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Product News : Percona Delivers Open Source In-Memory Engine for Percona Server for MongoDB
on 2016/8/16 8:46:19 (659 reads)
Product News

Percona announced the availability of Percona Memory Engine for MongoDB, a 100 percent open source in-memory storage engine for Percona Server for MongoDB. Based on the in-memory storage engine used in MongoDB Enterprise Edition, WiredTiger, Percona Memory Engine for MongoDB delivers extremely high performance and reduced costs for a variety of use cases, including application cache, sophisticated data manipulation, session management and more. Optimized for demanding workloads, Percona Memory Engine for MongoDB is the first open source in-memory storage engine to work with a MongoDB variant.

An in-memory storage engine based on WiredTiger was included in MongoDB 3.2 Enterprise Edition, but it was not made available in the open source MongoDB Community Edition. With Percona Memory Engine for MongoDB, Percona has now delivered an open source in-memory storage engine that works with Percona Server for MongoDB, the open source drop-in replacement for the MongoDB Community Edition that includes enterprise-grade features and functionality.

Percona Memory Engine for MongoDB delivers high performance reads with predictable latency, as well as high performance writes with no need to persist data on a disk. The solution can reduce infrastructure costs by eliminating or reducing the need to purchase additional high performance storage. This is especially advantageous for cloud-based solutions where high performance storage is costly. Percona Memory Engine for MongoDB command line and configuration options are similar to the memory engine in MongoDB 3.2 Enterprise Edition, making the transition to Percona Server for MongoDB straightforward. Typical use cases for Percona Memory Engine for MongoDB include:
* Application Cache -- Replace services such as memcached and custom application-level data structures. Adds the full power of MongoDB features to an application's memory cache.
* Real-time Analytics -- Uses in-memory computing in situations where response time is more critical than persistence.
* Sophisticated Data Manipulation -- Increase the performance of data manipulation operations such as aggregation and map reduction.
* Session Management -- Keep active user sessions in memory to decrease application response times.
* Transient Runtime State -- Store application stateful runtime data that doesn't require on-disk storage.
* Multi-tier object sharing -- Facilitate sharing of data in multi-tier/multi-language applications.
* Application Testing -- Reduce turnaround time for automated application tests.

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