Java SDK – Split Help Center

This guide provides detailed information about our Java SDK. All of our SDKs are open source. Go to our Java SDK GitHub repository to see the source code.

Language support

The Java SDK supports JDK8 and later.

Initialization

To get started, set up Split in your code base using the following two steps.

1. Import the SDK into your project

Import the SDK into your project using one of the following two methods:

<dependency>
    <groupId>io.split.client</groupId>
    <artifactId>java-client</artifactId>
    <version>4.12.1</version>
</dependency>

compile 'io.split.client:java-client:4.12.1'

If you cannot find the dependency, it may be due to the lag in the sync time between Sonatype and Maven central. In this case, use the following repository:

<repositories>
  <repository>
    <id>sonatype releases</id>
    <url>https://oss.sonatype.org/content/repositories/releases/</url>
  </repository>
</repositories>

2. Instantiate the SDK and create a new Split client

If upgrading an existing SDK - Block until ready changes

Starting version 3.0.1, SplitClientConfig#ready(int) is deprecated and migrated to a two part implementation:

Set the desired value in SplitClientConfig#setBlockUntilReadyTimeout(int).
Call SplitClient#blockUntilReady() or SplitManager#blockUntilReady().

When the SDK is instantiated, it starts background tasks to update an in-memory cache with small amounts of data fetched from Split servers. This process can take up to a few hundred milliseconds, depending on the size of data. If the SDK is asked to evaluate which treatment to show to a customer for a specific feature flag while it's in this intermediate state, it may not have the data necessary to run the evaluation. In this case, the SDK does not fail, rather, it returns the control treatment.

To make sure the SDK is properly loaded before asking it for a treatment, block until the SDK is ready. Do this by setting the desired wait using .setBlockUntilReadyTimeout() in the configuration and calling blockUntilReady() on the client. Do this all as a part of the startup sequence of your application.

We recommend instantiating the SDK once as a singleton and reusing it throughout your application.

Use the code snippet below and plug in your API key. The API key is available on your Organization settings page, on the APIs tab. The API key is of type sdk. For more information, see Understanding API Keys.

import io.split.client.SplitClient;
import io.split.client.SplitClientConfig;
import io.split.client.SplitFactory;
import io.split.client.SplitFactoryBuilder;

SplitClientConfig config = SplitClientConfig.builder()
   .setBlockUntilReadyTimeout(10000)
   .build();

SplitFactory splitFactory = SplitFactoryBuilder.build("YOUR_SDK_KEY", config);
SplitClient client = splitFactory.client();
try {
    client.blockUntilReady();
} catch (TimeoutException | InterruptedException e) {
    // log & handle
}

import io.split.client.SplitClient
import io.split.client.SplitClientConfig
import io.split.client.SplitFactory
import io.split.client.SplitFactoryBuilder

val config: SplitClientConfig = SplitClientConfig.builder()
            .setBlockUntilReadyTimeout(10000)
            .build()
val splitFactory: SplitFactory = SplitFactoryBuilder.build("YOUR_SDK_KEY", config)
val client: SplitClient = splitFactory.client()
try {
    client.blockUntilReady()
} catch (e: Exception) {
    // log & handle
}

Now you can start asking the SDK to evaluate treatments for your customers.

Using the SDK

Basic use

After you instantiate the SDK client, you can start using the getTreatment method of the SDK client to decide what version of your features your customers are served. The method requires the FEATURE_FLAG_NAME attribute that you want to ask for a treatment and a unique key attribute that corresponds to the end user that you are serving the feature to.

Then use an if-else-if block as shown below and insert the code for the different treatments that you defined in the Split user interface. Remember the final else branch in your code to handle the client returning the control treatment.

// The key here represents the ID of the user/account/etc you're trying to evaluate a treatment for
String treatment = client.getTreatment("key","FEATURE_FLAG_NAME");

if (treatment.equals("on")) {
    // insert code here to show on treatment
} else if (treatment.equals("off")) {
    // insert code here to show off treatment
} else {
    // insert your control treatment code here
}

// The key here represents the ID of the user/account/etc you're trying to evaluate a treatment for
val treatment = client.getTreatment("key","FEATURE_FLAG_NAME")

when (treatment) {
    "on" -> {
        // insert code here to show on treatment
    }
    "off" -> {
        // insert code here to show off treatment
    }
    else -> {
        // insert your control treatment code here
    }
}

Attribute syntax

To target based on custom attributes, the SDK's getTreatment method needs to be passed an attribute map at runtime.

In the example below, we are rolling out a feature flag to users. The provided attributes plan_type, registered_date, permissions, paying_customer, and deal_size are passed to the getTreatment call. These attributes are compared and evaluated against the attributes used in the Rollout plan as defined in the Split user interface to decide whether to show the on or off treatment to this account.

The getTreatment method supports five types of attributes: strings, numbers, dates, booleans, and sets. The proper data type and syntax for each are:

Strings: Use type String.
Numbers: Use type java.lang.Long or java.lang.Integer.
Dates: Express the value in milliseconds since epoch. In Java, milliseconds since epoch is of type java.lang.Long. For example, the value for the registered_date attribute below is System.currentTimeInMillis(), which is a long.
Booleans: Use type java.lang.boolean.
Sets: Use type java.util.Collection.

import io.codigo.client.SplitClient;
import io.split.client.SplitFactory;
import io.split.client.SplitFactoryBuilder;

import java.util.Map;
import java.util.HashMap;
import java.util.Date;

SplitFactory splitFactory = SplitFactoryBuilder.build("YOUR_SDK_KEY");
SplitClient client = splitFactory.client();

Map<String, Object> attributes = new HashMap<String, Object>();
attributes.put("plan_type", "growth");
attributes.put("registered_date", System.currentTimeMillis());
attributes.put("deal_size", 1000);
attributes.put("paying_customer", true);
List<String> perms = Arrays.asList("read", "write");
attributes.put("permissions", perms);

