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In our experience messaging uses are often comparatively low-throughput, but may require low end-to-end latency and often depend on the strongdurability guarantees Kafka provides.In this domain Kafka is comparable to traditional messaging systems such as ActiveMQ orRabbitMQ.Website Activity TrackingThe original use case for Kafka was to be able to rebuild a user activity tracking pipeline as a set of real-time publish-subscribe feeds.This means site activity (page views, searches, or other actions users may take) is published to central topics with one topic per activity type.These feeds are available for subscription for a range of use cases including real-time processing, real-time monitoring, and loading into Hadoop oroffline data warehousing systems for offline processing and reporting.Activity tracking is often very high volume as many activity messages are generated for each user page view.MetricsKafka is often used for operational monitoring data.This involves aggregating statistics from distributed applications to produce centralized feeds of operational data.Log AggregationMany people use Kafka as a replacement for a log aggregation solution.Log aggregation typically collects physical log files off servers and puts them in a central place (a file server or HDFS perhaps) for processing.Kafka abstracts away the details of files and gives a cleaner abstraction of log or event data as a stream of messages.This allows for lower-latency processing and easier support for multiple data sources and distributed data consumption.In comparison to log-centric systems like Scribe or Flume, Kafka offers equally good performance, stronger durability guarantees due to replication,and much lower end-to-end latency.Stream ProcessingMany users of Kafka process data in processing pipelines consisting of multiple stages, where raw input data is consumed from Kafka topics and thenaggregated, enriched, or otherwise transformed into new topics for further consumption or follow-up processing.For example, a processing pipeline for recommending news articles might crawl article content from RSS feeds and publish it to an "articles" topic;further processing might normalize or deduplicate this content and publish the cleansed article content to a new topic;a final processing stage might attempt to recommend this content to users.Such processing pipelines create graphs of real-time data flows based on the individual topics.Starting in 0.10.0.0, a light-weight but powerful stream processing library called Kafka Streamsis available in Apache Kafka to perform such data processing as described above.Apart from Kafka Streams, alternative open source stream processing tools include Apache Storm andApache Samza.Event SourcingEvent sourcing is a style of application design where state changes are logged as atime-ordered sequence of records. Kafka's support for very large stored log data makes it an excellent backend for an application built in this style.Commit LogKafka can serve as a kind of external commit-log for a distributed system. The log helps replicate data between nodes and acts as a re-syncingmechanism for failed nodes to restore their data.The log compaction feature in Kafka helps support this usage.In this usage Kafka is similar to Apache BookKeeper project. 1.3 Quick Start /*Licensed to the Apache Software Foundation (ASF) under one or morecontributor license agreements. See the NOTICE file distributed withthis work for additional information regarding copyright ownership.The ASF licenses this file to You under the Apache License, Version 2.0(the "License"); you may not use this file except in compliance withthe License. You may obtain a copy of the License at -2.0Unless required by applicable law or agreed to in writing, softwaredistributed under the License is distributed on an "AS IS" BASIS,WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.See the License for the specific language governing permissions andlimitations under the License.*/// Define variables for doc templatesvar context= "version": "26", "dotVersion": "2.6", "fullDotVersion": "2.6.3", "scalaVersion": "2.13";This tutorial assumes you are starting fresh and have no existing Kafka or ZooKeeper data.Since Kafka console scripts are different for Unix-based and Windows platforms, on Windows platforms use bin\windows\ instead of bin/, and change the script extension to .bat.
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NOTE: any prefixed ACLs added to a cluster, even after the cluster is fully upgraded, will be ignored should the cluster be downgraded again. Notable changes in 2.0.0 KIP-186 increases the default offset retention time from 1 day to 7 days. This makes it less likely to "lose" offsets in an application that commits infrequently. It also increases the active set of offsets and therefore can increase memory usage on the broker. Note that the console consumer currently enables offset commit by default and can be the source of a large number of offsets which this change will now preserve for 7 days instead of 1. You can preserve the existing behavior by setting the broker config offsets.retention.minutes to 1440.
Support for Java 7 has been dropped, Java 8 is now the minimum version required.
The default value for ssl.endpoint.identification.algorithm was changed to https, which performs hostname verification (man-in-the-middle attacks are possible otherwise). Set ssl.endpoint.identification.algorithm to an empty string to restore the previous behaviour.
KAFKA-5674 extends the lower interval of max.connections.per.ip minimum to zero and therefore allows IP-based filtering of inbound connections.
