This is a revert of a revert, re-enabling parallel data load. It avoid
the race condition by explicitly configuring the temporary directory in
question in load-data.py.
When the parallel data load change went in, we discovered
a race with a signature of:
java.io.FileNotFoundException: File
/tmp/hadoop-jenkins/mapred/local/1508958341829_tmp does not exist
The number in this path is milliseconds since the epoch, and the race
occurs when two queries submitted to HiveServer2, running with the local
runner, hit the same millisecond time stamp. The upstream bug is
https://issues.apache.org/jira/browse/MAPREDUCE-6441, and I described the
symptoms in https://issues.apache.org/jira/browse/MAPREDUCE-6992 (which
is now marked as a dupe).
I've tested this by running data load 5 times on the same machines
where it failed before. I also ran data load manually and inspected
the system to make sure that the temporary directories are getting
created as expected in /tmp/impala-data-load-*.
Change-Id: I60d65794da08de4bb3eb439a2414c095f5be0c10
Reviewed-on: http://gerrit.cloudera.org:8080/8405
Reviewed-by: Tim Armstrong <tarmstrong@cloudera.com>
Tested-by: Impala Public Jenkins
This is a migration from an old and broken script from another
repository. Example use:
bin/single_node_perf_run.py --ninja --workloads targeted-perf \
--load --scale 4 --iterations 20 --num_impalads 3 \
--start_minicluster --query_names PERF_AGG-Q3 \
$(git rev-parse HEAD~1) $(git rev-parse HEAD)
The script can load data, run benchmarks, and compare the statistics
of those runs for significant differences in performance. It glues
together buildall.sh, bin/load-data.py, bin/run-workload.py, and
tests/benchmark/report_benchmark_results.py.
Change-Id: I70ba7f3c28f612a370915615600bf8dcebcedbc9
Reviewed-on: http://gerrit.cloudera.org:8080/6818
Reviewed-by: Jim Apple <jbapple-impala@apache.org>
Tested-by: Impala Public Jenkins
This patch lays the groundwork for loading data and running end-to-end
tests on a remote CDH cluster. The requirements for the cluster to run
the tests are:
- Managed by Cloudera Manager (CM)
- GPL Extras need to be installed
- KMS and KeyTrustee installed and available as a service
- SERDEPROPERTIES in the Hive DB modified to accept wide tables
- Hive warehouse dir points to /test-warehouse
The actual data loading is done via a new script, remote_data_load.py,
which takes the CM host as an argument. It can be run from a client
machine that is not a node of the cluster, but it needs to have the
Impala repo checked out and Impala built. This insures that all of the
necessary data load scripts are available, as well as setting up the
environment properly (client binaries like beeline and the hbase shell
are available, python libraries like cm_api are installed, necessary
environment variables are defined, etc.)
It should be noted that running remote_data_load.py will overwrite
any local XML config files with the configurations downloaded from
the remote cluster.
Usage: remote_data_load.py [options] <cm_host address>
Options:
-h, --help show this help message and exit
--snapshot-file=SNAPSHOT_FILE
Path to the test-warehouse archive
--cm-user=CM_USER Cloudera Manager admin user
--cm-pass=CM_PASS Cloudera Manager admin user password
--gateway=GATEWAY Gateway host to upload the data from. If not
set, uses the CM host as gateway.
--ssh-user=SSH_USER System user on the remote machine with
passwordless SSH configured.
--no-load Do not try to load the snapshot
--exploration-strategy=EXPLORATION_STRATEGY
--test Run end-to-end tests against cluster
Testing:
This patch is being submitted with the understanding that there are
still clean up issues that need to be addressed in the remote data
load script, for which JIRA's have been filed.
However, since many of the existing build scripts also had to be
modified, it is more important to make sure that no regressions were
inadvertently introduced into the existing data load process. Loading
data to a local mini-cluster was checked repeatedly while this patch
was being developed, as well as running it against the Jenkins job
that provides the test-warehouse snapshot used by the many other
Impala CI builds that run daily.
