21 (i.e. a width of 8 has a bitWidth of 3, dividing the 64 bits of the uint into 21 separate per-height indexes). Careful placement of indexes could create extremely sub-optimal forms with large heights connecting leaf nodes that are sparsely packed. The overhead of the large number of intermediate nodes required to connect leaf nodes in AMTs that contain high indexes can be abused to create perverse forms that contain large numbers of nodes to store a minimal number of elements. Minimal nodes will be created where indexes are all in the lower-range. The optimal case for an AMT is contiguous index values starting from zero. As larger indexes are introduced that span beyond the current maximum, more nodes are required to address the new nodes _and_ the existing lower index nodes. Consider a case where a width=8 AMT is only addressing indexes less than 8 and requiring a single height. The introduction of a single index within 8 of the maximum 64-bit unsigned integer range will require the new root to have a height of 21 and have enough connecting nodes between it and both the existing elements and the new upper index. This pattern of behavior may be acceptable if there is significant density of entries under a particular maximum index. There is a direct relationship between the sparseness of index values and the number of nodes required to address the entries. This should be the key consideration when determining whether an AMT is a suitable data-structure for a given application.

Package saml contains a partial implementation of the SAML standard in golang. SAML is a standard for identity federation, i.e. either allowing a third party to authenticate your users or allowing third parties to rely on us to authenticate their users. In SAML parlance an Identity Provider (IDP) is a service that knows how to authenticate users. A Service Provider (SP) is a service that delegates authentication to an IDP. If you are building a service where users log in with someone else's credentials, then you are a Service Provider. This package supports implementing both service providers and identity providers. The core package contains the implementation of SAML. The package samlsp provides helper middleware suitable for use in Service Provider applications. The package samlidp provides a rudimentary IDP service that is useful for testing or as a starting point for other integrations. Version 0.4.0 introduces a few breaking changes to the _samlsp_ package in order to make the package more extensible, and to clean up the interfaces a bit. The default behavior remains the same, but you can now provide interface implementations of _RequestTracker_ (which tracks pending requests), _Session_ (which handles maintaining a session) and _OnError_ which handles reporting errors. Public fields of _samlsp.Middleware_ have changed, so some usages may require adjustment. See [issue 231](https://github.com/joshuaalewis/saml/issues/231) for details. The option to provide an IDP metadata URL has been deprecated. Instead, we recommend that you use the `FetchMetadata()` function, or fetch the metadata yourself and use the new `ParseMetadata()` function, and pass the metadata in _samlsp.Options.IDPMetadata_. Similarly, the _HTTPClient_ field is now deprecated because it was only used for fetching metdata, which is no longer directly implemented. The fields that manage how cookies are set are deprecated as well. To customize how cookies are managed, provide custom implementation of _RequestTracker_ and/or _Session_, perhaps by extending the default implementations. The deprecated fields have not been removed from the Options structure, but will be in future. In particular we have deprecated the following fields in _samlsp.Options_: - `Logger` - This was used to emit errors while validating, which is an anti-pattern. - `IDPMetadataURL` - Instead use `FetchMetadata()` - `HTTPClient` - Instead pass httpClient to FetchMetadata - `CookieMaxAge` - Instead assign a custom CookieRequestTracker or CookieSessionProvider - `CookieName` - Instead assign a custom CookieRequestTracker or CookieSessionProvider - `CookieDomain` - Instead assign a custom CookieRequestTracker or CookieSessionProvider - `CookieDomain` - Instead assign a custom CookieRequestTracker or CookieSessionProvider Let us assume we have a simple web application to protect. We'll modify this application so it uses SAML to authenticate users. ```golang package main import ( ) ``` Each service provider must have an self-signed X.509 key pair established. You can generate your own with something like this: We will use `samlsp.Middleware` to wrap the endpoint we want to protect. Middleware provides both an `http.Handler` to serve the SAML specific URLs and a set of wrappers to require the user to be logged in. We also provide the URL where the service provider can fetch the metadata from the IDP at startup. In our case, we'll use [samltest.id](https://samltest.id/), an identity provider designed for testing. ```golang package main import ( ) ``` Next we'll have to register our service provider with the identity provider to establish trust from the service provider to the IDP. For [samltest.id](https://samltest.id/), you can do something like: Navigate to https://samltest.id/upload.php and upload the file you fetched. Now you should be able to authenticate. The flow should look like this: 1. You browse to `localhost:8000/hello` 1. The middleware redirects you to `https://samltest.id/idp/profile/SAML2/Redirect/SSO` 1. samltest.id prompts you for a username and password. 1. samltest.id returns you an HTML document which contains an HTML form setup to POST to `localhost:8000/saml/acs`. The form is automatically submitted if you have javascript enabled. 1. The local service validates the response, issues a session cookie, and redirects you to the original URL, `localhost:8000/hello`. 1. This time when `localhost:8000/hello` is requested there is a valid session and so the main content is served. Please see `example/idp/` for a substantially complete example of how to use the library and helpers to be an identity provider. The SAML standard is huge and complex with many dark corners and strange, unused features. This package implements the most commonly used subset of these features required to provide a single sign on experience. The package supports at least the subset of SAML known as [interoperable SAML](http://saml2int.org). This package supports the Web SSO profile. Message flows from the service provider to the IDP are supported using the HTTP Redirect binding and the HTTP POST binding. Message flows from the IDP to the service provider are supported via the HTTP POST binding. The package can produce signed SAML assertions, and can validate both signed and encrypted SAML assertions. It does not support signed or encrypted requests. The _RelayState_ parameter allows you to pass user state information across the authentication flow. The most common use for this is to allow a user to request a deep link into your site, be redirected through the SAML login flow, and upon successful completion, be directed to the originally requested link, rather than the root. Unfortunately, _RelayState_ is less useful than it could be. Firstly, it is not authenticated, so anything you supply must be signed to avoid XSS or CSRF. Secondly, it is limited to 80 bytes in length, which precludes signing. (See section 3.6.3.1 of SAMLProfiles.) The SAML specification is a collection of PDFs (sadly): - [SAMLCore](http://docs.oasis-open.org/security/saml/v2.0/saml-core-2.0-os.pdf) defines data types. - [SAMLBindings](http://docs.oasis-open.org/security/saml/v2.0/saml-bindings-2.0-os.pdf) defines the details of the HTTP requests in play. - [SAMLProfiles](http://docs.oasis-open.org/security/saml/v2.0/saml-profiles-2.0-os.pdf) describes data flows. - [SAMLConformance](http://docs.oasis-open.org/security/saml/v2.0/saml-conformance-2.0-os.pdf) includes a support matrix for various parts of the protocol. [SAMLtest](https://samltest.id/) is a testing ground for SAML service and identity providers. Please do not report security issues in the issue tracker. Rather, please contact me directly at ross@kndr.org ([PGP Key `78B6038B3B9DFB88`](https://keybase.io/crewjam)).

Package selenium provides a client to drive web browser-based automation and testing. See the example below for how to get started with this API. This package can depend on several binaries being available, depending on which browsers will be used and how. To avoid needing to manage these dependencies, use a cloud-based browser testing environment, like Sauce Labs, BrowserStack or similar. Otherwise, use the methods provided by this API to specify the paths to the dependencies, which will have to be downloaded separately. This example shows how to navigate to a http://play.golang.org page, input a short program, run it, and inspect its output. If you want to actually run this example:

Package log15 provides an opinionated, simple toolkit for best-practice logging that is both human and machine readable. It is modeled after the standard library's io and net/http packages. This package enforces you to only log key/value pairs. Keys must be strings. Values may be any type that you like. The default output format is logfmt, but you may also choose to use JSON instead if that suits you. Here's how you log: This will output a line that looks like: To get started, you'll want to import the library: Now you're ready to start logging: Because recording a human-meaningful message is common and good practice, the first argument to every logging method is the value to the *implicit* key 'msg'. Additionally, the level you choose for a message will be automatically added with the key 'lvl', and so will the current timestamp with key 't'. You may supply any additional context as a set of key/value pairs to the logging function. log15 allows you to favor terseness, ordering, and speed over safety. This is a reasonable tradeoff for logging functions. You don't need to explicitly state keys/values, log15 understands that they alternate in the variadic argument list: If you really do favor your type-safety, you may choose to pass a log.Ctx instead: Frequently, you want to add context to a logger so that you can track actions associated with it. An http request is a good example. You can easily create new loggers that have context that is automatically included with each log line: This will output a log line that includes the path context that is attached to the logger: The Handler interface defines where log lines are printed to and how they are formated. Handler is a single interface that is inspired by net/http's handler interface: Handlers can filter records, format them, or dispatch to multiple other Handlers. This package implements a number of Handlers for common logging patterns that are easily composed to create flexible, custom logging structures. Here's an example handler that prints logfmt output to Stdout: Here's an example handler that defers to two other handlers. One handler only prints records from the rpc package in logfmt to standard out. The other prints records at Error level or above in JSON formatted output to the file /var/log/service.json This package implements three Handlers that add debugging information to the context, CallerFileHandler, CallerFuncHandler and CallerStackHandler. Here's an example that adds the source file and line number of each logging call to the context. This will output a line that looks like: Here's an example that logs the call stack rather than just the call site. This will output a line that looks like: The "%+v" format instructs the handler to include the path of the source file relative to the compile time GOPATH. The github.com/go-stack/stack package documents the full list of formatting verbs and modifiers available. The Handler interface is so simple that it's also trivial to write your own. Let's create an example handler which tries to write to one handler, but if that fails it falls back to writing to another handler and includes the error that it encountered when trying to write to the primary. This might be useful when trying to log over a network socket, but if that fails you want to log those records to a file on disk. This pattern is so useful that a generic version that handles an arbitrary number of Handlers is included as part of this library called FailoverHandler. Sometimes, you want to log values that are extremely expensive to compute, but you don't want to pay the price of computing them if you haven't turned up your logging level to a high level of detail. This package provides a simple type to annotate a logging operation that you want to be evaluated lazily, just when it is about to be logged, so that it would not be evaluated if an upstream Handler filters it out. Just wrap any function which takes no arguments with the log.Lazy type. For example: If this message is not logged for any reason (like logging at the Error level), then factorRSAKey is never evaluated. The same log.Lazy mechanism can be used to attach context to a logger which you want to be evaluated when the message is logged, but not when the logger is created. For example, let's imagine a game where you have Player objects: You always want to log a player's name and whether they're alive or dead, so when you create the player object, you might do: Only now, even after a player has died, the logger will still report they are alive because the logging context is evaluated when the logger was created. By using the Lazy wrapper, we can defer the evaluation of whether the player is alive or not to each log message, so that the log records will reflect the player's current state no matter when the log message is written: If log15 detects that stdout is a terminal, it will configure the default handler for it (which is log.StdoutHandler) to use TerminalFormat. This format logs records nicely for your terminal, including color-coded output based on log level. Becasuse log15 allows you to step around the type system, there are a few ways you can specify invalid arguments to the logging functions. You could, for example, wrap something that is not a zero-argument function with log.Lazy or pass a context key that is not a string. Since logging libraries are typically the mechanism by which errors are reported, it would be onerous for the logging functions to return errors. Instead, log15 handles errors by making these guarantees to you: - Any log record containing an error will still be printed with the error explained to you as part of the log record. - Any log record containing an error will include the context key LOG15_ERROR, enabling you to easily (and if you like, automatically) detect if any of your logging calls are passing bad values. Understanding this, you might wonder why the Handler interface can return an error value in its Log method. Handlers are encouraged to return errors only if they fail to write their log records out to an external source like if the syslog daemon is not responding. This allows the construction of useful handlers which cope with those failures like the FailoverHandler. log15 is intended to be useful for library authors as a way to provide configurable logging to users of their library. Best practice for use in a library is to always disable all output for your logger by default and to provide a public Logger instance that consumers of your library can configure. Like so: Users of your library may then enable it if they like: The ability to attach context to a logger is a powerful one. Where should you do it and why? I favor embedding a Logger directly into any persistent object in my application and adding unique, tracing context keys to it. For instance, imagine I am writing a web browser: When a new tab is created, I assign a logger to it with the url of the tab as context so it can easily be traced through the logs. Now, whenever we perform any operation with the tab, we'll log with its embedded logger and it will include the tab title automatically: There's only one problem. What if the tab url changes? We could use log.Lazy to make sure the current url is always written, but that would mean that we couldn't trace a tab's full lifetime through our logs after the user navigate to a new URL. Instead, think about what values to attach to your loggers the same way you think about what to use as a key in a SQL database schema. If it's possible to use a natural key that is unique for the lifetime of the object, do so. But otherwise, log15's ext package has a handy RandId function to let you generate what you might call "surrogate keys" They're just random hex identifiers to use for tracing. Back to our Tab example, we would prefer to set up our Logger like so: Now we'll have a unique traceable identifier even across loading new urls, but we'll still be able to see the tab's current url in the log messages. For all Handler functions which can return an error, there is a version of that function which will return no error but panics on failure. They are all available on the Must object. For example: All of the following excellent projects inspired the design of this library: code.google.com/p/log4go github.com/op/go-logging github.com/technoweenie/grohl github.com/Sirupsen/logrus github.com/kr/logfmt github.com/spacemonkeygo/spacelog golang's stdlib, notably io and net/http https://xkcd.com/927/

Package log15 provides an opinionated, simple toolkit for best-practice logging that is both human and machine readable. It is modeled after the standard library's io and net/http packages. This package enforces you to only log key/value pairs. Keys must be strings. Values may be any type that you like. The default output format is logfmt, but you may also choose to use JSON instead if that suits you. Here's how you log: This will output a line that looks like: To get started, you'll want to import the library: Now you're ready to start logging: Because recording a human-meaningful message is common and good practice, the first argument to every logging method is the value to the *implicit* key 'msg'. Additionally, the level you choose for a message will be automatically added with the key 'lvl', and so will the current timestamp with key 't'. You may supply any additional context as a set of key/value pairs to the logging function. log15 allows you to favor terseness, ordering, and speed over safety. This is a reasonable tradeoff for logging functions. You don't need to explicitly state keys/values, log15 understands that they alternate in the variadic argument list: If you really do favor your type-safety, you may choose to pass a log.Ctx instead: Frequently, you want to add context to a logger so that you can track actions associated with it. An http request is a good example. You can easily create new loggers that have context that is automatically included with each log line: This will output a log line that includes the path context that is attached to the logger: The Handler interface defines where log lines are printed to and how they are formated. Handler is a single interface that is inspired by net/http's handler interface: Handlers can filter records, format them, or dispatch to multiple other Handlers. This package implements a number of Handlers for common logging patterns that are easily composed to create flexible, custom logging structures. Here's an example handler that prints logfmt output to Stdout: Here's an example handler that defers to two other handlers. One handler only prints records from the rpc package in logfmt to standard out. The other prints records at Error level or above in JSON formatted output to the file /var/log/service.json This package implements three Handlers that add debugging information to the context, CallerFileHandler, CallerFuncHandler and CallerStackHandler. Here's an example that adds the source file and line number of each logging call to the context. This will output a line that looks like: Here's an example that logs the call stack rather than just the call site. This will output a line that looks like: The "%+v" format instructs the handler to include the path of the source file relative to the compile time GOPATH. The github.com/go-stack/stack package documents the full list of formatting verbs and modifiers available. The Handler interface is so simple that it's also trivial to write your own. Let's create an example handler which tries to write to one handler, but if that fails it falls back to writing to another handler and includes the error that it encountered when trying to write to the primary. This might be useful when trying to log over a network socket, but if that fails you want to log those records to a file on disk. This pattern is so useful that a generic version that handles an arbitrary number of Handlers is included as part of this library called FailoverHandler. Sometimes, you want to log values that are extremely expensive to compute, but you don't want to pay the price of computing them if you haven't turned up your logging level to a high level of detail. This package provides a simple type to annotate a logging operation that you want to be evaluated lazily, just when it is about to be logged, so that it would not be evaluated if an upstream Handler filters it out. Just wrap any function which takes no arguments with the log.Lazy type. For example: If this message is not logged for any reason (like logging at the Error level), then factorRSAKey is never evaluated. The same log.Lazy mechanism can be used to attach context to a logger which you want to be evaluated when the message is logged, but not when the logger is created. For example, let's imagine a game where you have Player objects: You always want to log a player's name and whether they're alive or dead, so when you create the player object, you might do: Only now, even after a player has died, the logger will still report they are alive because the logging context is evaluated when the logger was created. By using the Lazy wrapper, we can defer the evaluation of whether the player is alive or not to each log message, so that the log records will reflect the player's current state no matter when the log message is written: If log15 detects that stdout is a terminal, it will configure the default handler for it (which is log.StdoutHandler) to use TerminalFormat. This format logs records nicely for your terminal, including color-coded output based on log level. Becasuse log15 allows you to step around the type system, there are a few ways you can specify invalid arguments to the logging functions. You could, for example, wrap something that is not a zero-argument function with log.Lazy or pass a context key that is not a string. Since logging libraries are typically the mechanism by which errors are reported, it would be onerous for the logging functions to return errors. Instead, log15 handles errors by making these guarantees to you: - Any log record containing an error will still be printed with the error explained to you as part of the log record. - Any log record containing an error will include the context key LOG15_ERROR, enabling you to easily (and if you like, automatically) detect if any of your logging calls are passing bad values. Understanding this, you might wonder why the Handler interface can return an error value in its Log method. Handlers are encouraged to return errors only if they fail to write their log records out to an external source like if the syslog daemon is not responding. This allows the construction of useful handlers which cope with those failures like the FailoverHandler. log15 is intended to be useful for library authors as a way to provide configurable logging to users of their library. Best practice for use in a library is to always disable all output for your logger by default and to provide a public Logger instance that consumers of your library can configure. Like so: Users of your library may then enable it if they like: The ability to attach context to a logger is a powerful one. Where should you do it and why? I favor embedding a Logger directly into any persistent object in my application and adding unique, tracing context keys to it. For instance, imagine I am writing a web browser: When a new tab is created, I assign a logger to it with the url of the tab as context so it can easily be traced through the logs. Now, whenever we perform any operation with the tab, we'll log with its embedded logger and it will include the tab title automatically: There's only one problem. What if the tab url changes? We could use log.Lazy to make sure the current url is always written, but that would mean that we couldn't trace a tab's full lifetime through our logs after the user navigate to a new URL. Instead, think about what values to attach to your loggers the same way you think about what to use as a key in a SQL database schema. If it's possible to use a natural key that is unique for the lifetime of the object, do so. But otherwise, log15's ext package has a handy RandId function to let you generate what you might call "surrogate keys" They're just random hex identifiers to use for tracing. Back to our Tab example, we would prefer to set up our Logger like so: Now we'll have a unique traceable identifier even across loading new urls, but we'll still be able to see the tab's current url in the log messages. For all Handler functions which can return an error, there is a version of that function which will return no error but panics on failure. They are all available on the Must object. For example: All of the following excellent projects inspired the design of this library: code.google.com/p/log4go github.com/op/go-logging github.com/technoweenie/grohl github.com/Sirupsen/logrus github.com/kr/logfmt github.com/spacemonkeygo/spacelog golang's stdlib, notably io and net/http https://xkcd.com/927/

