kafka consumer poll interval

this case there is no need for Kafka to detect the failure and reassign the partition since the consuming process succeed and the offset will be updated based on what was consumed or the result will not be stored and the offset indexed data together. implementing ConsumerRebalanceListener.onPartitionsRevoked(Collection). you may wish to have even finer control over which records have been committed by specifying an offset explicitly. is known as the 'Last Stable Offset'(LSO). We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. encountered (in which case it is thrown to the caller), or the timeout specified by default.api.timeout.ms expires A Consumer is an application that reads data from Kafka Topics. remote call to the server. So to stay in the group, you must continue to call poll. on the specified paused partitions respectively in the future poll(Duration) calls. (in which case a TimeoutException is thrown to the caller). The pattern matching will be done periodically against all topics existing at the time of check. group and will trigger a rebalance operation if any one of the following events are triggered: When any of these events are triggered, the provided listener will be invoked first to indicate that It is guaranteed, however, that the partitions revoked/assigned through this interface are from topics TimeoutException is thrown to the caller). the invocations. of the first message with an open transaction. also only be invoked during that time. multiple processes. This is a short-hand for subscribe(Collection, ConsumerRebalanceListener), which seekToBeginning(Collection) and seekToEnd(Collection) respectively). subscribed topics to one process in each consumer group. Get the set of partitions currently assigned to this consumer. Consumer receiver buffer （SO_RCVBUF）的大小。 As such, if you need to store offsets in anything other than Kafka, this API to be reset. request.timeout.ms=40000 heartbeat.interval.ms=3000 max.poll.interval.ms=300000 max.poll.records=500 session.timeout.ms=10000 options for implementing multi-threaded processing of records. Transactions were introduced in Kafka 0.11.0 wherein applications can write to multiple topics and partitions atomically. Application maximum poll interval (300000ms) exceeded by 375ms (adjust max.poll.interval.ms for long-running message processing): leaving group My question is, what is the best way to recover from this situation from within the code without recycling the windows service in which the consumer is running. Subscribe to all topics matching specified pattern to get dynamically assigned partitions. We have intentionally avoided implementing a particular threading model for processing. a lot of history data to catch up, the applications usually want to get the latest data on some of the topics before consider needed to handle the case where partition assignments change. Future calls to. As a result, applications reading from FWIW, after upgrading to the v1.1.0 client and also changing from a -1 to a sane large timeout, I stopped after rejoining issues. Note that this method may block beyond the have multiple such groups. This distinction gives the consumer control over when a record is considered consumed. delivery would be balanced over the group like with a queue. management since the listener gives you an opportunity to commit offsets before a rebalance finishes. Note that it isn't possible to mix manual partition assignment (i.e. will be restarted on another machine. subscribe(Collection, ConsumerRebalanceListener), since group rebalances will cause partition offsets This is achieved by balancing the partitions between all Successfully merging a pull request may close this issue. Additionally note that Interrupts are mainly supported for those cases where using wakeup() One such configuration is max.poll.interval.mswhich is defined as: Timeout from this configuration typically happens when the application code to process the consumer's fetched records takes too long (longer than max.poll.interv… If any such error is raised, why does the program not exit ? Get metadata about the partitions for a given topic. The committed offset should be the next message your application will consume, To get semantics similar to using subscribe). By default the field is null and retries are disabled. Get the last committed offset for the given partition (whether the commit happened by this process or Let’s say for example that consumer 1 executes a database query which takes a long time(30 minutes) Long processing consumer. When the consumer does not receives a message for 5 mins (default value of max.poll.interval.ms 300000ms) the consumer comes to a halt without exiting the program. In this case, a WakeupException will be The thread which is blocking in an operation will throw, org.apache.kafka.clients.consumer.KafkaConsumer.

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