Dependency Injection by annotations for Golang https://github.com/lokstory/digo
Package goldi implements a lazy dependency injection framework for go. Goldi is MIT-Licensed
Package controllerruntime alias' common functions and types to improve discoverability and reduce the number of imports for simple Controllers. 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. TODO(pwittrock): Update this example when we have better dependency injection support
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 controllerruntime alias' common functions and types to improve discoverability and reduce the number of imports for simple Controllers. 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. TODO(pwittrock): Update this example when we have better dependency injection support
Package controllerruntime alias' common functions and types to improve discoverability and reduce the number of imports for simple Controllers. 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. TODO(pwittrock): Update this example when we have better dependency injection support
Package inject provides utilities for mapping and injecting dependencies in various ways.
Package inject is dependency injection operation package
Package controllerruntime alias' common functions and types to improve discoverability and reduce the number of imports for simple Controllers. 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. TODO(pwittrock): Update this example when we have better dependency injection support
Package Goij provides a fully automatic recursive dependency injector for dependency initialisation and injection.
Package controllerruntime alias' common functions and types to improve discoverability and reduce the number of imports for simple Controllers. 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. TODO(pwittrock): Update this example when we have better dependency injection support
Package inject provides utilities for mapping and injecting dependencies in various ways.
Package controllerruntime alias' common functions and types to improve discoverability and reduce the number of imports for simple Controllers. 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. TODO(pwittrock): Update this example when we have better dependency injection support
Package inject provides utilities for mapping and injecting dependencies in various ways.
Package inject provides utilities for mapping and injecting dependencies. Package inject provides utilities for mapping and injecting dependencies in various ways.
Package execstub provides stubbing for a usage of a command line api which is based on an executable discovered using the environment <PATH> or some sort of <HOME>+<Bin> configuration. What is a usage of a command line api? Its when you spawn a sub-process using a programming language API. Think go exec.Cmd.Run(..), java Runtime.exec(..), python subprocess.run(..). The sub-process execution will yield an outcome consisting of: side effects (e.g. a file created when executing touch). bytes or string in std-out and/or std-err. an exit code. The executed binary is typically specified using a path like argument. It can be: a absolute path a relative path the name of the binary Discovering the executable How the binary is discovered depends on the art the specified path like argument. The goal of the discovery process is to transform the path like specification into an absolute path, so that if the specification is done with: absolute path nothing is left to be done relative path it will be appended to the current directory to form an absolute path name of the binary a file with that name will be search in the directories specified by the PATH environment variable. The first found will be used. On some platforms known and specified file extensions (e.g. .exe) will be appended to the name during the search. Even when using absolute paths, you may decide to put a customization strategy using some process environment variable. We call this the home-bin-dir strategy. E.g. Java executed base on JAVA_HOME or JRE_HOME with the java executable located at ${JAVA_HOME}/bin/java Stubbing mechanism The aim os stubbing is to replace at execution time the actual executable with a fake one which will yield some kind of pre-defined known outcome. This can be done if there is step in the binary discovery which can be tweaked so that the stud-executable is discovered instead of the actual one. It is the case if binary is specified by name and discovered using PATH or when absolute path is used but with a customization layer in place based on defined environment home variable. The mechanism will just have to mutate the PATH or set the home variable accordingly. Stubbing become complicated if the specification is based on absolute (without customization layer) or relative path because it will require e.g.: a file overley to hide the original executable a change of the current directory an in the flight replacement of the original