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Displaying posts with tag: Fractal Tree™ indexes (reset)
OLTP and OLAP – Have Your Cake and Eat it Too!

Looks like we’ll be having some more fun at the Percona Live MySQL Conference! In addition to our booth and my colleague Tim’s talk, my lightning talk was accepted. The title is “OLTP and OLAP – Have Your Cake and Eat it Too!” The lightning talks, given in a TBD order, will start Wednesday evening (April 11th) at around 6:30 pm.

Below is the abstract I submitted.


OLTP and OLAP – Have Your Cake and Eat it Too!

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Fractal Tree Indexes and Mead – MySQL Meetup

Thanks again to Sheeri Cabral  for having me at the Boston MySQL Meetup on Monday for the talk on “Fractal Tree® Indexes – Theoretical Overview and Customer Use Cases.” The crowd was very interactive, and I appreciated that over 50 people signed up for the event and left some very positive comments and reviews.

In addition, the conversation spilled over late into the night as we made our way over to nearby Mead Hall afterwards for a few drinks, some food, and to continue the discussion.

The presentation is …

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FictionPress Selects TokuDB for Consistent Performance and Fast Disaster Recovery


Issues addressed:

  • Support complex and efficient indexes at 100+ million rows.
  • Predicable and consistent performance regardless of data size growth.
  • Fast recovery.

Ensuring Predictable Performance at Scale

The Company:  FictionPress operates both and and is home to over 6 million works of fiction, with millions of writers/readers participating from around the world in over 30 languages

The Challenge: FictionPress offers a number of interactive features to its large user base. These …

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Write Optimization: Myths, Comparison, Clarifications, Part 2

In my last post, we talked about the read/write tradeoff of indexing data structures, and some ways that people augment B-trees in order to get better write performance. We also talked about the significant drawbacks of each method, and I promised to show some more fundamental approaches.

We had two “workload-based” techniques: inserting in sequential order, and using fewer indexes, and two “data structure-based” techniques: a write buffer, and OLAP. Remember, the most common thing people do when faced with an insertion bottleneck is to use fewer indexes, and this kills query performance. So keep in mind that all our work on write-optimization is really work for read-optimization, in that write-optimized indexes are cheap enough that you can keep all the ones you need to get good read performance.

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Are You Forcing MySQL to Do Twice as Many JOINs as Necessary?
Baron Schwartz
This guest post is from our friends at Percona. They’re hosting Percona Live London from October 24-25, 2011. Percona Live is a two day summit with 100% technical sessions led by some of the most established speakers in the MySQL field.

In the London area and interested in attending? We are giving away two free passes in the next few days. Watch our @tokutek twitter feed for a chance to win.

Did you know that the following query actually performs a JOIN? You can’t see it, but it’s there:

SELECT the_day, COUNT(*), SUM(clicks), SUM(cost)
FROM ad_clicks_by_day
WHERE the_day >= …
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Write Optimization: Myths, Comparison, Clarifications

Some indexing structures are write optimized in that they are better than B-trees at ingesting data. Other indexing structures are read optimized in that they are better than B-trees at query time. Even within B-trees, there is a tradeoff between write performance and read performance. For example, non-clustering B-trees (such as MyISAM) are typically faster at indexing than clustering B-trees (such as InnoDB), but are then slower at queries.

This post is the first of two about how to understand write optimization, what it means for overall performance, and what the difference is between different write-optimized indexing schemes. We’ll be talking about how to deal with workloads that don’t fit in memory—in particular, if we had our data in B-trees, only the internal nodes (perhaps not even all of them) would fit in memory.

As I’ve already said, there is a tradeoff between write and read …

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Online Advertiser Intent Media Selects TokuDB over InnoDB and NoSQL for Big Data Ad-Hoc Analysis

Intent Media

Issue addressed: Ad hoc analytics on clickstream data arriving too fast for InnoDB or NoSQL to handle.

TokuDB powers an online advertising application

The Company: Headquartered in New York, Intent Media is a fast-growing online advertising startup. The company helps some of the largest online retailers monetize their traffic more efficiently at scale by showing highly relevant and targeted advertising to the 97+% of e-commerce visitors who do not transact.

The Challenge: The Intent Media platform processes hundreds of millions of events a day generated by media placements across leading e-commerce sites — a textbook “Big Data” challenge. Intent Media’s data is used to optimize media placements, drive segmentation models, and …

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Ask What Your Database Can Do for Your Country

How many in your household again?

One of President John Kennedy’s most memorable phrases is “ask not what your country can do for you –  ask what can you do for your country”.  I got to thinking about this over lunch with a fellow colleague in the big data space. After comparing named customers for a while, we realized we had forgotten one of the biggest “big data” customers whom we both have in common – the government.

Whether you believe in small or big government, one thing is for certain – it has some very big data on its hands. Some of this is freely available, such as the …

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It Actually is Easy Being Green

(Fractal) Tree Frog

Fractal Tree™ indexes are green. They have the potential to be greener still. Here’s why:

Remarkably, data centers consume 1-3 percent of all the US electricity. A majority of this power is used to drive servers and storage systems. Significant energy savings remain on the table.

Here’s why Fractal Tree indexing enables more energy-efficient storage: Data centers typically use many small-capacity disks rather than a few large-capacity disks. Why? One reason is to harness more spindles to obtain more I/Os per second. In some high-performance applications, users go so far as to employ techniques such as “ …

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Cage Match: OldSQL, NoSQL and NewSQL


When I interviewed at Tokutek, I met a team of distinguished academics and engineers who could calmly and thoughtfully wax eloquent about the finer points of B-tree and Fractal Tree™ indexing,  drive I/Os, and database engines. Soon after, I discovered that several of my colleagues have a second passion — they practice Mixed Martial Arts (MMA). As Wikipedia explains, MMA showcases the “fighters of different disciplines, including boxing, Brazilian Jiu-Jitsu, wrestling, Muay Thai, karate and others.” I’ve since learned about many different fighting styles.

This was useful to understand when an MMA-style fight broke out in the MySQL world earlier this month between the different variants or …

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