594c9c5a3e
## What changes were proposed in this pull request? This patch does pooling for both kafka consumers as well as fetched data. The overall benefits of the patch are following: * Both pools support eviction on idle objects, which will help closing invalid idle objects which topic or partition are no longer be assigned to any tasks. * It also enables applying different policies on pool, which helps optimization of pooling for each pool. * We concerned about multiple tasks pointing same topic partition as well as same group id, and existing code can't handle this hence excess seek and fetch could happen. This patch properly handles the case. * It also makes the code always safe to leverage cache, hence no need to maintain reuseCache parameter. Moreover, pooling kafka consumers is implemented based on Apache Commons Pool, which also gives couple of benefits: * We can get rid of synchronization of KafkaDataConsumer object while acquiring and returning InternalKafkaConsumer. * We can extract the feature of object pool to outside of the class, so that the behaviors of the pool can be tested easily. * We can get various statistics for the object pool, and also be able to enable JMX for the pool. FetchedData instances are pooled by custom implementation of pool instead of leveraging Apache Commons Pool, because they have CacheKey as first key and "desired offset" as second key which "desired offset" is changing - I haven't found any general pool implementations supporting this. This patch brings additional dependency, Apache Commons Pool 2.6.0 into `spark-sql-kafka-0-10` module. ## How was this patch tested? Existing unit tests as well as new tests for object pool. Also did some experiment regarding proving concurrent access of consumers for same topic partition. * Made change on both sides (master and patch) to log when creating Kafka consumer or fetching records from Kafka is happening. * branches * master: https://github.com/HeartSaVioR/spark/tree/SPARK-25151-master-ref-debugging * patch: https://github.com/HeartSaVioR/spark/tree/SPARK-25151-debugging * Test query (doing self-join) * https://gist.github.com/HeartSaVioR/d831974c3f25c02846f4b15b8d232cc2 * Ran query from spark-shell, with using `local[*]` to maximize the chance to have concurrent access * Collected the count of fetch requests on Kafka via command: `grep "creating new Kafka consumer" logfile | wc -l` * Collected the count of creating Kafka consumers via command: `grep "fetching data from Kafka consumer" logfile | wc -l` Topic and data distribution is follow: ``` truck_speed_events_stream_spark_25151_v1:0:99440 truck_speed_events_stream_spark_25151_v1:1:99489 truck_speed_events_stream_spark_25151_v1:2:397759 truck_speed_events_stream_spark_25151_v1:3:198917 truck_speed_events_stream_spark_25151_v1:4:99484 truck_speed_events_stream_spark_25151_v1:5:497320 truck_speed_events_stream_spark_25151_v1:6:99430 truck_speed_events_stream_spark_25151_v1:7:397887 truck_speed_events_stream_spark_25151_v1:8:397813 truck_speed_events_stream_spark_25151_v1:9:0 ``` The experiment only used smallest 4 partitions (0, 1, 4, 6) from these partitions to finish the query earlier. The result of experiment is below: branch | create Kafka consumer | fetch request -- | -- | -- master | 1986 | 2837 patch | 8 | 1706 Closes #22138 from HeartSaVioR/SPARK-25151. Lead-authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com> Co-authored-by: Jungtaek Lim <kabhwan@gmail.com> Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com> |
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_data | ||
_includes | ||
_layouts | ||
_plugins | ||
css | ||
img | ||
js | ||
_config.yml | ||
api.md | ||
building-spark.md | ||
cloud-integration.md | ||
cluster-overview.md | ||
configuration.md | ||
contributing-to-spark.md | ||
graphx-programming-guide.md | ||
hadoop-provided.md | ||
hardware-provisioning.md | ||
index.md | ||
job-scheduling.md | ||
ml-advanced.md | ||
ml-ann.md | ||
ml-classification-regression.md | ||
ml-clustering.md | ||
ml-collaborative-filtering.md | ||
ml-datasource.md | ||
ml-decision-tree.md | ||
ml-ensembles.md | ||
ml-features.md | ||
ml-frequent-pattern-mining.md | ||
ml-guide.md | ||
ml-linear-methods.md | ||
ml-migration-guides.md | ||
ml-pipeline.md | ||
ml-statistics.md | ||
ml-survival-regression.md | ||
ml-tuning.md | ||
mllib-classification-regression.md | ||
mllib-clustering.md | ||
mllib-collaborative-filtering.md | ||
mllib-data-types.md | ||
mllib-decision-tree.md | ||
mllib-dimensionality-reduction.md | ||
mllib-ensembles.md | ||
mllib-evaluation-metrics.md | ||
mllib-feature-extraction.md | ||
mllib-frequent-pattern-mining.md | ||
