This removes Impala-lzo from the Impala development environment.
Impala-lzo is not built as part of the Impala build. The LZO plugin
is no longer loaded. LZO tables are not loaded during dataload,
and LZO is no longer tested.
This removes some obsolete scan APIs that were only used by Impala-lzo.
With this commit, Impala-lzo would require code changes to build
against Impala.
The plugin infrastructure is not removed, and this leaves some
LZO support code in place. If someone were to decide to revive
Impala-lzo, they would still be able to load it as a plugin
and get the same functionality as before. This plugin support
may be removed later.
Testing:
- Dryrun of GVO
- Modified TestPartitionMetadataUncompressedTextOnly's
test_unsupported_text_compression() to add LZO case
Change-Id: I3a4f12247d8872b7e14c9feb4b2c58cfd60d4c0e
Reviewed-on: http://gerrit.cloudera.org:8080/15814
Reviewed-by: Bikramjeet Vig <bikramjeet.vig@cloudera.com>
Tested-by: Joe McDonnell <joemcdonnell@cloudera.com>
Full ACID row format looks like this:
{
"operation": 0,
"originalTransaction": 1,
"bucket": 536870912,
"rowId": 0,
"currentTransaction": 1,
"row": {"i": 1}
}
User columns are nested under "row". In the frontend we need to create
slot descriptors that correspond to the file schema. In the catalog we
could mimic the file schema but that would introduce several
complexities and corner cases in column resolution. Also in query
results the heading of the above user column would be "row.i". Star
expansion should also be modified, etc.
Because of that in the Catalog I create the exact opposite of the above
schema:
{
"row__id":
{
"operation": 0,
"originalTransaction": 1,
"bucket": 536870912,
"rowId": 0,
"currentTransaction": 1
}
"i": 1
}
This way very little modification is needed in the frontend. And the
hidden columns can be easily retrieved via 'SELECT row__id.*' when we
need those for debugging/testing.
We only need to change Path.getAbsolutePath() to return a schema path
that corresponds to the file schema. Also in the backend we need some
extra juggling in OrcSchemaResolver::ResolveColumn() to retrieve the
table schema path from the file schema path.
Testing:
I changed data loading to load ORC files in full ACID format by default.
With this change we should be able to scan full ACID tables that are
not minor-compacted, don't have deleted rows, and don't have original
files.
Newly added Tests:
* specific queries about hidden columns (full-acid-rowid.test)
* SHOW CREATE TABLE (show-create-table-full-acid.test)
* DESCRIBE [FORMATTED] TABLE (describe-path.test)
* INSERT should be forbidden (acid-negative.test)
* added tests for column masking (
ranger_column_masking_complex_types.test)
Change-Id: Ic2e2afec00c9a5cf87f1d61b5fe52b0085844bcb
Reviewed-on: http://gerrit.cloudera.org:8080/15395
Reviewed-by: Impala Public Jenkins <impala-public-jenkins@cloudera.com>
Tested-by: Impala Public Jenkins <impala-public-jenkins@cloudera.com>
We don't support reading UNION columns. Queries on tables containing
UNION types will fail in planning. Error message is metadata loading
error. However, scanner may need to read an ORC file with UNION types if
the table schema doesn't map to the UNION columns. Though the UNION
values won't be read, the scanner need to resolve the file schema,
including the UNION types, correctly.
In OrcSchemaResolver::BuildSchemaPath, we create a map from ORC type ids
to Impala SchemaPath representation for all types of the file. We should
deal with UNION types as well.
This patch also include some refactor to improve code readability.
Tests:
- Add tests for table schema and file schema mismatching on all complex
types.
Change-Id: I452d27b4e281eada00b62ac58af773a3479163ec
Reviewed-on: http://gerrit.cloudera.org:8080/15103
Reviewed-by: Impala Public Jenkins <impala-public-jenkins@cloudera.com>
Tested-by: Impala Public Jenkins <impala-public-jenkins@cloudera.com>
Implements read path for the date type in ORC scanner. The internal
representation of a date is an int32 meaning the number of days since
Unix epoch using proleptic Gregorian calendar.