executable and a rollback. These are either not desirable, or too heavy especially in a unit-test-ish context. The execstub module will therefore only provide stubbing for PATH and Home-Bin-Dir based discovery. Design elements Modeling the command line usage We have basically 3 aspects to model: Starting the process It is modeled using StubRequest which hold the the command name and the execution argument so that the fake execution process can be function of them. Execution The outcome can be a static sum of known in advance stderr, stdout and exit code. This is referred to as static mode. It may also be desirale to have some side effects, e.g.: related to the execution itself like creating a file or related to the unit-test as counting the number of calls In other constellations the outocome may requires some computation to determine stderr/stdout/exicode as function of the request. We refer to these cases as dynamic mode. The execution is therefore modelled as function namely StubFunc. Thus, if required the StubFunc corresponding to the current stubbing will be looked-up and run. Outcome The stderr/stdout/exit-code part of the outcome is modeled as ExecOutcome. The execution the the stubfunc may result however in an error. Such error is not an actual sub-process execution erroneous outcome. It therefore must be modeled separately as opposed to be added to stderr and setting a non-zero exit code. Such and error can be communicated by using the ExecOutcome field InternalErrTxt. Stub-Executable It is the fake executable used to effectuate the overall outcome. It may release the outcome by itself. It may also cooperate with a test helper to achieve the outcome. The following command line is used: /tmp/go-build720053430/b001/xyz.test -test.run=TestHelperProcess -- arg1 arg2 arg3 It is not possible to use the test helper directly because we will need to inject the args test.run and -- at the actual execution call site. Two kinds of stub-executable are provided: bash based, which uses a bash script exec base, which used an go based executable Obviously the go based one is bound to support more platform than the bash based one. Both executable are genric and need context information about the stubbing for which they will be used. This context data is modeled as CmdConfig and saved as file alongside the executable. Inter process communication (IPC) to support dynamic outcomes The invocation of a StubFunc to produce a dynamic outcome is done in the unit test process. There is no means to access the stub function directly from the fake process execution. IPC is used here to allow the fake process to issue a StubRequest and receive an ExecOutcome. The Serialization/Deserialization mechanism needed for IPC must be understood by both parties (mind bash) and satisfy a domain specific requirement of been able to cope with multiline sterr and stdout. We choose a combination of : base64 to encode the discrete data (e.g. stderr, stdout) and comma separated value (easily encoded and decoded in bash) for envelope encoding Named pipes are used for the actual data transport, because they are file based and easily handle in different setups (e.g. in bash). Stubbing Setup An ExecStubber is provided to manage the stubbing setup. Its key feature is to setup an invokation of a StubFunc to replace the execution of a process, See ExecStubber.WhenExecDoStubFunc(...). It allows settings beyond the basic requirement of specifying the executable to be stubbed and a StubFunc replacement. This is modeled using Settings, which provides the following configuration options: Selecting and customizing the discovery mode (PATH vs. Home-Bin-Dir) Selecting the stub executable (Bash vs. Exec(go based)) Selecting the stubbing mode (static vs. dynamic) Specifying the test process helper method name specifying a timeout for a stub sub-process execution A stubbing setup is identified by a key. The key can be use to: unwind the setup(Unregister(..)) access the stubbing data basis of static requests (FindAllPersistedStubRequests, DeleteAllPersistedStubRequests ). Concurrency and parallelism The mutation of the process environment is the key enabling mechanism of Execstub. The process environment must therefore be guarded against issues of concurrency and parallelism. This also mean that is not possible to have a parallel stubbing setup for the same executable within a test process. The outcome will otherwise be non-deterministic because the setting will likely override each other. ExecStubber provides a locking mechanism to realize the serialization of the mutation of environment. For this to work correctly however there must only be one ExecStubber per unit test process. Note that the code under test can still execute its sub-processes concurrently or in parallel. The correctness of the outcome here dependents on the implementation of the StubFunc function being used. Static outcomes without side effect are of course always deterministic.
