Testing Framework

Package toml provides facilities for decoding and encoding TOML configuration files via reflection. There is also support for delaying decoding with the Primitive type, and querying the set of keys in a TOML document with the MetaData type. The specification implemented: https://github.com/toml-lang/toml The sub-command github.com/BurntSushi/toml/cmd/tomlv can be used to verify whether a file is a valid TOML document. It can also be used to print the type of each key in a TOML document. There are two important types of tests used for this package. The first is contained inside '*_test.go' files and uses the standard Go unit testing framework. These tests are primarily devoted to holistically testing the decoder and encoder. The second type of testing is used to verify the implementation's adherence to the TOML specification. These tests have been factored into their own project: https://github.com/BurntSushi/toml-test The reason the tests are in a separate project is so that they can be used by any implementation of TOML. Namely, it is language agnostic. Example StrictDecoding shows how to detect whether there are keys in the TOML document that weren't decoded into the value given. This is useful for returning an error to the user if they've included extraneous fields in their configuration. Example UnmarshalTOML shows how to implement a struct type that knows how to unmarshal itself. The struct must take full responsibility for mapping the values passed into the struct. The method may be used with interfaces in a struct in cases where the actual type is not known until the data is examined. Example Unmarshaler shows how to decode TOML strings into your own custom data type.

Package toml provides facilities for decoding and encoding TOML configuration files via reflection. There is also support for delaying decoding with the Primitive type, and querying the set of keys in a TOML document with the MetaData type. The specification implemented: https://github.com/toml-lang/toml The sub-command github.com/BurntSushi/toml/cmd/tomlv can be used to verify whether a file is a valid TOML document. It can also be used to print the type of each key in a TOML document. There are two important types of tests used for this package. The first is contained inside '*_test.go' files and uses the standard Go unit testing framework. These tests are primarily devoted to holistically testing the decoder and encoder. The second type of testing is used to verify the implementation's adherence to the TOML specification. These tests have been factored into their own project: https://github.com/BurntSushi/toml-test The reason the tests are in a separate project is so that they can be used by any implementation of TOML. Namely, it is language agnostic. Example StrictDecoding shows how to detect whether there are keys in the TOML document that weren't decoded into the value given. This is useful for returning an error to the user if they've included extraneous fields in their configuration. Example UnmarshalTOML shows how to implement a struct type that knows how to unmarshal itself. The struct must take full responsibility for mapping the values passed into the struct. The method may be used with interfaces in a struct in cases where the actual type is not known until the data is examined. Example Unmarshaler shows how to decode TOML strings into your own custom data type.

