Why we love Cassandra

Why do we love Cassandra?Posted onApril 22, 2015by Ajay tiwari-app42 backend as a Service

APP42 provides lots of readymade APIs for developers and each API solves different problem of App development. To solve a different problem you need a different solution. APP42 architecture uses hybrid solution for each of the Services on database layer. Some Services is a good candidate for RDBMS however others is for NoSQL and Some require in-memory persistence.

APP42 performs lots of Analytics on the data and also provides a service to APP developers in the form of Marketing Automa tion. Implementing Analytics solution requires different persistence solution on DB layer. We chose Cassandra as our DB layer for implementation and fell deeply in love with it. There were other candidates like HBase and MongoDB for the solution however we decide to go ahead do with Cassandra and Here is the reasons why.

1. Cassandra Scales linearly with massive write.

App42 Analytics generates quite a lot of data when an event is generated. Events through a  single app may result with thousands of insertions on the database. We process billions of events and we wanted to has a storage which can withstand very heavy write operations and scale. We were stuck with the options for our requirement here, one is Cassandra and other is HBase. Though MongoDB is also a candidate however due to write lock issue on database level and cascading poor insertion perform Ance, it is out from the list at the very beginning of our selection process. Cassandra and HBase both is good with heavy write operations however we opted to go along with Cassandra looking at the B Enchmarks (http://planetcassandra.org/nosql-performance-benchmarks/) available in the market and considering  the Ease of managing the cluster. For us Cassandra is the perfect choice for heavy write load scenarios and it scales linearly as new machines is added in The ring.

2. Cassandra is a excellent choice for real-time analytic workloads

Due to their ability of supporting heavy write operations, it becomes naturally a good choice for Real time Analytics. Thumb rule of performing real time analytics is so you should has your data already calculated and should persist in th E database. If you know the reports your want to show in real time, you can have your schema defined accordingly and generate your data At real time. Batch mutation and distributed Global Counter is something, we really liked while using Cassandra. If you is looking for similar kind of the solution most likely Casssandra would suffice your needs.

3. Cassandra can integrated with Hadoop, Hive and Apache Spark for batch processing

As illustrated above Cassandra is a good candidate for real time analytics, however there might being scenarios where you MIG HT has to perform batch processing on the stored data. Cassandra can easily integrated with Hadoop and Hive to achieve this. Also, On-demand in-memory Analytics can be done through Apache Spark integration.

4. tunable consistency and CAP parameters.

Every database can provide parameters out of consistency (C) availability (A) and Network Partitioning tolerance (P) A T a time according to CAP theorem (Http://en.wikipedia.org/wiki/CAP_theorem). It's impossible to achieve all at the same time. Cassandra allows you to configure and tune these parameters based on your priority. By the default it is categorized under AP category.

There is many other features however these were certain points of considerations for us and we chose Cassandra based on T Hat

Hope This post helps others who is thinking of architecting their products which requires analytics over large amount of Data and want be resilient against scalability.

If you had a requirement of Big Data Analytics for heavy write operation, Cassandra can stand off to be a perfect choice For you. Your feedback and suggestion on Post is heartily welcome and you is free to reach out to us at [email protected] for further query or feedback.

src:http://blogs.shephertz.com/2015/04/22/love-cassandra/

Why we love Cassandra