Package gosnowflake is a pure Go Snowflake driver for the database/sql package. Clients can use the database/sql package directly. For example: Use the Open() function to create a database handle with connection parameters: The Go Snowflake Driver supports the following connection syntaxes (or data source name (DSN) 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 connection string (DSN) can contain both connection parameters (described below) and session parameters (https://docs.snowflake.com/en/sql-reference/parameters.html). 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 multi-factor authentication (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 interpreted as session parameters (https://docs.snowflake.com/en/sql-reference/parameters.html). For example, the TIMESTAMP_OUTPUT_FORMAT session parameter can be set by adding: A complete connection string looks similar to the following: Session-level parameters can also be set by using the SQL command "ALTER SESSION" (https://docs.snowflake.com/en/sql-reference/sql/alter-session.html). Alternatively, use OpenWithConfig() function to create a database handle with the specified Config. 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 exposing logrus's FieldLogger and default at INFO level. Users can use SetLogger in driver.go to set a customized logger for gosnowflake package. In order to enable debug logging for the driver, user could use SetLogLevel("debug") in SFLogger interface as shown in demo code at cmd/logger.go. To redirect the logs SFlogger.SetOutput method could do the work. Specific query request ID can be set in the context and will be passed through in place of the default randomized request ID. For example: 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 strings representing Snowflake data types. The following table shows those strings, the corresponding Snowflake data type, and the corresponding Golang native data type. The columns are: This table shows the data types: Footnotes: Note: SQL NULL values are converted to Golang nil values, and vice-versa. Version 1.3.9 (and later) of the Go Snowflake Driver supports the ability to bind an array variable to a parameter in an SQL INSERT statement. You can use this technique to insert multiple rows in a single batch. As an example, the following code inserts rows into a table that contains integer, float, boolean, and string columns. The example binds arrays to the parameters in the INSERT statement. Note: For alternative ways to load data into the Snowflake database (including bulk loading using the COPY command), see Loading Data Into Snowflake (https://docs.snowflake.com/en/user-guide-data-load.html). Go's database/sql package supports the ability to bind a parameter in an SQL statement to a time.Time variable. However, when the client binds data to send to the server, the driver cannot determine the correct Snowflake date/timestamp data type to associate with the binding parameter. 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. The Go Snowflake Driver supports JWT (JSON Web Token) authentication. To enable this feature, construct the DSN with fields "authenticator=SNOWFLAKE_JWT&privateKey=<your_private_key>", or using a 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 the base64.URLEncoding.EncodeToString() function. On the server side, you 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 the base64.StdEncoding.EncodeToString() function. To generate the valid key pair, you can execute the following commands in the shell: Note: As of February 2020, Golang's official library does not support passcode-encrypted PKCS8 private key. For security purposes, Snowflake highly recommends that you store the passcode-encrypted private key on the disk and decrypt the key in your application using a library you trust. This feature is available in version 1.3.8 or later of the driver. By default, Snowflake returns an error for queries issued with multiple statements. This restriction helps protect against SQL Injection attacks (https://en.wikipedia.org/wiki/SQL_injection). The multi-statement feature allows users skip this restriction and execute multiple SQL statements through a single Golang function call. However, this opens up the possibility for SQL injection, so it should be used carefully. The risk can be reduced by specifying the exact number of statements to be executed, which makes it more difficult to inject a statement by appending it. More details are below. The Go Snowflake Driver provides two functions that can execute multiple SQL statements in a single call: To compose a multi-statement query, simply create a string that contains all the queries, separated by semicolons, in the order in which the statements should be executed. To protect against SQL Injection attacks while using the multi-statement feature, pass a Context that specifies the number of statements in the string. For example: When multiple queries are executed by a single call to QueryContext(), multiple result sets are returned. After you process the first result set, get the next result set (for the next SQL statement) by calling NextResultSet(). The following pseudo-code shows how to process multiple result sets: The function db.execContext() returns a single result, which is the sum of the results of the individual statements. For example, if your multi-statement query executed two UPDATE statements, each of which updated 10 rows, then the result returned would be 20. Individual results for individual statements are not available. The following code shows how to retrieve the result of a multi-statement query executed through db.ExecContext(): Note: Because a multi-statement ExecContext() returns a single value, you cannot detect offsetting errors. For example, suppose you expected the return value to be 20 because you expected each UPDATE statement to update 10 rows. If one UPDATE statement updated 15 rows and the other UPDATE statement updated only 5 rows, the total would still be 20. You would see no indication that the UPDATES had not functioned as expected. The ExecContext() function does not return an error if passed a query (e.g. a SELECT statement). However, it still returns only a single value, not a result set, so using it to execute queries (or a mix of queries and non-query statements) is impractical. The QueryContext() function does not return an error if passed non-query statements (e.g. DML). The function returns a result set for each statement, whether or not the statement is a query. For each non-query statement, the result set contains a single row that contains a single column; the value is the number of rows changed by the statement. If you want to execute a mix of query and non-query statements (e.g. a mix of SELECT and DML statements) in a multi-statement query, use QueryContext(). You can retrieve the result sets for the queries, and you can retrieve or ignore the row counts for the non-query statements. If any of the SQL statements fail to compile or execute, execution is aborted. Any previous statements that ran before are unaffected. For example, if the statements below are run as one multi-statement query, the multi-statement query fails on the third statement, and an exception is thrown. If you then query the contents of the table named "test", the values 1 and 2 would be present. When using the QueryContext() and ExecContext() functions, golang code can check for errors the usual way. For example: Preparing statements and using bind variables are also not supported for multi-statement queries. GET and PUT operations are unsupported.
