TiSpark is a thin layer built for running Apache Spark on top of TiDB/TiKV to answer complex OLAP queries. It takes advantages of both the Spark platform and the distributed TiKV cluster while seamlessly glues to TiDB, the distributed OLTP database, to provide a Hybrid Transactional/Analytical Processing (HTAP), and serves as a one-stop solution for online transactions and analysis.

Read the Quick Start.

The current stable version is TiSpark 2.1.1 which is compatible with Spark 2.3.0+ and Spark 2.4.0+.

The latest stable version compatible with Spark 2.1.0+ is TiSpark 1.2.1

When using TiSpark 1.2.1, please follow the document for Spark 2.1

When using TiSpark 2.1.2 with Spark 2.3.0+, please use version 2.1.2-spark_2.3 and follow the document for Spark 2.3+

When using TiSpark 2.1.2 with Spark 2.4.0+, please use version 2.1.2-spark_2.4 and follow the document for Spark 2.3+

You may also build from sources to try the new features on TiSpark master branch.

If you are using maven(recommended), add the following to your pom.xml:

<dependencies>
    <dependency>
      <groupId>com.pingcap.tispark</groupId>
      <artifactId>tispark-core</artifactId>
      <version>2.1.2-spark_${spark.version}</version>
    </dependency>
</dependencies>

For other build tools, you can visit search.maven.org and search with GroupId (This search will also list all available modules of TiSpark including tikv-client).

TiSpark now supports Spark 2.3.0+/2.4.0+. The previous version for Spark 2.1.0+ will only contain bug fixes in future, you may still get Spark 2.1 support until TiSpark 1.2.1.

git clone https://github.com/pingcap/tispark.git

To build all TiSpark modules from sources, please run the command under TiSpark root directory:

mvn clean install -Dmaven.test.skip=true -P spark-2.3
or
mvn clean install -Dmaven.test.skip=true -P spark-2.4

Please note that you need to specify the major version of Spark according to the Spark version you are using.

Remember to add -Dmaven.test.skip=true to skip all the tests if you don't need to run them.

Each latest TiSpark version guarantees backward compatibility for TiDB components, i.e., supports TiDB/TiKV/PD until a certain release. Its reason varies, amongst which the most common one is that the new features and bug-fixes provided by TiDB components require an update on API, dependencies, etc.

While TiSpark provides downward compatibility for TiDB, it guarantees restricted Spark version support for means of catching up to the latest Datasource API changes.

For users using Spark 2.1 who wish to migrate to latest TiSpark on Spark 2.3/2.4, please download or install Spark 2.3+/2.4+ following instructions on Apache Spark Site and overwrite the old spark version in $SPARK_HOME.

TiSpark currently only supports scala-2.11.

• TiSpark integrates with Spark Catalyst Engine deeply. It provides precise control of computing, which allows Spark to read data from TiKV efficiently. It also supports index seek, which improves the performance of the point query execution significantly.

• It utilizes several strategies to push down the computing to reduce the size of dataset handling by Spark SQL, which accelerates the query execution. It also uses the TiDB built-in statistical information for the query plan optimization.

• From the data integration point of view, TiSpark + TiDB provides a solution runs both transaction and analysis directly on the same platform without building and maintaining any ETLs. It simplifies the system architecture and reduces the cost of maintenance.

• In addition, you can deploy and utilize tools from the Spark ecosystem for further data processing and manipulation on TiDB. For example, using TiSpark for data analysis and ETL; retrieving data from TiKV as a machine learning data source; generating reports from the scheduling system and so on.

TiSpark depends on the existence of TiKV clusters and PDs. It also needs to set up and use Spark clustering platform.

A thin layer of TiSpark. Most of the logic is inside tikv-client library. https://github.com/pingcap/tispark/tree/master/tikv-client

Before everything starts, you must add spark.sql.extensions org.apache.spark.sql.TiExtensions in spark-defaults.conf You should also confirm that spark.tispark.pd.addresses is set correctly

From Spark-shell:

./bin/spark-shell --jars /wherever-it-is/tispark-${name_with_version}.jar

For TiSpark version >= 2.0:

spark.sql("use tpch_test")

spark.sql("select count(*) from lineitem").show

For TiSpark version < 2.0:

import org.apache.spark.sql.TiContext
val ti = new TiContext (spark)
ti.tidbMapDatabase ("tpch_test")

spark.sql("select count(*) from lineitem").show

**Please Note: For now even if you use TiSpark 2.0+, for spark-submit on pyspark, tidbMapDatabase is still required and TiExtension is not supported yet. We are working on it.

spark.sql("select ti_version()").show

When using the TiDB Data Source API, please follow the document for TiDB Data Source API User Guide.

Below configurations can be put together with spark-defaults.conf or passed in the same way as other Spark config properties.

When you start spark-shell or spark-sql and run query, you might see the following warnings:

Failed to get database ****, returning NoSuchObjectException
Failed to get database ****, returning NoSuchObjectException

where **** is the name of database.

This is due to spark cannot find **** in its own catalog. The two warning messages are benign, you can just ignore them.

If you want to get rid of them, you can append the following text to ${SPARK_HOME}/conf/log4j.properties.

# tispark disable "WARN ObjectStore:568 - Failed to get database"
log4j.logger.org.apache.hadoop.hive.metastore.ObjectStore=ERROR

Time Zone can be set by using -Duser.timezone system property, e.g. -Duser.timezone=GMT-7, which will affect Timestamp type. Please do not use spark.sql.session.timeZone.

If you want to know how TiSpark could benefit from TiDB's statistic information, read more here.

TiDB starts to support view from tidb-3.0.

TiSpark currently does not support view. Users will not be able to observe or access data through views with TiSpark.

tidb-3.0 supports both Range Partition and Hash Partition.

TiSpark currently supports Range Partition and Hash Partition. Users can select data from Range Partition table and Hash Partition table through TiSpark.

In most cases, TiSpark will use a full table scan. Only in some cases, TiSpark will apply partition pruning (read more here.

We use docker-compose to provide tidb cluster service which allows you to run test across different platforms. It is recommended to install docker in order to test locally, or you can set up your own TiDB cluster locally as you wish.

If you prefer the docker way, you can use docker-compose up -d to launch tidb cluster service under tispark home directory. If you want to see tidb cluster's log you can launch via docker-compose up. You can use docker-compose down to shutdown entire tidb cluster service. All data is stored in the data directory at the root of this project. Feel free to change it.

You can read more about the test here.

@PingCAP

tidb-user@googlegroups.com

Google Group

TiSpark is under the Apache 2.0 license. See the LICENSE file for details.