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-${name_with_version}.jarTiSpark sample data and import scripts can be downloaded from TiSpark sample data.
tispark-sample-data/
Prepare the environment
Install JDK on the TiDB instance
Download the latest version of JDK 1.8 from Oracle JDK official download page. The version used in the following example is jdk-8u141-linux-x64.tar.gz
.
Extract the package and set the environment variables based on your JDK deployment directory.
Edit the ~/.bashrc
file. For example:
export JAVA_HOME=/home/pingcap/jdk1.8.0_144
export PATH=$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
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.