Adds MAX_MEM_ESTIMATE_FOR_ADMISSION query option, which takes
effect if and only if
* Memory-based admission control is enabled for the pool
* No mem_limit is set (i.e. best practices are not being followed)
In that case min(MAX_MEM_ESTIMATE_FOR_ADMISSION, mem_estimate)
is used for admission control instead of mem_estimate.
This provides a way to override the planner's estimate if
it happens to be incorrect and are preventing the query from
running. Setting MEM_LIMIT is usually a better alternative
but sometimes it is not feasible to set MEM_LIMIT for each
individual query.
Testing:
Added an admission control test to verify that query option allows
queries with high estimates to run.
Also tested manually on a minicluster started with:
start-impala-cluster.py --impalad_args='-vmodule admission-controller=3 \
-default_pool_mem_limit 12884901888'
Change-Id: Ia5fc32a507ad0f00f564dfe4f954a829ac55d14e
Reviewed-on: http://gerrit.cloudera.org:8080/10058
Reviewed-by: Tim Armstrong <tarmstrong@cloudera.com>
Tested-by: Impala Public Jenkins <impala-public-jenkins@cloudera.com>
The patch puts the output of Sentry to
$IMPALA_CLUSTER_LOGS_DIR/sentry/sentry.out to follow the
same convention as other service output logs.
Testing:
- Injected some failure in run-sentry-service.sh script to see if the
error message was captured
Change-Id: I76627bb5b986a548ec6e4f12b555bd6fc8c4dab8
Reviewed-on: http://gerrit.cloudera.org:8080/10064
Reviewed-by: Vuk Ercegovac <vercegovac@cloudera.com>
Reviewed-by: Philip Zeyliger <philip@cloudera.com>
Tested-by: Impala Public Jenkins <impala-public-jenkins@cloudera.com>
This changes generate-schema-statements.py to produce
separate SQL files for different file formats for Hive.
This changes load-data.py to go parallel on these
separate Hive SQL files. For correctness, the text
version of all tables must be loaded before any
of the other file formats.
load-data.py runs DDLs to create the tables in Impala
and goes parallel. Currently, there are some minor
dependencies so that text tables must be created
prior to creating the other table formats. This
changes the definitions of some tables in
testdata/datasets/functional/functional_schema_template.sql
to remove these dependencies. Now, the DDLs for the
text tables can run in parallel to the other file formats.
To unify the parallelism for Impala and Hive, load-data.py
now uses a single fixed-size pool of processes to run all
SQL files rather than spawning a thread per SQL file.
This also modifies the locations that do invalidate to
use refresh where possible and eliminate global
invalidates.
For debuggability, different SQL executions output to
different log files rather than to standard out. If an
error occurs, this will point out the relevant log
file.
This saves about 10-15 minutes on dataload (including
for GVO).
Change-Id: I34b71e6df3c8f23a5a31451280e35f4dc015a2fd
Reviewed-on: http://gerrit.cloudera.org:8080/8894
Reviewed-by: Joe McDonnell <joemcdonnell@cloudera.com>
Tested-by: Impala Public Jenkins <impala-public-jenkins@cloudera.com>
IMPALA-4794 changed the distinct aggregation behavior to shuffling by
both grouping exprs and the distinct expr. It's slower in queries
where the NDVs of grouping exprs are high and data are uniformly
distributed among groups. This patch adds a query option controlling
this behavior, letting users switch to the old plan.
Change-Id: Icb4b4576fb29edd62cf4b4ba0719c0e0a2a5a8dc
Reviewed-on: http://gerrit.cloudera.org:8080/9949
Reviewed-by: Tianyi Wang <twang@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>
Before this commit it was quite random which DDL oprations
returned a result set and which didn't.
With this commit, every DDL operations return a summary of
its execution. They declare their result set schema in
Frontend.java, and provide the summary in CalatogOpExecutor.java.
Updated the tests according to the new behavior.
Change-Id: Ic542fb8e49e850052416ac663ee329ee3974e3b9
Reviewed-on: http://gerrit.cloudera.org:8080/9090
Reviewed-by: Alex Behm <alex.behm@cloudera.com>
Tested-by: Impala Public Jenkins <impala-public-jenkins@cloudera.com>
Remove Yarn from minicluster by default.
Turns out that we start Yarn as part of the minicluster, but we never use it.
(HiveServer2 is configured to run MR jobs "locally" in process.) Likely, this
Yarn integration is a vestige of Yarn/Llama integration. We can save memory by
not starting it by default.
There are some less-common tooks like tests/comparison/cluster.py which use
Yarn (and Hadoop Streaming). In deference to those tools, I've left a mechanism
to start Yarn rather than excising it altogether. After running
buildall the regular way, add Yarn to the cluster by running:
testdata/cluster/admin -y start_cluster
I tested by running core tests. I did not test the kerberized minicluster.
[Due to a git mishap, a version of this was previously checked in and reverted.]
Change-Id: I97053a44bbe32048e6c35cc28680d1c7696af13f
Reviewed-on: http://gerrit.cloudera.org:8080/9970
Reviewed-by: Michael Brown <mikeb@cloudera.com>
Tested-by: Impala Public Jenkins <impala-public-jenkins@cloudera.com>
Turns out that we start Yarn as part of the minicluster, but we never use it.
(HiveServer2 is configured to run MR jobs "locally" in process.) Likely, this
Yarn integration is a vestige of Yarn/Llama integration. We can save memory by
not starting it by default.
There are some less-common tooks like tests/comparison/cluster.py which use
Yarn (and Hadoop Streaming). In deference to those tools, I've left a mechanism
to start Yarn rather than excising it altogether. After running
buildall the regular way, add Yarn to the cluster by running:
testdata/cluster/admin -y start_cluster
I tested by running core tests. I did not test the kerberized minicluster.
Change-Id: I5504cc40b89e3c6d53fac0b7aa4b395fa63e8d79
This is the compatibility-breaking part of Jinchul Kim's change
to add additional units. To support nanoseconds we need to
widen the output type of these functions. We also change
the meaning of "milliseconds" to include the seconds component.
