Google Git
Sign in
apache / mnemonic / ff1131fe535b6ab870ad87170ba6e277e01943e8
commitff1131fe535b6ab870ad87170ba6e277e01943e8[log] [tgz]
authorZhen Li <rhythmwind@gmail.com>Thu Mar 31 23:10:57 2022 -0700
committerYanhui Zhao <wildyz.yky@gmail.com>Fri Apr 01 21:21:44 2022 -0700
treefac4f4fb54391c44040791cda97f266134f55be4
parent0b700efa041283a0607f43abbf337d2de52eec40 [diff]
MNEMONIC-738: gradle build error caused by updated class major version

Signed-off-by: Zhen Li <rhythmwind@gmail.com>
1 file changed
tree: fac4f4fb54391c44040791cda97f266134f55be4
  1. .github/
  2. .vscode/
  3. docker/
  4. gradle/
  5. mnemonic-benches/
  6. mnemonic-collections/
  7. mnemonic-common/
  8. mnemonic-computing-services/
  9. mnemonic-core/
  10. mnemonic-examples/
  11. mnemonic-hadoop/
  12. mnemonic-memory-services/
  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