Confluent Cloud

Destination Confluent Cloud #

In the following steps, we refer the extracted replicant-cli as the $REPLICANT_HOME directory.

Don’t have a Confluent Cloud account? Get one here.

I. Set up connection configuration #

Specify your Confluent Cloud connection details to Replicant with a connection configuration file. You can find a sample connection configuration file confluent.yaml in the $REPLICANT_HOME/conf/conn directory.

Specify your connection in the following manner:

type: KAFKA

username: 'CLUSTER_API_KEY'
password: 'CLUSTER_API_SECRET'

auth-type: SASL 
brokers:
  broker1:
    host: 'BOOTSTRAP_SERVER_NAME'
    port: 'BOOTSTRAP_SERVER_PORT'

is-cloud-instance: true
max-connections: 30

Replace the following:

In the preceding configuration, max-connections specifies the maximum number of connections Replicant can open in Confluent Cloud. Feel free to change its value as you need.

In Arcion Cloud, fill up the connection details in the Connection form tab. The Connection form requires the same set of connection details as Arcion self-hosted CLI:

  • Enter a name for your connection in the Connection name field.
  • Enter the bootstrap hostname and port number in the Bootstrap Host and Port fields respectively.
  • Enter the resource-specific API key to access your Kafka cluster in the Key field.
  • Enter the secret associated with your Key in the Secret field.
  • Specify the maximum number of connections Replicant can open in Confluent Cloud in the Max connections field. Defaults to 30.
  • Specify the duration in milliseconds Replicant waits before retrying a failed operation in the Retry wait durations in ms field. Defaults to 1000.
  • Specify the number of times Replicant retries a failed operation in the Max retries field. Defaults to 30.

II. Configure mapper file (optional) #

If you want to define data mapping from your source to Confluent Cloud, specify the mapping rules in the mapper file. For more information on how to define the mapping rules and run Replicant CLI with the mapper file, see Mapper Configuration.

When mapping source object names to Kafka topics, you can choose between two delimiters for topic names. For more information, see Delimiter in Kafka topic and Redis stream names.

III. Set up Applier configuration #

  1. From $REPLICANT_HOME, naviagte to the sample Confluent Cloud Applier configuration file:

    vi conf/dst/confluent.yaml

  2. The configuration file contains global Applier parameters, with snapshot and realtime parameters following the global parameters:

    Global configuration parameters #

    Global configuration parameters live at the topmost level of the Applier configuration file. So you must specify them at the topmost place of the Applier configuration file. The global configuration parameters affect both snapshot and real-time replication.

    The following global Applier configuration parameters are available.

    replication-format #

    The structure of the published events.

    The following values are allowed:

    For snapshot mode, the following Confluent Cloud-specific parameters are available:

    replication-factor [v21.12.02.6] #

    Replication factor for data topics. For Kafka cluster setup this defines the factor in which Kafka topic partitions are replicated on different brokers. We pass this config value to Kafka and Kafka drives the partition level replication.

    num-shards [v21.12.02.6] #

    Number of partitions per data topic. By default this is set to a number of applier threads for getting the best possible scaling by allowing each individual applier thread to write to an independent partition of a Kafka topic.

    shard-key [v21.12.02.6] #

    Shard key to be used for partitioning data topics.

    shard-function [v21.12.02.6] #

    Sharding function to be used to deduce the partition allotment based on shard-key for all data topics. Values allowed are MOD and NONE.

    Default: By default, this parameter is set to NONE, meaning Kafka will use it’s partitioning algorithm.

    kafka-compression-type [v20.05.12.3] #

    Compression type. Allowed values are lz4, snappy, gzip, and none.

    Default: By default, this parameter is set to lz4.

    kafka-batch-size-in-bytes [v20.05.12.3] #

    Batch size for Kafka producer.

    Default: By default, this parameter is set to 100000.

    kafka-buffer-memory-size-in-bytes* [v20.05.12.3] #

    Memory allocated to Kafka client to store unsent messages. (Default set to 67108864)

    Default: By default, this parameter is set to 67108864.

    kafka-linger-ms [v20.05.12.3] #

    Config used to give more time for Kafka batches to fill (in milliseconds).

    Default: By default, this parameter is set to 10.

    kafka-interceptor-classes [v21.09.17.2] #

    Config used to specify list of interceptor classes. It corresponds to Kafka’s ProducerConfig.INTERCEPTOR_CLASSES_CONFIG.

    producer-max-block-ms [v22.07.19.7] #

    Corresponds to the max.block.ms parameter of Kafka Producer.

    Default: Default value is 60_000.

    create-topic-timeout-ms [v22.07.19.7] #

    Specifies the timeout for topic creation.

    Default: Default value is 60_000.

    per-table-config [v20.12.04.6] #

    This configuration allows you to specify various properties for target tables on a per table basis like the following:

    replication-factor [v21.12.02.6]
    Replication factor for data topics. For Kafka cluster setup, this defines the factor in which Kafka topic partitions are replicated on different brokers. We pass this config value to Kafka and Kafka drives the partition level replication.
    num-shards [v21.12.02.6]
    Number of partitions per data topic. By default this is set to a number of applier threads for getting the best possible scaling by allowing each individual applier thread to write to an independent partition of a Kafka topic.
    shard-key [v21.12.02.6]
    Shard key to be used for partitioning data topic.
    shard-function [v21.12.02.6]
    Sharding function to be used to deduce the partition allotment based on `shard-key` for all data topics. Values allowed are MOD and NONE.