Similarly to the Parquet implementation (IMPALA-7370) this
representation introduces an interoperability issue between Impala
and older versions of Hive (before 3.1). For more details see the
commit message of the mentioned Parquet implementation.
Change-Id: I672a2cdd2452a46b676e0e36942fd310f55c4956
Reviewed-on: http://gerrit.cloudera.org:8080/14982
Reviewed-by: Impala Public Jenkins <impala-public-jenkins@cloudera.com>
Tested-by: Impala Public Jenkins <impala-public-jenkins@cloudera.com>
Hive can write timestamps that are outside Impala's valid
range (Impala: 1400-9999 Hive: 0001-9999). This change adds
validation logic to ORC reading that replaces out-of-range
timestamps with NULLs and adds a warning to the query.
The logic is very similar to the existing validation in
Parquet. Some differences:
- "time of day" is not checked separately as it doesn't make
sense with ORC's encoding
- instead of column name only column id is added to the warning
Testing:
- added a simple EE test that scans an existing ORC file
Change-Id: I8ee2ba83a54f93d37e8832e064f2c8418b503490
Reviewed-on: http://gerrit.cloudera.org:8080/14832
Reviewed-by: Impala Public Jenkins <impala-public-jenkins@cloudera.com>
Tested-by: Impala Public Jenkins <impala-public-jenkins@cloudera.com>
Before this patch the supported year range for DATE type started with
year 0. This contradicts the ANSI SQL standard that defines the valid
DATE value range to be 0001-01-01 to 9999-12-31.
Change-Id: Iefdf1c036834763f52d44d0c39a25a1f04e41e07
Reviewed-on: http://gerrit.cloudera.org:8080/14349
Reviewed-by: Attila Jeges <attilaj@cloudera.com>
Tested-by: Impala Public Jenkins <impala-public-jenkins@cloudera.com>
This change is a follow-up to IMPALA-7368 and adds support for DATE
type to the avro scanner.
Similarly to parquet, avro uses DATE logical type for dates. DATE
logical type annotates an INT32 that stores the number of days since
the unix epoch, 1 January 1970.
This representation introduces an avro interoperability issue between
Impala and older versions of Hive:
- Before version 3.1, Hive used Julian calendar to represent dates
up to 1582-10-05 and Gregorian calendar for dates starting with
1582-10-15. Dates between 1582-10-05 and 1582-10-15 were lost.
- Impala uses proleptic Gregorian calendar, extending the Gregorian
calendar backward to dates preceding its official introduction in
1582-10-15.
This means that pre-1582-10-15 dates written to an avro table by Hive
will be read back incorrectly by Impala.
Note that Hive 3.1 switched to proleptic Gregorian calendar too, so
for Hive 3.1+ this is no longer an issue.
Dependency changes:
- BE uses avro 1.7.4-p5 from native-toolchain.
Change-Id: I7a9d5b93a22cf3a00244037e187f8c145cacc959
Reviewed-on: http://gerrit.cloudera.org:8080/13944
Reviewed-by: Impala Public Jenkins <impala-public-jenkins@cloudera.com>
Tested-by: Impala Public Jenkins <impala-public-jenkins@cloudera.com>
DATE values describe a particular year/month/day in the form
yyyy-MM-dd. For example: DATE '2019-02-15'. DATE values do not have a
time of day component. The range of values supported for the DATE type
is 0000-01-01 to 9999-12-31.
This initial DATE type support covers TEXT and HBASE fileformats only.
'DateValue' is used as the internal type to represent DATE values.
The changes are as follows:
- Support for DATE literal syntax.
- Explicit casting between DATE and other types (note that invalid
casts will fail with an error just like invalid DECIMAL_V2 casts,
while failed casts to other types do no lead to warning or error):
- from STRING to DATE. The string value must be formatted as
yyyy-MM-dd HH:mm:ss.SSSSSSSSS. The date component is mandatory,
the time component is optional. If the time component is
present, it will be truncated silently.
- from DATE to STRING. The resulting string value is formatted as
yyyy-MM-dd.
- from TIMESTAMP to DATE. The source timestamp's time of day
component is ignored.
