【转载】rocksdb对leveldb做了哪些优化？

作者：godj
链接：https://www.zhihu.com/question/328622742/answer/713388283

RocksDB Features that are not in LevelDB

Performance

• Multithread compaction
• Multithread memtable inserts
• Reduced DB mutex holding
• Optimized level-based compaction style and universal compaction style
• Prefix bloom filter
• Memtable bloom filter
• Single bloom filter covering the whole SST file
• Write lock optimization
• Improved Iter::Prev() performance
• Fewer comparator calls during SkipList searches
• Allocate memtable memory using huge page.

Features

• Column Families
• Transactions and WriteBatchWithIndex
• Backup and Checkpoints
• Merge Operators
• Compaction Filters
• RocksDB Java
• Manual Compactions Run in Parallel with Automatic Compactions
• Persistent Cache
• Bulk loading
• Forward Iterators/ Tailing iterator
• Single delete
• Delete files in range
• Pin iterator key/value

Alternative Data Structures And Formats

• Plain Table format for memory-only use cases
• Vector-based and hash-based memtable format
• Clock-based cache (coming soon)
• Pluggable information log
• Annotate transaction log write with blob (for replication)

Tunability

• Rate limiting
• Tunable Slowdown and Stop threshold
• Option to keep all files open
• Option to keep all index and bloom filter blocks in block cache
• Multiple WAL recovery modes
• Fadvise hints for readahead and to avoid caching in OS page cache
• Option to pin indexes and bloom filters of L0 files in memory
• More Compression Types: zlib, lz4, zstd
• Compression Dictionary
• Checksum Type: xxhash
• Different level size multiplier and compression type for each level.

Manageability

• Statistics
• Thread-local profiling
• More commands in command-line tools
• User-defined table properties
• Event listeners
• More DB Properties
• Dynamic option changes
• Get options from a string or map
• Persistent options to option files

标签：files,leveldb,based,rocksdb,filter,cache,compaction,转载,bloom
来源： https://www.cnblogs.com/jiangshifu/p/16246986.html