Android SDK quickstart

Part 1: Create a sample app

1. Get a free account

You need an Optimizely account to follow this guide. If you do not have an account, you can register for a free account. If you already have an account navigate to your Flags-enabled project.

2. Get your SDK key

To find your SDK Key in your Optimizely project:

Go to Settings > Primary Environment.
Copy and save the SDK Key for your primary environment.

📘
Note
Each environment has its own SDK key.

3. Copy the sample code

To try out the SDK:

In Android Studio, create a new Project, Empty activity android project called optimizely-android-quickstart with com.example.optimizely_android_quickstart as Package Name.
Install the Optimizely Feature Experimentation Android SDK.
In build.gradle (Project), add MavenCentral to repositories.

repositories {
    mavenCentral()
        google()
        jcenter()

}

In build.gradle (Module), add Optimizely Android SDK dependency and sync gradle by clicking Sync Now.

dependencies {
    implementation 'com.optimizely.ab:android-sdk:+'
}

The Android SDK is distributed through MavenCentral. The full source code is available on GitHub.

Copy the following code sample into the MainActivity.java file for your app.
Replace <Your_SDK_Key> with the SDK key you found in a previous step.

package com.example.optimizely_android_quickstart;

import android.os.Bundle;
import android.util.Log;

import androidx.annotation.Nullable;
import androidx.appcompat.app.AppCompatActivity;

import com.optimizely.ab.OptimizelyUserContext;
import com.optimizely.ab.android.sdk.OptimizelyClient;
import com.optimizely.ab.android.sdk.OptimizelyManager;
import com.optimizely.ab.config.parser.JsonParseException;
import com.optimizely.ab.optimizelydecision.OptimizelyDecision;

import java.io.BufferedInputStream;
import java.io.IOException;
import java.net.URL;
import java.util.Random;
import java.util.Timer;
import java.util.TimerTask;

public class MainActivity extends AppCompatActivity {
    OptimizelyManager optimizelyManager;

    String SDK_KEY = "Your_SDK_Key";
    String LOG_TAG = "OPTIMIZELY_QUICK_START";

    private TimerTask task = new DatafilePoller();

    @Override
    protected void onDestroy() {
        super.onDestroy();
        task.cancel();
    }

    @Override
    protected void onCreate(@Nullable Bundle savedInstanceState) {
        super.onCreate(savedInstanceState);
        setContentView(R.layout.activity_main);

        // this Optimizely initialization is synchronous. for other methods see the Android SDK reference
        //Initializing Optimizely Manager
        optimizelyManager = OptimizelyManager.builder()
                .withSDKKey(SDK_KEY)
                .build(this);

        Timer timer = new Timer();
        long intevalPeriod = 1000;
        // schedules the task to be run in an interval
        timer.scheduleAtFixedRate(task, 0,
                intevalPeriod);
    }

    private void runQuickStart(String datafile) {
        // Initialize optimizelyClient
        OptimizelyClient optimizelyClient = optimizelyManager.initialize(this, datafile);
        if (optimizelyClient.isValid()) {
            /* --------------------------------
             * to get rapid demo results, generate random users. Each user always sees the same variation unless you reconfigure the flag rule.
             * --------------------------------
             */
            Random rnd = new Random();

            boolean hasOnFlags = false;

            for (int i = 0; i < 10; i++) {
                String userId = (rnd.nextInt(9999 - 1000) + 1000) + "";
          /* --------------------------------
             Create hardcoded user & bucket user into a flag variation
             --------------------------------
          */
                OptimizelyUserContext user = optimizelyClient.createUserContext(userId);
                // "product_sort" corresponds to a flag key in your Optimizely project
                OptimizelyDecision decision = user.decide("product_sort");
                // did decision fail with a critical error?
                if (decision.getVariationKey() == null) {
                    Log.e(LOG_TAG, "\n\ndecision error: " + decision.getReasons());
                }
                // get a dynamic configuration variable
                // "sort_method" corresponds to a variable key in your Optimizely project
                String sortMethod = null;
                try {
                    sortMethod = decision.getVariables().getValue("sort_method", String.class);
                } catch (JsonParseException e) {
                    e.printStackTrace();
                }

