Summary:
When dynamically linking two binaries together, different builds of RocksDB from two sources might cause errors. To provide a tool for user to solve the problem, the RocksDB namespace is changed to a flag which can be overridden in build time.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/6433
Test Plan: Build release, all and jtest. Try to build with ROCKSDB_NAMESPACE with another flag.
Differential Revision: D19977691
fbshipit-source-id: aa7f2d0972e1c31d75339ac48478f34f6cfcfb3e
Summary:
This is a simple edit to have two #include file paths be consistent within range_del_aggregator.{h,cc} with everywhere else.
The impact of this inconsistency is that it actual breaks a Bazel based build on the Windows platform. The same pragma once failure occurs with both Windows Visual C++ 2019 and clang for Windows 9.0. Bazel's "sandboxing" of the builds causes both compilers to not properly recognize "rocksdb/types.h" and "include/rocksdb/types.h" to be the same file (also comparator.h). My guess is that the backslash versus forward slash mixing within path names is the underlying issue.
But, everything builds fine once the include paths in these two source files are consistent with the rest of the repository.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/6321
Differential Revision: D19506585
Pulled By: ltamasi
fbshipit-source-id: 294c346607edc433ab99eaabc9c880ee7426817a
Summary: Add hash table (under persistent cache) to CMake list
Test Plan: Run hash_test in windows and make check in Linux
Reviewers: sdong
Subscribers: andrewkr, dhruba, leveldb
Differential Revision: https://reviews.facebook.net/D59151
Summary:
Persistent read cache isn't very applicable for lite builds. Wrapping
the code with #ifndef ROCKSDB_LITE .. #endif
Test Plan: Run unit, lite, lite_test
Reviewers: sdong
Subscribers: andrewkr, dhruba, leveldb
Differential Revision: https://reviews.facebook.net/D58563
Summary:
We expect the persistent read cache to perform at speeds upto 8 GB/s. In order
to accomplish that, we need build a index mechanism which operate in the order
of multiple millions per sec rate.
This patch provide the basic data structure to accomplish that:
(1) Hash table implementation with lock contention spread
It is based on the StripedHashSet<T> implementation in
The Art of multiprocessor programming by Maurice Henry & Nir Shavit
(2) LRU implementation
Place holder algorithm for further optimizing
(3) Evictable Hash Table implementation
Building block for building index data structure that evicts data like files
etc
TODO:
(1) Figure if the sharded hash table and LRU can be used instead
(2) Figure if we need to support configurable eviction algorithm for
EvictableHashTable
Test Plan: Run unit tests
Subscribers: andrewkr, dhruba, leveldb
Differential Revision: https://reviews.facebook.net/D55785
sdong