TeraSort benchmark for Spark

This is an example Spark program for running TeraSort benchmarks. It is based on work from Reynold Xin's branch, but it is not the same TeraSort program that currently holds the record. That program is here.

Building

mvn install

The default is to link against Spark 2.4.4 jars (released September 2019). If you plan to run using an older version of Spark (e.g. 1.6) you will have to try -Dspark.version=1.6. If possible, it's probably a better idea to just update to a more recent version or Spark.

Running

cd to your your Spark install.

Generate data

./bin/spark-submit --class com.github.ehiggs.spark.terasort.TeraGen 
path/to/spark-terasort/target/spark-terasort-1.2-SNAPSHOT-jar-with-dependencies.jar 
1g file://$HOME/data/terasort_in 

Sort the data

./bin/spark-submit --class com.github.ehiggs.spark.terasort.TeraSort
path/to/spark-terasort/target/spark-terasort-1.2-SNAPSHOT-jar-with-dependencies.jar 
file://$HOME/data/terasort_in file://$HOME/data/terasort_out

Validate the data

./bin/spark-submit --class com.github.ehiggs.spark.terasort.TeraValidate
path/to/spark-terasort/target/spark-terasort-1.2-SNAPSHOT-jar-with-dependencies.jar 
file://$HOME/data/terasort_out file://$HOME/data/terasort_validate

Known issues

Performance

This terasort doesn't use the partitioning scheme that Hadoop's Terasort uses. This results in not very good performance. I could copy the Partitioning code from the Hadoop tree verbatim but I thought it would be more appropriate to rewrite more of it in Scala.

I haven't pulled the DaytonaPartitioner from the record holding sort yet because it's pretty intertwined into the rest of the code and AFAIK it's not really idiomatic Spark.

Functionality on native file systems

TeraValidate can read the file parts in the wrong order on native file systems (e.g. if you run Spark on your laptop, on Lustre, Panasas, etc). HDFS apparently always returns the files in alphanumeric order so most Hadoop users aren't affected. I thought I fixed this in the TeraInputFormat, but I was able to reproduce it since migrating the code from my Spark terasort branch.

Contributing

PRs are very welcome!