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RocksDB vs UnQLite: What are the differences?

# Introduction
In the world of database management systems, RocksDB and UnQLite are two popular choices, each offering unique features and benefits. Understanding the key differences between these two options can help in making an informed decision based on specific project requirements.

1. **Storage Engine**: RocksDB is a key-value store based on Log-Structured Merge-Tree (LSM) while UnQLite is a document store with a b-tree key-value store. RocksDB utilizes LSM to provide high write throughput and efficient disk space utilization, making it suitable for applications with high write workloads. On the other hand, UnQLite's b-tree structure allows for faster read operations and is ideal for scenarios where read latency is critical.

2. **Language Support**: RocksDB is originally written in C++ but provides bindings for various programming languages such as Java, Python, and Go. In contrast, UnQLite is written in C and provides a native C API, making it more suitable for projects that require direct interaction with C language programs. 

3. **Consistency Model**: RocksDB is optimized for high write throughput and is categorized as an eventually consistent database, meaning it sacrifices some level of consistency for performance. UnQLite, however, follows a strict transaction model with configurable locking mechanisms, ensuring data consistency at the cost of some performance trade-offs.

4. **Memory Management**: RocksDB operates on the principle of memory-mapped files, which can lead to higher memory consumption compared to UnQLite's in-memory storage engine. UnQLite stores data primarily in memory and periodically flushes to disk, making it efficient in terms of memory utilization.

5. **Community Support**: RocksDB is backed by a strong community of developers contributing to its continuous improvement and feature enhancements. UnQLite, while stable and reliable, may have a smaller developer community, leading to potentially slower updates and support for new features or bug fixes.

6. **Scaling Capabilities**: RocksDB is designed to scale horizontally, allowing for distributed deployments across multiple nodes for increased performance and fault tolerance. In contrast, UnQLite's single-node architecture may limit its scalability options for large-scale deployments requiring distributed systems.

In Summary, Understanding the key differences between RocksDB and UnQLite can help in choosing the right database solution based on specific project requirements.

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Pros of RocksDB
Pros of UnQLite
  • 5
    Very fast
  • 3
    Made by Facebook
  • 2
    Consistent performance
  • 1
    Ability to add logic to the database layer where needed
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    Cons of RocksDB
    Cons of UnQLite
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      • 1
        Different compilation for each platform

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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 UnQLite?

      UnQLite is a in-process software library which implements a self-contained, serverless, zero-configuration, transactional NoSQL database engine. UnQLite is a document store database similar to MongoDB, Redis, CouchDB etc. as well a standard Key/Value store similar to BerkeleyDB, LevelDB, etc.

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      What companies use RocksDB?
      What companies use UnQLite?
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        What tools integrate with RocksDB?
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        Blog Posts

        Jan 26 2022 at 4:34AM


        Amazon EC2RocksDBOpenTSDB+3
        What are some alternatives to RocksDB and UnQLite?
        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.
        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 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.
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        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.
        See all alternatives