mllib-guide.md | ||
mllib-isotonic-regression.md | ||
mllib-linear-methods.md | ||
mllib-migration-guides.md | ||
mllib-naive-bayes.md | ||
mllib-optimization.md | ||
mllib-pmml-model-export.md | ||
mllib-statistics.md | ||
monitoring.md | ||
programming-guide.md | ||
quick-start.md | ||
rdd-programming-guide.md | ||
README.md | ||
running-on-kubernetes.md | ||
running-on-mesos.md | ||
running-on-yarn.md | ||
security.md | ||
spark-standalone.md | ||
sparkr.md | ||
sql-data-sources-avro.md | ||
sql-data-sources-binaryFile.md | ||
sql-data-sources-hive-tables.md | ||
sql-data-sources-jdbc.md | ||
sql-data-sources-json.md | ||
sql-data-sources-load-save-functions.md | ||
sql-data-sources-orc.md | ||
sql-data-sources-parquet.md | ||
sql-data-sources-troubleshooting.md | ||
sql-data-sources.md | ||
sql-distributed-sql-engine.md | ||
sql-getting-started.md | ||
sql-keywords.md | ||
sql-migration-guide-hive-compatibility.md | ||
sql-migration-guide-upgrade.md | ||
sql-migration-guide.md | ||
sql-performance-tuning.md | ||
sql-programming-guide.md | ||
sql-pyspark-pandas-with-arrow.md | ||
sql-ref-arithmetic-ops.md | ||
sql-ref-datatypes.md | ||
sql-ref-functions-builtin-aggregate.md | ||
sql-ref-functions-builtin-scalar.md | ||
sql-ref-functions-builtin.md | ||
sql-ref-functions-udf-aggregate.md | ||
sql-ref-functions-udf-scalar.md | ||
sql-ref-functions-udf.md | ||
sql-ref-functions.md | ||
sql-ref-nan-semantics.md | ||
sql-ref-syntax-aux-analyze-table.md | ||
sql-ref-syntax-aux-analyze.md | ||
sql-ref-syntax-aux-cache-cache-table.md | ||
sql-ref-syntax-aux-cache-clear-cache.md | ||
sql-ref-syntax-aux-cache-uncache-table.md | ||
sql-ref-syntax-aux-cache.md | ||
sql-ref-syntax-aux-conf-mgmt-reset.md | ||
sql-ref-syntax-aux-conf-mgmt-set.md | ||
sql-ref-syntax-aux-conf-mgmt.md | ||
sql-ref-syntax-aux-describe-database.md | ||
sql-ref-syntax-aux-describe-function.md | ||
sql-ref-syntax-aux-describe-query.md | ||
sql-ref-syntax-aux-describe-table.md | ||
sql-ref-syntax-aux-describe.md | ||
sql-ref-syntax-aux-resource-mgmt-add-file.md | ||
sql-ref-syntax-aux-resource-mgmt-add-jar.md | ||
sql-ref-syntax-aux-resource-mgmt.md | ||
sql-ref-syntax-aux-show-columns.md | ||
sql-ref-syntax-aux-show-create-table.md | ||
sql-ref-syntax-aux-show-databases.md | ||
sql-ref-syntax-aux-show-functions.md | ||
sql-ref-syntax-aux-show-partitions.md | ||
sql-ref-syntax-aux-show-table.md | ||
sql-ref-syntax-aux-show-tables.md | ||
sql-ref-syntax-aux-show-tblproperties.md | ||
sql-ref-syntax-aux-show.md | ||
sql-ref-syntax-aux.md | ||
sql-ref-syntax-ddl-alter-database.md | ||
sql-ref-syntax-ddl-alter-table.md | ||
sql-ref-syntax-ddl-alter-view.md | ||
sql-ref-syntax-ddl-create-database.md | ||
sql-ref-syntax-ddl-create-function.md | ||
sql-ref-syntax-ddl-create-table.md | ||
sql-ref-syntax-ddl-create-view.md | ||
sql-ref-syntax-ddl-drop-database.md | ||
sql-ref-syntax-ddl-drop-function.md | ||
sql-ref-syntax-ddl-drop-table.md | ||
sql-ref-syntax-ddl-drop-view.md | ||
sql-ref-syntax-ddl-repair-table.md | ||
sql-ref-syntax-ddl-truncate-table.md | ||
sql-ref-syntax-ddl.md | ||
sql-ref-syntax-dml-insert-into.md | ||
sql-ref-syntax-dml-insert-overwrite-directory-hive.md | ||
sql-ref-syntax-dml-insert-overwrite-directory.md | ||
sql-ref-syntax-dml-insert-overwrite-table.md | ||
sql-ref-syntax-dml-insert.md | ||
sql-ref-syntax-dml-load.md | ||
sql-ref-syntax-dml.md | ||
sql-ref-syntax-qry-aggregation.md | ||
sql-ref-syntax-qry-explain.md | ||
sql-ref-syntax-qry-sampling.md | ||
sql-ref-syntax-qry-select-cte.md | ||
sql-ref-syntax-qry-select-distinct.md | ||
sql-ref-syntax-qry-select-groupby.md | ||
sql-ref-syntax-qry-select-having.md | ||
sql-ref-syntax-qry-select-hints.md | ||
sql-ref-syntax-qry-select-join.md | ||
sql-ref-syntax-qry-select-limit.md | ||
sql-ref-syntax-qry-select-orderby.md | ||
sql-ref-syntax-qry-select-setops.md | ||
sql-ref-syntax-qry-select-subqueries.md | ||
sql-ref-syntax-qry-select.md | ||
sql-ref-syntax-qry-window.md | ||
sql-ref-syntax-qry.md | ||
sql-ref-syntax.md | ||
sql-ref.md | ||
storage-openstack-swift.md | ||
streaming-custom-receivers.md | ||
streaming-kafka-0-10-integration.md | ||
streaming-kafka-integration.md | ||
streaming-kinesis-integration.md | ||
streaming-programming-guide.md | ||
structured-streaming-kafka-integration.md | ||
structured-streaming-programming-guide.md | ||
submitting-applications.md | ||
tuning.md | ||
web-ui.md |
license |
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Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this file to You under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. |
Welcome to the Spark documentation!
