import ChangeLog from '../changelog/connector-jdbc.md';
JDBC source connector
Read external data source data through JDBC.
:::tip
Warn: for license compliance, you have to provide database driver yourself, copy to $SEATUNNEL_HOME/lib/ directory in order to make them work.
e.g. If you use MySQL, should download and copy mysql-connector-java-xxx.jar to $SEATUNNEL_HOME/lib/. For Spark/Flink, you should also copy it to $SPARK_HOME/jars/ or $FLINK_HOME/lib/.
:::
- You need to ensure that the jdbc driver jar package has been placed in directory
${SEATUNNEL_HOME}/plugins/.
- You need to ensure that the jdbc driver jar package has been placed in directory
${SEATUNNEL_HOME}/lib/.
supports query SQL and can achieve projection effect.
| name | type | required | default value | description |
|---|---|---|---|---|
| url | String | Yes | - | The URL of the JDBC connection. Refer to a case: jdbc:postgresql://localhost/test |
| driver | String | Yes | - | The jdbc class name used to connect to the remote data source, if you use MySQL the value is com.mysql.cj.jdbc.Driver. |
| user | String | No | - | userName |
| password | String | No | - | password |
| query | String | No | - | Query statement |
| compatible_mode | String | No | - | The compatible mode of database, required when the database supports multiple compatible modes. For example, when using OceanBase database, you need to set it to 'mysql' or 'oracle'. when using starrocks, you need set it to starrocks |
| connection_check_timeout_sec | Int | No | 30 | The time in seconds to wait for the database operation used to validate the connection to complete. |
| partition_column | String | No | - | The column name for split data. |
| partition_upper_bound | Long | No | - | The partition_column max value for scan, if not set SeaTunnel will query database get max value. |
| partition_lower_bound | Long | No | - | The partition_column min value for scan, if not set SeaTunnel will query database get min value. |
| partition_num | Int | No | job parallelism | Not recommended for use, The correct approach is to control the number of split through split.sizeNote: This parameter takes effect only when using the query parameter. It does not take effect when using the table_path parameter. |
| decimal_type_narrowing | Boolean | No | true | Decimal type narrowing, if true, the decimal type will be narrowed to the int or long type if without loss of precision. Only support for Oracle at now. Please refer to decimal_type_narrowing below |
| use_select_count | Boolean | No | false | Use select count for table count rather then other methods in dynamic chunk split stage. This is currently only available for jdbc-oracle.In this scenario, select count directly is used when it is faster to update statistics using sql from analysis table |
| skip_analyze | Boolean | No | false | Skip the analysis of table count in dynamic chunk split stage. This is currently only available for jdbc-oracle.In this scenario, you schedule analysis table sql to update related table statistics periodically or your table data does not change frequently |
| fetch_size | Int | No | 0 | For queries that return a large number of objects, you can configure the row fetch size used in the query to improve performance by reducing the number database hits required to satisfy the selection criteria. Zero means use jdbc default value. |
| properties | Map | No | - | Additional connection configuration parameters,when properties and URL have the same parameters, the priority is determined by the specific implementation of the driver. For example, in MySQL, properties take precedence over the URL. |
| table_path | String | No | - | The path to the full path of table, you can use this configuration instead of query. examples: - mysql: "testdb.table1" - oracle: "test_schema.table1" - sqlserver: "testdb.test_schema.table1" - postgresql: "testdb.test_schema.table1" - iris: "test_schema.table1" |
| table_list | Array | No | - | The list of tables to be read, you can use this configuration instead of table_path |
| where_condition | String | No | - | Common row filter conditions for all tables/queries, must start with where. for example where id > 100 |
| split.size | Int | No | 8096 | How many rows in one split, captured tables are split into multiple splits when read of table. Note: This parameter takes effect only when using the table_path parameter. It does not take effect when using the query parameter. |