Cherry-picks: not for 2.x
Change-Id: I42d83712d9bb3a4900bec38a9c009dcf2a1fe019
Reviewed-on: http://gerrit.cloudera.org:8080/9957
Reviewed-by: Tim Armstrong <tarmstrong@cloudera.com>
Tested-by: Impala Public Jenkins <impala-public-jenkins@cloudera.com>
The underlying issue in IMPALA-6338 causes successful queries that are
cancelled internally due to all results having been returned to, in
rare cases, have info missing from the profile. This has caused flaky
tests but has low impact on users, and unfortunately with the current
query lifecycle logic in the coordinator, there is no simple solution.
There is ongoing work to improve query lifecycle logic in the
coordinator holistically, see IMPALA-5384. This work will eventually
address the underlying cause of IMPALA-6338. Until then, we disable
the tests that have been flaky.
Change-Id: Ie30b88fb8fb7780fc3a7153c05fdc3606145ce35
Reviewed-on: http://gerrit.cloudera.org:8080/9822
Reviewed-by: Thomas Tauber-Marshall <tmarshall@cloudera.com>
Reviewed-by: Tim Armstrong <tarmstrong@cloudera.com>
Tested-by: Impala Public Jenkins <impala-public-jenkins@cloudera.com>
Allows running the tests that make up the "core" suite in about 2 hours.
By comparison, https://jenkins.impala.io/job/ubuntu-16.04-from-scratch/buildTimeTrend
tends to run in about 3.5 hours.
This commit:
* Adds "echo" statements in a few places, to facilitate timing.
* Adds --skip-parallel/--skip-serial flags to run-tests.py,
and exposes them in run-all-tests.sh.
* Marks TestRuntimeFilters as a serial test. This test runs
queries that need > 1GB of memory, and, combined with
other tests running in parallel, can kill the parallel test
suite.
* Adds "test-with-docker.py", which runs a full build, data load,
and executes tests inside of Docker containers, generating
a timeline at the end. In short, one container is used
to do the build and data load, and then this container is
re-used to run various tests in parallel. All logs are
left on the host system.
Besides the obvious win of getting test results more quickly, this
commit serves as an example of how to get various bits of Impala
development working inside of Docker containers. For example, Kudu
relies on atomic rename of directories, which isn't available in most
Docker filesystems, and entrypoint.sh works around it.
In addition, the timeline generated by the build suggests where further
optimizations can be made. Most obviously, dataload eats up a precious
~30-50 minutes, on a largely idle machine.
This work is significantly CPU and memory hungry. It was developed on a
32-core, 120GB RAM Google Compute Engine machine. I've worked out
parallelism configurations such that it runs nicely on 60GB of RAM
(c4.8xlarge) and over 100GB (eg., m4.10xlarge, which has 160GB). There is
some simple logic to guess at some knobs, and there are knobs. By and
large, EC2 and GCE price machines linearly, so, if CPU usage can be kept
up, it's not wasteful to run on bigger machines.
Change-Id: I82052ef31979564968effef13a3c6af0d5c62767
Reviewed-on: http://gerrit.cloudera.org:8080/9085
Reviewed-by: Philip Zeyliger <philip@cloudera.com>
Tested-by: Impala Public Jenkins <impala-public-jenkins@cloudera.com>
This commit slightly loosens the coupling between IMPALA_HIVE_VERSION
and "hive.version" in the Maven sense.
Cherry-picks: not for 2.x
Change-Id: Ifbe6f5208b4ad0ffc9cbfe4e93d712ce698beb23
Reviewed-on: http://gerrit.cloudera.org:8080/9925
Reviewed-by: Tim Armstrong <tarmstrong@cloudera.com>
Tested-by: Impala Public Jenkins
Fixes a bug that introduced default initialized values in the set data
structure used to check for set membership that can cause wrong results.
Testing:
Added a test case that checks for the same.
Change-Id: I7e776dbcb7ee4a9b64e1295134a27d332f5415b6
Reviewed-on: http://gerrit.cloudera.org:8080/9891
Reviewed-by: Sailesh Mukil <sailesh@cloudera.com>
Reviewed-by: Tim Armstrong <tarmstrong@cloudera.com>
Tested-by: Impala Public Jenkins
This patch fixes the NullPointerException in SHOW CREATE TABLE for HBase
tables.
Testing:
- Moved the content of back hbase-show-create-table.test to
show-create-table.test
- Ran show-create-table end-to-end tests
Change-Id: Ibe018313168fac5dcbd80be9a8f28b71a2c0389b
Reviewed-on: http://gerrit.cloudera.org:8080/9884
Reviewed-by: Alex Behm <alex.behm@cloudera.com>
Tested-by: Impala Public Jenkins
Some builds are experiencing slow HBase region moves in the
test minicluster. Trying to increase the timeout from 10s to 60s.
Change-Id: Ic62719f1b1aad463bcdc18d0803e780ebb0f8b18
Reviewed-on: http://gerrit.cloudera.org:8080/9892
Reviewed-by: Zach Amsden <zamsden@cloudera.com>
Tested-by: Impala Public Jenkins
This patch removes restriction on creating a function with the same name
as the built-in function. The reason for lifting the restriction is to
avoid a name clash when introducing new built-in functions. The patch
also fixes some inconsistent behavior when creating or dropping a function
when the name specified is fully-qualified or not.
Refer to the below tables for more information.