Package ramfs implements a 9P2000 file server keeping all files in memory. A 9P2000 server is an agent that provides one or more hierarchical file systems -- file trees -- that may be accessed by processes. A server responds to requests by clients to navigate the hierarchy, and to create, remove, read, and write files. References:

Package saml contains a partial implementation of the SAML standard in golang. SAML is a standard for identity federation, i.e. either allowing a third party to authenticate your users or allowing third parties to rely on us to authenticate their users. In SAML parlance an Identity Provider (IDP) is a service that knows how to authenticate users. A Service Provider (SP) is a service that delegates authentication to an IDP. If you are building a service where users log in with someone else's credentials, then you are a Service Provider. This package supports implementing both service providers and identity providers. The core package contains the implementation of SAML. The package samlsp provides helper middleware suitable for use in Service Provider applications. The package samlidp provides a rudimentary IDP service that is useful for testing or as a starting point for other integrations. Version 0.4.0 introduces a few breaking changes to the _samlsp_ package in order to make the package more extensible, and to clean up the interfaces a bit. The default behavior remains the same, but you can now provide interface implementations of _RequestTracker_ (which tracks pending requests), _Session_ (which handles maintaining a session) and _OnError_ which handles reporting errors. Public fields of _samlsp.Middleware_ have changed, so some usages may require adjustment. See [issue 231](https://github.com/crewjam/saml/issues/231) for details. The option to provide an IDP metadata URL has been deprecated. Instead, we recommend that you use the `FetchMetadata()` function, or fetch the metadata yourself and use the new `ParseMetadata()` function, and pass the metadata in _samlsp.Options.IDPMetadata_. Similarly, the _HTTPClient_ field is now deprecated because it was only used for fetching metdata, which is no longer directly implemented. The fields that manage how cookies are set are deprecated as well. To customize how cookies are managed, provide custom implementation of _RequestTracker_ and/or _Session_, perhaps by extending the default implementations. The deprecated fields have not been removed from the Options structure, but will be in future. In particular we have deprecated the following fields in _samlsp.Options_: - `Logger` - This was used to emit errors while validating, which is an anti-pattern. - `IDPMetadataURL` - Instead use `FetchMetadata()` - `HTTPClient` - Instead pass httpClient to FetchMetadata - `CookieMaxAge` - Instead assign a custom CookieRequestTracker or CookieSessionProvider - `CookieName` - Instead assign a custom CookieRequestTracker or CookieSessionProvider - `CookieDomain` - Instead assign a custom CookieRequestTracker or CookieSessionProvider - `CookieDomain` - Instead assign a custom CookieRequestTracker or CookieSessionProvider Let us assume we have a simple web application to protect. We'll modify this application so it uses SAML to authenticate users. ```golang package main import ( ) ``` Each service provider must have an self-signed X.509 key pair established. You can generate your own with something like this: We will use `samlsp.Middleware` to wrap the endpoint we want to protect. Middleware provides both an `http.Handler` to serve the SAML specific URLs and a set of wrappers to require the user to be logged in. We also provide the URL where the service provider can fetch the metadata from the IDP at startup. In our case, we'll use [samltest.id](https://samltest.id/), an identity provider designed for testing. ```golang package main import ( ) ``` Next we'll have to register our service provider with the identity provider to establish trust from the service provider to the IDP. For [samltest.id](https://samltest.id/), you can do something like: Navigate to https://samltest.id/upload.php and upload the file you fetched. Now you should be able to authenticate. The flow should look like this: 1. You browse to `localhost:8000/hello` 1. The middleware redirects you to `https://samltest.id/idp/profile/SAML2/Redirect/SSO` 1. samltest.id prompts you for a username and password. 1. samltest.id returns you an HTML document which contains an HTML form setup to POST to `localhost:8000/saml/acs`. The form is automatically submitted if you have javascript enabled. 1. The local service validates the response, issues a session cookie, and redirects you to the original URL, `localhost:8000/hello`. 1. This time when `localhost:8000/hello` is requested there is a valid session and so the main content is served. Please see `example/idp/` for a substantially complete example of how to use the library and helpers to be an identity provider. The SAML standard is huge and complex with many dark corners and strange, unused features. This package implements the most commonly used subset of these features required to provide a single sign on experience. The package supports at least the subset of SAML known as [interoperable SAML](http://saml2int.org). This package supports the Web SSO profile. Message flows from the service provider to the IDP are supported using the HTTP Redirect binding and the HTTP POST binding. Message flows from the IDP to the service provider are supported via the HTTP POST binding. The package can produce signed SAML assertions, and can validate both signed and encrypted SAML assertions. It does not support signed or encrypted requests. The _RelayState_ parameter allows you to pass user state information across the authentication flow. The most common use for this is to allow a user to request a deep link into your site, be redirected through the SAML login flow, and upon successful completion, be directed to the originally requested link, rather than the root. Unfortunately, _RelayState_ is less useful than it could be. Firstly, it is not authenticated, so anything you supply must be signed to avoid XSS or CSRF. Secondly, it is limited to 80 bytes in length, which precludes signing. (See section 3.6.3.1 of SAMLProfiles.) The SAML specification is a collection of PDFs (sadly): - [SAMLCore](http://docs.oasis-open.org/security/saml/v2.0/saml-core-2.0-os.pdf) defines data types. - [SAMLBindings](http://docs.oasis-open.org/security/saml/v2.0/saml-bindings-2.0-os.pdf) defines the details of the HTTP requests in play. - [SAMLProfiles](http://docs.oasis-open.org/security/saml/v2.0/saml-profiles-2.0-os.pdf) describes data flows. - [SAMLConformance](http://docs.oasis-open.org/security/saml/v2.0/saml-conformance-2.0-os.pdf) includes a support matrix for various parts of the protocol. [SAMLtest](https://samltest.id/) is a testing ground for SAML service and identity providers. Please do not report security issues in the issue tracker. Rather, please contact me directly at ross@kndr.org ([PGP Key `78B6038B3B9DFB88`](https://keybase.io/crewjam)).

Package halgo is used to create application/hal+json representations of Go structs, and provides a client for navigating HAL-compliant APIs. There are two sides to halgo: serialisation and navigation. Serialisation is based around the Links struct, which you can embed in your own structures to provide HAL compliant links when you serialise your structs into JSON. Links has a little builder API which can make it somewhat more succinct to generate these links than modelling the structures yourself. Navigation, specifically through the Navigator func, is for when you want to consume a HAL-compliant API and walk its relations.

Package bst and its sub-packages specify an extensible Binary Search Tree (BST) API. Instead of trying to think of every method a user might want on a BST, we declare a set of extensible methods with the hopes of enabling a user to create any functionality they need. Packages and sub-packages: -------------------------- * bst Declares basic BST methods (Insert, Get, Remove, etc). * bst/finder Declares extensible method, Find, which allows you to direct navigation down a tree, looking for a specific value. * bst/visitor Declares extensible method, Visit, which allows you to defer taking an action on a tree node (insert, get, update, delete) until after you've visited that node (i.e. inspect then change with one visit to the node). * bst/walker Declares extensible method, Walk, which allows you to freely traverse a tree, either up and down (left, right, parent) or in sequential order (prev, next). * bst/simple Provides a reference implementation of an Extensible BST that implements all of the above-declared methods. License ------- This package is released under the MIT License. See included file 'LICENSE' for more details. Contributors ------------ David Farell <DavidPFarrell@yahoo.com>

Package log15 provides an opinionated, simple toolkit for best-practice logging that is both human and machine readable. It is modeled after the standard library's io and net/http packages. This package enforces you to only log key/value pairs. Keys must be strings. Values may be any type that you like. The default output format is logfmt, but you may also choose to use JSON instead if that suits you. Here's how you log: This will output a line that looks like: To get started, you'll want to import the library: Now you're ready to start logging: Because recording a human-meaningful message is common and good practice, the first argument to every logging method is the value to the *implicit* key 'msg'. Additionally, the level you choose for a message will be automatically added with the key 'lvl', and so will the current timestamp with key 't'. You may supply any additional context as a set of key/value pairs to the logging function. log15 allows you to favor terseness, ordering, and speed over safety. This is a reasonable tradeoff for logging functions. You don't need to explicitly state keys/values, log15 understands that they alternate in the variadic argument list: If you really do favor your type-safety, you may choose to pass a log.Ctx instead: Frequently, you want to add context to a logger so that you can track actions associated with it. An http request is a good example. You can easily create new loggers that have context that is automatically included with each log line: This will output a log line that includes the path context that is attached to the logger: The Handler interface defines where log lines are printed to and how they are formated. Handler is a single interface that is inspired by net/http's handler interface: Handlers can filter records, format them, or dispatch to multiple other Handlers. This package implements a number of Handlers for common logging patterns that are easily composed to create flexible, custom logging structures. Here's an example handler that prints logfmt output to Stdout: Here's an example handler that defers to two other handlers. One handler only prints records from the rpc package in logfmt to standard out. The other prints records at Error level or above in JSON formatted output to the file /var/log/service.json This package implements three Handlers that add debugging information to the context, CallerFileHandler, CallerFuncHandler and CallerStackHandler. Here's an example that adds the source file and line number of each logging call to the context. This will output a line that looks like: Here's an example that logs the call stack rather than just the call site. This will output a line that looks like: The "%+v" format instructs the handler to include the path of the source file relative to the compile time GOPATH. The github.com/go-stack/stack package documents the full list of formatting verbs and modifiers available. The Handler interface is so simple that it's also trivial to write your own. Let's create an example handler which tries to write to one handler, but if that fails it falls back to writing to another handler and includes the error that it encountered when trying to write to the primary. This might be useful when trying to log over a network socket, but if that fails you want to log those records to a file on disk. This pattern is so useful that a generic version that handles an arbitrary number of Handlers is included as part of this library called FailoverHandler. Sometimes, you want to log values that are extremely expensive to compute, but you don't want to pay the price of computing them if you haven't turned up your logging level to a high level of detail. This package provides a simple type to annotate a logging operation that you want to be evaluated lazily, just when it is about to be logged, so that it would not be evaluated if an upstream Handler filters it out. Just wrap any function which takes no arguments with the log.Lazy type. For example: If this message is not logged for any reason (like logging at the Error level), then factorRSAKey is never evaluated. The same log.Lazy mechanism can be used to attach context to a logger which you want to be evaluated when the message is logged, but not when the logger is created. For example, let's imagine a game where you have Player objects: You always want to log a player's name and whether they're alive or dead, so when you create the player object, you might do: Only now, even after a player has died, the logger will still report they are alive because the logging context is evaluated when the logger was created. By using the Lazy wrapper, we can defer the evaluation of whether the player is alive or not to each log message, so that the log records will reflect the player's current state no matter when the log message is written: If log15 detects that stdout is a terminal, it will configure the default handler for it (which is log.StdoutHandler) to use TerminalFormat. This format logs records nicely for your terminal, including color-coded output based on log level. Becasuse log15 allows you to step around the type system, there are a few ways you can specify invalid arguments to the logging functions. You could, for example, wrap something that is not a zero-argument function with log.Lazy or pass a context key that is not a string. Since logging libraries are typically the mechanism by which errors are reported, it would be onerous for the logging functions to return errors. Instead, log15 handles errors by making these guarantees to you: - Any log record containing an error will still be printed with the error explained to you as part of the log record. - Any log record containing an error will include the context key LOG15_ERROR, enabling you to easily (and if you like, automatically) detect if any of your logging calls are passing bad values. Understanding this, you might wonder why the Handler interface can return an error value in its Log method. Handlers are encouraged to return errors only if they fail to write their log records out to an external source like if the syslog daemon is not responding. This allows the construction of useful handlers which cope with those failures like the FailoverHandler. log15 is intended to be useful for library authors as a way to provide configurable logging to users of their library. Best practice for use in a library is to always disable all output for your logger by default and to provide a public Logger instance that consumers of your library can configure. Like so: Users of your library may then enable it if they like: The ability to attach context to a logger is a powerful one. Where should you do it and why? I favor embedding a Logger directly into any persistent object in my application and adding unique, tracing context keys to it. For instance, imagine I am writing a web browser: When a new tab is created, I assign a logger to it with the url of the tab as context so it can easily be traced through the logs. Now, whenever we perform any operation with the tab, we'll log with its embedded logger and it will include the tab title automatically: There's only one problem. What if the tab url changes? We could use log.Lazy to make sure the current url is always written, but that would mean that we couldn't trace a tab's full lifetime through our logs after the user navigate to a new URL. Instead, think about what values to attach to your loggers the same way you think about what to use as a key in a SQL database schema. If it's possible to use a natural key that is unique for the lifetime of the object, do so. But otherwise, log15's ext package has a handy RandId function to let you generate what you might call "surrogate keys" They're just random hex identifiers to use for tracing. Back to our Tab example, we would prefer to set up our Logger like so: Now we'll have a unique traceable identifier even across loading new urls, but we'll still be able to see the tab's current url in the log messages. For all Handler functions which can return an error, there is a version of that function which will return no error but panics on failure. They are all available on the Must object. For example: All of the following excellent projects inspired the design of this library: code.google.com/p/log4go github.com/op/go-logging github.com/technoweenie/grohl github.com/Sirupsen/logrus github.com/kr/logfmt github.com/spacemonkeygo/spacelog golang's stdlib, notably io and net/http https://xkcd.com/927/

