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

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

Guozhang Wang

Here comes the July 2016 edition of Log Compaction, a monthly digest of highlights in the Apache Kafka and stream processing community. Want to share some exciting news on this blog? Let us know.

  • A lot of improvements have been proposed since the latest 0.10.0.0 release:
    • KIP-33 – proposed by Jiangjie Qin, will add a time log index to enhance the accuracy of various functionalities such as searching offset by timestamp, time-based log rolling and retention, etc. It has been adopted with the target release version 0.10.1.0.
    • KIP-62 – proposed by Jason Gustafson, will separate the session timeout configuration for consumer hard failure detection from the processing timeout configuration, so that users have more flexibility specifying liveness criterion for different scenarios. It has been adopted with the target release version 0.10.1.0.
    • KIP-4 – proposed by Joe Stein and led by Grant Henke, will introduce request protocols for different administration operations, such as topics / configs / ACLs, etc. The topics admin request protocols has been under busy discussions and development.
    • We have a bunch of other KIPs under discussion and voting as well, such as KIP-63 and KIP-67 for improving the Streams API in Kafka, KIP-55 and KIP-48 for adding more features into Kafka Security, etc. We would love to encourage anyone from the community who are interested in these specific topics to get involved!
  • Want to learn about the Streams API in Kafka? Read this nice blog by Michael Noll on building your first real-time stream aggregation application, and watch the presentation by Guozhang Wang at Hadoop Summit San Jose!
  • LinkedIn hosted its first-ever Stream Processing Meetup. Shuyi Chen, Cameron Lee and Shubhanhu Nagar talk about how they use Kafka and Samza as the backbones for their streaming applications, at Uber and LinkedIn.
  • Considering using Kafka to simplify your microservices? Check out Jim Riecken’s talk at Scala Days New York this month.
  • Twitter has open sourced Heron, a new distributed stream computation system after Apache Storm.
  • Kafka was BIG at Berlin Buzzwords! Checkout Neha Narkhede’s keynote on using it for application development in the new paradigm of stream processing.

Subscribe to the Confluent Blog

Subscribe

More Articles Like This

Event Streaming Pipeline
Ilayaperumal Gopinathan

Spring for Apache Kafka Deep Dive – Part 4: Continuous Delivery of Event Streaming Pipelines

Ilayaperumal Gopinathan .

For event streaming application developers, it is important to continuously update the streaming pipeline based on the need for changes in the individual applications in the pipeline. It is also ...

Figure 1. Dataflow in architecture
Jendrik Poloczek

Reliable, Fast Access to On-Chain Data Insights

Jendrik Poloczek .

At TokenAnalyst, we are building the core infrastructure to integrate, clean, and analyze blockchain data. Data on a blockchain is also known as on-chain data. We offer both historical and ...

Apache Kafka + Spring Cloud Data Flow
Ilayaperumal Gopinathan

Spring for Apache Kafka Deep Dive – Part 3: Apache Kafka and Spring Cloud Data Flow

Ilayaperumal Gopinathan .

Following part 1 and part 2 of the Spring for Apache Kafka Deep Dive blog series, here in part 3 we will discuss another project from the Spring team: Spring ...

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.