Create function:
+---------+-------------+-------------------------+-------------------------------+-------------------------------+
| FQ Name | Built-in DB | Function Name | Existing Behavior | New Behavior |
+---------+-------------+-------------------------+-------------------------------+-------------------------------+
| Yes | Yes | Same as built-in | Same name exception | Cannot modify system database |
| Yes | Yes | Different than built-in | Cannot modify system database | Cannot modify system database |
| Yes | No | Same as built-in | Function created | Function created |
| Yes | No | Different than built-in | Function created | Function created |
| No | Yes | Same as built-in | Same name exception | Cannot modify system database |
| No | Yes | Different than built-in | Cannot modify system database | Cannot modify system database |
| No | No | Same as built-in | Same name exception | Function created |
| No | No | Different than built-in | Function created | Function created |
+---------+-------------+-------------------------+-------------------------------+-------------------------------+
Drop function:
+---------+-------------+-------------------------+-------------------------------+-------------------------------+
| FQ Name | Built-in DB | Function Name | Existing Behavior | New Behavior |
+---------+-------------+-------------------------+-------------------------------+-------------------------------+
| Yes | Yes | Same as built-in | Cannot modify system database | Cannot modify system database |
| Yes | Yes | Different than built-in | Cannot modify system database | Cannot modify system database |
| Yes | No | Same as built-in | Function dropped | Function dropped |
| Yes | No | Different than built-in | Function dropped | Function dropped |
| No | Yes | Same as built-in | Cannot modify system database | Cannot modify system database |
| No | Yes | Different than built-in | Cannot modify system database | Cannot modify system database |
| No | No | Same as built-in | Cannot modify system database | Function dropped |
| No | No | Different than built-in | Function dropped | Function dropped |
+---------+-------------+-------------------------+-------------------------------+-------------------------------+
Select function (no new behavior):
+---------+-------------+-------------------------+--------------------------------------------------------+
| FQ Name | Built-in DB | Function Name | Behavior |
+---------+-------------+-------------------------+--------------------------------------------------------+
| Yes | Yes | Same as built-in | Function in the specified database (built-in) executed |
| Yes | Yes | Different than built-in | Unknown function exception |
| Yes | No | Same as built-in | Function in the specified database executed |
| Yes | No | Different than built-in | Function in the specified database executed |
| No | Yes | Same as built-in | Built-in function executed |
| No | Yes | Different than built-in | Unknown function exception |
| No | No | Same as built-in | Built-in function executed |
| No | No | Different than built-in | Function in the current database executed |
+---------+-------------+-------------------------+--------------------------------------------------------+
Testing:
- Ran front-end tests
- Added end-to-end DDL function tests
Cherry-picks: not for 2.x
Change-Id: Ic30df56ac276970116715c14454a5a2477b185fa
Reviewed-on: http://gerrit.cloudera.org:8080/9800
Reviewed-by: Alex Behm <alex.behm@cloudera.com>
Tested-by: Impala Public Jenkins
The patch fixes issues with executing ALTER TABLE SET statements when
there are no matching partitions.
The patch also removes incorrect precondition i.e.
(partitionSet == null || !partitionSet.isEmpty()) in ALTER TABLE SET
statements because a partitionSet can be null when PARTITION is not
specified in the ALTER TABLE SET statement and partitionSet can be
empty when there is no matching partition. For example:
Matching partitions (partitionSet != null && !partitionSet.isEmpty()):
> alter table functional.alltypesagg partition(year=2009, month=1)
set fileformat parquet;
No matching partitions (partitionSet != null && partitionSet.isEmpty()):
> alter table functional.alltypesagg partition(year=2009, month=1)
set fileformat parquet;
No partition specified (partitionSet == null):
> alter table functional.alltypesagg set fileformat parquet;
Testing:
- Added a new test
- Ran all front-end tests
Change-Id: I793e827d5cf5b7986bd150dd9706df58da3417f3
Reviewed-on: http://gerrit.cloudera.org:8080/9819
Reviewed-by: Alex Behm <alex.behm@cloudera.com>
Tested-by: Impala Public Jenkins
A concern was brought up that Impala might not handle kudu master
addresses containing whitespace correctly. Turns out that the Kudu
client takes care of stripping whitespace, so it works, but it would
be good to have a test to ensure it continues to work.
Change-Id: I1857b8dbcb5af66d69f7620368cd3b9b85ae7576
Reviewed-on: http://gerrit.cloudera.org:8080/9876
Reviewed-by: Alex Behm <alex.behm@cloudera.com>
Tested-by: Impala Public Jenkins
Before this patch, the output type of round() ceil() floor() trunc() was
not always the same as the input type. It was also inconsistent in
general. For example, round(double) returned an integer, but
round(double, int) returned a double.
After looking at other database systems, we decided that the guideline
should be that the output type should be the same as the input type. In
this patch, we change the behavior of the previously mentioned functions
so that if a double is given then a double is returned.
We also modify the rounding behavior to always round away from zero.
Before, we were rounding towards positive infinity in some cases.
Testinging:
- Updated tests
- Ran an exhaustive build which passed.
Cherry-picks: not for 2.x
Change-Id: I77541678012edab70b182378b11ca8753be53f97
Reviewed-on: http://gerrit.cloudera.org:8080/9346
Reviewed-by: Alex Behm <alex.behm@cloudera.com>
Tested-by: Impala Public Jenkins
When an impalad is in executor-only mode, it receives no
catalog updates. As a result, lib-cache entries are never
refreshed. A consequence is that udf queries can return
incorrect results or may not run due to resolution issues.
Both cases are caused by the executor using a stale copy
of the lib file. For incorrect results, an old version of
the method may be used. Resolution issues can come up if
a method is added to a lib file.
The solution in this change is to capture the coordinator's
view of the lib file's last modified time when planning.
This last modified time is then shipped with the plan to
executors. Executors must then use both the lib file path
and the last modified time as a key for the lib-cache.
If the coordinator's last modified time is more recent than
the executor's lib-cache entry, then the entry is refreshed.
Brief discussion of alternatives:
- lib-cache always checks last modified time
+ easy/local change to lib-cache
- adds an fs lookup always. rejected for this reason
- keep the last modified time in the catalog
- bound on staleness is too loose. consider the case where
fn's f1, f2, f3 are created with last modified times of
t1, t2, t3. treat the fn's last modified time as a low-watermark;
if the cache entry has a more recent time, use it. Such a scheme
would allow the version at t2 to persist. An old fn may keep the
state from converging to the latest. This could end up with strange
cases where different versions of the lib are used across executors
for a single query.
In contrast, the change in this path relies on the statestore to
push versions forward at all coordinators, so will push all
versions at all caches forward as well.
Testing:
- added an e2e custom cluster test
Change-Id: Icf740ea8c6a47e671427d30b4d139cb8507b7ff6
Reviewed-on: http://gerrit.cloudera.org:8080/9697
Reviewed-by: Alex Behm <alex.behm@cloudera.com>
Tested-by: Impala Public Jenkins
Adds support for building against two sets of Hadoop ecosystem
components. The control variable is IMPALA_MINICLUSTER_PROFILE_OVERRIDE,
which can either be set to 2 (for Hadoop 2, Hive 1, and so on) or 3 (for
Hadoop 3, Hive 2, and so on).
