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RocksDB vs Speedb: What are the differences?
## Introduction
RocksDB and Speedb are both high-performance key-value storage engines designed to provide efficient data storage.
1. **Storage Model**: RocksDB is a persistent key-value store whereas Speedb is an in-memory key-value store.
2. **Durability**: RocksDB provides durability by writing data to disk, while Speedb sacrifices durability for speed by keeping data in memory to avoid disk writes.
3. **Performance Impact**: RocksDB may experience latency due to disk writes, while Speedb offers faster performance but at the risk of potential data loss if the system crashes.
4. **Use Cases**: RocksDB is suitable for applications that require durability and can tolerate lower performance, while Speedb is ideal for applications where speed is critical and data loss is acceptable.
5. **Optimization**: RocksDB focuses on persistent storage optimizations, such as reducing disk I/O and maintaining data integrity, while Speedb optimizes for in-memory operations and high-speed data access.
6. **Scalability**: RocksDB can handle larger data sets due to its use of disk storage, while Speedb is limited by available memory but excels at handling smaller, high-speed data sets.
In Summary, RocksDB prioritizes durability and persistent storage, whereas Speedb prioritizes speed and in-memory operations.
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Learn MorePros of RocksDB
Pros of Speedb
Pros of RocksDB
- Very fast5
- Made by Facebook3
- Consistent performance2
- Ability to add logic to the database layer where needed1
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What is RocksDB?
RocksDB is an embeddable persistent key-value store for fast storage. RocksDB can also be the foundation for a client-server database but our current focus is on embedded workloads. RocksDB builds on LevelDB to be scalable to run on servers with many CPU cores, to efficiently use fast storage, to support IO-bound, in-memory and write-once workloads, and to be flexible to allow for innovation.
What is Speedb?
Speedb’s Log-Structured Merge (LSM)-based key value store supports petabyte scaling of datasets with billions of objects, while maintaining high performance and low hardware requirements. It is based on a patented compaction method that reduces the write amplification factor (WAF) up to 6X and adds enhancements that eliminate latency issues and IO stalls.
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What are some alternatives to RocksDB and Speedb?
Redis
Redis is an open source (BSD licensed), in-memory data structure store, used as a database, cache, and message broker. Redis provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes, and streams.
Cassandra
Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.
MongoDB
MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
Badger
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HBase
Apache HBase is an open-source, distributed, versioned, column-oriented store modeled after Google' Bigtable: A Distributed Storage System for Structured Data by Chang et al. Just as Bigtable leverages the distributed data storage provided by the Google File System, HBase provides Bigtable-like capabilities on top of Apache Hadoop.