- from DATE to TIMESTAMP. The target timestamp's time of day
component is set to 00:00:00.
- Implicit casting between DATE and other types:
- from STRING to DATE if the source string value is used in a
context where a DATE value is expected.
- from DATE to TIMESTAMP if the source date value is used in a
context where a TIMESTAMP value is expected.
- Since STRING -> DATE, STRING -> TIMESTAMP and DATE -> TIMESTAMP
implicit conversions are now all possible, the existing function
overload resolution logic is not adequate anymore.
For example, it resolves the
if(false, '2011-01-01', DATE '1499-02-02') function call to the
if(BOOLEAN, TIMESTAMP, TIMESTAMP) version of the overloaded
function, instead of the if(BOOLEAN, DATE, DATE) version.
This is clearly wrong, so the function overload resolution logic had
to be changed to resolve function calls to the best-fit overloaded
function definition if there are multiple applicable candidates.
An overloaded function definition is an applicable candidate for a
function call if each actual parameter in the function call either
matches the corresponding formal parameter's type (without casting)
or is implicitly castable to that type.
When looking for the best-fit applicable candidate, a parameter
match score (i.e. the number of actual parameters in the function
call that match their corresponding formal parameter's type without
casting) is calculated and the applicable candidate with the highest
parameter match score is chosen.
There's one more issue that the new resolution logic has to address:
if two applicable candidates have the same parameter match score and
the only difference between the two is that the first one requires a
STRING -> TIMESTAMP implicit cast for some of its parameters while
the second one requires a STRING -> DATE implicit cast for the same
parameters then the first candidate has to be chosen not to break
backward compatibility.
E.g: year('2019-02-15') function call must resolve to
year(TIMESTAMP) instead of year(DATE). Note, that year(DATE) is not
implemented yet, so this is not an issue at the moment but it will
be in the future.
When the resolution algorithm considers overloaded function
definitions, first it orders them lexicographically by the types in
their parameter lists. To ensure the backward compatible behavior
Primitivetype.DATE enum value has to come after
PrimitiveType.TIMESTAMP.
- Codegen infrastructure changes for expression evaluation.
- 'IS [NOT] NULL' and '[NOT] IN' predicates.
- Common comparison operators (including the 'BETWEEN' operator).
- Infrastructure changes for built-in functions.
- Some built-in functions: conditional, aggregate, analytical and
math functions.
- C++ UDF/UDA support.
- Support partitioning and grouping by DATE.
- Beeswax, HiveServer2 support.
These items are tightly coupled and it makes sense to implement them
in one change-set.
Testing:
- A new partitioned TEXT table 'functional.date_tbl' (and the
corresponding HBASE table 'functional_hbase.date_tbl') was
introduced for DATE-related tests.
- BE and FE tests were extended to cover DATE type.
- E2E tests:
- since DATE type is supported for TEXT and HBASE fileformats
only, most DATE tests were implemented separately in
tests/query_test/test_date_queries.py.
Note, that this change-set is not a complete DATE type implementation,
but it lays the foundation for future work:
- Add date support to the random query generator.
- Implement a complete set of built-in functions.
- Add Parquet support.
- Add Kudu support.
- Optionally support Avro and ORC.
For further details, see IMPALA-6169.
Change-Id: Iea8155ef09557e0afa2f8b2d0b2dc9d0896dc30f
Reviewed-on: http://gerrit.cloudera.org:8080/12481
Reviewed-by: Impala Public Jenkins <impala-public-jenkins@cloudera.com>
Tested-by: Impala Public Jenkins <impala-public-jenkins@cloudera.com>
We've supported reading primitive types from ORC files (IMPALA-5717).
In this patch we add support for complex types (struct/array/map).
In IMPALA-5717, we leverage the ORC lib to parse ORC binaries (data in
io buffer read from DiskIoMgr). The ORC lib can materialize ORC column
binaries into its representation (orc::ColumnVectorBatch). Then we
transform values in orc::ColumnVectorBatch into impala::Tuples in
hdfs-orc-scanner. We don't need to do anything about decoding/decompression
since they are handled by the ORC lib. Fortunately, the ORC lib already
supports complex types, we can still leverage it to support complex types.