Change-Id: I1f443a1728a1d28168090c6f54e82dec2cb073e9
Reviewed-on: http://gerrit.cloudera.org:8080/4769
Reviewed-by: Taras Bobrovytsky <tbobrovytsky@cloudera.com>
Tested-by: Internal Jenkins
This change removes some of the occurrences of the strings 'CDH'/'cdh'
from the Impala repository. References to Cloudera-internal Jiras have
been replaced with upstream Jira issues on issues.cloudera.org.
For several categories of occurrences (e.g. pom.xml files,
DOWNLOAD_CDH_COMPONENTS) I also created a list of follow-up Jiras to
remove the occurrences left after this change.
Change-Id: Icb37e2ef0cd9fa0e581d359c5dd3db7812b7b2c8
Reviewed-on: http://gerrit.cloudera.org:8080/4187
Reviewed-by: Jim Apple <jbapple@cloudera.com>
Reviewed-by: Alex Behm <alex.behm@cloudera.com>
Tested-by: Internal Jenkins
For files that have a Cloudera copyright (and no other copyright
notice), make changes to follow the ASF source file header policy here:
http://www.apache.org/legal/src-headers.html#headers
Specifically:
1) Remove the Cloudera copyright.
2) Modify NOTICE.txt according to
http://www.apache.org/legal/src-headers.html#notice
to follow that format and add a line for Cloudera.
3) Replace or add the existing ASF license text with the one given
on the website.
Much of this change was automatically generated via:
git grep -li 'Copyright.*Cloudera' > modified_files.txt
cat modified_files.txt | xargs perl -n -i -e 'print unless m#Copyright.*Cloudera#i;'
cat modified_files_txt | xargs fix_apache_license.py [1]
Some manual fixups were performed following those steps, especially when
license text was completely missing from the file.
[1] https://gist.github.com/anonymous/ff71292094362fc5c594 with minor
modification to ORIG_LICENSE to match Impala's license text.
Change-Id: I2e0bd8420945b953e1b806041bea4d72a3943d86
Reviewed-on: http://gerrit.cloudera.org:8080/3779
Reviewed-by: Dan Hecht <dhecht@cloudera.com>
Tested-by: Internal Jenkins
The generated data is identical to the pregenerated tpch.tar.gz
and tpcds.tar.gz data that was used previously and were not
publically accessible.
This adds a "preload" hook to bin/load-data.py that can execute custom
logic for each data set. This is used to call the TPC-H and TPC-DS data
generation utilities that are already available in the Impala toolchain.
Testing:
Ran private test job with loading from snapshot disabled and without
the tpch/tpcds tarballs available.
Change-Id: Ieccfbd7d8d4a91bffddbe35abb7f5572e71a71cf
Reviewed-on: http://gerrit.cloudera.org:8080/3761
Reviewed-by: Tim Armstrong <tarmstrong@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>
All logs, test results and SQL files generated during data
loading and testing are now consolidated under a single new
directory $IMPALA_HOME/logs. The goal is to simplify archiving
in Jenkins runs and debugging.
The new structure is as follows:
$IMPALA_HOME/logs/cluster
- logs of Hadoop components and Impala
$IMPALA_HOME/logs/data_loading
- logs and SQL files produced in data loading
$IMPALA_HOME/logs/fe_tests
- logs and test output of Frontend unit tests
$IMPALA_HOME/logs/be_tests
- logs and test output of Backend unit tests
$IMPALA_HOME/logs/ee_tests
- logs and test output of end-to-end tests
$IMPALA_HOME/logs/custom_cluster_tests
- logs and test output of custom cluster tests
I tested this change with a full data load which
was successful.
