filmov
tv
Magma: Couchbase Storage Engine

Показать описание
In this month's Meetup, we will be talking about the new storage engine, Magma introduced in Couchbase Server 7.1.
We look forward to having you join us on June 15th at 10 a.m. PDT. The Meetup will be streamed on Youtube, Twitch & Twitter and if you miss it, we'll be uploading the recording the following day.
Session: Magma: New Couchbase Storage Engine
Speaker: Shivani Gupta, Director Product Management, Couchbase
Abstract
Magma is Couchbase’s latest storage engine that is designed for high performance with very large datasets that do not fit in memory. It is ideal for use cases that rely primarily on disk access. 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 the disk.
In this talk, you can learn more about Magma including the performance improvements along with the scenarios in which you could benefit from using Magma as the storage engine for your Couchbase buckets.
Speaker Bio:
Shivani Gupta is a Director of Product Management at Couchbase for the Core Server. Shivani has over 20 years of varied experience in Big Data, Distributed Systems, and Databases at different companies including Oracle, Microsoft, VMWare, Hortonworks and now Couchbase.
We look forward to having you join us on June 15th at 10 a.m. PDT. The Meetup will be streamed on Youtube, Twitch & Twitter and if you miss it, we'll be uploading the recording the following day.
Session: Magma: New Couchbase Storage Engine
Speaker: Shivani Gupta, Director Product Management, Couchbase
Abstract
Magma is Couchbase’s latest storage engine that is designed for high performance with very large datasets that do not fit in memory. It is ideal for use cases that rely primarily on disk access. 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 the disk.
In this talk, you can learn more about Magma including the performance improvements along with the scenarios in which you could benefit from using Magma as the storage engine for your Couchbase buckets.
Speaker Bio:
Shivani Gupta is a Director of Product Management at Couchbase for the Core Server. Shivani has over 20 years of varied experience in Big Data, Distributed Systems, and Databases at different companies including Oracle, Microsoft, VMWare, Hortonworks and now Couchbase.
Комментарии