Package cli provides a framework to build command line applications in Go with most of the burden of arguments parsing and validation placed on the framework instead of the user. To create a new application, initialize an app with cli.App. Specify a name and a brief description for the application: To attach code to execute when the app is launched, assign a function to the Action field: To assign a version to the application, use Version method and specify the flags that will be used to invoke the version command: Finally, in the main func, call Run passing in the arguments for parsing: To add one or more command line options (also known as flags), use one of the short-form StringOpt, StringsOpt, IntOpt, IntsOpt, or BoolOpt methods on App (or Cmd if adding flags to a command or subcommand). For example, to add a boolean flag to the cp command that specifies recursive mode, use the following: The first argument is a space separated list of names for the option without the dashes. Generally, both short and long forms are specified, but this is not mandatory. Additional names (aliases) can be provide if desired, but these are not shown in the auto-generated help. The second argument is the default value for the option if it is not supplied by the user. And, the third argument is the description to be shown in help messages. There is also a second set of methods on App called String, Strings, Int, Ints, and Bool, which accept a long-form struct of the type: cli.StringOpt, cli.StringsOpt, cli.IntOpt, cli.IntsOpt, cli.BoolOpt. The struct describes the option and allows the use of additional features not available in the short-form methods described above: Two features, EnvVar and SetByUser, can be defined in the long-form struct method. EnvVar is a space separated list of environment variables used to initialize the option if a value is not provided by the user. When help messages are shown, the value of any environment variables will be displayed. SetByUser is a pointer to a boolean variable that is set to true if the user specified the value on the command line. This can be useful to determine if the value of the option was explicitly set by the user or set via the default value. The result of both short- and long-forms is a pointer to a value, which will be populated after the command line arguments are parsed. You can only access the values stored in the pointers in the Action func, which is invoked after argument parsing has been completed. This precludes using the value of one option as the default value of another. On the command line, the following syntaxes are supported when specifying options. Boolean options: String and int options: Slice options (StringsOpt, IntsOpt) where option is repeated to accumulate values in a slice: To add one or more command line arguments (not prefixed by dashes), use one of the short-form StringArg, StringsArg, IntArg, IntsArg, or BoolArg methods on App (or Cmd if adding arguments to a command or subcommand). For example, to add two string arguments to our cp command, use the following calls: The first argument is the name of the argument as displayed in help messages. Argument names must be specified as all uppercase. The second argument is the default value for the argument if it is not supplied. And the third, argument is the description to be shown in help messages. There is also a second set of methods on App called String, Strings, Int, Ints, and Bool, which accept a long-form struct of the type: cli.StringArg, cli.StringsArg, cli.IntArg, cli.IntsArg, cli.BoolArg. The struct describes the arguments and allows the use of additional features not available in the short-form methods described above: Two features, EnvVar and SetByUser, can be defined in the long-form struct method. EnvVar is a space separated list of environment variables used to initialize the argument if a value is not provided by the user. When help messages are shown, the value of any environment variables will be displayed. SetByUser is a pointer to a boolean variable that is set to true if the user specified the value on the command line. This can be useful to determine if the value of the argument was explicitly set by the user or set via the default value. The result of both short- and long-forms is a pointer to a value which will be populated after the command line arguments are parsed. You can only access the values stored in the pointers in the Action func, which is invoked after argument parsing has been completed. This precludes using the value of one argument as the default value of another. The -- operator marks the end of command line options. Everything that follows will be treated as an argument, even if starts with a dash. For example, the standard POSIX touch command, which takes a filename as an argument (and possibly other options that we'll ignore here), could be defined as: If we try to create a file named "-f" via our touch command: It will fail because the -f will be parsed as an option, not as an argument. The fix is to insert -- after all flags have been specified, so the remaining arguments are parsed as arguments instead of options as follows: This ensures the -f is parsed as an argument instead of a flag named f. This package supports nesting of commands and subcommands. Declare a top-level command by calling the Command func on the top-level App struct. For example, the following creates an application called docker that will have one command called run: The first argument is the name of the command the user will specify on the command line to invoke this command. The second argument is the description of the command shown in help messages. And, the last argument is a CmdInitializer, which is a function that receives a pointer to a Cmd struct representing the command. Within this function, define the options and arguments for the command by calling the same methods as you would with top-level App struct (BoolOpt, StringArg, ...). To execute code when the command is invoked, assign a function to the Action field of the Cmd struct. Within that function, you can safely refer to the options and arguments as command line parsing will be completed at the time the function is invoked: Optionally, to provide a more extensive description of the command, assign a string to LongDesc, which is displayed when a user invokes --help. A LongDesc can be provided for Cmds as well as the top-level App: Subcommands can be added by calling Command on the Cmd struct. They can by defined to any depth if needed: Command and subcommand aliases are also supported. To define one or more aliases, specify a space-separated list of strings to the first argument of Command: With the command structure defined above, users can invoke the app in a variety of ways: As a convenience, to assign an Action to a func with no arguments, use ActionCommand when defining the Command. For example, the following two statements are equivalent: Please note that options, arguments, specs, and long descriptions cannot be provided when using ActionCommand. This is intended for very simple command invocations that take no arguments. Finally, as a side-note, it may seem a bit weird that this package uses a function to initialize a command instead of simply returning a command struct. The motivation behind this API decision is scoping: as with the standard flag package, adding an option or an argument returns a pointer to a value which will be populated when the app is run. Since you'll want to store these pointers in variables, and to avoid having dozens of them in the same scope (the main func for example or as global variables), this API was specifically tailored to take a func parameter (called CmdInitializer), which accepts the command struct. With this design, the command's specific variables are limited in scope to this function. Interceptors, or hooks, can be defined to be executed before and after a command or when any of its subcommands are executed. For example, the following app defines multiple commands as well as a global flag which toggles verbosity: Instead of duplicating the check for the verbose flag and setting the debug level in every command (and its sub-commands), a Before interceptor can be set on the top-level App instead: Whenever a valid command is called by the user, all the Before interceptors defined on the app and the intermediate commands will be called, in order from the root to the leaf. Similarly, to execute a hook after a command has been called, e.g. to cleanup resources allocated in Before interceptors, simply set the After field of the App struct or any other Command. After interceptors will be called, in order, from the leaf up to the root (the opposite order of the Before interceptors). The following diagram shows when and in which order multiple Before and After interceptors are executed: To exit the application, use cli.Exit function, which accepts an exit code and exits the app with the provided code. It is important to use cli.Exit instead of os.Exit as the former ensures that all of the After interceptors are executed before exiting. An App or Command's invocation syntax can be customized using spec strings. This can be useful to indicate that an argument is optional or that two options are mutually exclusive. The spec string is one of the key differentiators between this package and other CLI packages as it allows the developer to express usage in a simple, familiar, yet concise grammar. To define option and argument usage for the top-level App, assign a spec string to the App's Spec field: Likewise, to define option and argument usage for a command or subcommand, assign a spec string to the Command's Spec field: The spec syntax is mostly based on the conventions used in POSIX command line applications (help messages and man pages). This syntax is described in full below. If a user invokes the app or command with the incorrect syntax, the app terminates with a help message showing the proper invocation. The remainder of this section describes the many features and capabilities of the spec string grammar. Options can use both short and long option names in spec strings. In the example below, the option is mandatory and must be provided. Any options referenced in a spec string MUST be explicitly declared, otherwise this package will panic. I.e. for each item in the spec string, a corresponding *Opt or *Arg is required: Arguments are specified with all-uppercased words. In the example below, both SRC and DST must be provided by the user (two arguments). Like options, any argument referenced in a spec string MUST be explicitly declared, otherwise this package will panic: With the exception of options, the order of the elements in a spec string is respected and enforced when command line arguments are parsed. In the example below, consecutive options (-f and -g) are parsed regardless of the order they are specified (both "-f=5 -g=6" and "-g=6 -f=5" are valid). Order between options and arguments is significant (-f and -g must appear before the SRC argument). The same holds true for arguments, where SRC must appear before DST: Optionality of options and arguments is specified in a spec string by enclosing the item in square brackets []. If the user does not provide an optional value, the app will use the default value specified when the argument was defined. In the example below, if -x is not provided, heapSize will default to 1024: Choice between two or more items is specified in a spec string by separating each choice with the | operator. Choices are mutually exclusive. In the examples below, only a single choice can be provided by the user otherwise the app will terminate displaying a help message on proper usage: Repetition of options and arguments is specified in a spec string with the ... postfix operator to mark an item as repeatable. Both options and arguments support repitition. In the example below, users may invoke the command with multiple -e options and multiple SRC arguments: Grouping of options and arguments is specified in a spec string with parenthesis. When combined with the choice | and repetition ... operators, complex syntaxes can be created. The parenthesis in the example below indicate a repeatable sequence of a -e option followed by an argument, and that is mutually exclusive to a choice between -x and -y options. Option groups, or option folding, are a shorthand method to declaring a choice between multiple options. I.e. any combination of the listed options in any order with at least one option selected. The following two statements are equivalent: Option groups are typically used in conjunction with optionality [] operators. I.e. any combination of the listed options in any order or none at all. The following two statements are equivalent: All of the options can be specified using a special syntax: [OPTIONS]. This is a special token in the spec string (not optionality and not an argument called OPTIONS). It is equivalent to an optional repeatable choice between all the available options. For example, if an app or a command declares 4 options a, b, c and d, then the following two statements are equivalent: Inline option values are specified in the spec string with the =<some-text> notation immediately following an option (long or short form) to provide users with an inline description or value. The actual inline values are ignored by the spec parser as they exist only to provide a contextual hint to the user. In the example below, "absolute-path" and "in seconds" are ignored by the parser: The -- operator can be used to automatically treat everything following it as arguments. In other words, placing a -- in the spec string automatically inserts a -- in the same position in the program call arguments. This lets you write programs such as the POSIX time utility for example: Below is the full EBNF grammar for the Specs language: By combining a few of these building blocks together (while respecting the grammar above), powerful and sophisticated validation constraints can be created in a simple and concise manner without having to define in code. This is one of the key differentiators between this package and other CLI packages. Validation of usage is handled entirely by the package through the spec string. Behind the scenes, this package parses the spec string and constructs a finite state machine used to parse the command line arguments. It also handles backtracking, which allows it to handle tricky cases, or what I like to call "the cp test": Without backtracking, this deceptively simple spec string cannot be parsed correctly. For instance, docopt can't handle this case, whereas this package does. By default an auto-generated spec string is created for the app and every command unless a spec string has been set by the user. This can simplify use of the package even further for simple syntaxes. The following logic is used to create an auto-generated spec string: 1) start with an empty spec string, 2) if at least one option was declared, append "[OPTIONS]" to the spec string, and 3) for each declared argument, append it, in the order of declaration, to the spec string. For example, given this command declaration: The auto-generated spec string, which should suffice for simple cases, would be: If additional constraints are required, the spec string must be set explicitly using the grammar documented above. By default, the following types are supported for options and arguments: bool, string, int, strings (slice of strings), and ints (slice of ints). You can, however, extend this package to handle other types, e.g. time.Duration, float64, or even your own struct types. To define your own custom type, you must implement the flag.Value interface for your custom type, and then declare the option or argument using VarOpt or VarArg respectively if using the short-form methods. If using the long-form struct, then use Var instead. The following example defines a custom type for a duration. It defines a duration argument that users will be able to invoke with strings in the form of "1h31m42s": To make a custom type to behave as a boolean option, i.e. doesn't take a value, it must implement the IsBoolFlag method that returns true: To make a custom type behave as a multi-valued option or argument, i.e. takes multiple values, it must implement the Clear method, which is called whenever the values list needs to be cleared, e.g. when the value was initially populated from an environment variable, and then explicitly set from the CLI: To hide the default value of a custom type, it must implement the IsDefault method that returns a boolean. The help message generator will use the return value to decide whether or not to display the default value to users:

Package fx is a framework that makes it easy to build applications out of reusable, composable modules. Fx applications use dependency injection to eliminate globals without the tedium of manually wiring together function calls. Unlike other approaches to dependency injection, Fx works with plain Go functions: you don't need to use struct tags or embed special types, so Fx automatically works well with most Go packages. Basic usage is explained in the package-level example below. If you're new to Fx, start there! Advanced features, including named instances, optional parameters, and value groups, are explained under the In and Out types. To test functions that use the Lifecycle type or to write end-to-end tests of your Fx application, use the helper functions and types provided by the go.uber.org/fx/fxtest package.

Package scenarigo provides end-to-end scenario testing framework for APIs. It loads YAML files as scenarios and executes them.

