d4540654e9
Summary: CPU profiling reveals GetApproximateSizes as a bottleneck for performance. The current implementation is sub-optimal, it scans every file in every level to compute the result. We can take advantage of the fact that all levels above 0 are sorted in the increasing order of key ranges and use binary search to locate the starting index. This can reduce the number of comparisons required to compute the result. Test Plan: We have good test coverage. Run the tests. Reviewers: sdong, igor, rven, dynamike Subscribers: dynamike, maykov, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D37755
RocksDB: A Persistent Key-Value Store for Flash and RAM Storage
RocksDB is developed and maintained by Facebook Database Engineering Team. It is built on earlier work on LevelDB by Sanjay Ghemawat (sanjay@google.com) and Jeff Dean (jeff@google.com)
This code is a library that forms the core building block for a fast key value server, especially suited for storing data on flash drives. It has a Log-Structured-Merge-Database (LSM) design with flexible tradeoffs between Write-Amplification-Factor (WAF), Read-Amplification-Factor (RAF) and Space-Amplification-Factor (SAF). It has multi-threaded compactions, making it specially suitable for storing multiple terabytes of data in a single database.
Start with example usage here: https://github.com/facebook/rocksdb/tree/master/examples
See the github wiki for more explanation.
The public interface is in include/. Callers should not include or
rely on the details of any other header files in this package. Those
internal APIs may be changed without warning.
Design discussions are conducted in https://www.facebook.com/groups/rocksdb.dev/