String treatment = client.getTreatment("key", "FEATURE_FLAG_NAME", attributes);

if (treatment.equals("on")) {
    // insert on code here
} else if (treatment.equals("off")) {
    // insert off code here
} else {
    // insert control code here
}

import io.split.client.SplitClient
import io.split.client.SplitFactory
import io.split.client.SplitFactoryBuilder

val splitFactory: SplitFactory = SplitFactoryBuilder.build("YOUR_SDK_KEY")
val client: SplitClient = splitFactory.client()

val perms = listOf("read", "write")
val attributes = mapOf("plan_type" to "growth",
                        "registered_date" to System.currentTimeMillis(),
                        "deal_size" to 1000,
                        "paying_customer" to true,
                        "permissions" to perms)

val treatment = client.getTreatment("key", "FEATURE_FLAG_NAME", attributes)

when (treatment) {
    "on" -> {
        // insert code here to show on treatment
    }
    "off" -> {
        // insert code here to show off treatment
    }
    else -> {
        // insert your control treatment code here
    }
}

Multiple evaluations at once

In some instances, you may want to evaluate treatments for multiple feature flags at once. Use the different variations of getTreatments from the Split client to do this.

getTreatments: Pass a list of the feature flag names you want treatments for.
getTreatmentsByFlagSet: Evaluate all flags that are part of the provided set name and are cached on the SDK instance.
getTreatmentsByFlagSets: Evaluate all flags that are part of the provided set names and are cached on the SDK instance.

// getTreatments
List<String> featureFlagNames = Arrays.asList("FEATURE_FLAG_NAME_1", "FEATURE_FLAG_NAME_2");
Map<String, String> treatments = client.getTreatments("KEY", featureFlagNames);

// getTreatmentsByFlagSet
Map<String, String> treatmentsBySet = client.getTreatmentsByFlagSet("KEY", "backend");

// getTreatmentsByFlagSets
List<String> flagSetNames = Arrays.asList("backend", "server_side");
Map<String, String> treatmentsBySets = client.getTreatmentsByFlagSets("KEY", flagSetNames);

// getTreatments
val featureFlagNames = listOf("FEATURE_FLAG_NAME_1", "FEATURE_FLAG_NAME_2")
val treatments = client.getTreatments("KEY", featureFlagNames)

// getTreatmentsByFlagSet
val treatmentsBySet = client.getTreatmentsByFlagSet("KEY", "backend")

// getTreatmentsByFlagSets
val flagSetNames = listOf("backend", "server_side")
val treatmentsBySets = client.getTreatmentsByFlagSets("KEY", flagSetNames)

Get treatments with configurations

To leverage dynamic configurations with your treatments, you should use the getTreatmentWithConfig method. This method returns an object containing the treatment and associated configuration.

The config element is a stringified version of the configuration JSON defined in the Split user interface. If there is no configuration defined for a treatment, the SDK returns null for the config parameter.

This method takes the exact same set of arguments as the standard getTreatment method. See below for examples on proper usage:

SplitResult result = client.getTreatmentWithConfig("KEY", "FEATURE_FLAG_NAME");
String treatment = result.treatment();
if (null != result.config()) {
    MyConfiguration config = gson.fromJson(result.config(), MyConfiguration.class);
}

val result: SplitResult = client.getTreatmentWithConfig("KEY", "FEATURE_FLAG_NAME")
val config: String = result.config()
val treatment: String = result.treatment()

If you need to get multiple evaluations at once, you can also use the getTreatmentsWithConfig methods. These methods take the exact same arguments as the getTreatments methods but return a mapping of feature flag names to SplitResult instead of strings. Example usage below:

// getTreatmentsWithConfig
List<String> featureFlagNames = Arrays.asList("FEATURE_FLAG_NAME_1", "FEATURE_FLAG_NAME_2");
Map<String, SplitResult> treatments = client.getTreatmentsWithConfig("KEY", featureFlagNames, attributes);

// getTreatmentsWithConfigByFlagSet
Map<String, SplitResult> treatmentsBySet = client.getTreatmentsWithConfigByFlagSet("KEY", "backend");

// getTreatmentsWithConfigByFlagSets
List<String> flagSetNames = Arrays.asList("backend", "server_side");
Map<String, SplitResult> treatmentsBySets = client.getTreatmentsWithConfigByFlagSets("KEY", flagSetNames);

// getTreatmentsWithConfig
val featureFlagNames = listOf("FEATURE_FLAG_NAME_1", "FEATURE_FLAG_NAME_2")
val treatments: Map<String, SplitResult> = client.getTreatmentsWithConfig("KEY", featureFlagNames)

// getTreatmentsWithConfigByFlagSet
val treatmentsBySet: Map<String, SplitResult> = client.getTreatmentsWithConfigByFlagSet("KEY", "backend")

// getTreatmentsWithConfigByFlagSets
val flagSetNames = listOf("backend", "server_side")
val treatmentsBySets: Map<String, SplitResult> = client.getTreatmentsWithConfigByFlagSets("KEY", flagSetNames)

Shutdown

Make sure to call .destroy() before letting a process using the SDK exit as it gracefully shuts down the Split SDK by stopping all background threads, clearing caches, closing connections, and flushing the remaining unpublished impressions and events. The Java SDK specifically subscribes to the JVM shutdown hook (SIGTERM signal) which in normal circumstances is invoked automatically by the JVM during a shutdown process. This means that on a graceful shutdown of the server, the client will automatically call destroy() and will flush the buffers and release the resources.

In cases where you don't want our SDK to automatically destroy on shutdown, you can use the config: disableDestroyOnShutDown() (example usage in the Configuration section below) and set it to true. If you do this, the SDK ignores any signals like SIGTERM and it is your responsibility to properly call destroy at the right time. If a manual shutdown is required, you can then call:

client.destroy();

client.destroy()

After destroy() is called, any subsequent invocations to client.getTreatment() or manager methods result in control or empty list, respectively.

Important!

A call to the destroy() method also destroys the factory object. When creating new client instance, first create a new factory instance.

Track

Use the track method to record any actions your customers perform. Each action is known as an event and corresponds to an event type. Calling track through one of our SDKs or via the API is the first step to getting experimentation data into Split and allows you to measure the impact of your feature flags on your users' actions and metrics.

Refer to the Events guide for more information about using track events in feature flags.