Package controllerruntime alias' common functions and types to improve discoverability and reduce the number of imports for simple Controllers. 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. TODO(pwittrock): Update this example when we have better dependency injection support
Package controllerruntime alias' common functions and types to improve discoverability and reduce the number of imports for simple Controllers. 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. TODO(pwittrock): Update this example when we have better dependency injection support
Package di is an dependency injection container implements. Source code and other details for the project are available at GitHub:
Package controllerruntime alias' common functions and types to improve discoverability and reduce the number of imports for simple Controllers. 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. TODO(pwittrock): Update this example when we have better dependency injection support
Package inject provides utilities for mapping and injecting dependencies in various ways.
Package controllerruntime alias' common functions and types to improve discoverability and reduce the number of imports for simple Controllers. 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. TODO(pwittrock): Update this example when we have better dependency injection support
Package injector provides a reflect-based injection solution where each dependency is identified by an unique name. A large application built with dependency injection in mind find difficulties in managing and injecting dependencies. This library attempts to take care of it by containing all dependencies in a central container and injecting requested dependencies automatically. Its use is simple that you use Component method to register a dependency. The library will search for tagged fields and try to inject requested dependencies. It works using Go's reflection package and is inherently limited in what it can do as opposed to a code-gen system with respect to private fields. The usage pattern for the library involves struct tags. It requires the tag format used by the various standard libraries, like json, xml etc. It involves tags in one of the form below: The above form is asking for a named dependency called "logger".
Package controllerruntime alias' common functions and types to improve discoverability and reduce the number of imports for simple Controllers. 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. TODO(pwittrock): Update this example when we have better dependency injection support
Package controllerruntime alias' common functions and types to improve discoverability and reduce the number of imports for simple Controllers. 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. TODO(pwittrock): Update this example when we have better dependency injection support
Package xstats is a generic client for service instrumentation. xstats is inspired from Go-kit's metrics (https://github.com/go-kit/kit/tree/master/metrics) package but it takes a slightly different path. Instead of having to create an instance for each metric, xstats use a single instance to log every metrics you want. This reduces the boiler plate when you have a lot a metrics in your app. It's also easier in term of dependency injection. Talking about dependency injection, xstats comes with a xhandler.Handler integration so it can automatically inject the xstats client within the net/context of each request. Each request's xstats instance have its own tags storage ; This let you inject some per request contextual tags to be included with all observations sent within the lifespan of the request. xstats is pluggable and comes with integration for StatsD and DogStatsD, the Datadog (http://datadoghq.com) augmented version of StatsD with support for tags. More integration may come later (PR welcome).
Package `di` implements basic dependency injection (DI) container.
Package inject provides a reflect based injector. A large application built with dependency injection in mind will typically involve the boring work of setting up the object graph. This library attempts to take care of this boring work by creating and connecting the various objects. Its use involves you seeding the object graph with some (possibly incomplete) objects, where the underlying types have been tagged for injection. Given this, the library will populate the objects creating new ones as necessary. It uses singletons by default, supports optional private instances as well as named instances. It works using Go's reflection package and is inherently limited in what it can do as opposed to a code-gen system with respect to private fields. The usage pattern for the library involves struct tags. It requires the tag format used by the various standard libraries, like json, xml etc. It involves tags in one of the three forms below: The first no value syntax is for the common case of a singleton dependency of the associated type. The second triggers creation of a private instance for the associated type. Finally the last form is asking for a named dependency called "dev logger".
