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TiSpark Quick Start Guide

To make it easy to try TiSpark, the TiDB cluster installed using TiDB Ansible integrates Spark, TiSpark jar package and TiSpark sample data by default.

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.

    spark/jars/tispark-${name_with_version}.jar
  • TiSpark sample data and import scripts can be downloaded from TiSpark sample data.

    tispark-sample-data/

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 192.168.0.2, the port is 4000, the user name is root, and the password is null.

wget http://download.pingcap.org/tispark-sample-data.tar.gz
tar -zxvf tispark-sample-data.tar.gz
cd tispark-sample-data

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

mysql --local-infile=1 -h 192.168.0.2 -P 4000 -u root < dss.ddl

Run the script:

./sample_data.sh

Note:

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           |
+--------------------+
| INFORMATION_SCHEMA |
| PERFORMANCE_SCHEMA |
| 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

First start the spark-shell:

$ cd spark
$ bin/spark-shell

Then query the TiDB table as you are using the native Spark SQL:

scala> spark.sql("use TPCH_001")
scala> spark.sql("select count(*) from lineitem").show

The result is:

+--------+
|count(1)|
+--------+
|   60175|
+--------+

Now run a more complex Spark SQL:

scala> spark.sql(
      """select
        |   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
        |from
        |   lineitem
        |where
        |   l_shipdate <= date '1998-12-01' - interval '90' day
        |group by
        |   l_returnflag,
        |   l_linestatus
        |order by
        |   l_returnflag,
        |   l_linestatus
      """.stripMargin).show

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|
+------------+------------+---------+--------------+--------------+
(Continued)
-----------------+---------+------------+--------+-----------+
       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).