Author Archive: Harold Hannon

July 16, 2013

Riak Performance Analysis: Bare Metal v. Virtual

In December, I posted a MongoDB performance analysis that showed the quantitative benefits of using bare metal servers for MongoDB workloads. It should come as no surprise that in the wake of SoftLayer's Riak launch, we've got some similar data to share about running Riak on bare metal.

To run this test, we started by creating five-node clusters with Riak 1.3.1 on SoftLayer bare metal servers and on a popular competitor's public cloud instances. For the SoftLayer environment, we created these clusters using the Riak Solution Designer, so the nodes were all provisioned, configured and clustered for us automatically when we ordered them. For the public cloud virtual instance Riak cluster, each node was provisioned indvidually using a Riak image template and manually configured into a cluster after all had come online. To optimize for Riak performance, I made a few tweaks at the OS level of our servers (running CentOS 64-bit):

Noatime
Nodiratime
barrier=0
data=writeback
ulimit -n 65536

The common Noatime and Nodiratime settings eliminate the need for writes during reads to help performance and disk wear. The barrier and writeback settings are a little less common and may not be what you'd normally set. Although those settings present a very slight risk for loss of data on disk failure, remember that the Riak solution is deployed in five-node rings with data redundantly available across multiple nodes in the ring. With that in mind and considering each node also being deployed with a RAID10 storage array, you can see that the minor risk for data loss on the failure of a single disk in the entire solution would have no impact on the entire data set (as there are plenty of redundant copies for that data available). Given the minor risk involved, the performance increases of those two settings justify their use.

With all of the nodes tweaked and configured into clusters, we set up Basho's test harness — Basho Bench — to remotely simulate load on the deployments. Basho Bench allows you to create a configurable test plan for a Riak cluster by configuring a number of workers to utilize a driver type to generate load. It comes packaged as an Erlang application with a config file example that you can alter to create the specifics for the concurrency, data set size, and duration of your tests. The results can be viewed as CSV data, and there is an optional graphics package that allows you to generate the graphs that I am posting in this blog. A simplified graphic of our test environment would look like this:

Riak Test Environment

The following Basho Bench config is what we used for our testing:

{mode, max}.
{duration, 120}.
{concurrent, 8}.
{driver, basho_bench_driver_riakc_pb}.
{key_generator,{int_to_bin,{uniform_int,1000000}}}.
{value_generator,{exponential_bin,4098,50000}}.
{riakc_pb_ips, [{10,60,68,9},{10,40,117,89},{10,80,64,4},{10,80,64,8},{10,60,68,7}]}.
{riakc_pb_replies, 2}.
{operations, [{get, 10},{put, 1}]}.

To spell it out a little simpler:

Tests Performed

Data Set: 400GB
10:1 Query-to-Update Operations
8 Concurrent Client Connections
Test Duration: 2 Hours

You may notice that in the test cases that use SoftLayer "Medium" Servers, the virtual provider nodes are running 26 virtual compute units against our dual proc hex-core servers (12 cores total). In testing with Riak, memory is important to the operations than CPU resources, so we provisioned the virtual instances to align with the 36GB of memory in each of the "Medium" SoftLayer servers. In the public cloud environment, the higher level of RAM was restricted to packages with higher CPU, so while the CPU counts differ, the RAM amounts are as close to even as we could make them.

One final "housekeeping" note before we dive into the results: The graphs below are pulled directly from the optional graphics package that displays Basho Bench results. You'll notice that the scale on the left-hand side of graphs differs dramatically between the two environments, so a cursory look at the results might not tell the whole story. Click any of the graphs below for a larger version. At the end of each test case, we'll share a few observations about the operations per second and latency results from each test. When we talk about latency in the "key observation" sections, we'll talk about the 99th percentile line — 99% of the results had latency below this line. More simply you could say, "This is the highest latency we saw on this platform in this test." The primary reason we're focusing on this line is because it's much easier to read on the graphs than the mean/median lines in the bottom graphs.