Package controllerruntime alias' common functions and types to improve discoverability and reduce the number of imports for simple Controllers. 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. TODO(pwittrock): Update this example when we have better dependency injection support
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/master/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 (pkg/runtime/inject), 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/events) 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://godoc.org/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. TODO(pwittrock): Update this example when we have better dependency injection support 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/apiserver/blob/master/pkg/apis/config/v1alpha1/defaults.go * Create a new application for ReplicaSets that manages Pods owned by the ReplicaSet and calls into ReplicaSetReconciler. * Start the application. TODO(pwittrock): Update this example when we have better dependency injection support
Package inject provides utilities for mapping and injecting dependencies in various ways.
Package controllerruntime alias' common functions and types to improve discoverability and reduce the number of imports for simple Controllers. 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. TODO(pwittrock): Update this example when we have better dependency injection support
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 controllerruntime alias' common functions and types to improve discoverability and reduce the number of imports for simple Controllers. 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. TODO(pwittrock): Update this example when we have better dependency injection support
Package controllerruntime alias' common functions and types to improve discoverability and reduce the number of imports for simple Controllers. 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. TODO(pwittrock): Update this example when we have better dependency injection support
Package iris implements the highest realistic performance, easy to learn Go web framework. Iris provides a beautifully expressive and easy to use foundation for your next website, API, or distributed app. Low-level handlers compatible with `net/http` and high-level fastest MVC implementation and handlers dependency injection. Easy to learn for new gophers and advanced features for experienced, it goes as far as you dive into it! Source code and other details for the project are available at GitHub: 12.1.8 The only requirement is the Go Programming Language, at least version 1.13. Wiki: Examples: Middleware: Home Page:
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 hiboot is a web/cli app application framework Hiboot is a cloud native web and cli application framework written in Go. Hiboot integrates the popular libraries but make them simpler, easier to use. It borrowed some of the Spring features like dependency injection, aspect oriented programming, and auto configuration. You can integrate any other libraries easily by auto configuration with dependency injection support. hiboot-data is the typical project that implement customized hiboot starters. see https://godoc.org/hidevops.io/hiboot-data Overview One of the most significant feature of Hiboot is Dependency Injection. Hiboot implements JSR-330 standard. Let's say that we have two implementations of AuthenticationService, below will explain how does Hiboot work. In Hiboot the injection into fields is triggered by `inject:""` struct tag. when inject tag is present on a field, Hiboot tries to resolve the object to inject by the type of the field. If several implementations of the same service interface are available, you have to disambiguate which implementation you want to be injected. This can be done by naming the field to specific implementation. Although Field Injection is pretty convenient, but the Constructor Injection is the first-class citizen, we usually advise people to use constructor injection as it has below advantages, It's testable, easy to implement unit test. Syntax validation, with syntax validation on most of the IDEs to avoid typo. No need to use a dedicated mechanism to ensure required properties are set. type userController struct { web.Controller basicAuthenticationService AuthenticationService } // Hiboot will inject the implementation of AuthenticationService func newUserController(basicAuthenticationService AuthenticationService) { return &userController{ basicAuthenticationService: basicAuthenticationService, } } func init() { app.Register(newUserController) } Features This section will show you how to create and run a simplest hiboot application. Let’s get started! Get the source code Source Code This is a simple hello world example
Package dinit performs Dependency Injection (DI) for initializer functions.
Package hub implements a simple publish/subscribe API for application events. Different subsystems within an application can send notifications without having hard dependencies on each other. Dependency injection often benefits from this approach. Examples include sending server start and stop events to other components and service discovery events from libraries like consul. This package is an example of the Mediator design pattern (see https://en.wikipedia.org/wiki/Mediator_pattern). It is focused on managing application-layer events within a process. It is not intended as a general pub/sub library for distributed notifications, although it can serve as a basis for such an implementation. Rather than requiring a specific listener interface or approach to handling events, this package supports several kinds of listeners via the Subscriber interface: (1) A function with exactly one input argument and no outputs. The input argument cannot be an interface type. The sole input type is the event type that can be passed to Publish. (2) A bidrectional or send-only channel. The channel's element type cannot be an interface. The channel's element type can be passed to Publish, which will then place it onto the channel. NOTE: If a channel subscription is cancelled, the channel passed to Subscribe is NOT closed by default. Clients can explicitly close the channel in an afterCancel closure: (3) Any type (not just a struct) with a single method of the same signature described in (1). Any other type passed to Subscribe results in ErrInvalidListener.
Package controllerruntime alias' common functions and types to improve discoverability and reduce the number of imports for simple Controllers. 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. TODO(pwittrock): Update this example when we have better dependency injection support
Package hiboot is a web/cli app application framework Hiboot is a cloud native web and cli application framework written in Go. Hiboot integrates the popular libraries but make them simpler, easier to use. It borrowed some of the Spring features like dependency injection, aspect oriented programming, and auto configuration. You can integrate any other libraries easily by auto configuration with dependency injection support. hiboot-data is the typical project that implement customized hiboot starters. see https://godoc.org/hidevops.io/hiboot-data Overview One of the most significant feature of Hiboot is Dependency Injection. Hiboot implements JSR-330 standard. Let's say that we have two implementations of AuthenticationService, below will explain how does Hiboot work. In Hiboot the injection into fields is triggered by `inject:""` struct tag. when inject tag is present on a field, Hiboot tries to resolve the object to inject by the type of the field. If several implementations of the same service interface are available, you have to disambiguate which implementation you want to be injected. This can be done by naming the field to specific implementation. Although Field Injection is pretty convenient, but the Constructor Injection is the first-class citizen, we usually advise people to use constructor injection as it has below advantages, It's testable, easy to implement unit test. Syntax validation, with syntax validation on most of the IDEs to avoid typo. No need to use a dedicated mechanism to ensure required properties are set. type userController struct { web.Controller basicAuthenticationService AuthenticationService } // Hiboot will inject the implementation of AuthenticationService func newUserController(basicAuthenticationService AuthenticationService) { return &userController{ basicAuthenticationService: basicAuthenticationService, } } func init() { app.Register(newUserController) } Features This section will show you how to create and run a simplest hiboot application. Let’s get started! Get the source code Source Code This is a simple hello world example
Package iris implements the highest realistic performance, easy to learn Go web framework. Iris provides a beautifully expressive and easy to use foundation for your next website, API, or distributed app. Low-level handlers compatible with `net/http` and high-level fastest MVC implementation and handlers dependency injection. Easy to learn for new gophers and advanced features for experienced, it goes as far as you dive into it! Source code and other details for the project are available at GitHub: 11.2.8 The only requirement is the Go Programming Language, at least version 1.12. Wiki: Examples: Middleware: Home Page:
Package di implements a dependency injection (DI) container.
Package azcore implements an HTTP request/response middleware pipeline used by Azure SDK clients. The middleware consists of three components. A Policy can be implemented in two ways; as a first-class function for a stateless Policy, or as a method on a type for a stateful Policy. Note that HTTP requests made via the same pipeline share the same Policy instances, so if a Policy mutates its state it MUST be properly synchronized to avoid race conditions. A Policy's Do method is called when an HTTP request wants to be sent over the network. The Do method can perform any operation(s) it desires. For example, it can log the outgoing request, mutate the URL, headers, and/or query parameters, inject a failure, etc. Once the Policy has successfully completed its request work, it must call the Next() method on the *policy.Request instance in order to pass the request to the next Policy in the chain. When an HTTP response comes back, the Policy then gets a chance to process the response/error. The Policy instance can log the response, retry the operation if it failed due to a transient error or timeout, unmarshal the response body, etc. Once the Policy has successfully completed its response work, it must return the *http.Response and error instances to its caller. Template for implementing a stateless Policy: Template for implementing a stateful Policy: The Transporter interface is responsible for sending the HTTP request and returning the corresponding HTTP response or error. The Transporter is invoked by the last Policy in the chain. The default Transporter implementation uses a shared http.Client from the standard library. The same stateful/stateless rules for Policy implementations apply to Transporter implementations. To use the Policy and Transporter instances, an application passes them to the runtime.NewPipeline function. The specified Policy instances form a chain and are invoked in the order provided to NewPipeline followed by the