Cassandra vs Riak: 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, Riak is detailed as "A distributed, decentralized data storage system". Riak is a distributed database designed to deliver maximum data availability by distributing data across multiple servers. As long as your client can reach one Riak server, it should be able to write data. In most failure scenarios, the data you want to read should be available, although it may not be the most up-to-date version of that data.
Cassandra and Riak can be primarily classified as "Databases" tools.
"Distributed" is the top reason why over 96 developers like Cassandra, while over 9 developers mention "High Performance " as the leading cause for choosing Riak.
Cassandra and Riak are both open source tools. Cassandra with 5.23K GitHub stars and 2.33K forks on GitHub appears to be more popular than Riak with 3.22K GitHub stars and 526 GitHub forks.
According to the StackShare community, Cassandra has a broader approval, being mentioned in 337 company stacks & 230 developers stacks; compared to Riak, which is listed in 15 company stacks and 10 developer stacks.
What is Cassandra?
What is Riak?
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What are the cons of using Riak?
What tools integrate with Cassandra?
What tools integrate with Riak?
Stitch 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.
Cassandra 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.
While we experimented with Cassandra in the past, we are no longer using it. It is, however, open for consideration in future projects.
We 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