                if (decision.getEnabled()) {
                    // Keep count how many visitors had the flag enabled
                    hasOnFlags = true;
                }
                  /* --------------------------------
                   Mock what the users sees with print statements (in production, use flag variables to implement feature configuration)
                   --------------------------------
                */
                // always returns false until you enable a flag rule in your Optimizely project
                Log.d(LOG_TAG, "\n\nFlag " + (decision.getEnabled()? "on": "off") + ". User number " + user.getUserId() + " saw flag variation: " + decision.getVariationKey() + " and got products sorted by: " + sortMethod + " config variable as part of flag rule: " + decision.getRuleKey());
              
            }

            if (!hasOnFlags) {
                Log.d(LOG_TAG, "\n\nFlag was off for everyone. Some reasons could include:" +
                        "\n1. Your sample size of visitors was too small. Rerun, or increase the iterations in the FOR loop" +
                        "\n2. By default you have 2 keys for 2 project environments (dev/prod). Verify in Settings>Environments that you used the right key for the environment where your flag is toggled to ON." +
                        "\nCheck your key at  https://app.optimizely.com/v2/projects/" + optimizelyClient.getProjectConfig().getProjectId() + "settings/implementation");
            }
        } else {
            Log.d(LOG_TAG, "Optimizely client invalid. Verify in Settings>Environments that you used the primary environment's SDK key");
        }
    }

    /**
     * Downloads latest datafile from DATAFILE_URL. Then verify and call {@link MainActivity#runQuickStart(String)} (String)} if datafile got updated.
     * Fetch any datafile changes, which result from configuration updates you make to traffic percentage sliders, flag variable values, etc.
     */
    public class DatafilePoller extends TimerTask {
        static final String DATAFILE_URL = "https://cdn.optimizely.com/datafiles/%s.json";
        private String currentDatafile = "";

        @Override
        public void run() {
            try {
                BufferedInputStream in = new BufferedInputStream(new URL(String.format(DATAFILE_URL, SDK_KEY)).openStream());
                byte[] contents = new byte[1024];

                int bytesRead;
                String latestDatafile = "";
                while ((bytesRead = in.read(contents)) != -1) {
                    latestDatafile += new String(contents, 0, bytesRead);
                }
                if (!currentDatafile.equals(latestDatafile)) {
                    currentDatafile = latestDatafile;
                    runQuickStart(currentDatafile);
                }
            } catch (IOException e) {
                e.printStackTrace();
            }
        }
    }
}

📘
Note
Do not run your app yet, because you still need to set up the flag in the Optimizely app.

Part 2: Run your app

After completing Part 1, your app does nothing. You need to create a flag and a flag rule in the Optimizely app to enable the app. See logcat for the logs. Search OPTIMIZELY_QUICK_START in logs.

1. Create the feature flag

A feature flag lets you control the users that are exposed to a new feature code in your app. For this quickstart, imagine that you are rolling out a redesigned sorting feature for displaying products.

Create a flag in Optimizely named product_sort and give it a variable named sort_method:

Go to Flags > Create Flag.
Name the flag key product_sort and click Create Flag, which corresponds to the flag key in your sample app.
Go to Default Variables and click New (+).
Set the variable type to "String".
Name the variable sort_method, which corresponds to the variable key in your sample app.
Set the variable default value to alphabetical, which represents your old sorting method.

Click Save at the lower right corner to save the variable.
Go to Variations and click the default "on" variation. A variation is a wrapper for a collection of variable values.
Set the sort_method variable value to popular_first, which represents your new sorting method.

Click Save.

2. Create the flag delivery rule

Your sample app still does not do anything because you need to make and enable a flag rule in the app.

Make a targeted delivery rule for the "on" variation for the product_sort flag. A targeted delivery lets you gradually release a feature flag to users, but with the flexibility to roll it back if you encounter bugs.

Verify that you are in your primary environment (since you are using the primary environment SDK key):

verify the environment in which you make the rule

Click Add Rule and select Targeted Delivery.
Set the traffic slider to 50%. This delivers the flag to 50% of everyone who triggers the flag in this environment. You can roll out or roll back the product_sort flag to a percentage of traffic whenever you want.
From the Deliver drop-down, select On.
Click Save.

