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Learn MorePros of Couchbase
Pros of MySQL
Pros of RocksDB
Pros of Couchbase
- High performance18
- Flexible data model, easy scalability, extremely fast18
- Mobile app support9
- You can query it with Ansi-92 SQL7
- All nodes can be read/write6
- Equal nodes in cluster, allowing fast, flexible changes5
- Both a key-value store and document (JSON) db5
- Open source, community and enterprise editions5
- Automatic configuration of sharding4
- Local cache capability4
- Easy setup3
- Linearly scalable, useful to large number of tps3
- Easy cluster administration3
- Cross data center replication3
- SDKs in popular programming languages3
- Elasticsearch connector3
- Web based management, query and monitoring panel3
- Map reduce views2
- DBaaS available2
- NoSQL2
- Buckets, Scopes, Collections & Documents1
- FTS + SQL together1
Pros of MySQL
- Sql800
- Free679
- Easy562
- Widely used528
- Open source489
- High availability180
- Cross-platform support160
- Great community104
- Secure78
- Full-text indexing and searching75
- Fast, open, available25
- SSL support16
- Reliable15
- Robust14
- Enterprise Version8
- Easy to set up on all platforms7
- NoSQL access to JSON data type2
- Relational database1
- Easy, light, scalable1
- Sequel Pro (best SQL GUI)1
- Replica Support1
Pros of RocksDB
- Very fast5
- Made by Facebook3
- Consistent performance2
- Ability to add logic to the database layer where needed1
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Cons of Couchbase
Cons of MySQL
Cons of RocksDB
Cons of Couchbase
- Terrible query language3
Cons of MySQL
- Owned by a company with their own agenda16
- Can't roll back schema changes3
Cons of RocksDB
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- No public GitHub repository available -
What is Couchbase?
Developed as an alternative to traditionally inflexible SQL databases, the Couchbase NoSQL database is built on an open source foundation and architected to help developers solve real-world problems and meet high scalability demands.
What is MySQL?
The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.
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.
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What tools integrate with Couchbase?
What tools integrate with MySQL?
What tools integrate with RocksDB?
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What are some alternatives to Couchbase, MySQL, and RocksDB?
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.
CouchDB
Apache CouchDB is a database that uses JSON for documents, JavaScript for MapReduce indexes, and regular HTTP for its API. CouchDB is a database that completely embraces the web. Store your data with JSON documents. Access your documents and query your indexes with your web browser, via HTTP. Index, combine, and transform your documents with JavaScript.
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.
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.
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.