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Cassandra vs RocksDB: What are the differences?
Developers describe Cassandra as "A partitioned row store. Rows are organized into tables with a required primary key". 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. On the other hand, RocksDB is detailed as "Embeddable persistent key-value store for fast storage, developed and maintained by Facebook Database Engineering Team". 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.
Cassandra and RocksDB can be categorized as "Databases" tools.
"Distributed" is the primary reason why developers consider Cassandra over the competitors, whereas "Very fast" was stated as the key factor in picking RocksDB.
Cassandra and RocksDB are both open source tools. It seems that RocksDB with 14.3K GitHub stars and 3.12K forks on GitHub has more adoption than Cassandra with 5.27K GitHub stars and 2.35K GitHub forks.
According to the StackShare community, Cassandra has a broader approval, being mentioned in 342 company stacks & 239 developers stacks; compared to RocksDB, which is listed in 6 company stacks and 7 developer stacks.
What is Cassandra?
What is RocksDB?
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Why do developers choose Cassandra?
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Why do developers choose RocksDB?
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What are the cons of using Cassandra?
What are the cons of using RocksDB?
What companies use RocksDB?
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What tools integrate with Cassandra?
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CassandraStitch is a wrapper around a Cassandra database. It has a web application that provides read-access to the counts through an HTTP API. The counts are written to Cassandra in two distinct ways, and it's possible to use either or both of them:
Real-time: For real-time updates, Stitch has a processor application that handles a stream of events coming from a broker and increments the appropriate counts in Cassandra.
Batch: The batch part is a MapReduce job running on Hadoop that reads event logs, calculates the overall totals, and bulk loads this into Cassandra.
CassandraCassandra is our data management workhorse. It handles all our key-value services, supports time-series data storage and retrieval, securely stores all our audit trails, and backs our Datomic database.
CassandraWhile we experimented with Cassandra in the past, we are no longer using it. It is, however, open for consideration in future projects.
CassandraWe are using Cassandra in a few of our apps. One of them is as a count service application to track the number of shares, clicks.. etc
Cassandra