StackImpact Go 个人资料r

Overview

StackImpact is a production-grade performance profiler built for both production and development environments. It gives developers continuous and historical code-level view of application performance that is essential for locating CPU, memory allocation and I/O hot spots as well as latency bottlenecks. Included runtime metrics and error monitoring complement profiles for extensive performance analysis. Learn more at stackimpact.com.

Features

• Continuous hot spot profiling of CPU usage, memory allocation and blocking calls.
• Continuous latency bottleneck tracing.
• Error and panic monitoring.
• Health monitoring including CPU, memory, garbage collection and other runtime metrics.
• Alerts on profile anomalies.
• Team access.

Learn more on the features page (with screenshots).

How it works

The StackImpact profiler agent is imported into a program and used as a normal package. When the program runs, various sampling profilers are started and stopped automatically by the agent and/or programmatically using the agent methods. The agent periodically reports recorded profiles and metrics to the StackImpact 仪表盘. The agent can also operate in manual mode, which should be used in development only.

Documentation

See full documentation for reference.

Requirements

Linux, OS X or Windows. Go version 1.5+.

Getting started

Create StackImpact account

注册 for a free trial account at stackimpact.com (also with GitHub login).

Installing the agent

Install the Go agent by running

go get github.com/stackimpact/stackimpact-go

And import the package github.com/stackimpact/stackimpact-go in your application.

Configuring the agent

Start the agent by specifying the agent key and application name. The agent key can be found in your account's Configuration section.

agent := stackimpact.Start(stackimpact.Options{
	AgentKey: "agent key here",
	AppName: "MyGoApp",
})

All initialization options:

• AgentKey (Required) The access key for communication with the StackImpact servers.
• AppName (Required) A name to identify and group application data. Typically, a single codebase, deployable unit or executable module corresponds to one application. Sometimes also referred as a service.
• AppVersion (Optional) Sets application version, which can be used to associate profiling information with the source code release.
• AppEnvironment (Optional) Used to differentiate applications in different environments.
• HostName (Optional) By default, host name will be the OS hostname.
• ProxyAddress (Optional) Proxy server URL to use when connecting to the 仪表盘 servers.
• HTTPClient (Optional) An http.Client instance to be used instead of the default client for reporting data to 仪表盘 servers.
• DisableAutoProfiling (Optional) If set to true, disables the default automatic profiling and reporting. Focused or manual profiling should be used instead. Useful for environments without support for timers or background tasks.
• Debug (Optional) Enables debug logging.
• Logger (Optional) A log.Logger instance to be used instead of default STDOUT logger.

Basic example

package main

import (
	"fmt"
	"net/http"

	"github.com/stackimpact/stackimpact-go"
)

func handler(w http.ResponseWriter, r *http.Request) {
	fmt.Fprintf(w, "Hello world!")
}

func main() {
	agent := stackimpact.Start(stackimpact.Options{
		AgentKey: "agent key here",
		AppName: "Basic Go Server",
		AppVersion: "1.0.0",
		AppEnvironment: "production",
	})

	http.HandleFunc(agent.个人资料HandlerFunc("/", handler)) 
	http.ListenAndServe(":8080", nil)
}

Focused profiling

Focused profiling is suitable for repeating code, such as request or event handlers. By default, the agent starts and stops profiling automatically. In order to make sure the agent profiles the most relevant execution intervals, the following methods can be used. In addition to more precise profiling, timing information will also be reported for the profiled spans.

// Use this method to instruct the agent to start and stop 
// profiling. It does not guarantee that any profiler will be 
// started. The decision is made by the agent based on the 
// overhead constraints. The method returns Span object, on 
// which the Stop() method should be called. 
span := agent.个人资料();
defer span.Stop();

// This method is similar to the 个人资料() method. It additionally
// allows to specify a span name to group span timing measurements.
span := agent.个人资料WithName(name);
defer span.Stop();

// A helper function to profile HTTP handler execution by wrapping 
// http.Handle method parameters.
// Usage example:
//   http.Handle(agent.个人资料Handler("/some-path", someHandler))
pattern, wrappedHandler := agent.个人资料Handler(pattern, handler)

// A helper function to profile HTTP handler function execution 
// by wrapping http.HandleFunc method parameters.
// Usage example:
//   http.HandleFunc(agent.个人资料HandlerFunc("/some-path", someHandlerFunc))
pattern, wrappedHandlerFunc := agent.个人资料HandlerFunc(pattern, handlerFunc)

Error reporting

To monitor exceptions and panics with stack traces, the error recording API can be used.

Recording handled errors:

// Aggregates and reports errors with regular intervals.
agent.RecordError(someError)

Recording panics without recovering:

// Aggregates and reports panics with regular intervals.
defer agent.RecordPanic()

Recording and recovering from panics:

// Aggregates and reports panics with regular intervals. This function also
// recovers from panics.
defer agent.RecordAndRecoverPanic()

Manual profiling

Manual profiling should not be used in production!

By default, the agent starts and stops profiling automatically. Manual profiling allows to start and stop profilers directly. It is suitable for profiling short-lived programs and should not be used for long-running production applications. Automatic profiling should be disabled with DisableAutoProfiling: true.

// Start CPU profiler.
agent.StartCPU个人资料r();

// Stop CPU profiler and report the recorded profile to the 仪表盘.
// Automatic profiling should be disabled.
agent.StopCPU个人资料r();

// Start blocking call profiler.
agent.StartBlock个人资料r();

// Stop blocking call profiler and report the recorded profile to the 仪表盘.
agent.StopBlock个人资料r();

// Report current allocation profile to the 仪表盘.
agent.ReportAllocation个人资料();

Analyzing performance data in the 仪表盘

Once your application is restarted, you can start observing continuously recorded CPU, memory, I/O, and other hot spot profiles, execution bottlenecks as well as process metrics in the 仪表盘.

Troubleshooting

To enable debug logging, add Debug: true to startup options. If the debug log doesn't give you any hints on how to fix a problem, please report it to our support team in your account's Support section.

Overhead

The agent overhead is measured to be less than 1% for applications under high load.