Mandala is a framework for writing Android native applications in Go using the Goandroid[1] toolchain. You can develop, test and run your application on your desktop and then deploy it to an Android device. It encourages the use of idiomatic Go for writing Android applications: communication happens through channels, no callbacks. The framework is not to be considered as an high-level game engine but as a basic layer onto which game engines can be build or existing ones can be used. In my opinion, this opens interesting scenarios in the developing of native Android applications/games in Go. Goandroid's native_activity[2] example was the initial source of inspiration for this project. Please consider that Mandala is in a very early stage of development: API will change, test coverage is not so good for now. Last but not least, Go doesn't officially supports native Android development. Regarding this point, I hope that the present work could act as a sort of incentive in the direction of an official Android support by the Go Team. Have a nice Mandala! [1] - https://github.com/eliasnaur/goandroid [2] - https://github.com/eliasnaur/goandroid/tree/master/native-activity

Package gosnowflake is a pure Go Snowflake driver for the database/sql package. Clients can use the database/sql package directly. For example: Use Open to create a database handle with connection parameters: The Go Snowflake Driver supports the following connection syntaxes (or data source name formats): where all parameters must be escaped or use `Config` and `DSN` to construct a DSN string. The following example opens a database handle with the Snowflake account myaccount where the username is jsmith, password is mypassword, database is mydb, schema is testschema, and warehouse is mywh: The following connection parameters are supported: account <string>: Specifies the name of your Snowflake account, where string is the name assigned to your account by Snowflake. In the URL you received from Snowflake, your account name is the first segment in the domain (e.g. abc123 in https://abc123.snowflakecomputing.com). This parameter is optional if your account is specified after the @ character. If you are not on us-west-2 region or AWS deployment, then append the region after the account name, e.g. “<account>.<region>”. If you are not on AWS deployment, then append not only the region, but also the platform, e.g., “<account>.<region>.<platform>”. Account, region, and platform should be separated by a period (“.”), as shown above. If you are using a global url, then append connection group and "global", e.g., "account-<connection_group>.global". Account and connection group are separated by a dash ("-"), as shown above. region <string>: DEPRECATED. You may specify a region, such as “eu-central-1”, with this parameter. However, since this parameter is deprecated, it is best to specify the region as part of the account parameter. For details, see the description of the account parameter. database: Specifies the database to use by default in the client session (can be changed after login). schema: Specifies the database schema to use by default in the client session (can be changed after login). warehouse: Specifies the virtual warehouse to use by default for queries, loading, etc. in the client session (can be changed after login). role: Specifies the role to use by default for accessing Snowflake objects in the client session (can be changed after login). passcode: Specifies the passcode provided by Duo when using MFA for login. passcodeInPassword: false by default. Set to true if the MFA passcode is embedded in the login password. Appends the MFA passcode to the end of the password. loginTimeout: Specifies the timeout, in seconds, for login. The default is 60 seconds. The login request gives up after the timeout length if the HTTP response is success. authenticator: Specifies the authenticator to use for authenticating user credentials: To use the internal Snowflake authenticator, specify snowflake (Default). To authenticate through Okta, specify https://<okta_account_name>.okta.com (URL prefix for Okta). To authenticate using your IDP via a browser, specify externalbrowser. To authenticate via OAuth, specify oauth and provide an OAuth Access Token (see the token parameter below). application: Identifies your application to Snowflake Support. insecureMode: false by default. Set to true to bypass the Online Certificate Status Protocol (OCSP) certificate revocation check. IMPORTANT: Change the default value for testing or emergency situations only. token: a token that can be used to authenticate. Should be used in conjunction with the "oauth" authenticator. client_session_keep_alive: Set to true have a heartbeat in the background every hour to keep the connection alive such that the connection session will never expire. Care should be taken in using this option as it opens up the access forever as long as the process is alive. ocspFailOpen: true by default. Set to false to make OCSP check fail closed mode. validateDefaultParameters: true by default. Set to false to disable checks on existence and privileges check for Database, Schema, Warehouse and Role when setting up the connection All other parameters are taken as session parameters. For example, TIMESTAMP_OUTPUT_FORMAT session parameter can be set by adding: The Go Snowflake Driver honors the environment variables HTTP_PROXY, HTTPS_PROXY and NO_PROXY for the forward proxy setting. NO_PROXY specifies which hostname endings should be allowed to bypass the proxy server, e.g. :code:`no_proxy=.amazonaws.com` means that AWS S3 access does not need to go through the proxy. NO_PROXY does not support wildcards. Each value specified should be one of the following: The end of a hostname (or a complete hostname), for example: ".amazonaws.com" or "xy12345.snowflakecomputing.com". An IP address, for example "192.196.1.15". If more than one value is specified, values should be separated by commas, for example: By default, the driver's builtin logger is NOP; no output is generated. This is intentional for those applications that use the same set of logger parameters not to conflict with glog, which is incorporated in the driver logging framework. In order to enable debug logging for the driver, add a build tag sfdebug to the go tool command lines, for example: For tests, run the test command with the tag along with glog parameters. For example, the following command will generate all acitivty logs in the standard error. Likewise, if you build your application with the tag, you may specify the same set of glog parameters. To get the logs for a specific module, use the -vmodule option. For example, to retrieve the driver.go and connection.go module logs: Note: If your request retrieves no logs, call db.Close() or glog.flush() to flush the glog buffer. Note: The logger may be changed in the future for better logging. Currently if the applications use the same parameters as glog, you cannot collect both application and driver logs at the same time. From 0.5.0, a signal handling responsibility has moved to the applications. If you want to cancel a query/command by Ctrl+C, add a os.Interrupt trap in context to execute methods that can take the context parameter, e.g., QueryContext, ExecContext. See cmd/selectmany.go for the full example. Queries return SQL column type information in the ColumnType type. The DatabaseTypeName method returns the following strings representing Snowflake data types: Go's database/sql package limits Go's data types to the following for binding and fetching: Fetching data isn't an issue since the database data type is provided along with the data so the Go Snowflake Driver can translate Snowflake data types to Go native data types. When the client binds data to send to the server, however, the driver cannot determine the date/timestamp data types to associate with binding parameters. For example: To resolve this issue, a binding parameter flag is introduced that associates any subsequent time.Time type to the DATE, TIME, TIMESTAMP_LTZ, TIMESTAMP_NTZ or BINARY data type. The above example could be rewritten as follows: The driver fetches TIMESTAMP_TZ (timestamp with time zone) data using the offset-based Location types, which represent a collection of time offsets in use in a geographical area, such as CET (Central European Time) or UTC (Coordinated Universal Time). The offset-based Location data is generated and cached when a Go Snowflake Driver application starts, and if the given offset is not in the cache, it is generated dynamically. Currently, Snowflake doesn't support the name-based Location types, e.g., America/Los_Angeles. For more information about Location types, see the Go documentation for https://golang.org/pkg/time/#Location. Internally, this feature leverages the []byte data type. As a result, BINARY data cannot be bound without the binding parameter flag. In the following example, sf is an alias for the gosnowflake package: The driver directly downloads a result set from the cloud storage if the size is large. It is required to shift workloads from the Snowflake database to the clients for scale. The download takes place by goroutine named "Chunk Downloader" asynchronously so that the driver can fetch the next result set while the application can consume the current result set. The application may change the number of result set chunk downloader if required. Note this doesn't help reduce memory footprint by itself. Consider Custom JSON Decoder. Experimental: Custom JSON Decoder for parsing Result Set The application may have the driver use a custom JSON decoder that incrementally parses the result set as follows. This option will reduce the memory footprint to half or even quarter, but it can significantly degrade the performance depending on the environment. The test cases running on Travis Ubuntu box show five times less memory footprint while four times slower. Be cautious when using the option. (Private Preview) JWT authentication ** Not recommended for production use until GA Now JWT token is supported when compiling with a golang version of 1.10 or higher. Binary compiled with lower version of golang would return an error at runtime when users try to use JWT authentication feature. To enable this feature, one can construct DSN with fields "authenticator=SNOWFLAKE_JWT&privateKey=<your_private_key>", or using Config structure specifying: The <your_private_key> should be a base64 URL encoded PKCS8 rsa private key string. One way to encode a byte slice to URL base 64 URL format is through base64.URLEncoding.EncodeToString() function. On the server side, one can alter the public key with the SQL command: The <your_public_key> should be a base64 Standard encoded PKI public key string. One way to encode a byte slice to base 64 Standard format is through base64.StdEncoding.EncodeToString() function. To generate the valid key pair, one can do the following command on the shell script: GET and PUT operations are unsupported.

