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Confluent Platform v3.0.1 Release Notes

Confluent Platform 3.0.1 contains a number of bug fixes included in the Kafka 0.10.0.1 release. Details of the changes to Kafka in this patch release are found in the Kafka Release Notes. Details of the changes to other components of the Confluent Platform are listed in the respective changelogs such as Kafka REST Proxy changelog.

Highlights

Confluent Control Center

Control Center added performance improvements to reduce running overhead, including reducing the number of Kafka topics necessary and optimizing webclient fetches from the server. We also added the ability to delete a running connector and support for connecting to SSL/SASL secured Kafka clusters.

Kafka Streams

New Feature: Application Reset Tool

A common situation when implementing stream processing applications in practice is to tell an application to reprocess its data from scratch. This may be required for a number of reasons, including but not limited to: during development and testing, when addressing bugs in production, when doing A/B testing of algorithms and campaigns, when giving demos to customers, and so on.

Previously, resetting an application was a manual task that was cumbersome and error-prone. Kafka now includes an Application Reset Tool for Kafka Streams through which you can quickly reset an application so that it will reprocess its data from scratch – think: an application “reset button”.

Apache Kafka 0.10.0.1

Here is a quick overview of the notable Kafka-related changes in the release:

New Feature

  • KAFKA-3538: Abstract the creation/retrieval of Producer for stream sinks for unit testing

Improvement

  • KAFKA-3479: Add new consumer metrics documentation
  • KAFKA-3667: Improve Section 7.2 Encryption and Authentication using SSL to include proper hostname verification configuration
  • KAFKA-3683: Add file descriptor recommendation to ops guide
  • KAFKA-3699: Update protocol page on website to explain how KIP-35 should be used
  • KAFKA-3725: Update documentation with regards to XFS
  • KAFKA-3747: Close RecordBatch.records when append to batch fails
  • KAFKA-3785: Fetcher spending unnecessary time during metrics recording
  • KAFKA-3836: RocksDBStore.get() should not pass nulls to Deserializers
  • KAFKA-3880: Disallow Join Windows with size zero
  • KAFKA-3922: Add a copy-constructor to AbstractStream
  • KAFKA-4034: Consumer need not lookup coordinator when using manual assignment

