patron

Patron is a framework for creating microservices, originally created by Sotiris Mantzaris (https://github.com/mantzas). This fork is maintained by Beat Engineering (https://thebeat.co)

Patron is french for template or pattern, but it means also boss which we found out later (no pun intended).

The entry point of the framework is the Service. The Service uses Components to handle the processing of sync and async requests. The Service starts by default a HTTP Component which hosts the debug, health and metric endpoints. Any other endpoints will be added to the default HTTP Component as Routes. The service set's up by default logging with zerolog, tracing and metrics with jaeger and prometheus.

Patron provides abstractions for the following functionality of the framework:

• service, which orchestrates everything
• components and processors, which provide a abstraction of adding processing functionality to the service
  • asynchronous message processing (RabbitMQ, Kafka)
  • synchronous processing (HTTP)
• metrics and tracing
• logging

Patron provides same defaults for making the usage as simple as possible.

How to Contribute

Please see CONTRIBUTING.md.

patron-cli

The framework supplies a cli in order to simplify repository generation with the following features:

• git repository creation
• cmd folder and main.go creation with build version support (go build -ldflags '-X main.version=1.0.0' main.go)
• go module support and vendoring
• Dockerfile with version support (docker build --build-arg version=1.0.0)

The latest version can be installed with

go get github.com/mantzas/patron/cmd/patron

The below is an example of a service created with the cli that has a module name github.com/mantzas/test and will be created in the test folder in the current directory.

patron -m "github.com/mantzas/test" -p "test"

Service

The Service has the role of glueing all of the above together, which are:

• setting up logging
• setting up default HTTP component with the following endpoints configured:
  • profiling via pprof
  • health check
  • info endpoint for returning information about the service
• setting up termination by os signal
• starting and stopping components
• handling component errors
• setting up metrics and tracing

The service has some default settings which can be changed via environment variables:

• Service HTTP port, for setting the default HTTP components port to 50000 with PATRON_HTTP_DEFAULT_PORT
• Log level, for setting zerolog with INFO log level with PATRON_LOG_LEVEL
• Tracing, for setting up jaeger tracing with
  • agent address 0.0.0.0:6831 with PATRON_JAEGER_AGENT
  • sampler type probabilisticwith PATRON_JAEGER_SAMPLER_TYPE
  • sampler param 0.1 with PATRON_JAEGER_SAMPLER_PARAM

Component

A Component is a interface that exposes the following API:

type Component interface {
  Run(ctx context.Context) error  
  Info() map[string]interface{}
}

The above API gives the Service the ability to start and gracefully shutdown a component via context cancellation. Furthermore the component describes itself by implementing the Info method and thus giving the service the ability to report the information of all components. The framework divides the components in 2 categories:

• synchronous, which are components that follow the request/response pattern and
• asynchronous, which consume messages from a source but don't respond anything back

The following component implementations are available:

• HTTP (sync)
• RabbitMQ consumer (async)
• Kafka consumer (async)

Adding to the above list is as easy as implementing a Component and a Processor for that component.

Example

Setting up a new service with a HTTP Component is as easy as the following code:

  // Set up HTTP routes
  routes := make([]sync_http.Route, 0)
  routes = append(routes, sync_http.NewRoute("/", http.MethodGet, processor, true))
  
  srv, err := patron.New("test", patron.Routes(routes))
  if err != nil {
    log.Fatalf("failed to create service %v", err)
  }

  err = srv.Run()
  if err != nil {
    log.Fatalf("failed to create service %v", err)
  }

The above is pretty much self-explanatory. The processor follows the sync pattern.

Processors

Synchronous

The implementation of the processor is responsible to create a Request by providing everything that is needed (Headers, Fields, decoder, raw io.Reader) pass it to the implementation by invoking the Process method and handle the Response or the error returned by the processor.