Package p9p implements a compliant 9P2000 client and server library for use in modern, production Go services. This package differentiates itself in that is has departed from the plan 9 implementation primitives and better follows idiomatic Go style. The package revolves around the session type, which is an enumeration of raw 9p message calls. A few calls, such as flush and version, have been elided, deferring their usage to the server implementation. Sessions can be trivially proxied through clients and servers. The best place to get started is with Serve. Serve can be provided a connection and a handler. A typical implementation will call Serve as part of a listen/accept loop. As each network connection is created, Serve can be called with a handler for the specific connection. The handler can be implemented with a Session via the Dispatch function or can generate sessions for dispatch in response to client messages. (See cmd/9ps for an example) On the client side, NewSession provides a 9p session from a connection. After a version negotiation, methods can be called on the session, in parallel, and calls will be sent over the connection. Call timeouts can be controlled via the context provided to each method call. This package has the beginning of a nice client-server framework for working with 9p. Some of the abstractions aren't entirely fleshed out, but most of this can center around the Handler. Missing from this are a number of tools for implementing 9p servers. The most glaring are directory read and walk helpers. Other, more complex additions might be a system to manage in memory filesystem trees that expose multi-user sessions. The largest difference between this package and other 9p packages is simplification of the types needed to implement a server. To avoid confusing bugs and odd behavior, the components are separated by each level of the protocol. One example is that requests and responses are separated and they no longer hold mutable state. This means that framing, transport management, encoding, and dispatching are componentized. Little work will be required to swap out encodings, transports or connection implementations. This package has been wired from top to bottom to support context-based resource management. Everything from startup to shutdown can have timeouts using contexts. Not all close methods are fully in place, but we are very close to having controlled, predictable cleanup for both servers and clients. Timeouts can be very granular or very course, depending on the context of the timeout. For example, it is very easy to set a short timeout for a stat call but a long timeout for reading data. Currently, there is not multiversion support. The hooks and functionality are in place to add multi-version support. Generally, the correct space to do this is in the codec. Types, such as Dir, simply need to be extended to support the possibility of extra fields. The real question to ask here is what is the role of the version number in the 9p protocol. It really comes down to the level of support required. Do we just need it at the protocol level, or do handlers and sessions need to be have differently based on negotiated versions? This package has a number of TODOs to make it easier to use. Most of the existing code provides a solid base to work from. Don't be discouraged by the sawdust. In addition, the testing is embarassingly lacking. With time, we can get full testing going and ensure we have confidence in the implementation.

Package health provides a generic health checking framework. The health package works expvar style. By importing the package the debug server is getting a "/debug/health" endpoint that returns the current status of the application. If there are no errors, "/debug/health" will return a HTTP 200 status, together with an empty JSON reply "{}". If there are any checks with errors, the JSON reply will include all the failed checks, and the response will be have an HTTP 503 status. A Check can either be run synchronously, or asynchronously. We recommend that most checks are registered as an asynchronous check, so a call to the "/debug/health" endpoint always returns immediately. This pattern is particularly useful for checks that verify upstream connectivity or database status, since they might take a long time to return/timeout. To install health, just import it in your application: You can also (optionally) import "health/api" that will add two convenience endpoints: "/debug/health/down" and "/debug/health/up". These endpoints add "manual" checks that allow the service to quickly be brought in/out of rotation. After importing these packages to your main application, you can start registering checks. The recommended way of registering checks is using a periodic Check. PeriodicChecks run on a certain schedule and asynchronously update the status of the check. This allows CheckStatus to return without blocking on an expensive check. A trivial example of a check that runs every 5 seconds and shuts down our server if the current minute is even, could be added as follows: Alternatively, you can also make use of "RegisterPeriodicThresholdFunc" to implement the exact same check, but add a threshold of failures after which the check will be unhealthy. This is particularly useful for flaky Checks, ensuring some stability of the service when handling them. The lowest-level way to interact with the health package is calling "Register" directly. Register allows you to pass in an arbitrary string and something that implements "Checker" and runs your check. If your method returns an error with nil, it is considered a healthy check, otherwise it will make the health check endpoint "/debug/health" start returning a 503 and list the specific check that failed. Assuming you wish to register a method called "currentMinuteEvenCheck() error" you could do that by doing: CheckFunc is a convenience type that implements Checker. Another way of registering a check could be by using an anonymous function and the convenience method RegisterFunc. An example that makes the status endpoint always return an error: You could also use the health checker mechanism to ensure your application only comes up if certain conditions are met, or to allow the developer to take the service out of rotation immediately. An example that checks database connectivity and immediately takes the server out of rotation on err: You can also use the predefined Checkers that come included with the health package. First, import the checks: After that you can make use of any of the provided checks. An example of using a `FileChecker` to take the application out of rotation if a certain file exists can be done as follows: After registering the check, it is trivial to take an application out of rotation from the console: You could also test the connectivity to a downstream service by using a "HTTPChecker", but ensure that you only mark the test unhealthy if there are a minimum of two failures in a row:

Package go-ftw is a Framework for Testing Web Application Firewalls It is derived from the work made with the pytest plugin `ftw`

Package toml provides facilities for decoding and encoding TOML configuration files via reflection. There is also support for delaying decoding with the Primitive type, and querying the set of keys in a TOML document with the MetaData type. The specification implemented: https://github.com/toml-lang/toml The sub-command github.com/BurntSushi/toml/cmd/tomlv can be used to verify whether a file is a valid TOML document. It can also be used to print the type of each key in a TOML document. There are two important types of tests used for this package. The first is contained inside '*_test.go' files and uses the standard Go unit testing framework. These tests are primarily devoted to holistically testing the decoder and encoder. The second type of testing is used to verify the implementation's adherence to the TOML specification. These tests have been factored into their own project: https://github.com/BurntSushi/toml-test The reason the tests are in a separate project is so that they can be used by any implementation of TOML. Namely, it is language agnostic. Example StrictDecoding shows how to detect whether there are keys in the TOML document that weren't decoded into the value given. This is useful for returning an error to the user if they've included extraneous fields in their configuration. Example UnmarshalTOML shows how to implement a struct type that knows how to unmarshal itself. The struct must take full responsibility for mapping the values passed into the struct. The method may be used with interfaces in a struct in cases where the actual type is not known until the data is examined. Example Unmarshaler shows how to decode TOML strings into your own custom data type.