Package log15 provides an opinionated, simple toolkit for best-practice logging that is both human and machine readable. It is modeled after the standard library's io and net/http packages. This package enforces you to only log key/value pairs. Keys must be strings. Values may be any type that you like. The default output format is logfmt, but you may also choose to use JSON instead if that suits you. Here's how you log: This will output a line that looks like: To get started, you'll want to import the library: Now you're ready to start logging: Because recording a human-meaningful message is common and good practice, the first argument to every logging method is the value to the *implicit* key 'msg'. Additionally, the level you choose for a message will be automatically added with the key 'lvl', and so will the current timestamp with key 't'. You may supply any additional context as a set of key/value pairs to the logging function. log15 allows you to favor terseness, ordering, and speed over safety. This is a reasonable tradeoff for logging functions. You don't need to explicitly state keys/values, log15 understands that they alternate in the variadic argument list: If you really do favor your type-safety, you may choose to pass a log.Ctx instead: Frequently, you want to add context to a logger so that you can track actions associated with it. An http request is a good example. You can easily create new loggers that have context that is automatically included with each log line: This will output a log line that includes the path context that is attached to the logger: The Handler interface defines where log lines are printed to and how they are formated. Handler is a single interface that is inspired by net/http's handler interface: Handlers can filter records, format them, or dispatch to multiple other Handlers. This package implements a number of Handlers for common logging patterns that are easily composed to create flexible, custom logging structures. Here's an example handler that prints logfmt output to Stdout: Here's an example handler that defers to two other handlers. One handler only prints records from the rpc package in logfmt to standard out. The other prints records at Error level or above in JSON formatted output to the file /var/log/service.json This package implements three Handlers that add debugging information to the context, CallerFileHandler, CallerFuncHandler and CallerStackHandler. Here's an example that adds the source file and line number of each logging call to the context. This will output a line that looks like: Here's an example that logs the call stack rather than just the call site. This will output a line that looks like: The "%+v" format instructs the handler to include the path of the source file relative to the compile time GOPATH. The github.com/go-stack/stack package documents the full list of formatting verbs and modifiers available. The Handler interface is so simple that it's also trivial to write your own. Let's create an example handler which tries to write to one handler, but if that fails it falls back to writing to another handler and includes the error that it encountered when trying to write to the primary. This might be useful when trying to log over a network socket, but if that fails you want to log those records to a file on disk. This pattern is so useful that a generic version that handles an arbitrary number of Handlers is included as part of this library called FailoverHandler. Sometimes, you want to log values that are extremely expensive to compute, but you don't want to pay the price of computing them if you haven't turned up your logging level to a high level of detail. This package provides a simple type to annotate a logging operation that you want to be evaluated lazily, just when it is about to be logged, so that it would not be evaluated if an upstream Handler filters it out. Just wrap any function which takes no arguments with the log.Lazy type. For example: If this message is not logged for any reason (like logging at the Error level), then factorRSAKey is never evaluated. The same log.Lazy mechanism can be used to attach context to a logger which you want to be evaluated when the message is logged, but not when the logger is created. For example, let's imagine a game where you have Player objects: You always want to log a player's name and whether they're alive or dead, so when you create the player object, you might do: Only now, even after a player has died, the logger will still report they are alive because the logging context is evaluated when the logger was created. By using the Lazy wrapper, we can defer the evaluation of whether the player is alive or not to each log message, so that the log records will reflect the player's current state no matter when the log message is written: If log15 detects that stdout is a terminal, it will configure the default handler for it (which is log.StdoutHandler) to use TerminalFormat. This format logs records nicely for your terminal, including color-coded output based on log level. Becasuse log15 allows you to step around the type system, there are a few ways you can specify invalid arguments to the logging functions. You could, for example, wrap something that is not a zero-argument function with log.Lazy or pass a context key that is not a string. Since logging libraries are typically the mechanism by which errors are reported, it would be onerous for the logging functions to return errors. Instead, log15 handles errors by making these guarantees to you: - Any log record containing an error will still be printed with the error explained to you as part of the log record. - Any log record containing an error will include the context key LOG15_ERROR, enabling you to easily (and if you like, automatically) detect if any of your logging calls are passing bad values. Understanding this, you might wonder why the Handler interface can return an error value in its Log method. Handlers are encouraged to return errors only if they fail to write their log records out to an external source like if the syslog daemon is not responding. This allows the construction of useful handlers which cope with those failures like the FailoverHandler. log15 is intended to be useful for library authors as a way to provide configurable logging to users of their library. Best practice for use in a library is to always disable all output for your logger by default and to provide a public Logger instance that consumers of your library can configure. Like so: Users of your library may then enable it if they like: The ability to attach context to a logger is a powerful one. Where should you do it and why? I favor embedding a Logger directly into any persistent object in my application and adding unique, tracing context keys to it. For instance, imagine I am writing a web browser: When a new tab is created, I assign a logger to it with the url of the tab as context so it can easily be traced through the logs. Now, whenever we perform any operation with the tab, we'll log with its embedded logger and it will include the tab title automatically: There's only one problem. What if the tab url changes? We could use log.Lazy to make sure the current url is always written, but that would mean that we couldn't trace a tab's full lifetime through our logs after the user navigate to a new URL. Instead, think about what values to attach to your loggers the same way you think about what to use as a key in a SQL database schema. If it's possible to use a natural key that is unique for the lifetime of the object, do so. But otherwise, log15's ext package has a handy RandId function to let you generate what you might call "surrogate keys" They're just random hex identifiers to use for tracing. Back to our Tab example, we would prefer to set up our Logger like so: Now we'll have a unique traceable identifier even across loading new urls, but we'll still be able to see the tab's current url in the log messages. For all Handler functions which can return an error, there is a version of that function which will return no error but panics on failure. They are all available on the Must object. For example: All of the following excellent projects inspired the design of this library: code.google.com/p/log4go github.com/op/go-logging github.com/technoweenie/grohl github.com/Sirupsen/logrus github.com/kr/logfmt github.com/spacemonkeygo/spacelog golang's stdlib, notably io and net/http https://xkcd.com/927/

Package log15 provides an opinionated, simple toolkit for best-practice logging that is both human and machine readable. It is modeled after the standard library's io and net/http packages. This package enforces you to only log key/value pairs. Keys must be strings. Values may be any type that you like. The default output format is logfmt, but you may also choose to use JSON instead if that suits you. Here's how you log: This will output a line that looks like: To get started, you'll want to import the library: Now you're ready to start logging: Because recording a human-meaningful message is common and good practice, the first argument to every logging method is the value to the *implicit* key 'msg'. Additionally, the level you choose for a message will be automatically added with the key 'lvl', and so will the current timestamp with key 't'. You may supply any additional context as a set of key/value pairs to the logging function. log15 allows you to favor terseness, ordering, and speed over safety. This is a reasonable tradeoff for logging functions. You don't need to explicitly state keys/values, log15 understands that they alternate in the variadic argument list: If you really do favor your type-safety, you may choose to pass a log.Ctx instead: Frequently, you want to add context to a logger so that you can track actions associated with it. An http request is a good example. You can easily create new loggers that have context that is automatically included with each log line: This will output a log line that includes the path context that is attached to the logger: The Handler interface defines where log lines are printed to and how they are formated. Handler is a single interface that is inspired by net/http's handler interface: Handlers can filter records, format them, or dispatch to multiple other Handlers. This package implements a number of Handlers for common logging patterns that are easily composed to create flexible, custom logging structures. Here's an example handler that prints logfmt output to Stdout: Here's an example handler that defers to two other handlers. One handler only prints records from the rpc package in logfmt to standard out. The other prints records at Error level or above in JSON formatted output to the file /var/log/service.json This package implements three Handlers that add debugging information to the context, CallerFileHandler, CallerFuncHandler and CallerStackHandler. Here's an example that adds the source file and line number of each logging call to the context. This will output a line that looks like: Here's an example that logs the call stack rather than just the call site. This will output a line that looks like: The "%+v" format instructs the handler to include the path of the source file relative to the compile time GOPATH. The github.com/go-stack/stack package documents the full list of formatting verbs and modifiers available. The Handler interface is so simple that it's also trivial to write your own. Let's create an example handler which tries to write to one handler, but if that fails it falls back to writing to another handler and includes the error that it encountered when trying to write to the primary. This might be useful when trying to log over a network socket, but if that fails you want to log those records to a file on disk. This pattern is so useful that a generic version that handles an arbitrary number of Handlers is included as part of this library called FailoverHandler. Sometimes, you want to log values that are extremely expensive to compute, but you don't want to pay the price of computing them if you haven't turned up your logging level to a high level of detail. This package provides a simple type to annotate a logging operation that you want to be evaluated lazily, just when it is about to be logged, so that it would not be evaluated if an upstream Handler filters it out. Just wrap any function which takes no arguments with the log.Lazy type. For example: If this message is not logged for any reason (like logging at the Error level), then factorRSAKey is never evaluated. The same log.Lazy mechanism can be used to attach context to a logger which you want to be evaluated when the message is logged, but not when the logger is created. For example, let's imagine a game where you have Player objects: You always want to log a player's name and whether they're alive or dead, so when you create the player object, you might do: Only now, even after a player has died, the logger will still report they are alive because the logging context is evaluated when the logger was created. By using the Lazy wrapper, we can defer the evaluation of whether the player is alive or not to each log message, so that the log records will reflect the player's current state no matter when the log message is written: If log15 detects that stdout is a terminal, it will configure the default handler for it (which is log.StdoutHandler) to use TerminalFormat. This format logs records nicely for your terminal, including color-coded output based on log level. Becasuse log15 allows you to step around the type system, there are a few ways you can specify invalid arguments to the logging functions. You could, for example, wrap something that is not a zero-argument function with log.Lazy or pass a context key that is not a string. Since logging libraries are typically the mechanism by which errors are reported, it would be onerous for the logging functions to return errors. Instead, log15 handles errors by making these guarantees to you: - Any log record containing an error will still be printed with the error explained to you as part of the log record. - Any log record containing an error will include the context key LOG15_ERROR, enabling you to easily (and if you like, automatically) detect if any of your logging calls are passing bad values. Understanding this, you might wonder why the Handler interface can return an error value in its Log method. Handlers are encouraged to return errors only if they fail to write their log records out to an external source like if the syslog daemon is not responding. This allows the construction of useful handlers which cope with those failures like the FailoverHandler. log15 is intended to be useful for library authors as a way to provide configurable logging to users of their library. Best practice for use in a library is to always disable all output for your logger by default and to provide a public Logger instance that consumers of your library can configure. Like so: Users of your library may then enable it if they like: The ability to attach context to a logger is a powerful one. Where should you do it and why? I favor embedding a Logger directly into any persistent object in my application and adding unique, tracing context keys to it. For instance, imagine I am writing a web browser: When a new tab is created, I assign a logger to it with the url of the tab as context so it can easily be traced through the logs. Now, whenever we perform any operation with the tab, we'll log with its embedded logger and it will include the tab title automatically: There's only one problem. What if the tab url changes? We could use log.Lazy to make sure the current url is always written, but that would mean that we couldn't trace a tab's full lifetime through our logs after the user navigate to a new URL. Instead, think about what values to attach to your loggers the same way you think about what to use as a key in a SQL database schema. If it's possible to use a natural key that is unique for the lifetime of the object, do so. But otherwise, log15's ext package has a handy RandId function to let you generate what you might call "surrogate keys" They're just random hex identifiers to use for tracing. Back to our Tab example, we would prefer to set up our Logger like so: Now we'll have a unique traceable identifier even across loading new urls, but we'll still be able to see the tab's current url in the log messages. For all Handler functions which can return an error, there is a version of that function which will return no error but panics on failure. They are all available on the Must object. For example: All of the following excellent projects inspired the design of this library: code.google.com/p/log4go github.com/op/go-logging github.com/technoweenie/grohl github.com/Sirupsen/logrus github.com/kr/logfmt github.com/spacemonkeygo/spacelog golang's stdlib, notably io and net/http https://xkcd.com/927/

Package selenium provides a client to drive web browser-based automation and testing. See the example below for how to get started with this API. This package can depend on several binaries being available, depending on which browsers will be used and how. To avoid needing to manage these dependencies, use a cloud-based browser testing environment, like Sauce Labs, BrowserStack or similar. Otherwise, use the methods provided by this API to specify the paths to the dependencies, which will have to be downloaded separately. This example shows how to navigate to a http://play.golang.org page, input a short program, run it, and inspect its output. If you want to actually run this example:

Package log15 provides an opinionated, simple toolkit for best-practice logging that is both human and machine readable. It is modeled after the standard library's io and net/http packages. This package enforces you to only log key/value pairs. Keys must be strings. Values may be any type that you like. The default output format is logfmt, but you may also choose to use JSON instead if that suits you. Here's how you log: This will output a line that looks like: To get started, you'll want to import the library: Now you're ready to start logging: Because recording a human-meaningful message is common and good practice, the first argument to every logging method is the value to the *implicit* key 'msg'. Additionally, the level you choose for a message will be automatically added with the key 'lvl', and so will the current timestamp with key 't'. You may supply any additional context as a set of key/value pairs to the logging function. log15 allows you to favor terseness, ordering, and speed over safety. This is a reasonable tradeoff for logging functions. You don't need to explicitly state keys/values, log15 understands that they alternate in the variadic argument list: If you really do favor your type-safety, you may choose to pass a log.Ctx instead: Frequently, you want to add context to a logger so that you can track actions associated with it. An http request is a good example. You can easily create new loggers that have context that is automatically included with each log line: This will output a log line that includes the path context that is attached to the logger: The Handler interface defines where log lines are printed to and how they are formated. Handler is a single interface that is inspired by net/http's handler interface: Handlers can filter records, format them, or dispatch to multiple other Handlers. This package implements a number of Handlers for common logging patterns that are easily composed to create flexible, custom logging structures. Here's an example handler that prints logfmt output to Stdout: Here's an example handler that defers to two other handlers. One handler only prints records from the rpc package in logfmt to standard out. The other prints records at Error level or above in JSON formatted output to the file /var/log/service.json This package implements three Handlers that add debugging information to the context, CallerFileHandler, CallerFuncHandler and CallerStackHandler. Here's an example that adds the source file and line number of each logging call to the context. This will output a line that looks like: Here's an example that logs the call stack rather than just the call site. This will output a line that looks like: The "%+v" format instructs the handler to include the path of the source file relative to the compile time GOPATH. The github.com/go-stack/stack package documents the full list of formatting verbs and modifiers available. The Handler interface is so simple that it's also trivial to write your own. Let's create an example handler which tries to write to one handler, but if that fails it falls back to writing to another handler and includes the error that it encountered when trying to write to the primary. This might be useful when trying to log over a network socket, but if that fails you want to log those records to a file on disk. This pattern is so useful that a generic version that handles an arbitrary number of Handlers is included as part of this library called FailoverHandler. Sometimes, you want to log values that are extremely expensive to compute, but you don't want to pay the price of computing them if you haven't turned up your logging level to a high level of detail. This package provides a simple type to annotate a logging operation that you want to be evaluated lazily, just when it is about to be logged, so that it would not be evaluated if an upstream Handler filters it out. Just wrap any function which takes no arguments with the log.Lazy type. For example: If this message is not logged for any reason (like logging at the Error level), then factorRSAKey is never evaluated. The same log.Lazy mechanism can be used to attach context to a logger which you want to be evaluated when the message is logged, but not when the logger is created. For example, let's imagine a game where you have Player objects: You always want to log a player's name and whether they're alive or dead, so when you create the player object, you might do: Only now, even after a player has died, the logger will still report they are alive because the logging context is evaluated when the logger was created. By using the Lazy wrapper, we can defer the evaluation of whether the player is alive or not to each log message, so that the log records will reflect the player's current state no matter when the log message is written: If log15 detects that stdout is a terminal, it will configure the default handler for it (which is log.StdoutHandler) to use TerminalFormat. This format logs records nicely for your terminal, including color-coded output based on log level. Becasuse log15 allows you to step around the type system, there are a few ways you can specify invalid arguments to the logging functions. You could, for example, wrap something that is not a zero-argument function with log.Lazy or pass a context key that is not a string. Since logging libraries are typically the mechanism by which errors are reported, it would be onerous for the logging functions to return errors. Instead, log15 handles errors by making these guarantees to you: - Any log record containing an error will still be printed with the error explained to you as part of the log record. - Any log record containing an error will include the context key LOG15_ERROR, enabling you to easily (and if you like, automatically) detect if any of your logging calls are passing bad values. Understanding this, you might wonder why the Handler interface can return an error value in its Log method. Handlers are encouraged to return errors only if they fail to write their log records out to an external source like if the syslog daemon is not responding. This allows the construction of useful handlers which cope with those failures like the FailoverHandler. log15 is intended to be useful for library authors as a way to provide configurable logging to users of their library. Best practice for use in a library is to always disable all output for your logger by default and to provide a public Logger instance that consumers of your library can configure. Like so: Users of your library may then enable it if they like: The ability to attach context to a logger is a powerful one. Where should you do it and why? I favor embedding a Logger directly into any persistent object in my application and adding unique, tracing context keys to it. For instance, imagine I am writing a web browser: When a new tab is created, I assign a logger to it with the url of the tab as context so it can easily be traced through the logs. Now, whenever we perform any operation with the tab, we'll log with its embedded logger and it will include the tab title automatically: There's only one problem. What if the tab url changes? We could use log.Lazy to make sure the current url is always written, but that would mean that we couldn't trace a tab's full lifetime through our logs after the user navigate to a new URL. Instead, think about what values to attach to your loggers the same way you think about what to use as a key in a SQL database schema. If it's possible to use a natural key that is unique for the lifetime of the object, do so. But otherwise, log15's ext package has a handy RandId function to let you generate what you might call "surrogate keys" They're just random hex identifiers to use for tracing. Back to our Tab example, we would prefer to set up our Logger like so: Now we'll have a unique traceable identifier even across loading new urls, but we'll still be able to see the tab's current url in the log messages. For all Handler functions which can return an error, there is a version of that function which will return no error but panics on failure. They are all available on the Must object. For example: All of the following excellent projects inspired the design of this library: code.google.com/p/log4go github.com/op/go-logging github.com/technoweenie/grohl github.com/Sirupsen/logrus github.com/kr/logfmt github.com/spacemonkeygo/spacelog golang's stdlib, notably io and net/http https://xkcd.com/927/

