Connect to OceanBase Database by using the Spark catalog|V4.4.2|OceanBase Database| docs|Distributed Database

The OceanBase Spark Connector supports the Spark catalog since version 1.1. By using the Spark catalog, you can access and operate OceanBase Database in a more concise and consistent manner.

This topic describes how to configure and use the OceanBase Spark catalog. After you complete the tutorial, you will be able to access and operate OceanBase Database by using the Spark catalog.

Prerequisites

You have deployed OceanBase Database and created a MySQL-compatible user tenant. For more information about how to create a user tenant, see Create a user tenant.

Procedure

Step 1: Obtain the database connection information

Contact the deployment personnel or administrator of OceanBase Database to obtain the database connection string. For example:

obclient -h$host -P$port -u$user_name -p$password -D$database_name

Parameter description:

$host: the IP address for connecting to OceanBase Database. For the connection through OceanBase Database Proxy (ODP), use the IP address of an ODP. For direct connection, use the IP address of an OBServer node.
$port: the port for connecting to OceanBase Database. For the connection through ODP, the default port is 2883, which can be customized during ODP deployment. For direct connection, the default port is 2881, which can be customized during OceanBase Database deployment.
$database_name: the name of the database to be accessed.

Notice

The user for connecting to the tenant must have the CREATE, INSERT, DROP, and SELECT privileges on the database. For more information about user privileges, see Privilege types in MySQL-compatible mode.
$user_name: the tenant connection account. For the connection through ODP, the common format is username@tenant name#cluster name or cluster name:tenant name:username. For direct connection, the format is username@tenant name.
$password: the password of the account.

For more information about the connection string, see Connect to an OceanBase tenant by using OBClient.

Step 2: Prepare the Spark environment

Download Spark 3.4.4 and extract it to the specified directory. Use the following commands to navigate to the Spark directory and set the SPARK_HOME environment variable to the extracted directory:

Follow these steps to download and configure Spark:

Download Spark 3.4.4.

wget https://dlcdn.apache.org/spark/spark-3.4.4/spark-3.4.4-bin-hadoop3.tgz

Extract spark-3.4.4-bin-hadoop3.tgz to the specified directory.
```
tar -zxvf spark-3.4.4-bin-hadoop3.tgz -C $SPARK_HOME
```
Example:

Extract spark-3.4.4-bin-hadoop3.tgz to the specified directory /home/admin/test_spark_catalog.
```
tar -zxvf spark-3.4.4-bin-hadoop3.tgz -C /home/admin/test_spark_catalog
```

Set the SPARK_HOME environment variable to the extracted directory.

export SPARK_HOME=$(pwd)

Example:

export SPARK_HOME=/home/admin/test_spark_catalog/spark-3.4.4-bin-hadoop3

Step 3: Configure OceanBase Catalog

Download the OceanBase Spark Connector.
1. Download the spark-connector-oceanbase JAR file.
2. Move the JAR file to the jars directory of Spark Home:
```
cp spark-connector-oceanbase-3.4_2.12-1.1.jar $SPARK_HOME/jars/
```
Alternatively, you can directly download the spark-connector-oceanbase JAR file in the specified directory after decompressing spark-3.4.4-bin-hadoop3.tgz.
1. Navigate to the jars directory of Spark Home.
  
  Example:
```
cd /home/admin/test_spark_catalog/spark-3.4.4-bin-hadoop3/jars
```
2. Download the spark-connector-oceanbase.
```
wget https://repo1.maven.org/maven2/com/oceanbase/spark-connector-oceanbase-3.4_2.12/1.1/spark-connector-oceanbase-3.4_2.12-1.1.jar
```
Download the MySQL driver.
1. Download the MySQL driver JAR file.
2. Move the JAR file to the jars directory of Spark Home:
```
cp mysql-connector-j-8.2.0.jar $SPARK_HOME/jars/
```
Alternatively, you can directly download the MySQL driver JAR file in the specified directory after decompressing spark-3.4.4-bin-hadoop3.tgz.
1. Navigate to the jars directory of Spark Home.
  
  Example:
```
cd /home/admin/test_spark_catalog/spark-3.4.4-bin-hadoop3/jars
```
2. Download the MySQL driver.
```
wget https://repo1.maven.org/maven2/com/mysql/mysql-connector-j/8.2.0/mysql-connector-j-8.2.0.jar
```
Edit the Spark configuration file, which is located at $SPARK_HOME/conf/spark-defaults.conf by default.