We intend (in a trivial follow-on change soon) to make 3 the new default
and to explicitly deprecate 2, but this change only does not switch the
default yet. We support both to facilitate a smoother transition, but
support will be removed soon in the Impala 3.x line.
The switch is done at build time, following the pattern from IMPALA-5184
(build fe against both Hive 1 & 2 APIs). Switching back and forth
requires running 'cmake' again. Doing this at build-time avoids
complicating the Java code with classloader configuration.
There are relatively few incompatible APIs. This implementation
encapsulates that by extracting some Java code into
fe/src/compat-minicluminicluster-profile-{2,3}. (This follows the
pattern established by IMPALA-5184, but, to avoid a proliferation
of directories, I've moved the Hive files into the same tree.)
pattern from IMPALA-5184 (build fe against both Hive 1 & 2 APIs). I
consolidated the Hive changes into the same directory structure.
For Maven, I introduced Maven "profiles" to handle the two cases where
the dependencies (and exclusions) differ. These are driven by the
$IMPALA_MINICLUSTER_PROFILE environment variable.
For Sentry, exception class names changed. We work around this by adding
"isSentry...(Exception)" methods with two different implementations.
Sentry is also doing some odd shading, whereby some exceptions are
"sentry.org.apache.sentry..."; we handle both. Similarly, the mechanism
to create a SentryAuthProvider is slightly different. The easiest way to
see the differences is to run:
diff -u fe/src/compat-minicluster-profile-{2,3}/java/org/apache/impala/util/SentryUtil.java
diff -u fe/src/compat-minicluster-profile-{2,3}/java/org/apache/impala/authorization/SentryAuthProvider.java
The Sentry work is based on a change by Zach Amsden.
In addition, we recently added an explicit "refresh" permission. In
Sentry 2, this required creating an ImpalaPrivilegeModel to capture
that. It's a slight customization of Hive's equivalent class.
For Parquet, the difference is even more mechanical. The package names
gone from "parquet" to "org.apache.parquet". The affected code
was extracted into ParquetHelper, but only one copy exists. The second
copy is generated at build-time using sed.
In the rare cases where we need to behave differently at runtime,
MiniclusterProfile.MINICLUSTER_PROFILE is a class which encapsulates
what version we were built aginst. One of the cases is the results
expected by various frontend tests. I avoided the issue by translating
one error string into another, which handled the diversion in one place,
rather than complicating the several locations which look for "No
FileSystem for scheme..." errors.
The HBase APIs we use for splitting regions at test time changed.
This patch includes a re-write of that code for the new APIs. This
piece was contributed by Zach Amsden.
To work with newer versions of dependencies, I updated the version of
httpcomponents.core we use to 4.4.9.
We (Thomas Tauber-Marshall and I) uploaded new Hadoop/Hive/Sentry/HBase
binaries to s3://native-toolchain, and amended the shell scripts to
launch the right things. There are minor mechanical differences. Some
of this was based on earlier work by Joe McDonnell and Zach Amsden.
Hive's logging is changed in Hive 2, necessitating creating a
log4j2.properties template and using it appropriately. Furthermore,
Hadoop3's new shell script re-writes do a certain amount of classpath
de-duplication, causing some issues with locating the relevant logging
configurations. Accomodations exist in the code to deal with that.
parquet-filtering.test was updated to turn off stats filtering. Older
Hive didn't write Parquet statistics, but newer Hive does. By turning
off stats filtering, we test what the test had intended to test.
For views-compatibility.test, it seems that Hive 2 has fixed certain
bugs that we were testing for in Hive. I've added a
HIVE=SUCCESS_PROFILE_3_ONLY mechanism to capture that.
For AuthorizationTest, different hive versions show slightly different
things for extended output.
To facilitate easier reviewing, the following files are 100% renames as identified by git; nothing
to see here.
rename fe/src/{compat-hive-1 => compat-minicluster-profile-2}/java/org/apache/hive/service/rpc/thrift/TGetCatalogsReq.java (100%)
rename fe/src/{compat-hive-1 => compat-minicluster-profile-2}/java/org/apache/hive/service/rpc/thrift/TGetColumnsReq.java (100%)
rename fe/src/{compat-hive-1 => compat-minicluster-profile-2}/java/org/apache/hive/service/rpc/thrift/TGetFunctionsReq.java (100%)
rename fe/src/{compat-hive-1 => compat-minicluster-profile-2}/java/org/apache/hive/service/rpc/thrift/TGetInfoReq.java (100%)
rename fe/src/{compat-hive-1 => compat-minicluster-profile-2}/java/org/apache/hive/service/rpc/thrift/TGetSchemasReq.java (100%)
rename fe/src/{compat-hive-1 => compat-minicluster-profile-2}/java/org/apache/hive/service/rpc/thrift/TGetTablesReq.java (100%)
rename fe/src/{compat-hive-1 => compat-minicluster-profile-2}/java/org/apache/impala/compat/MetastoreShim.java (100%)
rename fe/src/{compat-hive-2 => compat-minicluster-profile-3}/java/org/apache/impala/compat/MetastoreShim.java (100%)
rename testdata/cluster/node_templates/{cdh5 => common}/etc/hadoop/conf/kms-acls.xml.tmpl (100%)
rename testdata/cluster/node_templates/{cdh5 => common}/etc/hadoop/conf/kms-site.xml.tmpl (100%)
rename testdata/cluster/node_templates/{cdh5 => common}/etc/hadoop/conf/yarn-site.xml.tmpl (100%)
rename testdata/cluster/node_templates/{cdh5 => common}/etc/init.d/kudu-common (100%)
rename testdata/cluster/node_templates/{cdh5 => common}/etc/init.d/kudu-master (100%)
rename testdata/cluster/node_templates/{cdh5 => common}/etc/init.d/kudu-tserver (100%)
rename testdata/cluster/node_templates/{cdh5 => common}/etc/kudu/master.conf.tmpl (100%)
rename testdata/cluster/node_templates/{cdh5 => common}/etc/kudu/tserver.conf.tmpl (100%)
CreateTableLikeFileStmt had a chunk of code moved to ParquetHelper.java. This
was done manually, but without changing anything except what Java required in
terms of accessibility and boilerplate.
rewrite fe/src/main/java/org/apache/impala/analysis/CreateTableLikeFileStmt.java (80%)
copy fe/src/{main/java/org/apache/impala/analysis/CreateTableLikeFileStmt.java => compat-minicluster-profile-3/java/org/apache/impala/analysis/ParquetHelper.java} (77%)
Testing: Ran core & exhaustive tests with both profiles.
