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apache / mnemonic / f828d32fdff5e524c81c6e422cfe99c244eefdf8
commitf828d32fdff5e524c81c6e422cfe99c244eefdf8[log] [tgz]
authorGary <garyw@apache.org>Sun Aug 30 13:03:08 2020 -0700
committerGordon King <10856796+bigdata-memory@users.noreply.github.com>Tue Sep 01 21:59:48 2020 -0700
tree3a430faf277c706e8096b19473dcd1ce63b84155
parentcb82114848cde3355c01d0b1bde897cf9927243b [diff]
MNEMONIC-534: Deploy workflows of Github Actions

Signed-off-by: Gary <garyw@apache.org>
2 files changed
tree: 3a430faf277c706e8096b19473dcd1ce63b84155
  1. .circleci/
  2. .github/
  3. bin/
  4. docker/
  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-query/
  14. mnemonic-sessions/
  15. mnemonic-spark/
  16. .asf.yaml
  17. .gitattributes
  18. .gitignore
  19. .travis.yml
  20. KEYS
  21. LICENSE
  22. NOTICE
  23. pom.xml
  24. README.md
README.md

================================

Mnemonic Official Website

CI Build Status

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