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Displaying posts with tag: Benchmarking (reset)
10x Insertion Performance Increase for MongoDB with Fractal Tree Indexes

The challenge of handling massive data processing workloads has spawned many new innovations and techniques in the database world, from indexing innovations like our Fractal Tree® technology to a myriad of “NoSQL” solutions (here is our Chief Scientist’s perspective). Among the most popular and widely adopted NoSQL solutions is MongoDB and we became curious if our Fractal Tree indexing could offer some advantage when combined with it. The answer seems to be a strong “yes”.

Earlier in the summer we kicked off a small side project and here’s what we did: we implemented a “version 2” IndexInterface as a Fractal Tree index and ran some benchmarks. Note that our integration only affects MongoDB’s secondary indexes; primary indexes continue to rely on MongoDB’s indexing code. All the changes we made to the MongoDB source …

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Real World Compression

Benchmarking is a tricky thing, especially when it comes to compression. Some data compresses quite well while other data does not compress at all. Storing jpeg images in a BLOB column produces 0% compression, but storing the string “AAAAAAAAAAAAAAAAAAAA” in a VARCHAR(20) column produces extremely high (and unrealistic) compression numbers.

This week I was assisting a TokuDB customer understand the insertion performance of TokuDB versus InnoDB and MyISAM for their actual data. The table contained a single VARCHAR(50), multiple INTEGER, one SET, one DECIMAL, and a surrogate primary key.  To support a varied query workload they needed 6 indexes.

Here is an obfuscated schema of the table:

col1 varchar(50) NOT NULL,
col2 int(40) NOT NULL DEFAULT '0',
col3 int(10) NOT NULL DEFAULT '0',
col4 int(10) NOT NULL DEFAULT '0',
col5 int(10) NOT NULL DEFAULT '0',
col6 set('val1', 'val2', ..., ‘val19’, 'val20',) NOT NULL DEFAULT …
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TokuDB v6.0: Download Available

TokuDB v6.0 is full of great improvements, like getting rid of slave lag, better compression, improved checkpointing, and support for XA.

I’m happy to announce that TokuDB v6.0 is now generally available and can be downloaded here.

Sysbench Performance

I wanted to take this time to talk about one more under-the-hood goody we’ve added to v6.0. In particular, we’ve been working on our locking schemes and have made …

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Benchmarking your MySQL servers

Benchmarking tools like Sysbench and DBT2 has helped alot of DBAs in measuring their MySQL databases performance. By benchmarking you will really know how far your current setup will go. In this part, you will learn how to install the sysbench in Ubuntu and other Enterprise linux.

1. Installing sysbench to Ubuntu is never been easy as issuing the apt-get command. You can also download the source from sourceforge.

In ubuntu, execute below.

sudo apt-get install sysbench

2. Installing to enterprise linux like Oracle Linux/ Red Hat Linux. Download the rpm file from or any sites providing rpm downloads. You can also check this source.

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TokuDB v6.0: Frequent Checkpoints with No Performance Hit

Checkpointing — which involves periodically writing out dirty pages from memory — is central to the design of crash recovery for both TokuDB and InnoDB. A key issue in designing a checkpointing system is how often to checkpoint, and TokuDB takes a very different approach from InnoDB. How often and how much InnoDB checkpoints is complicated, but under certain workloads it can be relatively infrequent. In contrast, TokuDB runs a complete checkpoint starting one minute after the last one ended.

Frequent checkpoints make for fast recovery. Once MySQL crashes, the storage engine needs to replay the log to get back to a correct state. The length of the log is a function of the time since the last checkpoint for TokuDB and a more complicated function of the workload for InnoDB. And replaying the log is single threaded. So TokuDB recovers in minutes, and …

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TokuDB v6.0: Even Better Compression

A key feature of our new TokuDB v6.0 release, which I have been blogging about this week, is compression. Compression is always on in TokuDB, and the compression we’ve achieved in the past has been quite good. See a previous post on the 18x compression achieved by TokuDB v5.0 on one benchmark. In our latest release, we’ve updated the way compression works and got 50% improvement on compression.

I decided to present numbers on the same set of data as the old post, so see that post for experimental details.

But first, what are the changes? TokuDB compresses large blocks of data — on the order of MB, rather than the 16KB that InnoDB uses — …

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MySQL Conference and Expo Talk on Benchmarking

I’ll be speaking on April 11th at 4:30 pm in Room 4 in at the Percona Conference and Expo Talk. The topic will be “Creating a Benchmark Infrastructure That Just Works.

Throughout my career I’ve been involved with maintaining the performance of database applications and therefore created many benchmark frameworks. At Tokutek, an important part of my role is measuring the performance of our storage engine over time and versus competing solutions. There is nothing proprietary about what I’ve created, it can be used anywhere.

My presentation will cover how I created the benchmark infrastructure at Tokutek:

  • Hardware and software …
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1 Billion Insertions – The Wait is Over!

iiBench measures the rate at which a database can insert new rows while maintaining several secondary indexes. We ran this for 1 billion rows with TokuDB and InnoDB starting last week, right after we launched TokuDB v5.2. While TokuDB completed it in 15 hours, InnoDB took 7 days.

The results are shown below. At the end of the test, TokuDB’s insertion rate remained at 17,028 inserts/second whereas InnoDB had dropped to 1,050 inserts/second. That is a difference of over 16x. Our complete set of benchmarks for TokuDB v5.2 can be found here.

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Compression Benchmarking: Size vs. Speed (I want both)

I’m creating a library of benchmarks and test suites that will run as part of a Continuous Integration (CI) process here at Tokutek. My goal is to regularly measure several aspects of our storage engine over time: performance, correctness, memory/CPU/disk utilization, etc. I’ll also be running tests against InnoDB and other databases for comparative analysis. I plan on posting a series of blog entries as my CI framework evolves, for now I have the results of my first benchmark.

Compression is an always-on feature of TokuDB. There are no server/session variables to enable compression or change the compression level (one goal of TokuDB is to have as few tuning parameters as possible). My compression benchmark uses iiBench to measure the insert performance and compression achieved by …

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MySQL Community – what do you want in a load testing framework?

So I’ve been doing a fair number of automated load tests these past six months. Primarily with Sysbench, which is a fine, fine tool. First I started using some simple bash based loop controls to automate my overnight testing, but as usually happens with shell scripts they grew unwieldy and I rewrote them in python. Now I have some flexible and easily configurable code for sysbench based MySQL benchmarking to offer the community. I’ve always been a fan of giving back to such a helpful group of people – you’ll never hear me complain about “my time isn’t free”. So, let me know what you want in an ideal testing environment (from a load testing framework automation standpoint) and I’ll integrate it into my existing framework and then release it via the BSD license. The main goal here is to have a standardized modular framework, based on sysbench, that allows anyone to compare their server performance via repeatable tests. It’s fun to see …

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Showing entries 31 to 40 of 62
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