#169 – In-Memory Computing and Apache Ignite

#169 – In-Memory Computing and Apache Ignite

Chris EvansData Management, Guest Speakers

This week Chris and Martin talk to Nikita Ivanov CTO and founder of GridGain Systems. The topic is in-memory computing and specifically Apache Ignite, an open-source key-value store that also supports SQL99 and POSIX-compliant file interfaces.

The idea of running applications purely from memory isn’t a new one. DRAM is the fastest “storage” component but isn’t designed as a long-term storage medium. Consequently, in-memory solutions such as Apache Ignite require features to ensure data resiliency and consistency. Ignite and similar solutions have a heavy focus on data distribution and protection in order to meet resiliency needs.

We also have to remember that memory and storage use different semantics. Memory is byte-orientated, through LOAD/STORE type instructions, whereas storage operates at a block level through read/write introductions. This difference provides both opportunities and challenges. As Nikita indicates, the new wave of storage-class memory products (persistent memory) such as Optane may seem to offer benefits, but might not offer significant benefit through the addition of persistence.

You can learn more about GridGain at https://www.gridgain.com/ and Apache Ignite at https://ignite.apache.org/

Elapsed Time: 00:45:35


  • 00:00:00 – Intros
  • 00:01:10 – What is Apache Ignite?
  • 00:02:30 – Effective in-memory computing introduces multiple machines & distributed systems
  • 00:06:20 – Memory and storage have different access semantics
  • 00:09:00 – In-memory computing has driven the most advanced distributed systems
  • 00:10:24 – What data models does Apache Ignite support?
  • 00:12:00 – Ignite offers SQL99, Key Value and POSIX file system semantics
  • 00:13:19 – Ignite suits between 8 and 64 nodes
  • 00:16:00 – Ignite is aimed at high-end in-memory requirements
  • 00:18:21 – Is in-memory computing a replacement for faster hardware?
  • 00:22:30 – GPUs offer the ability to manage small-scale analytics
  • 00:23:50 – How can we differentiate between in-memory solutions?
  • 00:25:00 – Complexity is a challenge for in-memory computing
  • 00:27:30 – Do we need to modify in-memory computing to be more consumable?
  • 00:32:10 – How do we differentiate between the multiple in-memory solutions?
  • 00:34:00 – How will new media influence in-memory development?
  • 00:39:00 – The next challenge for non-volatile media is integration
  • 00:40:30 – Wrap Up

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