Bug

  • KAFKA-3185: Allow users to cleanup internal data
  • KAFKA-3258: BrokerTopicMetrics of deleted topics are never deleted
  • KAFKA-3393: Update site docs and javadoc based on max.block.ms changes
  • KAFKA-3500: KafkaOffsetBackingStore set method needs to handle null
  • KAFKA-3718: propagate all KafkaConfig __consumer_offsets configs to OffsetConfig instantiation
  • KAFKA-3728: EndToEndAuthorizationTest offsets_topic misconfigured
  • KAFKA-3781: Errors.exceptionName() can throw NPE
  • KAFKA-3782: Transient failure with kafkatest.tests.connect.connect_distributed_test.ConnectDistributedTest.test_bounce.clean=True
  • KAFKA-3783: Race condition on last ACL removal for a resource fails with a ZkBadVersionException
  • KAFKA-3784: TimeWindows#windowsFor misidentifies some windows if TimeWindows#advanceBy is used
  • KAFKA-3787: Preserve message timestamp in mirror mkaer
  • KAFKA-3789: Upgrade Snappy to fix snappy decompression errors
  • KAFKA-3802: log mtimes reset on broker restart
  • KAFKA-3805: Running multiple instances of a Streams app on the same machine results in Java_org_rocksdb_RocksDB_write error
  • KAFKA-3817: KTableRepartitionMap should handle null inputs
  • KAFKA-3830: getTGT() debug logging exposes confidential information
  • KAFKA-3850: WorkerSinkTask should retry commits if woken up during rebalance or shutdown
  • KAFKA-3851: Add references to important installation/upgrade notes to release notes
  • KAFKA-3852: Clarify how to handle message format upgrade without killing performance
  • KAFKA-3854: Subsequent regex subscription calls fail
  • KAFKA-3855: Guard race conditions in TopologyBuilder
  • KAFKA-3864: Kafka Connect Struct.get returning defaultValue from Struct not the field schema
  • KAFKA-3879: KafkaConsumer with auto commit enabled gets stuck when killed after broker is dead
  • KAFKA-3887: StreamBounceTest.test_bounce and StreamSmokeTest.test_streams failing
  • KAFKA-3890: Kafka Streams: task assignment is not maintained on cluster restart or rolling restart
  • KAFKA-3898: KStream.leftJoin(…) is missing a Serde for thisVal. This can cause failures after mapValues etc
  • KAFKA-3902: Optimize KTable.filter() to reduce unnecessary traffic
  • KAFKA-3915: LogCleaner IO buffers do not account for potential size difference due to message format change
  • KAFKA-3924: Data loss due to halting when LEO is larger than leader's LEO
  • KAFKA-3933: Kafka OOM During Log Recovery Due to Leaked Native Memory
  • KAFKA-3935: ConnectDistributedTest.test_restart_failed_task.connector_type=sink system test failing
  • KAFKA-3941: Avoid applying eviction listener in InMemoryKeyValueLoggedStore
  • KAFKA-3950: kafka mirror maker tool is not respecting whitelist option
  • KAFKA-3952: VerifyConsumerRebalance cannot succeed when checking partition owner
  • KAFKA-3960: Committed offset not set after first assign
  • KAFKA-3977: KafkaConsumer swallows exceptions raised from message deserializers
  • KAFKA-3983: It would be helpful if SocketServer's Acceptors logged both the SocketChannel port and the processor ID upon registra
  • KAFKA-3996: ByteBufferMessageSet.writeTo() should be non-blocking
  • KAFKA-4008: Module "tools" should not be dependent on "core"
  • KAFKA-4018: Streams causing older slf4j-log4j library to be packaged along with newer version
  • KAFKA-4073: MirrorMaker should handle mirroring messages w/o timestamp better
  • PR-1735 - Add application id prefix for copartitionGroups in TopologyBuilder

Test

  • KAFKA-3863: Add system test for connector failure/restart

Sub-task

  • KAFKA-3660: Log exception message in ControllerBrokerRequestBatch
  • KAFKA-3865: Transient failures in org.apache.kafka.connect.runtime.WorkerSourceTaskTest.testSlowTaskStart
  • KAFKA-3931: kafka.api.PlaintextConsumerTest.testPatternUnsubscription transient failure

Previous CP releases

Confluent Platform 3.0.0 Release Notes

This is a major release of the Confluent Platform that provides Confluent users with Apache Kafka 0.10.0.0, the latest stable version of Kafka. In addition, this release includes the new Confluent Control Center application as well as the new Kafka Streams library that ships with Apache Kafka 0.10.0.0.

Confluent Platform users are encouraged to upgrade to CP 3.0.0 as it includes both new major functionality as well as important bug fixes. The technical details of this release are summarized in the What’s New section below.

Highlights

We have added several new features to the Confluent Platform in CP 3.0, to provide a more complete, easier to use, and higher performacne Stream Processing Platform:

Kafka Streams

We’re very excited to introduce Kafka Streams. Kafka Streams is included in Apache Kafka 0.10.0.0. Kafka Streams is a library that turns Apache Kafka into a full featured, modern stream processing system. Kafka Streams includes a high level language for describing common stream operations (such as joining, filtering, and aggregating records), allowing developers to quickly develop powerful streaming applications. Kafka Streams applications can easily be deployed on many different systems— they can run on YARN, be deployed on Mesos, run in Docker containers, or just embedded into exisiting Java applications.

Further information is available in the Kafka Streams documentation. If you want to give it a quick spin, head straight to the Kafka Streams Quickstart.