Package saml contains a partial implementation of the SAML standard in golang. SAML is a standard for identity federation, i.e. either allowing a third party to authenticate your users or allowing third parties to rely on us to authenticate their users. In SAML parlance an Identity Provider (IDP) is a service that knows how to authenticate users. A Service Provider (SP) is a service that delegates authentication to an IDP. If you are building a service where users log in with someone else's credentials, then you are a Service Provider. This package supports implementing both service providers and identity providers. The core package contains the implementation of SAML. The package samlsp provides helper middleware suitable for use in Service Provider applications. The package samlidp provides a rudimentary IDP service that is useful for testing or as a starting point for other integrations. Version 0.4.0 introduces a few breaking changes to the _samlsp_ package in order to make the package more extensible, and to clean up the interfaces a bit. The default behavior remains the same, but you can now provide interface implementations of _RequestTracker_ (which tracks pending requests), _Session_ (which handles maintaining a session) and _OnError_ which handles reporting errors. Public fields of _samlsp.Middleware_ have changed, so some usages may require adjustment. See [issue 231](https://github.com/crewjam/saml/issues/231) for details. The option to provide an IDP metadata URL has been deprecated. Instead, we recommend that you use the `FetchMetadata()` function, or fetch the metadata yourself and use the new `ParseMetadata()` function, and pass the metadata in _samlsp.Options.IDPMetadata_. Similarly, the _HTTPClient_ field is now deprecated because it was only used for fetching metdata, which is no longer directly implemented. The fields that manage how cookies are set are deprecated as well. To customize how cookies are managed, provide custom implementation of _RequestTracker_ and/or _Session_, perhaps by extending the default implementations. The deprecated fields have not been removed from the Options structure, but will be in future. In particular we have deprecated the following fields in _samlsp.Options_: - `Logger` - This was used to emit errors while validating, which is an anti-pattern. - `IDPMetadataURL` - Instead use `FetchMetadata()` - `HTTPClient` - Instead pass httpClient to FetchMetadata - `CookieMaxAge` - Instead assign a custom CookieRequestTracker or CookieSessionProvider - `CookieName` - Instead assign a custom CookieRequestTracker or CookieSessionProvider - `CookieDomain` - Instead assign a custom CookieRequestTracker or CookieSessionProvider - `CookieDomain` - Instead assign a custom CookieRequestTracker or CookieSessionProvider Let us assume we have a simple web application to protect. We'll modify this application so it uses SAML to authenticate users. ```golang package main import ( ) ``` Each service provider must have an self-signed X.509 key pair established. You can generate your own with something like this: We will use `samlsp.Middleware` to wrap the endpoint we want to protect. Middleware provides both an `http.Handler` to serve the SAML specific URLs and a set of wrappers to require the user to be logged in. We also provide the URL where the service provider can fetch the metadata from the IDP at startup. In our case, we'll use [samltest.id](https://samltest.id/), an identity provider designed for testing. ```golang package main import ( ) ``` Next we'll have to register our service provider with the identity provider to establish trust from the service provider to the IDP. For [samltest.id](https://samltest.id/), you can do something like: Navigate to https://samltest.id/upload.php and upload the file you fetched. Now you should be able to authenticate. The flow should look like this: 1. You browse to `localhost:8000/hello` 1. The middleware redirects you to `https://samltest.id/idp/profile/SAML2/Redirect/SSO` 1. samltest.id prompts you for a username and password. 1. samltest.id returns you an HTML document which contains an HTML form setup to POST to `localhost:8000/saml/acs`. The form is automatically submitted if you have javascript enabled. 1. The local service validates the response, issues a session cookie, and redirects you to the original URL, `localhost:8000/hello`. 1. This time when `localhost:8000/hello` is requested there is a valid session and so the main content is served. Please see `example/idp/` for a substantially complete example of how to use the library and helpers to be an identity provider. The SAML standard is huge and complex with many dark corners and strange, unused features. This package implements the most commonly used subset of these features required to provide a single sign on experience. The package supports at least the subset of SAML known as [interoperable SAML](http://saml2int.org). This package supports the Web SSO profile. Message flows from the service provider to the IDP are supported using the HTTP Redirect binding and the HTTP POST binding. Message flows from the IDP to the service provider are supported via the HTTP POST binding. The package can produce signed SAML assertions, and can validate both signed and encrypted SAML assertions. It does not support signed or encrypted requests. The _RelayState_ parameter allows you to pass user state information across the authentication flow. The most common use for this is to allow a user to request a deep link into your site, be redirected through the SAML login flow, and upon successful completion, be directed to the originally requested link, rather than the root. Unfortunately, _RelayState_ is less useful than it could be. Firstly, it is not authenticated, so anything you supply must be signed to avoid XSS or CSRF. Secondly, it is limited to 80 bytes in length, which precludes signing. (See section 3.6.3.1 of SAMLProfiles.) The SAML specification is a collection of PDFs (sadly): - [SAMLCore](http://docs.oasis-open.org/security/saml/v2.0/saml-core-2.0-os.pdf) defines data types. - [SAMLBindings](http://docs.oasis-open.org/security/saml/v2.0/saml-bindings-2.0-os.pdf) defines the details of the HTTP requests in play. - [SAMLProfiles](http://docs.oasis-open.org/security/saml/v2.0/saml-profiles-2.0-os.pdf) describes data flows. - [SAMLConformance](http://docs.oasis-open.org/security/saml/v2.0/saml-conformance-2.0-os.pdf) includes a support matrix for various parts of the protocol. [SAMLtest](https://samltest.id/) is a testing ground for SAML service and identity providers. Please do not report security issues in the issue tracker. Rather, please contact me directly at ross@kndr.org ([PGP Key `78B6038B3B9DFB88`](https://keybase.io/crewjam)).

Package log15 provides an opinionated, simple toolkit for best-practice logging that is both human and machine readable. It is modeled after the standard library's io and net/http packages. This package enforces you to only log key/value pairs. Keys must be strings. Values may be any type that you like. The default output format is logfmt, but you may also choose to use JSON instead if that suits you. Here's how you log: This will output a line that looks like: To get started, you'll want to import the library: Now you're ready to start logging: Because recording a human-meaningful message is common and good practice, the first argument to every logging method is the value to the *implicit* key 'msg'. Additionally, the level you choose for a message will be automatically added with the key 'lvl', and so will the current timestamp with key 't'. You may supply any additional context as a set of key/value pairs to the logging function. log15 allows you to favor terseness, ordering, and speed over safety. This is a reasonable tradeoff for logging functions. You don't need to explicitly state keys/values, log15 understands that they alternate in the variadic argument list: If you really do favor your type-safety, you may choose to pass a log.Ctx instead: Frequently, you want to add context to a logger so that you can track actions associated with it. An http request is a good example. You can easily create new loggers that have context that is automatically included with each log line: This will output a log line that includes the path context that is attached to the logger: The Handler interface defines where log lines are printed to and how they are formated. Handler is a single interface that is inspired by net/http's handler interface: Handlers can filter records, format them, or dispatch to multiple other Handlers. This package implements a number of Handlers for common logging patterns that are easily composed to create flexible, custom logging structures. Here's an example handler that prints logfmt output to Stdout: Here's an example handler that defers to two other handlers. One handler only prints records from the rpc package in logfmt to standard out. The other prints records at Error level or above in JSON formatted output to the file /var/log/service.json //This package implements three Handlers that add debugging information to the //context, CallerFileHandler, CallerFuncHandler and CallerStackHandler. Here's //an example that adds the source file and line number of each logging call to //the context. // // h := log.CallerFileHandler(log.StdoutHandler) // log.Root().SetHandler(h) // ... // log.Error("open file", "err", err) // //This will output a line that looks like: // // lvl=eror t=2014-05-02T16:07:23-0700 msg="open file" err="file not found" caller=data.go:42 // //Here's an example that logs the call stack rather than just the call site. // // h := log.CallerStackHandler("%+v", log.StdoutHandler) // log.Root().SetHandler(h) // ... // log.Error("open file", "err", err) // //This will output a line that looks like: // // lvl=eror t=2014-05-02T16:07:23-0700 msg="open file" err="file not found" stack="[pkg/data.go:42 pkg/cmd/main.go]" // //The "%+v" format instructs the handler to include the path of the source file //relative to the compile time GOPATH. The github.com/go-stack/stack package //documents the full list of formatting verbs and modifiers available. The Handler interface is so simple that it's also trivial to write your own. Let's create an example handler which tries to write to one handler, but if that fails it falls back to writing to another handler and includes the error that it encountered when trying to write to the primary. This might be useful when trying to log over a network socket, but if that fails you want to log those records to a file on disk. This pattern is so useful that a generic version that handles an arbitrary number of Handlers is included as part of this library called FailoverHandler. Sometimes, you want to log values that are extremely expensive to compute, but you don't want to pay the price of computing them if you haven't turned up your logging level to a high level of detail. This package provides a simple type to annotate a logging operation that you want to be evaluated lazily, just when it is about to be logged, so that it would not be evaluated if an upstream Handler filters it out. Just wrap any function which takes no arguments with the log.Lazy type. For example: If this message is not logged for any reason (like logging at the Error level), then factorRSAKey is never evaluated. The same log.Lazy mechanism can be used to attach context to a logger which you want to be evaluated when the message is logged, but not when the logger is created. For example, let's imagine a game where you have Player objects: You always want to log a player's name and whether they're alive or dead, so when you create the player object, you might do: Only now, even after a player has died, the logger will still report they are alive because the logging context is evaluated when the logger was created. By using the Lazy wrapper, we can defer the evaluation of whether the player is alive or not to each log message, so that the log records will reflect the player's current state no matter when the log message is written: If log15 detects that stdout is a terminal, it will configure the default handler for it (which is log.StdoutHandler) to use TerminalFormat. This format logs records nicely for your terminal, including color-coded output based on log level. Becasuse log15 allows you to step around the type system, there are a few ways you can specify invalid arguments to the logging functions. You could, for example, wrap something that is not a zero-argument function with log.Lazy or pass a context key that is not a string. Since logging libraries are typically the mechanism by which errors are reported, it would be onerous for the logging functions to return errors. Instead, log15 handles errors by making these guarantees to you: - Any log record containing an error will still be printed with the error explained to you as part of the log record. - Any log record containing an error will include the context key LOG15_ERROR, enabling you to easily (and if you like, automatically) detect if any of your logging calls are passing bad values. Understanding this, you might wonder why the Handler interface can return an error value in its Log method. Handlers are encouraged to return errors only if they fail to write their log records out to an external source like if the syslog daemon is not responding. This allows the construction of useful handlers which cope with those failures like the FailoverHandler. log15 is intended to be useful for library authors as a way to provide configurable logging to users of their library. Best practice for use in a library is to always disable all output for your logger by default and to provide a public Logger instance that consumers of your library can configure. Like so: Users of your library may then enable it if they like: The ability to attach context to a logger is a powerful one. Where should you do it and why? I favor embedding a Logger directly into any persistent object in my application and adding unique, tracing context keys to it. For instance, imagine I am writing a web browser: When a new tab is created, I assign a logger to it with the url of the tab as context so it can easily be traced through the logs. Now, whenever we perform any operation with the tab, we'll log with its embedded logger and it will include the tab title automatically: There's only one problem. What if the tab url changes? We could use log.Lazy to make sure the current url is always written, but that would mean that we couldn't trace a tab's full lifetime through our logs after the user navigate to a new URL. Instead, think about what values to attach to your loggers the same way you think about what to use as a key in a SQL database schema. If it's possible to use a natural key that is unique for the lifetime of the object, do so. But otherwise, log15's ext package has a handy RandId function to let you generate what you might call "surrogate keys" They're just random hex identifiers to use for tracing. Back to our Tab example, we would prefer to set up our Logger like so: Now we'll have a unique traceable identifier even across loading new urls, but we'll still be able to see the tab's current url in the log messages. For all Handler functions which can return an error, there is a version of that function which will return no error but panics on failure. They are all available on the Must object. For example: All of the following excellent projects inspired the design of this library: code.google.com/p/log4go github.com/op/go-logging github.com/technoweenie/grohl github.com/Sirupsen/logrus github.com/kr/logfmt github.com/spacemonkeygo/spacelog golang's stdlib, notably io and net/http https://xkcd.com/927/

This executable provides an HTTP server that watches for file system changes to .go files within the working directory (and all nested go packages). Navigating to the configured host and port in a web browser will display the latest results of running `go test` in each go package.

MongoTUI is a MongoDb TUI client which allows to connect to multiple MongoDB instances. Press <Ctrl>-<c> to connect to a MongoDB instance, you can enter the connection parameters individually or the connection URI as well. Notice that the connection URI always wins, if the individual fields and the connection URI are filled. The open database connections, accessed by <Ctrl>-<d>, their databases and collections are displayed as a tree view in the left application panel. You can navigate through the nodes with the arrow keys or left-click on them. The command editor is accessible by <Ctrl>-<e>. The commands are fired on the database which are selected in the tree view by pressing <Enter> or <Return> in the command editor. The command result is shown in the result panel as a tree view. You can access it with <Ctrl>-<r> and navigate through the nodes with the arrow keys. <Ctrl>-<t> disconnects (terminates) the selected connection. <Ctrl>-<q> disconnects the open connections and quits the application.

This executable provides an HTTP server that watches for file system changes to .go files within the working directory (and all nested go packages). Navigating to the configured host and port in a web browser will display the latest results of running `go test` in each go package.

