JDBC

JDBC source connector

Description​

Read external data source data through JDBC.

Key features​

• batch

• stream

• exactly-once

• column projection

supports query SQL and can achieve projection effect.

• parallelism

• support user-defined split

• support multiple table read

Options​

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'.
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 Nexus will query database get max value.
partition_lower_bound	Long	No	-	The partition_column min value for scan, if not set Nexus 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.size How many splits do we need to split into, only support positive integer. default value is job parallelism.
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.
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.

Parallel Reader​

The JDBC Source connector supports parallel reading of data from tables. Nexus 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:

1. If partition_column is not null, It will be used to calculate split. The column must in Supported split data type.

2. If partition_column is null, Nexus 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), because guid id not in supported split data type, so the column name will be used to split data.

Supported split data type:

• String

• Number(int, bigint, decimal, ...)

• Date

tips​

If the table can not be split(for example, table have no Primary Key or Unique Index, and partition_column is not set), it will run in single concurrency.

Use table_path to replace query for single table reading. If you need to read multiple tables, use table_list.

appendix​

there are some reference value for params above.

datasource	driver	url
mysql	com.mysql.cj.jdbc.Driver	jdbc:mysql://localhost:3306/test
postgresql	org.postgresql.Driver	jdbc:postgresql://localhost:5432/postgres
dm	dm.jdbc.driver.DmDriver	jdbc:dm://localhost:5236
phoenix	org.apache.phoenix.queryserver.client.Driver	jdbc:phoenix:thin:url=http://localhost:8765;serialization=PROTOBUF
sqlserver	com.microsoft.sqlserver.jdbc.SQLServerDriver	jdbc:sqlserver://localhost:1433
oracle	oracle.jdbc.OracleDriver	jdbc:oracle:thin:@localhost:1521/xepdb1
sqlite	org.sqlite.JDBC	jdbc:sqlite:test.db
gbase8a	com.gbase.jdbc.Driver	jdbc:gbase://e2e_gbase8aDb:5258/test
starrocks	com.mysql.cj.jdbc.Driver	jdbc:mysql://localhost:3306/test
db2	com.ibm.db2.jcc.DB2Driver	jdbc:db2://localhost:50000/testdb
tablestore	com.alicloud.openservices.tablestore.jdbc.OTSDriver	"jdbc:ots:http s://myinstance.cn-hangzhou.ots.aliyuncs.com/myinstance"
saphana	com.sap.db.jdbc.Driver	jdbc:sap://localhost:39015
doris	com.mysql.cj.jdbc.Driver	jdbc:mysql://localhost:3306/test
teradata	com.teradata.jdbc.TeraDriver	jdbc:teradata://localhost/DBS_PORT=1025,DATABASE=test
Snowflake	net.snowflake.client.jdbc.SnowflakeDriver	jdbc:snowflake://<account_name>.snowflakecomputing.com
Redshift	com.amazon.redshift.jdbc42.Driver	jdbc:redshift://localhost:5439/testdb?defaultRowFetchSize=1000
Vertica	com.vertica.jdbc.Driver	jdbc:vertica://localhost:5433
Kingbase	com.kingbase8.Driver	jdbc:kingbase8://localhost:54321/db_test
OceanBase	com.oceanbase.jdbc.Driver	jdbc:oceanbase://localhost:2881
Hive	org.apache.hive.jdbc.HiveDriver	jdbc:hive2://localhost:10000
xugu	com.xugu.cloudjdbc.Driver	jdbc:xugu://localhost:5138
InterSystems IRIS	com.intersystems.jdbc.IRISDriver	jdbc:IRIS://localhost:1972/%SYS

Example​

simple​

Case 1​

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"
}

Case 3 Use the select NUM_ROWS from all_tables for the table rows but skip the analyze table.​

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"
}

parallel by partition_column​

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"
        split.size = 10000
        # Read start boundary
        #partition_lower_bound = ...
        # Read end boundary
        #partition_upper_bound = ...
    }
}

sink {
  Console {}
}

Parallel Boundary:​

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
        }
    }
}

parallel by Primary Key or Unique Index​

Configuring table_path will turn on auto split, you can configure split.* 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 {}
}

multiple table read:​

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
}