Riak Test 1: "Small" Bare Metal 5-Node Cluster vs Virtual 5-Node Cluster

Servers

SoftLayer Small Riak Server Node
Single 4-core Intel 1270 CPU
64-bit CentOS
8GB RAM
4 x 500GB SATAII – RAID10
1Gb Bonded Network
Virtual Provider Node
4 Virtual Compute Units
64-bit CentOS
7.5GB RAM
4 x 500GB Network Storage – RAID10
1Gb Network
 

Results

Riak Performance Analysis

Riak Performance Analysis

Key Observations

The SoftLayer environment showed much more consistency in operations per second with an average throughput around 450 Op/sec. The virtual environment throughput varied significantly between about 50 operations per second to more than 600 operations per second with the trend line fluctuating slightly between about 220 Op/sec and 350 Op/sec.

Comparing the latency of get and put requests, the 99th percentile of results in the SoftLayer environment stayed around 50ms for gets and under 200ms for puts while the same metric for the virtual environment hovered around 800ms in gets and 4000ms in puts. The scale of the graphs is drastically different, so if you aren't looking closely, you don't see how significantly the performance varies between the two.

Riak Test 2: "Medium" Bare Metal 5-Node Cluster vs Virtual 5-Node Cluster

Servers

SoftLayer Medium Riak Server Node
Dual 6-core Intel 5670 CPUs
64-bit CentOS
36GB RAM
4 x 300GB 15K SAS – RAID10
1Gb Network – Bonded
Virtual Provider Node
26 Virtual Compute Units
64-bit CentOS
30GB RAM
4 x 300GB Network Storage
1Gb Network
 

Results

Riak Performance Analysis

Riak Performance Analysis

Key Observations

Similar to the results of Test 1, the throughput numbers from the bare metal environment are more consistent (and are consistently higher) than the throughput results from the virtual instance environment. The SoftLayer environment performed between 1500 and 1750 operations per second on average while the virtual provider environment averaged around 1200 operations per second throughout the test.

The latency of get and put requests in Test 2 also paints a similar picture to Test 1. The 99th percentile of results in the SoftLayer environment stayed below 50ms and under 400ms for puts while the same metric for the virtual environment averaged about 250ms in gets and over 1000ms in puts. Latency in a big data application can be a killer, so the results from the virtual provider might be setting off alarm bells in your head.

Riak Test 3: "Medium" Bare Metal 5-Node Cluster vs Virtual 5-Node Cluster

Servers

SoftLayer Medium Riak Server Node
Dual 6-core Intel 5670 CPUs
64-bit CentOS
36GB RAM
4 x 128GB SSD – RAID10
1Gb Network – Bonded
Virtual Provider Node
26 Virtual Compute Units
64-bit CentOS
30GB RAM
4 x 300GB Network Storage
1Gb Network
 

Results

Riak Performance Analysis

Riak Performance Analysis

Key Observations

In Test 3, we're using the same specs in our virtual provider nodes, so the results for the virtual node environment are the same in Test 3 as they are in Test 2. In this Test, the SoftLayer environment substitutes SSD hard drives for the 15K SAS drives used in Test 2, and the throughput numbers show the impact of that improved I/O. The average throughput of the bare metal environment with SSDs is between 1750 and 2000 operations per second. Those numbers are slightly higher than the SoftLayer environment in Test 2, further distancing the bare metal results from the virtual provider results.

The latency of gets for the SoftLayer environment is very difficult to see in this graph because the latency was so low throughout the test. The 99th percentile of puts in the SoftLayer environment settled between 500ms and 625ms, which was a little higher than the bare metal results from Test 2 but still well below the latency from the virtual environment.

Summary

The results show that — similar to the majority of data-centric applications that we have tested — Riak has more consistent, better performing, and lower latency results when deployed onto bare metal instead of a cluster of public cloud instances. The stark differences in consistency of the results and the latency are noteworthy for developers looking to host their big data applications. We compared the 99th percentile of latency, but the mean/median results are worth checking out as well. Look at the mean and median results from the SoftLayer SSD Node environment: For gets, the mean latency was 2.5ms and the median was somewhere around 1ms. For puts, the mean was between 7.5ms and 11ms and the median was around 5ms. Those kinds of results are almost unbelievable (and that's why I've shared everything involved in completing this test so that you can try it yourself and see that there's no funny business going on).