This readme will walk you through navigating and building the Spark documentation, which is included here with the Spark source code. You can also find documentation specific to release versions of Spark at https://spark.apache.org/documentation.html.
Read on to learn more about viewing documentation in plain text (i.e., markdown) or building the documentation yourself. Why build it yourself? So that you have the docs that correspond to whichever version of Spark you currently have checked out of revision control.
Prerequisites
The Spark documentation build uses a number of tools to build HTML docs and API docs in Scala, Java, Python, R and SQL.
You need to have Ruby and Python installed. Also install the following libraries:
$ sudo gem install jekyll jekyll-redirect-from pygments.rb
$ sudo pip install Pygments
# Following is needed only for generating API docs
$ sudo pip install sphinx pypandoc mkdocs
$ sudo Rscript -e 'install.packages(c("knitr", "devtools", "rmarkdown"), repos="https://cloud.r-project.org/")'
$ sudo Rscript -e 'devtools::install_version("roxygen2", version = "5.0.1", repos="https://cloud.r-project.org/")'
$ sudo Rscript -e 'devtools::install_version("testthat", version = "1.0.2", repos="https://cloud.r-project.org/")'
Note: If you are on a system with both Ruby 1.9 and Ruby 2.0 you may need to replace gem with gem2.0.
Note: Other versions of roxygen2 might work in SparkR documentation generation but RoxygenNote
field in $SPARK_HOME/R/pkg/DESCRIPTION
is 5.0.1, which is updated if the version is mismatched.
Generating the Documentation HTML
We include the Spark documentation as part of the source (as opposed to using a hosted wiki, such as the github wiki, as the definitive documentation) to enable the documentation to evolve along with the source code and be captured by revision control (currently git). This way the code automatically includes the version of the documentation that is relevant regardless of which version or release you have checked out or downloaded.
In this directory you will find text files formatted using Markdown, with an ".md" suffix. You can
read those text files directly if you want. Start with index.md
.
Execute jekyll build
from the docs/
directory to compile the site. Compiling the site with
Jekyll will create a directory called _site
containing index.html
as well as the rest of the
compiled files.
$ cd docs
$ jekyll build
You can modify the default Jekyll build as follows:
# Skip generating API docs (which takes a while)
$ SKIP_API=1 jekyll build
# Serve content locally on port 4000
$ jekyll serve --watch
# Build the site with extra features used on the live page
$ PRODUCTION=1 jekyll build
API Docs (Scaladoc, Javadoc, Sphinx, roxygen2, MkDocs)
You can build just the Spark scaladoc and javadoc by running ./build/sbt unidoc
from the $SPARK_HOME
directory.
Similarly, you can build just the PySpark docs by running make html
from the
$SPARK_HOME/python/docs
directory. Documentation is only generated for classes that are listed as
public in __init__.py
. The SparkR docs can be built by running $SPARK_HOME/R/create-docs.sh
, and
the SQL docs can be built by running $SPARK_HOME/sql/create-docs.sh
after building Spark first.
When you run jekyll build
in the docs
directory, it will also copy over the scaladoc and javadoc for the various
Spark subprojects into the docs
directory (and then also into the _site
directory). We use a
jekyll plugin to run ./build/sbt unidoc
before building the site so if you haven't run it (recently) it
may take some time as it generates all of the scaladoc and javadoc using Unidoc.
The jekyll plugin also generates the PySpark docs using Sphinx, SparkR docs
using roxygen2 and SQL docs
using MkDocs.
NOTE: To skip the step of building and copying over the Scala, Java, Python, R and SQL API docs, run SKIP_API=1 jekyll build
. In addition, SKIP_SCALADOC=1
, SKIP_PYTHONDOC=1
, SKIP_RDOC=1
and SKIP_SQLDOC=1
can be used
to skip a single step of the corresponding language. SKIP_SCALADOC
indicates skipping both the Scala and Java docs.