Example:

Note

The IP address and password in the example are desensitized. You need to replace them with the actual values in your environment.
1. Navigate to the $SPARK_HOME/conf directory.
```
cd /home/admin/test_spark_catalog/spark-3.4.4-bin-hadoop3/conf
```
2. Configure OceanBase Catalog.
```
vi spark-defaults.conf
```
  Enter the following content:
```
spark.sql.catalog.ob=com.oceanbase.spark.catalog.OceanBaseCatalog
spark.sql.catalog.ob.url=jdbc:mysql://10.10.10.1:2881
spark.sql.catalog.ob.username=root@mysql001
spark.sql.catalog.ob.password=******
spark.sql.catalog.ob.schema-name=test
```

Start the Spark SQL CLI.

$SPARK_HOME/bin/spark-sql

Here is an example:

/home/admin/test_spark_catalog/spark-3.4.4-bin-hadoop3/bin/spark-sql

The return result is as follows:

Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
25/04/07 15:52:02 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
25/04/07 15:52:05 WARN HiveConf: HiveConf of name hive.stats.jdbc.timeout does not exist
25/04/07 15:52:05 WARN HiveConf: HiveConf of name hive.stats.retries.wait does not exist
25/04/07 15:52:09 WARN ObjectStore: Version information not found in metastore. hive.metastore.schema.verification is not enabled so recording the schema version 2.3.0
25/04/07 15:52:09 WARN ObjectStore: setMetaStoreSchemaVersion called but recording version is disabled: version = 2.3.0, comment = Set by MetaStore admin@10.10.10.1
25/04/07 15:52:09 WARN ObjectStore: Failed to get database default, returning NoSuchObjectException
Spark master: local[*], Application Id: local-1744012324296
spark-sql (default)>

Enter use ob; in the Spark SQL CLI to switch to the OceanBase catalog configured in step 3.

spark-sql (default)> use ob;

The return result is as follows:

25/04/07 16:11:06 WARN ObjectStore: Failed to get database global_temp, returning NoSuchObjectException
Time taken: 2.544 seconds
spark-sql (test)>

Step 4: Use OceanBase Catalog with Spark CLI

Create an OceanBase database table by using Spark SQL

In the Spark SQL CLI, execute the following statement:

spark-sql (test)> CREATE TABLE orders (
    order_id      INT COMMENT 'order id',
    order_date    TIMESTAMP,
    customer_name STRING,
    price         DOUBLE,
    product_id    INT,
    order_status  BOOLEAN);

The execution result is as follows:

25/04/07 16:14:25 WARN OceanBaseMySQLDialect: Ignored unsupported table property: owner
Time taken: 0.085 seconds

After the execution is successful, connect to the OceanBase database by using OBClient. You can see that the orders table has been created.

$obclient -h10.10.10.1 -P2881 -uroot@mysql001 -p****** -A test
Welcome to the OceanBase.  Commands end with ; or \g.
Your OceanBase connection id is 3221487689
Server version: OceanBase 4.2.1.8 (r108020012024111712-585a11c3514ac7882b041453a529050ac62c6180) (Built Nov 17 2024 12:49:45)

Copyright (c) 2000, 2018, OceanBase and/or its affiliates. All rights reserved.

Type 'help;' or '\h' for help. Type '\c' to clear the current input statement.

obclient [test]> show tables;
+----------------+
| Tables_in_test |
+----------------+
| orders         |
+----------------+
1 row in set

You can also execute show tables; in the Spark SQL CLI to query the orders table:
```
spark-sql (test)> show tables;
```
The execution result is as follows:
```
orders
Time taken: 0.044 seconds, Fetched 1 row(s)
```

Write data to an OceanBase Database table by using Spark SQL

Run the following write statement in the Spark SQL CLI:

spark-sql (test)> INSERT INTO orders VALUES
    (1, now(), 'zs', 12.2, 12, true),
    (2, now(), 'ls', 121.2, 12, true),
    (3, now(), 'xx', 123.2, 12, true),
    (4, now(), 'jac', 124.2, 12, false),
    (5, now(), 'dot', 111.25, 12, true);

The return result is as follows:

Time taken: 1.433 seconds

Then, connect to OceanBase Database by using OBClient and query the data in the orders table.

obclient [test]> SELECT * FROM orders;

The return result is as follows:

+----------+---------------------+---------------+--------+------------+--------------+
| order_id | order_date          | customer_name | price  | product_id | order_status |
+----------+---------------------+---------------+--------+------------+--------------+
|        1 | 2025-04-07 16:22:34 | zs            |   12.2 |         12 |            1 |
|        2 | 2025-04-07 16:22:34 | ls            |  121.2 |         12 |            1 |
|        4 | 2025-04-07 16:22:34 | jac           |  124.2 |         12 |            0 |
|        5 | 2025-04-07 16:22:34 | dot           | 111.25 |         12 |            1 |
|        3 | 2025-04-07 16:22:34 | xx            |  123.2 |         12 |            1 |
+----------+---------------------+---------------+--------+------------+--------------+
5 rows in set