It's commonly understood that local single-tenant resources that bare metal will always perform better than network storage resources, but by putting some concrete numbers on paper, the difference in performance is pretty amazing. Virtualizing on multi-tenant solutions with network attached storage often introduces latency issues, and performance will vary significantly depending on host load. These results may seem obvious, but sometimes the promise of quick and easy deployments on public cloud environments can lure even the sanest and most rational developer. Some applications are suited for public cloud, but big data isn't one of them. But when you have data-centric apps that require extreme I/O traffic to your storage medium, nothing can beat local high performance resources.

-Harold

December 20, 2012

MongoDB Performance Analysis: Bare Metal v. Virtual

Developers can be cynical. When "the next great thing in technology" is announced, I usually wait to see how it performs before I get too excited about it ... Show me how that "next great thing" compares apples-to-apples with the competition, and you'll get my attention. With the launch of MongoDB at SoftLayer, I'd guess a lot of developers outside of SoftLayer and 10gen have the same "wait and see" attitude about the new platform, so I put our new MongoDB engineered servers to the test.

When I shared MongoDB architectural best practices, I referenced a few of the significant optimizations our team worked with 10gen to incorporate into our engineered servers (cheat sheet). To illustrate the impact of these changes in MongoDB performance, we ran 10gen's recommended benchmarking harness (freely available for download and testing of your own environment) on our three tiers of engineered servers alongside equivalent shared virtual environments commonly deployed by the MongoDB community. We've made a pretty big deal about the performance impact of running MongoDB on optimized bare metal infrastructure, so it's time to put our money where our mouth is.

The Testing Environment

For each of the available SoftLayer MongoDB engineered servers, data sets of 512kb documents were preloaded onto single MongoDB instances. The data sets were created with varying size compared to available memory to allow for data sets that were both larger (2X) and smaller than available memory. Each test also ensured that the data set was altered during the test run frequently enough to prevent the queries from caching all of the data into memory.

Once the data sets were created, JMeter server instances with 4 cores and 16GB of RAM were used to drive 'benchrun' from the 10gen benchmarking harness. This diagram illustrates how we set up the testing environment (click for a better look):

MongoDB Performance Analysis Setup

These Jmeter servers function as the clients generating traffic on the MongoDB instances. Each client generated random query and update requests with a ratio of six queries per update (The update requests in the test were to ensure that data was not allowed to fully cache into memory and never exercise reads from disk). These tests were designed to create an extreme load on the servers from an exponentially increasing number of clients until the system resources became saturated, and we recorded the resulting performance of the MongoDB application.

At the Medium (MD) and Large (LG) engineered server tiers, performance metrics were run separately for servers using 15K SAS hard drive data mounts and servers using SSD hard drive data mounts. If you missed the post comparing the IOPS statistics between different engineered server hard drive configurations, be sure to check it out. For a better view of the results in a given graph, click the image included in the results below to see a larger version.

Test Case 1: Small MongoDB Engineered Servers vs Shared Virtual Instance

Servers

Small (SM) MongoDB Engineered Server
Single 4-core Intel 1270 CPU
64-bit CentOS
8GB RAM
2 x 500GB SATAII - RAID1
1Gb Network
Virtual Provider Instance
4 Virtual Compute Units
64-bit CentOS
7.5GB RAM
2 x 500GB Network Storage - RAID1
1Gb Network
 

Tests Performed

Small Data Set (8GB of .5mb documents)
200 iterations of 6:1 query-to-update operations
Concurrent client connections exponentially increased from 1 to 32
Test duration spanned 48 hours
Average Read Operations per Second
by Concurrent Client
MongoDB Performance Analysis
Peak Read Operations per Second
by Concurrent ClientMongoDB Performance Analysis
Average Write Operations per Second
by Concurrent Client
MongoDB Performance Analysis
Peak Write Operations per Second
by Concurrent ClientMongoDB Performance Analysis

