MongoDB CDC source connector

Support Those Engines

SeaTunnel Zeta
Flink

Key Features

• batch
• stream
• exactly-once
• column projection
• parallelism
• support user-defined split

Description

The MongoDB CDC connector allows for reading snapshot data and incremental data from MongoDB database.

Supported DataSource Info

In order to use the Mongodb CDC connector, the following dependencies are required. They can be downloaded via install-plugin.sh or from the Maven central repository.

Availability Settings

1.MongoDB version: MongoDB version >= 4.0.

2.Cluster deployment: replica sets or sharded clusters.

3.Storage Engine: WiredTiger Storage Engine.

4.Permissions:changeStream and read

use admin;
db.createRole(
    {
        role: "strole",
        privileges: [{
            resource: { db: "", collection: "" },
            actions: [
                "splitVector",
                "listDatabases",
                "listCollections",
                "collStats",
                "find",
                "changeStream" ]
        }],
        roles: [
            { role: 'read', db: 'config' }
        ]
    }
);
db.createUser(
  {
      user: 'stuser',
      pwd: 'stpw',
      roles: [
         { role: 'strole', db: 'admin' }
      ]
  }
);

Data Type Mapping

The following table lists the field data type mapping from MongoDB BSON type to Seatunnel data type.

For specific types in MongoDB, we use Extended JSON format to map them to Seatunnel STRING type.

Tips

1.When using the DECIMAL type in SeaTunnel, be aware that the maximum range cannot exceed 34 digits, which means you should use decimal(34, 18).

Source Options

Tips:

1.If the collection changes at a slow pace, it is strongly recommended to set an appropriate value greater than 0 for the heartbeat.interval.ms parameter. When we recover a Seatunnel job from a checkpoint or savepoint, the heartbeat events can push the resumeToken forward to avoid its expiration.
2.MongoDB has a limit of 16MB for a single document. Change documents include additional information, so even if the original document is not larger than 15MB, the change document may exceed the 16MB limit, resulting in the termination of the Change Stream operation.
3.It is recommended to use immutable shard keys. In MongoDB, shard keys allow modifications after transactions are enabled, but changing the shard key can cause frequent shard migrations, resulting in additional performance overhead. Additionally, modifying the shard key can also cause the Update Lookup feature to become ineffective, leading to inconsistent results in CDC (Change Data Capture) scenarios.

How to Create a MongoDB CDC Data Synchronization Jobs

CDC Data Print to Client

The following example demonstrates how to create a data synchronization job that reads cdc data from MongoDB and prints it on the local client:

env {
  # You can set engine configuration here
  parallelism = 1
  job.mode = "STREAMING"
  checkpoint.interval = 5000
}
source {
  MongoDB-CDC {
    hosts = "mongo0:27017"
    database = ["inventory"]
    collection = ["inventory.products"]
    username = stuser
    password = stpw
    schema = {
      fields {
        "_id" : string,
        "name" : string,
        "description" : string,
        "weight" : string
      }
    }
  }
}
# Console printing of the read Mongodb data
sink {
  Console {
    parallelism = 1
  }
}

CDC Data Write to MysqlDB

The following example demonstrates how to create a data synchronization job that reads cdc data from MongoDB and write to mysql database:

env {
  # You can set engine configuration here
  parallelism = 1
  job.mode = "STREAMING"
  checkpoint.interval = 5000
}
source {
  MongoDB-CDC {
    hosts = "mongo0:27017"
    database = ["inventory"]
    collection = ["inventory.products"]
    username = stuser
    password = stpw
  }
}
sink {
  jdbc {
    url = "jdbc:mysql://mysql_cdc_e2e:3306"
    driver = "com.mysql.cj.jdbc.Driver"
    user = "st_user"
    password = "seatunnel"
    generate_sink_sql = true
    # You need to configure both database and table
    database = mongodb_cdc
    table = products
    primary_keys = ["_id"]
  }
}

Multi-table Synchronization

The following example demonstrates how to create a data synchronization job that read the cdc data of multiple library tables mongodb and prints it on the local client:

env {
  # You can set engine configuration here
  parallelism = 1
  job.mode = "STREAMING"
  checkpoint.interval = 5000
}
source {
  MongoDB-CDC {
    hosts = "mongo0:27017"
    database = ["inventory","crm"]
    collection = ["inventory.products","crm.test"]
    username = stuser
    password = stpw
  }
}
# Console printing of the read Mongodb data
sink {
  Console {
    parallelism = 1
  }
}

Tips:

1.The cdc synchronization of multiple library tables cannot specify the schema, and can only output json data downstream. This is because MongoDB does not provide metadata information for querying, so if you want to support multiple tables, all tables can only be read as one structure.

Regular Expression Matching for Multiple Tables

The following example demonstrates how to create a data synchronization job that through regular expression read the data of multiple library tables mongodb and prints it on the local client:

env {
  # You can set engine configuration here
  parallelism = 1
  job.mode = "STREAMING"
  checkpoint.interval = 5000
}
source {
  MongoDB-CDC {
    hosts = "mongo0:27017"
    # So this example is used (^(test).*|^(tpc).*|txc|.*[p$]|t{2}).(t[5-8]|tt),matching txc.tt、test2.test5.
    database = ["(^(test).*|^(tpc).*|txc|.*[p$]|t{2})"]
    collection = ["(t[5-8]|tt)"]
    username = stuser
    password = stpw
  }
}
# Console printing of the read Mongodb data
sink {
  Console {
    parallelism = 1
  }
}

Format of real-time streaming data

{
   _id : { <BSON Object> },        // Identifier of the open change stream, can be assigned to the 'resumeAfter' parameter for subsequent resumption of this change stream
   "operationType" : "<operation>",        // The type of change operation that occurred, such as: insert, delete, update, etc.
   "fullDocument" : { <document> },      // The full document data involved in the change operation. This field does not exist in delete operations
   "ns" : {   
      "db" : "<database>",         // The database where the change operation occurred
      "coll" : "<collection>"     // The collection where the change operation occurred
   },
   "to" : {   // These fields are displayed only when the operation type is 'rename'
      "db" : "<database>",         // The new database name after the change
      "coll" : "<collection>"     // The new collection name after the change
   },
   "source":{
        "ts_ms":"<timestamp>",     // The timestamp when the change operation occurred
        "table":"<collection>"     // The collection where the change operation occurred
        "db":"<database>",         // The database where the change operation occurred
        "snapshot":"false"         // Identify the current stage of data synchronization
    },
   "documentKey" : { "_id" : <value> },  // The _id field value of the document involved in the change operation
   "updateDescription" : {    // Description of the update operation
      "updatedFields" : { <document> },  // The fields and values that the update operation modified
      "removedFields" : [ "<field>", ... ]     // The fields and values that the update operation removed
   }
   "clusterTime" : <Timestamp>,     // The timestamp of the Oplog log entry corresponding to the change operation
   "txnNumber" : <NumberLong>,    // If the change operation is executed in a multi-document transaction, this field and value are displayed, representing the transaction number
   "lsid" : {          // Represents information related to the Session in which the transaction is located
      "id" : <UUID>,  
      "uid" : <BinData> 
   }
}