Package nject is a general purpose dependency injection framework. It provides wrapping, pruning, and indirect variable passing. It is type safe and using it requires no type assertions. There are two main injection APIs: Run and Bind. Bind is designed to be used at program initialization and does as much work as possible then rather than during main execution. The API for nject is a list of providers (injectors) that are run in order. The final function in the list must be called. The other functions are called if their value is consumed by a later function that must be called. Here is a simple example: In this example, context.Background and log.Default are not invoked because their outputs are not used by the final function (http.ListenAndServe). The basic idea of nject is to assemble a Collection of providers and then use that collection to supply inputs for functions that may use some or all of the provided types. One big win from dependency injection with nject is the ability to reshape various different functions into a single signature. For example, having a bunch of functions with different APIs all bound as http.HandlerFunc is easy. Providers produce or consume data. The data is distinguished by its type. If you want to three different strings, then define three different types: Then you can have a function that does things with the three types: The above function would be a valid injector or final function in a provider Collection. For example: This creates a sequence and executes it. Run injects a myFirst value and the sequence of providers runs: genSecond() injects a mySecond and myStringFunc() combines the myFirst and mySecond to create a myThird. Then the function given in run saves that final value. The expected output is Providers are grouped as into linear sequences. When building an injection chain, the providers are grouped into several sets: LITERAL, STATIC, RUN. The LITERAL and STATIC sets run once per initialization. The RUN set runs once per invocation. Providers within a set are executed in the order that they were originally specified. Providers whose outputs are not consumed are omitted unless they are marked Required(). Collections are bound with Bind(&invocationFunction, &initializationFunction). The invocationFunction is expected to be used over and over, but the initializationFunction is expected to be used less frequently. The STATIC set is re-invoked each time the initialization function is run. The LITERAL set is just the literal values in the collection. The STATIC set is composed of the cacheable injectors. The RUN set if everything else. All injectors have the following type signature: None of the input or output parameters may be anonymously-typed functions. An anoymously-typed function is a function without a named type. Injectors whose output values are not used by a downstream handler are dropped from the handler chain. They are not invoked. Injectors that have no output values are a special case and they are always retained in the handler chain. In injector that is annotated as Cacheable() may promoted to the STATIC set. An injector that is annotated as MustCache() must be promoted to the STATIC set: if it cannot be promoted then the collection is deemed invalid. An injector may not be promoted to the STATIC set if it takes as input data that comes from a provider that is not in the STATIC or LITERAL sets. For example, arguments to the invocation function, if the invoke function takes an int as one of its inputs, then no injector that takes an int as an argument may be promoted to the STATIC set. Injectors in the STATIC set will be run exactly once per set of input values. If the inputs are consistent, then the output will be a singleton. This is true across injection chains. If the following provider is used in multiple chains, as long as the same integer is injected, all chains will share the same pointer. Injectors in the STATIC set are only run for initialization. For some things, like opening a database, that may still be too often. Injectors that are marked Memoized must be promoted to the static set. Memoized injectors are only run once per combination of inputs. Their outputs are remembered. If called enough times with different arguments, memory will be exhausted. Memoized injectors may not have more than 90 inputs. Memoized injectors may not have any inputs that are go maps, slices, or functions. Arrays, structs, and interfaces are okay. This requirement is recursive so a struct that that has a slice in it is not okay. Fallible injectors are special injectors that change the behavior of the injection chain if they return error. Fallible injectors in the RUN set, that return error will terminate execution of the injection chain. A non-wrapper function that returns nject.TerminalError is a fallible injector. The TerminalError does not have to be the last return value. The nject package converts TerminalError objects into error objects so only the fallible injector should use TerminalError. Anything that consumes the TerminalError should do so by consuming error instead. Fallible injectors can be in both the STATIC set and the RUN set. Their behavior is a bit different. If a non-nil value is returned as the TerminalError from a fallible injector in the RUN set, none of the downstream providers will be called. The provider chain returns from that point with the TerminalError as a return value. Since all return values must be consumed by a middleware provider or the bound invoke function, fallible injectors must come downstream from a middleware handler that takes error as a returned value if the invoke function (function that runs a bound injection chain) does not return error. If a fallible injector returns nil for the TerminalError, the other output values are made available for downstream handlers to consume. The other output values are not considered return values and are not available to be consumed by upstream middleware handlers. The error returned by a fallible injector is not available downstream. If a non-nil value is returned as the TerminalError from a fallible injector in the STATIC set, the rest of the STATIC set will be skipped. If there is an init function and it returns error, then the value returned by the fallible injector will be returned via init function. Unlike fallible injectors in the RUN set, the error output by a fallible injector in the STATIC set is available downstream (but only in the RUN set -- nothing else in the STATIC set will execute). Some examples: A wrap function interrupts the linear sequence of providers. It may or may invoke the remainder of the sequence that comes after it. The remainder of the sequence is provided to the wrap function as a function that it may call. The type signature of a wrap function is a function that receives an function as its first parameter. That function must be of an anonymous type: For example: When this wrappper function runs, it is responsible for invoking the rest of the provider chain. It does this by calling inner(). The parameters to inner are available as inputs to downstream providers. The value(s) returned by inner come from the return values of other wrapper functions and from the return value(s) of the final function. Wrap functions can call inner() zero or more times. The values returned by wrap functions must be consumed by another upstream wrap function or by the init function (if using Bind()). Wrap functions have a small amount of runtime overhead compared to other kinds of functions: one call to reflect.MakeFunc(). Wrap functions serve the same role as middleware, but are usually easier to write. Wrap functions that invoke inner() multiple times in parallel are are not well supported at this time and such invocations must have the wrap function decorated with Parallel(). Final functions are simply the last provider in the chain. They look like regular Go functions. Their input parameters come from other providers. Their return values (if any) must be consumed by an upstream wrapper function or by the init function (if using Bind()). Wrap functions that return error should take error as a returned value so that they do not mask a downstream error. Wrap functions should not return TerminalError because they internally control if the downstream chain is called. Literal values are values in the provider chain that are not functions. Provider chains can be invalid for many reasons: inputs of a type not provided earlier in the chain; annotations that cannot be honored (eg. MustCache & Memoize); return values that are not consumed; functions that take or return functions with an anymous type other than wrapper functions; A chain that does not terminate with a function; etc. Bind() and Run() will return error when presented with an invalid provider chain. Bind() and Run() will return error rather than panic. After Bind()ing an init and invoke function, calling them will not panic unless a provider panic()s A wrapper function can be used to catch panics and turn them into errors. When doing that, it is important to propagate any errors that are coming up the chain. If there is no guaranteed function that will return error, one can be added with Shun(). Bind() uses a complex and somewhat expensive O(n^2) set of rules to evaluate which providers should be included in a chain and which can be dropped. The goal is to keep the ones you want and remove the ones you don't want. Bind() tries to figure this out based on the dependencies and the annotations. MustConsume, not Desired: Only include if at least one output is transitively consumed by a Required or Desired chain element and all outputs are consumed by some other provider. Not MustConsume, not Desired: only include if at least one output is transitively consumed by a Required or Desired provider. Not MustConsume, Desired: Include if all inputs are available. MustConsume, Desired: Only include if all outputs are transitively consumed by a required or Desired chain element. When there are multiple providers of a type, Bind() tries to get it from the closest provider. Providers that have unmet dependencies will be eliminated from the chain unless they're Required. The remainder of this document consists of suggestions for how to use nject. Contributions to this section would be welcome. Also links to blogs or other discussions of using nject in practice. The best practice for using nject inside a large project is to have a few common chains that everyone imports. Most of the time, these common chains will be early in the sequence of providers. Customization of the import chains happens in many places. This is true for services, libraries, and tests. For tests, a wrapper that includes the standard chain makes it easier to write tests. See github.com/memsql/ntest for helper functions and more examples. If nject cannot bind or run a chain, it will return error. The returned error is generally very good, but it does not contain the full debugging output. The full debugging output can be obtained with the DetailedError function. If the detailed error shows that nject has a bug, note that part of the debug output includes a regression test that can be turned into an nject issue. Remove the comments to hide the original type names. The Reorder() decorator allows injection chains to be fully or partially reordered. Reorder is currently limited to a single pass and does not know which injectors are ultimately going to be included in the final chain. It is likely that if you mark your entire chain with Reorder, you'll have unexpected results. On the other hand, Reorder provides safe and easy way to solve some common problems. For example: providing optional options to an injected dependency. Because the default options are marked as Shun, they'll only be included if they have to be included. If a user of thingChain wants to override the options, they simply need to mark their override as Reorder. To make this extra friendly, a helper function to do the override can be provided and used. Recommended best practice is to have injectors shutdown the things they themselves start. They should do their own cleanup. Inside tests, an injector can use t.Cleanup() for this. For services, something like t.Cleanup can easily be built: Alternatively, any wrapper function can do it's own cleanup in a defer that it defines. Wrapper functions have a small runtime performance penalty, so if you have more than a couple of providers that need cleanup, it makes sense to include something like CleaningService. The normal direction of forced inclusion is that an upstream provider is required because a downstream provider uses a type produced by the upstream provider. There are times when the relationship needs to be reversed. For example, a type gets modified by a downstream injector. The simplest option is to combine the providers into one function. Another possibility is to mark the upstream provider with MustConsume and have it produce a type that is only consumed by the downstream provider. Lastly, the providers can be grouped with Cluster so that they'll be included or excluded as a group. Example shows what gets included and what does not for several injection chains. These examples are meant to show the subtlety of what gets included and why. This example explores injecting a database handle or transaction only when they're used.

Package toml provides facilities for decoding and encoding TOML configuration files via reflection. There is also support for delaying decoding with the Primitive type, and querying the set of keys in a TOML document with the MetaData type. The specification implemented: https://github.com/toml-lang/toml The sub-command github.com/BurntSushi/toml/cmd/tomlv can be used to verify whether a file is a valid TOML document. It can also be used to print the type of each key in a TOML document. There are two important types of tests used for this package. The first is contained inside '*_test.go' files and uses the standard Go unit testing framework. These tests are primarily devoted to holistically testing the decoder and encoder. The second type of testing is used to verify the implementation's adherence to the TOML specification. These tests have been factored into their own project: https://github.com/BurntSushi/toml-test The reason the tests are in a separate project is so that they can be used by any implementation of TOML. Namely, it is language agnostic. Example StrictDecoding shows how to detect whether there are keys in the TOML document that weren't decoded into the value given. This is useful for returning an error to the user if they've included extraneous fields in their configuration. Example UnmarshalTOML shows how to implement a struct type that knows how to unmarshal itself. The struct must take full responsibility for mapping the values passed into the struct. The method may be used with interfaces in a struct in cases where the actual type is not known until the data is examined. Example Unmarshaler shows how to decode TOML strings into your own custom data type.

