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apache / mnemonic / 8edc75b662a703966f4a328a79aed455fe65c6d5
commit8edc75b662a703966f4a328a79aed455fe65c6d5[log] [tgz]
authorchenyang <chenyang@apache.org>Fri Mar 17 00:15:23 2023 +0000
committerchenyang <chenyang@apache.org>Fri Mar 17 00:15:23 2023 +0000
tree4797a24bf99149bc53b0911c922112e1de8bb5a0
parentdb817c2c74d3424331a09d73deb91d6aa2003512 [diff]
MNEMONIC-776: update gpg public key into KEYS file as the previous one is expired

Signed-off-by: chenyang <chenyang@apache.org>
1 file changed
tree: 4797a24bf99149bc53b0911c922112e1de8bb5a0
  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