Cherry-picks: not for 2.x.
Change-Id: I7a2ab50331986c7394c2bbfd6c865232bca975f7
Reviewed-on: http://gerrit.cloudera.org:8080/9716
Reviewed-by: Alex Behm <alex.behm@cloudera.com>
Tested-by: Impala Public Jenkins
Impala already supported RLE encoding for levels and dictionary pages, so
the only task was to integrate it into BoolColumnReader.
A new benchmark, rle-benchmark.cc is added to test the speed of RLE
decoding for different bit widths and run lengths.
There might be a small performance impact on PLAIN encoded booleans,
because of the additional branch when the cache of BoolColumnReader is
filled. As the cache size is 128, I considered this to be outside the
"hot loop".
Testing:
As Impala cannot write RLE encoded bool columns at the moment, parquet-mr
was used to create a test file, testdata/data/rle_encoded_bool.parquet
tests/query_test/test_scanners.py#test_rle_encoded_bools creates a table
that uses this file, and tries to query from it.
Change-Id: I4644bf8cf5d2b7238b05076407fbf78ab5d2c14f
Reviewed-on: http://gerrit.cloudera.org:8080/9403
Reviewed-by: Tim Armstrong <tarmstrong@cloudera.com>
Tested-by: Impala Public Jenkins
In wait-hdfs-replication, the frequent and eager restart might slow the
HDFS replication down. HDFS should be restarted only if no progress is
made in a certain amount of time, and we should wait longer before
failing the data loading.
Testing: It's tested with a fake HDFS fsck script.
Change-Id: Ib059480254643dc032731b4b3c55204a93b61e77
Reviewed-on: http://gerrit.cloudera.org:8080/9698
Reviewed-by: Alex Behm <alex.behm@cloudera.com>
Tested-by: Impala Public Jenkins
Currently when using a SET_LOOKUP strategy for in-predicates in impala
we use a std:set object for checking membership. This patch takes a
hybrid approach based on benchmarking results and uses boost::flat_set
for int, big int, and float datatypes and boost::unordered_set for the
rest (tiny int, small int, double, string, timestamp, decimal).
The intent of this change is to fix a regression when upgrading the
toolchain to use LLVM 5.0.1 (IMPALA-5980).
Performance:
Ran a query for each data type with a large in predicate containing
500 elements on a single node with mt_dop set to 1.
+-----------+---------------+----------+---------------+----------+
| Data Type | Llvm 3 hybrid | Llvm 3 | Llvm 5 hybrid | Llvm 5 |
+-----------+---------------+----------+---------------+----------+
| Table used: tpch100_parquet.lineitem |
+-----------+---------------+----------+--------------+-----------+
| big int | 17s782ms | 13s941ms | 13s201ms | 25s604ms |
| string | 40s750ms | 64s | 40s723ms | 73s |
| decimal | 13s929ms | 22s272ms | 13s710ms | 34s338ms |
| int | 19s368ms | 11s308ms | 9s169ms | 15s254ms |
+-----------+---------------+----------+--------------+-----------+
| Table used: alltypes with 33638400 rows |
+-----------+---------------+----------+--------------+-----------+
| double | 5s726ms | 5s894ms | 5s595ms | 6s592ms |
| small int | 4s776ms | 5s057ms | 4s740ms | 5s358ms |
| float | 7s223ms | 6s397ms | 6s287ms | 6s926ms |
+-----------+---------------+----------+---------------+----------+
Also added a targeted perf query that uses a large in-predicate
over a decimal column.
Testing:
- Ran expr-test and test_exprs successfully.
Change-Id: Ifd1627d779d10a16468cc3c2d0bc26a497e048df
Reviewed-on: http://gerrit.cloudera.org:8080/9570
Reviewed-by: Bikramjeet Vig <bikramjeet.vig@cloudera.com>
Reviewed-by: Dan Hecht <dhecht@cloudera.com>
Tested-by: Impala Public Jenkins
We use Java 1.7 in fe/pom.xml, where most of our Java code is. For
consistency, this updates the rest of our Maven configurations to use
the same version of Java. A change I'm working with uses
try-with-resources in HBase splitting, which is how I ran into
this.
Testing: ran core tests
Change-Id: I6cecddf367f00185a14a8b08c03456e3b756bd70
Reviewed-on: http://gerrit.cloudera.org:8080/9600
Reviewed-by: Philip Zeyliger <philip@cloudera.com>
Tested-by: Impala Public Jenkins
The DCHECK was only valid if the Parquet file metadata is internally
consistent, with the number of values reported by the metadata
matching the number of encoded levels.
The DCHECK was intended to directly detect misuse of the RleBatchDecoder
interface, which would lead to incorrect results. However, our other
test coverage for reading Parquet files is sufficient to test the
correctness of level decoding.
Testing:
Added a minimal corrupt test file that reproduces the issue.
Change-Id: Idd6e09f8c8cca8991be5b5b379f6420adaa97daa
Reviewed-on: http://gerrit.cloudera.org:8080/9556
Reviewed-by: Tim Armstrong <tarmstrong@cloudera.com>
Tested-by: Impala Public Jenkins
The bug was that the SortInfo of analytics was given
ordering exprs that were not fully resolved against their
input (e.g. inline views were not resolved).
As a result, the SortInfo logic did not materialize exprs
like rand() coming from inline views.
The fix is to pass fully resolved exprs to the analytic
SortInfo, and then the existing materialization logic
properly handles non-deterministic built-ins and UDFs.
The code around sort generation was rather convoluted
and difficult to understand. I overhauled SortInfo to
unify the different uses of it under a common codepath
After that cleanup, the fix for this issue was trivial.
