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apache / mnemonic / ee471c97534b368167ee12ead47b8d54c5691eda
commitee471c97534b368167ee12ead47b8d54c5691eda[log] [tgz]
authorKatarina <104290148+katarinaking@users.noreply.github.com>Sun Dec 29 18:59:01 2024 -0800
committerGitHub <noreply@github.com>Sun Dec 29 18:59:01 2024 -0800
tree09df58a5baf7bd3576a728e19f6accb815b4f57c
parent02378cc7b5410743d8a6d8e22b2d8484d8a833dd [diff]
parent5f2219615ae7c4f8af98860cf0991c5743ade164 [diff]
Merge pull request #403 from katarinaking/841

MNEMONIC-841: Added comments for better clarity.
tree: 09df58a5baf7bd3576a728e19f6accb815b4f57c
  1. .devcontainer/
  2. .github/
  3. .vscode/
  4. docker/
  5. gradle/
  6. mnemonic-benches/
  7. mnemonic-collections/
  8. mnemonic-common/
  9. mnemonic-computing-services/
  10. mnemonic-core/
  11. mnemonic-examples/
  12. mnemonic-hadoop/
  13. mnemonic-memory-services/
  14. mnemonic-protocol/
  15. mnemonic-sessions/
  16. mnemonic-spark/
  17. tools/
  18. .asf.yaml
  19. .gitattributes
  20. .gitignore
  21. build.gradle
  22. gradlew
  23. gradlew.bat
  24. KEYS
  25. LICENSE
  26. NOTICE
  27. pom.xml
  28. README.md
  29. 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