Testing Framework

sigs.k8s.io/controller-runtime

Package controllerruntime provides tools to construct Kubernetes-style controllers that manipulate both Kubernetes CRDs and aggregated/built-in Kubernetes APIs. It defines easy helpers for the common use cases when building CRDs, built on top of customizable layers of abstraction. Common cases should be easy, and uncommon cases should be possible. In general, controller-runtime tries to guide users towards Kubernetes controller best-practices. The main entrypoint for controller-runtime is this root package, which contains all of the common types needed to get started building controllers: The examples in this package walk through a basic controller setup. The kubebuilder book (https://book.kubebuilder.io) has some more in-depth walkthroughs. controller-runtime favors structs with sane defaults over constructors, so it's fairly common to see structs being used directly in controller-runtime. A brief-ish walkthrough of the layout of this library can be found below. Each package contains more information about how to use it. Frequently asked questions about using controller-runtime and designing controllers can be found at https://github.com/kubernetes-sigs/controller-runtime/blob/main/FAQ.md. Every controller and webhook is ultimately run by a Manager (pkg/manager). A manager is responsible for running controllers and webhooks, and setting up common dependencies, like shared caches and clients, as well as managing leader election (pkg/leaderelection). Managers are generally configured to gracefully shut down controllers on pod termination by wiring up a signal handler (pkg/manager/signals). Controllers (pkg/controller) use events (pkg/event) to eventually trigger reconcile requests. They may be constructed manually, but are often constructed with a Builder (pkg/builder), which eases the wiring of event sources (pkg/source), like Kubernetes API object changes, to event handlers (pkg/handler), like "enqueue a reconcile request for the object owner". Predicates (pkg/predicate) can be used to filter which events actually trigger reconciles. There are pre-written utilities for the common cases, and interfaces and helpers for advanced cases. Controller logic is implemented in terms of Reconcilers (pkg/reconcile). A Reconciler implements a function which takes a reconcile Request containing the name and namespace of the object to reconcile, reconciles the object, and returns a Response or an error indicating whether to requeue for a second round of processing. Reconcilers use Clients (pkg/client) to access API objects. The default client provided by the manager reads from a local shared cache (pkg/cache) and writes directly to the API server, but clients can be constructed that only talk to the API server, without a cache. The Cache will auto-populate with watched objects, as well as when other structured objects are requested. The default split client does not promise to invalidate the cache during writes (nor does it promise sequential create/get coherence), and code should not assume a get immediately following a create/update will return the updated resource. Caches may also have indexes, which can be created via a FieldIndexer (pkg/client) obtained from the manager. Indexes can used to quickly and easily look up all objects with certain fields set. Reconcilers may retrieve event recorders (pkg/recorder) to emit events using the manager. Clients, Caches, and many other things in Kubernetes use Schemes (pkg/scheme) to associate Go types to Kubernetes API Kinds (Group-Version-Kinds, to be specific). Similarly, webhooks (pkg/webhook/admission) may be implemented directly, but are often constructed using a builder (pkg/webhook/admission/builder). They are run via a server (pkg/webhook) which is managed by a Manager. Logging (pkg/log) in controller-runtime is done via structured logs, using a log set of interfaces called logr (https://pkg.go.dev/github.com/go-logr/logr). While controller-runtime provides easy setup for using Zap (https://go.uber.org/zap, pkg/log/zap), you can provide any implementation of logr as the base logger for controller-runtime. Metrics (pkg/metrics) provided by controller-runtime are registered into a controller-runtime-specific Prometheus metrics registry. The manager can serve these by an HTTP endpoint, and additional metrics may be registered to this Registry as normal. You can easily build integration and unit tests for your controllers and webhooks using the test Environment (pkg/envtest). This will automatically stand up a copy of etcd and kube-apiserver, and provide the correct options to connect to the API server. It's designed to work well with the Ginkgo testing framework, but should work with any testing setup. This example creates a simple application Controller that is configured for ReplicaSets and Pods. * Create a new application for ReplicaSets that manages Pods owned by the ReplicaSet and calls into ReplicaSetReconciler. * Start the application. This example creates a simple application Controller that is configured for ExampleCRDWithConfigMapRef CRD. Any change in the configMap referenced in this Custom Resource will cause the re-reconcile of the parent ExampleCRDWithConfigMapRef due to the implementation of the .Watches method of "sigs.k8s.io/controller-runtime/pkg/builder".Builder. This example creates a simple application Controller that is configured for ReplicaSets and Pods. This application controller will be running leader election with the provided configuration in the manager options. If leader election configuration is not provided, controller runs leader election with default values. Default values taken from: https://github.com/kubernetes/component-base/blob/master/config/v1alpha1/defaults.go * defaultLeaseDuration = 15 * time.Second * defaultRenewDeadline = 10 * time.Second * defaultRetryPeriod = 2 * time.Second * Create a new application for ReplicaSets that manages Pods owned by the ReplicaSet and calls into ReplicaSetReconciler. * Start the application.