Testing:
- Locally ran planner tests
- Locally ran analytic EE tests in test_queries.py
- Core/hdfs run passed
Change-Id: Id2b3f4e5e3f1fd441a63160db3c703c432fbb072
Reviewed-on: http://gerrit.cloudera.org:8080/9631
Reviewed-by: Alex Behm <alex.behm@cloudera.com>
Tested-by: Impala Public Jenkins
The retries in split-hbase.sh don't work in the common case,
because $MINIKDC_PRINC_HIVE is not set in non-kerberized (common)
environments. The regular data load scripts (create-load-data.sh)
have code to manage that, but split-hbase.sh blindly forges ahead,
leading to errors like:
/home/impdev/Impala/testdata/bin/split-hbase.sh: line 49: MINIKDC_PRINC_HIVE: unbound variable
Error in /home/impdev/Impala/testdata/bin/create-load-data.sh at line 48: LOAD_DATA_ARGS=""
Since this hasn't been working, I opted to remove it entirely, as a failure on
the line where HBase splitting actually failed would be significantly more
useful than the error here. A search of mailing lists suggested that I was at
least the second person to have run into this. (In my case, I did break HBase
splitting, but it took me a second to identify the error, since the log was
spammed with unrelated information relating to the cluster restart.)
Testing: core tests.
Change-Id: I715891c9e744f21002330c3ae3ebc14095d94ffd
Reviewed-on: http://gerrit.cloudera.org:8080/9588
Reviewed-by: Philip Zeyliger <philip@cloudera.com>
Tested-by: Impala Public Jenkins
Before Kudu supported DECIMAL columns the TPCDS and TPCH
columns were djusted to use DOUBLE in place of DECIMAL. This
patch undoes that change now that Kudu supports DECIMAL.
Testing:
- Updated concurrent_select.py
- Updated test_tpch_queries.py
- Excersized by the Kudu planner tests
Change-Id: I2f7e4464dc6705cadd610a82c459390a9c0dfe4f
Reviewed-on: http://gerrit.cloudera.org:8080/9484
Reviewed-by: Thomas Tauber-Marshall <tmarshall@cloudera.com>
Tested-by: Impala Public Jenkins
This change allows casting of a string in 'lazy' date/time
format to timestamp. The supported lazy date formats are:
yyyy-[M]M-[d]d
yyyy-[M]M-[d]d [H]H:[m]m:[s]s[.SSSSSSSSS]
[H]H:[m]m:[s]s[.SSSSSSSSS]
We will incur a SCAN performance penalty (approximately 1/2
TotalReadThroughput) when the string is in one of these
lazy date/time format.
Testing:
Benchmarked the performance consequence by executing this SQL on
a private build over 3.8 billion rows:
select min(cast (time_string as timestamp)) from private.impala_5315
Added tests for valid and invalid date/time format strings
in expr-test.cc to be inline with existing tests for CAST() function.
Added end-to-end tests into exprs.test and
select-lazy-timestamp.test to exercise the new function within
the context of a query.
Added tests to exercise the leading and trailing white space trimming
behaviour in default and lazy date/time string format (IMPALA-6630).
Change-Id: Ib9a184a09d7e7783f04d47588537612c2ecec28f
Reviewed-on: http://gerrit.cloudera.org:8080/7009
Reviewed-by: Tim Armstrong <tarmstrong@cloudera.com>
Tested-by: Impala Public Jenkins
This patch enables pushing scan predicates on
DECIMAL columns down to Kudu.
Testing:
- Added Planner decimal predicate test to kudu.test
- Added Planner decimal in-list test to kudu-selectivity.test
Change-Id: I2569a9e1d58f1c58884d58633d46348364888ed7
Reviewed-on: http://gerrit.cloudera.org:8080/9578
Reviewed-by: Alex Behm <alex.behm@cloudera.com>
Tested-by: Impala Public Jenkins
We've observed empirically that giving Impala 80% of system memory
doesn't leave enough room for the minicluster and ASAN overhead, leading
to the OOM killer striking during test runs (sometimes). This commit
reduces the threshold to 70%.
This commit also reduces the memory usage of semi-joins-exhaustive.test
by roughly halving the number of records it deals with. This was
necessary for tests to pass on a machine with 32GB of RAM.
Testing: I've run the ASAN build (more) happily with this change.
I've run exhaustive tests on a 32GB machine.
Change-Id: Iabca7a95560bd27c2de2b0a147ee9a3c45199db7
Reviewed-on: http://gerrit.cloudera.org:8080/9395
Reviewed-by: Tim Armstrong <tarmstrong@cloudera.com>
Tested-by: Impala Public Jenkins
HDFS sometimes fails to fully replicate all the blocks in 30 seconds
and no progress is made. This patch tries to restart HDFS several times
before aborting the data loading.
Change-Id: Iefd4c2fc6c287f054e385de52bdc42b0bdbd7915
Reviewed-on: http://gerrit.cloudera.org:8080/9469
Reviewed-by: Alex Behm <alex.behm@cloudera.com>
Tested-by: Impala Public Jenkins
Quick fix of Parquet write path until the Parquet community
agrees on the ordering of floating point numbers.
The behavior follows the way fmax()/fmin() works, ie. Impala
will only write NaN into the stats when all the values are NaNs.
This behavior is aligned with the quick fix of Parquet-CPP.
Added e2e tests as well.
Change-Id: I3957806948f7c661af4be5495f2ec92d1e9fc9d6
Reviewed-on: http://gerrit.cloudera.org:8080/9381
Reviewed-by: Lars Volker <lv@cloudera.com>
Tested-by: Impala Public Jenkins
IMPALA-6592 revealed a gap in test coverage for files with
invalid/unsupported Parquet codecs. This adds a test that reproduces the
bug that was present in my IMPALA-4835 patch. master is unaffected by
this bug.
I also hid the conversion tables and made the conversion go through
functions that validate the enum values, to make it easier to track down
problems like this in the future.
Testing:
Ran exhaustive tests.
Change-Id: I1502ea7b7f39aa09f0ed2677e84219b37c64c416
Reviewed-on: http://gerrit.cloudera.org:8080/9500
Reviewed-by: Tim Armstrong <tarmstrong@cloudera.com>
Tested-by: Impala Public Jenkins
The bug is that the right child of a blocking join node could be closed
before the builder if an error was encountered when sending a batch to
the sink. This hits a DCHECK because Buffers owned by the sink may still
be accounted against the child node.
Testing:
Added the test that originally triggered the problem. It reproduced the
failure when based on the IMPALA-4835 patch, but I can't reproduce
the failure after rebase onto master.
Change-Id: Ie46b87a4889d7cee907124796c830db41125cf15
Reviewed-on: http://gerrit.cloudera.org:8080/9493
Tested-by: Impala Public Jenkins
Reviewed-by: Tim Armstrong <tarmstrong@cloudera.com>
Before this patch, when there was an error when converting a string to
a decimal, a NULL was returned. In this patch, we change this behavior
so that an error is returned if decimal_v2 is enabled. We also add a
warning if there is an underflow.
The reasoning is that we want stricter behavior in decimal_v2.
Testing:
- Added some EE tests.
- Ran an exhaustive build, which passed.
Change-Id: Icffccac1c1c2361447ae4b0de9b6c2ec7de071db
Reviewed-on: http://gerrit.cloudera.org:8080/9339
Reviewed-by: Dan Hecht <dhecht@cloudera.com>
Tested-by: Impala Public Jenkins
Revert "IMPALA-6585: increase test_low_mem_limit_q21 limit"
This reverts commit 25bcb258df.
Revert "IMPALA-6588: don't add empty list of ranges in text scan"
This reverts commit d57fbec6f6.
Revert "IMPALA-4835: Part 3: switch I/O buffers to buffer pool"
This reverts commit 24b4ed0b29.
Revert "IMPALA-4835: Part 2: Allocate scan range buffers upfront"
This reverts commit 5699b59d0c.
Revert "IMPALA-4835: Part 1: simplify I/O mgr mem mgmt and cancellation"
This reverts commit 65680dc421.
Change-Id: Ie5ca451cd96602886b0a8ecaa846957df0269cbb
Reviewed-on: http://gerrit.cloudera.org:8080/9480
Reviewed-by: Dan Hecht <dhecht@cloudera.com>
Tested-by: Impala Public Jenkins
When loading from an up-to-date snapshot, dataload will
load all of the metadata and load data into HDFS. Then,
it will skip load-data.py for functional/exhaustive,
tpch/core, and tpcds/core. It will invoke a special
round of load-data.py calls to populate Kudu tables,
and it always runs these with a force reload.
However, when loading from an old snapshot, dataload will
still load all of the metadata and load the data into
HDFS, but then it will still invoke load-data.py for
functional/exhaustive, tpch/core, and tpcds/core.
These invocations mostly do DDLs with very few load
statements. However, these invocations are a problem
for Kudu. The metadata of Impala tables referencing
Kudu entities have been imported along with all the other
metadata, but the Kudu entities have not been created, as
they are separate from HDFS. This means that Kudu tables
are not really valid in this circumstance.
Since Kudu has been added to the list of data formats
for tpch/core (see IMPALA-6475), load-data.py with
tpch/core will attempt to insert into these invalid
Kudu tables.
To avoid this, always force reload any Kudu tables.
generate-schema-statements.py will always generate a
drop table statement before any create of a Kudu table.
This guarantees that the create will also create the
corresponding Kudu entity.
Change-Id: I2d07f3513c543e2590f2f62b96b37472316868ee
Reviewed-on: http://gerrit.cloudera.org:8080/9445
Reviewed-by: Joe McDonnell <joemcdonnell@cloudera.com>
Tested-by: Impala Public Jenkins
IMPALA-5752 added support for Kudu decimal. As a part
of that, it added Kudu versions of decimal_tbl and
decimal_tiny. Kudu tables are created and loaded even
on local tests, so these tables are loaded when they
previously weren't. The LOAD sections for these tables
rely on executing HDFS commmands to copy data to
appropriate locations. These HDFS commands cannot work
on local tests, causing this failure.
Untangling when to execute LOAD sections is complicated,
so this simply switches the decimal_tbl and decimal_tiny
to do LOAD DATA LOCAL calls, which do not rely on HDFS
commands.
Change-Id: I1f717917269d116c07a6f17944583f5e8faf2932
Reviewed-on: http://gerrit.cloudera.org:8080/9438
Reviewed-by: Tim Armstrong <tarmstrong@cloudera.com>
Tested-by: Impala Public Jenkins
This is the final patch to switch the Disk I/O manager to allocate all
buffer from the buffer pool and to reserve the buffers required for
a query upfront.
* The planner reserves enough memory to run a single scanner per
scan node.
* The multi-threaded scan node must increase reservation before
spinning up more threads.
* The scanner implementations must be careful to stay within their
assigned reservation.
The row-oriented scanners were most straightforward, since they only
have a single scan range active at a time. A single I/O buffer is
sufficient to scan the whole file but more I/O buffers can improve I/O
throughput.
Parquet is more complex because it issues a scan range per column and
the sizes of the columns on disk are not known during planning. To
deal with this, the reservation in the frontend is based on a
heuristic involving the file size and # columns. The Parquet scanner
can then divvy up reservation to columns based on the size of column
data on disk.
I adjusted how the 'mem_limit' is divided between buffer pool and non
buffer pool memory for low mem_limits to account for the increase in
buffer pool memory.
Testing:
* Added more planner tests to cover reservation calcs for scan node.
* Test scanners for all file formats with the reservation denial debug
action, to test behaviour when the scanners hit reservation limits.
* Updated memory and buffer pool limits for tests.
* Added unit tests for dividing reservation between columns in parquet,
since the algorithm is non-trivial.
Perf:
I ran TPC-H and targeted perf locally comparing with master. Both
showed small improvements of a few percent and no regressions of
note. Cluster perf tests showed no significant change.
Change-Id: Ic09c6196b31e55b301df45cc56d0b72cfece6786
Reviewed-on: http://gerrit.cloudera.org:8080/8966
Reviewed-by: Tim Armstrong <tarmstrong@cloudera.com>
Tested-by: Impala Public Jenkins
crashes impala
Impala crashes on creating a UDF from a shared library (.so file) which
was renamed to have .ll extension. CreateFile() call in GetSymbols()
fails and returns on error and does not close the codegen object. This
patch closes the codegen object on failure. This avoids hitting a DCHECK
later up in the stack.
The chain of failures also invokes the DiagnosticHandlerFn. RuntimeState
object is NULL when the DiagnosticHandlerFn gets called in this case.
This change also adds a check before accessing it for logging.