    Default: By default, this parameter is set to NONE, meaning Kafka will use it’s partitioning algorithm.

    Below is a sample config for snapshot mode:

    snapshot:
  threads: 16
  txn-size-rows: 10000
  replication-factor: 1
  schema-dictionary: SCHEMA_DUMP  # Allowed values: POJO | SCHEMA_DUMP| NONE
  kafka-compression-type: lz4
  kafka-batch-size-in-bytes: 100000
  kafka-buffer-memory-size-in-bytes: 67108864
  kafka-linger-ms: 10
  skip-tables-on-failures : false
  kafka-interceptor-classes: ["KafkaInterceptors.SampleInterceptor"]
  producer-max-block-ms: 60_000
  create-topic-timeout-ms: 100_000

    If you want to operate in realtime mode, you can use a realtime section to specify your configuration. The following Kafka-specific parameters are available:

    split-topic #

    true or false.

    Creates a separate topic for snapshot and CDC data if true. If false, a single topic contains the data for snapshot and CDC. split-topic is a global parameter for realtime mode. So you can’t change it on a per-table basis.

    Default: true.

    split-topic is applicable only when replication-format is set to JSON.

    replication-factor [v21.12.02.6] #

    Replication factor for CDC topics. For Kafka cluster setup this defines the factor in which Kafka topic partitions are replicated on different brokers. We pass this config value to Kafka and Kafka drives the partition level replication.

    num-shards[v21.12.02.6] #

    Number of partitions to be created for all CDC log topics.

    shard-key [v21.12.02.6] #

    Shard key to be used for partitioning CDC logs in all target topics.

    shard-function [v21.12.02.6] #

    Sharding function to be used to deduce the partition allotment based on shard-key for all CDC log topics. Values allowed are MOD and NONE.

    Default: By default, this parameter is set to NONE, meaning Kafka will use it’s partitioning algorithm.

    kafka-compression-type [v20.05.12.3] #

    Compression type. Allowed values are lz4, snappy, gzip, and none.

    Default: By default, this parameter is set to lz4.

    kafka-batch-size-in-bytes [v20.05.12.3] #

    Batch size for Kafka producer.

    Default: By default, this parameter is set to 100000.

    kafka-buffer-memory-size-in-bytes* [v20.05.12.3] #

    Memory allocated to Kafka client to store unsent messages. (Default set to 67108864)

    Default: By default, this parameter is set to 67108864.

    kafka-linger-ms [v20.05.12.3] #

    Config used to give more time for Kafka batches to fill (in milliseconds).

    Default: By default, this parameter is set to 10.

    kafka-interceptor-classes [v21.09.17.2] #

    Config used to specify list of interceptor classes. It corresponds to Kafka’s ProducerConfig.INTERCEPTOR_CLASSES_CONFIG.

    producer-max-block-ms [v22.07.19.7] #

    Corresponds to the max.block.ms parameter of Kafka Producer.

    Default: Default value is 60_000.

    create-topic-timeout-ms [v22.07.19.7] #

    Specifies the timeout for topic creation.

    Default: Default value is 60_000.

    per-table-config [v20.12.04.6] #

    This configuration allows you to specify various properties for target tables on a per table basis like the following:

    replication-factor [v21.12.02.6]
    Replication factor for data topics. For Kafka cluster setup, this defines the factor in which Kafka topic partitions are replicated on different brokers. We pass this config value to Kafka and Kafka drives the partition level replication.
    num-shards [v21.12.02.6]
    Number of partitions per data topic. By default this is set to a number of applier threads for getting the best possible scaling by allowing each individual applier thread to write to an independent partition of a Kafka topic.
    shard-key [v21.12.02.6]
    Shard key to be used for partitioning data topic.
    shard-function [v21.12.02.6]
    Sharding function to be used to deduce the partition allotment based on `shard-key` for all data topics. Values allowed are MOD and NONE.

    Default: By default, this parameter is set to NONE, meaning Kafka will use it’s partitioning algorithm.

    Below is a sample config for realtime mode:

    realtime:
  txn-size-rows: 1000
  before-image-format: ALL  # Allowed values : KEY, ALL
  after-image-format: ALL   # Allowed values : UPDATED, ALL
  kafka-compression-type: lz4
  shard-key: id
  num-shards: 1
  shard-function: MOD # Allowed values: MOD, NONE. NONE means storage will use its default sharding
  skip-tables-on-failures : false
  producer-max-block-ms: 60_000
  create-topic-timeout-ms: 100_000

  per-table-config:
  - tables:
      io_blitzz_nation:
        shard-key: id
        num-shards: 16 #default: 1
        shard-function: NONE
      io_blitzz_region:
        shard-key: id
      io_blitzz_customer:
        shard-key: custkey
        num-shards: 16

Attention:

  • During replication, Replicant stores metadata information related to replicated tables in a special topic with the prefix replicate_io_replication_schema. You can configure the replication factor and partitioning for this topic using the replication-factor and num-shards parameters respectively in the snapshot section of the Applier configuration file. You must set these parameters for the metadata topic in the snapshot section of your Applier configuration file, even if you’re operating in realtime mode. Metadata topic is common to snapshot, realtime, and full modes of Replicant. So its settings are included in the snapshot section.

    For more information about how different Replicant modes work, see Running Replicant.

  • Replicant uses Kafka’s transactional API for writing data in batches to Kafka. Transactional API ensures exactly-once delivery semantics.

  • Replicant doesn’t address realtime changes for views when replicating from the following databases to Kafka:

For a detailed explanation of configuration parameters in the applier file, see Applier Reference.