Everybody understands that memory is much faster than disk – even the fastest solid state storage can’t compete with it. As such the choice for the most demanding workloads, where performance and predictable latency are paramount, is in-memory computing.
MongoDB is no exception. MongoDB can benefit from a storage engine option that stores data in memory. In fact, MongoDB introduced it in the 3.2 release with their In-Memory Storage Engine. Unfortunately, their engine is only available in their closed source MongoDB Enterprise Edition. Users of their open source MongoDB Community Edition were out of luck. Until now.
At Percona we strive to provide the best open source MongoDB variant software with Percona Server for MongoDB. To meet this goal, we spent the last few months working on an open source implementation of an in-memory storage engine: introducing Percona Memory Engine for MongoDB!
Percona Memory Engine for MongoDB provides the same performance gains as the current implementation of MongoDB’s in-memory engine. Both are based on WiredTiger, but optimize it for cases where data fits in memory and does not need to be persistent.
To make migrating from MongoDB Enterprise Edition to Percona Server for MongoDB as simple as possible, we made our command line and configuration options as compatible as possible with the MongoDB In-Memory Storage Engine.
Look for more blog posts showing the performance advantages of Percona Memory Engine for MongoDB compared to conventional disk-based engines, as well as some use cases and best practices for using Percona Memory Engine in your MongoDB deployments. Below is a quick list of advantages that in-memory processing provides:
- Reduced costs. Storing data in memory means you do not have to have additional costs for high-performance storage, which provides a great advantage for cloud systems (where high-performance storage comes at a premium).
- Very high performance reads. In-memory processing provides highly predictable latency as all reads come from memory instead of being pulled from a disk.
- Very high performance writes. In-memory processing removes the need for persisted data on disk, which very useful for cases where data durability is not critical.
From a developer standpoint, Percona Memory Engine addresses several practical use cases:
- Application cache. Replace services such as memcached and custom application-level data structures with the full power of MongoDB features.
- Sophisticated data manipulation. Augment performance for data manipulation operations such as aggregation and map reduction.
- Session management. Decrease application response times by keeping active user sessions in memory.
- Transient Runtime State. Store application stateful runtime data that doesn’t require on-disk storage.
- Real-time Analytics. Use in-memory computing in situations where response time is more critical than persistence.
- Multi-tier object sharing. Facilitate data sharing in multi-tier/multi-language applications.
- Application Testing. Reduce turnaround time for automated application tests.
I’m including a simple benchmark result for very intensive write workloads that compares Percona Memory Engine and WiredTiger. As you can see, you can get dramatically better performance with Percona Memory Engine!