The Hive HBase spec specifies that the key column mapping can either be
defined explicitly (using the :key syntax) or left out completely in
which case a mapping to the first table column is implied. This change
updates Impala to support implicit key mappings and also adds some
checks in our ALTER TABLE DDL to unsure we cannot get into this state by
dropping a column from an Hbase table (a similar restriction that Hive
puts in place)
Change-Id: I920d642261659ee3e881da2553ffe83300923af8
Reviewed-on: http://gerrit.ent.cloudera.com:8080/554
Reviewed-by: Lenni Kuff <lskuff@cloudera.com>
Tested-by: Lenni Kuff <lskuff@cloudera.com>
Our data generation doesn't appear to fully support comments in the
base table name section. This fixes the data generation, but we should
follow on by improving our comment support in the framework.
Change-Id: I68274bd98d5b0d54868d6c80b7137a59e7329229
Reviewed-on: http://gerrit.ent.cloudera.com:8080/465
Reviewed-by: Nong Li <nong@cloudera.com>
Tested-by: Lenni Kuff <lskuff@cloudera.com>
* Changed frontend analysis for HBase tables
* Changed Thrift messages to allow HBase as a sink type.
* JNI Wrapper around htable
* Create hbase-table-sink
* Create hbase-table-writer
* Static init lots of JNI related code for HBase.
* Cleaned up some cpplint issues.
* Changed junit analysis tests
* Create a new HBase test table.
* Added functional tests for HBase inserts.
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.
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
Moved this out of the data loading framework because it is kind of a special
case. I will consider how we can update the framework to address mixed format
tables.
