{"id":13192,"date":"2022-05-12T08:10:55","date_gmt":"2022-05-12T15:10:55","guid":{"rendered":"https:\/\/www.couchbase.com\/blog\/?p=13192"},"modified":"2023-05-22T07:59:26","modified_gmt":"2023-05-22T14:59:26","slug":"low-tco-with-couchbase","status":"publish","type":"post","link":"https:\/\/www.couchbase.com\/blog\/low-tco-with-couchbase\/","title":{"rendered":"Reduce TCO By 10x Using Couchbase 7.1 For Large Multi-Terabyte Databases"},"content":{"rendered":"

Store hundreds of terabytes of JSON data and query in milliseconds with Couchbase 7.1. Our new storage engine in 7.1, Couchbase Magma,<\/strong> makes Couchbase the most cost-effective and performant database, lowering TCO.<\/span><\/p>\n

With Magma storage engine, Couchbase 7.1 becomes the ideal database for your data intensive use cases. Some examples of such use cases include write heavy applications such as IoT and logging; read heavy applications with large datasets such as customer portals; as well as mixed workloads with large datasets such as metadata and content stores (photo\/video stores) and user profiles.<\/p>\n

Let us dive further into benefits of Magma and how you can start using it.<\/p>\n

Magma TCO reduction<\/h2>\n

Magma’s design allows it to function with minimal amounts of memory – it is operationally stable at as low as 1% memory-to-data ratio. For example, if you want to store 1TB of data in a node, you need only 10GB of memory to run with Magma if you want to access everything primarily from disk. If you want in-memory (sub-millisecond) access speed for your working set you will probably want to use a memory-to-data ratio that represents your working set.\u00a0<\/span><\/p>\n

Magma can also store larger amounts of data per node – we tested and certified up to 10TB of data per node (this includes primary and replica data). Hence, to store 100TB of data, you need only 10 servers in a Couchbase cluster.<\/span><\/p>\n

If you\u2019re familiar with the existing Couchbase storage engine called Couchstore, you know that Couchstore requires a minimum of 10% memory-to-data ratio, and recommends no more than 2-3TB of data per node.<\/span><\/p>\n

The reduction in memory-to-data ratio from 10% to 1%, and increase in node density to 10TB of data translates to 10x reduction in servers for most scenarios, and a corresponding 10x reduction in TCO.<\/span><\/p>\n

Magma performance gains<\/h2>\n

For disk-based workloads<\/strong>, Magma significantly improves performance. Some highlights of performance improvements are:<\/span><\/p>\n