This change includes a number of improvements for the test data loading framework:
* Named sections for schema template definitions
* Removal of uneeded sections from schema template definitions (ex. ANALYZE TABLE)
* More granular data loading via table name filters
* Improved robustness in detecting failed data loads
* Table level constraints for specific file formats
* Re-written compute stats script
Add support for generating ANALYZE TABLE ... COMPUTE STATISTICS statements to the data loading
workflow. This allows for capturing simple table stats such as number of rows, number of
partitions, and table size in bytes. These are stored into a new mysql database with the same
name as the metastore except with a '_Stats' suffix. If using Derby a new database results are
stored in a new derby database.
Fixed a problem where we were not properly looking up the dataset associated
with the given workload if it had non-word characters in its name (a-z & _). Also cut down
on the execution time of the hive-benchmark workload under the "core" vector.
This change updates the run-benchmark script to enable it to target one or more
workloads. Now benchmarks can be run like:
./run-benchmark --workloads=hive-benchmark,tpch
We lookup the workload in the workloads directory, then read the associated
query .test files and start executing them.
To ensure the queries are not duplicated between benchmark and query tests, I
moved all existing queries (under fe/src/test/resources/* to the workloads
directory. You do NOT need to look through all the .test files, I've just moved
them. The one new file is the 'hive-benchmark.test' which contains the hive
benchmark queries.
Also added support for generating schema for different scale factors as well as
executing against these scale factors. For example, let's say we have a dataset
with a scale factor called "SF1". We would first generate the schema using:
./generate_schema_statements --workload=<workload> --scale_factor="SF3"
This will create tables with a unique names from the other scale factors.
Run the generated .sql file to load the data. Alternatively, the data can loaded
by running a new python script:
./bin/load-data.py -w <workload1>,<workload2> -e <exploration strategy> -s [scale factor]
For example: load-data.sh -w tpch -e core -s SF3
Then run against this:
./run-benchmark --workloads=<workload> --scale_factor=SF3
This changeset also includes a few other minor tweaks to some of the test
scripts.
Change-Id: Ife8a8d91567d75c9612be37bec96c1e7780f50d6