What we need to add in IMPALA-6503 are two things:
1. Specify which nested columns we need in the form required by the ORC
lib (Get list of ORC type ids from tuple descriptors)
2. Transform outputs of ORC lib (nested orc::ColumnVectorBatch) into
Impala's representation (Slots/Tuples/RowBatches)
To format the materialization, we implement several ORC column readers
in hdfs-orc-scanner. Each kind of reader treats a column type and
transforms outputs of the ORC lib into tuple/slot values.
Tests:
* Enable existing tests for complex types (test_nested_types.py,
test_tpch_nested_queries.py) for ORC.
* Run exhaustive tests in DEBUG and RELEASE builds.
Change-Id: I244dc9d2b3e425393f90e45632cb8cdbea6cf790
Reviewed-on: http://gerrit.cloudera.org:8080/12168
Reviewed-by: Impala Public Jenkins <impala-public-jenkins@cloudera.com>
Tested-by: Impala Public Jenkins <impala-public-jenkins@cloudera.com>
This patch integrates the orc library into Impala and implements
HdfsOrcScanner as a middle layer between them. The HdfsOrcScanner
supplies input needed from the orc-reader, tracks memory consumption of
the reader and transfers the reader's output (orc::ColumnVectorBatch)
into impala::RowBatch. The ORC version we used is release-1.4.3.
A startup option --enable_orc_scanner is added for this feature. It's
set to true by default. Setting it to false will fail queries on ORC
tables.
Currently, we only support reading primitive types. Writing into ORC
table has not been supported neither.
Tests
- Most of the end-to-end tests can run on ORC format.
- Add tpcds, tpch tests for ORC.
- Add some ORC specific tests.
- Haven't enabled test_scanner_fuzz for ORC yet, since the ORC library
is not robust for corrupt files (ORC-315).
Change-Id: Ia7b6ae4ce3b9ee8125b21993702faa87537790a4
Reviewed-on: http://gerrit.cloudera.org:8080/9134
Reviewed-by: Quanlong Huang <huangquanlong@gmail.com>
Reviewed-by: Tim Armstrong <tarmstrong@cloudera.com>
Tested-by: Impala Public Jenkins <impala-public-jenkins@cloudera.com>
Implements HdfsScanner::GetNext() for the Avro, RC File, and
Sequence File scanners. Changes ProcessSplit() to repeatedly call
GetNext() to share the core scanning code between the legacy
ProcessSplit() interface (ProcessSplit()) and the new GetNext()
interface.
Summary of changes:
- Slightly change code flow for initial scan range that
only parses the file header. The new code sets
'only_parsing_header_' in Open() and then honors
that flag in GetNextInternal(). Before, all the logic
was inside ProcessSpit().
- Replace 'finished_' with 'eos_'.
- Add a RowBatch parameter to various functions.
- Change Close() to free all resources when a nullptr
RowBatch is passed.
Testing:
- Exhaustive tests passed on debug
- Core tests passed on asan
- TODO: Perf testing on cluster
Change-Id: Ie18f57b0d3fe0052a8ccd361b6a5fcdf979d0669
Reviewed-on: http://gerrit.cloudera.org:8080/6527
Reviewed-by: Alex Behm <alex.behm@cloudera.com>
Tested-by: Impala Public Jenkins
CodegenWriteSlot() receives negative length values for the lengths of
the slots passed to it if the slots contain escape characters. (This
is currently only for non-string types, as we do not codegen string
types with escaped characters). The DelimitedTextParser is responsible
for identifying escape characters and assigning the negative lengths
appropriately. CodegenWriteCompleteTuple() passes this length to
CodegenWriteSlot() as it is. This differs from the behavior of
WriteSlot() where the length passed to it is always positive, as all
the callers of WriteSlot() make sure of that (including
WriteCompleteTuple()).
The IrIsNullString() and IrGenericIsNullString() functions are
responsibe for checking if the given data contains a NULL pattern.
