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GrowthBook Go SDK

Installation

go get github.com/growthbook/growthbook-golang

Quick Usage

The main public API for the SDK is provided through the Context and GrowthBook types. The Context type provides a means to pass settings to the main GrowthBook type, while the GrowthBook type provides a Feature method for accessing feature values, and a Run method for running inline experiments.

// Parse feature map from JSON data.
features := growthbook.ParseFeatureMap(jsonBody)

// Create context and main GrowthBook object.
context := growthbook.NewContext().WithFeatures(features)
gb := growthbook.New(context)

// Perform feature test.
if gb.Feature("my-feature").On {
  // ...
}

color := gb.Feature("signup-button-color").GetValueWithDefault("blue")
fmt.Println(color)

experiment :=
  growthbook.NewExperiment("my-experiment").
    WithVariations("A", "B")

result := gb.Run(experiment)

fmt.Println(result.Value)

experiment2 :=
  growthbook.NewExperiment("complex-experiment").
    WithVariations(
      map[string]string{"color": "blue", "size": "small"},
      map[string]string{"color": "green", "size": "large"},
    ).
    WithWeights(0.8, 0.2).
    WithCoverage(0.5)

result2 := gb.Run(experiment2)
fmt.Println(result2.Value.(map[string]string)["color"],
  result2.Value.(map[string]string)["size"])

The GrowthBook Context

A new Context is created using the NewContext function and its fields can be set using the WithEnabled, WithAttributes, WithURL, WithFeatures, WithForcedVariations, WithQAMode and WithTrackingCallback methods. These With... methods return a Context pointer to enable call chaining. Context details can alternatively be parsed from JSON data (see JSON data representations). The fields in a Context include information about the user for whom feature results will be evaluated (the Attributes), the features that are defined, plus some additional values to control forcing of feature results under some circumstances.

Given a Context value, a new GrowthBook value can be created using the New function. The GrowthBook type has some getter and setter methods (setters are methods with names of the form With...) for fields of the associated Context. As well as providing access to the underlying Context and exposing the main Feature and Run methods, the GrowthBook type also keeps track of the results of experiments that are performed, in order to implement tracking and experiment subscription callbacks.

For example, assuming that the growthbook package is imported with name "growthbook", the following code will create a Context and GrowthBook value using features parsed from JSON data and some fixed attributes:

// Parse feature map from JSON.
features := growthbook.ParseFeatureMap(featureJSON)

// Create context and main GrowthBook object.
context := growthbook.NewContext().
  WithFeatures(features).
  WithAttributes(growthbook.Attributes{
    "country": "US",
    "browser": "firefox",
  })
gb := growthbook.New(context)

Features

The WithFeatures method of Context takes a FeatureMap value, which is defined as map[string]*Feature, and which can be created from JSON data using the ParseFeatureMap function (see JSON data representations). You can pass a feature map generated this way to the WithFeatures method of Context or GrowthBook:

featureMap := ParseFeatureMap([]byte(
  `{ "feature-1": {...},
     "feature-2": {...},
     "another-feature": {...}
   }`))

gb := NewContext().WithFeatures(featureMap)

If you need to load feature definitions from a remote source like an API or database, you can update the context at any time with WithFeatures.

If you use the GrowthBook App to manage your features, you don't need to build this JSON file yourself -- it will auto-generate one for you and make it available via an API endpoint.

If you prefer to build this file by hand or you want to know how it works under the hood, check out the detailed Feature Definitions section below.

Attributes

You can specify attributes about the current user and request. These are used for two things:

  • Feature targeting (e.g. paid users get one value, free users get another);
  • Assigning persistent variations in A/B tests (e.g. user id "123" always gets variation B).