In the examples below you can see that the .track() method can take up to five arguments. The proper data type and syntax for each are:

key: The key variable used in the getTreatment call and firing this track event. The expected data type is String.
TRAFFIC_TYPE: The traffic type of the key in the track call. The expected data type is String. You can only pass values that match the names of traffic types that you defined in your instance of Split.
EVENT_TYPE: The event type that this event should correspond to. The expected data type is String. Full requirements on this argument are:
- Contains 63 characters or fewer.
- Starts with a letter or number.
- Contains only letters, numbers, hyphen, underscore, or period.
- This is the regular expression we use to validate the value: [a-zA-Z0-9][-_\.a-zA-Z0-9]{0,62}
VALUE: (Optional) The value used in creating the metric. This field can be sent in as null or 0 if you intend to only use the count function when creating a metric. The expected data type is Integer or Float.
PROPERTIES: (Optional) A map of key value pairs that can filter your metrics. To learn more about event property capture, refer to the Events property capture guide. Split currently supports three types of properties: strings, numbers, and booleans.

The track method returns a boolean value of true or false to indicate whether or not the SDK successfully queued the event to be sent back to Split's servers on the next event post. The SDK returns false if the current queue size is equal to the config set by eventsQueueSize or if an incorrect input to the track method is provided.

In the case that a bad input has been provided, you can read more about our SDK's expected behavior in our Events guide.

// If you would like to send an event without a value
boolean trackEvent = client.track("key", "TRAFFIC_TYPE", "EVENT_TYPE");
// Example
boolean trackEvent = client.track("john@doe.com", "user", "page_load_time");

// If you would like to associate a value to an event
boolean trackEvent = client.track("key", "TRAFFIC_TYPE", "EVENT_TYPE", VALUE);
// Example
boolean trackEvent = client.track("john@doe.com", "user", "page_load_time", 83.334);

// If you would like to associate a value and properties to an event
boolean trackEvent = client.track("key", "TRAFFIC_TYPE", "EVENT_TYPE", VALUE, {PROPERTIES});
// Example
HashMap<String, Object> properties = new HashMap<>();
properties.put("package", "premium");
properties.put("admin", true);
properties.put("discount", 50);

boolean trackEvent = client.track("john@doe.com", "user", "page_load_time", 83.334, properties);

// If you would like to associate just properties to an event
boolean trackEvent = client.track("key", "TRAFFIC_TYPE", "EVENT_TYPE", {PROPERTIES});
// Example
HashMap<String, Object> properties = new HashMap<>();
properties.put("package", "premium");
properties.put("admin", true);
properties.put("discount", 50);

boolean trackEvent = client.track("john@doe.com", "user", "page_load_time", properties);

// If you would like to send an event without a value
val trackEvent: Boolean = client.track("key", "TRAFFIC_TYPE", "EVENT_TYPE")
// Example
val trackEvent: Boolean = client.track("john@doe.com", "user", "page_load_time")

// If you would like to associate a value to an event
val trackEvent: Boolean = client.track("key", "TRAFFIC_TYPE", "EVENT_TYPE", VALUE)
// Example
val trackEvent: Boolean = client.track("john@doe.com", "user", "page_load_time", 83.334)

// If you would like to associate a value and properties to an event
val trackEvent: Boolean = client.track("key", "TRAFFIC_TYPE", "EVENT_TYPE", VALUE, {PROPERTIES})
// Example
val properties = mapOf("package" to "premium",
                                    "admin" to true,
                                    "discount" to 50)

val trackEvent: Boolean = client.track("john@doe.com", "user", "page_load_time", 83.334, properties)

// If you would like to associate just properties to an event
val trackEvent: Boolean = client.track("key", "TRAFFIC_TYPE", "EVENT_TYPE", {PROPERTIES})
// Example
val properties = mapOf("package" to "premium",
                                    "admin" to true,
                                    "discount" to 50)

val trackEvent: Boolean = client.track("john@doe.com", "user", "page_load_time", properties)

Configuration

The SDK has a number of knobs for configuring performance. Each knob is tuned to a reasonable default. However, you can override the value while instantiating the SDK. The parameters available for configuration are shown below.

Configuration	Description	Default value
featuresRefreshRate	The SDK polls Split servers for changes to feature rollout plans. This parameter controls this polling period in seconds.	60 seconds
segmentsRefreshRate	The SDK polls Split servers for changes to segments at this rate (in seconds).	60 seconds
impressionsRefreshRate	The treatment log captures which customer saw what treatment (on, off, etc.) at what time. This log is periodically flushed back to Split servers. This configuration controls how quickly the cache expires after a write (in seconds).	60 seconds
telemetryRefreshRate	The SDK caches diagnostic data that it periodically sends to Split servers. This configuration controls how frequently this data is sent back to Split servers (in seconds).	3600 seconds
eventsQueueSize	When using `.track`, the number of events to be kept in memory.	500
eventFlushIntervalInMillis	When using `.track`, how often (in milliseconds) the events queue is flushed to Split servers.	30000 ms
connectionTimeout	HTTP client connection timeout (in ms).	15000ms
readTimeout	HTTP socket read timeout (in ms).	15000ms
setBlockUntilReadyTimeout	If specified, the client building process blocks until the SDK is ready to serve traffic or the specified time has elapsed. If the SDK is not ready within the specified time, a `TimeOutException` is thrown (in ms).	0ms
impressionsQueueSize	Default queue size for impressions.	30K
disableLabels	Disable labels from being sent to Split backend. Labels may contain sensitive information.	enabled
disableIPAddress	Disable sending IP Address & hostname to the backend.	enabled
proxyHost	The location of the proxy.	localhost
proxyPort	The port of the proxy.	-1 (not set)
proxyUsername	Username to authenticate against the proxy server.	null
proxyPassword	Password to authenticate against the proxy server.	null
streamingEnabled	Boolean flag to enable the streaming service as default synchronization mechanism. In the event of an issue with streaming, the SDK falls back to the polling mechanism. If false, the SDK polls for changes as usual without attempting to use streaming.	true
impressionsMode	Defines how impressions are queued on the SDK. Supported modes are OPTIMIZED, NONE, and DEBUG. In OPTIMIZED mode, only unique impressions are queued and posted to Split; this is the recommended mode for experimentation use cases. In NONE mode, no impression is tracked in Split and only minimum viable data to support usage stats is, so never use this mode if you are experimenting with that instance impressions. Use NONE when you want to optimize for feature flagging only use cases and reduce impressions network and storage load. In DEBUG mode, all impressions are queued and sent to Split; this is useful for validations. Use DEBUG mode when you want every impression to be logged in the Split user interface when trying to debug your SDK setup. This setting does not impact the impression listener which receives all generated impressions locally.	OPTIMIZED
operationMode	Defines how the SDK synchronizes its data. Two operation modes are currently supported: - STANDALONE. - CONSUMER	STANDALONE
storageMode	Defines what kind of storage the SDK is going to use. With MEMORY, the SDK uses its own storage and runs as STANDALONE mode. Set REDIS mode if you want the SDK to run with this implementation as CONSUMER mode.	MEMORY
flagSetsFilter	This setting allows the SDK to only synchronize the feature flags in the specified flag sets, avoiding unused or unwanted flags from being synced on the SDK instance, bringing all the benefits from a reduced payload.	null
threadFactory	Defines what kind of thread the SDK is going to use. Allows the SDK to use Virtual Threads.	null
inputStream	This setting allows the SDK supports InputStream to use localhost inside a JAR.	null
FileTypeEnum	Defines which kind of file is going to be the inputStream. Supported files are YAML and JSON for inputStream.	null