Package selenium provides a client to drive web browser-based automation and testing. See the example below for how to get started with this API. This package can depend on several binaries being available, depending on which browsers will be used and how. To avoid needing to manage these dependencies, use a cloud-based browser testing environment, like Sauce Labs, BrowserStack or similar. Otherwise, use the methods provided by this API to specify the paths to the dependencies, which will have to be downloaded separately. This example shows how to navigate to a http://play.golang.org page, input a short program, run it, and inspect its output. If you want to actually run this example:

Package saml contains a partial implementation of the SAML standard in golang. SAML is a standard for identity federation, i.e. either allowing a third party to authenticate your users or allowing third parties to rely on us to authenticate their users. In SAML parlance an Identity Provider (IDP) is a service that knows how to authenticate users. A Service Provider (SP) is a service that delegates authentication to an IDP. If you are building a service where users log in with someone else's credentials, then you are a Service Provider. This package supports implementing both service providers and identity providers. The core package contains the implementation of SAML. The package samlsp provides helper middleware suitable for use in Service Provider applications. The package samlidp provides a rudimentary IDP service that is useful for testing or as a starting point for other integrations. Version 0.4.0 introduces a few breaking changes to the _samlsp_ package in order to make the package more extensible, and to clean up the interfaces a bit. The default behavior remains the same, but you can now provide interface implementations of _RequestTracker_ (which tracks pending requests), _Session_ (which handles maintaining a session) and _OnError_ which handles reporting errors. Public fields of _samlsp.Middleware_ have changed, so some usages may require adjustment. See [issue 231](https://github.com/haalcala/saml/issues/231) for details. The option to provide an IDP metadata URL has been deprecated. Instead, we recommend that you use the `FetchMetadata()` function, or fetch the metadata yourself and use the new `ParseMetadata()` function, and pass the metadata in _samlsp.Options.IDPMetadata_. Similarly, the _HTTPClient_ field is now deprecated because it was only used for fetching metdata, which is no longer directly implemented. The fields that manage how cookies are set are deprecated as well. To customize how cookies are managed, provide custom implementation of _RequestTracker_ and/or _Session_, perhaps by extending the default implementations. The deprecated fields have not been removed from the Options structure, but will be in future. In particular we have deprecated the following fields in _samlsp.Options_: - `Logger` - This was used to emit errors while validating, which is an anti-pattern. - `IDPMetadataURL` - Instead use `FetchMetadata()` - `HTTPClient` - Instead pass httpClient to FetchMetadata - `CookieMaxAge` - Instead assign a custom CookieRequestTracker or CookieSessionProvider - `CookieName` - Instead assign a custom CookieRequestTracker or CookieSessionProvider - `CookieDomain` - Instead assign a custom CookieRequestTracker or CookieSessionProvider - `CookieDomain` - Instead assign a custom CookieRequestTracker or CookieSessionProvider Let us assume we have a simple web application to protect. We'll modify this application so it uses SAML to authenticate users. ```golang package main import ( ) ``` Each service provider must have an self-signed X.509 key pair established. You can generate your own with something like this: We will use `samlsp.Middleware` to wrap the endpoint we want to protect. Middleware provides both an `http.Handler` to serve the SAML specific URLs and a set of wrappers to require the user to be logged in. We also provide the URL where the service provider can fetch the metadata from the IDP at startup. In our case, we'll use [samltest.id](https://samltest.id/), an identity provider designed for testing. ```golang package main import ( ) ``` Next we'll have to register our service provider with the identity provider to establish trust from the service provider to the IDP. For [samltest.id](https://samltest.id/), you can do something like: Navigate to https://samltest.id/upload.php and upload the file you fetched. Now you should be able to authenticate. The flow should look like this: 1. You browse to `localhost:8000/hello` 1. The middleware redirects you to `https://samltest.id/idp/profile/SAML2/Redirect/SSO` 1. samltest.id prompts you for a username and password. 1. samltest.id returns you an HTML document which contains an HTML form setup to POST to `localhost:8000/saml/acs`. The form is automatically submitted if you have javascript enabled. 1. The local service validates the response, issues a session cookie, and redirects you to the original URL, `localhost:8000/hello`. 1. This time when `localhost:8000/hello` is requested there is a valid session and so the main content is served. Please see `example/idp/` for a substantially complete example of how to use the library and helpers to be an identity provider. The SAML standard is huge and complex with many dark corners and strange, unused features. This package implements the most commonly used subset of these features required to provide a single sign on experience. The package supports at least the subset of SAML known as [interoperable SAML](http://saml2int.org). This package supports the Web SSO profile. Message flows from the service provider to the IDP are supported using the HTTP Redirect binding and the HTTP POST binding. Message flows from the IDP to the service provider are supported via the HTTP POST binding. The package can produce signed SAML assertions, and can validate both signed and encrypted SAML assertions. It does not support signed or encrypted requests. The _RelayState_ parameter allows you to pass user state information across the authentication flow. The most common use for this is to allow a user to request a deep link into your site, be redirected through the SAML login flow, and upon successful completion, be directed to the originally requested link, rather than the root. Unfortunately, _RelayState_ is less useful than it could be. Firstly, it is not authenticated, so anything you supply must be signed to avoid XSS or CSRF. Secondly, it is limited to 80 bytes in length, which precludes signing. (See section 3.6.3.1 of SAMLProfiles.) The SAML specification is a collection of PDFs (sadly): - [SAMLCore](http://docs.oasis-open.org/security/saml/v2.0/saml-core-2.0-os.pdf) defines data types. - [SAMLBindings](http://docs.oasis-open.org/security/saml/v2.0/saml-bindings-2.0-os.pdf) defines the details of the HTTP requests in play. - [SAMLProfiles](http://docs.oasis-open.org/security/saml/v2.0/saml-profiles-2.0-os.pdf) describes data flows. - [SAMLConformance](http://docs.oasis-open.org/security/saml/v2.0/saml-conformance-2.0-os.pdf) includes a support matrix for various parts of the protocol. [SAMLtest](https://samltest.id/) is a testing ground for SAML service and identity providers. Please do not report security issues in the issue tracker. Rather, please contact me directly at ross@kndr.org ([PGP Key `78B6038B3B9DFB88`](https://keybase.io/crewjam)).

Package iris provides a beautifully expressive and easy to use foundation for your next website, API, or distributed app. Source code and other details for the project are available at GitHub: 11.1.1 The only requirement is the Go Programming Language, at least version 1.8 but 1.11.1 and above is highly recommended. Example code: You can start the server(s) listening to any type of `net.Listener` or even `http.Server` instance. The method for initialization of the server should be passed at the end, via `Run` function. Below you'll see some useful examples: UNIX and BSD hosts can take advantage of the reuse port feature. Example code: That's all with listening, you have the full control when you need it. Let's continue by learning how to catch CONTROL+C/COMMAND+C or unix kill command and shutdown the server gracefully. In order to manually manage what to do when app is interrupted, we have to disable the default behavior with the option `WithoutInterruptHandler` and register a new interrupt handler (globally, across all possible hosts). Example code: Access to all hosts that serve your application can be provided by the `Application#Hosts` field, after the `Run` method. But the most common scenario is that you may need access to the host before the `Run` method, there are two ways of gain access to the host supervisor, read below. First way is to use the `app.NewHost` to create a new host and use one of its `Serve` or `Listen` functions to start the application via the `iris#Raw` Runner. Note that this way needs an extra import of the `net/http` package. Example Code: Second, and probably easier way is to use the `host.Configurator`. Note that this method requires an extra import statement of "github.com/kataras/iris/core/host" when using go < 1.9, if you're targeting on go1.9 then you can use the `iris#Supervisor` and omit the extra host import. All common `Runners` we saw earlier (`iris#Addr, iris#Listener, iris#Server, iris#TLS, iris#AutoTLS`) accept a variadic argument of `host.Configurator`, there are just `func(*host.Supervisor)`. Therefore the `Application` gives you the rights to modify the auto-created host supervisor through these. Example Code: Read more about listening and gracefully shutdown by navigating to: All HTTP methods are supported, developers can also register handlers for same paths for different methods. The first parameter is the HTTP Method, second parameter is the request path of the route, third variadic parameter should contains one or more iris.Handler executed by the registered order when a user requests for that specific resouce path from the server. Example code: In order to make things easier for the user, iris provides functions for all HTTP Methods. The first parameter is the request path of the route, second variadic parameter should contains one or more iris.Handler executed by the registered order when a user requests for that specific resouce path from the server. Example code: A set of routes that are being groupped by path prefix can (optionally) share the same middleware handlers and template layout. A group can have a nested group too. `.Party` is being used to group routes, developers can declare an unlimited number of (nested) groups. Example code: iris developers are able to register their own handlers for http statuses like 404 not found, 500 internal server error and so on. Example code: With the help of iris's expressionist router you can build any form of API you desire, with safety. Example code: At the previous example, we've seen static routes, group of routes, subdomains, wildcard subdomains, a small example of parameterized path with a single known parameter and custom http errors, now it's time to see wildcard parameters and macros. iris, like net/http std package registers route's handlers by a Handler, the iris' type of handler is just a func(ctx iris.Context) where context comes from github.com/kataras/iris/context. Iris has the easiest and the most powerful routing process you have ever meet. At the same time, iris has its own interpeter(yes like a programming language) for route's path syntax and their dynamic path parameters parsing and evaluation, We call them "macros" for shortcut. How? It calculates its needs and if not any special regexp needed then it just registers the route with the low-level path syntax, otherwise it pre-compiles the regexp and adds the necessary middleware(s). Standard macro types for parameters: if type is missing then parameter's type is defaulted to string, so {param} == {param:string}. If a function not found on that type then the "string"'s types functions are being used. i.e: Besides the fact that iris provides the basic types and some default "macro funcs" you are able to register your own too!. Register a named path parameter function: at the func(argument ...) you can have any standard type, it will be validated before the server starts so don't care about performance here, the only thing it runs at serve time is the returning func(paramValue string) bool. Example Code: Last, do not confuse ctx.Values() with ctx.Params(). Path parameter's values goes to ctx.Params() and context's local storage that can be used to communicate between handlers and middleware(s) goes to ctx.Values(), path parameters and the rest of any custom values are separated for your own good. Run Static Files Example code: More examples can be found here: https://github.com/kataras/iris/tree/master/_examples/beginner/file-server Middleware is just a concept of ordered chain of handlers. Middleware can be registered globally, per-party, per-subdomain and per-route. Example code: iris is able to wrap and convert any external, third-party Handler you used to use to your web application. Let's convert the https://github.com/rs/cors net/http external middleware which returns a `next form` handler. Example code: Iris supports 5 template engines out-of-the-box, developers can still use any external golang template engine, as `context/context#ResponseWriter()` is an `io.Writer`. All of these five template engines have common features with common API, like Layout, Template Funcs, Party-specific layout, partial rendering and more. Example code: View engine supports bundled(https://github.com/shuLhan/go-bindata) template files too. go-bindata gives you two functions, asset and assetNames, these can be setted to each of the template engines using the `.Binary` func. Example code: A real example can be found here: https://github.com/kataras/iris/tree/master/_examples/view/embedding-templates-into-app. Enable auto-reloading of templates on each request. Useful while developers are in dev mode as they no neeed to restart their app on every template edit. Example code: Note: In case you're wondering, the code behind the view engines derives from the "github.com/kataras/iris/view" package, access to the engines' variables can be granded by "github.com/kataras/iris" package too. Each one of these template engines has different options located here: https://github.com/kataras/iris/tree/master/view . This example will show how to store and access data from a session. You don’t need any third-party library, but If you want you can use any session manager compatible or not. In this example we will only allow authenticated users to view our secret message on the /secret page. To get access to it, the will first have to visit /login to get a valid session cookie, which logs him in. Additionally he can visit /logout to revoke his access to our secret message. Example code: Running the example: Sessions persistence can be achieved using one (or more) `sessiondb`. Example Code: More examples: In this example we will create a small chat between web sockets via browser. Example Server Code: Example Client(javascript) Code: Running the example: Iris has first-class support for the MVC pattern, you'll not find these stuff anywhere else in the Go world. Example Code: // GetUserBy serves // Method: GET // Resource: http://localhost:8080/user/{username:string} // By is a reserved "keyword" to tell the framework that you're going to // bind path parameters in the function's input arguments, and it also // helps to have "Get" and "GetBy" in the same controller. // // func (c *ExampleController) GetUserBy(username string) mvc.Result { // return mvc.View{ // Name: "user/username.html", // Data: username, // } // } Can use more than one, the factory will make sure that the correct http methods are being registered for each route for this controller, uncomment these if you want: Iris web framework supports Request data, Models, Persistence Data and Binding with the fastest possible execution. Characteristics: All HTTP Methods are supported, for example if want to serve `GET` then the controller should have a function named `Get()`, you can define more than one method function to serve in the same Controller. Register custom controller's struct's methods as handlers with custom paths(even with regex parametermized path) via the `BeforeActivation` custom event callback, per-controller. Example: Persistence data inside your Controller struct (share data between requests) by defining services to the Dependencies or have a `Singleton` controller scope. Share the dependencies between controllers or register them on a parent MVC Application, and ability to modify dependencies per-controller on the `BeforeActivation` optional event callback inside a Controller, i.e Access to the `Context` as a controller's field(no manual binding is neede) i.e `Ctx iris.Context` or via a method's input argument, i.e Models inside your Controller struct (set-ed at the Method function and rendered by the View). You can return models from a controller's method or set a field in the request lifecycle and return that field to another method, in the same request lifecycle. Flow as you used to, mvc application has its own `Router` which is a type of `iris/router.Party`, the standard iris api. `Controllers` can be registered to any `Party`, including Subdomains, the Party's begin and done handlers work as expected. Optional `BeginRequest(ctx)` function to perform any initialization before the method execution, useful to call middlewares or when many methods use the same collection of data. Optional `EndRequest(ctx)` function to perform any finalization after any method executed. Session dynamic dependency via manager's `Start` to the MVC Application, i.e Inheritance, recursively. Access to the dynamic path parameters via the controller's methods' input arguments, no binding is needed. When you use the Iris' default syntax to parse handlers from a controller, you need to suffix the methods with the `By` word, uppercase is a new sub path. Example: Register one or more relative paths and able to get path parameters, i.e Response via output arguments, optionally, i.e Where `any` means everything, from custom structs to standard language's types-. `Result` is an interface which contains only that function: Dispatch(ctx iris.Context) and Get where HTTP Method function(Post, Put, Delete...). Iris has a very powerful and blazing fast MVC support, you can return any value of any type from a method function and it will be sent to the client as expected. * if `string` then it's the body. * if `string` is the second output argument then it's the content type. * if `int` then it's the status code. * if `bool` is false then it throws 404 not found http error by skipping everything else. * if `error` and not nil then (any type) response will be omitted and error's text with a 400 bad request will be rendered instead. * if `(int, error)` and error is not nil then the response result will be the error's text with the status code as `int`. * if `custom struct` or `interface{}` or `slice` or `map` then it will be rendered as json, unless a `string` content type is following. * if `mvc.Result` then it executes its `Dispatch` function, so good design patters can be used to split the model's logic where needed. Examples with good patterns to follow but not intend to be used in production of course can be found at: https://github.com/kataras/iris/tree/master/_examples/#mvc. By creating components that are independent of one another, developers are able to reuse components quickly and easily in other applications. The same (or similar) view for one application can be refactored for another application with different data because the view is simply handling how the data is being displayed to the user. If you're new to back-end web development read about the MVC architectural pattern first, a good start is that wikipedia article: https://en.wikipedia.org/wiki/Model%E2%80%93view%E2%80%93controller. But you should have a basic idea of the framework by now, we just scratched the surface. If you enjoy what you just saw and want to learn more, please follow the below links: Examples: Middleware: Home Page: Book (in-progress):

This executable provides an HTTP server that watches for file system changes to .go files within the working directory (and all nested go packages). Navigating to the configured host and port in a web browser will display the latest results of running `go test` in each go package.