Attributes can be any JSON data type -- boolean, integer, string, array, or object and are represented by the Attributes type, which is an alias for the generic map[string]interface{} type that Go uses for JSON objects. If you know them up front, you can pass them into Context or GrowthBook using WithAttributes:

gb := growthbook.New(context).
  WithAttributes(Attributes{
    "id":       "123",
    "loggedIn": true,
    "deviceId": "abc123def456",
    "company":  "acme",
    "paid":     false,
    "url":      "/pricing",
    "browser":  "chrome",
    "mobile":   false,
    "country":  "US",
  })

You can also set or update attributes asynchronously at any time with the WithAttributes method. This will completely overwrite the attributes object with whatever you pass in. If you want to merge attributes instead, you can get the existing ones with Attributes:

attrs := gb.Attributes()
attrs["url"] = "/checkout"
gb.WithAttributes(attrs)

Be aware that changing attributes may change the assigned feature values. This can be disorienting to users if not handled carefully. A common approach is to only refresh attributes on navigation, when the window is focused, and/or after a user performs a major action like logging in.

Tracking Callback

Any time an experiment is run to determine the value of a feature, we can run a callback function so you can record the assigned value in your event tracking or analytics system of choice.

context.WithTrackingCallback(func(experiment *growthbook.Experiment,
  result *growthbook.ExperimentResult) {
    // Example using Segment.io
    client.Enqueue(analytics.Track{
      UserId: context.Attributes()["id"],
      Event: "Experiment Viewed",
      Properties: analytics.NewProperties().
        Set("experimentId", experiment.Key).
        Set("variationId", result.VariationID)
    })
  }
)

Error handling

The GrowthBook public API does not return errors under any normal circumstances. The intention is for developers to be able to use the SDK in both development and production smoothly. To this end, error reporting is provided by a configurable logging interface.

For development use, the DevLogger type provides a suitable implementation of the logging interface: it prints all logged messages to standard output, and exits on errors.

For production use, a logger that directs log messages to a suitable centralised logging facility and ignores all errors would be suitable. The logger can of course also signal error and warning conditions to other parts of the program in which it is used.

To be specific about this:

  • None of the functions that create or update Context, GrowthBook or Experiment values return errors.

  • The main GrowthBook.Feature and GrowthBook.Run methods never return errors.

  • None of the functions that create values from JSON data return errors.

For most common use cases, this means that the GrowthBook SDK can be used transparently, without needing to care about error handling. Your server code will never crash because of problems in the GrowthBook SDK. The only effect of error conditions in the inputs to the SDK may be that feature values and results of experiments are not what you expect.

Using Features

The main method, GrowthBook.Feature(key), takes a feature key and uses the stored feature definitions and attributes to evaluate the feature value. It returns a FeatureResult value with the following fields:

  • Value: the JSON value of the feature (or null if not defined), as a FeatureValue value (which is just an alias for interface{}, using Go's default behavior for handling JSON values);
  • On and Off: the JSON value cast to booleans (to make your code easier to read);
  • Source: a value of type FeatureResultSource, telling why the value was assigned to the user. One of UnknownFeatureResultSource, DefaultValueResultSource, ForceResultSource, or ExperimentResultSource.
  • Experiment: information about the experiment (if any) which was used to assign the value to the user.
  • ExperimentResult: the result of the experiment (if any) which was used to assign the value to the user.

Here's an example that uses all of them:

result := gb.Feature("my-feature")

// The JSON value (might be null, string, boolean, number, array, or
// object).
fmt.Println(result.Value)

if result.On {
  // Feature value is truthy (in a Javascript sense)
}
if result.Off {
  // Feature value is falsy
}

// If the feature value was assigned as part of an experiment
if result.Source == growthbook.ExperimentResultSource {
  // Get all the possible variations that could have been assigned
  fmt.Println(result.Experiment.Variations)
}

Defaulting of the values of feature results is assisted by the GetValueWithDefault method on the FeatureResult type. For example, this code evaluates the result of a feature and returns the feature value, defaulting to "blue" if the feature has no value:

color := gb.Feature("signup-button-color").GetValueWithDefault("blue")

Feature Definitions

For details of the JSON format used for feature definitions, consult the documentation for the GrowthBook Javascript SDK. The Go SDK uses exactly the same logic for processing features, and can ingest the same JSON feature definitions as are used by the Javascript SDK (see JSON data representations).

