MongoDB Source Connector

Support Those Engines

Spark
Flink
SeaTunnel Zeta

Key Features

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

Description

The MongoDB Connector provides the ability to read and write data from and to MongoDB. This document describes how to set up the MongoDB connector to run data reads against MongoDB.

Supported DataSource Info

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

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.The parameter match.query is compatible with the historical old version parameter matchQuery, and they are equivalent replacements.

How to Create a MongoDB Data Synchronization Jobs

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

# Set the basic configuration of the task to be performed
env {
  parallelism = 1
  job.mode = "BATCH"
}
# Create a source to connect to Mongodb
source {
  MongoDB {
    uri = "mongodb://user:password@127.0.0.1:27017"
    database = "test_db"
    collection = "source_table"
    schema = {
      fields {
        c_map = "map<string, string>"
        c_array = "array<int>"
        c_string = string
        c_boolean = boolean
        c_int = int
        c_bigint = bigint
        c_double = double
        c_bytes = bytes
        c_date = date
        c_decimal = "decimal(38, 18)"
        c_timestamp = timestamp
        c_row = {
          c_map = "map<string, string>"
          c_array = "array<int>"
          c_string = string
          c_boolean = boolean
          c_int = int
          c_bigint = bigint
          c_double = double
          c_bytes = bytes
          c_date = date
          c_decimal = "decimal(38, 18)"
          c_timestamp = timestamp
        }
      }
    }
  }
}
# Console printing of the read Mongodb data
sink {
  Console {
    parallelism = 1
  }
}

Parameter Interpretation

MongoDB Database Connection URI Examples

Unauthenticated single node connection:

mongodb://192.168.0.100:27017/mydb

Replica set connection:

mongodb://192.168.0.100:27017/mydb?replicaSet=xxx

Authenticated replica set connection:

mongodb://admin:password@192.168.0.100:27017/mydb?replicaSet=xxx&authSource=admin

Multi-node replica set connection:

mongodb://192.168.0.1:27017,192.168.0.2:27017,192.168.0.3:27017/mydb?replicaSet=xxx

Sharded cluster connection:

mongodb://192.168.0.100:27017/mydb

Multiple mongos connections:

mongodb://192.168.0.1:27017,192.168.0.2:27017,192.168.0.3:27017/mydb

Note: The username and password in the URI must be URL-encoded before being concatenated into the connection string.

MatchQuery Scan

In data synchronization scenarios, the matchQuery approach needs to be used early to reduce the number of documents that need to be processed by subsequent operators, thus improving performance. Here is a simple example of a seatunnel using match.query

source {
  MongoDB {
    uri = "mongodb://user:password@127.0.0.1:27017"
    database = "test_db"
    collection = "orders"
    match.query = "{status: \"A\"}"
    schema = {
      fields {
        id = bigint
        status = string
      }
    }
  }
}

The following are examples of MatchQuery query statements of various data types:

# Query Boolean type
"{c_boolean:true}"
# Query string type
"{c_string:\"OCzCj\"}"
# Query the integer
"{c_int:2}"
# Type of query time
"{c_date:ISODate(\"2023-06-26T16:00:00.000Z\")}"
# Query floating point type
{c_double:{$gte:1.71763202185342e+308}}

Please refer to how to write the syntax of match.query：https://www.mongodb.com/docs/manual/tutorial/query-documents

Projection Scan

In MongoDB, Projection is used to control which fields are included in the query results. This can be accomplished by specifying which fields need to be returned and which fields do not. In the find() method, a projection object can be passed as a second argument. The key of the projection object indicates the fields to include or exclude, and a value of 1 indicates inclusion and 0 indicates exclusion. Here is a simple example, assuming we have a collection named users:

# Returns only the name and email fields
db.users.find({}, { name: 1, email: 0 });

In data synchronization scenarios, projection needs to be used early to reduce the number of documents that need to be processed by subsequent operators, thus improving performance. Here is a simple example of a seatunnel using projection:

source {
  MongoDB {
    uri = "mongodb://user:password@127.0.0.1:27017"
    database = "test_db"
    collection = "users"
    match.projection = "{ name: 1, email: 0 }"
    schema = {
      fields {
        name = string
      }
    }
  }
}