They are called by CodegenWriteSlot(). A NULL pattern usually contains
an escaped character which means that the length of that slot will be
a negative length. However, the IrIsNullString() and
IrGenericIsNullString() that take the same length argument from
CodegenWriteSlot() always expect a positive length argument. So, no
slots were ever marked as NULL by these NULL-checking functions when
codegen was enabled.
NULL slots were still detected accidentally because of some incorrect
code in CodegenWriteSlot() that marked invalid slots and NULL slots as
NULL. Therefore, due to this code, even invalid slots were not marked
as invalid and did not return an error. Instead they were just
sliently marked as NULL.
This patch makes sure that only positive lengths are passed to
CodegenWriteSlot() so that NULL checking is correct and it also
makes sure that invalid slots are not silently marked as NULL.
Testing: Re-enabled an older hdfs-scan-node-errors test. Formatted
it to fit new error message format after IMPALA-3859 and IMPALA-3895.
Change-Id: I858e427ad7c2b2da8c2bb657be06b7443655781f
Reviewed-on: http://gerrit.cloudera.org:8080/5377
Reviewed-by: Sailesh Mukil <sailesh@cloudera.com>
Tested-by: Internal Jenkins
This patch prevents an invalid decimal type in an Avro file schema from
crashing Impala. Most invalid Avro schemas are caught by the frontend,
but file schemas still need to be validated by the backend.
After this patch files with bad schemas are skipped.
Testing:
This was hit very rarely by the scanner fuzzing. Added a regression test that
scans a file with a bad schema.
Change-Id: I25a326ee2220bc14d3b5f887dc288b4adf859cfc
Reviewed-on: http://gerrit.cloudera.org:8080/4876
Reviewed-by: Tim Armstrong <tarmstrong@cloudera.com>
Tested-by: Internal Jenkins
Logging file or table data is a bad idea, and doing it by default is
particularly bad. This patch changes HdfsScanNode::LogRowParseError() to
log a file and offset only.
Testing: See rewritten tests.
To support testing this change, we also fix IMPALA-3895, by introducing
a canonical string __HDFS_FILENAME__ that all Hadoop filenames in the ERROR
output are replaced with before comparing with the expected
results. This fixes a number of issues with the old way of matching
filenames which purported to be a regex, but really wasn't. In
particular, we can now match the rest of an ERROR line after the
filename, which was not possible before.
In some cases, we don't want to substitute filenames because the ERROR
output is looking for a very specific output. In that case we can write:
$NAMENODE/<filename>
and this patch will not perform _any_ filename substitutions on ERROR
sections that contain the $NAMENODE string.
Finally, this patch fixes a bug where a test that had an ERRORS section
but no RESULTS section would silently pass without testing anything.
Change-Id: I5a604f8784a9ff7b4bf878f82ee7f56697df3272
Reviewed-on: http://gerrit.cloudera.org:8080/4020
Reviewed-by: Henry Robinson <henry@cloudera.com>
Tested-by: Internal Jenkins
Also fix a stale comment in the avro scanner header.
The main work here is to fix the handling of empty result sets in the
test result verifier. This is a problem because we wanted to verify
that the results in the test file were a superset of the rows
returned, and this was thrown off by superflous '' rows in the expected
and actual result sets.
The basic problem is that the way test file sections
was parsed conflated an empty result section with non-empty result
section that had a single empty string. I.e.:
---- RESULTS
====
vs
---- RESULTS
====
both got resolved to [''].
Change-Id: Ia007e558d92c7e4ce30be90446fdbb1f50a0ebc4
Reviewed-on: http://gerrit.cloudera.org:8080/3413
Tested-by: Internal Jenkins
Reviewed-by: Tim Armstrong <tarmstrong@cloudera.com>
This patch adds error checking to the Avro scanner (both the codegen'd
and interepted paths), including out-of-bounds checks and data
validity checks.
I ran a local benchmark using the following queries:
set num_scanner_threads=1;
select count(i) from default.avro_bigints_big; # file contains only longs
select max(l_orderkey) from biglineitem_avro; # file has tpch.lineitem schema
Both benchmark queries see negligable or no performance impact.