Package log15 provides an opinionated, simple toolkit for best-practice logging that is both human and machine readable. It is modeled after the standard library's io and net/http packages. This package enforces you to only log key/value pairs. Keys must be strings. Values may be any type that you like. The default output format is logfmt, but you may also choose to use JSON instead if that suits you. Here's how you log: This will output a line that looks like: To get started, you'll want to import the library: Now you're ready to start logging: Because recording a human-meaningful message is common and good practice, the first argument to every logging method is the value to the *implicit* key 'msg'. Additionally, the level you choose for a message will be automatically added with the key 'lvl', and so will the current timestamp with key 't'. You may supply any additional context as a set of key/value pairs to the logging function. log15 allows you to favor terseness, ordering, and speed over safety. This is a reasonable tradeoff for logging functions. You don't need to explicitly state keys/values, log15 understands that they alternate in the variadic argument list: If you really do favor your type-safety, you may choose to pass a log.Ctx instead: Frequently, you want to add context to a logger so that you can track actions associated with it. An http request is a good example. You can easily create new loggers that have context that is automatically included with each log line: This will output a log line that includes the path context that is attached to the logger: The Handler interface defines where log lines are printed to and how they are formated. Handler is a single interface that is inspired by net/http's handler interface: Handlers can filter records, format them, or dispatch to multiple other Handlers. This package implements a number of Handlers for common logging patterns that are easily composed to create flexible, custom logging structures. Here's an example handler that prints logfmt output to Stdout: Here's an example handler that defers to two other handlers. One handler only prints records from the rpc package in logfmt to standard out. The other prints records at Error level or above in JSON formatted output to the file /var/log/service.json This package implements three Handlers that add debugging information to the context, CallerFileHandler, CallerFuncHandler and CallerStackHandler. Here's an example that adds the source file and line number of each logging call to the context. This will output a line that looks like: Here's an example that logs the call stack rather than just the call site. This will output a line that looks like: The "%+v" format instructs the handler to include the path of the source file relative to the compile time GOPATH. The github.com/go-stack/stack package documents the full list of formatting verbs and modifiers available. The Handler interface is so simple that it's also trivial to write your own. Let's create an example handler which tries to write to one handler, but if that fails it falls back to writing to another handler and includes the error that it encountered when trying to write to the primary. This might be useful when trying to log over a network socket, but if that fails you want to log those records to a file on disk. This pattern is so useful that a generic version that handles an arbitrary number of Handlers is included as part of this library called FailoverHandler. Sometimes, you want to log values that are extremely expensive to compute, but you don't want to pay the price of computing them if you haven't turned up your logging level to a high level of detail. This package provides a simple type to annotate a logging operation that you want to be evaluated lazily, just when it is about to be logged, so that it would not be evaluated if an upstream Handler filters it out. Just wrap any function which takes no arguments with the log.Lazy type. For example: If this message is not logged for any reason (like logging at the Error level), then factorRSAKey is never evaluated. The same log.Lazy mechanism can be used to attach context to a logger which you want to be evaluated when the message is logged, but not when the logger is created. For example, let's imagine a game where you have Player objects: You always want to log a player's name and whether they're alive or dead, so when you create the player object, you might do: Only now, even after a player has died, the logger will still report they are alive because the logging context is evaluated when the logger was created. By using the Lazy wrapper, we can defer the evaluation of whether the player is alive or not to each log message, so that the log records will reflect the player's current state no matter when the log message is written: If log15 detects that stdout is a terminal, it will configure the default handler for it (which is log.StdoutHandler) to use TerminalFormat. This format logs records nicely for your terminal, including color-coded output based on log level. Becasuse log15 allows you to step around the type system, there are a few ways you can specify invalid arguments to the logging functions. You could, for example, wrap something that is not a zero-argument function with log.Lazy or pass a context key that is not a string. Since logging libraries are typically the mechanism by which errors are reported, it would be onerous for the logging functions to return errors. Instead, log15 handles errors by making these guarantees to you: - Any log record containing an error will still be printed with the error explained to you as part of the log record. - Any log record containing an error will include the context key LOG15_ERROR, enabling you to easily (and if you like, automatically) detect if any of your logging calls are passing bad values. Understanding this, you might wonder why the Handler interface can return an error value in its Log method. Handlers are encouraged to return errors only if they fail to write their log records out to an external source like if the syslog daemon is not responding. This allows the construction of useful handlers which cope with those failures like the FailoverHandler. log15 is intended to be useful for library authors as a way to provide configurable logging to users of their library. Best practice for use in a library is to always disable all output for your logger by default and to provide a public Logger instance that consumers of your library can configure. Like so: Users of your library may then enable it if they like: The ability to attach context to a logger is a powerful one. Where should you do it and why? I favor embedding a Logger directly into any persistent object in my application and adding unique, tracing context keys to it. For instance, imagine I am writing a web browser: When a new tab is created, I assign a logger to it with the url of the tab as context so it can easily be traced through the logs. Now, whenever we perform any operation with the tab, we'll log with its embedded logger and it will include the tab title automatically: There's only one problem. What if the tab url changes? We could use log.Lazy to make sure the current url is always written, but that would mean that we couldn't trace a tab's full lifetime through our logs after the user navigate to a new URL. Instead, think about what values to attach to your loggers the same way you think about what to use as a key in a SQL database schema. If it's possible to use a natural key that is unique for the lifetime of the object, do so. But otherwise, log15's ext package has a handy RandId function to let you generate what you might call "surrogate keys" They're just random hex identifiers to use for tracing. Back to our Tab example, we would prefer to set up our Logger like so: Now we'll have a unique traceable identifier even across loading new urls, but we'll still be able to see the tab's current url in the log messages. For all Handler functions which can return an error, there is a version of that function which will return no error but panics on failure. They are all available on the Must object. For example: All of the following excellent projects inspired the design of this library: code.google.com/p/log4go github.com/op/go-logging github.com/technoweenie/grohl github.com/Sirupsen/logrus github.com/kr/logfmt github.com/spacemonkeygo/spacelog golang's stdlib, notably io and net/http https://xkcd.com/927/

Package dht implements a distributed hash table that satisfies the ipfs routing interface. This DHT is modeled after kademlia with S/Kademlia modifications. Package dht implements a distributed hash table that satisfies the ipfs routing interface. This DHT is modeled after Kademlia with S/Kademlia modifications. package query implement a query manager to drive concurrent workers to query the DHT. A query is setup with a target key, a queryFunc tasked to communicate with a peer, and a set of initial peers. As the query progress, queryFunc can return closer peers that will be used to navigate closer to the target key in the DHT until an answer is reached.

citadel is a family of Security Operations (SecOps) microservices In Citadel, each service functions independently, allowing you to use all of them or just the service that fits your needs. The goal of the project is to provide a baseline set of services that a team could easily use to store, query, and interact with Security Operations resources. REST API Documentation can be found in the following places: Download the project, install dependencies, and generate updated docs and services Usage (Server) Navigate to the cmd/citadel directory to start up the server Usage (CLI) Navigate to the cmd/citadel-cli directory to use the command line interface

Package log15 provides an opinionated, simple toolkit for best-practice logging that is both human and machine readable. It is modeled after the standard library's io and net/http packages. This package enforces you to only log key/value pairs. Keys must be strings. Values may be any type that you like. The default output format is logfmt, but you may also choose to use JSON instead if that suits you. Here's how you log: This will output a line that looks like: To get started, you'll want to import the library: Now you're ready to start logging: Because recording a human-meaningful message is common and good practice, the first argument to every logging method is the value to the *implicit* key 'msg'. Additionally, the level you choose for a message will be automatically added with the key 'lvl', and so will the current timestamp with key 't'. You may supply any additional context as a set of key/value pairs to the logging function. log15 allows you to favor terseness, ordering, and speed over safety. This is a reasonable tradeoff for logging functions. You don't need to explicitly state keys/values, log15 understands that they alternate in the variadic argument list: If you really do favor your type-safety, you may choose to pass a log.Ctx instead: Frequently, you want to add context to a logger so that you can track actions associated with it. An http request is a good example. You can easily create new loggers that have context that is automatically included with each log line: This will output a log line that includes the path context that is attached to the logger: The Handler interface defines where log lines are printed to and how they are formated. Handler is a single interface that is inspired by net/http's handler interface: Handlers can filter records, format them, or dispatch to multiple other Handlers. This package implements a number of Handlers for common logging patterns that are easily composed to create flexible, custom logging structures. Here's an example handler that prints logfmt output to Stdout: Here's an example handler that defers to two other handlers. One handler only prints records from the rpc package in logfmt to standard out. The other prints records at Error level or above in JSON formatted output to the file /var/log/service.json This package implements three Handlers that add debugging information to the context, CallerFileHandler, CallerFuncHandler and CallerStackHandler. Here's an example that adds the source file and line number of each logging call to the context. This will output a line that looks like: Here's an example that logs the call stack rather than just the call site. This will output a line that looks like: The "%+v" format instructs the handler to include the path of the source file relative to the compile time GOPATH. The github.com/go-stack/stack package documents the full list of formatting verbs and modifiers available. The Handler interface is so simple that it's also trivial to write your own. Let's create an example handler which tries to write to one handler, but if that fails it falls back to writing to another handler and includes the error that it encountered when trying to write to the primary. This might be useful when trying to log over a network socket, but if that fails you want to log those records to a file on disk. This pattern is so useful that a generic version that handles an arbitrary number of Handlers is included as part of this library called FailoverHandler. Sometimes, you want to log values that are extremely expensive to compute, but you don't want to pay the price of computing them if you haven't turned up your logging level to a high level of detail. This package provides a simple type to annotate a logging operation that you want to be evaluated lazily, just when it is about to be logged, so that it would not be evaluated if an upstream Handler filters it out. Just wrap any function which takes no arguments with the log.Lazy type. For example: If this message is not logged for any reason (like logging at the Error level), then factorRSAKey is never evaluated. The same log.Lazy mechanism can be used to attach context to a logger which you want to be evaluated when the message is logged, but not when the logger is created. For example, let's imagine a game where you have Player objects: You always want to log a player's name and whether they're alive or dead, so when you create the player object, you might do: Only now, even after a player has died, the logger will still report they are alive because the logging context is evaluated when the logger was created. By using the Lazy wrapper, we can defer the evaluation of whether the player is alive or not to each log message, so that the log records will reflect the player's current state no matter when the log message is written: If log15 detects that stdout is a terminal, it will configure the default handler for it (which is log.StdoutHandler) to use TerminalFormat. This format logs records nicely for your terminal, including color-coded output based on log level. Becasuse log15 allows you to step around the type system, there are a few ways you can specify invalid arguments to the logging functions. You could, for example, wrap something that is not a zero-argument function with log.Lazy or pass a context key that is not a string. Since logging libraries are typically the mechanism by which errors are reported, it would be onerous for the logging functions to return errors. Instead, log15 handles errors by making these guarantees to you: - Any log record containing an error will still be printed with the error explained to you as part of the log record. - Any log record containing an error will include the context key LOG15_ERROR, enabling you to easily (and if you like, automatically) detect if any of your logging calls are passing bad values. Understanding this, you might wonder why the Handler interface can return an error value in its Log method. Handlers are encouraged to return errors only if they fail to write their log records out to an external source like if the syslog daemon is not responding. This allows the construction of useful handlers which cope with those failures like the FailoverHandler. log15 is intended to be useful for library authors as a way to provide configurable logging to users of their library. Best practice for use in a library is to always disable all output for your logger by default and to provide a public Logger instance that consumers of your library can configure. Like so: Users of your library may then enable it if they like: The ability to attach context to a logger is a powerful one. Where should you do it and why? I favor embedding a Logger directly into any persistent object in my application and adding unique, tracing context keys to it. For instance, imagine I am writing a web browser: When a new tab is created, I assign a logger to it with the url of the tab as context so it can easily be traced through the logs. Now, whenever we perform any operation with the tab, we'll log with its embedded logger and it will include the tab title automatically: There's only one problem. What if the tab url changes? We could use log.Lazy to make sure the current url is always written, but that would mean that we couldn't trace a tab's full lifetime through our logs after the user navigate to a new URL. Instead, think about what values to attach to your loggers the same way you think about what to use as a key in a SQL database schema. If it's possible to use a natural key that is unique for the lifetime of the object, do so. But otherwise, log15's ext package has a handy RandId function to let you generate what you might call "surrogate keys" They're just random hex identifiers to use for tracing. Back to our Tab example, we would prefer to set up our Logger like so: Now we'll have a unique traceable identifier even across loading new urls, but we'll still be able to see the tab's current url in the log messages. For all Handler functions which can return an error, there is a version of that function which will return no error but panics on failure. They are all available on the Must object. For example: All of the following excellent projects inspired the design of this library: code.google.com/p/log4go github.com/op/go-logging github.com/technoweenie/grohl github.com/Sirupsen/logrus github.com/kr/logfmt github.com/spacemonkeygo/spacelog golang's stdlib, notably io and net/http https://xkcd.com/927/

Package nokiahealth is a client module for working with the Nokia Health (previously Withings) API. The current version (v2) of this module has been updated to work with the newer Oauth2 implementation of the API. As with all Oauth2 APIs, you must obtain authorization to access the users data. To do so you must first register your application with the nokia health api to obtain a ClientID and ClientSecret. Once you have your client information you can now create a new client. You will need to also provide the redirectURL you provided during application registration. This URL is where the user will be redirected to and will include the code you need to generate the accessToken needed to access the users data. Under normal situations you should have an http server listening on that address and pull the code from the URL query parameters. You can now use the client to generate the authorization URL the user needs to navigate to. This URL will take the user to a page that will prompt them to allow your application to access their data. The state string returned by the method is a randomly generated BASE64 string using the crypto/rand module. It will be returned in the redirect as a query parameter and the two values verified they match. It's not required, but useful for security reasons. Using the code returned by the redirect in the query parameters, you can generate a new user. This user struct can be used to immediately perform actions against the users data. It also contains the token you should save somewhere for reuse. Obviously use whatever context you would like here. Make sure the save at least the refreshToken for accessing the user data at a later date. You may also save the accessToken, but it does expire and creating a new client from saved token data only requires the refreshToken. You can easily create a user from a saved token using the NewUserFromRefreshToken method. A working configured client is required for the user generated from this method to work. The user struct has various methods associated with each API endpoint to perform data retrieval. The methods take a specific param struct specifying the api options to use on the request. The API is a bit "special" so the params vary a bit between each method. The client does what it can to smooth those out but there is only so much that can be done. Every method has two forms, one that accepts a context and one that does now. This allows you to provide a context on each request if you would like to. By default all methods utilize a context to timeout the request to the API. The value of the timeout is stored on the Client and can be access as/set on Client.Timeout. Setting is _not_ thread safe and should only be set on client creation. If you need to change the timeout for different requests use the methodCtx variant of the method. By default the state generated by the AuthCodeURL utilized crypto/rand. If you would like to implement your own random method you can do so by assigning the function to Rand field of the Client struct. The function should support the Rand type. Also this is _not_ thread safe so only perform this action on client creation. By default every returned response will be parsed and the parsed data returned. If you need access to the raw request data you can enable it by setting the SaveRawResponse field of the client struct to true. This should be done at client creation time. With it set to true the RawResponse field of the returned structs will include the raw response. Some data request methods include a parseResponse field on the params struct. If this is included additional parsing is performed to make the data more usable. This can be seen on GetBodyMeasures for example. You can include the path fields sent to the API by setting IncludePath to true on the client. This is primarily used for debugging but could be helpful in some situations. By default the client will request all known scopes. If you would like to pair down this you can change the scope by using the SetScope method of the client. Consts in the form of ScopeXxx are provided to aid selection.

Package log15 provides an opinionated, simple toolkit for best-practice logging that is both human and machine readable. It is modeled after the standard library's io and net/http packages. This package enforces you to only log key/value pairs. Keys must be strings. Values may be any type that you like. The default output format is logfmt, but you may also choose to use JSON instead if that suits you. Here's how you log: This will output a line that looks like: To get started, you'll want to import the library: Now you're ready to start logging: Because recording a human-meaningful message is common and good practice, the first argument to every logging method is the value to the *implicit* key 'msg'. Additionally, the level you choose for a message will be automatically added with the key 'lvl', and so will the current timestamp with key 't'. You may supply any additional context as a set of key/value pairs to the logging function. log15 allows you to favor terseness, ordering, and speed over safety. This is a reasonable tradeoff for logging functions. You don't need to explicitly state keys/values, log15 understands that they alternate in the variadic argument list: If you really do favor your type-safety, you may choose to pass a log.Ctx instead: Frequently, you want to add context to a logger so that you can track actions associated with it. An http request is a good example. You can easily create new loggers that have context that is automatically included with each log line: This will output a log line that includes the path context that is attached to the logger: The Handler interface defines where log lines are printed to and how they are formated. Handler is a single interface that is inspired by net/http's handler interface: Handlers can filter records, format them, or dispatch to multiple other Handlers. This package implements a number of Handlers for common logging patterns that are easily composed to create flexible, custom logging structures. Here's an example handler that prints logfmt output to Stdout: Here's an example handler that defers to two other handlers. One handler only prints records from the rpc package in logfmt to standard out. The other prints records at Error level or above in JSON formatted output to the file /var/log/service.json This package implements three Handlers that add debugging information to the context, CallerFileHandler, CallerFuncHandler and CallerStackHandler. Here's an example that adds the source file and line number of each logging call to the context. This will output a line that looks like: Here's an example that logs the call stack rather than just the call site. This will output a line that looks like: The "%+v" format instructs the handler to include the path of the source file relative to the compile time GOPATH. The log15/stack package documents the full list of formatting verbs and modifiers available. The Handler interface is so simple that it's also trivial to write your own. Let's create an example handler which tries to write to one handler, but if that fails it falls back to writing to another handler and includes the error that it encountered when trying to write to the primary. This might be useful when trying to log over a network socket, but if that fails you want to log those records to a file on disk. This pattern is so useful that a generic version that handles an arbitrary number of Handlers is included as part of this library called FailoverHandler. Sometimes, you want to log values that are extremely expensive to compute, but you don't want to pay the price of computing them if you haven't turned up your logging level to a high level of detail. This package provides a simple type to annotate a logging operation that you want to be evaluated lazily, just when it is about to be logged, so that it would not be evaluated if an upstream Handler filters it out. Just wrap any function which takes no arguments with the log.Lazy type. For example: If this message is not logged for any reason (like logging at the Error level), then factorRSAKey is never evaluated. The same log.Lazy mechanism can be used to attach context to a logger which you want to be evaluated when the message is logged, but not when the logger is created. For example, let's imagine a game where you have Player objects: You always want to log a player's name and whether they're alive or dead, so when you create the player object, you might do: Only now, even after a player has died, the logger will still report they are alive because the logging context is evaluated when the logger was created. By using the Lazy wrapper, we can defer the evaluation of whether the player is alive or not to each log message, so that the log records will reflect the player's current state no matter when the log message is written: If log15 detects that stdout is a terminal, it will configure the default handler for it (which is log.StdoutHandler) to use TerminalFormat. This format logs records nicely for your terminal, including color-coded output based on log level. Becasuse log15 allows you to step around the type system, there are a few ways you can specify invalid arguments to the logging functions. You could, for example, wrap something that is not a zero-argument function with log.Lazy or pass a context key that is not a string. Since logging libraries are typically the mechanism by which errors are reported, it would be onerous for the logging functions to return errors. Instead, log15 handles errors by making these guarantees to you: - Any log record containing an error will still be printed with the error explained to you as part of the log record. - Any log record containing an error will include the context key LOG15_ERROR, enabling you to easily (and if you like, automatically) detect if any of your logging calls are passing bad values. Understanding this, you might wonder why the Handler interface can return an error value in its Log method. Handlers are encouraged to return errors only if they fail to write their log records out to an external source like if the syslog daemon is not responding. This allows the construction of useful handlers which cope with those failures like the FailoverHandler. log15 is intended to be useful for library authors as a way to provide configurable logging to users of their library. Best practice for use in a library is to always disable all output for your logger by default and to provide a public Logger instance that consumers of your library can configure. Like so: Users of your library may then enable it if they like: The ability to attach context to a logger is a powerful one. Where should you do it and why? I favor embedding a Logger directly into any persistent object in my application and adding unique, tracing context keys to it. For instance, imagine I am writing a web browser: When a new tab is created, I assign a logger to it with the url of the tab as context so it can easily be traced through the logs. Now, whenever we perform any operation with the tab, we'll log with its embedded logger and it will include the tab title automatically: There's only one problem. What if the tab url changes? We could use log.Lazy to make sure the current url is always written, but that would mean that we couldn't trace a tab's full lifetime through our logs after the user navigate to a new URL. Instead, think about what values to attach to your loggers the same way you think about what to use as a key in a SQL database schema. If it's possible to use a natural key that is unique for the lifetime of the object, do so. But otherwise, log15's ext package has a handy RandId function to let you generate what you might call "surrogate keys" They're just random hex identifiers to use for tracing. Back to our Tab example, we would prefer to set up our Logger like so: Now we'll have a unique traceable identifier even across loading new urls, but we'll still be able to see the tab's current url in the log messages. For all Handler functions which can return an error, there is a version of that function which will return no error but panics on failure. They are all available on the Must object. For example: All of the following excellent projects inspired the design of this library: code.google.com/p/log4go github.com/op/go-logging github.com/technoweenie/grohl github.com/Sirupsen/logrus github.com/kr/logfmt github.com/spacemonkeygo/spacelog golang's stdlib, notably io and net/http https://xkcd.com/927/

