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apache / mnemonic / 4522474b2795bc355ea97d12db75432fd33e528c
commit4522474b2795bc355ea97d12db75432fd33e528c[log] [tgz]
authorXiaojin Jiao <xjiao@apache.org>Tue Oct 18 20:04:46 2022 -0700
committerYanhui Zhao <wildyz.yky@gmail.com>Thu Oct 20 21:35:15 2022 -0700
tree22e455ea127182c6c35a205050c1f9a2f1c434db
parent0c98e2690542ee96e0947d4a86cfffaac0a82a4d [diff]
MNEMONIC-766: Correct JNI class name from VMemServiceImpl to VMemKindServiceImpl

Signed-off-by: Xiaojin Jiao <xjiao@apache.org>
3 files changed
tree: 22e455ea127182c6c35a205050c1f9a2f1c434db
  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-sessions/
  15. mnemonic-spark/
  16. tools/
  17. .asf.yaml
  18. .gitattributes
  19. .gitignore
  20. build.gradle
  21. gradlew
  22. gradlew.bat
  23. KEYS
  24. LICENSE
  25. NOTICE
  26. pom.xml
  27. README.md
  28. 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