Enable the flag for your flag rule:

3. Run your sample app

In Android studio, click Run for the sample app you created earlier. Output appears similar to the following in the logcat:

Flag on. User number 6998 saw flag variation: on and got products sorted by: popular_first config variable as part of flag rule: targeted_delivery

Flag on. User number 1177 saw flag variation: on and got products sorted by: popular_first config variable as part of flag rule: targeted_delivery

Flag on. User number 9714 saw flag variation: on and got products sorted by: popular_first config variable as part of flag rule: targeted_delivery

Flag on. User number 4140 saw flag variation: on and got products sorted by: popular_first config variable as part of flag rule: targeted_delivery

Flag on. User number 4994 saw flag variation: on and got products sorted by: popular_first config variable as part of flag rule: targeted_delivery

Flag off. User number 8700 saw flag variation: off and got products sorted by: alphabetical config variable as part of flag rule: default-rollout-208-19963693913

Flag off. User number 9912 saw flag variation: off and got products sorted by: alphabetical config variable as part of flag rule: default-rollout-208-19963693913

Flag on. User number 6560 saw flag variation: on and got products sorted by: popular_first config variable as part of flag rule: targeted_delivery

Flag on. User number 9252 saw flag variation: on and got products sorted by: popular_first config variable as part of flag rule: targeted_delivery

Flag on. User number 6582 saw flag variation: on and got products sorted by: popular_first config variable as part of flag rule: targeted_delivery

📘
Note
You will not get exactly 50% of your user traffic in the "on" variation, since you are working with such small numbers of visitors. Also, the users who got an "off" flag did not make it into the 50% traffic you set, so they fell through to the default "Off" rule (default-rollout in the preceding print statements):

4. How it works

So far, you have:

Created a flag, flag variable, and a flag variation (wrapper for your variables) in the Optimizely app
Implemented a flag in your app with the Decide method

What is going on in your sample app?

How it works: decide to show a user a flag

The SDK’s Decide method determines whether to show or hide the feature flag for a specific user.

📘
Note
You can reuse this method for different flag rules -- whether for delivering more traffic, or running an experiment to show different sorting methods to just a portion of users.

After you learn which sorting method works best to increase sales, roll out the product sort flag to all traffic with the method set to the optimum value.

In your sample app:

OptimizelyUserContext user = optimizelyClient.createUserContext(userId);
// "product_sort" corresponds to the flag key you create in the Optimizely app
OptimizelyDecision decision = user.decide("product_sort");

📘
Note
Optionally include attributes when you create your user (not shown in your sample app ), so that you can target specific audiences. For example:
Map attributes = new HashMap<String, Object>();
attributes.put("logged_in", true);
OptimizelyUserContext user = optimizelyClient.createUserContext("user123", attributes);

How it works: configure flag variations

You can dynamically configure a flag variation using flag variables. In your sample app:

// always returns false until you enable a flag rule in the Optimizely app
if (decision.getEnabled()) {
  // "sort_method" corresponds to variable key you define in Optimizely app
  String sortMethod = decision.getVariables().getValue("sort_method", String.class);
    Log.d(LOG_TAG, "sort_method: "+ sortMethod.toString());
}

For your product_sort flag, you can configure variations with different sort_method values, sorting by popular products, relevant products, promoted products, and so on. You can set different values for the sort method at any time in the Optimizely app.

Part 3: Run an A/B test

This tutorial just guided you through a targeted delivery because it is the simplest flag rule. However, you often want to A/B test how users react to feature flag variations before you roll out a feature flag delivery.

The following table shows the difference between flag deliveries and A/B tests:

Targeted delivery rule	A/B test rule
You can roll out your flag to a percentage of your general user base (or to specific audiences), or roll back if you encounter bugs.	Experiment by A/B testing a flag before you invest in delivering, so you know what to build. Track how users behave in flag variations, then interpret your experiment results using the Optimizely Stats Engine.

Now A/B test the "on" variation of your product_sort flag.

1. Add event tracking

You need to add a Track Event method to your sample app, so you can mock up user events and then see metrics.

