We just published results with improvements in Thread Pool in Percona Server:
Percona Server: Thread Pool Improvements for Transactional Workloads
Percona Server: Improve Scalability with Thread Pool

What I am happy to see is that Percona Server is able to handle a tremendous amount of user connections. From our charts you can see it can go to 16000 active connections without a decline in throughput.

In fact, in our experiments we used 25000 connections and even more, so I think now we face OS limitations in handling connections as …

In a previous thread pool post, I mentioned that in Percona Server we used an open source implementation of MariaDB’s thread pool, and enhanced/improved it further. Below I would like to describe some of these improvements for transactional workloads.

When we were evaluating MariaDB’s thread pool implementation, we observed that it improves scalability for AUTOCOMMIT statements. However, it does not scale well with multi-statement transactions. The UPDATE_NO_KEY test which was run as an AUTOCOMMIT statement and inside a transaction gave the following results:

After analysis, we identified the major cause of that inefficiency: …

By default, for every client connection the MySQL server spawns a separate thread which will process all statements for this connection. This is the ‘one-thread-per-connection’ model. It’s simple and efficient until some number of connections N is reached. After this point performance of the MySQL server will degrade, mostly due to various contentions caused by N threads that are trying to access shared resources: either system ones like CPU, IO, memory or MySQL specific: structures/locks/etc. To keep the system stable and avoid degradation in the performance we need to limit the number of active threads, and at the same time we do not want to limit number of the client connections. The ‘Thread Pool’ model helps us to achieve that. It allows mapping N client connections to M number of active threads (actually performing work) while demonstrate a smooth and stable throughput for the MySQL server.

There are several implementations of …

Or I could place in the title – “Yes, we done it!”

After reaching 500K QPS in Read-Only on SQL queries, it was natural to expect a much higher performance level from InnoDB Memcached Plugin which is by-passing all SQL related layers.. However the story is not simple, and yet far from finished

While for today we have already our first “preview” results showing that we’re able to reach over 1,000,000 Query/sec level with the latest MySQL 5.7 code:

click here to read the full article..

This is part two on a two-part series on the performance implications of in-flight data encryption with MySQL. In the first part, I focused specifically on the impact of using MySQL’s built-in SSL support with some rather surprising results. Certainly it was expected that query throughput would be lower with SSL than without, but I was rather surprised by the magnitude of the performance hit incurred at connection setup time. These results naturally lended themselves to some further investigation; in particular, I wanted to compare performance differences between MySQL’s built-in SSL encryption facilities and external encryption technologies, such as SSH tunneling. I’ll also be using this post to address a couple of questions posed in the comments on my original article. So, without further ado….

Test Environment

The …

NOTE: This is part 1 of what will be a two-part series on the performance implications of using in-flight data encryption.

Some of you may recall my security webinar from back in mid-August; one of the follow-up questions that I was asked was about the performance impact of enabling SSL connections. My answer was 25%, based on some 2011 data that I had seen over on yaSSL’s website, but I included the caveat that it is workload-dependent, because the most expensive part of using SSL is establishing the connection. Not long thereafter, I received a request to conduct some more specific benchmarks surrounding SSL usage in MySQL, and today I’m going to show the results.

First, the testing …

Yesterday we announced the GA release of Percona Server 5.6, the latest release of our enhanced, drop-in replacement for MySQL. Percona Server 5.6 is the best free MySQL alternative for demanding applications. Our third major release, Percona Server 5.6 offers all the improvements found in MySQL 5.6 Community Edition plus scalability, availability, backup, and security features some of which are found only in MySQL 5.6 Enterprise Edition.

Percona Server 5.6 comes with:

• General performance improvements
• Extensive diagnostics via TABLE/INDEX/USER …

With the close of call for papers earlier this month, the Percona Live London conference committee was in full swing this past week reviewing all of the many submissions for November’s Percona Live London MySQL Conference.

The submissions are far ranging and cover some really interesting topics, making the lineup for Percona Live London really strong! What the committee looks for in a submission is how much “value” a talk will bring to the conference – this is to say it needs to be far more that a product demo. As such, real-world experiences are receiving much more favorable reviews, along with talks that cover methodologies the attendees will …

This post is a continuation of my research of TokuDB’s  storage engine to understand if it is suitable for timeseries workloads.

While inserting LOAD DATA INFILE into an empty table shows great results for TokuDB, what’s more interesting is seeing some realistic workloads.

So this time let’s take a look at the INSERT benchmark.

What I am going to do is to insert data in 16 parallel threads into the table from the previous post:

CREATE TABLE `sensordata` (
  `ts` int(10) unsigned NOT NULL DEFAULT '0',
  `sensor_id` int(10) unsigned NOT NULL,
  `data1` double NOT NULL,
  `data2` double NOT NULL,
  `data3` double NOT NULL,
  `data4` double NOT NULL,
  `data5` double NOT …

I am working on a customer’s system where the requirement is to store a lot of timeseries data from different sensors.

For performance reasons we are going to use SSD, and therefore there is a list of requirements for the architecture:

• Provide high insertion rate
• Provide a good compression rate to store more data on expensive SSDs
• Engine should be SSD friendly (less writes per timeperiod to help with SSD wear)
• Provide a reasonable response time (within ~50 ms) on SELECT queries on hot recently inserted data

Looking on these requirements I actually think that TokuDB might be a good fit for this task.

There are several aspects to consider. This time I want to compare TokuDB vs InnoDB on an initial load time and space consumption.

Let’s …