This commit fixes issue CDH-18969 where Impala returns wrong results
when querying an HBase table. This issue is triggered when a column family
sorts lexicographically before ":key", which is the column family of the
row key, thereby causing the wrong column to be used as a row key by the
backend.
The following changes are included:
1. Modified the load function in HBaseTable.java to make sure the
catalog object of an HBase table always stores the row key column first.
Change-Id: Icd7ebc973d81672c04d5c7c8bbabd813338d5eac
Reviewed-on: http://gerrit.ent.cloudera.com:8080/2513
Reviewed-by: Dimitris Tsirogiannis <dtsirogiannis@cloudera.com>
Tested-by: jenkins
Reviewed-on: http://gerrit.ent.cloudera.com:8080/2602
Allows reading decimal columns with or without codegen. Includes tests
based on a data file posted on HIVE-5823.
Change-Id: Ie541c6b98bd24543691850cb45a434af60b5a5a6
(cherry picked from commit 6983dcefdf70cce14724e17d03bc061ffb8f671c)
Reviewed-on: http://gerrit.ent.cloudera.com:8080/2596
Reviewed-by: Skye Wanderman-Milne <skye@cloudera.com>
Tested-by: jenkins
For wide Avro tables, ReadZLong() would get inlined many times into a
single function body, causing LLVM to crash. Not inlining doesn't seem
to have a performance impact on narrow tables, and helps with wide
tables.
This change also adds tests over wide (i.e. many-column) tables. The
test tables are produced by specifying shell commands to generate test
tables in functional_schema_template.sql, which are executed in
generate-schema-statements.py. In the SQL templates, sections starting
with a ` are treated as shell commands. The output of the shell
command is then used as the section text. This is only a starting
point; it isn't currently implemented for all sections, and may have
to be tweaked if we use this mechanism for all tables.
Change-Id: Ife0d857d19b21534167a34c8bc06bc70bef34910
Reviewed-on: http://gerrit.ent.cloudera.com:8080/2206
Reviewed-by: Skye Wanderman-Milne <skye@cloudera.com>
Tested-by: Skye Wanderman-Milne <skye@cloudera.com>
(cherry picked from commit 1c5951e3cce25a048208ab9bb3a3aed95e41cf67)
Reviewed-on: http://gerrit.ent.cloudera.com:8080/2353
Tested-by: jenkins
This change updates our DDL syntax support to allow for using 'STORED AS PARQUET'
as well as 'STORED AS PARQUETFILE'. Moving forward we should prefer the new syntax,
but continue to support the old. I made the same change for 'AVROFILE', but since
we have not yet documented the 'AVROFILE' syntax I left out support for the old syntax.
Change-Id: I10c73a71a94ee488c9ae205485777b58ab8957c9
Reviewed-on: http://gerrit.ent.cloudera.com:8080/1053
Reviewed-by: Marcel Kornacker <marcel@cloudera.com>
Tested-by: jenkins
Currently, we execute all the queries involved in data loading serially. This change
creates a separate .sql file for each file format, compression codec and compression
scheme combination, and executes all the files in parallel. Additionally, we now store all the
.sql files (independent of workload) in $IMPALA_HOME/data_load_files/<dataset_name>. Note
that only data loaded through Impala is parallelized, data loaded through hive and hbase
remains serial.
On our build machines, the time taken to load all the data from snapshot was on the order
of 15 minutes.
Change-Id: If8a862c43f0e75b506ca05d83eacdc05621cbbf8
Reviewed-on: http://gerrit.ent.cloudera.com:8080/804
Reviewed-by: Ishaan Joshi <ishaan@cloudera.com>
Tested-by: Ishaan Joshi <ishaan@cloudera.com>
Tested-by: jenkins
This change adds Impala DDL support for creation of AVRO tables.
Additionally, it add Impala support for CREATE and ALTER SERDEPROPERTIES
which are used when creating Avro backed tables. This syntax is not
exactly the same as the Hive support since it introduces a new
fileformat (AVROFILE) that implies the needed Serialization library,
input format, and output format.
Change-Id: I5047e419198a89599e9d014fdedfee1a20437a7d
Reviewed-on: http://gerrit.ent.cloudera.com:8080/464
Reviewed-by: Lenni Kuff <lskuff@cloudera.com>
Tested-by: Lenni Kuff <lskuff@cloudera.com>
This is the first set of changes required to start getting our functional test
infrastructure moved from JUnit to Python. After investigating a number of
option, I decided to go with a python test executor named py.test
(http://pytest.org/). It is very flexible, open source (MIT licensed), and will
enable us to do some cool things like parallel test execution.
As part of this change, we now use our "test vectors" for query test execution.
This will be very nice because it means if load the "core" dataset you know you
will be able to run the "core" query tests (specified by --exploration_strategy
when running the tests).
You will see that now each combination of table format + query exec options is
treated like an individual test case. this will make it much easier to debug
exactly where something failed.
These new tests can be run using the script at tests/run-tests.sh
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