Ginkgo is a BDD-style testing framework for Golang The godoc documentation describes Ginkgo's API. More comprehensive documentation (with examples!) is available at http://onsi.github.io/ginkgo/ Ginkgo's preferred matcher library is [Gomega](http://github.com/onsi/gomega) Ginkgo on Github: http://github.com/onsi/ginkgo Ginkgo is MIT-Licensed

Gomega is the Ginkgo BDD-style testing framework's preferred matcher library. The godoc documentation describes Gomega's API. More comprehensive documentation (with examples!) is available at http://onsi.github.io/gomega/ Gomega on Github: http://github.com/onsi/gomega Learn more about Ginkgo online: http://onsi.github.io/ginkgo Ginkgo on Github: http://github.com/onsi/ginkgo Gomega is MIT-Licensed

Package godog is the official Cucumber BDD framework for Golang, it merges specification and test documentation into one cohesive whole. Godog does not intervene with the standard "go test" command and it's behavior. You can leverage both frameworks to functionally test your application while maintaining all test related source code in *_test.go files. Godog acts similar compared to go test command. It uses go compiler and linker tool in order to produce test executable. Godog contexts needs to be exported same as Test functions for go test. For example, imagine you’re about to create the famous UNIX ls command. Before you begin, you describe how the feature should work, see the example below.. Example: Now, wouldn’t it be cool if something could read this sentence and use it to actually run a test against the ls command? Hey, that’s exactly what this package does! As you’ll see, Godog is easy to learn, quick to use, and will put the fun back into tests. Godog was inspired by Behat and Cucumber the above description is taken from it's documentation.

Code generated .* DO NOT EDIT. This project is created as the alternative to https://github.com/DATA-DOG/godog and is inspirited by it. There are a few differences between both solutions: - GoBDD uses the built-in testing framework - GoBDD is run as standard test case (not a separate program) - you can use every Go native feature like build tags, pprof and so on - the context in every test case contains all the required information to run (values passed from previous steps). More information can be found in the readme file https://github.com/go-bdd/gobdd/blob/master/README.md

Package pi provides the top-level repository for the GoPi interactive parser system. The code is organized into the various sub-packages, dealing with the different stages of parsing etc. * pi: integrates all the parsing elements into the overall parser framework. * langs: has the parsers for specific languages, including Go (of course), markdown and tex (latter are lexer-only) * filecat: is the core file categorization (e.g., mime-type, etc) framework that associates files with their extensions and provides other type-level meta data that enables the Pi system and GoGi and other GoKi framework tools to know how to deal with different types of files. Note that the GUI editor framework for creating and testing parsers is in the Gide package: https://github.com/goki/gide under the piv sub-package.

Package newstorage contains common tests for newstorage implementation.

Package httpexpect helps with end-to-end HTTP and REST API testing. See example directory: There are two common ways to test API with httpexpect: The second approach works only if the server is a Go module and its handler can be imported in tests. Concrete behaviour is determined by Client implementation passed to Config struct. If you're using http.Client, set its Transport field (http.RoundTriper) to one of the following: Note that http handler can be usually obtained from http framework you're using. E.g., echo framework provides either http.Handler. You can also provide your own implementation of RequestFactory (creates http.Request), or Client (gets http.Request and returns http.Response). If you're starting server from tests, it's very handy to use net/http/httptest. Whenever values are checked for equality in httpexpect, they are converted to "canonical form": This is equivalent to subsequently json.Marshal() and json.Unmarshal() the value and currently is implemented so. When some check fails, failure is reported. If non-fatal failures are used (see Reporter interface), execution is continued and instance that was checked is marked as failed. If specific instance is marked as failed, all subsequent checks are ignored for this instance and for any child instances retrieved after failure. Example: If you want to be informed about every asserion made, successful or failed, you can use AssertionHandler interface. Default implementation of this interface ignores successful assertions and reports failed assertions using Formatter and Reporter objects. Custom AssertionHandler can handle all assertions (e.g. dump them in JSON format) and is free to use or not to use Formatter and Reporter in its sole discretion.

Package noleak complements the Gingko/Gomega testing and matchers framework with matchers for Goroutine leakage detection. To start with, returns information about all (non-dead) goroutines at a particular moment. This is useful to capture a known correct snapshot and then later taking a new snapshot and comparing these two snapshots for leaked goroutines. Next, the matcher filters out well-known and expected "non-leaky" goroutines from an actual list of goroutines (passed from Eventually or Expect), hopefully ending up with an empty list of leaked goroutines. If there are still goroutines left after filtering, then HaveLeaked() will succeed ... which usually is actually considered to be failure. So, this can be rather declared to be "suckcess" because no one wants leaked goroutines. A typical pattern to detect goroutines leaked in individual tests is as follows: Using Eventually instead of Expect ensures that there is some time given for temporary goroutines to finally wind down. Gomega's default values apply: the 1s timeout and 10ms polling interval. Please note that the form is the same as the slightly longer, but also more expressive variant: Depending on your tests and the dependencies used, you might need to identify additional goroutines as not being leaks. The noleak packages comes with the following predefined goroutine "filter" matchers that can be specified as arguments to HaveLeaked(...): In addition, you can use any other GomegaMatcher, as long as it can work on a (single) goroutine.Goroutine. For instance, Gomega's HaveField and WithTransform matchers are good foundations for writing project-specific noleak matchers. By default, when noleak's HaveLeaked matcher finds one or more leaked goroutines, it dumps the goroutine backtraces in a condensed format that uses only a single line per call instead of two lines. Moreover, the backtraces abbreviate the source file location in the form of package/source.go:lineno: By setting noleak.ReportFilenameWithPath=true the leaky goroutine backtraces will show full path names for each source file: noleak has been heavily inspired by the Goroutine leak detector github.com/uber-go/goleak. That's definitely a fine piece of work! But then why another goroutine leak package? After a deep analysis of Uber's goleak we decided against crunching goleak somehow half-assed into the Gomega TDD matcher ecosystem. In particular, reusing and wrapping of the existing Uber implementation would have become very awkward: goleak.Find combines all the different elements of getting actual goroutines information, filtering them, arriving at a leak conclusion, and even retrying multiple times all in just one single exported function. Unfortunately, goleak makes gathering information about all goroutines an internal matter, so we cannot reuse such functionality elsewhere. Users of the Gomega ecosystem are already experienced in arriving at conclusions and retrying temporarily failing expectations: Gomega does it in form of Eventually().ShouldNot(), and (without the trying aspect) with Expect().NotTo(). So what is missing is only a goroutine leak detector in form of the HaveLeaked matcher, as well as the ability to specify goroutine filters in order to sort out the non-leaking (and therefore expected) goroutines, using a few filter criteria. That is, a few new goroutine-related matchers. In this architecture, even existing Gomega matchers can optionally be (re)used as the need arises. https://github.com/onsi/gomega and https://github.com/onsi/ginkgo.

