A Comparative Study of RocksDB and K4 Storage Engines
This write up presents a comparative analysis of RocksDB and K4, two storage engines based on the Log-Structured Merge (LSM) tree architecture. We examine their respective strengths in terms of read and write performance, concurrency, durability, and ease of integration. Benchmark results and unique features of each engine are discussed to help developers and system architects make an informed choice based on application requirements.
Storage engines are crucial components in modern data-intensive applications, particularly those requiring high-performance key-value stores for both random and sequential reads and writes. A popular storage engine RocksDB and new storage engine K4, are both optimized for high-speed access and durability. This write up aims to compare these two engines to highlight their performance characteristics, trade-offs, and unique features.
RocksDB is a widely-used, high-performance LSM-based key-value store developed by Facebook and is a fork of LevelDB created at Google. It is known for its strong performance in write-heavy workloads and support for a wide range of configurations and fine-grained tuning. K4, on the other hand, is a newer open-source storage engine designed for both high-performance writes and reads, with a focus on minimal latency and optimized storage techniques, such as but not limited to a custom cuckoo filter for fast lookups.
Both RocksDB and K4 are based on the Log-Structured Merge Tree (LSM) architecture, which is designed to optimize write performance by writing data sequentially in memory and periodically flushing it to disk in sorted chunks (SSTables). However, their internal implementations differ significantly in terms of optimization techniques and added features.
- Optimized for Write-Heavy Workloads RocksDB is designed to handle large volumes of writes efficiently, using techniques like write-ahead logging (WAL) and compaction to ensure durability and consistency.
- Bloom Filters For faster read performance, RocksDB uses Bloom filters to test whether a key exists in a particular SSTable before actually accessing it.
- Fine-Grained Tuning RocksDB offers numerous configuration options, allowing for detailed control over aspects like compaction strategies, block cache, and compression.
- Support for Transactions RocksDB provides ACID transactions with snapshot isolation, allowing for safe and atomic operations.
- Compression Support Built-in support for compression (e.g., Snappy, Zlib) helps to reduce disk usage, though it may impact CPU utilization.
- Optimized for Both Reads and Writes K4 balances write performance with fast random read access, using an innovative cuckoo filter integrated into SSTables to speed up key lookups.
- Custom Cuckoo Filter The cuckoo filter provides better performance than traditional Bloom filters in terms of false positives and memory usage, leading to faster read operations with fewer disk accesses. The custom sstable filter enables O(P) best case is O(1) searches on equi functions.
- Atomic Transactions Like RocksDB, K4 supports atomic transactions for PUT and DELETE operations, ensuring consistency.
- Parallel Compaction K4 features paired and merged multi-threaded compaction, which reduces disk I/O during background operations, leading to better read performance and concurrent writes.
- Durability and Recovery K4 uses Write-Ahead Logging (WAL) for durability, ensuring that all write operations are recorded before being applied, and it offers the ability to recover from WAL logs if SSTables are missing.
- TTL Support K4 supports time-to-live (TTL) for keys, enabling automatic expiry of data after a set duration. K4 uses a skiplist for the memtable; On traversals there are check's to tombstone key's if their expired.
- Compression Support Built-in support for compression LZ77 inspired
To evaluate the performance of RocksDB and K4, we conducted benchmarks on an 11th Gen Intel i7-11700K CPU and a WDC HDD. The results provide insights into both read and write performance under various conditions, including sequential writes, random reads and writes, and concurrent workloads. More benchmarks with different systems will be conducted in the future.
RocksDB excels in sequential write performance due to its optimized compaction and write buffering mechanisms. It can efficiently handle high throughput write-heavy workloads. K4 also performs well in sequential writes, with its multi-threaded compaction mechanism allowing it to handle large volumes of writes while minimizing disk I/O.
RocksDB’s random read performance is improved by its Bloom filter, but the performance can degrade during compaction, especially with large datasets and frequent updates. K4 demonstrates faster random read performance, thanks to its custom cuckoo filter. The cuckoo filter minimizes false positives, resulting in fewer disk accesses during random read operations. K4-Go also outperforms RocksDB in write performance in random access workloads.
RocksDB performs reasonably well under concurrent workloads, but its compaction processes and background tasks can introduce latency, particularly when there are frequent write and read operations. K4 is optimized for concurrent access, with granular page locking and parallel compaction, which enables it to handle multiple concurrent read and write operations more efficiently than RocksDB.
RocksDB typically has lower latency for sequential reads and writes, but its performance can degrade in scenarios with high random access and concurrent operations. K4, on the other hand, shows lower latency for random reads and writes due to its optimized filter and efficient background compaction processes.
Both RocksDB and K4 offer robust durability guarantees using WAL (Write-Ahead Logging). However, K4 has an added benefit with atomic transactions and the ability to recover from WAL files, ensuring consistency even when SSTables are missing.
RocksDB has an extensive ecosystem and support for multiple languages, making it a solid choice for developers working in different environments. It is well-suited for use with C++, Java, Python, and other languages.
K4 offers a simpler integration experience, with native support for Go and a C library that can be used with foreign function interfaces (FFIs) in languages like Python, Ruby, and Java.
Both RocksDB and K4 are powerful storage engines that provide excellent performance for data-intensive applications, but each has its unique strengths.
RocksDB is well-suited for environments that require highly customizable performance tuning and where sequential writes are the dominant workload. It is highly reliable and widely adopted across the industry. K4, on the other hand, stands out due to its optimized random read performance, custom cuckoo filter, and parallel compaction. K4 is particularly well-suited for Go-based applications and scenarios with mixed read-write workloads and concurrent access patterns.
In terms of raw performance, K4 seems to offer an edge in random read and write scenarios, especially when concurrent operations are a factor. If your application demands high-speed random access and low latency, K4 could be the better choice. However, for traditional write-heavy applications and environments requiring extensive fine-grained configuration, RocksDB remains a robust, proven solution.
Ultimately, the choice between RocksDB and K4 depends on your specific workload, required features, and the language you are working with. For Go-based applications requiring fast, low-latency storage with high concurrency, K4 may indeed be the faster and more efficient choice.