Change-Id: Ief1f58f3320ec39d31b3c6bc6ef87f58ff7dfdfa
Reviewed-on: http://gerrit.cloudera.org:8080/2456
Reviewed-by: Alex Behm <alex.behm@cloudera.com>
Tested-by: Internal Jenkins
Python tests and infra scripts will now use "python" from the virtualenv
via $IMPALA_HOME/bin/impala-python. Some scripts could be simplified now
that python 2.6 and a dependable set of third-party libraries are
available but that is not done as part of this commit.
Change-Id: If1cf96898d6350e78ea107b9026b12ba63a4162f
Reviewed-on: http://gerrit.cloudera.org:8080/603
Reviewed-by: Taras Bobrovytsky <tbobrovytsky@cloudera.com>
Tested-by: Internal Jenkins
This patch improves the performance of the planning phase of a query
querying HBase tables. It removes an unnecessary second call to compute
stats and adds a new version for estimating the row count in a table.
This patch adds an incremental version to estimate the number of rows
for a set of regions. This incremental version will start querying up to
five regions to calculate the average row size and use this value to
estimate the row count based on the size of the regions on disk. Only if
the standard deviation from the average is larger than 15% query an
additional region, it will query additional regions to calculate an
average with more confidence.
If the data is balanced it will not be necessary to retrieve data from
all regions but only from a subset. In the worst case, all regions are
queried.
Change-Id: Idcb3bea81b11cb08da6d9329ba66c86aca23e170
Reviewed-on: http://gerrit.sjc.cloudera.com:8080/5258
Tested-by: jenkins
Reviewed-by: Martin Grund <mgrund@cloudera.com>
This is the first iteration of a kerberized development environment.
All the daemons start and use kerberos, with the sole exception of the
hive metastore. This is sufficient to test impala authentication.
When buildall.sh is run using '-kerberize', it will stop before
loading data or attempting to run tests.
Loading data into the cluster is known to not work at this time, the
root causes being that Beeline -> HiveServer2 -> MapReduce throws
errors, and Beeline -> HiveServer2 -> HBase has problems. These are
left for later work.
However, the impala daemons will happily authenticate using kerberos
both from clients (like the impala shell) and amongst each other.
This means that if you can get data into the mini-cluster, you could
query it.
Usage:
* Supply a '-kerberize' option to buildall.sh, or
* Supply a '-kerberize' option to create-test-configuration.sh, then
'run-all.sh -format', re-source impala-config.sh, and then start
impala daemons as usual. You must reformat the cluster because
kerberizing it will change all the ownership of all files in HDFS.
Notable changes:
* Added clean start/stop script for the llama-minikdc
* Creation of Kerberized HDFS - namenode and datanodes
* Kerberized HBase (and Zookeeper)
* Kerberized Hive (minus the MetaStore)
* Kerberized Impala
* Loading of data very nearly working
Still to go:
* Kerberize the MetaStore
* Get data loading working
* Run all tests
* The unknown unknowns
* Extensive testing
Change-Id: Iee3f56f6cc28303821fc6a3bf3ca7f5933632160
Reviewed-on: http://gerrit.sjc.cloudera.com:8080/4019
Reviewed-by: Michael Yoder <myoder@cloudera.com>
Tested-by: jenkins
While loading parquet, there are a few table creation queries that use the 'like'
keyword; this ends up opening a small race window when all the table formats are created
concurrently. With this change, we create the text tables first before attempting to
parallelize the rest of the data loading.
Change-Id: Ib84cf0e5120b3588d3f0503d7119ca055e08e53f
Reviewed-on: http://gerrit.ent.cloudera.com:8080/1241
Reviewed-by: Ishaan Joshi <ishaan@cloudera.com>
Tested-by: jenkins
During a full data load, we load all the data (except parquet) via hive, and then load the
parquet data via Impala. The catalog service does not update the metadata of tables
changed outside Impala, so we need to explicitly invalidate the metadata before loading
parquet data.