It is possible to create Feature values in the Go SDK by hand, simply by creating Go values of the appropriate types (Feature, FeatureValue, FeatureRule), but the most common use case is likely to be ingesting feature definitions from JSON data using the ParseFeatureMap function.

Inline Experiments

Experiments can be defined and run using the Experiment type and the Run method of the GrowthBook type. Experiment definitions can be created directly as values of the Experiment type, or parsed from JSON definitions using the ParseExperiment function. Passing an Experiment value to the Run method of the GrowthBook type will run the experiment, returing an ExperimentResult value that contains the resulting feature value. This allows users to run arbitrary experiments without providing feature definitions up-front.

experiment :=
  growthbook.NewExperiment("my-experiment").
    WithVariations("red", "blue", "green")

result := gb.Run(experiment)

All other experiment settings (weights, hash attribute, coverage, namespace, condition) are supported when using inline experiments: the Experiment type has With... methods that allow these fields to be set easily (i.e. WithWeights, WithHashAttribute, WithCoverage, WithNamespace, WithCondition).

In addition, there are a few other settings that only really make sense for inline experiments:

  • Force can be set to one of the variation array indexes. Everyone will be immediately assigned the specified value.
  • Active can be set to false to disable the experiment and return the control for everyone.

Inline Experiment Return Value

A call to GrowthBook.Run(experiment) returns a value of type *ExperimentResult:

experiment := growthbook.NewExperiment("my-experiment").
  WithVariations("A", "B")
result := gb.Run(experiment)

// If user is part of the experiment
fmt.Println(result.InExperiment) // true or false

// The index of the assigned variation
fmt.Println(result.VariationID) // 0 or 1

// The value of the assigned variation
fmt.Println(result.Value) // "A" or "B"

// The user attribute used to assign a variation
fmt.Println(result.HashAttribute) // "id"

// The value of that attribute
fmt.Println(result.HashValue) // e.g. "123"

The InExperiment flag is only set to true if the user was randomly assigned a variation. If the user failed any targeting rules or was forced into a specific variation, this flag will be false.

JSON data representations

For interoperability of the GrowthBook Go SDK with versions of the SDK in other languages, the core "input" values of the SDK (in particular, Context and Experiment values and maps of feature definitions) can be created by parsing JSON data. A common use case is to download feature definitions from a central location as JSON, to parse them into a feature map that can be applied to a GrowthBook Context, then using this context to create a GrowthBook value that can be used for feature tests.

A contrived example of how this might work is:

// Download JSON feature file and read file body.
resp, err := http.Get("https://s3.amazonaws.com/myBucket/features.json")
if err != nil {
    log.Fatal(err)
}
defer resp.Body.Close()
body, err := ioutil.ReadAll(resp.Body)
if err != nil {
    log.Fatal(err)
}

// Parse feature map from JSON.
features, err := ParseFeatureMap(body)
if err != nil {
    log.Fatal(err)
}

// Create context and main GrowthBook object.
context := NewContext().WithFeatures(features)
growthbook := New(context)

// Perform feature test.
if growthbook.Feature("my-feature").On {
    // ...
}

The functions that implement this JSON processing functionality have names like ParseContext, BuildContext, and so on. Each Parse... function process raw JSON data (as a []byte value), while the Build... functions process JSON objects unmarshalled to Go values of type map[string]interface{}. This provides flexibility in ingestion of JSON data.

Tracking and subscriptions

The Context value supports a "tracking callback", which is a function that is called any time an experiment is run to determine the value of a feature, so that users can record the assigned value in an external event tracking or analytics system.

In addition to the tracking callback, the GrowthBook type also supports more general "subscriptions", which are callback functions that are called any time Run is called, irrespective of whether or not a user is included in an experiment. The subscription system ensures that subscription callbacks are only called when the result of an experiment changes, or a new experiment is run.