To set each of the parameters defined above, use the following syntax:

import io.split.client.SplitClient;
import io.split.client.SplitClientConfig;
import io.split.client.SplitFactory;
import io.split.client.SplitFactoryBuilder;

SplitClientConfig config = SplitClientConfig.builder()
                            .impressionsRefreshRate(60)
                            .connectionTimeout(15000)
                            .readTimeout(15000)
                            .enableDebug()
                            .setBlockUntilReadyTimeout(10000)
                            .flagSetsFilter(Arrays.asList("backend", "server_side"))
                            .build();

SplitFactory splitFactory = SplitFactoryBuilder.build("YOUR_SDK_KEY",config);
SplitClient client = splitFactory.client();
client.blockUntilReady();

import io.split.client.SplitClient;
import io.split.client.SplitClientConfig;
import io.split.client.SplitFactory;
import io.split.client.SplitFactoryBuilder;

val config: SplitClientConfig = SplitClientConfig.builder()
            .impressionsRefreshRate(60)
            .connectionTimeout(15000)
            .readTimeout(15000)
            .enableDebug()
            .setBlockUntilReadyTimeout(10000)
            .flagSetsFilter(Arrays.asList("backend", "server_side"))
            .build()

val splitFactory: SplitFactory = SplitFactoryBuilder.build("YOUR_SDK_KEY", config)
val client: SplitClient = splitFactory.client()
client.blockUntilReady()

Connecting to a Split Proxy instance

The SDK can connect to a Split Proxy instance as though it was connecting to our CDN, and the Proxy synchronizes the data and writes impressions and events back to the Split server. Be sure to install the Split Proxy by following the steps in Split Proxy guide.

Use the .endpoint() property in the SplitClientConfig builder object to point the Java SDK to the Synchronizer, making sure to use the same port specified in the Proxy command line. When creating the SplitFactory object, use the custom API key specified in the client-apikeys parameter for the Proxy. The Proxy uses the Split SDK key when connecting to Split. Refer to the following code example to connect to a Proxy instance:

import io.split.client.SplitClient;
import io.split.client.SplitClientConfig;
import io.split.client.SplitFactory;
import io.split.client.SplitFactoryBuilder;

public class SplitSD {
    public static void main(String[] args) {
      SplitClientConfig config = SplitClientConfig.builder()
           .setBlockUntilReadyTimeout(10000)
           .endpoint("https://myproxy.com","https://myproxy.com")
           .authServiceURL("https://myproxy.com" + "/api/auth")
           .telemetryURL("https://myproxy.com" + "/api/v1")
           .build();
      SplitFactory splitFactory = SplitFactoryBuilder.build("YOUR_SDK_KEY", config);
      SplitClient client = splitFactory.client();
      try {
        client.blockUntilReady()
        String treatment = client.getTreatment("user10","sample_feature_flag");
        if (treatment.equals("on")) {
          System.out.print("Treatment is on");
        } else if (treatment.equals("off")) {
          System.out.print("Treatment is off");
        } else {
          System.out.print("SDK Not ready");
        }
      } catch (Exception e) {
          System.out.print("Exception: "+e.getMessage());
      }
    }
}

import io.split.client.SplitFactoryBuilder
import io.split.client.SplitClient
import io.split.client.SplitClientConfig
import io.split.client.SplitFactory

fun main (args: Array<String>){
    val config: SplitClientConfig = SplitClientConfig.builder()
                .setBlockUntilReadyTimeout(10000)
                .endpoint("https://myproxy.com","https://myproxy.com")
                .authServiceURL("https://myproxy.com" + "/api/auth")
                .telemetryURL("https://myproxy.com" + "/api/v1")
                .build()
    val splitFactory: SplitFactory = SplitFactoryBuilder.build("YOUR_SDK_KEY", config)
    val client: SplitClient = splitFactory.client()
    try {
        client.blockUntilReady()
        val treatment = client.getTreatment("key", "FEATURE_FLAG_NAME", attributes)
        when (treatment) {
            "on" -> {
                println("Treatment is on")
            }
            "off" -> {
                println("Treatment is off")
            }
            else -> {
                println("SDK Not ready")
            }
        }
    } catch (e: Exception) {
        println("Exception: " + e.message)    
    }
}

Localhost mode

For testing, a developer can put code behind feature flags on their development machine without the SDK requiring network connectivity. To achieve this, the Split SDK can be started in localhost mode (aka off-the-grid mode). In this mode, the SDK neither polls nor updates Split servers. Instead, it uses an in-memory data structure to determine what treatments to show to the logged in customer for each of the features. To use the SDK in localhost mode, you must replace the API Key with "localhost" value.

With this mode, you can instantiate the SDKS using one of the following methods:

JSON: Full support, for advanced cases or replicating an environment by pulling rules from Split cloud (from version 4.7.0).
YAML: Supports dynamic configs, individual targets and default rules (from version 3.1.0).
.split: Legacy option, only treatment result.