Partitioned Scan

To speed up reading data in parallel source task instances, seatunnel provides a partitioned scan feature for MongoDB collections. The following partitioning strategies are provided. Users can control data sharding by setting the partition.split-key for sharding keys and partition.split-size for sharding size.

source {
  MongoDB {
    uri = "mongodb://user:password@127.0.0.1:27017"
    database = "test_db"
    collection = "users"
    partition.split-key = "id"
    partition.split-size = 1024
    schema = {
      fields {
        id = bigint
        status = string
      }
    }
  }
}

Flat Sync String

By utilizing flat.sync-string, only one field attribute value can be set, and the field type must be a String. This operation will perform a string mapping on a single MongoDB data entry.

env {
  parallelism = 10
  job.mode = "BATCH"
}
source {
  MongoDB {
    uri = "mongodb://user:password@127.0.0.1:27017"
    database = "test_db"
    collection = "users"
    flat.sync-string = true
    schema = {
      fields {
        data = string
      }
    }
  }
}
sink {
  Console {}
}

Use the data samples synchronized with modified parameters, such as the following:

{
  "_id":{
    "$oid":"643d41f5fdc6a52e90e59cbf"
  },
  "c_map":{
    "OQBqH":"jllt",
    "rkvlO":"pbfdf",
    "pCMEX":"hczrdtve",
    "DAgdj":"t",
    "dsJag":"voo"
  },
  "c_array":[
    {
      "$numberInt":"-865590937"
    },
    {
      "$numberInt":"833905600"
    },
    {
      "$numberInt":"-1104586446"
    },
    {
      "$numberInt":"2076336780"
    },
    {
      "$numberInt":"-1028688944"
    }
  ],
  "c_string":"bddkzxr",
  "c_boolean":false,
  "c_tinyint":{
    "$numberInt":"39"
  },
  "c_smallint":{
    "$numberInt":"23672"
  },
  "c_int":{
    "$numberInt":"-495763561"
  },
  "c_bigint":{
    "$numberLong":"3768307617923954543"
  },
  "c_float":{
    "$numberDouble":"5.284220288280258E37"
  },
  "c_double":{
    "$numberDouble":"1.1706091642478246E308"
  },
  "c_bytes":{
    "$binary":{
      "base64":"ZWJ4",
      "subType":"00"
    }
  },
  "c_date":{
    "$date":{
      "$numberLong":"1686614400000"
    }
  },
  "c_decimal":{
    "$numberDecimal":"683265300"
  },
  "c_timestamp":{
    "$date":{
      "$numberLong":"1684283772000"
    }
  },
  "c_row":{
    "c_map":{
      "OQBqH":"cbrzhsktmm",
      "rkvlO":"qtaov",
      "pCMEX":"tuq",
      "DAgdj":"jzop",
      "dsJag":"vwqyxtt"
    },
    "c_array":[
      {
        "$numberInt":"1733526799"
      },
      {
        "$numberInt":"-971483501"
      },
      {
        "$numberInt":"-1716160960"
      },
      {
        "$numberInt":"-919976360"
      },
      {
        "$numberInt":"727499700"
      }
    ],
    "c_string":"oboislr",
    "c_boolean":true,
    "c_tinyint":{
      "$numberInt":"-66"
    },
    "c_smallint":{
      "$numberInt":"1308"
    },
    "c_int":{
      "$numberInt":"-1573886733"
    },
    "c_bigint":{
      "$numberLong":"4877994302999518682"
    },
    "c_float":{
      "$numberDouble":"1.5353209063652051E38"
    },
    "c_double":{
      "$numberDouble":"1.1952441956458565E308"
    },
    "c_bytes":{
      "$binary":{
        "base64":"cWx5Ymp0Yw==",
        "subType":"00"
      }
    },
    "c_date":{
      "$date":{
        "$numberLong":"1686614400000"
      }
    },
    "c_decimal":{
      "$numberDecimal":"656406177"
    },
    "c_timestamp":{
      "$date":{
        "$numberLong":"1684283772000"
      }
    }
  },
  "id":{
    "$numberInt":"2"
  }
}

Changelog

2.2.0-beta 2022-09-26

• Add MongoDB Source Connector

Next Version

• [Feature]Refactor mongodb source connector(4620)