Delete your old sample code, and paste in the following code.
Remember to replace your SDK key again.
Do not run your app yet because you still need to set up the A/B test in the Optimizely app.

📘
Note
You also need to add implementation group: 'com.google.code.gson', name: 'gson', version: '2.8.6' dependency in gradle project as it is getting used in the given code.

package com.example.optimizely_android_quickstart;

import android.os.Bundle;
import android.util.Log;

import androidx.annotation.Nullable;
import androidx.appcompat.app.AppCompatActivity;

import com.optimizely.ab.OptimizelyUserContext;
import com.optimizely.ab.android.sdk.OptimizelyClient;
import com.optimizely.ab.android.sdk.OptimizelyManager;
import com.optimizely.ab.optimizelydecision.OptimizelyDecision;

import java.io.BufferedInputStream;
import java.io.IOException;
import java.net.URL;
import java.util.Random;
import java.util.Timer;
import java.util.TimerTask;

public class MainActivity extends AppCompatActivity {
    OptimizelyManager optimizelyManager;

    String SDK_KEY = "YOUR_SDK_KEY";
    String LOG_TAG = "OPTIMIZELY_QUICK_START";

    private TimerTask task = new DatafilePoller();

    @Override
    protected void onDestroy() {
        super.onDestroy();
        task.cancel();
    }

    @Override
    protected void onCreate(@Nullable Bundle savedInstanceState) {
        super.onCreate(savedInstanceState);
        setContentView(R.layout.activity_main);

        // this Optimizely initialization is synchronous. for other methods see the Java SDK reference
        //Initializing Optimizely Manager
        optimizelyManager = OptimizelyManager.builder()
                .withSDKKey(SDK_KEY)
                .build(this);

        Timer timer = new Timer();
        long intevalPeriod = 1000;
        // schedules the task to be run in an interval
        timer.scheduleAtFixedRate(task, 0,
                intevalPeriod);
    }

    private void runQuickStart(String datafile) {
        OptimizelyClient optimizelyClient = optimizelyManager.initialize(this, datafile);
        if (!optimizelyClient.isValid()) {
            Log.d(LOG_TAG,"Optimizely client invalid. Verify in Settings>Environments that you used the primary environment's SDK key");
            return;
        }  
        /* --------------------------------
         OPTIONAL: Add a notification listener so you can integrate with third-party analytics platforms
         --------------------------------
        */
      /*
        int notificationId = optimizelyClient.getNotificationCenter().addNotificationHandler(DecisionNotification.class, decisionNotification -> {
            if ("flag".equals(decisionNotification.getType())) {
                Gson gsonObj = new Gson();
                String serializedJsonInfo = gsonObj.toJson(decisionNotification.getDecisionInfo());
                Log.d(LOG_TAG,"Feature flag access related information: " + serializedJsonInfo);
                // Send data to analytics provider here
            }
        });
            */
        /* --------------------------------
         * to get rapid demo experiment results, generate random users. Each user is deterministically hashed into a variation.
         * --------------------------------
         */
        Random rnd = new Random();
        boolean hasOnFlags = false;
        for (int i = 0; i < 5; i++) {
            String userId = (rnd.nextInt(9999 - 1000) + 1000) + "";
        /* --------------------------------
           Bucket user into a flag variation and mock experiment results
           --------------------------------
        */
            OptimizelyUserContext user = optimizelyClient.createUserContext(userId);
            OptimizelyDecision decision = user.decide("product_sort");
            // did decision fail with a critical error?
            if (decision.getVariationKey() != null && !decision.getVariationKey().isEmpty()) {
                Log.d(LOG_TAG,"decision error: " + decision.getReasons());
            }
            Object sortMethod = decision.getVariables().toMap().get("sort_method");
            if (decision.getEnabled()) {
                hasOnFlags = true;
            }
            Log.d(LOG_TAG, "\n\nFlag " + (decision.getEnabled()? "on": "off") + ". User number " + user.getUserId() + " saw flag variation: " + decision.getVariationKey() + " and got products sorted by: " + sortMethod + " config variable as part of flag rule: " + decision.getRuleKey());
            MockPurchase(user);
        }
        if (!hasOnFlags) {
            Log.d(LOG_TAG,"\n\nFlag was off for everyone. Some reasons could include:" +
                    "\n1. Your sample size of visitors was too small. Rerun, or increase the iterations in the FOR loop" +
                    "\n2. By default you have 2 keys for 2 project environments (dev/prod). Verify in Settings>Environments that you used the right key for the environment where your flag is toggled to ON." +
                    "\n\nCheck your key at  https://app.optimizely.com/v2/projects/" + optimizelyClient.getProjectConfig().getProjectId() + "settings/implementation");
        } else {
                    Log.d(LOG_TAG,"\n\nDone with your mocked A/B test.");
                Log.d(LOG_TAG,"Check out your report at  https://app.optimizely.com/v2/projects/" + optimizelyClient.getProjectConfig().getProjectId() + "/reports");
                Log.d(LOG_TAG,"Be sure to select the environment that corresponds to your SDK key");
        }
    }