Package log15 provides an opinionated, simple toolkit for best-practice logging that is both human and machine readable. It is modeled after the standard library's io and net/http packages. This package enforces you to only log key/value pairs. Keys must be strings. Values may be any type that you like. The default output format is logfmt, but you may also choose to use JSON instead if that suits you. Here's how you log: This will output a line that looks like: To get started, you'll want to import the library: Now you're ready to start logging: Because recording a human-meaningful message is common and good practice, the first argument to every logging method is the value to the *implicit* key 'msg'. Additionally, the level you choose for a message will be automatically added with the key 'lvl', and so will the current timestamp with key 't'. You may supply any additional context as a set of key/value pairs to the logging function. log15 allows you to favor terseness, ordering, and speed over safety. This is a reasonable tradeoff for logging functions. You don't need to explicitly state keys/values, log15 understands that they alternate in the variadic argument list: If you really do favor your type-safety, you may choose to pass a log.Ctx instead: Frequently, you want to add context to a logger so that you can track actions associated with it. An http request is a good example. You can easily create new loggers that have context that is automatically included with each log line: This will output a log line that includes the path context that is attached to the logger: The Handler interface defines where log lines are printed to and how they are formated. Handler is a single interface that is inspired by net/http's handler interface: Handlers can filter records, format them, or dispatch to multiple other Handlers. This package implements a number of Handlers for common logging patterns that are easily composed to create flexible, custom logging structures. Here's an example handler that prints logfmt output to Stdout: Here's an example handler that defers to two other handlers. One handler only prints records from the rpc package in logfmt to standard out. The other prints records at Error level or above in JSON formatted output to the file /var/log/service.json This package implements three Handlers that add debugging information to the context, CallerFileHandler, CallerFuncHandler and CallerStackHandler. Here's an example that adds the source file and line number of each logging call to the context. This will output a line that looks like: Here's an example that logs the call stack rather than just the call site. This will output a line that looks like: The "%+v" format instructs the handler to include the path of the source file relative to the compile time GOPATH. The github.com/go-stack/stack package documents the full list of formatting verbs and modifiers available. The Handler interface is so simple that it's also trivial to write your own. Let's create an example handler which tries to write to one handler, but if that fails it falls back to writing to another handler and includes the error that it encountered when trying to write to the primary. This might be useful when trying to log over a network socket, but if that fails you want to log those records to a file on disk. This pattern is so useful that a generic version that handles an arbitrary number of Handlers is included as part of this library called FailoverHandler. Sometimes, you want to log values that are extremely expensive to compute, but you don't want to pay the price of computing them if you haven't turned up your logging level to a high level of detail. This package provides a simple type to annotate a logging operation that you want to be evaluated lazily, just when it is about to be logged, so that it would not be evaluated if an upstream Handler filters it out. Just wrap any function which takes no arguments with the log.Lazy type. For example: If this message is not logged for any reason (like logging at the Error level), then factorRSAKey is never evaluated. The same log.Lazy mechanism can be used to attach context to a logger which you want to be evaluated when the message is logged, but not when the logger is created. For example, let's imagine a game where you have Player objects: You always want to log a player's name and whether they're alive or dead, so when you create the player object, you might do: Only now, even after a player has died, the logger will still report they are alive because the logging context is evaluated when the logger was created. By using the Lazy wrapper, we can defer the evaluation of whether the player is alive or not to each log message, so that the log records will reflect the player's current state no matter when the log message is written: If log15 detects that stdout is a terminal, it will configure the default handler for it (which is log.StdoutHandler) to use TerminalFormat. This format logs records nicely for your terminal, including color-coded output based on log level. Becasuse log15 allows you to step around the type system, there are a few ways you can specify invalid arguments to the logging functions. You could, for example, wrap something that is not a zero-argument function with log.Lazy or pass a context key that is not a string. Since logging libraries are typically the mechanism by which errors are reported, it would be onerous for the logging functions to return errors. Instead, log15 handles errors by making these guarantees to you: - Any log record containing an error will still be printed with the error explained to you as part of the log record. - Any log record containing an error will include the context key LOG15_ERROR, enabling you to easily (and if you like, automatically) detect if any of your logging calls are passing bad values. Understanding this, you might wonder why the Handler interface can return an error value in its Log method. Handlers are encouraged to return errors only if they fail to write their log records out to an external source like if the syslog daemon is not responding. This allows the construction of useful handlers which cope with those failures like the FailoverHandler. log15 is intended to be useful for library authors as a way to provide configurable logging to users of their library. Best practice for use in a library is to always disable all output for your logger by default and to provide a public Logger instance that consumers of your library can configure. Like so: Users of your library may then enable it if they like: The ability to attach context to a logger is a powerful one. Where should you do it and why? I favor embedding a Logger directly into any persistent object in my application and adding unique, tracing context keys to it. For instance, imagine I am writing a web browser: When a new tab is created, I assign a logger to it with the url of the tab as context so it can easily be traced through the logs. Now, whenever we perform any operation with the tab, we'll log with its embedded logger and it will include the tab title automatically: There's only one problem. What if the tab url changes? We could use log.Lazy to make sure the current url is always written, but that would mean that we couldn't trace a tab's full lifetime through our logs after the user navigate to a new URL. Instead, think about what values to attach to your loggers the same way you think about what to use as a key in a SQL database schema. If it's possible to use a natural key that is unique for the lifetime of the object, do so. But otherwise, log15's ext package has a handy RandId function to let you generate what you might call "surrogate keys" They're just random hex identifiers to use for tracing. Back to our Tab example, we would prefer to set up our Logger like so: Now we'll have a unique traceable identifier even across loading new urls, but we'll still be able to see the tab's current url in the log messages. For all Handler functions which can return an error, there is a version of that function which will return no error but panics on failure. They are all available on the Must object. For example: All of the following excellent projects inspired the design of this library: code.google.com/p/log4go github.com/op/go-logging github.com/technoweenie/grohl github.com/Sirupsen/logrus github.com/kr/logfmt github.com/spacemonkeygo/spacelog golang's stdlib, notably io and net/http https://xkcd.com/927/

Package iris provides a beautifully expressive and easy to use foundation for your next website, API, or distributed app. Source code and other details for the project are available at GitHub: 11.1.1 The only requirement is the Go Programming Language, at least version 1.8 but 1.11.1 and above is highly recommended. Example code: You can start the server(s) listening to any type of `net.Listener` or even `http.Server` instance. The method for initialization of the server should be passed at the end, via `Run` function. Below you'll see some useful examples: UNIX and BSD hosts can take advantage of the reuse port feature. Example code: That's all with listening, you have the full control when you need it. Let's continue by learning how to catch CONTROL+C/COMMAND+C or unix kill command and shutdown the server gracefully. In order to manually manage what to do when app is interrupted, we have to disable the default behavior with the option `WithoutInterruptHandler` and register a new interrupt handler (globally, across all possible hosts). Example code: Access to all hosts that serve your application can be provided by the `Application#Hosts` field, after the `Run` method. But the most common scenario is that you may need access to the host before the `Run` method, there are two ways of gain access to the host supervisor, read below. First way is to use the `app.NewHost` to create a new host and use one of its `Serve` or `Listen` functions to start the application via the `iris#Raw` Runner. Note that this way needs an extra import of the `net/http` package. Example Code: Second, and probably easier way is to use the `host.Configurator`. Note that this method requires an extra import statement of "github.com/kataras/iris/core/host" when using go < 1.9, if you're targeting on go1.9 then you can use the `iris#Supervisor` and omit the extra host import. All common `Runners` we saw earlier (`iris#Addr, iris#Listener, iris#Server, iris#TLS, iris#AutoTLS`) accept a variadic argument of `host.Configurator`, there are just `func(*host.Supervisor)`. Therefore the `Application` gives you the rights to modify the auto-created host supervisor through these. Example Code: Read more about listening and gracefully shutdown by navigating to: All HTTP methods are supported, developers can also register handlers for same paths for different methods. The first parameter is the HTTP Method, second parameter is the request path of the route, third variadic parameter should contains one or more iris.Handler executed by the registered order when a user requests for that specific resouce path from the server. Example code: In order to make things easier for the user, iris provides functions for all HTTP Methods. The first parameter is the request path of the route, second variadic parameter should contains one or more iris.Handler executed by the registered order when a user requests for that specific resouce path from the server. Example code: A set of routes that are being groupped by path prefix can (optionally) share the same middleware handlers and template layout. A group can have a nested group too. `.Party` is being used to group routes, developers can declare an unlimited number of (nested) groups. Example code: iris developers are able to register their own handlers for http statuses like 404 not found, 500 internal server error and so on. Example code: With the help of iris's expressionist router you can build any form of API you desire, with safety. Example code: At the previous example, we've seen static routes, group of routes, subdomains, wildcard subdomains, a small example of parameterized path with a single known parameter and custom http errors, now it's time to see wildcard parameters and macros. iris, like net/http std package registers route's handlers by a Handler, the iris' type of handler is just a func(ctx iris.Context) where context comes from github.com/kataras/iris/context. Iris has the easiest and the most powerful routing process you have ever meet. At the same time, iris has its own interpeter(yes like a programming language) for route's path syntax and their dynamic path parameters parsing and evaluation, We call them "macros" for shortcut. How? It calculates its needs and if not any special regexp needed then it just registers the route with the low-level path syntax, otherwise it pre-compiles the regexp and adds the necessary middleware(s). Standard macro types for parameters: if type is missing then parameter's type is defaulted to string, so {param} == {param:string}. If a function not found on that type then the "string"'s types functions are being used. i.e: Besides the fact that iris provides the basic types and some default "macro funcs" you are able to register your own too!. Register a named path parameter function: at the func(argument ...) you can have any standard type, it will be validated before the server starts so don't care about performance here, the only thing it runs at serve time is the returning func(paramValue string) bool. Example Code: Last, do not confuse ctx.Values() with ctx.Params(). Path parameter's values goes to ctx.Params() and context's local storage that can be used to communicate between handlers and middleware(s) goes to ctx.Values(), path parameters and the rest of any custom values are separated for your own good. Run Static Files Example code: More examples can be found here: https://github.com/kataras/iris/tree/master/_examples/beginner/file-server Middleware is just a concept of ordered chain of handlers. Middleware can be registered globally, per-party, per-subdomain and per-route. Example code: iris is able to wrap and convert any external, third-party Handler you used to use to your web application. Let's convert the https://github.com/rs/cors net/http external middleware which returns a `next form` handler. Example code: Iris supports 5 template engines out-of-the-box, developers can still use any external golang template engine, as `context/context#ResponseWriter()` is an `io.Writer`. All of these five template engines have common features with common API, like Layout, Template Funcs, Party-specific layout, partial rendering and more. Example code: View engine supports bundled(https://github.com/shuLhan/go-bindata) template files too. go-bindata gives you two functions, asset and assetNames, these can be setted to each of the template engines using the `.Binary` func. Example code: A real example can be found here: https://github.com/kataras/iris/tree/master/_examples/view/embedding-templates-into-app. Enable auto-reloading of templates on each request. Useful while developers are in dev mode as they no neeed to restart their app on every template edit. Example code: Note: In case you're wondering, the code behind the view engines derives from the "github.com/kataras/iris/view" package, access to the engines' variables can be granded by "github.com/kataras/iris" package too. Each one of these template engines has different options located here: https://github.com/kataras/iris/tree/master/view . This example will show how to store and access data from a session. You don’t need any third-party library, but If you want you can use any session manager compatible or not. In this example we will only allow authenticated users to view our secret message on the /secret page. To get access to it, the will first have to visit /login to get a valid session cookie, which logs him in. Additionally he can visit /logout to revoke his access to our secret message. Example code: Running the example: Sessions persistence can be achieved using one (or more) `sessiondb`. Example Code: More examples: In this example we will create a small chat between web sockets via browser. Example Server Code: Example Client(javascript) Code: Running the example: Iris has first-class support for the MVC pattern, you'll not find these stuff anywhere else in the Go world. Example Code: // GetUserBy serves // Method: GET // Resource: http://localhost:8080/user/{username:string} // By is a reserved "keyword" to tell the framework that you're going to // bind path parameters in the function's input arguments, and it also // helps to have "Get" and "GetBy" in the same controller. // // func (c *ExampleController) GetUserBy(username string) mvc.Result { // return mvc.View{ // Name: "user/username.html", // Data: username, // } // } Can use more than one, the factory will make sure that the correct http methods are being registered for each route for this controller, uncomment these if you want: Iris web framework supports Request data, Models, Persistence Data and Binding with the fastest possible execution. Characteristics: All HTTP Methods are supported, for example if want to serve `GET` then the controller should have a function named `Get()`, you can define more than one method function to serve in the same Controller. Register custom controller's struct's methods as handlers with custom paths(even with regex parametermized path) via the `BeforeActivation` custom event callback, per-controller. Example: Persistence data inside your Controller struct (share data between requests) by defining services to the Dependencies or have a `Singleton` controller scope. Share the dependencies between controllers or register them on a parent MVC Application, and ability to modify dependencies per-controller on the `BeforeActivation` optional event callback inside a Controller, i.e Access to the `Context` as a controller's field(no manual binding is neede) i.e `Ctx iris.Context` or via a method's input argument, i.e Models inside your Controller struct (set-ed at the Method function and rendered by the View). You can return models from a controller's method or set a field in the request lifecycle and return that field to another method, in the same request lifecycle. Flow as you used to, mvc application has its own `Router` which is a type of `iris/router.Party`, the standard iris api. `Controllers` can be registered to any `Party`, including Subdomains, the Party's begin and done handlers work as expected. Optional `BeginRequest(ctx)` function to perform any initialization before the method execution, useful to call middlewares or when many methods use the same collection of data. Optional `EndRequest(ctx)` function to perform any finalization after any method executed. Session dynamic dependency via manager's `Start` to the MVC Application, i.e Inheritance, recursively. Access to the dynamic path parameters via the controller's methods' input arguments, no binding is needed. When you use the Iris' default syntax to parse handlers from a controller, you need to suffix the methods with the `By` word, uppercase is a new sub path. Example: Register one or more relative paths and able to get path parameters, i.e Response via output arguments, optionally, i.e Where `any` means everything, from custom structs to standard language's types-. `Result` is an interface which contains only that function: Dispatch(ctx iris.Context) and Get where HTTP Method function(Post, Put, Delete...). Iris has a very powerful and blazing fast MVC support, you can return any value of any type from a method function and it will be sent to the client as expected. * if `string` then it's the body. * if `string` is the second output argument then it's the content type. * if `int` then it's the status code. * if `bool` is false then it throws 404 not found http error by skipping everything else. * if `error` and not nil then (any type) response will be omitted and error's text with a 400 bad request will be rendered instead. * if `(int, error)` and error is not nil then the response result will be the error's text with the status code as `int`. * if `custom struct` or `interface{}` or `slice` or `map` then it will be rendered as json, unless a `string` content type is following. * if `mvc.Result` then it executes its `Dispatch` function, so good design patters can be used to split the model's logic where needed. Examples with good patterns to follow but not intend to be used in production of course can be found at: https://github.com/kataras/iris/tree/master/_examples/#mvc. By creating components that are independent of one another, developers are able to reuse components quickly and easily in other applications. The same (or similar) view for one application can be refactored for another application with different data because the view is simply handling how the data is being displayed to the user. If you're new to back-end web development read about the MVC architectural pattern first, a good start is that wikipedia article: https://en.wikipedia.org/wiki/Model%E2%80%93view%E2%80%93controller. But you should have a basic idea of the framework by now, we just scratched the surface. If you enjoy what you just saw and want to learn more, please follow the below links: Examples: Middleware: Home Page: Book (in-progress):

