The following changes are included in this commit:
1. Modified the alltypesagg table to include an additional partition key
that has nulls.
2. Added a number of tests in hdfs.test that exercise the partition
pruning logic (see IMPALA-887).
3. Modified all the tests that are affected by the change in alltypesagg.
Change-Id: I1a769375aaa71273341522eb94490ba5e4c6f00d
Reviewed-on: http://gerrit.ent.cloudera.com:8080/2874
Reviewed-by: Dimitris Tsirogiannis <dtsirogiannis@cloudera.com>
Tested-by: jenkins
Reviewed-on: http://gerrit.ent.cloudera.com:8080/3236
count(x) with no distinct and no group-by expressions returns NULL on empty input
if other distinct aggs (e.g. COUNT(distinct x) are present.
This happens because the COUNT is transformed to SUM(COUNT()),
with the inner COUNT being evaluated WITH a group-by expression (e.g. x).
SUM over empty input returns NULL, but COUNT should return 0.
This patch fixes this by replacing COUNT with zeroifnull(COUNT) before AggregateInfo
is generated if there are distinct aggs and no group-bys. The logic in AggregateInfo
itself has not been modified.
Change-Id: I902e3fdd95767135b2f3fe423e8802ef57366af1
Reviewed-on: http://gerrit.ent.cloudera.com:8080/1921
Reviewed-by: Srinath Shankar <sshankar@cloudera.com>
Tested-by: jenkins
Having predicates need to be transferred to the 2nd phase merge agg
for distinct + non-distinct aggregates without group by.
For distinct + non-distinct aggregates with group by, it is correct
to evaluate the predicates at the 2nd phase (non-merge) agg.
Change-Id: I71d73c4ef92becbb81e142bc0cb5f54e790b1fb5
Reviewed-on: http://gerrit.ent.cloudera.com:8080/1743
Reviewed-by: Alex Behm <alex.behm@cloudera.com>
Tested-by: jenkins
Reviewed-on: http://gerrit.ent.cloudera.com:8080/1817
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
- created new class PlanFragment, which encapsulates everything having to do with a single
plan fragment, including its partition, output exprs, destination node, etc.
- created new class DataPartition
- explicit classes for fragment and plan node ids, to avoid getting them mixed up, which is easy to do with ints
- Adding IdGenerator class.
- moved PlanNode.ExplainPlanLevel to Types.thrift, so it can also be used for
PlanFragment.getExplainString()
- Changed planner interface to return scan ranges with a complete list of server locations,
instead of making a server assignment.
Also included: cleaned up AggregateInfo:
- the 2nd phase of a DISTINCT aggregation is now captured separately from a merge aggregation.
- moved analysis functionality into AggregateInfo
Removing broken test cases from workload functional-planner (they're being handled correctly in functional-newplanner).
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