Confluent Control Center

Control Center is a web-based management and monitoring tool for Apache Kafka. In version 3.0.0, Control Center allows you to configure, edit, and manage connectors in Kafka Connect. It also includes Stream Monitoring: a system for measuring and monitoring your data streams end to end, from producer to consumer. To get started with Control Center, see Installing Confluent Control Center Server.

A term license for Confluent Control Center is available for Confluent Platform Enterprise Subscribers, but any user may download and try Confluent Control Center for free for 30 days.

Apache Kafka 0.10.0.0

Apache Kafka 0.10.0.0 is a major release of Apache Kafka and includes a number of new features and enhancements. Highlights include

  • Kafka Streams. As described above, Kafka Streams adds a simple but powerful streaming library to Apache Kafka.
  • Relative Offsets in Compressed Messages. In older versions of Kafka, recompression occurred when a broker received a batch of messages from ther producer. In 0.10.0.0, we have changed from using absolute offsets to relative offsets to avoid the recompression, reducing latency and reducing load on Kafka brokers. KAFKA-2511
  • Rack Awareness. Kafka can now run with a rack awareness feature that isolates replicas so they are guaranteed to span multiple racks or availability zones. This allows all of Kafka’s durability guarantees to be applied to these larger architectural units, significantly increasing availability. KAFKA-1215
  • Timestamps in Mesages. Messages are now tagged with timestamps at the time they are produced, allowing a number of future features including looking up message by time and measuring timing. KAFKA-2511
  • Kafka Consumer Max Records. In 0.9.0.0, developers had little control over the number of mesages returned when calling poll() for the new consumer. This feature introduces a new parameter max.poll.records that allows developers to limit the number of messages returned. KAKFA-3007
  • Client-Side Interceptors. We have introduced a new plugin architecture that allows developers to easily add “plugins” to Kafka clients. This allows developers to easily deploy additional code to inspect or modify Kafka messages. KAFKA-3162
  • Standardize Client Sequences. This features changed the arguments to some methods in the new consumer to work nore consistently with Java Collections. KAFKA-3006
  • List Connectors REST API. You can now query a distributed Kafka Connect cluster to discover the available connector classes. KAFKA-3316
  • Admin API changes. Some changes were made in the metadata request/response, improving performance in some situations. KAFKA-1694
  • Protocol Version Improvements. Kafka brokers now support a request that returns all supported protocol API versions. (This will make it easier for future Kafka clients to support multiple broker versions with a single client.) KAFKA-3307
  • SASL Improvements. Kafka 0.9.0.0 introduced new security features to Kafka, including support for Kerberos through SASL. In 0.10.0.0, Kafka now includes support for more SASL features, including external authentication servers, supporting multiple types of SASL authentication on one server, and other improvements. KAFKA-3149
  • Connect Status/Control APIs. In Kafka 0.10.0.0, we have continued to improve Kafka Connect. Previously, users had to monitor logs to view the status of connectors and their tasks, but we now support a status API for easier monitoring. We’ve also added control APIs, which allow you to pause a connector’s message processing in order to perform maintenance, and to manually restart tasks which have failed. KAFKA-3093, KAFKA-2370, KAFKA-3506
  • Allow cross origin HTTP requests on all HTTP methods. In Kafka 0.9.0.0, Kafka Connect only supported requests from the same domain; this enhancement removes that restriction. KAFKA-3578
  • Kafka LZ4 framing. Kafka’s implementation of LZ4 did not follow the standard LZ4 specification, creating problems for third party clients that wanted to leverage existing libraries. Kafka now conforms to the standard. KAFKA-3160

Note

Upgrading a Kafka Connect running in distributed mode from 0.9 versions of Kafka to 0.10 versions requires making a configuration change before the upgrade. See the Kafka Connect Upgrade Notes for more details.

For a complete list of features added and bugs fixed, see the Apache Kafka Release Notes.