v0.19.1 • last month

go.uber.org/fx

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. If you're new to Fx, start there! Advanced features, including named instances, optional parameters, and value groups, are explained in this section further down. 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. Fx constructors declare their dependencies as function parameters. This can quickly become unreadable if the constructor has a lot of dependencies. To improve the readability of constructors like this, create a struct that lists all the dependencies as fields and change the function to accept that struct instead. The new struct is called a parameter struct. Fx has first class support for parameter structs: any struct embedding fx.In gets treated as a parameter struct, so the individual fields in the struct are supplied via dependency injection. Using a parameter struct, we can make the constructor above much more readable: Though it's rarelly necessary to mix the two, constructors can receive any combination of parameter structs and parameters. Result structs are the inverse of parameter structs. These structs represent multiple outputs from a single function as fields. Fx treats all structs embedding fx.Out as result structs, so other constructors can rely on the result struct's fields directly. Without result structs, we sometimes have function definitions like this: With result structs, we can make this both more readable and easier to modify in the future: Some use cases require the application container to hold multiple values of the same type. A constructor that produces a result struct can tag any field with `name:".."` to have the corresponding value added to the graph under the specified name. An application may contain at most one unnamed value of a given type, but may contain any number of named values of the same type. Similarly, a constructor that accepts a parameter struct can tag any field with `name:".."` to have the corresponding value injected by name. Note that both the name AND type of the fields on the parameter struct must match the corresponding result struct. Constructors often have optional dependencies on some types: if those types are missing, they can operate in a degraded state. Fx supports optional dependencies via the `optional:"true"` tag to fields on parameter structs. If an optional field isn't available in the container, the constructor receives the field's zero value. Constructors that declare optional dependencies MUST gracefully handle situations in which those dependencies are absent. The optional tag also allows adding new dependencies without breaking existing consumers of the constructor. The optional tag may be combined with the name tag to declare a named value dependency optional. To make it easier to produce and consume many values of the same type, Fx supports named, unordered collections called value groups. Constructors can send values into value groups by returning a result struct tagged with `group:".."`. Any number of constructors may provide values to this named collection, but the ordering of the final collection is unspecified. Value groups require parameter and result structs to use fields with different types: if a group of constructors each returns type T, parameter structs consuming the group must use a field of type []T. Parameter structs can request a value group by using a field of type []T tagged with `group:".."`. This will execute all constructors that provide a value to that group in an unspecified order, then collect all the results into a single slice. Note that values in a value group are unordered. Fx makes no guarantees about the order in which these values will be produced. By default, when a constructor declares a dependency on a value group, all values provided to that value group are eagerly instantiated. That is undesirable for cases where an optional component wants to constribute to a value group, but only if it was actually used by the rest of the application. A soft value group can be thought of as a best-attempt at populating the group with values from constructors that have already run. In other words, if a constructor's output type is only consumed by a soft value group, it will not be run. Note that Fx randomizes the order of values in the value group, so the slice of values may not match the order in which constructors were run. To declare a soft relationship between a group and its constructors, use the `soft` option on the input group tag (`group:"[groupname],soft"`). This option is only valid for input parameters. With such a declaration, a constructor that provides a value to the 'server' value group will be called only if there's another instantiated component that consumes the results of that constructor. NewHandlerAndLogger will be called because the Logger is consumed by the application, but NewHandler will not be called because it's only consumed by the soft value group. By default, values of type T produced to a value group are consumed as []T. This means that if the producer produces []T, the consumer must consume [][]T. There are cases where it's desirable for the producer (the fx.Out) to produce multiple values ([]T), and for the consumer (the fx.In) consume them as a single slice ([]T). Fx offers flattened value groups for this purpose. To provide multiple values for a group from a result struct, produce a slice and use the `,flatten` option on the group tag. This indicates that each element in the slice should be injected into the group individually. By default, a type that embeds fx.In may not have any unexported fields. The following will return an error if used with Fx. If you have need of unexported fields on such a type, you may opt-into ignoring unexported fields by adding the ignore-unexported struct tag to the fx.In. For example,

v1.23.0 • last month

github.com/gorilla/csrf

Package csrf (gorilla/csrf) provides Cross Site Request Forgery (CSRF) prevention middleware for Go web applications & services. It includes: * The `csrf.Protect` middleware/handler provides CSRF protection on routes attached to a router or a sub-router. * A `csrf.Token` function that provides the token to pass into your response, whether that be a HTML form or a JSON response body. * ... and a `csrf.TemplateField` helper that you can pass into your `html/template` templates to replace a `{{ .csrfField }}` template tag with a hidden input field. gorilla/csrf is easy to use: add the middleware to individual handlers with the below: ... and then collect the token with `csrf.Token(r)` before passing it to the template, JSON body or HTTP header (you pick!). gorilla/csrf inspects the form body (first) and HTTP headers (second) on subsequent POST/PUT/PATCH/DELETE/etc. requests for the token. Note that the authentication key passed to `csrf.Protect([]byte(key))` should be 32-bytes long and persist across application restarts. Generating a random key won't allow you to authenticate existing cookies and will break your CSRF validation. Here's the common use-case: HTML forms you want to provide CSRF protection for, in order to protect malicious POST requests being made: Note that the CSRF middleware will (by necessity) consume the request body if the token is passed via POST form values. If you need to consume this in your handler, insert your own middleware earlier in the chain to capture the request body. You can also send the CSRF token in the response header. This approach is useful if you're using a front-end JavaScript framework like Ember or Angular, or are providing a JSON API: If you're writing a client that's supposed to mimic browser behavior, make sure to send back the CSRF cookie (the default name is _gorilla_csrf, but this can be changed with the CookieName Option) along with either the X-CSRF-Token header or the gorilla.csrf.Token form field. In addition: getting CSRF protection right is important, so here's some background: * This library generates unique-per-request (masked) tokens as a mitigation against the BREACH attack (http://breachattack.com/). * The 'base' (unmasked) token is stored in the session, which means that multiple browser tabs won't cause a user problems as their per-request token is compared with the base token. * Operates on a "whitelist only" approach where safe (non-mutating) HTTP methods (GET, HEAD, OPTIONS, TRACE) are the *only* methods where token validation is not enforced. * The design is based on the battle-tested Django (https://docs.djangoproject.com/en/1.8/ref/csrf/) and Ruby on Rails (http://api.rubyonrails.org/classes/ActionController/RequestForgeryProtection.html) approaches. * Cookies are authenticated and based on the securecookie (https://github.com/gorilla/securecookie) library. They're also Secure (issued over HTTPS only) and are HttpOnly by default, because sane defaults are important. * Go's `crypto/rand` library is used to generate the 32 byte (256 bit) tokens and the one-time-pad used for masking them. This library does not seek to be adventurous.

