Connect [PORTABLE]

[Bobby Thomas]

Increase your data sales by connecting with premium publishers and marketers committed to better strategies to reach their target audiences across all digital environments. Connect drives value through continuous enhancement and innovation, keeping you ahead of the pack with future-proofed tools and opportunities.

connect

Download ⚙⚙⚙ https://t.co/WrAahtwuEV

You probably have lots of data in existing systems like relational databases or traditional messaging systems, along with many applications that already use these systems. Kafka Connect allows you to continuously ingest data from external systems into Kafka, and vice versa. It is an extensible tool that runs connectors, which implement the custom logic for interacting with an external system. It is thus very easy to integrate existing systems with Kafka. To make this process even easier, there are hundreds of such connectors readily available.

First, make sure to add connect-file-fullDotVersion.jar to the plugin.path property in the Connect worker's configuration. For the purpose of this quickstart we'll use a relative path and consider the connectors' package as an uber jar, which works when the quickstart commands are run from the installation directory. However, it's worth noting that for production deployments using absolute paths is always preferable. See plugin.path for a detailed description of how to set this config.

Next, we'll start two connectors running in standalone mode, which means they run in a single, local, dedicated process. We provide three configuration files as parameters. The first is always the configuration for the Kafka Connect process, containing common configuration such as the Kafka brokers to connect to and the serialization format for data. The remaining configuration files each specify a connector to create. These files include a unique connector name, the connector class to instantiate, and any other configuration required by the connector.

These sample configuration files, included with Kafka, use the default local cluster configuration you started earlier and create two connectors: the first is a source connector that reads lines from an input file and produces each to a Kafka topic and the second is a sink connector that reads messages from a Kafka topic and produces each as a line in an output file.

During startup you'll see a number of log messages, including some indicating that the connectors are being instantiated. Once the Kafka Connect process has started, the source connector should start reading lines from test.txt and producing them to the topic connect-test, and the sink connector should start reading messages from the topic connect-test and write them to the file test.sink.txt. We can verify the data has been delivered through the entire pipeline by examining the contents of the output file:

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=Produce. This metric now becomes kafka.network:type=RequestMetrics,name=RequestsPerSec,request=FetchConsumer,version=0. 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.

Note: If you are willing to accept downtime, you can simply take all the brokers down, update the code and start all of them. They will start with the new protocol by default.Note: Bumping the protocol version and restarting can be done any time after the brokers were upgraded. It does not have to be immediately after.Upgrading a 0.10.1 Kafka Streams Application

• Upgrading your Streams application from 0.10.1 to 0.10.2 does not require a broker upgrade. A Kafka Streams 0.10.2 application can connect to 0.10.2 and 0.10.1 brokers (it is not possible to connect to 0.10.0 brokers though).
• You need to recompile your code. Just swapping the Kafka Streams library jar file will not work and will break your application.
• If you use a custom (i.e., user implemented) timestamp extractor, you will need to update this code, because the TimestampExtractor interface was changed.
• If you register custom metrics, you will need to update this code, because the StreamsMetric interface was changed.
• See Streams API changes in 0.10.2 for more details.

Upgrading a 0.10.0 Kafka Streams Application

• Upgrading your Streams application from 0.10.0 to 0.10.2 does require a broker upgrade because a Kafka Streams 0.10.2 application can only connect to 0.10.2 or 0.10.1 brokers.
• There are couple of API changes, that are not backward compatible (cf. Streams API changes in 0.10.2 for more details). Thus, you need to update and recompile your code. Just swapping the Kafka Streams library jar file will not work and will break your application.
• Upgrading from 0.10.0.x to 0.10.2.2 requires two rolling bounces with config upgrade.from="0.10.0" set for first upgrade phase (cf. KIP-268). As an alternative, an offline upgrade is also possible.
  • prepare your application instances for a rolling bounce and make sure that config upgrade.from is set to "0.10.0" for new version 0.10.2.2
  • bounce each instance of your application once
  • prepare your newly deployed 0.10.2.2 application instances for a second round of rolling bounces; make sure to remove the value for config upgrade.from
  • bounce each instance of your application once more to complete the upgrade
• Upgrading from 0.10.0.x to 0.10.2.0 or 0.10.2.1 requires an offline upgrade (rolling bounce upgrade is not supported)
  • stop all old (0.10.0.x) application instances
  • update your code and swap old code and jar file with new code and new jar file
  • restart all new (0.10.2.0 or 0.10.2.1) application instances

Notable changes in 0.10.2.2

• New configuration parameter upgrade.from added that allows rolling bounce upgrade from version 0.10.0.x

Notable changes in 0.10.2.1

• The default values for two configurations of the StreamsConfig class were changed to improve the resiliency of Kafka Streams applications. The internal Kafka Streams producer retries default value was changed from 0 to 10. The internal Kafka Streams consumer max.poll.interval.ms default value was changed from 300000 to Integer.MAX_VALUE.

Notable changes in 0.10.2.0

• The Java clients (producer and consumer) have acquired the ability to communicate with older brokers. Version 0.10.2 clients can talk to version 0.10.0 or newer brokers. Note that some features are not available or are limited when older brokers are used.
• Several methods on the Java consumer may now throw InterruptException if the calling thread is interrupted. Please refer to the KafkaConsumer Javadoc for a more in-depth explanation of this change.
• Java consumer now shuts down gracefully. By default, the consumer waits up to 30 seconds to complete pending requests. A new close API with timeout has been added to KafkaConsumer to control the maximum wait time.
• Multiple regular expressions separated by commas can be passed to MirrorMaker with the new Java consumer via the --whitelist option. This makes the behaviour consistent with MirrorMaker when used the old Scala consumer.
• Upgrading your Streams application from 0.10.1 to 0.10.2 does not require a broker upgrade. A Kafka Streams 0.10.2 application can connect to 0.10.2 and 0.10.1 brokers (it is not possible to connect to 0.10.0 brokers though).
• The Zookeeper dependency was removed from the Streams API. The Streams API now uses the Kafka protocol to manage internal topics instead of modifying Zookeeper directly. This eliminates the need for privileges to access Zookeeper directly and "StreamsConfig.ZOOKEEPER_CONFIG" should not be set in the Streams app any more. If the Kafka cluster is secured, Streams apps must have the required security privileges to create new topics.
• Several new fields including "security.protocol", "connections.max.idle.ms", "retry.backoff.ms", "reconnect.backoff.ms" and "request.timeout.ms" were added to StreamsConfig class. User should pay attention to the default values and set these if needed. For more details please refer to 3.5 Kafka Streams Configs.

New Protocol Versions

• KIP-88: OffsetFetchRequest v2 supports retrieval of offsets for all topics if the topics array is set to null.
• KIP-88: OffsetFetchResponse v2 introduces a top-level error_code field.
• KIP-103: UpdateMetadataRequest v3 introduces a listener_name field to the elements of the end_points array.
• KIP-108: CreateTopicsRequest v1 introduces a validate_only field.
• KIP-108: CreateTopicsResponse v1 introduces an error_message field to the elements of the topic_errors array.

Upgrading from 0.8.x, 0.9.x or 0.10.0.X to 0.10.1.00.10.1.0 has wire protocol changes. By following the recommended rolling upgrade plan below, you guarantee no downtime during the upgrade.However, please notice the Potential breaking changes in 0.10.1.0 before upgrade.
Note: Because new protocols are introduced, it is important to upgrade your Kafka clusters before upgrading your clients (i.e. 0.10.1.x clientsonly support 0.10.1.x or later brokers while 0.10.1.x brokers also support older clients).For a rolling upgrade:

df19127ead