Change-Id: Iec39db9ea46e4a11b17589881732629a56444120
Reviewed-on: http://gerrit.ent.cloudera.com:8080/1207
Reviewed-by: Ishaan Joshi <ishaan@cloudera.com>
Tested-by: jenkins
Instead of calling 'invalidate metadata' before loading each workload
we should call it once, after loading all test data. This will allow
us to pickup data inserted by Hive. The only reason this worked before
is because we restart Impala before running the tests. This will also
be a bit faster if loading multiple workloads.
Change-Id: I28d42bbf5d7a24b5fde687d67a4b41472ec4b897
Reviewed-on: http://gerrit.ent.cloudera.com:8080/1153
Reviewed-by: Ishaan Joshi <ishaan@cloudera.com>
Tested-by: jenkins
Currently, we execute all the queries involved in data loading serially. This change
creates a separate .sql file for each file format, compression codec and compression
scheme combination, and executes all the files in parallel. Additionally, we now store all the
.sql files (independent of workload) in $IMPALA_HOME/data_load_files/<dataset_name>. Note
that only data loaded through Impala is parallelized, data loaded through hive and hbase
remains serial.
On our build machines, the time taken to load all the data from snapshot was on the order
of 15 minutes.
Change-Id: If8a862c43f0e75b506ca05d83eacdc05621cbbf8
Reviewed-on: http://gerrit.ent.cloudera.com:8080/804
Reviewed-by: Ishaan Joshi <ishaan@cloudera.com>
Tested-by: Ishaan Joshi <ishaan@cloudera.com>
Tested-by: jenkins
At the moment, a query is the default unit of execution and parallelism in the Impala
performance suite. With this change, we now have the ability to treat a workload as the
unit of execution. A workload is defined as a unique combination of the dataset, scale
factor, a subset (or all) of the queries in the dataset, and a table format (file format,
compression codec and compression scheme).
It introduces two new command line options in bin/run-workload.py:
* --execution_scope
The default scope is 'query', and it maintains previous semantics. The
new scope is 'workload', which toggles the unit of execution to a workload.
* --shuffle_query_exec_order.
Shuffles the order in which queries are executed (only applicable when the
execution_scope if workload), defaults to False.
Change-Id: I790d75f0896210cda8eb999015b0be04246e4c45
Reviewed-on: http://gerrit.ent.cloudera.com:8080/503
Reviewed-by: Ishaan Joshi <ishaan@cloudera.com>
Tested-by: Ishaan Joshi <ishaan@cloudera.com>
This change adds Impala DDL support for creation of AVRO tables.
Additionally, it add Impala support for CREATE and ALTER SERDEPROPERTIES
which are used when creating Avro backed tables. This syntax is not
exactly the same as the Hive support since it introduces a new
fileformat (AVROFILE) that implies the needed Serialization library,
input format, and output format.
Change-Id: I5047e419198a89599e9d014fdedfee1a20437a7d
Reviewed-on: http://gerrit.ent.cloudera.com:8080/464
Reviewed-by: Lenni Kuff <lskuff@cloudera.com>
Tested-by: Lenni Kuff <lskuff@cloudera.com>
This change adds support for auxiliary worksloads, tests, and datasets. This is useful
to augment the regular test runs with some additional tests that do not belong in the
main Impala repo.
This change includes a number of improvements for the test data loading framework:
* Named sections for schema template definitions
* Removal of uneeded sections from schema template definitions (ex. ANALYZE TABLE)
* More granular data loading via table name filters
* Improved robustness in detecting failed data loads
* Table level constraints for specific file formats
* Re-written compute stats script
Add support for generating ANALYZE TABLE ... COMPUTE STATISTICS statements to the data loading
workflow. This allows for capturing simple table stats such as number of rows, number of
partitions, and table size in bytes. These are stored into a new mysql database with the same
name as the metastore except with a '_Stats' suffix. If using Derby a new database results are
stored in a new derby database.
Fixed a problem where we were not properly looking up the dataset associated
with the given workload if it had non-word characters in its name (a-z & _). Also cut down
on the execution time of the hive-benchmark workload under the "core" vector.
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