v1.7.2 • last year

github.com/awsdocs/aws-doc-sdk-examples/gov2/testtools

Package testtools provides a set of tools to help test code that calls AWS services. **AWS Middleware Stubber** The AWS Middleware Stubber is a unit testing tool that hooks into the AWS SDK for Go middleware (https://aws.github.io/aws-sdk-go-v2/docs/middleware/) to short-circuit calls to AWS services, verify inputs, and return predefined outputs. This improves unit testing because you don't have to define mocks or change the way your code calls AWS. Tests run without calling AWS, which means tests run faster and don't incur charges or risk impacting your resources. To use AwsmStubber, first create an instance of AwsmStubber. The stubber is configured to handle all calls to AWS before the Serialize middleware step. Use the stubber config to create a service client. Define and add all service actions that are called by your test. During your test run, the stubber verifies that each call is made in the order that stubs are added to the stubber. The stubber also checks actual input against expected input. If the call is verified, either the specified output is returned or, if an error is requested, the error is returned. Run your test and verify the results. Use testtools helper functions to verify errors and run exit code. By using sub tests, you can use the same test code to test both error and non-error paths. The testtools.ExitTest helper verifies that all expected stubs were called during the test, so if your test exits early and leaves uncalled stubs, the test fails. **Framework** The framework section of the package provides a set of helper functions that you can use in your tests to perform common tasks, such as verifying that errors returned from the code under test match up with the expected errors, and running exit checks to verify all stubs were called. **Scenarios** The scenarios section of the package provides a set of helper functions that you can use to run scenario tests. Scenarios typically string together several actions in a narrative format. The scenario test functions let you define the expected actions of your scenario as a list of stubs. Then, your test function is called first with no errors, and subsequently with each stub set to return an error. **Mocks** The mocks section of the package provides mocks of components that are used in the code examples, such as a mock of the IQuestioner interface that lets you specify a list of expected answers. The mock questioner returns these answers in sequence during a test to mock user input.

v0.0.0-20240124140945-a3b438bc482d • 10 months ago

github.com/docker/go-healthcheck

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:

v0.1.0 • 7 years ago

github.com/jawher/mow.cli

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, Float64Opt, Floats64Opt, or BoolOpt methods on App (or Cmd if adding flags to a command or a subcommand). For example, to add a boolean flag to the cp command that specifies recursive mode, use the following: or: The first version returns a new pointer to a bool value which will be populated when the app is run, whereas the second version will populate a pointer to an existing variable you specify. The option name(s) is a space separated list of names (without the dashes). The one letter names can then be called with a single dash (short option, -R), the others with two dashes (long options, --recursive). You also specify the default value for the option if it is not supplied by the user. The last parameter 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.Float64Opt, cli.Floats64Opt, cli.BoolOpt. The struct describes the option and allows the use of additional features not available in the short-form methods described above: Or: The first version returns a new pointer to a value which will be populated when the app is run, whereas the second version will populate a pointer to an existing variable you specify. 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. 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, int and float options: Slice options (StringsOpt, IntsOpt, Floats64Opt) 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, Float64Arg, Floats64Arg, 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: Or: The first version returns a new pointer to a value which will be populated when the app is run, whereas the second version will populate a pointer to an existing variable you specify. You then specify the argument as will be displayed in help messages. Argument names must be specified as all uppercase. The next parameter is the default value for the argument if it is not supplied. And the last is the description to be shown in help messages. There is also a second set of methods on App called String, Strings, Int, Ints, Float64, Floats64 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: Or: The first version returns a new pointer to a value which will be populated when the app is run, whereas the second version will populate a pointer to an existing variable you specify. 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. 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: Commands can be hidden in the help messages. This can prove useful to deprecate a command so that it does not appear to new users in the help, but still exists to not break existing scripts. To hide a command, set the Hidden field to true: 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, float64, strings (slice of strings), ints (slice of ints) and floats64 (slice of float64). 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:

v1.2.0 • 4 years ago

golang.org/x/tools/go/pointer

Package pointer implements Andersen's analysis, an inclusion-based pointer analysis algorithm first described in (Andersen, 1994). A pointer analysis relates every pointer expression in a whole program to the set of memory locations to which it might point. This information can be used to construct a call graph of the program that precisely represents the destinations of dynamic function and method calls. It can also be used to determine, for example, which pairs of channel operations operate on the same channel. The package allows the client to request a set of expressions of interest for which the points-to information will be returned once the analysis is complete. In addition, the client may request that a callgraph is constructed. The example program in example_test.go demonstrates both of these features. Clients should not request more information than they need since it may increase the cost of the analysis significantly. Our algorithm is INCLUSION-BASED: the points-to sets for x and y will be related by pts(y) ⊇ pts(x) if the program contains the statement y = x. It is FLOW-INSENSITIVE: it ignores all control flow constructs and the order of statements in a program. It is therefore a "MAY ALIAS" analysis: its facts are of the form "P may/may not point to L", not "P must point to L". It is FIELD-SENSITIVE: it builds separate points-to sets for distinct fields, such as x and y in struct { x, y *int }. It is mostly CONTEXT-INSENSITIVE: most functions are analyzed once, so values can flow in at one call to the function and return out at another. Only some smaller functions are analyzed with consideration of their calling context. It has a CONTEXT-SENSITIVE HEAP: objects are named by both allocation site and context, so the objects returned by two distinct calls to f: are distinguished up to the limits of the calling context. It is a WHOLE PROGRAM analysis: it requires SSA-form IR for the complete Go program and summaries for native code. See the (Hind, PASTE'01) survey paper for an explanation of these terms. The analysis is fully sound when invoked on pure Go programs that do not use reflection or unsafe.Pointer conversions. In other words, if there is any possible execution of the program in which pointer P may point to object O, the analysis will report that fact. By default, the "reflect" library is ignored by the analysis, as if all its functions were no-ops, but if the client enables the Reflection flag, the analysis will make a reasonable attempt to model the effects of calls into this library. However, this comes at a significant performance cost, and not all features of that library are yet implemented. In addition, some simplifying approximations must be made to ensure that the analysis terminates; for example, reflection can be used to construct an infinite set of types and values of those types, but the analysis arbitrarily bounds the depth of such types. Most but not all reflection operations are supported. In particular, addressable reflect.Values are not yet implemented, so operations such as (reflect.Value).Set have no analytic effect. The pointer analysis makes no attempt to understand aliasing between the operand x and result y of an unsafe.Pointer conversion: It is as if the conversion allocated an entirely new object: The analysis cannot model the aliasing effects of functions written in languages other than Go, such as runtime intrinsics in C or assembly, or code accessed via cgo. The result is as if such functions are no-ops. However, various important intrinsics are understood by the analysis, along with built-ins such as append. The analysis currently provides no way for users to specify the aliasing effects of native code. ------------------------------------------------------------------------ The remaining documentation is intended for package maintainers and pointer analysis specialists. Maintainers should have a solid understanding of the referenced papers (especially those by H&L and PKH) before making making significant changes. The implementation is similar to that described in (Pearce et al, PASTE'04). Unlike many algorithms which interleave constraint generation and solving, constructing the callgraph as they go, this implementation for the most part observes a phase ordering (generation before solving), with only simple (copy) constraints being generated during solving. (The exception is reflection, which creates various constraints during solving as new types flow to reflect.Value operations.) This improves the traction of presolver optimisations, but imposes certain restrictions, e.g. potential context sensitivity is limited since all variants must be created a priori. A type is said to be "pointer-like" if it is a reference to an object. Pointer-like types include pointers and also interfaces, maps, channels, functions and slices. We occasionally use C's x->f notation to distinguish the case where x is a struct pointer from x.f where is a struct value. Pointer analysis literature (and our comments) often uses the notation dst=*src+offset to mean something different than what it means in Go. It means: for each node index p in pts(src), the node index p+offset is in pts(dst). Similarly *dst+offset=src is used for store constraints and dst=src+offset for offset-address constraints. Nodes are the key datastructure of the analysis, and have a dual role: they represent both constraint variables (equivalence classes of pointers) and members of points-to sets (things that can be pointed at, i.e. "labels"). Nodes are naturally numbered. The numbering enables compact representations of sets of nodes such as bitvectors (or BDDs); and the ordering enables a very cheap way to group related nodes together. For example, passing n parameters consists of generating n parallel constraints from caller+i to callee+i for 0<=i<n. The zero nodeid means "not a pointer". For simplicity, we generate flow constraints even for non-pointer types such as int. The pointer equivalence (PE) presolver optimization detects which variables cannot point to anything; this includes not only all variables of non-pointer types (such as int) but also variables of pointer-like types if they are always nil, or are parameters to a function that is never called. Each node represents a scalar part of a value or object. Aggregate types (structs, tuples, arrays) are recursively flattened out into a sequential list of scalar component types, and all the elements of an array are represented by a single node. (The flattening of a basic type is a list containing a single node.) Nodes are connected into a graph with various kinds of labelled edges: simple edges (or copy constraints) represent value flow. Complex edges (load, store, etc) trigger the creation of new simple edges during the solving phase. Conceptually, an "object" is a contiguous sequence of nodes denoting an addressable location: something that a pointer can point to. The first node of an object has a non-nil obj field containing information about the allocation: its size, context, and ssa.Value. Objects include: Many objects have no Go types. For example, the func, map and chan type kinds in Go are all varieties of pointers, but their respective objects are actual functions (executable code), maps (hash tables), and channels (synchronized queues). Given the way we model interfaces, they too are pointers to "tagged" objects with no Go type. And an *ssa.Global denotes the address of a global variable, but the object for a Global is the actual data. So, the types of an ssa.Value that creates an object is "off by one indirection": a pointer to the object. The