Test Case 2: Medium MongoDB Engineered Servers vs Shared Virtual Instance

Servers (15K SAS Data Mount Comparison)

Medium (MD) MongoDB Engineered Server
Dual 6-core Intel 5670 CPUs
64-bit CentOS
36GB RAM
2 x 64GB SSD - RAID1 (Journal Mount)
4 x 300GB 15K SAS - RAID10 (Data Mount)
1Gb Network - Bonded
Virtual Provider Instance
26 Virtual Compute Units
64-bit CentOS
30GB RAM
2 x 64GB Network Storage - RAID1 (Journal Mount)
4 x 300GB Network Storage - RAID10 (Data Mount)
1Gb Network
 

Tests Performed

Small Data Set (32GB of .5mb documents)
200 iterations of 6:1 query-to-update operations
Concurrent client connections exponentially increased from 1 to 128
Test duration spanned 48 hours
Average Read Operations per Second
by Concurrent Client
MongoDB Performance Analysis
Peak Read Operations per Second
by Concurrent ClientMongoDB Performance Analysis
Average Write Operations per Second
by Concurrent Client
MongoDB Performance Analysis
Peak Write Operations per Second
by Concurrent ClientMongoDB Performance Analysis

Servers (SSD Data Mount Comparison)

Medium (MD) MongoDB Engineered Server
Dual 6-core Intel 5670 CPUs
64-bit CentOS
36GB RAM
2 x 64GB SSD - RAID1 (Journal Mount)
4 x 400GB SSD - RAID10 (Data Mount)
1Gb Network - Bonded
Virtual Provider Instance
26 Virtual Compute Units
64-bit CentOS
30GB RAM
2 x 64GB Network Storage - RAID1 (Journal Mount)
4 x 300GB Network Storage - RAID10 (Data Mount)
1Gb Network
 

Tests Performed

Small Data Set (32GB of .5mb documents)
200 iterations of 6:1 query-to-update operations
Concurrent client connections exponentially increased from 1 to 128
Test duration spanned 48 hours
Average Read Operations per Second
by Concurrent Client
MongoDB Performance Analysis
Peak Read Operations per Second
by Concurrent ClientMongoDB Performance Analysis
Average Write Operations per Second
by Concurrent Client
MongoDB Performance Analysis
Peak Write Operations per Second
by Concurrent ClientMongoDB Performance Analysis

Test Case 3: Large MongoDB Engineered Servers vs Shared Virtual Instance

Servers (15K SAS Data Mount Comparison)

Large (LG) MongoDB Engineered Server
Dual 8-core Intel E5-2620 CPUs
64-bit CentOS
128GB RAM
2 x 64GB SSD - RAID1 (Journal Mount)
6 x 600GB 15K SAS - RAID10 (Data Mount)
1Gb Network - Bonded
Virtual Provider Instance
26 Virtual Compute Units
64-bit CentOS
64GB RAM (Maximum available on this provider)
2 x 64GB Network Storage - RAID1 (Journal Mount)
6 x 600GB Network Storage - RAID10 (Data Mount)
1Gb Network
 

Tests Performed

Small Data Set (64GB of .5mb documents)
200 iterations of 6:1 query-to-update operations
Concurrent client connections exponentially increased from 1 to 128
Test duration spanned 48 hours
Average Read Operations per Second
by Concurrent Client
MongoDB Performance Analysis
Peak Read Operations per Second
by Concurrent ClientMongoDB Performance Analysis
Average Write Operations per Second
by Concurrent Client
MongoDB Performance Analysis
Peak Write Operations per Second
by Concurrent ClientMongoDB Performance Analysis

Servers (SSD Data Mount Comparison)