    // mock tracking a user event so you can see some experiment reports
    void MockPurchase(OptimizelyUserContext user) {
        Log.d(LOG_TAG,"Pretend that user made a purchase? y/n ");
        Random rnd = new Random();
        int yesOrNo = rnd.nextInt(2);
        // Assigning random yes and no
        String answer = yesOrNo == 1? "y" : "n";
        Log.d(LOG_TAG, answer);
        if (answer.toLowerCase().equals("y")) {
            // track a user event you defined in the Optimizely app
            user.trackEvent("purchase");
            Log.d(LOG_TAG,"Optimizely recorded a purchase in experiment results for user " + user.getUserId());
        } else {
            Log.d(LOG_TAG,"Optimizely didn't record a purchase in experiment results  for user " + user.getUserId());
        }
    }
  
    /**
     * Downloads latest datafile from DATAFILE_URL. Then verify and call {@link MainActivity#runQuickStart(String)} (String)} if datafile got updated.
     * Fetch any datafile changes, which result from configuration updates you make to traffic percentage sliders, flag variable values, etc.
     */
    public class DatafilePoller extends TimerTask {
        static final String DATAFILE_URL = "https://cdn.optimizely.com/datafiles/%s.json";
        private String currentDatafile = "";

        @Override
        public void run() {
            try {
                BufferedInputStream in = new BufferedInputStream(new URL(String.format(DATAFILE_URL, SDK_KEY)).openStream());
                byte[] contents = new byte[1024];

                int bytesRead;
                String latestDatafile = "";
                while ((bytesRead = in.read(contents)) != -1) {
                    latestDatafile += new String(contents, 0, bytesRead);
                }
                if (!currentDatafile.equals(latestDatafile)) {
                    currentDatafile = latestDatafile;
                    runQuickStart(currentDatafile);
                }
            } catch (IOException e) {
                e.printStackTrace();
            }
        }
    }
}

2. Pause other rules in free accounts

If you have a free account, you need to pause the Targeted Delivery you created earlier in this quickstart before you save your A/B test:

Select the specific Flag that contains the Targeted Delivery you plan on pausing.

Select the Environment and the Targeted Delivery you want to pause.
Click the Disable Rule button in the upper right-hand corner.

3. Create the A/B test

To create and launch an experiment in your Optimizely project:

Go to Rules for your flag.
Click Add Rule > A/B Test.

4. Add an event

In an experiment, you will track users' relevant actions to measure how users react to your feature flag variations. You need to define the actions you want to track:

Click on the Metrics field.
Click Create new event.

For the Event Key, enter purchase and click Create Event. (You want to know whether the new sorting flag helps customers figure out what to buy, so track whether the user makes a purchase after they were shown the products in a new order.)

Leave the defaults (measure Increase in unique conversions).

Click Add Metric.
Leave the default "Off" variation as a control. Select the "On" variation you configured in a previous step:

Click Save.