JSON

Since version 4.7.0, our SDK supports localhost mode by using the JSON format. This version allows the user to map feature flags and segment definitions in the same format as the APIs receive the data.

This new mode needs extra configuration to be set

Name	Description	Type
splitFile	Indicates the path of the feature flags file location to read	String
segmentDirectory	Indicates the path where all the segment files are located	String
localhostRefreshEnabled	Flag to run synchronization refresh for feature flags and segments in localhost mode.	Boolean

splitFile

The following splitFile is a JSON that represents a SplitChange:

public class SplitChange {
    public List<Split> splits;
    public long since;
    public long till;
}

public class Split {
    public String name;
    public int seed;
    public Status status;
    public boolean killed;
    public String defaultTreatment;
    public List<Condition> conditions;
    public String trafficTypeName;
    public long changeNumber;
    public Integer trafficAllocation;
    public Integer trafficAllocationSeed;
    public int algo;
    public Map<String, String> configurations;
}

class Split(
    var splits: List<Split>?,
    var since: Long?,
    var till: Long?,
)

class Split(
    var name: String?,
    var seed: Int?,
    var status: Status?,
    var killed: Boolean?,
    var defaultTreatment: String?,
    var conditions: List<Condition>?,
    var trafficTypeName: String?,
    var changeNumber: Long?,
    var trafficAllocation: Int?,
    var trafficAllocationSeed: Int?,
    var algo: Int?,
    configurations: Map<String, String>?
)

{
  "splits": [
    {
      "trafficTypeName": "user",
      "name": "feature_flag_1",
      "trafficAllocation": 100,
      "trafficAllocationSeed": -1364119282,
      "seed": -605938843,
      "status": "ACTIVE",
      "killed": false,
      "defaultTreatment": "off",
      "changeNumber": 1660326991072,
      "algo": 2,
      "configurations": {},
      "conditions": [
        {
          "conditionType": "ROLLOUT",
          "matcherGroup": {
            "combiner": "AND",
            "matchers": [
              {
                "keySelector": {
                  "trafficType": "user",
                  "attribute": null
                },
                "matcherType": "IN_SEGMENT",
                "negate": false,
                "userDefinedSegmentMatcherData": {
                  "segmentName": "segment_1"
                },
                "whitelistMatcherData": null,
                "unaryNumericMatcherData": null,
                "betweenMatcherData": null,
                "booleanMatcherData": null,
                "dependencyMatcherData": null,
                "stringMatcherData": null
              }
            ]
          },
          "partitions": [
            {
              "treatment": "on",
              "size": 0
            },
            {
              "treatment": "off",
              "size": 100
            }
          ],
          "label": "in segment segment_1"
        },
        {
          "conditionType": "ROLLOUT",
          "matcherGroup": {
            "combiner": "AND",
            "matchers": [
              {
                "keySelector": {
                  "trafficType": "user",
                  "attribute": null
                },
                "matcherType": "ALL_KEYS",
                "negate": false,
                "userDefinedSegmentMatcherData": null,
                "whitelistMatcherData": null,
                "unaryNumericMatcherData": null,
                "betweenMatcherData": null,
                "booleanMatcherData": null,
                "dependencyMatcherData": null,
                "stringMatcherData": null
              }
            ]
          },
          "partitions": [
            {
              "treatment": "on",
              "size": 50
            },
            {
              "treatment": "off",
              "size": 50
            }
          ],
          "label": "default rule"
        }
      ]
    }
  ],
  "since": -1,
  "till": 1660326991072
}

segmentDirectory

The provided segment directory must have the json files of the corresponding segment linked to previous feature flag definitions. According to the Split file sample above: feature_flag_1 has segment_1 linked. That means that the segmentDirectory needs to have segment_1 definition.

public class SegmentChange {
    public String id;
    public String name;
    public List<String> added;
    public List<String> removed;
    public long since;
    public long till;
}

class SegmentChange(
    var id: String,
    var name: String,
    var added: List<String>,
    var removed: List<String>,
    var since: Long,
    var till: Long
)

{
  "name": "segment_1",
  "added": [
    "example1",
    "example2"
  ],
  "removed": [],
  "since": -1,
  "till": 1585948850110
}

SplitClientConfig config = SplitClientConfig.builder()
       .splitFile("parentRoot/featureFlags.json")
       .segmentDirectory("parentRoot/segments")
       .setBlockUntilReadyTimeout(10000)
       .build();

val config: SplitClientConfig = SplitClientConfig.builder()
                .splitFile("parentRoot/featureFlags.json")
                .segmentDirectory("parentRoot/segments")
                .setBlockUntilReadyTimeout(10000)
                .build()

YAML

Since version 3.1.0, our SDK supports a type of localhost feature flag definition file that uses the YAML format. This new format allows the user to map different keys to different treatments within a single feature flag and also add configurations to them. The format is a list of single-key maps (one per mapping feature-flag-keys-config) which is defined as follows:

# - feature_name:
#     treatment: "treatment_applied_to_this_entry"
#     keys: "single_key_or_list"
#     config: "{\"desc\" : \"this applies only to ON treatment\"}"

- my_feature_flag:
    treatment: "on"
    keys: "key"
    config: "{\"desc\" : \"this applies only to ON treatment\"}"
- some_other_feature_flag:
    treatment: "off"
- my_feature_flag:
    treatment: "off"
- other_feature_flag:
    treatment: "off"
    keys: ["key_1", "key_2"]
    config: "{\"desc\" : \"this overrides multiple keys and returns off treatment for those keys\"}"

In the example above, we have four entries:

The first entry defines that for feature flag my_feature_flag, the key key returns the treatment on and the on treatment is tied to the configuration {"desc" : "this applies only to ON treatment"}.
The second entry defines that the feature flag some_other_feature_flag always returns the off treatment and no configuration.
The third entry defines that my_feature_flag always returns off for all keys that don't match another entry (in this case, any key other than key).
The fourth entry shows how an example overrides a treatment for a set of keys.