This patch adds a new Avro scanner unit test and an end-to-end test
that queries several corrupted files, as well as updates the zig-zag
varlen int unit test.
Change-Id: I801a11c496a128e02c564c2a9c44baa5a97be132
Reviewed-on: http://gerrit.cloudera.org:8080/3072
Reviewed-by: Dan Hecht <dhecht@cloudera.com>
Tested-by: Internal Jenkins
Fix a bug in which Impala only reads the first stream
of a multi-stream bz2/gzip file.
Changes the bz2 decoder to read the file in a streaming
fashion rather than reading the entire file into memory
before it can be decompressed.
Change-Id: Icbe617d03a69953f0bf3aa0f7c30d34bc612f9f8
(cherry picked from commit b6d0b4e059329633dc50f1f73ebe35b7ac317a8e)
Reviewed-on: http://gerrit.cloudera.org:8080/2219
Reviewed-by: Juan Yu <jyu@cloudera.com>
Tested-by: Internal Jenkins
Stream::ReadBytes() could fail by other reasons than
'stale metadata'. Adding Errorcode Check to make sure
Impala return proper error message.
It also fixes IMPALA-2488 metadata.test_stale_metadata
fails on non-hdfs filesystem.
Change-Id: I9a25df3fb49f721bf68d1b07f42a96ce170abbaa
Reviewed-on: http://gerrit.cloudera.org:8080/1166
Reviewed-by: Juan Yu <jyu@cloudera.com>
Tested-by: Internal Jenkins
I did a local benchmark and there's minimal performance impact(<1%)
Change-Id: I8d84a145acad886c52587258b27d33cff96ea399
(cherry picked from commit 7e750ad5d90007cc85ebe493af4dce7a537ad7c0)
Reviewed-on: http://gerrit.cloudera.org:8080/189
Reviewed-by: Juan Yu <jyu@cloudera.com>
Tested-by: Internal Jenkins
Re-enables data error tests which were not being included in
run-tests.py. Broken tests were updated, with one exception which
is tracked by IMPALA-1862. Depends on a related change to
Impala-lzo.
Change-Id: I4c42498bdebf9155a8722695a3305b63ecc6e5f3
Reviewed-on: http://gerrit.cloudera.org:8080/194
Reviewed-by: Alex Behm <alex.behm@cloudera.com>
Tested-by: Internal Jenkins
Additionally, this patch also disabled the hbase/none test dimension if the
TARGET_FILESYSTEM environment variable is set to either s3 of isilon.
Change-Id: I63aecaa478d2ba9eb68de729e9640071359a2eeb
Reviewed-on: http://gerrit.cloudera.org:8080/74
Reviewed-by: Dan Hecht <dhecht@cloudera.com>
Tested-by: Internal Jenkins
Compressed text formats currently require entire compressed
files be read into memory to be decompressed in a single call
to the decompression codec. This changes the HdfsTextScanner
to drive gzip in a streaming mode, i.e. produce partial output
as input is consumed.
Change-Id: Id5c0805e18cf6b606bcf27a5df4b5f58895809fd
Reviewed-on: http://gerrit.sjc.cloudera.com:8080/5233
Reviewed-by: Matthew Jacobs <mj@cloudera.com>
Tested-by: jenkins
(cherry picked from commit 05c3cc55e7a601d97adc4eebe03f878c68a33e56)
Reviewed-on: http://gerrit.sjc.cloudera.com:8080/5385
This re-enables a subset of the stable data errors tests and updates them to
work in our test framework. This includes support for updating results via --update_results.
This also lets us remove a lot of old code that was there only to support these disabled
tests.
Change-Id: I4c40c3976d00dfc710d59f3f96c99c1ed33e7e9b
Reviewed-on: http://gerrit.ent.cloudera.com:8080/1952
Reviewed-by: Lenni Kuff <lskuff@cloudera.com>
Tested-by: jenkins
Reviewed-on: http://gerrit.ent.cloudera.com:8080/2277
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
verify that the exception message contains the correct error;
verify that excpected exception is thrown;
verify that no exception is thrown when abort_on_error is set to false
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