Package httpexpect helps with end-to-end HTTP and REST API testing. See example directory: There are two common ways to test API with httpexpect: The second approach works only if the server is a Go module and its handler can be imported in tests. Concrete behaviour is determined by Client implementation passed to Config struct. If you're using http.Client, set its Transport field (http.RoundTriper) to one of the following: Note that http handler can be usually obtained from http framework you're using. E.g., echo framework provides either http.Handler or fasthttp.RequestHandler. You can also provide your own implementation of RequestFactory (creates http.Request), or Client (gets http.Request and returns http.Response). If you're starting server from tests, it's very handy to use net/http/httptest. Whenever values are checked for equality in httpexpect, they are converted to "canonical form": This is equivalent to subsequently json.Marshal() and json.Unmarshal() the value and currently is implemented so. When some check fails, failure is reported. If non-fatal failures are used (see Reporter interface), execution is continued and instance that was checked is marked as failed. If specific instance is marked as failed, all subsequent checks are ignored for this instance and for any child instances retrieved after failure. Example:

Package skogul is a framework for receiving, processing and forwarding data, typically metric data or event-oriented data, at high throughput. It is designed to be as agnostic as possible with regards to how it transmits data and how it receives it, and the processors in between need not worry about how the data got there or how it will be treated in the next chain. This means you can use Skogul to receive data on a influxdb-like line-based TCP interface and send it on to postgres - or influxdb - without having to write explicit support, just set up the chain. The guiding principles of Skogul is: - Make as few assumptions as possible about how data is received - Be stupid fast End users should only need to worry about the cmd/skogul tool, which comes fully equipped with self-contained documentation. Adding new logic to Skogul should also be fairly easy. New developers should focus on understanding two things: 1. The skogul.Container data structure - which is the heart of Skogul. 2. The relationship from receiver to handler to sender. The Container is documented in this very package. Receivers are where data originates within Skogul. The typical Receiver will receive data from the outside world, e.g. by other tools posting data to a HTTP endpoint. Receivers can also be used to "create" data, either test data or, for example, log data. When skogul starts, it will start all receivers that are configured. Handlers determine what is done with the data once received. They are responsible for parsing raw data and optionally transform it. This is the only place where it is allowed to _modify_ data. Today, the only transformer is the "templater", which allows a collection of metrics which share certain attributes (e.g.: all collected at the same time and from the same machine) to provide these shared attributes in a template which the "templater" transformer then applies to all metrics. Other examples of transformations that make sense are: - Adding a metadata field - Removing a metadata field - Removing all but a specific set of fields - Converting nested metrics to multiple metrics or flatten them Once a handler has done its deed, it sends the Container to the sender, and this is where "the fun begins" so to speak. Senders consist of just a data structure that implements the Send() interface. They are not allowed to change the container, but besides that, they can do "whatever". The most obvious example is to send the container to a suitable storage system - e.g., a time series database. So if you want to add support for a new time series database in Skogul, you will write a sender. In addition to that, many senders serve only to add internal logic and pass data on to other senders. Each sender should only do one specific thing. For example, if you want to write data both to InfluxDB and MySQL, you need three senders: The "MySQL" and "InfluxDB" senders, and the "dupe" sender, which just takes a list of other senders and sends whatever it receives on to all of them. Today, Senders and Receivers both have an identical "Auto"-system, found in auto.go of the relevant directories. This is how the individual implementations are made discoverable to the configuration system, and how documentation is provided. Documentation for the settings of a sender/receiver is handled as struct tags. Once more parsers/transformers are added, they will likely also use a similar system.

Package go-ftw is a Framework for Testing Web Application Firewalls It is derived from the work made with the pytest plugin `ftw`

Package main is an application package for Findy Agency Service. Please be noted that the whole Findy Agency is still under construction, and there are many missing features for full production use. Also there are some refactoring candidates for restructuring the internal package layout. However, the findy-agent is currently tested for an extended period of pilot and development use, where it's proven to be stable. The current focus of the project is to offer efficient and straightforward multi-tenant agency with Aries compatible agent protocols. You can use the agency and related Go packages roughly for four purposes: 1. As a service agency for multiple Edge Agents at the same time by implementing them corresponding Cloud Agents. Allocated CAs implement Aries agent to agent protocols and interoperability. 2. As a CLI tool for setting up Edge Agent wallets, creating schemas and credential definitions into the wallet and writing them to the ledger. You can use findy-agent's own CLI for most of the needed tasks but for be usability we recommend to use findy CLI. 3. As an admin tool to monitor and maintain agency. 4. As a framework to implement Service Agents like issuers and verifiers. There are Go helpers to onboard EAs to agency and the Client, which hides the connections to the agency. The agency's compilation includes command and flag sets to operate it with minimal dependencies to other repos or utilities. The offered command set is minimal, but it offers everything to set up and maintain an agency. There is a separate CLI UI in other repo, which includes an extended command set with auto-completion scripts. The whole codebase is still heavily under construction, but the main principles are ready and ok. Documentation is very minimal and partially missing. findy-agent can be used as a service, as a framework and a CLI tool. It's structured to the following sub-packages:

Package zaptest implements test helpers that facilitate using a zap.Logger in unit tests. This package is useful when running unit tests with components that use the github.com/uber-go/zap logging framework. In unit tests we usually want to suppress any logging output as long as the tests are not failing or they are started in a verbose mode. Package zaptest is also compatible with the github.com/onsi/ginkgo BDD testing framework. Have a look at the zaptest unit tests to see an usage examples.

Package assert provides a set of comprehensive testing tools for use with the normal Go testing system. The following is a complete example using assert in a standard test function: if you assert many times, use the format below: Assertions allow you to easily write test code, and are global funcs in the `assert` package. All assertion functions take, as the first argument, the `*testing.T` object provided by the testing framework. This allows the assertion funcs to write the failings and other details to the correct place. Every assertion function also takes an optional string message as the final argument, allowing custom error messages to be appended to the message the assertion method outputs.

