The bug was that the number of materialized agg-tuple slots did not correspond to the number
of materialized agg functions, due to binding predicates against an AggNode causing slot
materialization after SelectStmt.materializeRequiredSlots().
This patch fixes the issue by taking binding predicates (bound to a slot in an agg tuple)
into consideration in SelectStmt.materializeRequiredSlots().
I added a new sanity check in AggregationNode.toThrift() surfaced another issue with slot
materialization that is also fixed in this patch. The ordering exprs must be marked before
the agg exprs in SelectStmt.materializeRequiredSlots() because the odering exprs may contain
agg exprs that are only referenced inside the ORDER BY clause.
Change-Id: I1bdc0466f583907bed625ce6608938e59faee83f
Reviewed-on: http://gerrit.ent.cloudera.com:8080/1639
Reviewed-by: Marcel Kornacker <marcel@cloudera.com>
Tested-by: jenkins
Reviewed-on: http://gerrit.ent.cloudera.com:8080/1818
Reviewed-by: Alex Behm <alex.behm@cloudera.com>
This patch cleans up analysis and execution of scalar and aggregate functions
so that there is no difference between how builtins and user functions are
handled. The only difference is that the catalog is populated with the builtins
all the time.
The BE always gets a TFunction object and just executes it (builtins will have
an empty hdfs file location).
This removes the opcode registry and all of the functionality is subsumed by
the catalog, most of which was already duplicated there anyway.
This also introduces the concept of a system database; databases that the
user cannot modify and is populated automatically on startup.
Change-Id: Iaa3f84dad0a1a57691f5c7d8df7305faf01d70ed
Reviewed-on: http://gerrit.ent.cloudera.com:8080/1386
Reviewed-by: Nong Li <nong@cloudera.com>
Tested-by: jenkins
Reviewed-on: http://gerrit.ent.cloudera.com:8080/1577
There are now 4 explain levels summarized as follows:
- Level 0: MINIMAL
Non-fragmented parallel plan only showing plan nodes with minimal attributes
- Level 1: STANDARD
Non-fragmented parallel plan with some details in plan nodes
- Level 2: EXTENDED
Non-fragmented parallel plan with full details in plan nodes including
the table/column stats, row size, #hosts, cardinality,
and estimated per-host memory requirement
- Level 3: VERBOSE
Fragmented parallel plan with full details (like level 2)
This patch also includes several bugfixes related to plan costing and/or
testing of explain plans.
Change-Id: I622310f01d1b3d53ea1031adaf3b3ffdd94eba30
Reviewed-on: http://gerrit.ent.cloudera.com:8080/1211
Reviewed-by: Alex Behm <alex.behm@cloudera.com>
Tested-by: jenkins
Introduces STRAIGHT_JOIN keyword to prevent join order optimization.
Structural changes to the planning framework:
- slot materialization: the decision whether to materialize a slot now happens *prior* to
plan generation. This is needed in order to be able to generate accurate cost estimates
at plan generation time. see QueryStmt.materializeRequiredSlots()
- added PlanNode.init(), which initializes the entire state of a PlanNode; this subsumes
finalize()
* computeMemLayout() now happens per-tuple in the corresponding ScanNode's init()
* init() calls computeStats() by default; also marks slots as materialized and calls
TupleDescriptor.computeMemLayout()
- added PlanNode.tblRefIds_
- restructured UnionStmt and union plan generation to fit pred propagation model:
all tuples are created (and equiv predicates registered) prior to plan generation
- added Expr.isAuxExpr
Change-Id: I475c1645bfca9e84ae6e5f529e7781d9532e5c9a
Reviewed-on: http://gerrit.ent.cloudera.com:8080/955
Reviewed-by: Alex Behm <alex.behm@cloudera.com>
Reviewed-by: Lenni Kuff <lskuff@cloudera.com>
Tested-by: jenkins
This patch redoes how the aggregation node is implemented. The functionality is
now split between aggregation-node, agg-expr and aggregate-functions. This is a working
progress (there's still a lot of debug stuff I added that needs to be cleaned up) but
it does pass the tests.
Aggregation-node is now very simple and now only deals with the grouping part.
Aggregate-expr serves as the glue between the agg node and the aggregate functions.
The aggregation functions are implemented with the UDA interface. I've reimplemented
our existing aggregate functions with this setup. For true UDAs, the binaries would be
loaded in aggregate-expr.
This also includes some preliminary changes in the FE. We now need to annotate each
AggNode as executing the update vs. merge phase (root aggs execute update, others
execute merge) and if it needs a finalize step (only the root does). This is more
general than our builtins which are too simple to need this structure.
There is a big TODO here to allow the intermediate types between agg nodes to change.
For example, in distinct estimate, the input type is the column type and the output type
is a bigint. We'd like the intermediate type to be CHAR(256). This is different since
currently, the intermediate type and output type have always been the same. We've hacked
around this by having both the intermediate and output type be TYPE_STRING. I've left
this for another patch (changing the BE to support this is trivial).
For aggregates that result in strings, we used to store some additional stuff past the
end of the tuple. The layout was:
<tuple> <length of 1st string buffer>,<length of 2nd string buffer>, etc
The rationale for this is that we want to reuse the buffer for min/max and grow the buffer
more quickly for group_concat. This breaks down the abstraction between agg-expr and
agg-node and is not something UDAs can use in general. Rather than try to hack around
this, I think the proper solution is to the intermediate type not be StringValue and
to contain the buffer length itself.
This patch also resurrects the distinct estimate code. The distinct estimate functions
exercise all of the code paths.
Change-Id: Ic152a2cd03bc1713967673681e1e6204dcd80346
Reviewed-on: http://gerrit.ent.cloudera.com:8080/564
Reviewed-by: Nong Li <nong@cloudera.com>
Tested-by: Nong Li <nong@cloudera.com>
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