[localhost:21000] > create function foo4 (string, string) returns string
location '/tmp/bad_udf.ll' symbol='MyAwesomeUdf';
Query: create function foo4 (string, string) returns string location
'/tmp/bad_udf.ll' symbol='MyAwesomeUdf'
ERROR: AnalysisException: Could not load binary: /tmp/bad_udf.ll
LLVM diagnostic error: Invalid bitcode signature
Change-Id: Id060668802ca9c80367cdc0e8a823b968d549bbb
Reviewed-on: http://gerrit.cloudera.org:8080/9154
Reviewed-by: Tim Armstrong <tarmstrong@cloudera.com>
Tested-by: Impala Public Jenkins
Adds support for the Kudu DECIMAL type introduced in Kudu 1.7.0.
Note: Adding support for Kudu decimal min/max filters is
tracked in IMPALA-6533.
Tests:
* Added Kudu create with decimal test to AnalyzeDDLTest.java
* Added Kudu table_format to test_decimal_queries.py
** Both decimal.test and decimal-exprs.test workloads
* Added decimal queries to the following Kudu workloads:
** kudu_create.test
** kudu_delete.test
** kudu_insert.test
** kudu_update.test
** kudu_upsert.test
Change-Id: I3a9fe5acadc53ec198585d765a8cfb0abe56e199
Reviewed-on: http://gerrit.cloudera.org:8080/9368
Reviewed-by: Dimitris Tsirogiannis <dtsirogiannis@cloudera.com>
Tested-by: Impala Public Jenkins
This change adds support for "clustered", "noclustered", "shuffle" and
"noshuffle" hints in CTAS statement.
Example:
create /*+ clustered,noshuffle */ table t partitioned by (year, month)
as select * from functional.alltypes
The effect of these hints are the same as in insert statements:
clustered:
Sort locally by partition expression before insert to ensure that only
one partition is written at a time. The goal is to reduce the number of
files kept open / buffers kept in memory simultaneously.
noclustered:
Do not sort by primary key before insert to Kudu table. No effect on HDFS
tables currently, as this is their default behavior.
shuffle:
Forces the planner to add an exchange node that repartitions by the
partition expression of the output table. This means that a partition
will be written only by a single node, which minimizes the global
number of simultaneous writes.
If only one partition is written (because all partitioning columns
are constant or the target table is not partitioned), then the shuffle
hint leads to a plan where all rows are merged at the coordinator where
the table sink is executed.
noshuffle:
Do not add exchange node before insert to partitioned tables.
The parser needed some modifications to be able to differentiate between
CREATE statements that allow hints (CTAS), and CREATE statements that
do not (every other type of CREATE statements). As a result, KW_CREATE
was moved from tbl_def_without_col_defs to statement rules.
Testing:
The parser tests mirror the tests of INSERT, while analysis and planner
tests are minimal, as the underlying logic is the same as for INSERT.
Query tests are not created, as the hints have no effect on
the DDL part of CTAS, and the actual query ran is the same as in
the insert case.
Change-Id: I8d74bca999da8ae1bb89427c70841f33e3c56ab0
Reviewed-on: http://gerrit.cloudera.org:8080/8400
Reviewed-by: Alex Behm <alex.behm@cloudera.com>
Tested-by: Impala Public Jenkins
If the first number in a row group written by Impala is NaN,
then Impala writes incorrect statistics in the metadata.
This will result in incorrect results when filtering the
data.
This commit fixes the read path when encountering NaNs in
Parquet min/max statistics. If min and max are both NaN, we
can't use the statistics at all. If only one of them is NaN,
the other still can be used.
I added some tests to QueryTest/parqet-stats.test
Change-Id: If3897fc1426541239223670812f59e2bed32f455
Reviewed-on: http://gerrit.cloudera.org:8080/9358
Reviewed-by: Tim Armstrong <tarmstrong@cloudera.com>
Tested-by: Impala Public Jenkins
The default value for rpc_max_message_size is 50MB.
Impala currently requires support for messages of
up to 2GB. This changes the value of rpc_max_message_size
to INT_MAX for Impala.
Testing:
- Added a test to test_very_large_strings that generates
a row with multiple large strings. This row requires
that the RPC framework successfully transmit over
400MB. This works for both KRPC and Thrift.
This query operates under the same amount of memory
as other queries in large_strings.test.
- Tested separately that larger row sizes also work,
including tests up to almost 2GB.
Change-Id: I876bba0536e1d85e41eacd9c0aeccfe5c2126e58
Reviewed-on: http://gerrit.cloudera.org:8080/9337
Reviewed-by: Joe McDonnell <joemcdonnell@cloudera.com>
Tested-by: Impala Public Jenkins
Previously, tuple pointers of a row batch are allocated from
the heap via malloc() and tuple data is allocated from the
MemPool associated with the RowBatch. This change converts
the allocations of tuple pointers and tuple data to using
BufferPool for row batches allocated from KrpcDataStreamRecvr.
The primary motivation for this change is to take advantage of
the fact that buffers allocated from BufferPool always go back
to the per-core arena they came from when they are freed. This
alleviates the TCMalloc imbalance between the RPC service threads
and the fragment execution threads. As described in IMPALA-5518,
row batches are always allocated from the service threads' TCMalloc
cache and placed into the fragment execution threads' TCMalloc cache
when they're freed. This leads to underflow and overflow in those
threads' caches and high contention for the spinlock of the central
free list. With BufferPool, the memory always went back to its
originating arena so this kind of imbalance is less likely to occur.
This also dovetails with the long term plan to put most allocations
under BufferPool and have each operators in the plan reserved
appropriate amount of memory before execution.
Note that the proper reservation mechanism of the exchange node
hasn't yet been implemented in this change so the buffer pool client
handle used for allocating buffers has an ad-hoc set-up of no reservation
limit and using root reservation tracker as parent. This needs to be
fixed as part of IMPALA-6524. The default buffer pool limit is also
bumped to 85% to account for the extra usage from the exchange nodes.
The minimum buffer size is also lowered to 8KB to reduce amount of memory
wastage as a row batch's tuple pointers / tuple data can sometimes be
much smaller than 64KB.
Testing done: Debug core build.
Change-Id: If4b1a45f68b9df0d3b539511e15aff15700246f2
Reviewed-on: http://gerrit.cloudera.org:8080/9344
Reviewed-by: Michael Ho <kwho@cloudera.com>
Tested-by: Impala Public Jenkins
