KafkaConsume
Value members
Abstract methods
Stream
where the elements themselves are Stream
s which continually
request records for a single partition. These Stream
s will have to be
processed in parallel, using parJoin
or parJoinUnbounded
. Note that
when using parJoin(n)
and n
is smaller than the number of currently
assigned partitions, then there will be assigned partitions which won't
be processed. For that reason, prefer parJoinUnbounded
and the actual
limit will be the number of assigned partitions.
If you do not want to process all partitions in parallel, then you
can use stream instead, where records for all partitions are in
a single Stream
.
Stream
where the elements themselves are Stream
s which continually
request records for a single partition. These Stream
s will have to be
processed in parallel, using parJoin
or parJoinUnbounded
. Note that
when using parJoin(n)
and n
is smaller than the number of currently
assigned partitions, then there will be assigned partitions which won't
be processed. For that reason, prefer parJoinUnbounded
and the actual
limit will be the number of assigned partitions.
If you do not want to process all partitions in parallel, then you
can use stream instead, where records for all partitions are in
a single Stream
.
- Note
you have to first use
subscribe
to subscribe the consumer before using thisStream
. If you forgot to subscribe, there will be a NotSubscribedException raised in theStream
.
Stream
where each element contains a current assignment. The current
assignment is the Map
, where keys is a TopicPartition
, and values are
streams with records for a particular TopicPartition
.
New assignments will be received on each rebalance. On rebalance,
Kafka revoke all previously assigned partitions, and after that assigned
new partitions all at once. partitionsMapStream
reflects this process
in a streaming manner.
Note, that partition streams for revoked partitions will
be closed after the new assignment comes.
This is the most generic Stream
method. If you don't need such control,
consider using partitionedStream
or stream
methods.
They are both based on a partitionsMapStream
.
Stream
where each element contains a current assignment. The current
assignment is the Map
, where keys is a TopicPartition
, and values are
streams with records for a particular TopicPartition
.
New assignments will be received on each rebalance. On rebalance,
Kafka revoke all previously assigned partitions, and after that assigned
new partitions all at once. partitionsMapStream
reflects this process
in a streaming manner.
Note, that partition streams for revoked partitions will
be closed after the new assignment comes.
This is the most generic Stream
method. If you don't need such control,
consider using partitionedStream
or stream
methods.
They are both based on a partitionsMapStream
.
- See also
- Note
you have to first use
subscribe
to subscribe the consumer before using thisStream
. If you forgot to subscribe, there will be a NotSubscribedException raised in theStream
.
Stops consuming new messages from Kafka.
This method could be used to implement a graceful shutdown.
This method has a few effects:
Stops consuming new messages from Kafka.
This method could be used to implement a graceful shutdown.
This method has a few effects:
- After this call no more data will be fetched from Kafka through the
poll
method. - All currently running streams will continue to run until all in-flight messages will be processed.
It means that streams will be completed when all fetched messages will be processed.
If some of the [[stream]] methods will be called after [[stopConsuming]] call, these methods will return empty streams.
More than one call of [[stopConsuming]] will have no effect.
Alias for partitionedStream.parJoinUnbounded
.
See partitionedStream for more information.
Alias for partitionedStream.parJoinUnbounded
.
See partitionedStream for more information.
- Note
you have to first use
subscribe
to subscribe the consumer before using thisStream
. If you forgot to subscribe, there will be a NotSubscribedException raised in theStream
.