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  • package root
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    root
  • package com
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    root
  • package adform
    Definition Classes
    com
  • package streamloader

    The entry point of the stream loader library is the StreamLoader class, which requires a $KafkaSource and a $Sink.

    The entry point of the stream loader library is the StreamLoader class, which requires a $KafkaSource and a $Sink. Once started it will subscribe to the provided topics and will start polling and sinking records. The sink has to be able to persist records and to look up committed offsets (technically this is optional, but without it there would be no way to provide any delivery guarantees). A large class of sinks are batch based, implemented as $RecordBatchingSink. This sink accumulate batches of records using some $RecordBatcher and once ready, stores them to some underlying $RecordBatchStorage. A common type of batch is file based, i.e. a batcher might write records to a temporary file and once the file is full the sink commits the file to some underlying storage, such as a database or a distributed file system like HDFS.

    A sketch of the class hierarchy illustrating the main classes and interfaces can be seen below.



    For concrete storage implementations see the clickhouse, hadoop, s3 and vertica packages. They also contain more file builder implementations than just the $CsvFileBuilder included in the core library.

    Definition Classes
    adform
  • package sink
    Definition Classes
    streamloader
  • package batch
    Definition Classes
    sink
  • package encoding
    Definition Classes
    sink
  • package file
    Definition Classes
    sink
  • PartitionGroupSinker
  • PartitionGroupingSink
  • RewindingPartitionGroupSinker
  • Sink
  • WrappedPartitionGroupSinker
  • WrappedSink
t

com.adform.streamloader.sink

PartitionGroupSinker

trait PartitionGroupSinker extends AnyRef

Base sinker responsible for loading a fixed set of Kafka topic partitions to persistent storage. Implementers must define committed stream position lookup from the underlying storage upon initialization.

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Abstract Value Members

  1. abstract def close(): Unit

    Cleans up and closes the sinker.

  2. abstract val groupName: String

    Name of the partition group, can be used in metrics and logs to differentiate between group sinkers.

  3. abstract val groupPartitions: Set[TopicPartition]

    The Kafka topic partitions that this sinker is responsible for.

  4. abstract def heartbeat(): Unit

    Notifies the sinker that record consumption is still active.

    Notifies the sinker that record consumption is still active. Gives the sinker an opportunity to perform flushing with very low traffic streams.

  5. abstract def initialize(kafkaContext: KafkaContext): Map[TopicPartition, Option[StreamPosition]]

    Initializes the sinker and returns the stream positions where topic partitions should be reset to before loading.

    Initializes the sinker and returns the stream positions where topic partitions should be reset to before loading. Should be called once before any subsequent calls to write. This is most likely a blocking call since it queries Kafka/storage for offsets.

    kafkaContext

    Kafka context to use when looking up / committing offsets to Kafka, if needed.

    returns

    The initial loader positions in the owned topic partitions. If no position is returned the position will not be reset explicitly, meaning that the consumer will either reset it to the earliest/latest based on the configuration value of auto.offset.reset, or will reset to the offset stored in Kafka, if any.

  6. abstract def write(record: StreamRecord): Unit

    Writes a given stream record to storage.

    Writes a given stream record to storage.

    Calling this method does not ensure that the record will be flushed to storage, e.g. the sinker might implement batching thus delaying the actual storage.

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