individual nodes of an object are sometimes referred to as "labels". For uniformity, all objects have a non-zero number of fields, even those of the empty type struct{}. (All arrays are treated as if of length 1, so there are no empty arrays. The empty tuple is never address-taken, so is never an object.) An tagged object has the following layout: The T node's typ field is the dynamic type of the "payload": the value v which follows, flattened out. The T node's obj has the otTagged flag. Tagged objects are needed when generalizing across types: interfaces, reflect.Values, reflect.Types. Each of these three types is modelled as a pointer that exclusively points to tagged objects. Tagged objects may be indirect (obj.flags ⊇ {otIndirect}) meaning that the value v is not of type T but *T; this is used only for reflect.Values that represent lvalues. (These are not implemented yet.) Variables of the following "scalar" types may be represented by a single node: basic types, pointers, channels, maps, slices, 'func' pointers, interfaces. Pointers: Nothing to say here, oddly. Basic types (bool, string, numbers, unsafe.Pointer): Currently all fields in the flattening of a type, including non-pointer basic types such as int, are represented in objects and values. Though non-pointer nodes within values are uninteresting, non-pointer nodes in objects may be useful (if address-taken) because they permit the analysis to deduce, in this example, that p points to s.x. If we ignored such object fields, we could only say that p points somewhere within s. All other basic types are ignored. Expressions of these types have zero nodeid, and fields of these types within aggregate other types are omitted. unsafe.Pointers are not modelled as pointers, so a conversion of an unsafe.Pointer to *T is (unsoundly) treated equivalent to new(T). Channels: An expression of type 'chan T' is a kind of pointer that points exclusively to channel objects, i.e. objects created by MakeChan (or reflection). 'chan T' is treated like *T. *ssa.MakeChan is treated as equivalent to new(T). *ssa.Send and receive (*ssa.UnOp(ARROW)) and are equivalent to store Maps: An expression of type 'map[K]V' is a kind of pointer that points exclusively to map objects, i.e. objects created by MakeMap (or reflection). map K[V] is treated like *M where M = struct{k K; v V}. *ssa.MakeMap is equivalent to new(M). *ssa.MapUpdate is equivalent to *y=x where *y and x have type M. *ssa.Lookup is equivalent to y=x.v where x has type *M. Slices: A slice []T, which dynamically resembles a struct{array *T, len, cap int}, is treated as if it were just a *T pointer; the len and cap fields are ignored. *ssa.MakeSlice is treated like new([1]T): an allocation of a *ssa.Index on a slice is equivalent to a load. *ssa.IndexAddr on a slice returns the address of the sole element of the slice, i.e. the same address. *ssa.Slice is treated as a simple copy. Functions: An expression of type 'func...' is a kind of pointer that points exclusively to function objects. A function object has the following layout: There may be multiple function objects for the same *ssa.Function due to context-sensitive treatment of some functions. The first node is the function's identity node. Associated with every callsite is a special "targets" variable, whose pts() contains the identity node of each function to which the call may dispatch. Identity words are not otherwise used during the analysis, but we construct the call graph from the pts() solution for such nodes. The following block of contiguous nodes represents the flattened-out types of the parameters ("P-block") and results ("R-block") of the function object. The treatment of free variables of closures (*ssa.FreeVar) is like that of global variables; it is not context-sensitive. *ssa.MakeClosure instructions create copy edges to Captures. A Go value of type 'func' (i.e. a pointer to one or more functions) is a pointer whose pts() contains function objects. The valueNode() for an *ssa.Function returns a singleton for that function. Interfaces: An expression of type 'interface{...}' is a kind of pointer that points exclusively to tagged objects. All tagged objects pointed to by an interface are direct (the otIndirect flag is clear) and concrete (the tag type T is not itself an interface type). The associated ssa.Value for an interface's tagged objects may be an *ssa.MakeInterface instruction, or nil if the tagged object was created by an instrinsic (e.g. reflection). Constructing an interface value causes generation of constraints for all of the concrete type's methods; we can't tell a priori which ones may be called. TypeAssert y = x.(T) is implemented by a dynamic constraint triggered by each tagged object O added to pts(x): a typeFilter constraint if T is an interface type, or an untag constraint if T is a concrete type. A typeFilter tests whether O.typ implements T; if so, O is added to pts(y). An untagFilter tests whether O.typ is assignable to T,and if so, a copy edge O.v -> y is added. ChangeInterface is a simple copy because the representation of tagged objects is independent of the interface type (in contrast to the "method tables" approach used by the gc runtime). y := Invoke x.m(...) is implemented by allocating contiguous P/R blocks for the callsite and adding a dynamic rule triggered by each tagged object added to pts(x). The rule adds param/results copy edges to/from each discovered concrete method. (Q. Why do we model an interface as a pointer to a pair of type and value, rather than as a pair of a pointer to type and a pointer to value? A. Control-flow joins would merge interfaces ({T1}, {V1}) and ({T2}, {V2}) to make ({T1,T2}, {V1,V2}), leading to the infeasible and type-unsafe combination (T1,V2). Treating the value and its concrete type as inseparable makes the analysis type-safe.) Type parameters: Type parameters are not directly supported by the analysis. Calls to generic functions will be left as if they had empty bodies. Users of the package are expected to use the ssa.InstantiateGenerics builder mode when building code that uses or depends on code containing generics. reflect.Value: A reflect.Value is modelled very similar to an interface{}, i.e. as a pointer exclusively to tagged objects, but with two generalizations. 1. a reflect.Value that represents an lvalue points to an indirect (obj.flags ⊇ {otIndirect}) tagged object, which has a similar layout to an tagged object except that the value is a pointer to the dynamic type. Indirect tagged objects preserve the correct aliasing so that mutations made by (reflect.Value).Set can be observed. Indirect objects only arise when an lvalue is derived from an rvalue by indirection, e.g. the following code: Whether indirect or not, the concrete type of the tagged object corresponds to the user-visible dynamic type, and the existence of a pointer is an implementation detail. (NB: indirect tagged objects are not yet implemented) 2. The dynamic type tag of a tagged object pointed to by a reflect.Value may be an interface type; it need not be concrete. This arises in code such as this: pts(eface) is a singleton containing an interface{}-tagged object. That tagged object's payload is an interface{} value, i.e. the pts of the payload contains only concrete-tagged objects, although in this example it's the zero interface{} value, so its pts is empty. reflect.Type: Just as in the real "reflect" library, we represent a reflect.Type as an interface whose sole implementation is the concrete type, *reflect.rtype. (This choice is forced on us by go/types: clients cannot fabricate types with arbitrary method sets.) rtype instances are canonical: there is