| split.even-distribution.factor.lower-bound | Double | No | 0.05 | Not recommended for use. The lower bound of the chunk key distribution factor. This factor is used to determine whether the table data is evenly distributed. If the distribution factor is calculated to be greater than or equal to this lower bound (i.e., (MAX(id) - MIN(id) + 1) / row count), the table chunks would be optimized for even distribution. Otherwise, if the distribution factor is less, the table will be considered as unevenly distributed and the sampling-based sharding strategy will be used if the estimated shard count exceeds the value specified by sample-sharding.threshold. The default value is 0.05. |
| split.even-distribution.factor.upper-bound | Double | No | 100 | Not recommended for use. The upper bound of the chunk key distribution factor. This factor is used to determine whether the table data is evenly distributed. If the distribution factor is calculated to be less than or equal to this upper bound (i.e., (MAX(id) - MIN(id) + 1) / row count), the table chunks would be optimized for even distribution. Otherwise, if the distribution factor is greater, the table will be considered as unevenly distributed and the sampling-based sharding strategy will be used if the estimated shard count exceeds the value specified by sample-sharding.threshold. The default value is 100.0. |
| split.sample-sharding.threshold | Int | No | 1000 | This configuration specifies the threshold of estimated shard count to trigger the sample sharding strategy. When the distribution factor is outside the bounds specified by chunk-key.even-distribution.factor.upper-bound and chunk-key.even-distribution.factor.lower-bound, and the estimated shard count (calculated as approximate row count / chunk size) exceeds this threshold, the sample sharding strategy will be used. This can help to handle large datasets more efficiently. The default value is 1000 shards. |
| split.inverse-sampling.rate | Int | No | 1000 | The inverse of the sampling rate used in the sample sharding strategy. For example, if this value is set to 1000, it means a 1/1000 sampling rate is applied during the sampling process. This option provides flexibility in controlling the granularity of the sampling, thus affecting the final number of shards. It's especially useful when dealing with very large datasets where a lower sampling rate is preferred. The default value is 1000. |
| common-options | No | - | Source plugin common parameters, please refer to Source Common Options for details. | |
| split.string_split_mode | String | No | sample | Supports different string splitting algorithms. By default, sample is used to determine the split by sampling the string value. You can switch to charset_based to enable charset-based string splitting algorithm. When set to charset_based, the algorithm assumes characters of partition_column are within ASCII range 32-126, which covers most character-based splitting scenarios. |
| split.string_split_mode_collate | String | No | - | Specifies the collation to use when string_split_mode is set to charset_based and the table has a special collation. If not specified, the database's default collation will be used. |
Decimal type narrowing, if true, the decimal type will be narrowed to the int or long type if without loss of precision. Only support for Oracle at now.
eg:
decimal_type_narrowing = true
| Oracle | SeaTunnel |
|---|---|
| NUMBER(1, 0) | Boolean |
| NUMBER(6, 0) | INT |
| NUMBER(10, 0) | BIGINT |
decimal_type_narrowing = false
| Oracle | SeaTunnel |
|---|---|
| NUMBER(1, 0) | Decimal(1, 0) |
| NUMBER(6, 0) | Decimal(6, 0) |
| NUMBER(10, 0) | Decimal(10, 0) |
The JDBC Source connector supports parallel reading of data from tables. SeaTunnel will use certain rules to split the data in the table, which will be handed over to readers for reading. The number of readers is determined by the parallelism option.
Split Key Rules:
- If
partition_columnis not null, It will be used to calculate split. The column must in Supported split data type. - If
partition_columnis null, seatunnel will read the schema from table and get the Primary Key and Unique Index. If there are more than one column in Primary Key and Unique Index, The first column which in the supported split data type will be used to split data. For example, the table have Primary Key(nn guid, name varchar), becauseguidid not in supported split data type, so the columnnamewill be used to split data.
Supported split data type:
- String
- Number(int, bigint, decimal, ...)