📘
Note
You are not limited to two variations; you can also create A/B tests with multiple variations. For example:

Double check your flag to ensure that it is on so your experiment can run:

5. Run the A/B test

Click Run in Android Studio and answer the command-line prompts. Output appears similar to the following:

Flag on. User number 1496 saw flag variation: on and got products sorted by: popular_first config variable as part of flag rule: experiment_1
Pretend that user made a purchase? y/n
n
Optimizely didn't record a purchase in experiment results for user 1496


Flag off. User number 1194 saw flag variation: off and got products sorted by: alphabetical config variable as part of flag rule: experiment_1
Pretend that user made a purchase? y/n
y
Optimizely recorded a purchase in experiment results for user 1194


Flag off. User number 5815 saw flag variation: off and got products sorted by: alphabetical config variable as part of flag rule: experiment_1
Pretend that user made a purchase? y/n
y
Optimizely recorded a purchase in experiment results for user 5815


Flag on. User number 1248 saw flag variation: on and got products sorted by: popular_first config variable as part of flag rule: experiment_1
Pretend that user made a purchase? y/n
y
Optimizely recorded a purchase in experiment results for user 1248


Flag off. User number 9580 saw flag variation: off and got products sorted by: alphabetical config variable as part of flag rule: experiment_1
Pretend that user made a purchase? y/n
n
Optimizely didn't record a purchase in experiment results for user 9580


Done with your mocked A/B test.
Check out your report at  https://app.optimizely.com/v2/projects/19957465438/reports
Be sure to select the environment that corresponds to your SDK key

6. See your A/B test results

Go to the Reports tab to see your experiment results.

Your results should look similar:

📘
Notes

You might not see the exact user traffic percentages you configured for your flag variations until you have larger numbers of users.

You might not see your user traffic immediately. Refresh the browser to refresh traffic.

Your experiment results will not tell you a winning variation until you have a large number of visitors, (on the order of 100,000).

7. How it works

For an A/B test, you need a way to tell Optimizely when a user made a purchase in your app and map this event in your app code to the specific event you created in Optimizely. Luckily the SDK has a method for that. Use the Track Event method and pass in the key for the event you created (purchase). In your sample app:

// Track how users behave when they see a flag variation
// e.g., after your app processed a purchase, let Optimizely know what happened:
user.trackEvent("purchased");

📘
Note
Optionally add tags to your event to enrich it (not shown in your sample app). You can also use reserve tag keys like revenue to track quantitative results. For example:
Map tags = new HashMap<String, Object>();
tags.put("category", "shoes");
tags.put("revenue", 6432);

user.trackEvent("purchase", tags);

Event tracking is currently supported only for experiment rules, not delivery rules. Tracking for deliveries will be supported in a future release.

Either way, you should include event tracking when you implement a flag because it can help you integrate with a third-party analytics platform, and it gives you flexibility when you create A/B tests.

Conclusion

Congratulations! You successfully set up and launched your first Optimizely Feature Experimentation experiment. While this example focused on optimizing sales, Optimizely’s experimentation platform can support an open-ended set of experimentation use cases.

See our complete Android SDK documentation to learn more ways to optimize your software using experimentation.

Android SDK quickstart

Part 1: Create a sample app

1. Get a free account

2. Get your SDK key

📘
Note

3. Copy the sample code

📘
Note

Part 2: Run your app

1. Create the feature flag

2. Create the flag delivery rule

3. Run your sample app

📘
Note

4. How it works

How it works: decide to show a user a flag

📘
Note

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Note

How it works: configure flag variations

Part 3: Run an A/B test

1. Add event tracking

📘
Note

2. Pause other rules in free accounts

3. Create the A/B test

4. Add an event

📘
Note

5. Run the A/B test

6. See your A/B test results

📘
Notes

7. How it works

📘
Note

Conclusion

Part 1: Create a sample app

1. Get a free account

2. Get your SDK key

📘Note

3. Copy the sample code

📘Note

Part 2: Run your app

1. Create the feature flag

2. Create the flag delivery rule

3. Run your sample app

📘Note

4. How it works

How it works: decide to show a user a flag

📘Note

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How it works: configure flag variations

Part 3: Run an A/B test

1. Add event tracking

📘Note

2. Pause other rules in free accounts

3. Create the A/B test

4. Add an event

📘Note

5. Run the A/B test

6. See your A/B test results

📘Notes

7. How it works

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Conclusion

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