Use the SplitConfigBuilder object to set the location of the Split localhost YAML file as shown in the example below:

import io.split.client.SplitClient;
import io.split.client.SplitClientConfig;
import io.split.client.SplitFactoryBuilder;

SplitClientConfig config = SplitClientConfig.builder()
       .splitFile("parentRoot/split.yaml")
       .setBlockUntilReadyTimeout(10000)
       .build();
SplitClient client = SplitFactoryBuilder.build("localhost", config).client();

import io.split.client.SplitFactoryBuilder
import io.split.client.SplitClient
import io.split.client.SplitClientConfig

val config: SplitClientConfig = SplitClientConfig.builder()
            .splitFile("parentRoot/split.yaml")
            .setBlockUntilReadyTimeout(10000)
            .build()
val client: SplitClient = SplitFactoryBuilder.build("localhost", config).client()

.SPLIT file

SplitClientConfig config = SplitClientConfig.builder().setBlockUntilReadyTimeout(10000).build();
SplitFactory splitFactory = SplitFactoryBuilder.build("localhost", config);
SplitClient client = splitFactory.client();

val config: SplitClientConfig = SplitClientConfig.builder().setBlockUntilReadyTimeout(10000).build()
val splitFactory: SplitFactory = SplitFactoryBuilder.build("localhost", config)
val client: SplitClient = splitFactory.client()

In this mode, the SDK loads a mapping of feature flag name to treatment from a file at $HOME/.split. For a given flag, the treatment specified in the file is returned for every customer.

getTreatment calls for a feature flag and only returns the one treatment that you defined in the file. You can then change the treatment as necessary for your testing in the file. Any feature that is not provided in the features map returns the control treatment if the SDK is asked to evaluate them.

The format of this file is two columns separated by a whitespace. The left column is the feature flag name and the right column is the treatment name. The following is a sample .split file:

reporting_v2 on # sdk.getTreatment(*, reporting_v2) will return 'on'

double_writes_to_cassandra off

new-navigation v3

Input Stream

Since version 4.9.0, the SDK supports InputStream to use localhost inside a JAR. To achieve this, we added new parameters in splitFile property to set the InputStream. The first param is an InputStream of the file that we want to read. And the second is a FileTypeEnum which can be either YAML, or JSON. Here is an example code to demonstrate how to use this new feature:

import io.split.client.SplitClient;
import io.split.client.SplitClientConfig;
import io.split.client.SplitFactoryBuilder;
import io.split.client.utils.FileTypeEnum;

import java.io.FileInputStream;
import java.io.InputStream;

InputStream inputStream = new FileInputStream("parentRoot/split.yaml");
SplitClientConfig config = SplitClientConfig.builder()
       .splitFile(inputStream, FileTypeEnum.YAML)
       .setBlockUntilReadyTimeout(10000)
       .build();
SplitClient client = SplitFactoryBuilder.build("localhost", config).client();

import io.split.client.SplitFactoryBuilder;
import io.split.client.SplitClient;
import io.split.client.SplitClientConfig;
import io.split.client.utils.FileTypeEnum;

import java.io.FileInputStream;
import java.io.InputStream;

InputStream inputStream = new FileInputStream("parentRoot/split.json");
SplitClientConfig config = SplitClientConfig.builder()
       .splitFile(inputStream, FileTypeEnum.JSON)
       .setBlockUntilReadyTimeout(10000)
       .build();
SplitClient client = SplitFactoryBuilder.build("localhost", config).client();

import io.split.client.SplitFactoryBuilder
import io.split.client.SplitClient
import io.split.client.SplitClientConfig
import io.split.client.utils.FileTypeEnum

import java.io.FileInputStream
import java.io.InputStream

val inputStream = new FileInputStream("parentRoot/split.json");
val config: SplitClientConfig = SplitClientConfig.builder()
            .splitFile(inputStream, FileTypeEnum.JSON)
            .setBlockUntilReadyTimeout(10000)
            .build()
val client: SplitClient = SplitFactoryBuilder.build("localhost", config).client()

Before you get started with the cache, download the correct version of Redis to your machine. Make sure to start your Redis server. Refer to the Redis documentation for help. After that, followi the additional three steps to set up the cache with Redis.

1. Install the Redis Wrapper into your project

Import the Redis Wrapper into your project using one of the two methods below:

<dependency>
    <groupId>io.split.client</groupId>
    <artifactId>redis-wrapper</artifactId>
    <version>3.1.0</version>
</dependency>

compile 'io.split.client:redis-wrapper:1.0.0'

2. Set up the Split Synchronizer

Set up the Split Synchronizer to sync data to a Redis cache. Once you set up the synchronizer, go to the following step #3 to instantiate:

3. Instantiate the SDK client with Redis enabled

To run the SDK with Redis, you need to provide the Redis storage wrapper. Refer to the following to provide the wrapper:

// Building the Redis storage wrapper with some configurations of choice.
CustomStorageWrapper redis = RedisInstance.builder()
                .host("localhost")
                .port(6379)
                .timeout(1000)
                .database(0)
                .prefix("java")
                .build();
// Building the SDK config with the Redis wrapper referenced.
SplitClientConfig splitConfig = SplitClientConfig.builder()
                        .customStorageWrapper(redis)
                        .operationMode(OperationMode.CONSUMER)
                        .storageMode(StorageMode.REDIS)
                        .build();
// Then just build the factory as usual.
SplitFactory splitFactory = SplitFactoryBuilder.build("YOUR_API_KEY", config);

// Building the Redis storage wrapper with some configurations of choice.
val redis: CustomStorageWrapper = RedisInstance.builder()
        .host("localhost")
        .port(6379)
        .timeout(1000)
        .database(0)
        .prefix("java")
        .build()
// Building the SDK config with the Redis wrapper referenced.
val splitConfig: SplitClientConfig = SplitClientConfig.builder()
                .customStorageWrapper(redis)
                .operationMode(OperationMode.CONSUMER)
                .storageMode(StorageMode.REDIS)
                .build()
// Then just build the factory as usual.
val splitFactory: SplitFactory = SplitFactoryBuilder.build("YOUR_API_KEY", config)

Redis wrapper configuration

When you create a new instance for the Redis wrapper, you can provide your own configurations for some values.