Deprecating Camus

Camus in Confluent Platform is deprecated in Confluent Platform 3.0 and may be removed in a release after Confluent Platform 3.1. To export data from Kafka to HDFS and Hive, we recommend Kafka Connect with the Confluent HDFS connector as an alternative.

Other Notable Changes

We have also added some additional features to the Confluent Platform in CP 3.0:

  • Preview release of Python Client. We’re introducing a fully supported, up to date client for Python. Over time, we will keep this client up to date with the latest Java clients, including support for new broker versions and Kafka features. Try it out and send us feedback, through the Confluent Platform Mailing List.
  • Security for Schema Registry. The Schema Registry now supports SSL both at its REST layer (via HTTPS) and in its communication with Kafka. The REST layer is the public, user-facing component, and the “communication with Kafka” is the backend communication with Kafka where schemas are stored.
  • Security for Kafka REST Proxy. The REST Proxy now supports REST calls over HTTPS. The REST Proxy does not currently support Kafka security.
  • We’ve removed the “beta” designation from the new Java consumer and encourage users to begin migration away from the old consumers (note that it is required to make use of Kafka security extensions).
  • The old Scala producer has been deprecated. Users should migrate to the Java producer as soon as possible.

What’s New in CP 2.0.1?

Confluent Platform 2.0.1 contains a number of bug fixes included in the Kafka 0.9.0.1 release. Details of the changes to Kafka in this patch release are found in the Kafka Release Notes. Details of the changes to other components of the Confluent Platform are listed in the respective changelogs such as Kafka REST Proxy changelog.

Here is a quick overview of the notable Kafka-related bug fixes in the release, grouped by the affected functionality:

  • KAFKA-2978: Topic partition is not sometimes consumed after rebalancing of consumer group
  • KAFKA-3179: Kafka consumer delivers message whose offset is earlier than sought offset.
  • KAFKA-3157: Mirror maker doesn’t commit offset with new consumer when there is no more messages
  • KAFKA-3170: Default value of fetch_min_bytes in new consumer is 1024 while doc says it is 1
  • KAFKA-2695: Handle null string/bytes protocol primitives
  • KAFKA-3100: Broker.createBroker should work if json is version > 2, but still compatible
  • KAFKA-3012: Avoid reserved.broker.max.id collisions on upgrade
  • KAFKA-3198: Ticket Renewal Thread exits prematurely due to inverted comparison
  • KAFKA-3152: kafka-acl doesn’t allow space in principal name
  • KAFKA-3169: Kafka broker throws OutOfMemory error with invalid SASL packet
  • KAFKA-2878: Kafka broker throws OutOfMemory exception with invalid join group request
  • KAFKA-3166: Disable SSL client authentication for SASL_SSL security protocol
  • KAFKA-3003: The fetch.wait.max.ms is not honored when new log segment rolled for low volume topics
  • KAFKA-3159: Kafka consumer 0.9.0.0 client poll is very CPU intensive under certain conditions
  • KAFKA-2988: Change default configuration of the log cleaner
  • KAFKA-2973: Fix leak of child sensors on remove
  • KAFKA-2937: Topics marked for delete in Zookeeper may become undeletable

What’s New in CP 2.0.0?

The CP 2.0.0 release includes a range of new features over the previous release CP 1.0.x.

This release includes three key security features built directly within Kafka itself. First we now authenticate users using either Kerberos or TLS client certificates, so we now know who is making each request to Kafka. Second we have added a unix-like permissions system (ACLs) to control which users can access which data. Third, we support encryption on the wire using TLS to protect sensitive data on an untrusted network.

For more information on security features and how to enable them, see Kafka Security.

Kafka Connect facilitates large-scale, real-time data import and export for Kafka. It abstracts away common problems that each such data integration tool needs to solve to be viable for 24x7 production environments: fault tolerance, partitioning, offset management and delivery semantics, operations, and monitoring. It offers the capability to run a pool of processes that host a large number of Kafka connectors while handling load balancing and fault tolerance.

