itroulli/DataflowSME

Tutorial for Cloud Dataflow

Tutorial for Cloud Dataflow

This is a collection of tutorial-style Dataflow exercises based on the Dataflow gaming example and inspired by the Beam tutorial.

In the gaming scenario, many users play a fictional game, as members of different teams, over the course of a day, and their events are logged for processing.

The exercises either read the batch data from CSV files on GCS or the streaming data from a PubSub topic (generated by the included Injector program). All exercises write their output to BigQuery.

NOTE: Python examples (WIP) have been added, for additional instructions for running these examples, please refer to this document. Streaming support for Python is currently in Beta for Dataflow, and there is a feature gap between Java and Python, see limitations.

Pull requests are encouraged!

Set up your environment

Tools

1. Install JDK version 8 or newer

2. Install Maven (for windows, mac, linux)

3. Install Google Cloud SDK

4. Install an IDE such as Eclipse or IntelliJ (optional)

5. To test your installation, open a terminal window and type:

   $ java -version
   $ mvn --version
   $ gcloud --version

Set up a project

1. Go to https://cloud.google.com/console.
2. Enable billing and create a project. For this project:
   1. Enable Google Dataflow API and BigQuery API.
   2. Create a GCS bucket and a folder inside the bucket, to use as a staging location.
   3. Create a BigQuery dataset to store the results.

Prepare the environment

1. Authenticate to Google Cloud using the gcloud command and set the default credentials and default project. You will need to replace YOUR-PROJECT-ID with the id of the project you created before:

   $ gcloud auth login
   $ gcloud auth application-default login
   $ gcloud config set project YOUR-PROJECT-ID

2. Get the project name with Gcloud and set it as an env variable:

   $ export PROJECT=`gcloud config get-value project`

3. Set other environment variables

   $ export STAGING_FOLDER=gs://<path of the bucket and folder that you created before>
   $ export BIGQUERY_DATASET=<name of the dataset that you created before>
   $ export USER=`whoami`

Download the code

• Clone the github repository

  $ git clone https://github.com/malo-denielou/DataflowSME.git
  $ cd DataflowSME

• Alternatively, download it from here, and then unzip it:

  $ unzip DataflowSME-master.zip
  $ cd DataflowSME-master

Exercise 0 (prework)

Goal: Use the provided Dataflow pipeline to import the input events from a file in GCS to BigQuery and run simple queries on the result.

Procedure:

1. Compile and run the pipeline:

   $ mvn compile exec:java \
        -Dexec.mainClass=org.apache.beam.examples.complete.game.Exercise0 \
        -Dexec.args="--project=$PROJECT \
                     --tempLocation=$STAGING_FOLDER \
                     --runner=DataflowRunner \
                     --outputDataset=$BIGQUERY_DATASET \
                     --outputTableName=events \
                     --input=gs://dataflow-samples/game/gaming_data1.csv"

2. Navigate to the Dataflow UI (the link is printed in the terminal, look for "To access the Dataflow monitoring console, please navigate to ...").

3. Once the pipeline finishes (should take about 15-20 minutes), the Job Status on the UI changes to Succeeded.

4. After the pipeline finishes, check the value of ParseGameEvent/ParseErrors aggregator on the UI. Scroll down in the Summary tab to find it.

5. Check the number of distinct teams in the created BigQuery table.

   $ bq query --project_id=$PROJECT \
       'select count(distinct team) from $BIGQUERY_DATASET.events;'

Exercise 1

Goal: Use Dataflow to calculate per-user scores and write them to BigQuery.

Procedure

1. Modify src/main/java8/org/apache/beam/examples/complete/game/Exercise1.java

2. Run the pipeline (using Direct runner):

   $ mvn compile exec:java \
        -Dexec.mainClass=org.apache.beam.examples.complete.game.Exercise1 \
        -Dexec.args="--project=$PROJECT \
                     --tempLocation=$STAGING_FOLDER \
                     --runner=DirectRunner \
                     --outputDataset=$BIGQUERY_DATASET \
                     --outputTableName=user_scores \
                     --input=gs://dataflow-sme-tutorial/gaming_data0.csv"

3. Once the pipeline finishes successfully check the score for 'user0_AmberDingo':

   $ bq query --project_id=$PROJECT \
       "select total_score from $BIGQUERY_DATASET.user_scores \
        where user = \"user0_AmberDingo\";"

4. Rerun the pipeline on the Dataflow service, but remove the BigQuery table first:

   $ bq rm --project_id=$PROJECT $BIGQUERY_DATASET.user_scores

   and then execute the above mvn command with

       --runner=DataflowRunner

Exercise 2

Goal: Use Dataflow to calculate per-hour team scores and write them to BigQuery.

Procedure:

1. Modify src/main/java8/org/apache/beam/examples/complete/game/Exercise2.java

2. Run the pipeline:

   $ mvn compile exec:java \
        -Dexec.mainClass=org.apache.beam.examples.complete.game.Exercise2 \
        -Dexec.args="--project=$PROJECT \
                     --tempLocation=$STAGING_FOLDER \
                     --runner=DirectRunner \
                     --outputDataset=$BIGQUERY_DATASET \
                     --outputTableName=hourly_team_scores \
                     --input=gs://dataflow-sme-tutorial/gaming_data0.csv"

3. Once the pipeline finishes successfully check the score for team 'AmberDingo':

   $ bq query --project_id=$PROJECT \
       "select total_score from $BIGQUERY_DATASET.hourly_team_scores \
        where team = \"AmberDingo\" and window_start = \"2017-03-18 16:00:00 UTC\";"

Exercise 3

Goal: Convert the previous pipeline to run in a streaming mode.

First, you need to set up the injector to publish scores via PubSub.

1. Create and download a JSON key for Google Application Credentials. See instructions. Make sure that the key's account has at least the following role:

   • Pub/Sub --> Editor

2. Open a second terminal window. In this terminal run the commands listed in steps 2 and 3 of the section "Prepare the enviroment" to set the same variables as in the first terminal (you do not need to do step 1).

3. In the new terminal set the new credentials by running:

   $ export GOOGLE_APPLICATION_CREDENTIALS=/path/to/your/credentials-key.json

4. Create a new topic:

   $ gcloud pubsub topics create game_events_$USER

5. In the second terminal run the injector:

   $ mvn exec:java \
     -Dexec.mainClass="org.apache.beam.examples.complete.game.injector.Injector" \
     -Dexec.args="$PROJECT game_events_$USER none none"

Now complete the exercise so that it runs the pipeline from Exercise 2 in either batch or streaming mode.

Procedure:

1. Modify src/main/java8/org/apache/beam/examples/complete/game/Exercise3.java

2. Run the pipeline in batch mode (this is equivalent to Exercise 2):

   $ mvn compile exec:java \
        -Dexec.mainClass=org.apache.beam.examples.complete.game.Exercise3 \
        -Dexec.args="--project=$PROJECT \
                     --tempLocation=$STAGING_FOLDER \
                     --runner=DirectRunner \
                     --outputDataset=$BIGQUERY_DATASET \
                     --outputTableName=hourly_team_scores \
                     --input=gs://dataflow-sme-tutorial/gaming_data0.csv"

3. Run the pipeline in streaming mode on Dataflow service:

   $ mvn compile exec:java \
        -Dexec.mainClass=org.apache.beam.examples.complete.game.Exercise3 \
        -Dexec.args="--project=$PROJECT \
                     --tempLocation=$STAGING_FOLDER \
                     --runner=DataflowRunner \
                     --outputDataset=$BIGQUERY_DATASET \
                     --outputTableName=hourly_team_scores \
                     --topic=projects/$PROJECT/topics/game_events_$USER"

Exercise 4

Goal: Use Dataflow to create a streaming per-minute BigQuery LeaderBoard of team scores for events coming through PubSub. Some of the logged game events may be late-arriving, if users play on mobile devices and go transiently offline for a period.

Procedure:

1. Modify src/main/java8/org/apache/beam/examples/complete/game/Exercise4.java

   You will need to make two sets of changes:

   • Calculate the total score for every user and publish speculative results every thirty seconds (under CalculateUserScores).
   • Calculate the team scores for each minute that the pipeline runs (under CalculateTeamScores).