Field name(s)	Description	Default value
timeout	Timeout that the connections is going to handle.	1000
host	Hostname where the Redis instance is.	localhost
port	HTTP port used in the connection.	6379
database	Numeric database to be used.	0
user	Redis cluster user. Leave empty if no User is used.	""
password	Redis cluster password. Leave empty if no password is used.	""
prefix	Best practice is to use a prefix in case the Redis instance is shared by many SDKs.	""
jedisPool	You can provide your own implementation of JedisPool.	null
maxTotal	Max number of pool connections.	8

Redis cluster support

The SDK supports Redis with Cluster. Note that a stable release of Cluster has shipped since Redis 3.0. For further information about Redis Cluster, refer to the Cluster documentation.

Use the following configuration for Redis in Cluster mode.

Variable	Type	Description
clusterNodes	Set	The list of cluster nodes.
jedis	JedisCluster	Jedis contains the list of cluster nodes.
keyHashTag	string	Custom hashtag to be used.

// Building the Redis storage wrapper with some configurations of choice.
Set<HostAndPort> jedisClusterNodes = new HashSet<HostAndPort>();
jedisClusterNodes.add(new HostAndPort("cluster-node1", 6071));
jedisClusterNodes.add(new HostAndPort("cluster-node2", 6072));
jedisClusterNodes.add(new HostAndPort("cluster-node3", 6073));
JedisCluster jedis = new JedisCluster(jedisClusterNodes);
CustomStorageWrapper redis = RedisInstance.builder()
                              .jedisCluster(jedis)
                              .hashtag("{SPLITIO}")
                              .prefix("java:")
                              .build();
SplitClientConfig config = SplitClientConfig.builder()
                            .customStorageWrapper(redis)
                            .operationMode(OperationMode.CONSUMER)
                            .storageMode(StorageMode.REDIS)
                            .setBlockUntilReadyTimeout(10000)
                            .enableDebug()
                            .build();
SplitFactory splitFactory =  SplitFactoryBuilder.build("apikey", config);

// Building the Redis storage wrapper with some configurations of choice.
val jedisClusterNodes = setOf<HostAndPort>(
                                HostAndPort("cluster-node1", 6071),
                                HostAndPort("cluster-node2", 6072),
                                HostAndPort("cluster-node3", 6073))
        val jedis = JedisCluster(jedisClusterNodes)
        val redis: CustomStorageWrapper = RedisInstance.builder()
            .jedisCluster(jedis)
            .hashtag("{SPLITIO}")
            .prefix("java:")
            .build();
        val splitConfig: SplitClientConfig = SplitClientConfig.builder()
            .customStorageWrapper(redis)
            .operationMode(OperationMode.CONSUMER)
            .storageMode(StorageMode.REDIS)
            .build()
val splitFactory: SplitFactory = SplitFactoryBuilder.build("apikey", config)

Redis Cluster

The Java SDK performs multi-key operations in certain methods such as mget (to return values of all specified keys) or keys (to return all the keys that matches a particular pattern) to avoid multiple calls to Redis. Redis Cluster does not allow these operations unless you use hashtags. Hashtags ensure that multiple keys are allocated in the same hash slot. The SDK allows you to use a custom key hashtag for storing keys. If this option is missing, it uses a default hashtag of {SPLITIO} when cluster mode is specified in the configuration. Keep in mind that multi-key operations may become unavailable during a resharding of the hash slots, calls to getTreatments, or manager.splitNames(), causing splitKeys to fail.

Manager

Use the Split Manager to get a list of features available to the Split client.

To instantiate a Manager in your code base, use the same factory that you used for your client.

SplitFactory splitFactory = SplitFactoryBuilder.build("YOUR_SDK_KEY");
SplitManager manager = splitFactory.manager();

val splitFactory: SplitFactory = SplitFactoryBuilder.build("YOUR_SDK_KEY")
val manager: SplitManager = splitFactory.manager()

The Manager then has the following methods available:

/**
 * Retrieves the feature flags that are currently registered with the
 * SDK.
 *
 * @return a List of SplitView or empty.
 */
List<SplitView> splits();

/**
 * Returns the feature flags registered with the SDK of this name.
 *
 * @return SplitView or null
 */
SplitView split(String SplitName);

/**
 * Returns the feature flag names registered with the SDK.
 *
 * @return a List of String containing feature flag names or empty
 */
List<String> splitNames();

/**
* Retrieves the feature flags that are currently registered with the
* SDK.
*/
fun splits(): List<SplitView>

/**
* Returns the feature flags registered with the SDK of this name.
*/
fun split(SplitName: String): SplitView

/**
* Returns the feature flag names registered with the SDK.
*/
fun splitNames(): List<String>

The SplitView object that you see referenced above has the following structure:

public class SplitView {
    public String name;
    public String trafficType;
    public boolean killed;
    public List<String> treatments;
    public long changeNumber;
    public Map<String, String> configs;
    public List<String> sets;
    public String defaultTreatment;
}

class SplitView(
    var name: String?,
    var trafficType: String?,
    var killed: Boolean,
    var treatments: List<String>?,
    var changeNumber: Long,
    var sets: List<String>?,
    var defaultTreatment: String?
)

Listener

Split SDKs send impression data back to Split servers periodically and as a result of evaluating feature flags. To additionally send this information to a location of your choice, define and attach an impression listener.

The SDK sends the generated impressions to the impression listener immediately. As a result, be careful while implementing handling logic to avoid blocking the main thread. As the second parameter, specify the size of the queue acting as a buffer (see the snippet below).

If the impression listener is slow at processing the incoming data, the queue fills up and any subsequent impressions are dropped.