at most one per dynamic type. (rtypes are in fact large structs but since identity is all that matters, we represent them by a single node.) The payload of each *rtype-tagged object is an *rtype pointer that points to exactly one such canonical rtype object. We exploit this by setting the node.typ of the payload to the dynamic type, not '*rtype'. This saves us an indirection in each resolution rule. As an optimisation, *rtype-tagged objects are canonicalized too. Aggregate types: Aggregate types are treated as if all directly contained aggregates are recursively flattened out. Structs: *ssa.Field y = x.f creates a simple edge to y from x's node at f's offset. *ssa.FieldAddr y = &x->f requires a dynamic closure rule to create The nodes of a struct consist of a special 'identity' node (whose type is that of the struct itself), followed by the nodes for all the struct's fields, recursively flattened out. A pointer to the struct is a pointer to its identity node. That node allows us to distinguish a pointer to a struct from a pointer to its first field. Field offsets are logical field offsets (plus one for the identity node), so the sizes of the fields can be ignored by the analysis. (The identity node is non-traditional but enables the distinction described above, which is valuable for code comprehension tools. Typical pointer analyses for C, whose purpose is compiler optimization, must soundly model unsafe.Pointer (void*) conversions, and this requires fidelity to the actual memory layout using physical field offsets.) *ssa.Field y = x.f creates a simple edge to y from x's node at f's offset. *ssa.FieldAddr y = &x->f requires a dynamic closure rule to create Arrays: We model an array by an identity node (whose type is that of the array itself) followed by a node representing all the elements of the array; the analysis does not distinguish elements with different indices. Effectively, an array is treated like struct{elem T}, a load y=x[i] like y=x.elem, and a store x[i]=y like x.elem=y; the index i is ignored. A pointer to an array is pointer to its identity node. (A slice is also a pointer to an array's identity node.) The identity node allows us to distinguish a pointer to an array from a pointer to one of its elements, but it is rather costly because it introduces more offset constraints into the system. Furthermore, sound treatment of unsafe.Pointer would require us to dispense with this node. Arrays may be allocated by Alloc, by make([]T), by calls to append, and via reflection. Tuples (T, ...): Tuples are treated like structs with naturally numbered fields. *ssa.Extract is analogous to *ssa.Field. However, tuples have no identity field since by construction, they cannot be address-taken. There are three kinds of function call: Cases 1 and 2 apply equally to methods and standalone functions. Static calls: A static call consists three steps: A static function call is little more than two struct value copies between the P/R blocks of caller and callee: Context sensitivity: Static calls (alone) may be treated context sensitively, i.e. each callsite may cause a distinct re-analysis of the callee, improving precision. Our current context-sensitivity policy treats all intrinsics and getter/setter methods in this manner since such functions are small and seem like an obvious source of spurious confluences, though this has not yet been evaluated. Dynamic function calls: Dynamic calls work in a similar manner except that the creation of copy edges occurs dynamically, in a similar fashion to a pair of struct copies in which the callee is indirect: (Recall that the function object's P- and R-blocks are contiguous.) Interface method invocation: For invoke-mode calls, we create a params/results block for the callsite and attach a dynamic closure rule to the interface. For each new tagged object that flows to the interface, we look up the concrete method, find its function object, and connect its P/R blocks to the callsite's P/R blocks, adding copy edges to the graph during solving. Recording call targets: The analysis notifies its clients of each callsite it encounters, passing a CallSite interface. Among other things, the CallSite contains a synthetic constraint variable ("targets") whose points-to solution includes the set of all function objects to which the call may dispatch. It is via this mechanism that the callgraph is made available. Clients may also elect to be notified of callgraph edges directly; internally this just iterates all "targets" variables' pts(·)s. We implement Hash-Value Numbering (HVN), a pre-solver constraint optimization described in Hardekopf & Lin, SAS'07. This is documented in more detail in hvn.go. We intend to add its cousins HR and HU in future. The solver is currently a naive Andersen-style implementation; it does not perform online cycle detection, though we plan to add solver optimisations such as Hybrid- and Lazy- Cycle Detection from (Hardekopf & Lin, PLDI'07). It uses difference propagation (Pearce et al, SQC'04) to avoid redundant re-triggering of closure rules for values already seen. Points-to sets are represented using sparse bit vectors (similar to those used in LLVM and gcc), which are more space- and time-efficient than sets based on Go's built-in map type or dense bit vectors. Nodes are permuted prior to solving so that object nodes (which may appear in points-to sets) are lower numbered than non-object (var) nodes. This improves the density of the set over which the PTSs range, and thus the efficiency of the representation. Partly thanks to avoiding map iteration, the execution of the solver is 100% deterministic, a great help during debugging. Andersen, L. O. 1994. Program analysis and specialization for the C programming language. Ph.D. dissertation. DIKU, University of Copenhagen. David J. Pearce, Paul H. J. Kelly, and Chris Hankin. 2004. Efficient field-sensitive pointer analysis for C. In Proceedings of the 5th ACM SIGPLAN-SIGSOFT workshop on Program analysis for software tools and engineering (PASTE '04). ACM, New York, NY, USA, 37-42. http://doi.acm.org/10.1145/996821.996835 David J. Pearce, Paul H. J. Kelly, and Chris Hankin. 2004. Online Cycle Detection and Difference Propagation: Applications to Pointer Analysis. Software Quality Control 12, 4 (December 2004), 311-337. http://dx.doi.org/10.1023/B:SQJO.0000039791.93071.a2 David Grove and Craig Chambers. 2001. A framework for call graph construction algorithms. ACM Trans. Program. Lang. Syst. 23, 6 (November 2001), 685-746. http://doi.acm.org/10.1145/506315.506316 Ben Hardekopf and Calvin Lin. 2007. The ant and the grasshopper: fast and accurate pointer analysis for millions of lines of code. In Proceedings of the 2007 ACM SIGPLAN conference on Programming language design and implementation (PLDI '07). ACM, New York, NY, USA, 290-299. http://doi.acm.org/10.1145/1250734.1250767 Ben Hardekopf and Calvin Lin. 2007. Exploiting pointer and location equivalence to optimize pointer analysis. In Proceedings of the 14th international conference on Static Analysis (SAS'07), Hanne Riis Nielson and Gilberto Filé (Eds.). Springer-Verlag, Berlin, Heidelberg, 265-280. Atanas Rountev and Satish Chandra. 2000. Off-line variable substitution for scaling points-to analysis. In Proceedings of the ACM SIGPLAN 2000 conference on Programming language design and implementation (PLDI '00). ACM, New York, NY, USA, 47-56. DOI=10.1145/349299.349310 http://doi.acm.org/10.1145/349299.349310 This program demonstrates how to use the pointer analysis to obtain a conservative call-graph of a Go program. It also shows how to compute the points-to set of a variable, in this case, (C).f's ch parameter.