Large (LG) MongoDB Engineered Server
Dual 8-core Intel E5-2620 CPUs
64-bit CentOS
128GB RAM
2 x 64GB SSD - RAID1 (Journal Mount)
6 x 400GB SSD - RAID10 (Data Mount)
1Gb Network - Bonded
Virtual Provider Instance
26 Virtual Compute Units
64-bit CentOS
64GB RAM (Maximum available on this provider)
2 x 64GB Network Storage - RAID1 (Journal Mount)
6 x 600GB Network Storage - RAID10 (Data Mount)
1Gb Network
 

Tests Performed

Small Data Set (64GB of .5mb documents)
200 iterations of 6:1 query-to-update operations
Concurrent client connections exponentially increased from 1 to 128
Test duration spanned over 48 hours
Average Read Operations per Second
by Concurrent Client
MongoDB Performance Analysis
Peak Read Operations per Second
by Concurrent ClientMongoDB Performance Analysis
Average Write Operations per Second
by Concurrent Client
MongoDB Performance Analysis
Peak Write Operations per Second
by Concurrent ClientMongoDB Performance Analysis

Impressions from Performance Testing

The results speak for themselves. Running a Mongo DB big data solution on a shared virtual environment has significant drawbacks when compared to running MongoDB on a single-tenant bare metal offering. Disk I/O is by far the most limiting resource for MongoDB, and relying on shared network-attached storage (with much lower disk I/O) makes this limitation very apparent. Beyond the average and peak statistics above, performance varied much more significantly in the virtual instance environment, so it's not as consistent and predictable as a bare metal.

Highlights:

  • When a working data set is smaller than available memory, query performance increases.
  • The number of clients performing queries has an impact on query performance because more data is being actively cached at a rapid rate.
  • The addition of a separate Journal Mount volume significantly improves performance. Because the Small (SM) engineered server does not include a secondary mount for Journals, whenever MongoDB began to journal, the disk I/O associated with journalling was disruptive to the query and update operations performed on the Data Mount.
  • The best deployments in terms of operations per second, stability and control were the configurations with a RAID10 SSD Data Mount and a RAID1 SSD Journal Mount. These configurations are available in both our Medium and Large offerings, and I'd highly recommend them.

-Harold

December 6, 2012

MongoDB: Architectural Best Practices

With the launch of our MongoDB solutions, developers can provision powerful, optimized, horizontally scaling NoSQL database clusters in real-time on bare metal infrastructure in SoftLayer data centers around the world. We worked tirelessly with our friends at 10gen — the creators of MongoDB — to build and tweak hardware and software configurations that enable peak MongoDB performance, and the resulting platform is pretty amazing. As Duke mentioned in his blog post, those efforts followed 10Gen's MongoDB best practices, but what he didn't mention was that we created some architectural best practices of our own for MongoDB in deployments on our platform.

The MongoDB engineered servers that you order from SoftLayer already implement several of the recommendations you'll see below, and I'll note which have been incorporated as we go through them. Given the scope of the topic, it's probably easiest to break down this guide into a few sections to make it a little more digestible. Let's take a look at the architectural best practices of running MongoDB through the phases of the roll-out process: Selecting a deployment strategy to prepare for your MongoDB installation, the installation itself, and the operational considerations of running it in production.

Deployment Strategy

When planning your MongoDB deployment, you should follow Sun Tzu's (modified) advice: "If you know the [friend] and know yourself, you need not fear the result of a hundred battles." "Friend" was substituted for the "enemy" in this advice because the other party is MongoDB. If you aren't familiar with MongoDB, the top of your to-do list should be to read MongoDB's official documentation. That information will give you the background you'll need as you build and use your database. When you feel comfortable with what MongoDB is all about, it's time to "know yourself."

Your most important consideration will be the current and anticipated sizes of your data set. Understanding the volume of data you'll need to accommodate will be the primary driver for your choice of individual physical nodes as well as your sharding plans. Once you've established an expected size of your data set, you need to consider the importance of your data and how tolerant you are of the possibility of lost or lagging data (especially in replicated scenarios). With this information in hand, you can plan and start testing your deployment strategy.

