Adds an aggregate function to compute equi-depth histograms. The UDA
creates a sample of the column values using weighted reservoir sampling
and computes the histogram from the sorted sample.
TODO:
* Extract highly frequent values into separate buckets (i.e. 'compressed
histogram').
* Expose separate finalize fn to produce samples and histogram data for stats
Change-Id: I314ce5fb8c73b935c4d61ea5bbd6816c59b3b41e
Reviewed-on: http://gerrit.ent.cloudera.com:8080/3552
Reviewed-by: Matthew Jacobs <mj@cloudera.com>
Tested-by: jenkins
(cherry picked from commit c5c475712f88244e15160befaf4e99d6e165a148)
Reviewed-on: http://gerrit.ent.cloudera.com:8080/3608
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
Float/Doubles are lossy so using those as the default literal type
is problematic.
Change-Id: I5a619dd931d576e2e6cd7774139e9bafb9452db9
Reviewed-on: http://gerrit.ent.cloudera.com:8080/2758
Reviewed-by: Nong Li <nong@cloudera.com>
Tested-by: jenkins
The previous implementation did not properly handle replacing the is_null
return argument from expr calls.
Change-Id: I96cd0dfca8876b4f914b0cbc4eb459ea3dcdf230
Reviewed-on: http://gerrit.ent.cloudera.com:8080/1795
Reviewed-by: Nong Li <nong@cloudera.com>
Tested-by: jenkins
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
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>
Implements a group_concat() function which concatenates all the values in a group together.
The format is group_concat(str_col, [separator]). The default separator is ', '. NULLs
are ignored.
Change-Id: If152df6f528401117dba81d66ef691bfb548cc7d
Reviewed-on: http://gerrit.ent.cloudera.com:8080/117
Reviewed-by: Aaron Davidson <aaron.davidson@cloudera.com>
Tested-by: Aaron Davidson <aaron.davidson@cloudera.com>
This patch adds support for the following SQL constructs
- Unary + operator
- The ALL keyword, in SELECT ALL and SELECT aggregate_func(ALL *)
- REAL and INTEGER as type synonyms for DOUBLE and INT respectively
- The AS keyword after a table spec. e.g. SELECT * FROM tbl AS t0
With this change the Python tests will now be called as part of buildall and
the corresponding Java tests have been disabled. The new tests can also be
invoked calling ./tests/run-tests.sh directly.
This includes a fix from Nong that caused wrong results for limit on non-io
manager formats.
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
"distinctpc" and "distinctpcsa".
We've gathered statistics on an internal dataset (all columns) which is
part of our regression data. It's roughly 400mb, ~100 columns,
int/bigint/string type.
On Hive, it took roughly 64sec.
On this Impala implementation, it took 35sec. By adding inline to hash-util.h (which we don't),
we can achieve 24~26sec.
Change-Id: Ibcba3c9512b49e8b9eb0c2fec59dfd27f14f84c3
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