v0.1.0-deprecated • last year

github.com/pemistahl/lingua-go

Package lingua accurately detects the natural language of written text, be it long or short. Its task is simple: It tells you which language some text is written in. This is very useful as a preprocessing step for linguistic data in natural language processing applications such as text classification and spell checking. Other use cases, for instance, might include routing e-mails to the right geographically located customer service department, based on the e-mails' languages. Language detection is often done as part of large machine learning frameworks or natural language processing applications. In cases where you don't need the full-fledged functionality of those systems or don't want to learn the ropes of those, a small flexible library comes in handy. So far, the only other comprehensive open source library in the Go ecosystem for this task is Whatlanggo (https://github.com/abadojack/whatlanggo). Unfortunately, it has two major drawbacks: 1. Detection only works with quite lengthy text fragments. For very short text snippets such as Twitter messages, it does not provide adequate results. 2. The more languages take part in the decision process, the less accurate are the detection results. Lingua aims at eliminating these problems. It nearly does not need any configuration and yields pretty accurate results on both long and short text, even on single words and phrases. It draws on both rule-based and statistical methods but does not use any dictionaries of words. It does not need a connection to any external API or service either. Once the library has been downloaded, it can be used completely offline. Compared to other language detection libraries, Lingua's focus is on quality over quantity, that is, getting detection right for a small set of languages first before adding new ones. Currently, 75 languages are supported. They are listed as variants of type Language. Lingua is able to report accuracy statistics for some bundled test data available for each supported language. The test data for each language is split into three parts: 1. a list of single words with a minimum length of 5 characters 2. a list of word pairs with a minimum length of 10 characters 3. a list of complete grammatical sentences of various lengths Both the language models and the test data have been created from separate documents of the Wortschatz corpora (https://wortschatz.uni-leipzig.de) offered by Leipzig University, Germany. Data crawled from various news websites have been used for training, each corpus comprising one million sentences. For testing, corpora made of arbitrarily chosen websites have been used, each comprising ten thousand sentences. From each test corpus, a random unsorted subset of 1000 single words, 1000 word pairs and 1000 sentences has been extracted, respectively. Given the generated test data, I have compared the detection results of Lingua, and Whatlanggo running over the data of Lingua's supported 75 languages. Additionally, I have added Google's CLD3 (https://github.com/google/cld3/) to the comparison with the help of the gocld3 bindings (https://github.com/jmhodges/gocld3). Languages that are not supported by CLD3 or Whatlanggo are simply ignored during the detection process. Lingua clearly outperforms its contenders. Every language detector uses a probabilistic n-gram (https://en.wikipedia.org/wiki/N-gram) model trained on the character distribution in some training corpus. Most libraries only use n-grams of size 3 (trigrams) which is satisfactory for detecting the language of longer text fragments consisting of multiple sentences. For short phrases or single words, however, trigrams are not enough. The shorter the input text is, the less n-grams are available. The probabilities estimated from such few n-grams are not reliable. This is why Lingua makes use of n-grams of sizes 1 up to 5 which results in much more accurate prediction of the correct language. A second important difference is that Lingua does not only use such a statistical model, but also a rule-based engine. This engine first determines the alphabet of the input text and searches for characters which are unique in one or more languages. If exactly one language can be reliably chosen this way, the statistical model is not necessary anymore. In any case, the rule-based engine filters out languages that do not satisfy the conditions of the input text. Only then, in a second step, the probabilistic n-gram model is taken into consideration. This makes sense because loading less language models means less memory consumption and better runtime performance. In general, it is always a good idea to restrict the set of languages to be considered in the classification process using the respective api methods. If you know beforehand that certain languages are never to occur in an input text, do not let those take part in the classifcation process. The filtering mechanism of the rule-based engine is quite good, however, filtering based on your own knowledge of the input text is always preferable. There might be classification tasks where you know beforehand that your language data is definitely not written in Latin, for instance. The detection accuracy can become better in such cases if you exclude certain languages from the decision process or just explicitly include relevant languages. Knowing about the most likely language is nice but how reliable is the computed likelihood? And how less likely are the other examined languages in comparison to the most likely one? In the example below, a slice of ConfidenceValue is returned containing those languages which the calling instance of LanguageDetector has been built from. The entries are sorted by their confidence value in descending order. Each value is a probability between 0.0 and 1.0. The probabilities of all languages will sum to 1.0. If the language is unambiguously identified by the rule engine, the value 1.0 will always be returned for this language. The other languages will receive a value of 0.0. By default, Lingua uses lazy-loading to load only those language models on demand which are considered relevant by the rule-based filter engine. For web services, for instance, it is rather beneficial to preload all language models into memory to avoid unexpected latency while waiting for the service response. If you want to enable the eager-loading mode, you can do it as seen below. Multiple instances of LanguageDetector share the same language models in memory which are accessed asynchronously by the instances. By default, Lingua returns the most likely language for a given input text. However, there are certain words that are spelled the same in more than one language. The word `prologue`, for instance, is both a valid English and French word. Lingua would output either English or French which might be wrong in the given context. For cases like that, it is possible to specify a minimum relative distance that the logarithmized and summed up probabilities for each possible language have to satisfy. It can be stated as seen below. Be aware that the distance between the language probabilities is dependent on the length of the input text. The longer the input text, the larger the distance between the languages. So if you want to classify very short text phrases, do not set the minimum relative distance too high. Otherwise Unknown will be returned most of the time as in the example below. This is the return value for cases where language detection is not reliably possible.

v1.4.0 • last year

github.com/snowlyg/iris-admin

<h1 align="center">IrisAdmin</h1> [![Build Status](https://app.travis-ci.com/snowlyg/iris-admin.svg?branch=master)](https://app.travis-ci.com/snowlyg/iris-admin) [![LICENSE](https://img.shields.io/github/license/snowlyg/iris-admin)](https://github.com/snowlyg/iris-admin/blob/master/LICENSE) [![go doc](https://godoc.org/github.com/snowlyg/iris-admin?status.svg)](https://godoc.org/github.com/snowlyg/iris-admin) [![go report](https://goreportcard.com/badge/github.com/snowlyg/iris-admin)](https://goreportcard.com/badge/github.com/snowlyg/iris-admin) [![Build Status](https://codecov.io/gh/snowlyg/iris-admin/branch/master/graph/badge.svg)](https://codecov.io/gh/snowlyg/iris-admin) [简体中文](./README.md) | English #### Project url [GITHUB](https://github.com/snowlyg/iris-admin) | [GITEE](https://gitee.com/snowlyg/iris-admin) **** > This project just for learning golang, welcome to give your suggestions! #### Documentation - [IRIS-ADMIN-DOC](https://doc.snowlyg.com) - [IRIS V12 document for chinese](https://github.com/snowlyg/iris/wiki) - [godoc](https://pkg.go.dev/github.com/snowlyg/iris-admin?utm_source=godoc) [![Gitter](https://badges.gitter.im/iris-go-tenancy/community.svg)](https://gitter.im/iris-go-tenancy/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge) [![Join the chat at https://gitter.im/iris-go-tenancy/iris-admin](https://badges.gitter.im/iris-go-tenancy/iris-admin.svg)](https://gitter.im/iris-go-tenancy/iris-admin?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) #### BLOG - [REST API with iris-go web framework](https://blog.snowlyg.com/iris-go-api-1/) - [How to user iris-go with casbin](https://blog.snowlyg.com/iris-go-api-2/) --- #### Getting started - Get master package , Notice must use `master` version. ```sh ``` #### Program introduction ##### The project consists of multiple plugins, each with different functions - [viper_server] ```go package cache import ( ) var CONFIG Redis // getViperConfig get initialize config db: ` + db + ` addr: "` + CONFIG.Addr + `" password: "` + CONFIG.Password + `" pool-size: ` + poolSize), ``` - [zap_server] ```go ``` - [database] ```go ``` - [casbin] ```go ``` - [cache] ```go ``` - [operation] - [cron_server] ```go ``` - [web] - ```go // WebFunc web framework // - GetTestClient test client // - GetTestLogin test for login // - AddWebStatic add web static path // - AddUploadStatic add upload static path // - Run start ``` - [mongodb] #### Initialize database ##### Simple - Use gorm's `AutoMigrate()` function to auto migrate database. ```go package main import ( ) ``` ##### Custom migrate tools - Use `gormigrate` third party package. Tt's helpful for database migrate and program development. - Detail is see [iris-admin-cmd](https://github.com/snowlyg/iris-admin-example/blob/main/iris/cmd/main.go). --- - Add main.go file. ```go package main import ( ) ``` #### Run project - When you first run this cmd `go run main.go` , you can see some config files in the `config` directory, - and `rbac_model.conf` will be created in your project root directory. ```sh go run main.go ``` #### Module - You can use [iris-admin-rbac](https://github.com/snowlyg/iris-admin-rbac) package to add rbac function for your project quickly. - Your can use AddModule() to add other modules . ```go package main import ( ) ``` #### Default static file path - A static file access path has been built in by default - Static files will upload to `/static/upload` directory. - You can set this config key `static-path` to change the default directory. ```yaml system: ``` #### Use with front-end framework , e.g. vue - Default,you must build vue to the `dist` directory. - Naturally you can set this config key `web-path` to change the default directory. ```go package main import ( ) ``` #### Example - [iris](https://github.com/snowlyg/iris-admin-example/tree/main/iris) - [gin](https://github.com/snowlyg/iris-admin-example/tree/main/gin) #### RBAC - [iris-admin-rbac](https://github.com/snowlyg/iris-admin-rbac) #### Unit test and documentation - Before start unit tests, you need to set two system environment variables `mysqlPwd` and `mysqlAddr`,that will be used when running the test instance。 - helper/tests(https://github.com/snowlyg/helper/tree/main/tests) package the unit test used, it's simple package base on httpexpect/v2(https://github.com/gavv/httpexpect). - [example for unit test](https://github.com/snowlyg/iris-admin-rbac/tree/main/iris/perm/tests) - [example for unit test](https://github.com/snowlyg/iris-admin-rbac/tree/main/gin/authority/test) Before create a http api unit test , you need create a base test file named `main_test.go` , this file have some unit test step ： ***Suggest use docker mysql, otherwise if the test fails, there will be a lot of test data left behind*** - 1.create database before test start and delete database when test finish. - 2.create tables and seed test data at once time. - 3.`PartyFunc` and `SeedFunc` use to custom someting for your test model. 内容如下所示: ***main_test.go*** ```go package test import ( ) var TestServer *web_gin.WebServer var TestClient *httptest.Client ``` ***index_test.go*** ```go package test import ( ) var ( ) ``` ## 🔋 JetBrains OS licenses <a href="https://www.jetbrains.com/?from=iris-admin" target="_blank"><img src="https://raw.githubusercontent.com/panjf2000/illustrations/master/jetbrains/jetbrains-variant-4.png" width="230" align="middle"/></a> ## ☕️ Buy me a coffee > Please be sure to leave your name, GitHub account or other social media accounts when you donate by the following means so that I can add it to the list of donors as a token of my appreciation. - [为爱发电](https://afdian.net/@snowlyg/plan) - [donating](https://paypal.me/snowlyg?country.x=C2&locale.x=zh_XC)

v1.2.15 • 2 years ago

github.com/charmbracelet/bubbletea

github.com/onsi/ginkgo

github.com/onsi/ginkgo/v2

sigs.k8s.io/controller-runtime

go.uber.org/fx

github.com/onsi/gomega

github.com/cucumber/godog

github.com/gavv/httpexpect

github.com/burntsushi/toml

github.com/gorilla/csrf

github.com/DATA-DOG/godog

github.com/gavv/httpexpect/v2

gopkg.in/gavv/httpexpect.v1

github.com/awsdocs/aws-doc-sdk-examples/gov2/testtools

github.com/docker/go-healthcheck

github.com/jawher/mow.cli

sigs.k8s.io/cluster-api/test/framework

github.com/orlangure/gnomock

github.com/aws/aws-sdk-go-v2/service/auditmanager

github.com/appleboy/gofight/v2

gopkg.in/gavv/httpexpect.v2

golang.org/x/tools/go/pointer

github.com/hyperledger/aries-framework-go/test/component

github.com/pemistahl/lingua-go

github.com/ysmood/got

github.com/appleboy/gofight

github.com/dimiro1/health

github.com/MarvinJWendt/testza

github.com/snowlyg/iris-admin

gopkg.in/appleboy/gofight.v2

sigs.k8s.io/testing_frameworks

github.com/vmware-tanzu/tanzu-framework/test/pkg

gopkg.in/appleboy/gofight.v1

github.com/eodnozerkin-ozon/allure-go-test/pkg/framework

hidevops.io/hiboot

github.com/alecthomas/assert

github.com/go-bdd/gobdd

github.com/Eun/go-hit

github.com/zannen/toml

github.com/nbio/st

github.com/uber-go/fx

github.com/hyperledger/aries-framework-go-ext/component/storage/mongodb

github.com/smartcontractkit/chainlink-testing-framework

github.com/adamluzsi/testcase

github.com/kong/kubernetes-testing-framework

github.com/jacobsa/oglematchers

github.com/hidevopsio/hiboot

gopkg.in/BurntSushi/toml.v0

github.com/hyperledger/aries-framework-go/test/newstorage

git.lenzplace.org/lenzj/testcli