This repository is a containerized version of the spark-native-yarn project - an improvement for Apache Spark by allowing pluggable execution contexts introduced with the SPARK-3561 JIRA. The new execution context is Apache Tez.
This Docker image depends on our previous Hadoop Docker image, available at the SequenceIQ GitHub page. The base Hadoop Docker image is also available as an official Docker image (sequenceiq/hadoop-docker).
###Pull the image from the Docker Repository
We suggest to always pull the container from the official Docker repository - as this is always maintained and supported by us.
docker pull sequenceiq/spark-native-yarn
Once you have pulled the container you are ready to run the image.
###Run the image
docker run -i -t -h sandbox sequenceiq/spark-native-yarn /etc/bootstrap.sh -bash
###Versions
Hadoop 2.5.1 and Apache Spark 1.1.0 and Apache Tez 0.5
You now have a fully configured Apache Spark, where the execution context is Apache Tez.
###Test the container
We have pushed sample data and tests from the code repository into the Docker container, thus you can start experimenting right away without writing one line of code.
####Calculate PI
Simplest example to test with is the PI calculation.
cd /usr/local/spark
./bin/spark-submit --class org.apache.spark.examples.SparkPi --master execution-context:org.apache.spark.tez.TezJobExecutionContext --conf update-classpath=true ./lib/spark-examples-1.1.0.2.1.5.0-702-hadoop2.4.0.2.1.5.0-695.jar
You should expect something like the following as the result:
Pi is roughly 3.14668
####Run a KMeans example
Run the KMeans example using the sample dataset.
./bin/spark-submit --class sample.KMeans --master execution-context:org.apache.spark.tez.TezJobExecutionContext --conf update-classpath=true ./lib/spark-native-yarn-samples-1.0.jar /sample-data/kmeans_data.txt
You should expect something like the following as the result:
Finished iteration (delta = 0.0)
Final centers:
DenseVector(0.15000000000000002, 0.15000000000000002, 0.15000000000000002)
DenseVector(9.2, 9.2, 9.2)
DenseVector(0.0, 0.0, 0.0)
DenseVector(9.05, 9.05, 9.05)
####Other examples (Join, Partition By, Source count, Word count)
Join
./bin/spark-submit --class sample.Join --master execution-context:org.apache.spark.tez.TezJobExecutionContext --conf update-classpath=true ./lib/spark-native-yarn-samples-1.0.jar /sample-data/join1.txt /sample-data/join2.txt
Partition By
./bin/spark-submit --class sample.PartitionBy --master execution-context:org.apache.spark.tez.TezJobExecutionContext --conf update-classpath=true ./lib/spark-native-yarn-samples-1.0.jar /sample-data/partitioning.txt
Source count
./bin/spark-submit --class sample.SourceCount --master execution-context:org.apache.spark.tez.TezJobExecutionContext --conf update-classpath=true ./lib/spark-native-yarn-samples-1.0.jar /sample-data/wordcount.txt
Word count
./bin/spark-submit --class sample.WordCount --master execution-context:org.apache.spark.tez.TezJobExecutionContext --conf update-classpath=true ./lib/spark-native-yarn-samples-1.0.jar /sample-data/wordcount.txt 1
Note that the last argument (1) is the number of reducers.
###Using the Spark Shell
The Spark shell works out of the box with the new Tez executor context, the only thing you will need to do is run:
./bin/spark-shell --master execution-context:org.apache.spark.tez.TezJobExecutionContext
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