Distributed In Memory Processing And Streaming With Hazelcast

Summary

In memory computing provides significant performance benefits, but brings along challenges for managing failures and scaling up. Hazelcast is a platform for managing stateful in-memory storage and computation across a distributed cluster of commodity hardware. On top of this foundation, the Hazelcast team has also built a streaming platform for reliable high throughput data transmission. In this episode Dale Kim shares how Hazelcast is implemented, the use cases that it enables, and how it complements on-disk data management systems.

Announcements

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• Your host is Tobias Macey and today I’m interviewing Dale Kim about Hazelcast, a distributed in-memory computing platform for data intensive applications

Interview

• Introduction
• How did you get involved in the area of data management?
• Can you start by describing what Hazelcast is and its origins?
• What are the benefits and tradeoffs of in-memory computation for data-intensive workloads?
• What are some of the common use cases for the Hazelcast in memory grid?
• How is Hazelcast implemented?
  • How has the architecture evolved since it was first created?
• How is the Jet streaming framework architected?
  • What was the motivation for building it?
  • How do the capabilities of Jet compare to systems such as Flink or Spark Streaming?
• How has the introduction of hardware capabilities such as NVMe drives influenced the market for in-memory systems?
• How is the governance of the open source grid and Jet projects handled?
  • What is the guiding heuristic for which capabilities or features to include in the open source projects vs. the commercial offerings?
• What is involved in building an application or workflow on top of Hazelcast?
• What are the common patterns for engineers who are building on top of Hazelcast?
• What is involved in deploying and maintaining an installation of the Hazelcast grid or Jet streaming?
• What are the scaling factors for Hazelcast?
  • What are the edge cases that users should be aware of?
• What are some of the most interesting, innovative, or unexpected ways that you have seen Hazelcast used?
• When is Hazelcast Grid or Jet the wrong choice?
• What is in store for the future of Hazelcast?

Contact Info

• LinkedIn

Parting Question

• From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

• Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
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Links

• HazelCast
• Istanbul
• Apache Spark
• OrientDB
• CAP Theorem
• NVMe
• Memristors
• Intel Optane Persistent Memory
• Hazelcast Jet
• Kappa Architecture
• IBM Cloud Paks
• Digital Integration Hub (Gartner)

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA