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From Guozhang Wang <guozh...@apache.org>
Subject [ANNOUNCE] Apache Kafka 1.0.0 Released
Date Wed, 01 Nov 2017 15:38:37 GMT
The Apache Kafka community is pleased to announce the release for Apache
Kafka 1.0.0.

This is a major release of the Kafka project, and is no mere bump of the
version number. The Apache Kafka Project Management Committee has packed a
number of valuable enhancements into the release. Let me summarize a few of
them:

** Since its introduction in version 0.10, the Streams API has become
hugely popular among Kafka users, including the likes of Pinterest,
Rabobank, Zalando, and The New York Times. In 1.0, the the API continues to
evolve at a healthy pace. To begin with, the builder API has been improved
(KIP-120). A new API has been added to expose the state of active tasks at
runtime (KIP-130). Debuggability gets easier with enhancements to the
print() and writeAsText() methods (KIP-160). And if that’s not enough,
check out KIP-138 and KIP-161 too. For more on streams, check out the
Apache Kafka Streams documentation (https://kafka.apache.org/docu
mentation/streams/), including some helpful new tutorial videos.

** Operating Kafka at scale requires that the system remain observable, and
to make that easier, we’ve made a number of improvements to metrics. These
are too many to summarize without becoming tedious, but Connect metrics
have been significantly improved (KIP-196), a litany of new health check
metrics are now exposed (KIP-188), and we now have a global topic and
partition count (KIP-168). Check out KIP-164 and KIP-187 for even more.

** We now support Java 9, leading, among other things, to significantly
faster TLS and CRC32C implementations. Over-the-wire encryption will be
faster now, which will keep Kafka fast and compute costs low when
encryption is enabled.

** In keeping with the security theme, KIP-152 cleans up the error handling
on Simple Authentication Security Layer (SASL) authentication attempts.
Previously, some authentication error conditions were indistinguishable
from broker failures and were not logged in a clear way. This is cleaner
now.

** Kafka can now tolerate disk failures better. Historically, JBOD storage
configurations have not been recommended, but the architecture has
nevertheless been tempting: after all, why not rely on Kafka’s own
replication mechanism to protect against storage failure rather than using
RAID? With KIP-112, Kafka now handles disk failure more gracefully. A
single disk failure in a JBOD broker will not bring the entire broker down;
rather, the broker will continue serving any log files that remain on
functioning disks.

** Since release 0.11.0, the idempotent producer (which is the producer
used in the presence of a transaction, which of course is the producer we
use for exactly-once processing) required max.in.flight.requests.per.connection
to be equal to one. As anyone who has written or tested a wire protocol can
attest, this put an upper bound on throughput. Thanks to KAFKA-5949, this
can now be as large as five, relaxing the throughput constraint quite a bit.


All of the changes in this release can be found in the release notes:

https://dist.apache.org/repos/dist/release/kafka/1.0.0/RELEASE_NOTES.html


You can download the source release from:

https://www.apache.org/dyn/closer.cgi?path=/kafka/1.0.0/kafka-1.0.0-src.tgz

and binary releases from:

https://www.apache.org/dyn/closer.cgi?path=/kafka/1.0.0/kafka_2.11-1.0.0.tgz
(Scala
2.11)
https://www.apache.org/dyn/closer.cgi?path=/kafka/1.0.0/kafka_2.12-1.0.0.tgz
(Scala
2.12)


------------------------------------------------------------
---------------------------------------

Apache Kafka is a distributed streaming platform with four four core APIs:

** The Producer API allows an application to publish a stream records to one
or more Kafka topics.

** The Consumer API allows an application to subscribe to one or more topics
and process the stream of records produced to them.

** The Streams API allows an application to act as a stream processor,
consuming
an input stream from one or more topics and producing an output stream to
one or more output topics, effectively transforming the input streams to
output streams.

** The Connector API allows building and running reusable producers or
consumers
that connect Kafka topics to existing applications or data systems. For
example, a connector to a relational database might capture every change to
a table.three key capabilities:


With these APIs, Kafka can be used for two broad classes of application:

** Building real-time streaming data pipelines that reliably get data between
systems or applications.

** Building real-time streaming applications that transform or react
to the streams
of data.


Apache Kafka is in use at large and small companies worldwide, including
Capital One, Goldman Sachs, ING, LinkedIn, Netflix, Pinterest, Rabobank,
Target, The New York Times, Uber, Yelp, and Zalando, among others.


A big thank you for the following 108 contributors to this release!

Abhishek Mendhekar, Xi Hu, Andras Beni, Andrey Dyachkov, Andy Chambers,
Apurva Mehta, Armin Braun, Attila Kreiner, Balint Molnar, Bart De Vylder,
Ben Stopford, Bharat Viswanadham, Bill Bejeck, Boyang Chen, Bryan Baugher,
Colin P. Mccabe, Koen De Groote, Dale Peakall, Damian Guy, Dana Powers,
Dejan Stojadinović, Derrick Or, Dong Lin, Zhendong Liu, Dustin Cote,
Edoardo Comar, Eno Thereska, Erik Kringen, Erkan Unal, Evgeny Veretennikov,
Ewen Cheslack-Postava, Florian Hussonnois, Janek P, Gregor Uhlenheuer,
Guozhang Wang, Gwen Shapira, Hamidreza Afzali, Hao Chen, Jiefang He, Holden
Karau, Hooman Broujerdi, Hugo Louro, Ismael Juma, Jacek Laskowski, Jakub
Scholz, James Cheng, James Chien, Jan Burkhardt, Jason Gustafson, Jeff
Chao, Jeff Klukas, Jeff Widman, Jeremy Custenborder, Jeyhun Karimov,
Jiangjie Qin, Joel Dice, Joel Hamill, Jorge Quilcate Otoya, Kamal C, Kelvin
Rutt, Kevin Lu, Kevin Sweeney, Konstantine Karantasis, Perry Lee, Magnus
Edenhill, Manikumar Reddy, Manikumar Reddy O, Manjula Kumar, Mariam John,
Mario Molina, Matthias J. Sax, Max Zheng, Michael Andre Pearce, Michael
André Pearce, Michael G. Noll, Michal Borowiecki, Mickael Maison, Nick
Pillitteri, Oleg Prozorov, Onur Karaman, Paolo Patierno, Pranav Maniar,
Qihuang Zheng, Radai Rosenblatt, Alex Radzish, Rajini Sivaram, Randall
Hauch, Richard Yu, Robin Moffatt, Sean McCauliff, Sebastian Gavril, Siva
Santhalingam, Soenke Liebau, Stephane Maarek, Stephane Roset, Ted Yu,
Thibaud Chardonnens, Tom Bentley, Tommy Becker, Umesh Chaudhary, Vahid
Hashemian, Vladimír Kleštinec, Xavier Léauté, Xianyang Liu, Xin Li, Linhua
Xin


We welcome your help and feedback. For more information on how to report
problems, and to get involved, visit the project website at
http://kafka.apache.org/




Thanks,
Guozhang Wang

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