2. Make sure that the injector is running on the second terminal.

3. Run the pipeline:

   $ mvn compile exec:java \
         -Dexec.mainClass=org.apache.beam.examples.complete.game.Exercise4 \
         -Dexec.args="--project=$PROJECT \
         --tempLocation=$STAGING_FOLDER \
         --runner=DataflowRunner \
         --topic=projects/$PROJECT/topics/game_events_$USER \
         --outputDataset=$BIGQUERY_DATASET \
         --outputTableName=leaderboard"

4. Check the user and team scores, eg:

   $ bq query --project_id=$PROJECT \
        "SELECT * FROM [$BIGQUERY_DATASET.leaderboard_team] WHERE \
         timing=\"ON_TIME\" ORDER BY window_start"

Exercise 5

Goal: Complete the CalculateSpammyUsers PTransform to (a) determine users who have a score that is 2.5x the global average in each window, and then (b) use the results to compute non-spammy team scores.

Procedure:

1. Modify src/main/java8/org/apache/beam/examples/complete/game/Exercise5.java

2. Make sure that the injector is running on the second terminal.

3. Run the pipeline:

   $ mvn compile exec:java \
         -Dexec.mainClass=org.apache.beam.examples.complete.game.Exercise5 \
         -Dexec.args="--project=$PROJECT \
         --tempLocation=$STAGING_FOLDER \
         --runner=DataflowRunner \
         --topic=projects/$PROJECT/topics/game_events_$USER \
         --outputDataset=$BIGQUERY_DATASET \
         --outputTableName=despammed_scores"

4. Check the de-spammed user scores:

   $ bq query --project_id=$PROJECT \
        "SELECT * FROM [$BIGQUERY_DATASET.despammed_scores] \
         ORDER BY window_start LIMIT 10"

Exercise 6

Goal: Compute periodic global mean session durations for users.

Procedure:

1. Modify src/main/java8/org/apache/beam/examples/complete/game/Exercise6.java

2. Make sure that the injector is running on the second terminal.

3. Run the pipeline:

   $ mvn compile exec:java \
         -Dexec.mainClass=org.apache.beam.examples.complete.game.Exercise6 \
         -Dexec.args="--project=$PROJECT \
         --tempLocation=$STAGING_FOLDER \
         --runner=DataflowRunner \
         --topic=projects/$PROJECT/topics/game_events_$USER \
         --outputDataset=$BIGQUERY_DATASET \
         --outputTableName=sessions"

4. Let the pipeline run for over 5 minutes (the default window length)

5. Check the de-spammed user scores and mean session lengths:

   $ bq query --project_id=$PROJECT \
        "SELECT * FROM [$BIGQUERY_DATASET.sessions] \
        ORDER BY window_start LIMIT 10"

Exercise 7

Goal: Implement a pipeline that filters spammy users based on latency between 'game play' events and 'game score' events.

Procedure:

1. Modify src/main/java8/org/apache/beam/examples/complete/game/Exercise7.java

2. Stop the injector that you ran on the second terminal and run it again with additional output for play events.

   $ mvn exec:java \
     -Dexec.mainClass="org.apache.beam.examples.complete.game.injector.Injector" \
     -Dexec.args="$PROJECT game_events_$USER play_events_$USER none"

3. Run the pipeline:

   $ mvn compile exec:java \
         -Dexec.mainClass=org.apache.beam.examples.complete.game.Exercise7 \
         -Dexec.args="--project=$PROJECT \
         --tempLocation=$STAGING_FOLDER \
         --runner=DataflowRunner \
         --topic=projects/$PROJECT/topics/game_events_$USER \
         --playEventsTopic=projects/$PROJECT/topics/play_events_$USER \
         --outputDataset=$BIGQUERY_DATASET \
         --outputTableName=exercise7"

4. Check the list of spammy users:

   $ bq query --project_id=$PROJECT \
        "SELECT * FROM [$BIGQUERY_DATASET.exercise7_bad_users] \
        ORDER BY time"