Quick Start Guide for the TiDB Connector for Spark

To make it easy to try the TiDB Connector for Spark, TiDB cluster integrates Spark, TiSpark jar package and TiSpark sample data by default, in both the Pre-GA and master versions installed using TiDB-Ansible.

Deployment information

  • Spark is deployed by default in the spark folder in the TiDB instance deployment directory.

  • The TiSpark jar package is deployed by default in the jars folder in the Spark deployment directory.

  • TiSpark sample data and import scripts are deployed by default in the TiDB-Ansible directory.


Prepare the environment

Install JDK on the TiDB instance

Download the latest version of JDK 1.8 from Oracle JDK official download page. The version used in the following example is jdk-8u141-linux-x64.tar.gz.

Extract the package and set the environment variables based on your JDK deployment directory.

Edit the ~/.bashrc file. For example:

export JAVA_HOME=/home/pingcap/jdk1.8.0_144
export PATH=$JAVA_HOME/bin:$PATH

Verify the validity of JDK:

$ java -version
java version "1.8.0_144"
Java(TM) SE Runtime Environment (build 1.8.0_144-b01)
Java HotSpot(TM) 64-Bit Server VM (build 25.144-b01, mixed mode)

Import the sample data

Assume that the TiDB cluster is started. The service IP of one TiDB instance is, the port is 4000, the user name is root, and the password is null.

cd tidb-ansible/resources/bin/tispark-sample-data

Edit the TiDB login information in sample_data.sh. For example:

mysql --local-infile=1 -h -P 4000 -u root < dss.ddl

Run the script:



You need to install the MySQL client on the machine that runs the script. If you are a CentOS user, you can install it through the command yum -y install mysql.

Log into TiDB and verify that the TPCH_001 database and the following tables are included.

$ mysql -uroot -P4000 -h192.168.0.2
MySQL [(none)]> show databases;
| Database           |
| TPCH_001           |
| mysql              |
| test               |
5 rows in set (0.00 sec)

MySQL [(none)]> use TPCH_001
Reading table information for completion of table and column names
You can turn off this feature to get a quicker startup with -A

Database changed
MySQL [TPCH_001]> show tables;
| Tables_in_TPCH_001 |
| CUSTOMER           |
| LINEITEM           |
| NATION             |
| ORDERS             |
| PART               |
| PARTSUPP           |
| REGION             |
| SUPPLIER           |
8 rows in set (0.00 sec)

Use example

Assume that the IP of your PD node is, and the port is 2379.

First start the spark-shell in the spark deployment directory:

$ cd spark
$ bin/spark-shell
import org.apache.spark.sql.TiContext
val ti = new TiContext(spark)

// Mapping all TiDB tables from `TPCH_001` database as Spark SQL tables

Then you can call Spark SQL directly:

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

The result is:

|   60175|

Now run a more complex Spark SQL:

scala> spark.sql(
        |   l_returnflag,
        |   l_linestatus,
        |   sum(l_quantity) as sum_qty,
        |   sum(l_extendedprice) as sum_base_price,
        |   sum(l_extendedprice * (1 - l_discount)) as sum_disc_price,
        |   sum(l_extendedprice * (1 - l_discount) * (1 + l_tax)) as sum_charge,
        |   avg(l_quantity) as avg_qty,
        |   avg(l_extendedprice) as avg_price,
        |   avg(l_discount) as avg_disc,
        |   count(*) as count_order
        |   lineitem
        |   l_shipdate <= date '1998-12-01' - interval '90' day
        |group by
        |   l_returnflag,
        |   l_linestatus
        |order by
        |   l_returnflag,
        |   l_linestatus

The result is:

|l_returnflag|l_linestatus|  sum_qty|sum_base_price|sum_disc_price|
|           A|           F|380456.00|  532348211.65|505822441.4861|
|           N|           F|  8971.00|   12384801.37| 11798257.2080|
|           N|           O|742802.00| 1041502841.45|989737518.6346|
|           R|           F|381449.00|  534594445.35|507996454.4067|
       sum_charge|  avg_qty|   avg_price|avg_disc|count_order|
 526165934.000839|25.575155|35785.709307|0.050081|      14876|
  12282485.056933|25.778736|35588.509684|0.047759|        348|
1029418531.523350|25.454988|35691.129209|0.049931|      29181|
 528524219.358903|25.597168|35874.006533|0.049828|      14902|

See more examples.