Package gochrome aims to be a complete Chrome DevTools Protocol Viewer implementation. Versioned packages are available. Curently the only version is `tot` or Tip-of-Tree. Stable versions will be made available in the future. This is beta software and hasn't been well exercised in real-world applications. See https://chromedevtools.github.io/devtools-protocol/ The Chrome DevTools Protocol allows for tools to instrument, inspect, debug and profile Chromium, Chrome and other Blink-based browsers. Many existing projects currently use the protocol. The Chrome DevTools uses this protocol and the team maintains its API. Instrumentation is divided into a number of domains (DOM, Debugger, Network etc.). Each domain defines a number of commands it supports and events it generates. Both commands and events are serialized JSON objects of a fixed structure. You can either debug over the wire using the raw messages as they are described in the corresponding domain documentation, or use extension JavaScript API. The latest (tip-of-tree) protocol (tot) It changes frequently and can break at any time. However it captures the full capabilities of the Protocol, whereas the stable release is a subset. There is no backwards compatibility support guaranteed for the capabilities it introduces. Resources Basics: Using DevTools as protocol client The Developer Tools front-end can attach to a remotely running Chrome instance for debugging. For this scenario to work, you should start your host Chrome instance with the remote-debugging-port command line switch: Then you can start a separate client Chrome instance, using a distinct user profile: Now you can navigate to the given port from your client and attach to any of the discovered tabs for debugging: http://localhost:9222 You will find the Developer Tools interface identical to the embedded one and here is why: In this scenario, you can substitute Developer Tools front-end with your own implementation. Instead of navigating to the HTML page at http://localhost:9222, your application can discover available pages by requesting: http://localhost:9222/json and getting a JSON object with information about inspectable pages along with the WebSocket addresses that you could use in order to start instrumenting them. Remote debugging is especially useful when debugging remote instances of the browser or attaching to the embedded devices. Blink port owners are responsible for exposing debugging connections to the external users. This is especially handy to understand how the DevTools frontend makes use of the protocol. First, run Chrome with the debugging port open: Then, select the Chromium Projects item in the Inspectable Pages list. Now that DevTools is up and fullscreen, open DevTools to inspect it. Cmd-R in the new inspector to make the first restart. Now head to Network Panel, filter by Websocket, select the connection and click the Frames tab. Now you can easily see the frames of WebSocket activity as you use the first instance of the DevTools. To allow chrome extensions to interact with the protocol, we introduced chrome.debugger extension API that exposes this JSON message transport interface. As a result, you can not only attach to the remotely running Chrome instance, but also instrument it from its own extension. Chrome Debugger Extension API provides a higher level API where command domain, name and body are provided explicitly in the `sendCommand` call. This API hides request ids and handles binding of the request with its response, hence allowing `sendCommand` to report result in the callback function call. One can also use this API in combination with the other Extension APIs. If you are developing a Web-based IDE, you should implement an extension that exposes debugging capabilities to your page and your IDE will be able to open pages with the target application, set breakpoints there, evaluate expressions in console, live edit JavaScript and CSS, display live DOM, network interaction and any other aspect that Developer Tools is instrumenting today. Opening embedded Developer Tools will terminate the remote connection and thus detach the extension. https://chromedevtools.github.io/devtools-protocol/#simultaneous The canonical protocol definitions live in the Chromium source tree: (browser_protocol.json and js_protocol.json). They are maintained manually by the DevTools engineering team. These files are mirrored (hourly) on GitHub in the devtools-protocol repo. The declarative protocol definitions are used across tools. Within Chromium, a binding layer is created for the Chrome DevTools to interact with, and separately the protocol is used for Chrome Headless’s C++ interface. What’s the protocol_externs file It’s created via generate_protocol_externs.py and useful for tools using closure compiler. The TypeScript story is here. Not yet. See bugger-daemon’s third-party docs. See also the endpoints implementation in Chromium. /json/protocol was added in Chrome 60. The endpoint is exposed as webSocketDebuggerUrl in /json/version. Note the browser in the URL, rather than page. If Chrome was launched with --remote-debugging-port=0 and chose an open port, the browser endpoint is written to both stderr and the DevToolsActivePort file in browser profile folder. Yes, as of Chrome 63! See Multi-client remote debugging support. Upon disconnnection, the outgoing client will receive a detached event. For example: View the enum of possible reasons. (For reference: the original patch). After disconnection, some apps have chosen to pause their state and offer a reconnect button.

Package surfdap provides primitives for navigating an LDAP directory tree.

Serve starts a simple temporary static file server in your current directory and prints your IP address to share with colleagues. Initially based on the Gist https://gist.github.com/paulmach/7271283/2a1116ca15e34ee23ac5a3a87e2a626451424993 by Paul Mach (https://github.com/paulmach). Usage: Navigating to http://localhost:8080 will display the index.html or directory listing file.

Package app runs service components, capturing panics and managing shutdown. When building services in Go, there is usually a number of tasks that are being done in a single binary. For example, you might launch an HTTP server, something to send heartbeats to a monitoring service, and something to listen for change events from a configuration service, and each of them has their own goroutines. The biggest headache then with having all these components is managing many goroutines; the most commonly used solution to that is sync.WaitGroup. However, if you want to also have shutdown triggered if an individual component fails, now you start passing state around. Also, issues arise with un-handled panics, so now all your goroutines need to be wrapped in recover() with handlers. app is a very small solution designed for navigating that mess. Features Run something in a goroutine: Wait for all tasks to finish: 1. You can run short tasks or even setup kinds of pieces inside an app.Go - it's just as applicable for short-running things as the long-running app portions. This lets you parallelize your app startup, for example. 2. app.Go can even be run inside another thing run within app.Go - no need to start everything from your 'main' goroutine 3. Closer errors primarily are there for symmetry and logging purposes. Use them if you need them, don't otherwise. 4. app.Main() is actually goroutine-safe; it can be run in multiple places and all of the instances of Main() will return when the app completes running. 5. os.Exit and facilities which call it (like log.Fatal) will cause your app to stop running immediately. This means all your cleanup code will never get a chance to run! Don't use them if you care about clean shutdown. Libraries which manage their own go-routines inside a specific scope and want to control their lifetime should instead use the pgroup sub-package: https://godoc.org/gopkg.in/crast/app.v0/pgroup

Package log15 provides an opinionated, simple toolkit for best-practice logging that is both human and machine readable. It is modeled after the standard library's io and net/http packages. This package enforces you to only log key/value pairs. Keys must be strings. Values may be any type that you like. The default output format is logfmt, but you may also choose to use JSON instead if that suits you. Here's how you log: This will output a line that looks like: To get started, you'll want to import the library: Now you're ready to start logging: Because recording a human-meaningful message is common and good practice, the first argument to every logging method is the value to the *implicit* key 'msg'. Additionally, the level you choose for a message will be automatically added with the key 'lvl', and so will the current timestamp with key 't'. You may supply any additional context as a set of key/value pairs to the logging function. log15 allows you to favor terseness, ordering, and speed over safety. This is a reasonable tradeoff for logging functions. You don't need to explicitly state keys/values, log15 understands that they alternate in the variadic argument list: If you really do favor your type-safety, you may choose to pass a log.Ctx instead: Frequently, you want to add context to a logger so that you can track actions associated with it. An http request is a good example. You can easily create new loggers that have context that is automatically included with each log line: This will output a log line that includes the path context that is attached to the logger: The Handler interface defines where log lines are printed to and how they are formated. Handler is a single interface that is inspired by net/http's handler interface: Handlers can filter records, format them, or dispatch to multiple other Handlers. This package implements a number of Handlers for common logging patterns that are easily composed to create flexible, custom logging structures. Here's an example handler that prints logfmt output to Stdout: Here's an example handler that defers to two other handlers. One handler only prints records from the rpc package in logfmt to standard out. The other prints records at Error level or above in JSON formatted output to the file /var/log/service.json This package implements three Handlers that add debugging information to the context, CallerFileHandler, CallerFuncHandler and CallerStackHandler. Here's an example that adds the source file and line number of each logging call to the context. This will output a line that looks like: Here's an example that logs the call stack rather than just the call site. This will output a line that looks like: The "%+v" format instructs the handler to include the path of the source file relative to the compile time GOPATH. The github.com/go-stack/stack package documents the full list of formatting verbs and modifiers available. The Handler interface is so simple that it's also trivial to write your own. Let's create an example handler which tries to write to one handler, but if that fails it falls back to writing to another handler and includes the error that it encountered when trying to write to the primary. This might be useful when trying to log over a network socket, but if that fails you want to log those records to a file on disk. This pattern is so useful that a generic version that handles an arbitrary number of Handlers is included as part of this library called FailoverHandler. Sometimes, you want to log values that are extremely expensive to compute, but you don't want to pay the price of computing them if you haven't turned up your logging level to a high level of detail. This package provides a simple type to annotate a logging operation that you want to be evaluated lazily, just when it is about to be logged, so that it would not be evaluated if an upstream Handler filters it out. Just wrap any function which takes no arguments with the log.Lazy type. For example: If this message is not logged for any reason (like logging at the Error level), then factorRSAKey is never evaluated. The same log.Lazy mechanism can be used to attach context to a logger which you want to be evaluated when the message is logged, but not when the logger is created. For example, let's imagine a game where you have Player objects: You always want to log a player's name and whether they're alive or dead, so when you create the player object, you might do: Only now, even after a player has died, the logger will still report they are alive because the logging context is evaluated when the logger was created. By using the Lazy wrapper, we can defer the evaluation of whether the player is alive or not to each log message, so that the log records will reflect the player's current state no matter when the log message is written: If log15 detects that stdout is a terminal, it will configure the default handler for it (which is log.StdoutHandler) to use TerminalFormat. This format logs records nicely for your terminal, including color-coded output based on log level. Becasuse log15 allows you to step around the type system, there are a few ways you can specify invalid arguments to the logging functions. You could, for example, wrap something that is not a zero-argument function with log.Lazy or pass a context key that is not a string. Since logging libraries are typically the mechanism by which errors are reported, it would be onerous for the logging functions to return errors. Instead, log15 handles errors by making these guarantees to you: - Any log record containing an error will still be printed with the error explained to you as part of the log record. - Any log record containing an error will include the context key LOG15_ERROR, enabling you to easily (and if you like, automatically) detect if any of your logging calls are passing bad values. Understanding this, you might wonder why the Handler interface can return an error value in its Log method. Handlers are encouraged to return errors only if they fail to write their log records out to an external source like if the syslog daemon is not responding. This allows the construction of useful handlers which cope with those failures like the FailoverHandler. log15 is intended to be useful for library authors as a way to provide configurable logging to users of their library. Best practice for use in a library is to always disable all output for your logger by default and to provide a public Logger instance that consumers of your library can configure. Like so: Users of your library may then enable it if they like: The ability to attach context to a logger is a powerful one. Where should you do it and why? I favor embedding a Logger directly into any persistent object in my application and adding unique, tracing context keys to it. For instance, imagine I am writing a web browser: When a new tab is created, I assign a logger to it with the url of the tab as context so it can easily be traced through the logs. Now, whenever we perform any operation with the tab, we'll log with its embedded logger and it will include the tab title automatically: There's only one problem. What if the tab url changes? We could use log.Lazy to make sure the current url is always written, but that would mean that we couldn't trace a tab's full lifetime through our logs after the user navigate to a new URL. Instead, think about what values to attach to your loggers the same way you think about what to use as a key in a SQL database schema. If it's possible to use a natural key that is unique for the lifetime of the object, do so. But otherwise, log15's ext package has a handy RandId function to let you generate what you might call "surrogate keys" They're just random hex identifiers to use for tracing. Back to our Tab example, we would prefer to set up our Logger like so: Now we'll have a unique traceable identifier even across loading new urls, but we'll still be able to see the tab's current url in the log messages. For all Handler functions which can return an error, there is a version of that function which will return no error but panics on failure. They are all available on the Must object. For example: All of the following excellent projects inspired the design of this library: code.google.com/p/log4go github.com/op/go-logging github.com/technoweenie/grohl github.com/Sirupsen/logrus github.com/kr/logfmt github.com/spacemonkeygo/spacelog golang's stdlib, notably io and net/http https://xkcd.com/927/

Package toml is a TOML markup language parser. This version supports the specification as described in https://github.com/toml-lang/toml/blob/master/versions/en/toml-v0.4.0.md TOML data may be parsed in two ways: by file, or by string. Either way, the result is a TomlTree object that can be used to navigate the structure and data within the original document. After parsing TOML data with Load() or LoadFile(), use the Has() and Get() methods on the returned TomlTree, to find your way through the document data. Go-toml has support for basic dot-separated key paths on the Has(), Get(), Set() and GetDefault() methods. These are the same kind of key paths used within the TOML specification for struct tames. TOML allows keys to contain '.', which can cause this syntax to be problematic for some documents. In such cases, use the GetPath(), HasPath(), and SetPath(), methods to explicitly define the path. This form is also faster, since it avoids having to parse the passed key for '.' delimiters. Note that this is distinct from the heavyweight query syntax supported by TomlTree.Query() and the Query() struct (see below). Each element within the TomlTree is stored with position metadata, which is invaluable for providing semantic feedback to a user. This helps in situations where the TOML file parses correctly, but contains data that is not correct for the application. In such cases, an error message can be generated that indicates the problem line and column number in the source TOML document. The TOML query path implementation is based loosely on the JSONPath specification: http://goessner.net/articles/JsonPath/ The idea behind a query path is to allow quick access to any element, or set of elements within TOML document, with a single expression. This is roughly equivalent to: err is nil if any parsing exception occurs. If no node in the tree matches the query, result will simply contain an empty list of items. As illustrated above, the query path is much more efficient, especially since the structure of the TOML file can vary. Rather than making assumptions about a document's structure, a query allows the programmer to make structured requests into the document, and get zero or more values as a result. The syntax of a query begins with a root token, followed by any number sub-expressions: Index expressions perform no bounds checking, and will contribute no values to the result set if the provided index or index range is invalid. Negative indexes represent values from the end of the array, counting backwards. Slice expressions are supported, by using ':' to separate a start/end index pair. Slice expressions also allow negative indexes for the start and stop arguments. Slice expressions may have an optional stride/step parameter: Slice start and end parameters are also optional: Query filters are used within a Union [,] or single Filter [] expression. A filter only allows nodes that qualify through to the next expression, and/or into the result set. There are several filters provided with the library: An executed query returns a QueryResult object. This contains the nodes in the TOML tree that qualify the query expression. Position information is also available for each value in the set. Queries may be executed directly on a TomlTree object, or compiled ahead of time and executed discretely. The former is more convienent, but has the penalty of having to recompile the query expression each time. Filter expressions may also be user defined by using the SetFilter() function on the Query object. The function must return true/false, which signifies if the passed node is kept or discarded, respectively.