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Confluent Platform 3.0.0 Release Notes

This is a major release of the Confluent Platform that provides Confluent users with Apache Kafka, 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

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.


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 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

Apache Kafka 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, 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, 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 introduced new security features to Kafka, including support for Kerberos through SASL. In, 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, 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, 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


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.

Previous CP releases

What’s New in CP 2.0.1?

Confluent Platform 2.0.1 contains a number of bug fixes included in the Kafka 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:

New Java consumer

  • 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 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

Performance/memory usage

  • KAFKA-3003: The is not honored when new log segment rolled for low volume topics
  • KAFKA-3159: Kafka consumer 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

Topic deletion

  • 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

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.

User Defined Quotas

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.

New Consumer

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.

New Client Library - librdkafka

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 800000 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.

Compatibility Notes

  • 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 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 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 broker configuration property accordingly.

How to Download

CP 3.0.0 is available for download at See section Installation for detailed information.

To upgrade Confluent Platform from 2.0.x to 3.0.0, check the Upgrade documentation.


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.