Confluent Platform includes a file connector for importing data from text files or exporting to text files, JDBC connector for importing data from relational databases and an HDFS connector for exporting data to HDFS / Hive in Avro and Parquet formats.

To learn more about Kafka Connect and the available connectors, see Kafka Connect.

Confluent Platform 2.0 and Kafka 0.9 now support user-defined quotas. Users have the ability to enforce quotas on a per-client basis. Producer-side quotas are defined in terms of bytes written per second per client id while consumer quotas are defined in terms of bytes read per second per client id.

Learn more about user defined quotas in the Enforcing Client Quotas section of the post-deployment documentation.

This release introduces beta support for the newly redesigned consumer client. At a high level, the primary difference in the new consumer is that it removes the distinction between the “high-level” ZooKeeper-based consumer and the “low-level” SimpleConsumer APIs, and instead offers a unified consumer API.

The new consumer allows the use of the group management facility (like the older high-level consumer) while still offering better control over offset commits at the partition level (like the older low-level consumer). It offers pluggable partition assignment amongst the members of a consumer group and ships with several assignment strategies. This completes a series of projects done in the last few years to fully decouple Kafka clients from Zookeeper, thus entirely removing the consumer client’s dependency on ZooKeeper.

To learn how to use the new consumer, refer to the Kafka Consumers documentation or the API docs.

In this release of the Confluent Platform we are packaging librdkafka. librdkafka is a C/C++ library implementation of the Apache Kafka protocol, containing both Producer and Consumer support. It was designed with message delivery reliability and high performance in mind, current figures exceed 800,000 msgs/second for the producer and 3 million msgs/second for the consumer.

You can learn how to use librdkafka side-by-side with the Java clients in our Kafka Clients documentation.

Proactive Support is a component of the Confluent Platform that improves Confluent’s support for the platform by collecting and reporting support metrics (“Metrics”) to Confluent. Proactive Support is enabled by default in the Confluent Platform. We do this to provide proactive support to our customers, to help us build better products, to help customers comply with support contracts, and to help guide our marketing efforts. With Metrics enabled, a Kafka broker is configured to collect and report certain broker and cluster metadata (“Metadata”) every 24 hours about your use of the Confluent Platform (including without limitation, your remote internet protocol address) to Confluent, Inc. (“Confluent”) or its parent, subsidiaries, affiliates or service providers.

Proactive Support is enabled by default in the Confluent Platform, but you can disable it by following the instructions in Proactive Support documentation. Please refer to the Confluent Privacy Policy for an in-depth description of how Confluent processes such information.

  • Kafka 0.9 no longer supports Java 6 or Scala 2.9. If you are still on Java 6, consider upgrading to a supported version.
  • Configuration parameter replica.lag.max.messages was removed. Partition leaders will no longer consider the number of lagging messages when deciding which replicas are in sync.
  • Configuration parameter replica.lag.time.max.ms now refers not just to the time passed since last fetch request from replica, but also to time since the replica last caught up. Replicas that are still fetching messages from leaders but did not catch up to the latest messages in replica.lag.time.max.ms will be considered out of sync.
  • MirroMaker no longer supports multiple target clusters. As a result it will only accept a single --consumer.config parameter. To mirror multiple source clusters, you will need at least one MirrorMaker instance per source cluster, each with its own consumer configuration.
  • Broker IDs above 1000 are now reserved by default to automatically assigned broker IDs. If your cluster has existing broker IDs above that threshold make sure to increase the reserved.broker.max.id broker configuration property accordingly.

How to Download

CP 3.0.1 is available for download at http://www.confluent.io/developer. See section Installation for detailed information.

To upgrade Confluent Platform to 3.0.1, check the Upgrade documentation.

Questions?

If you have questions regarding this release, feel free to reach out via the Confluent Platform mailing list. Confluent customers are encouraged to contact our support directly.