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apache / mnemonic / fed27662cd8383fb7ee2c6752400d3edee1035d9
commitfed27662cd8383fb7ee2c6752400d3edee1035d9[log] [tgz]
authorlcy0816 <lcy0816@hotmail.com>Fri Feb 25 12:48:18 2022 -0800
committerGitHub <noreply@github.com>Fri Feb 25 12:48:18 2022 -0800
treead0d45cdfe3ab35a9223d46bf4864a57da7f9bae
parenteca3dd5433dd35ff2671be468aa6f7b2d9336a16 [diff]
parentf73aab2855314b97367b55f450ed8feb8a31d37c [diff]
Merge pull request #270 from lcy0816/issue-725

MNEMONIC-725:Keep thread information when get InterruptedException
tree: ad0d45cdfe3ab35a9223d46bf4864a57da7f9bae
  1. .github/
  2. docker/
  3. gradle/
  4. mnemonic-benches/
  5. mnemonic-collections/
  6. mnemonic-common/
  7. mnemonic-computing-services/
  8. mnemonic-core/
  9. mnemonic-examples/
  10. mnemonic-hadoop/
  11. mnemonic-memory-services/
  12. mnemonic-sessions/
  13. mnemonic-spark/
  14. tools/
  15. .asf.yaml
  16. .gitattributes
  17. .gitignore
  18. build.gradle
  19. gradlew
  20. gradlew.bat
  21. KEYS
  22. LICENSE
  23. NOTICE
  24. pom.xml
  25. README.md
  26. settings.gradle
README.md

================================

Mnemonic Official Website

CI

Apache Mnemonic is a non-volatile hybrid memory storage oriented library, it proposed a non-volatile/durable Java object model and durable computing service that bring several advantages to significantly improve the performance of massive real-time data processing/analytics. developers are able to use this library to design their cache-less and SerDe-less high performance applications.

Features:

  • In-place data storage on local non-volatile memory
  • Durable Object Model (DOM)
  • Durable Native Computing Model (DNCM)
  • Object graphs lazy loading & sharing
  • Auto-reclaim memory resources and Mnemonic objects
  • Hierarchical cache pool for massive data caching
  • Extensible memory services for new device adoption and allocation optimization
  • Durable data structure collection(WIP)
  • Durable computing service
  • Minimize memory footprint of on-heap
  • Reduce GC Overheads as the following chart shown (collected from Apache Spark experiments)
  • Drop-in Hadoop MapReduce support
  • Drop-in Hadoop Spark support