The sync package contains only a function definition along with the models needed:

type ProcessorFunc func(context.Context, *Request) (*Response, error)

The Request model contains the following properties (which are provided when calling the "constructor" NewRequest)

• Fields, which may contain any fields associated with the request
• Raw, the raw request data (if any) in the form of a io.Reader
• Headers, the request headers in the form of map[string]string
• decode, which is a function of type encoding.Decode that decodes the raw reader

A exported function exists for decoding the raw io.Reader in the form of

Decode(v interface{}) error

The Response model contains the following properties (which are provided when calling the "constructor" NewResponse)

• Payload, which may hold a struct of type interface{}

Asynchronous

The implementation of the async processor follows exactly the same principle as the sync processor. The main difference is that:

• The Request is the Message and contains only data as []byte
• There is no Response, so the processor may return a error

type ProcessorFunc func(context.Context, *Message) error

Everything else is exactly the same.

Metrics and Tracing

Tracing and metrics are provided by Jaeger's implementation of the OpenTracing project. Every component has been integrated with the above library and produces traces and metrics. Metrics are provided with the default HTTP component at the /metrics route for Prometheus to scrape. Tracing will be send to a jaeger agent which can be setup though environment variables mentioned in the config section. Sane defaults are applied for making the use easy. We have included some clients inside the trace package which are instrumented and allow propagation of tracing to downstream systems. The tracing information is added to each implementations header. These clients are:

• HTTP
• AMQP
• Kafka
• SQL

Reliability

The reliability package contains the following implementations:

• Circuit Breaker

Circuit Breaker

The circuit breaker supports a half-open state which allows to probe for successful responses in order to close the circuit again. Every aspect of the circuit breaker is configurable via it's settings.

Clients

The following clients have been implemented:

• http, with distributed tracing and optional circuit breaker
• sql, with distributed tracing
• kafka, with distributed tracing
• amqp, with distributed tracing

Logging

The log package is designed to be a leveled logger with field support.

The log package defines the logger interface and a factory function type that needs to be implemented in order to set up the logging in this framework.

  // instantiate the implemented factory func type and fields (map[string]interface{})
  err := log.Setup(factory, fields)
  // handle error

If the setup is omitted the package will not setup any logging!

From there logging is as simple as

  log.Info("Hello world!")

The implementations should support following log levels:

• Debug, which should log the message with debug level
• Info, which should log the message with info level
• Warn, which should log the message with warn level
• Error, which should log the message with error level
• Panic, which should log the message with panic level and panics
• Fatal, which should log the message with fatal level and terminates the application

The first four (Debug, Info, Warn and Error) give the opportunity to differentiate the messages by severity. The last two (Panic and Fatal) do the same and do additional actions (panic and termination).

The package supports fields, which are logged along with the message, to augment the information further to ease querying in the log management system.

The following implementations are provided as sub-package and are by default wired up in the framework:

• zerolog, which supports the excellent zerolog library and is set up by default

Context Logging

Logs can be associated with some contextual data e.g. a request id. Every line logged should contain this id thus grouping the logs together. This is achieved with the usage of the context package like demonstrated bellow:

ctx := log.WithContext(r.Context(), log.Sub(map[string]interface{}{"requestID": uuid.New().String()}))

The context travels through the code as a argument and can be acquired as follows:

logger:=log.FromContext(ctx)
logger.Infof("request processed")

Benchmarks are provided to show the performance of this.

Every provided component creates a context logger which is then propagated in the context

Logger

The logger interface defines the actual logger.

type Logger interface {
  Fatal(...interface{})
  Fatalf(string, ...interface{})
  Panic(...interface{})
  Panicf(string, ...interface{})
  Error(...interface{})
  Errorf(string, ...interface{})
  Warn(...interface{})
  Warnf(string, ...interface{})
  Info(...interface{})
  Infof(string, ...interface{})
  Debug(...interface{})
  Debugf(string, ...interface{})
}

In order to be consistent with the design the implementation of the Fatal(f) have to terminate the application with an error and the Panic(f) need to panic.

Factory

The factory function type defines a factory for creating a logger.

type FactoryFunc func(map[string]interface{}) Logger

Security

The necessary abstraction are available to implement authentication in the following components:

• HTTP

HTTP

In order to use authentication, a authenticator has to be implement following the interface:

type Authenticator interface {
  Authenticate(req *http.Request) (bool, error)
}

This authenticator can then be used to set up routes with authentication.

The following authenticator are available:

• API key authenticator, see examples