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apache / mnemonic / 9e1450635b95c3f99d459836dab7b8c1b6d6799d
commit9e1450635b95c3f99d459836dab7b8c1b6d6799d[log] [tgz]
authorChunyong He <chunyong.he@gmail.com>Sun Mar 14 23:37:45 2021 -0700
committerYanhui Zhao <yzhao@apache.org>Tue Mar 16 15:58:13 2021 -0700
tree04bcbd98b400c45f7be81d259e4d12d02d400673
parentee934bc1f55ce8a61f9fce39d240eaa51867957e [diff]
MNEMONIC-635: Fix tools/tool.conf bug for DurablePersonRefBreakNGTest setting

Signed-off-by: Chunyong He <chunyong.he@gmail.com>
1 file changed
tree: 04bcbd98b400c45f7be81d259e4d12d02d400673
  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-query/
  13. mnemonic-sessions/
  14. mnemonic-spark/
  15. tools/
  16. .asf.yaml
  17. .gitattributes
  18. .gitignore
  19. build.gradle
  20. gradlew
  21. gradlew.bat
  22. KEYS
  23. LICENSE
  24. NOTICE
  25. pom.xml
  26. README.md
  27. 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