KIP-272 added API version tag to the metric kafka.network:type=RequestMetrics,name=RequestsPerSec,request=.... This metric now becomes kafka.network:type=RequestMetrics,name=RequestsPerSec,request=FetchConsumer,version=1. This will impact JMX monitoring tools that do not automatically aggregate. To get the total count for a specific request type, the tool needs to be updated to aggregate across different versions.
KIP-225 changed the metric "records.lag" to use tags for topic and partition. The original version with the name format "topic-partition.records-lag" has been removed.
The Scala consumers, which have been deprecated since 0.11.0.0, have been removed. The Java consumer has been the recommended option since 0.10.0.0. Note that the Scala consumers in 1.1.0 (and older) will continue to work even if the brokers are upgraded to 2.0.0.
The Scala producers, which have been deprecated since 0.10.0.0, have been removed. The Java producer has been the recommended option since 0.9.0.0. Note that the behaviour of the default partitioner in the Java producer differs from the default partitioner in the Scala producers. Users migrating should consider configuring a custom partitioner that retains the previous behaviour. Note that the Scala producers in 1.1.0 (and older) will continue to work even if the brokers are upgraded to 2.0.0.
MirrorMaker and ConsoleConsumer no longer support the Scala consumer, they always use the Java consumer.
The ConsoleProducer no longer supports the Scala producer, it always uses the Java producer.
A number of deprecated tools that rely on the Scala clients have been removed: ReplayLogProducer, SimpleConsumerPerformance, SimpleConsumerShell, ExportZkOffsets, ImportZkOffsets, UpdateOffsetsInZK, VerifyConsumerRebalance.
The deprecated kafka.tools.ProducerPerformance has been removed, please use org.apache.kafka.tools.ProducerPerformance.
New Kafka Streams configuration parameter upgrade.from added that allows rolling bounce upgrade from older version.
KIP-284 changed the retention time for Kafka Streams repartition topics by setting its default value to Long.MAX_VALUE.
Updated ProcessorStateManager APIs in Kafka Streams for registering state stores to the processor topology. For more details please read the Streams Upgrade Guide.
In earlier releases, Connect's worker configuration required the internal.key.converter and internal.value.converter properties. In 2.0, these are no longer required and default to the JSON converter. You may safely remove these properties from your Connect standalone and distributed worker configurations: internal.key.converter=org.apache.kafka.connect.json.JsonConverter internal.key.converter.schemas.enable=false internal.value.converter=org.apache.kafka.connect.json.JsonConverter internal.value.converter.schemas.enable=false
KIP-266 adds a new consumer configuration default.api.timeout.ms to specify the default timeout to use for KafkaConsumer APIs that could block. The KIP also adds overloads for such blocking APIs to support specifying a specific timeout to use for each of them instead of using the default timeout set by default.api.timeout.ms. In particular, a new poll(Duration) API has been added which does not block for dynamic partition assignment. The old poll(long) API has been deprecated and will be removed in a future version. Overloads have also been added for other KafkaConsumer methods like partitionsFor, listTopics, offsetsForTimes, beginningOffsets, endOffsets and close that take in a Duration.
Also as part of KIP-266, the default value of request.timeout.ms has been changed to 30 seconds. The previous value was a little higher than 5 minutes to account for maximum time that a rebalance would take. Now we treat the JoinGroup request in the rebalance as a special case and use a value derived from max.poll.interval.ms for the request timeout. All other request types use the timeout defined by request.timeout.ms
The internal method kafka.admin.AdminClient.deleteRecordsBefore has been removed. Users are encouraged to migrate to org.apache.kafka.clients.admin.AdminClient.deleteRecords.
The AclCommand tool --producer convenience option uses the KIP-277 finer grained ACL on the given topic.
KIP-176 removes the --new-consumer option for all consumer based tools. This option is redundant since the new consumer is automatically used if --bootstrap-server is defined.
KIP-290 adds the ability to define ACLs on prefixed resources, e.g. any topic starting with 'foo'.
KIP-283 improves message down-conversion handling on Kafka broker, which has typically been a memory-intensive operation. The KIP adds a mechanism by which the operation becomes less memory intensive by down-converting chunks of partition data at a time which helps put an upper bound on memory consumption. With this improvement, there is a change in FetchResponse protocol behavior where the broker could send an oversized message batch towards the end of the response with an invalid offset. Such oversized messages must be ignored by consumer clients, as is done by KafkaConsumer. KIP-283 also adds new topic and broker configurations message.downconversion.enable and log.message.downconversion.enable respectively to control whether down-conversion is enabled. When disabled, broker does not perform any down-conversion and instead sends an UNSUPPORTED_VERSION error to the client.