We believe these tests for throughput and latency demonstrate a major improvement in the write performance for MongoDB. As a result of these improvements, users should experience better, more predictable performance.
#DOWNLOAD MONGODB 3.0 UPDATE#
At the 95th percentile, update latency for MongoDB 3.0 is almost 90% lower than MongoDB 2.6, and over 80% lower at the 99th percentile. In the balanced workload, update latency is lower still. This is important - improving throughput should not come at the cost of greater latency as this will ultimately degrade the experience for users of the application. Update latency is significantly improved in MongoDB 3.0: it has been reduced by almost 90% at both the 95th and 99th percentiles. Here we compare the update latency at the 95th and 99th percentiles using the read-intensive workload.
For update latency, however, the results are more interesting. We see very little difference between MongoDB 2.6 and MongoDB 3.0 in terms of read latency: reads are consistently 1 ms or less across workloads. (One could argue these are insufficiently precise – most web sessions involve hundreds of requests, and so it is very likely that most users will experience latency at the 99th percentile during their session.) High latency queries are measured at the 95th and 99th percentiles – where observed latency is worse than 95% or 99% of all other latencies. Developers who want to ensure a consistently great, low-latency experience worry about the worst performing queries in their deployment.
Average latency measured across many operations is not the best metric. Measuring throughput isn’t enough – it is also important to consider the latency of operations. This is better than the 4x improvement we see with the 95% read workload because there are more writes.
Looking at this test, the more fine-grained concurrency control of MongoDB 3.0 clearly improves overall throughput.įinally, for the balanced workload we see over 6x better throughput with MongoDB 3.0. In MongoDB 2.6 concurrency control is managed at the database level, and writes can block reads, reducing overall throughput. This is a smaller improvement than observed for the load because writes are only 5% of all operations. Here we see approximately 4x better throughput with WiredTiger. The second test compares the two systems for a workload that is 95% reads and 5% updates. We should expect to see the biggest improvement for this workload because it is 100% writes, and WiredTiger’s document-level concurrency control is most beneficial to multi-threaded write workloads on servers with many processor cores. In YCSB tests, MongoDB 3.0 provides around 7x the throughput of MongoDB 2.6 for multi-threaded, batch inserts. In this post we’ll compare YCSB results for MongoDB 2.6 and 3.0. However, it is fairly popular and understood by users of MongoDB and other systems. YCSB is fairly basic and probably does not tell you everything you need to know about the performance of your application. YCSB is used by some organizations as part of their performance testing for several different database technologies. These tests reflect the diverse applications users build, and the ever-evolving environments in which they are deployed. To help us measure performance, we use hundreds of different tests that we have developed working with the community. Only your requirements, your data, and your infrastructure can tell you what you need to know. As such, there’s really no “standard” benchmark that will inform you about the best technology to use for your application. Use cases for MongoDB are diverse, and it is critical to use performance tests that reflect the needs of your application and the hardware you will use for your deployment.
#DOWNLOAD MONGODB 3.0 HOW TO#
To help illustrate what we’ve done and how to take advantage of the changes we’ve made, we will be publishing a series of blog posts comparing MongoDB 2.6 and 3.0 performance.Īs with any benchmarking, the applicability of the results to your application is not always clear. A major focus for MongoDB 3.0 has been improving performance, especially write performance and hardware utilization.
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