Transporter. Once the Pipeline has been created, create a runtime.Request instance and pass it to Pipeline's Do method. The Pipeline.Do method sends the specified Request through the chain of Policy and Transporter instances. The response/error is then sent through the same chain of Policy instances in reverse order. For example, assuming there are Policy types PolicyA, PolicyB, and PolicyC along with TransportA. The flow of Request and Response looks like the following: The Request instance passed to Pipeline's Do method is a wrapper around an *http.Request. It also contains some internal state and provides various convenience methods. You create a Request instance by calling the runtime.NewRequest function: If the Request should contain a body, call the SetBody method. A seekable stream is required so that upon retry, the retry Policy instance can seek the stream back to the beginning before retrying the network request and re-uploading the body. Operations like JSON-MERGE-PATCH send a JSON null to indicate a value should be deleted. This requirement conflicts with the SDK's default marshalling that specifies "omitempty" as a means to resolve the ambiguity between a field to be excluded and its zero-value. In the above example, Name and Count are defined as pointer-to-type to disambiguate between a missing value (nil) and a zero-value (0) which might have semantic differences. In a PATCH operation, any fields left as nil are to have their values preserved. When updating a Widget's count, one simply specifies the new value for Count, leaving Name nil. To fulfill the requirement for sending a JSON null, the NullValue() function can be used. This sends an explict "null" for Count, indicating that any current value for Count should be deleted. When the HTTP response is received, the *http.Response is returned directly. Each Policy instance can inspect/mutate the *http.Response. To enable logging, set environment variable AZURE_SDK_GO_LOGGING to "all" before executing your program. By default the logger writes to stderr. This can be customized by calling log.SetListener, providing a callback that writes to the desired location. Any custom logging implementation MUST provide its own synchronization to handle concurrent invocations. See the docs for the log package for further details. Pageable operations return potentially large data sets spread over multiple GET requests. The result of each GET is a "page" of data consisting of a slice of items. Pageable operations can be identified by their New*Pager naming convention and return type of *runtime.Pager[T]. The call to WidgetClient.NewListWidgetsPager() returns an instance of *runtime.Pager[T] for fetching pages and determining if there are more pages to fetch. No IO calls are made until the NextPage() method is invoked. Long-running operations (LROs) are operations consisting of an initial request to start the operation followed by polling to determine when the operation has reached a terminal state. An LRO's terminal state is one of the following values. LROs can be identified by their Begin* prefix and their return type of *runtime.Poller[T]. When a call to WidgetClient.BeginCreateOrUpdate() returns a nil error, it means that the LRO has started. It does _not_ mean that the widget has been created or updated (or failed to be created/updated). The *runtime.Poller[T] provides APIs for determining the state of the LRO. To wait for the LRO to complete, call the PollUntilDone() method. The call to PollUntilDone() will block the current goroutine until the LRO has reached a terminal state or the context is canceled/timed out. Note that LROs can take anywhere from several seconds to several minutes. The duration is operation-dependent. Due to this variant behavior, pollers do _not_ have a preconfigured time-out. Use a context with the appropriate cancellation mechanism as required. Pollers provide the ability to serialize their state into a "resume token" which can be used by another process to recreate the poller. This is achieved via the runtime.Poller[T].ResumeToken() method. Note that a token can only be obtained for a poller that's in a non-terminal state. Also note that any subsequent calls to poller.Poll() might change the poller's state. In this case, a new token should be created. After the token has been obtained, it can be used to recreate an instance of the originating poller. When resuming a poller, no IO is performed, and zero-value arguments can be used for everything but the Options.ResumeToken. Resume tokens are unique per service client and operation. Attempting to resume a poller for LRO BeginB() with a token from LRO BeginA() will result in an error. The fake package contains types used for constructing in-memory fake servers used in unit tests. This allows writing tests to cover various success/error conditions without the need for connecting to a live service. Please see https://github.com/Azure/azure-sdk-for-go/tree/main/sdk/samples/fakes for details and examples on how to use fakes.