Tag - Fractal Trees

MongoDB benchmark: sysbench-mongodb IO-bound workload comparison

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 highlight […]

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Understanding the Performance Characteristics of Partitioned Collections

In TokuMX 1.5 that is right around the corner, the big feature will be partitioned collections. This feature is similar to partitioned tables in Oracle, MySQL, SQL Server, and Postgres. A question many have is “why should I use partitioned tables?” In short, it’s complicated. The answer depends on your workload, your schema, and […]

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Thoughts on Small Datum – Part 2

If you did not read my first blog post about Mark Callaghan’s (@markcallaghan) benchmarks as documented in his blog, Small Datum, you may want to skim through it now for a little context.
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On March 11th, Mark, a former Google and now Facebook database guru, published an insertion rate benchmark comparing MySQL outfitted with the […]

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Thoughts on Small Datum – Part 1

A little background…
When I ventured into sales and marketing (I’m an engineer by education) I learned I would often have to interpret and simply summarize the business value that is sometimes hidden in benchmarks. Simply put, the people who approve the purchase of products like TokuDB® and TokuMX™ appreciate the executive summary.
Therefore, I plan to […]

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Fast Updates with TokuDB

With TokuDB v6.6 out now, I’m excited to present one of my favorite enhancements: fast updates with TokuDB. Update intensive applications can have their throughput limited by the random read capacity of the storage system. The cause of the throughput limit is the read-modify-write algorithm that MySQL uses when processing update statements. MySQL reads […]

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