Percona Memory Engine for MongoDB

Memory Engine vs WiredTiger Insert

InMemory engineThis post discusses Percona Server for MongoDB’s new in-memory storage engine, Percona Memory Engine for MongoDB.

Percona Server for MongoDB introduced the Memory Engine starting with the 3.2.8-2.0 version. To use it, run Percona Server for MongoDB with the --storageEngine=inMemory option.

In-memory is a special configuration of WiredTiger that doesn’t store user data on disk. With this engine, data fully resides in the virtual memory of the system (and might get lost on server shutdown).

Despite the fact that the engine is purely in-memory, it writes a small amount of diagnostic data and statistics to disk. The latter can be controlled with the --inMemoryStatisticsLogDelaySecs option. The --dbpath option controls where to store the files. Generally, in-memory cannot run on the database directory previously used by any other engine (including WiredTiger).

The engine uses the desired amount of memory when configured with the --inMemorySizeGB option. This option takes fractional numbers to allow precise memory size specification. When you reach the specified memory limit, aWT_CACHE_FULL error is returned for all kinds of operations that cause user data size to grow. These include inserting new documents, creating indexes, updating documents by adding or extending fields, running aggregation workflow and others. However, you can still perform read queries on a full engine.

Since Percona Memory Engine executes fewer operations and makes no disk I/O system calls, it performs better compared to conventional durable storage engines, including WiredTiger’s standard disk-based configuration.


The following graphs show Percona Memory Engine versus WiredTiger performance. Both engines use the default configuration with 140GB cache size specified. The hardware is 56-core Intel(R) Xeon(R) CPU E5-2683 v3 @ 2.00GHz with 256GB of RAM and RAID1 2xHDD. Test data set is about cache size and fully fits in memory.

Memory Engine vs WiredTiger Insert

Memory Engine vs WiredTiger OLTP

You can clearly see that Percona Memory Engine has better throughput and less jitter on all kinds of workloads. Checkpointing, however, can cause jitters in WiredTiger, and are absent in Percona Memory Engine as there’s no need to periodically sync in-memory data structures with their on-disk representations.

However, the performance of Percona Memory Engine drops when it’s about to become full (currently, when it’s 99% full). We’ve marked this issue as fixed ( but it still crops up in extreme cases.

Percona Memory Engine might use up to 1.5 times more memory above the set configuration when it’s close to full. WiredTiger almost never exceeds the specified cache memory limit. This might change in future versions. But current users should avoid possible swapping or OOM-killing of the server with Percona Memory Engine if (mis)configured to use all or close to all of available system RAM.

You can download the latest version of Percona Server for MongoDB, which includes the new Percona Memory Engine feature, here.


Introducing Percona Memory Engine for MongoDB

Percona Memory Engine for MongoDB

Percona Memory Engine for MongoDBI’m pleased to announce the latest Percona Server for MongoDB feature: Percona Memory Engine for MongoDB.

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!

Download Percona Memory Engine for MongoDB here.

Percona Memory Engine for MongoDB

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