Package cgi implements the common gateway interface (CGI) for Caddy 2, a modern, full-featured, easy-to-use web server. It has been forked from the fantastic work of Kurt Jung who wrote that plugin for Caddy 1. This plugin lets you generate dynamic content on your website by means of command line scripts. To collect information about the inbound HTTP request, your script examines certain environment variables such as PATH_INFO and QUERY_STRING. Then, to return a dynamically generated web page to the client, your script simply writes content to standard output. In the case of POST requests, your script reads additional inbound content from standard input. The advantage of CGI is that you do not need to fuss with server startup and persistence, long term memory management, sockets, and crash recovery. Your script is called when a request matches one of the patterns that you specify in your Caddyfile. As soon as your script completes its response, it terminates. This simplicity makes CGI a perfect complement to the straightforward operation and configuration of Caddy. The benefits of Caddy, including HTTPS by default, basic access authentication, and lots of middleware options extend easily to your CGI scripts. CGI has some disadvantages. For one, Caddy needs to start a new process for each request. This can adversely impact performance and, if resources are shared between CGI applications, may require the use of some interprocess synchronization mechanism such as a file lock. Your server's responsiveness could in some circumstances be affected, such as when your web server is hit with very high demand, when your script's dependencies require a long startup, or when concurrently running scripts take a long time to respond. However, in many cases, such as using a pre-compiled CGI application like fossil or a Lua script, the impact will generally be insignificant. Another restriction of CGI is that scripts will be run with the same permissions as Caddy itself. This can sometimes be less than ideal, for example when your script needs to read or write files associated with a different owner. Serving dynamic content exposes your server to more potential threats than serving static pages. There are a number of considerations of which you should be aware when using CGI applications. CGI scripts should be located outside of Caddy's document root. Otherwise, an inadvertent misconfiguration could result in Caddy delivering the script as an ordinary static resource. At best, this could merely confuse the site visitor. At worst, it could expose sensitive internal information that should not leave the server. Mistrust the contents of PATH_INFO, QUERY_STRING and standard input. Most of the environment variables available to your CGI program are inherently safe because they originate with Caddy and cannot be modified by external users. This is not the case with PATH_INFO, QUERY_STRING and, in the case of POST actions, the contents of standard input. Be sure to validate and sanitize all inbound content. If you use a CGI library or framework to process your scripts, make sure you understand its limitations. An error in a CGI application is generally handled within the application itself and reported in the headers it returns. Your CGI application can be executed directly or indirectly. In the direct case, the application can be a compiled native executable or it can be a shell script that contains as its first line a shebang that identifies the interpreter to which the file's name should be passed. Caddy must have permission to execute the application. On Posix systems this will mean making sure the application's ownership and permission bits are set appropriately; on Windows, this may involve properly setting up the filename extension association. In the indirect case, the name of the CGI script is passed to an interpreter such as lua, perl or python. - This module needs to be installed (obviously). - The directive needs to be registered in the Caddyfile: The basic cgi directive lets you add a handler in the current caddy router location with a given script and optional arguments. The matcher is a default caddy matcher that is used to restrict the scope of this directive. The directive can be repeated any reasonable number of times. Here is the basic syntax: For example: When a request such as https://example.com/report or https://example.com/report/weekly arrives, the cgi middleware will detect the match and invoke the script named /usr/local/cgi-bin/report. The current working directory will be the same as Caddy itself. Here, it is assumed that the script is self-contained, for example a pre-compiled CGI application or a shell script. Here is an example of a standalone script, similar to one used in the cgi plugin's test suite: The environment variables PATH_INFO and QUERY_STRING are populated and passed to the script automatically. There are a number of other standard CGI variables included that are described below. If you need to pass any special environment variables or allow any environment variables that are part of Caddy's process to pass to your script, you will need to use the advanced directive syntax described below. Beware that in Caddy v2 it is (currently) not possible to separate the path left of the matcher from the full URL. Therefore if you require your CGI program to know the SCRIPT_NAME, make sure to pass that explicitly: In order to specify custom environment variables, pass along one or more environment variables known to Caddy, or specify more than one match pattern for a given rule, you will need to use the advanced directive syntax. That looks like this: For example, The script_name subdirective helps the cgi module to separate the path to the script from the (virtual) path afterwards (which shall be passed to the script). env can be used to define a list of key=value environment variable pairs that shall be passed to the script. pass_env can be used to define a list of environment variables of the Caddy process that shall be passed to the script. If your CGI application runs properly at the command line but fails to run from Caddy it is possible that certain environment variables may be missing. For example, the ruby gem loader evidently requires the HOME environment variable to be set; you can do this with the subdirective pass_env HOME. Another class of problematic applications require the COMPUTERNAME variable. The pass_all_env subdirective instructs Caddy to pass each environment variable it knows about to the CGI excutable. This addresses a common frustration that is caused when an executable requires an environment variable and fails without a descriptive error message when the variable cannot be found. These applications often run fine from the command prompt but fail when invoked with CGI. The risk with this subdirective is that a lot of server information is shared with the CGI executable. Use this subdirective only with CGI applications that you trust not to leak this information. buffer_limit is used when a http request has Transfer-Endcoding: chunked. The Go CGI Handler refused to handle these kinds of requests, see https://github.com/golang/go/issues/5613. In order to work around this the chunked request is buffered by caddy and sent to the CGI application as a whole with the correct CONTENT_LENGTH set. The buffer_limit setting marks a threshold between buffering in memory and using a temporary file. Every request body smaller than the buffer_limit is buffered in-memory. It accepts all formats supported by go-humanize. Default: 4MiB. (An example of this is git push if the objects to push are larger than the http.postBuffer) With the unbuffered_output subdirective it is possible to instruct the CGI handler to flush output from the CGI script as soon as possible. By default, the output is buffered into chunks before it is being written to optimize the network usage and allow to determine the Content-Length. When unbuffered, bytes will be written as soon as possible. This will also force the response to be written in chunked encoding. If you run into unexpected results with the CGI plugin, you are able to examine the environment in which your CGI application runs. To enter inspection mode, add the subdirective inspect to your CGI configuration block. This is a development option that should not be used in production. When in inspection mode, the plugin will respond to matching requests with a page that displays variables of interest. In particular, it will show the replacement value of {match} and the environment variables to which your CGI application has access. For example, consider this example CGI block: When you request a matching URL, for example, the Caddy server will deliver a text page similar to the following. The CGI application (in this case, wapptclsh) will not be called. This information can be used to diagnose problems with how a CGI application is called. To return to operation mode, remove or comment out the inspect subdirective. In this example, the Caddyfile looks like this: Note that a request for /show gets mapped to a script named /usr/local/cgi-bin/report/gen. There is no need for any element of the script name to match any element of the match pattern. The contents of /usr/local/cgi-bin/report/gen are: The purpose of this script is to show how request information gets communicated to a CGI script. Note that POST data must be read from standard input. In this particular case, posted data gets stored in the variable POST_DATA. Your script may use a different method to read POST content. Secondly, the SCRIPT_EXEC variable is not a CGI standard. It is provided by this middleware and contains the entire command line, including all arguments, with which the CGI script was executed. When a browser requests the response looks like When a client makes a POST request, such as with the following command the response looks the same except for the following lines: This small example demonstrates how to write a CGI program in Go. The use of a bytes.Buffer makes it easy to report the content length in the CGI header. When this program is compiled and installed as /usr/local/bin/servertime, the following directive in your Caddy file will make it available: The module is written in a way that it expects the scripts you want it to execute to actually exist. A non-existing or non-executable file is considered a setup error and will yield a HTTP 500. If you want to make sure, only existing scripts are executed, use a more specific matcher, as explained in the Caddy docs. Example: When calling a url like /cgi/foo/bar.pl it will check if the local file ./app/foo/bar.pl exists and only then it will proceed with calling the CGI.

Package toml provides facilities for decoding and encoding TOML configuration files via reflection. There is also support for delaying decoding with the Primitive type, and querying the set of keys in a TOML document with the MetaData type. The specification implemented: https://github.com/toml-lang/toml The sub-command github.com/BurntSushi/toml/cmd/tomlv can be used to verify whether a file is a valid TOML document. It can also be used to print the type of each key in a TOML document. There are two important types of tests used for this package. The first is contained inside '*_test.go' files and uses the standard Go unit testing framework. These tests are primarily devoted to holistically testing the decoder and encoder. The second type of testing is used to verify the implementation's adherence to the TOML specification. These tests have been factored into their own project: https://github.com/BurntSushi/toml-test The reason the tests are in a separate project is so that they can be used by any implementation of TOML. Namely, it is language agnostic. Example StrictDecoding shows how to detect whether there are keys in the TOML document that weren't decoded into the value given. This is useful for returning an error to the user if they've included extraneous fields in their configuration. Example UnmarshalTOML shows how to implement a struct type that knows how to unmarshal itself. The struct must take full responsibility for mapping the values passed into the struct. The method may be used with interfaces in a struct in cases where the actual type is not known until the data is examined. Example Unmarshaler shows how to decode TOML strings into your own custom data type.

Package sandwich is a middleware framework for go that lets you write testable web servers. Sandwich allows writing robust middleware handlers that are easily tested: Sandwich is provides a basic PAT-style router. Here's a simple complete program using sandwich: Sandwich automatically calls your middleware with the necessary arguments to run them based on the types they require. These types can be provided by previous middleware or directly during the initial setup. For example, you can use this to provide your database to all handlers: Set(...) and SetAs(...) are excellent alternatives to using global values, plus they keep your functions easy to test! In many cases you want to initialize a value based on the request, for example extracting the user login: This starts to show off the real power of sandwich. For each request, the following occurs: This allows you to write small, independently testable functions and let sandwich chain them together for you. Sandwich works hard to ensure that you don't get annoying run-time errors: it's structured such that it must always be possible to call your functions when the middleware is initialized rather than when the http handler is being executed, so you don't get surprised while your server is running. When a handler returns an error, sandwich aborts the middleware chain and looks for the most recently registered error handler and calls that. Error handlers may accept any types that have been provided so far in the middleware stack as well as the error type. They must not have any return values. Sandwich also allows registering handlers to run during AND after the middleware (and error handling) stack has completed. This is especially useful for handles such as logging or gzip wrappers. Once the before handle is run, the 'after' handlers are queued to run and will be run regardless of whether an error aborts any subsequent middleware handlers. Typically this is done with the first function creating and initializing some state to pass to the deferred handler. For example, the logging handlers are: and are added to the chain using: In this case, the `Wrap` executes NewLogEntry during middleware processing that returns a *LogEntry which is provided to downstream handlers, including the deferred Commit handler -- in this case a method expression (https://golang.org/ref/spec#Method_expressions) that takes the *LogEntry as its value receiver. Unfortunately, providing interfaces is a little tricky. Since interfaces in Go are only used for static typing, the encapsulation isn't passed to functions that accept interface{}, like Set(). This means that if you have an interface and a concrete implementation, such as: You cannot provide this to handlers directly via the Set() call. Instead, you have to either use SetAs() or a dedicated middleware function: It's a bit silly, but that's how it is.

Package tea provides a framework for building rich terminal user interfaces based on the paradigms of The Elm Architecture. It's well-suited for simple and complex terminal applications, either inline, full-window, or a mix of both. It's been battle-tested in several large projects and is production-ready. A tutorial is available at https://github.com/charmbracelet/bubbletea/tree/master/tutorials Example programs can be found at https://github.com/charmbracelet/bubbletea/tree/master/examples