Confluent
Log Compaction | Highlights in the Kafka and Stream Processing Community | February 2016
Log Compaction

Log Compaction | Highlights in the Kafka and Stream Processing Community | February 2016

Gwen Shapira
Welcome to the February 2016 edition of Log Compaction, a monthly digest of highlights in the Apache Kafka and stream processing community. Got a newsworthy item? Let us know.

  • We’ve been discussing many improvement proposals this month
    • KIP-41 – a proposal to limit the number of records returned by KafkaConsumer.poll method has been accepted.
    • KIP-42 – a proposal to add interceptors to producers and consumers has been accepted. This improvement creates interesting new monitoring options and once this is implemented, it will be interesting to hear how to community is using the new APIs.
    • KIP-43 and KIP-44 propose improvements and extensions to Kafka’s authentication protocols. These are still under active discussion, and if you are interested in security in Kafka, I suggest reading the wiki and the discussion to see where we are heading.             
    • KIP-45 – a proposal to standardize the various collections that the KafkaConsumer API expects is still under discussion, with the benefits of more standardized approach being weighed against the desire to maintain backward compatibility for this new API.
  • Many of us are just learning the ins and outs of the new consumer. This recently published blog post, with a complete end-to-end example proves very useful.
  • A passionate developer wrote very detailed blog posts on Kafka integration with Spark Streaming. This includes the little-discussed question of how to write the results of the stream processing job back into Kafka.
  • LinkedIn wrote about new features in Samza. The blog post also includes sexy throughput numbers, description of their use-case and description of how Samza is used in their data products. Really cool stuff.
  • Google contributed their Dataflow API (but not implementation) to the Apache Software Foundation and are inviting other stream processing projects to implement their SDK. We are watching to see where this will take the active stream processing community.

More Articles Like This

Kafka Summit London

Welcome to Kafka Summit London 2018!

Tim Berglund . .

San Francisco. New York. San Francisco again. What is left to do? Why, Kafka Summit London, of course! The Kafka Community’s first Summit on this sceptred isle opens today. Kafka Summit ...

Adam Warski

Event Sourcing Using Apache Kafka

Adam Warski . .

Adam Warski is one of the co-founders of SoftwareMill, where he codes mainly using Scala and other interesting technologies. He is involved in open-source projects, such as sttp, MacWire, Quicklens, ...

Robin Moffatt

KSQL in Action: Real-Time Streaming ETL from Oracle Transactional Data

Robin Moffatt . .

In this post I’m going to show what streaming ETL looks like in practice. We’re replacing batch extracts with event streams, and batch transformation with in-flight transformation. But first, a ...

Leave a Reply

Your email address will not be published. Required fields are marked *

Try Confluent Platform

Download Now

We use cookies to understand how you use our site and to improve your experience. Click here to learn more or change your cookie settings. By continuing to browse, you agree to our use of cookies.