- Date
If the table can not be split(for example, table have no Primary Key or Unique Index, and
partition_columnis not set), it will run in single concurrency.Use
table_pathto replacequeryfor single table reading. If you need to read multiple tables, usetable_list.
there are some reference value for params above.
Jdbc {
url = "jdbc:mysql://localhost/test?serverTimezone=GMT%2b8"
driver = "com.mysql.cj.jdbc.Driver"
connection_check_timeout_sec = 100
user = "root"
password = "123456"
query = "select * from type_bin"
}
Case 2 Use the select count(*) instead of analysis table for count table rows in dynamic chunk split stage
Jdbc {
url = "jdbc:mysql://localhost/test?serverTimezone=GMT%2b8"
driver = "com.mysql.cj.jdbc.Driver"
connection_check_timeout_sec = 100
user = "root"
password = "123456"
use_select_count = true
query = "select * from type_bin"
}
Jdbc {
url = "jdbc:mysql://localhost/test?serverTimezone=GMT%2b8"
driver = "com.mysql.cj.jdbc.Driver"
connection_check_timeout_sec = 100
user = "root"
password = "123456"
skip_analyze = true
query = "select * from type_bin"
}
env {
parallelism = 10
job.mode = "BATCH"
}
source {
Jdbc {
url = "jdbc:mysql://localhost/test?serverTimezone=GMT%2b8"
driver = "com.mysql.cj.jdbc.Driver"
connection_check_timeout_sec = 100
user = "root"
password = "123456"
query = "select * from type_bin"
partition_column = "id"
partition_num = 10 # Replace split.size with partition_num
# Read start boundary
#partition_lower_bound = ...
# Read end boundary
#partition_upper_bound = ...
}
}
sink {
Console {}
}
It is more efficient to specify the data within the upper and lower bounds of the query. It is more efficient to read your data source according to the upper and lower boundaries you configured.
source {
Jdbc {
url = "jdbc:mysql://localhost:3306/test?serverTimezone=GMT%2b8&useUnicode=true&characterEncoding=UTF-8&rewriteBatchedStatements=true"
driver = "com.mysql.cj.jdbc.Driver"
connection_check_timeout_sec = 100
user = "root"
password = "123456"
# Define query logic as required
query = "select * from type_bin"
partition_column = "id"
# Read start boundary
partition_lower_bound = 1
# Read end boundary
partition_upper_bound = 500
partition_num = 10
properties {
useSSL=false
}
}
}
Configuring
table_pathwill turn on auto split, you can configuresplit.*to adjust the split strategy
env {
parallelism = 10
job.mode = "BATCH"
}
source {
Jdbc {
url = "jdbc:mysql://localhost/test?serverTimezone=GMT%2b8"
driver = "com.mysql.cj.jdbc.Driver"
connection_check_timeout_sec = 100
user = "root"
password = "123456"
table_path = "testdb.table1"
query = "select * from testdb.table1"
split.size = 10000
}
}
sink {
Console {}
}
Configuring table_list will turn on auto split, you can configure split.* to adjust the split strategy
Jdbc {
url = "jdbc:mysql://localhost/test?serverTimezone=GMT%2b8"
driver = "com.mysql.cj.jdbc.Driver"
connection_check_timeout_sec = 100
user = "root"
password = "123456"
table_list = [
{
# e.g. table_path = "testdb.table1"、table_path = "test_schema.table1"、table_path = "testdb.test_schema.table1"
table_path = "testdb.table1"
},
{
table_path = "testdb.table2"
# Use query filetr rows & columns
query = "select id, name from testdb.table2 where id > 100"
}
]
#where_condition= "where id > 100"
#split.size = 10000
#split.even-distribution.factor.upper-bound = 100
#split.even-distribution.factor.lower-bound = 0.05
#split.sample-sharding.threshold = 1000
#split.inverse-sampling.rate = 1000
}