It sounds a little strange to hear that you should test a deployment strategy, but when it comes to big data, you want to make sure your databases start with a strong foundation. You should perform load testing scenarios on a potential deployment strategy to confirm that a given architecture will meet your needs, and there are a few specific areas that you should consider:

Memory Sizing
MongoDB (like many data-oriented applications) works best when the data set can reside in memory. Nothing performs better than a MongoDB instance that does not require disk I/O. Whenever possible, select a platform that has more available RAM than your working data set size. If your data set exceeds the available RAM for a single node, then consider using sharding to increase the amount of available RAM in a cluster to accommodate the larger data set. This will maximize the overall performance of your deployment. If you notice page faults when you put your database under production load, they may indicate that you are exceeding the available RAM in your deployment.

Disk Type
If speed is not your primary concern or if you have a data set that is far larger than any available in memory strategy can support, selecting the proper disk type for your deployment is important. IOPS will be key in selecting your disk type and obviously the higher the IOPS the better the performance of MongoDB. Local disks should be used whenever possible (as network storage can cause high latency and poor performance for your deployment). It's also advised that you use RAID 10 when creating disk arrays.

To give you an idea of what kind of IOPS to expect from a given type of drive, these are the approximate ranges of IOPS per drive in SoftLayer MongoDB engineered servers:

SATA II – 100-200 IOPS
15K SAS – 300-400 IOPS
SSD – 7,000-8,000 IOPS (read) 19,000-20,000 IOPS (write)

CPU
Clock speed and the amount of available processors becomes a consideration if you anticipate using MapReduce. It has also been noted that when running a MongoDB instance with the majority of the data in memory, clock speed can have a major impact on overall performance. If you are planning to use MapReduce or you're able to operate with a majority of your data in memory, consider a deployment strategy that includes a CPU with a high clock/bus speed to maximize your operations per second.

Replication
Replication provides high availability of your data if a node fails in your cluster. It should be standard to replicate with at least three nodes in any MongoDB deployment. The most common configuration for replication with three nodes is a 2x1 deployment — having two primary nodes in a single data center with a backup server in a secondary data center:

MongoDB Replication

Sharding
If you anticipate a large, active data set, you should deploy a sharded MongoDB deployment. Sharding allows you to partition a single data set across multiple nodes. You can allow MongoDB to automatically distribute the data across nodes in the cluster or you may elect to define a shard key and create range-based sharding for that key.

Sharding may also help write performance, so you can also elect to shard even if your data set is small but requires a high amount of updates or inserts. It's important to note that when you deploy a sharded set, MongoDB will require three (and only three) config server instances which are specialized Mongo runtimes to track the current shard configuration. Loss of one of these nodes will cause the cluster to go into a read-only mode (for the configuration only) and will require that all nodes be brought back online before any configuration changes can be made.

Write Safety Mode
There are several write safety modes that govern how MongoDB will handle the persistence of the data to disk. It is important to consider which mode best fits your needs for both data integrity and performance. The following write safety modes are available:

None – This mode provides a deferred writing strategy that is non-blocking. This will allow for high performance, however there is a small opportunity in the case of a node failing that data can be lost. There is also the possibility that data written to one node in a cluster will not be immediately available on all nodes in that cluster for read consistency. The 'None' strategy will also not provide any sort of protection in the case of network failures. That lack of protection makes this mode highly unreliable and should only be used when performance is a priority and data integrity is not a concern.

Normal – This is the default for MongoDB if you do not select any other mode. It provides a deferred writing strategy that is non-blocking. This will allow for high performance, however there is a small opportunity in the case of a node failing that data can be lost. There is also the possibility that data written to one node in a cluster will not be immediately available on all nodes in that cluster for read consistency.

Safe – This mode will block until MongoDB has acknowledged that it has received the write request but will not block until the write is actually performed. This provides a better level of data integrity and will ensure that read consistency is achieved within a cluster.

Journal Safe – Journals provide a recovery option for MongoDB. Using this mode will ensure that the data has been acknowledged and a Journal update has been performed before returning.