SplitClientConfig config = SplitClientConfig.builder()
    .integrations(
            IntegrationsConfig.builder()
                    .impressionsListener(new MyImpressionListener(), 500)
                    .build())
    .build();

SplitFactoryBuilder.build("YOUR_SDK_KEY", config).client();


// Custom Impression listener class
static class MyImpressionListener implements ImpressionListener {

  @Override
  public void log(Impression impression) {
      // Send this data somewhere. Printing to console for now.
      System.out.println(impression);
  }

  @Override
  public void close() {
      // Do something
  }
}

val config: SplitClientConfig = SplitClientConfig.builder()
            .integrations(
                IntegrationsConfig.builder()
                    .impressionsListener(MyImpressionListener(), 500)
                    .build())
            .build()
SplitFactoryBuilder.build("YOUR_SDK_KEY", config).client()

// Custom Impression listener class
class MyImpressionListener : ImpressionListener {
    override fun log(impression: Impression) {
        // Send this data somewhere. Printing to console for now.
        println(impression);
    }
    override fun close() {
        // Do something
    }
}

Logging

The Java SDK uses slf4j-api for logging. If you do not provide an implementation for slf4j, you see the following error in your logs:

SLF4J: Failed to load class "org.slf4j.impl.StaticLoggerBinder".
SLF4J: Defaulting to no-operation (NOP) logger implementation
SLF4J: See http://www.slf4j.org/codes.html#StaticLoggerBinder for further details.

You can get the SDK to log by providing a concrete implementation for SLF4J. For instance, if you are using log4j, you should import the following dependency.

<dependency>
  <groupId>org.slf4j</groupId>
  <artifactId>slf4j-log4j12</artifactId>
  <version>1.7.21</version>
</dependency>

If you have a log4j.properties in your classpath, the SDK log is visible. The following is an example of log4j.properties entry:

log4j.rootLogger=DEBUG, A1
log4j.appender.A1=org.apache.log4j.ConsoleAppender
log4j.appender.A1.layout=org.apache.log4j.PatternLayout
log4j.appender.A1.layout.ConversionPattern=%-4r [%t] %-5p %c %x - %m%n

log4j.rootLogger=DEBUG, A1
log4j.appender.A1=org.apache.log4j.ConsoleAppender
log4j.appender.A1.layout=org.apache.log4j.PatternLayout
log4j.appender.A1.layout.ConversionPattern=%-4r [%t] %-5p %c %x - %m%n

The following is an example of initializing the logger object in Java:

import io.split.client.SplitClient;
import io.split.client.SplitClientConfig;
import io.split.client.SplitFactory;
import io.split.client.SplitFactoryBuilder;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

public class SplitSD {

    final static Logger logger = LoggerFactory.getLogger(SplitSD.class);
    public static void main(String[] args) {
        SplitClientConfig config = SplitClientConfig.builder()
                   .setBlockUntilReadyTimeout(10000)
                   .enableDebug()
                   .build();

import io.split.client.SplitClient;
import io.split.client.SplitClientConfig;
import io.split.client.SplitFactory;
import io.split.client.SplitFactoryBuilder;
import org.slf4j.LoggerFactory

class SplitSD {
    inline fun <reified T:Any> logger() = LoggerFactory.getLogger(T::class.java)
}

Thread Factory

Since version 4.10.0, the Java SDK provides support for Virtual Threads using the config threadFactory, instead of traditional threads. Below is an example of how to set it up:

import io.split.client.SplitClient;
import io.split.client.SplitClientConfig;
import io.split.client.SplitFactory;
import io.split.client.SplitFactoryBuilder;

import java.util.concurrent.ThreadFactory;

ThreadFactory virtualThreadFactory = Thread.ofVirtual().factory();
SplitClientConfig config = SplitClientConfig.builder()
                            .setBlockUntilReadyTimeout(10000)
                            .threadFactory(virtualThreadFactory)
                            .build();

SplitFactory splitFactory = SplitFactoryBuilder.build("YOUR_SDK_KEY",config);
SplitClient client = splitFactory.client();
client.blockUntilReady();

import io.split.client.SplitClient;
import io.split.client.SplitClientConfig;
import io.split.client.SplitFactory;
import io.split.client.SplitFactoryBuilder;

val virtualThreadFactory = Thread.ofVirtual().factory();
val config: SplitClientConfig = SplitClientConfig.builder()
            .setBlockUntilReadyTimeout(10000)
            .threadFactory(virtualThreadFactory)
            .build()

val splitFactory: SplitFactory = SplitFactoryBuilder.build("YOUR_SDK_KEY", config)
val client: SplitClient = splitFactory.client()
client.blockUntilReady()

Integrations

New Relic

The New Relic integration annotates New Relic transactions with Split feature flags information that can be used to correlate application metrics with feature flag changes. This integration is implemented as a synchronous impression listener and it can be enabled as shown below:

SplitClientConfig config = SplitClientConfig.builder()
    .integrations(
            IntegrationsConfig.builder()
                    .newRelicImpressionListener()
                    .build())
    .build();

SplitFactoryBuilder.build("YOUR_SDK_KEY", config).client();

val config: SplitClientConfig = SplitClientConfig.builder()
            .integrations(
                IntegrationsConfig.builder()
                    .newRelicImpressionListener()
                    .build())
            .build()

SplitFactoryBuilder.build("YOUR_SDK_KEY", config).client()

This integration is only enabled if Split SDK detects the New Relic agent in the classpath. If the agent is not detected, the following error will be displayed in the logs (if logging is enabled):

WARN [main] (IntegrationsConfig.java:72) - New Relic agent not found. Continuing without it

Network proxy

If you need to use a network proxy, you can configure proxies by setting the proxyHost and proxyPort options in the SDK configuration (refer Configuration section for more information). The SDK reads those variables and uses them to perform the server request.

Advanced: WebLogic container

WebLogic and the Split Java SDK contain a reference to Google Guava. If you are currently deploying a web application that contains our Java SDK into WebLogic, instruct the container to load Guava from the app classpath and not from the container.

If you have an existing weblogic.xml file in your deployment, add: <package-name>com.google.common.*</package-name> under the <prefer-application-packages> tag. If you do not, create the file and place it under the directory WEB-INF.

Here is a sample of a weblogic.xml file that includes the previously mentioned Guava classpath loading instruction.

<?xml version="1.0" encoding="UTF-8"?>
<weblogic-web-app xmlns="http://xmlns.oracle.com/weblogic/weblogic-web-app">
  <context-root>/testing-java</context-root>
  <container-descriptor>
    <prefer-web-inf-classes>false</prefer-web-inf-classes>
    <prefer-application-packages>
      <package-name>com.google.common.*</package-name>
    </prefer-application-packages>

  </container-descriptor>
</weblogic-web-app>