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Vadim TkachenkoThis blog will look at MySQL 5.7 sysbench OLTP read-only results to determine if they are faster than previous versions. As promised in my previous post, I have checked MySQL 5.7 performance against previous versions in a different workload. This time, I will use sysbench OLTP read-only transactions (read-write transactions are part of future research, […]
This blog examines MySQL 5.7’s primary key lookup results, and determines if MySQL 5.7 is really faster than its early versions. MySQL 5.7 was released some time ago, and now that the dust has settled it’s a good time to review its performance improvements. I’m not doing this just to satisfy my own curiosity! Many […]
In this blog post, we’ll be discussing Percona Server 5.7 performance improvements. Starting from the Percona Server 5.6 release, we’ve introduced several significant changes that help address performance problems for highly-concurrent I/O-bound workloads. Some of our research and improvements were re-implemented for MySQL 5.7 – one of the best MySQL releases. But even though MySQL 5.7 […]
In this blog, we’ll discuss how Percona XtraDB Cluster handled a high latency network environment. Recently I was working in an environment where Percona XtraDB Cluster was running over a 10GB network, but one of the nodes was located in a distant location and the ping time was higher than what you would typically expect. For […]
NOTE: This blog post contains some mistakes in data. While many of our conclusions are still valid, we’ve updated the evaluation in a new blog post. Please use that post in conjunction with this one. Vadim Tkachenko and I have been working with Fractal Tree® storage engines (Fractal Tree engines are available in Percona Server […]
This will be another post on using Percona Server via a Docker image. I want to follow up on my previous post regarding CPU/Network overhead in Docker “Measuring Percona Server Docker CPU/network overhead” by measuring if there is any docker IO overhead on operations. After running several tests, it appears (spoiler alert) that there is no Docker IO overhead. […]
Now that we have our Percona Server Docker images, I wanted to measure the performance overhead when we run the database in the container. Since Docker promises to use a lightweight container, in theory there should be very light overhead. We need to verify that claim, however. In this post I will show the numbers for CPU and network intensive workloads, […]
Docker is incredibly popular tool for deploying software, so we decided to provide a Percona Docker image for both Percona Server MySQL and Percona Server for MongoDB. We want to create an easy way to try our products. There are actually some images available from https://hub.docker.com/_/percona/, but these images are provided by Docker itself, not from Percona. […]
This is a followup to my previous post Apache Spark with Air ontime performance data. To recap an interesting point in that post: when using 48 cores with the server, the result was worse than with 12 cores. I wanted to understand the reason is was true, so I started digging. My primary suspicion was that Java (I […]
There is a growing interest in Apache Spark, so I wanted to play with it (especially after Alexander Rubin’s Using Apache Spark post). To start, I used the recently released Apache Spark 1.6.0 for this experiment, and I will play with “Airlines On-Time Performance” database from http://www.transtats.bts.gov/DL_SelectFields.asp?Table_ID=236&DB_Short_Name=On-Time. You can find the scripts I used here https://github.com/Percona-Lab/ontime-airline-performance. The uncompressed dataset is about 70GB, which is […]
We recently released the GA version of Percona Server for MongoDB, which comes with a variety of storage engines: RocksDB, PerconaFT and WiredTiger. Both RocksDB and PerconaFT are write-optimized engines, so I wanted to compare all engines in a workload oriented to data ingestions. For a benchmark I used iiBench-mongo (https://github.com/mdcallag/iibench-mongodb), and I inserted one billion […]
MongoDB 3.2 was recently released with WiredTiger as the default storage engine. In just over five years, MongoDB has evolved into a popular database. MongoDB 3.0 supported “pluggable storage engines.” The B-Tree-based WiredTiger should outperform IO-optimized RocksDB and PerconaFT in in-memory workloads, but it demonstrates performance degradation when we move into IO workloads. There are reports that WiredTiger […]
In my previous post Amazon Aurora – Looking Deeper, I promised benchmark results on Amazon Aurora. Amazon used quite a small dataset in their benchmark: 250 tables, with 25000 rows each, which in my calculation corresponds to 4.5GB worth of data. For this datasize, Amazon used r3.8xlarge instances, which provided 32 virtual CPUs and 244GB of […]
Recently my colleague (by Percona) Yves Trudeau and colleague (by industry) Marco Tusa published their materials on Amazon Aurora. Indeed, Amazon Aurora is a hot topic these days, and we have a stream of customer inquiries regarding this technology. I’ve decided to form my own opinion, and nothing is better than a personal, hands-on experience, which I […]
Intro The purpose of this project was to measure the potential overhead of VividCortex Agent, which is used by VividCortex.com database monitoring system. This benchmark is part of a consulting engagement with VividCortex and paid by the customer. The assumption is that VividCortex agent uses CPU processing time, and we should see an impact on […]
Recently Aphyr (Kyle Kingsbury) published https://aphyr.com/posts/327-call-me-maybe-mariadb-galera-cluster The article is technically valid, I am not going to dispute a conclusion Aphyr made, but it is also quite technically involved, so users who just jump to conclusion may get the wrong impression and we’re left with more questions than ever. So, let me state what is the […]
In my recent benchmarks for MongoDB, we can see that the two engines WiredTiger and TokuMX struggle from periodical drops in throughput, which is clearly related to a checkpoint interval – and therefore I correspond it to a checkpoint activity. The funny thing is that I thought we solved checkpointing issues in InnoDB once and […]
Previously I tested Tokutek’s Fractal Trees (TokuMX & TokuMXse) as MongoDB storage engines – today let’s look into the MySQL area. I am going to use modified LinkBench in a heavy IO-load. I compared InnoDB without compression, InnoDB with 8k compression, TokuDB with quicklz compression. Uncompressed datasize is 115GiB, and cachesize is 12GiB for InnoDB […]
As you may know, Tokutek is now part of Percona and I would like to explain some internals of TokuDB and TokuMX – what performance benefits they bring, along with further optimizations we are working on. However, before going into deep details, I feel it is needed to explain the fundamentals of Key-Value store, and […]
In this post I’ll share the results of a sysbench-mongodb benchmark I performed on my server. I compared MMAP, WiredTiger, RocksDB and TokuMXse (based on MongoDB 3.0) and TokuMX (based on MongoDB 2.4) in an IO-intensive workload. The full results are available here, and below I’ll just share the summary chart: I would like to […]