Fsync - This mode provides the highest level of data integrity and blocks until a physical write of the data has occurred. This comes with a degradation in performance and should be used only if data integrity is the primary concern for your application.

Testing the Deployment
Once you've determined your deployment strategy, test it with a data set similar to your production data. 10gen has several tools to help you with load testing your deployment, and the console has a tool named 'benchrun' which can execute operations from within a JavaScript test harness. These tools will return operation information as well as latency numbers for each of those operations. If you require more detailed information about the MongoDB instance, consider using the mongostat command or MongoDB Monitoring Service (MMS) to monitor your deployment during the testing.

Installation

When performing the installation of MongoDB, a few considerations can help create both a stable and performance-oriented solution. 10gen recommends the use CentOS (64-bit) as the base operating system if at all possible. If you try installing MongoDB on a 32-bit operating system, you might run into file size limits that cause issues, and if you feel the urge to install it on Windows, you'll see performance issues if virtual memory begins to be utilized by the OS to make up for a lack of RAM in your deployment. As a result, 32-bit operating systems and Windows operating systems should be avoided on MongoDB servers. SoftLayer provisions CentOS 6.X 64-bit operating systems by default on all of our MongoDB engineered server deployments.

When you've got CentOS 64-bit installed, you should also make the following changes to maximize your performance (all of which are included by default on all SoftLayer engineered servers):

Set SSD Read Ahead Defaults to 16 Blocks - SSD drives have excellent seek times allowing for shrinking the Read Ahead to 16 blocks. Spinning disks might require slight buffering so these have been set to 32 blocks.

noatime - Adding the noatime option eliminates the need for the system to make writes to the file system for files which are simply being read — or in other words: Faster file access and less disk wear.

Turn NUMA Off in BIOS - Linux, NUMA and MongoDB tend not to work well together. If you are running MongoDB on NUMA hardware, we recommend turning it off (running with an interleave memory policy). If you don't, problems will manifest in strange ways like massive slow downs for periods of time or high system CPU time.

Set ulimit - We have set the ulimit to 64000 for open files and 32000 for user processes to prevent failures due to a loss of available file handles or user processes.

Use ext4 - We have selected ext4 over ext3. We found ext3 to be very slow in allocating files (or removing them). Additionally, access within large files is poor with ext3.

One last tip on installation: Make the Journal and Data volumes be distinct physical volumes. If the Journal and Data directories reside on a single physical volume, flushes to the Journal will interrupt the access of data and provide spikes of high latency within your MongoDB deployment.

Operations

Once a MongoDB deployment has been promoted to production, there are a few recommendations for monitoring and optimizing performance. You should always have the MMS agent running on all MongoDB instances to help monitor the health and performance of your deployment. Additionally, this tool is also very useful if you have 10gen MongoDB Cloud Subscriptions because it provides useful debugging data for the 10gen team during support interactions. In addition to MMS, you can use the mongostat command (mentioned in the deployment section) to see runtime information about the performance of a MongoDB node. If either of these tools flags performance issues, sharding or indexing are first-line options to resolve them:

Indexes - Indexes should be created for a MongoDB deployment if monitoring tools indicate that field based queries are performing poorly. Always use indexes when you are querying data based on distinct fields to help boost performance.

Sharding - Sharding can be leveraged when the overall performance of the node is suffering because of a large operating data set. Be sure to shard before you get in the red; the system only splits chunks for sharding on insert or update so if you wait too long to shard you may have some uneven distribution for a period of time or forever depending on your data set and sharding key strategy.

I know it seems like we've covered a lot over the course of this blog post, but this list of best practices is far from exhaustive. If you want to learn more, the MongoDB forums are a great resource to connect with the rest of the MongoDB community and learn from their experiences, and the documentation on MongoDB's site is another phenomenal resource. The best people to talk to when it comes to questions about MongoDB are the folks at 10gen, so I also highly recommend taking advantage of MongoDB Cloud Subscriptions to get their direct support for your one-off questions and issues.

-Harold

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