Skip to main content
Version: Next

RocketMQ

RocketMQ source connector

Support Apache RocketMQ Version​

  • 4.9.0 (Or a newer version, for reference)

Support These Engines​

Spark
Flink
SeaTunnel Zeta

Key Features​

Description​

Source connector for Apache RocketMQ.

Source Options​

NameTypeRequiredDefaultDescription
topicsStringyes-RocketMQ topic name. If there are multiple topics, use , to split, for example: "tpc1,tpc2".
name.srv.addrStringyes-RocketMQ name server cluster address.
acl.enabledBooleannofalseIf true, access control is enabled, and access key and secret key need to be configured.
access.keyStringno
secret.keyStringnoWhen ACL_ENABLED is true, secret key cannot be empty.
batch.sizeintno100RocketMQ consumer pull batch size
consumer.groupStringnoSeaTunnel-Consumer-GroupRocketMQ consumer group id, used to distinguish different consumer groups.
commit.on.checkpointBooleannotrueIf true the consumer's offset will be periodically committed in the background.
schemano-The structure of the data, including field names and field types.
formatStringnojsonData format. The default format is json. Optional text format. The default field separator is ",".If you customize the delimiter, add the "field.delimiter" option.
field.delimiterStringno,Customize the field delimiter for data format
start.modeStringnoCONSUME_FROM_GROUP_OFFSETSThe initial consumption pattern of consumers,there are several types: [CONSUME_FROM_LAST_OFFSET],[CONSUME_FROM_FIRST_OFFSET],[CONSUME_FROM_GROUP_OFFSETS],[CONSUME_FROM_TIMESTAMP],[CONSUME_FROM_SPECIFIC_OFFSETS]
start.mode.offsetsno
start.mode.timestampLongnoThe time required for consumption mode to be "CONSUME_FROM_TIMESTAMP".
partition.discovery.interval.millislongno-1The interval for dynamically discovering topics and partitions.
common-optionsconfigno-Source plugin common parameters, please refer to Source Common Options for details.

start.mode.offsets​

The offset required for consumption mode to be "CONSUME_FROM_SPECIFIC_OFFSETS".

for example:

start.mode.offsets = {
  topic1-0 = 70
  topic1-1 = 10
  topic1-2 = 10
}

Task Example​

Simple:​

Consumer reads Rocketmq data and prints it to the console type

env {
  parallelism = 1
  job.mode = "BATCH"
}

source {
  Rocketmq {
    name.srv.addr = "rocketmq-e2e:9876"
    topics = "test_topic_json"
    plugin_output = "rocketmq_table"
    schema = {
      fields {
        id = bigint
        c_map = "map<string, smallint>"
        c_array = "array<tinyint>"
        c_string = string
        c_boolean = boolean
        c_tinyint = tinyint
        c_smallint = smallint
        c_int = int
        c_bigint = bigint
        c_float = float
        c_double = double
        c_decimal = "decimal(2, 1)"
        c_bytes = bytes
        c_date = date
        c_timestamp = timestamp
      }
    }
  }
}

transform {
  # If you would like to get more information about how to configure seatunnel and see full list of transform plugins,
  # please go to https://seatunnel.apache.org/docs/category/transform
}

sink {
  Console {
  }
}

Specified format consumption Simple:​

When I consume the topic data in json format parsing and pulling the number of bars each time is 400, the consumption starts from the original location

env {
  parallelism = 1
  job.mode = "BATCH"
}

source {
  Rocketmq {
    name.srv.addr = "localhost:9876"
    topics = "test_topic"
    plugin_output = "rocketmq_table"
    start.mode = "CONSUME_FROM_FIRST_OFFSET"
    batch.size = "400"
    consumer.group = "test_topic_group"
    format = "json"
    format = json
    schema = {
      fields {
        c_map = "map<string, string>"
        c_array = "array<int>"
        c_string = string
        c_boolean = boolean
        c_tinyint = tinyint
        c_smallint = smallint
        c_int = int
        c_bigint = bigint
        c_float = float
        c_double = double
        c_decimal = "decimal(30, 8)"
        c_bytes = bytes
        c_date = date
        c_timestamp = timestamp
      }
    }
  }
}

transform {
  # If you would like to get more information about how to configure seatunnel and see full list of transform plugins,
  # please go to https://seatunnel.apache.org/docs/category/transform
}
sink {
  Console {
  }
}

Specified timestamp Simple:​

This is to specify a time to consume, and I dynamically sense the existence of a new partition every 1000 milliseconds to pull the consumption

env {
  parallelism = 1
  spark.app.name = "SeaTunnel"
  spark.executor.instances = 2
  spark.executor.cores = 1
  spark.executor.memory = "1g"
  spark.master = local
  job.mode = "BATCH"
}

source {
  Rocketmq {
    name.srv.addr = "localhost:9876"
    topics = "test_topic"
    partition.discovery.interval.millis = "1000"
    start.mode.timestamp="1694508382000"
    consumer.group="test_topic_group"
    format="json"
    format = json
    schema = {
      fields {
        c_map = "map<string, string>"
        c_array = "array<int>"
        c_string = string
        c_boolean = boolean
        c_tinyint = tinyint
        c_smallint = smallint
        c_int = int
        c_bigint = bigint
        c_float = float
        c_double = double
        c_decimal = "decimal(30, 8)"
        c_bytes = bytes
        c_date = date
        c_timestamp = timestamp
      }
    }
  }
}

transform {
  # If you would like to get more information about how to configure seatunnel and see full list of transform plugins,
  # please go to https://seatunnel.apache.org/docs/category/transform
}

sink {
  Console {
  }
}