Query an OceanBase Database table by using Spark SQL

Run the following query statement in the Spark SQL CLI:

spark-sql (test)> SELECT * FROM orders;

The return result is as follows:

1       2025-04-07 16:22:34     zs      12.2    12      true
2       2025-04-07 16:22:34     ls      121.2   12      true
4       2025-04-07 16:22:34     jac     124.2   12      false
5       2025-04-07 16:22:34     dot     111.25  12      true
3       2025-04-07 16:22:34     xx      123.2   12      true
Time taken: 0.462 seconds, Fetched 5 row(s)

Step 5: Synchronize external system data to OceanBase Database.

Create a Hive table and insert data

In the Spark SQL CLI, execute the following statements to create a Hive table:

spark-sql (test)> DROP TABLE spark_catalog.default.orders;
Time taken: 0.827 seconds

spark-sql (test)> CREATE TABLE spark_catalog.default.orders (
    order_id      INT,
    order_date    TIMESTAMP,
    customer_name STRING,
    price         DOUBLE,
    product_id    INT,
    order_status  BOOLEAN
    ) USING PARQUET;

The output is as follows:

25/04/07 16:37:54 WARN SessionState: METASTORE_FILTER_HOOK will be ignored, since hive.security.authorization.manager is set to instance of HiveAuthorizerFactory.
25/04/07 16:37:54 WARN HiveConf: HiveConf of name hive.internal.ss.authz.settings.applied.marker does not exist
25/04/07 16:37:54 WARN HiveConf: HiveConf of name hive.stats.jdbc.timeout does not exist
25/04/07 16:37:54 WARN HiveConf: HiveConf of name hive.stats.retries.wait does not exist
Time taken: 0.457 seconds

In the Spark SQL CLI, execute the following statements to insert data into the Hive table:

spark-sql (test)> INSERT INTO spark_catalog.default.orders VALUES
    (1, now(), 'zs', 12.2, 12, true),
    (2, now(), 'ls', 121.2, 12, true),
    (3, now(), 'xx', 123.2, 12, true),
    (4, now(), 'jac', 124.2, 12, false),
    (5, now(), 'dot', 111.25, 12, true);

The output is as follows:

Time taken: 1.378 seconds

Synchronize data from Hive tables to OceanBase Database

Execute the following write statement in Spark SQL CLI.

spark-sql (test)> INSERT INTO orders
    SELECT * FROM spark_catalog.default.orders;

The return result is as follows:

Time taken: 0.523 seconds

Connect to OceanBase Database by using OBClient and query the orders table. The query result shows that the data is synchronized.

obclient [test]> SELECT * FROM orders;

The return result is as follows:

+----------+---------------------+---------------+--------+------------+--------------+
| order_id | order_date          | customer_name | price  | product_id | order_status |
+----------+---------------------+---------------+--------+------------+--------------+
|        1 | 2025-04-07 16:22:34 | zs            |   12.2 |         12 |            1 |
|        2 | 2025-04-07 16:22:34 | ls            |  121.2 |         12 |            1 |
|        4 | 2025-04-07 16:22:34 | jac           |  124.2 |         12 |            0 |
|        5 | 2025-04-07 16:22:34 | dot           | 111.25 |         12 |            1 |
|        3 | 2025-04-07 16:22:34 | xx            |  123.2 |         12 |            1 |
|        4 | 2025-04-07 16:42:04 | jac           |  124.2 |         12 |            0 |
|        5 | 2025-04-07 16:42:04 | dot           | 111.25 |         12 |            1 |
|        1 | 2025-04-07 16:42:04 | zs            |   12.2 |         12 |            1 |
|        2 | 2025-04-07 16:42:04 | ls            |  121.2 |         12 |            1 |
|        3 | 2025-04-07 16:42:04 | xx            |  123.2 |         12 |            1 |
+----------+---------------------+---------------+--------+------------+--------------+
10 rows in set

Clean up the environment

After you complete this tutorial, you can stop the Spark SQL CLI as needed by using the following command.

spark-sql (test)> quit;

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Connect to OceanBase Database by using the Spark catalog

Prerequisites

Procedure

Step 1: Obtain the database connection information

Notice

Step 2: Prepare the Spark environment

Step 3: Configure OceanBase Catalog

Note

Step 4: Use OceanBase Catalog with Spark CLI

Create an OceanBase database table by using Spark SQL

Write data to an OceanBase Database table by using Spark SQL

Query an OceanBase Database table by using Spark SQL

Step 5: Synchronize external system data to OceanBase Database.

Create a Hive table and insert data

Synchronize data from Hive tables to OceanBase Database

Clean up the environment