TPC-H queries implemented in Spark using the DataFrames API. Tested under Spark 2.4.0

Savvas Savvides

[email protected]

Under the dbgen directory do:

make

This should generate an executable called dbgen

./dbgen -h

gives you the various options for generating the tables. The simplest case is running:

./dbgen

which generates tables with extension .tbl with scale 1 (default) for a total of rougly 1GB size across all tables. For different size tables you can use the -s option:

./dbgen -s 10

will generate roughly 10GB of input data.

You can then either upload your data to hdfs or read them locally.

First compile using:

sbt package

Make sure you set the INPUT_DIR and OUTPUT_DIR in TpchQuery class before compiling to point to the location the of the input data and where the output should be saved.

You can then run a query using:

spark-submit --class "main.scala.TpchQuery" --master MASTER target/scala-2.11/spark-tpc-h-queries_2.11-1.0.jar ##

where ## is the number of the query to run e.g 1, 2, ..., 22 and MASTER specifies the spark-mode e.g local, yarn, standalone etc...

1. Data generator (http://www.tpc.org/tpch/)

2. TPC-H for Hive (https://issues.apache.org/jira/browse/hive-600)

3. TPC-H for PIG (https://github.com/ssavvides/tpch-pig)