Split out the encoder/type for parquet reader/writer. I think this puts us
in a better place to support future encodings.
On the tpch lineitem table, the results are:
Before:
BytesWritten: 236.45 MB
Per Column Sizes:
l_comment: 75.71 MB
l_commitdate: 8.64 MB
l_discount: 11.19 MB
l_extendedprice: 33.02 MB
l_linenumber: 4.56 MB
l_linestatus: 869.98 KB
l_orderkey: 8.99 MB
l_partkey: 27.02 MB
l_quantity: 11.58 MB
l_receiptdate: 8.65 MB
l_returnflag: 1.40 MB
l_shipdate: 8.65 MB
l_shipinstruct: 1.45 MB
l_shipmode: 2.17 MB
l_suppkey: 21.91 MB
l_tax: 10.68 MB
After:
BytesWritten: 198.63 MB (84%)
Per Column Sizes:
l_comment: 75.71 MB (100%)
l_commitdate: 8.64 MB (100%)
l_discount: 2.89 MB (25.8%)
l_extendedprice: 33.13 MB (100.33%)
l_linenumber: 1.50 MB (32.89%)
l_linestatus: 870.26 KB (100.032%)
l_orderkey: 9.18 MB (102.11%)
l_partkey: 27.10 MB (100.29%)
l_quantity: 4.32 MB (37.31%)
l_receiptdate: 8.65 MB (100%)
l_returnflag: 1.40 MB (100%)
l_shipdate: 8.65 MB (100%)
l_shipinstruct: 1.45 MB (100%)
l_shipmode: 2.17 MB (100%)
l_suppkey: 10.11 MB (46.14%)
l_tax: 2.89 MB (27.06%)
The table is overall 84% as big (i.e. 16% smaller). A few columns got marginally
bigger. If the file filled the 1 GB, I'd expect the overhead to decrease even
more.
The restructuring to use a virtual call doesn't seem to change things much and
will go away when we codegen the scanner.
Here's what they look like with this patch (note this is on the before data files,
so only string cols are dictionary encoded).
Before query times:
Insert Time: 8.5 sec
select *: 2.3 sec
select avg(l_orderkey): .33 sec
After query times:
Insert Time: 9.5 sec <-- Longer due to doing dictionary encoding
select *: 2.4 sec <-- kind of noisy, possibly a slight slow down
select avg(l_orderkey): .33 sec
Change-Id: I213fdca1bb972cc200dc0cd9fb14b77a8d36d9e6
Reviewed-on: http://gerrit.ent.cloudera.com:8080/238
Tested-by: jenkins <kitchen-build@cloudera.com>
Reviewed-by: Skye Wanderman-Milne <skye@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
