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Displaying posts with tag: tokumx (reset)
Maybe You Should Try Taking a Walk in My Shoes

The title of this post should really be, “Maybe He Should Try Taking a Walk in Your Shoes.”

The he I’m referring to is economist and author, Tim Harford. The you is the people who use NewSQL and NoSQL approaches to mine big data with database platforms like MySQL and MongoDB (or, preferably, our high-performance distributions of them, TokuDB and TokuMX).

Why should Mr. Harford take that walk? Well, he recently penned an article on big data in …

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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.


On March 11th, Mark, a former Google and now Facebook database guru, published an insertion rate benchmark comparing MySQL outfitted with the InnoDB storage engine with two NoSQL alternatives — basic MongoDB and …

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How TokuMX Secondaries Work in Replication

As I’ve mentioned in previous posts, TokuMX replication differs quite a bit from MongoDB’s replication. The differences are large enough such that we’ve completely redone some of MongoDB’s existing algorithms. One such area is how secondaries apply oplog data from a primary. In this post, I’ll explain how.

In designing how secondaries apply oplog data, we did not look closely at how MongoDB does it. In fact, I’ve currently forgotten all I’ve learned about MongoDB’s implementation, so I am not in a position to compare the two. I think I recall that MongoDB’s oplog idempotency was a key to their algorithms. Because we chose …

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My Favorite MongoDB Replication Feature: Crash Safety

At an extremely high level, replication in MongoDB and MySQL are similar. Both databases have exactly one machine, the primary (or master), that accepts writes from clients. With a single transaction (or atomic operation, in MongoDB’s case), the tables and oplog (or binary log in MySQL) are modified to reflect the change. The log captures what the change is so other secondaries (or slaves) can read the changes and process them, making the slaves identical to the master. (Note that I am NOT talking about multi-master replication.)

Underneath the covers, their implementations are quite different. And in peeking underneath the covers while developing TokuMX, I learned more about my favorite thing in …

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ClusterControl 1.2.5 Released

March 5, 2014 By Severalnines

The Severalnines team is pleased to announce the release of ClusterControl 1.2.5. This release contains key new features along with performance improvements and bug fixes. We have outlined some of the key features below. 

For additional details about the release:

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How TokuMX was Born

With TokuMX 1.4 coming out soon, with (teaser) wonderful improvements made to sharding and updates (and plenty of other goodies), I’ve recently reminisced about how we got TokuMX to this point. We (actually, really John) started dabbling with integrating Fractal Tree® indexes into MongoDB in the summer of 2012, where we (really, he) prototyped using Fractal Tree indexes only for secondary indexes. As cool as that

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The Effects of Database Heap Storage Choices in MongoDB

William Zola over at MongoDB gave a great talk called “The (Only) Three Reasons for Slow MongoDB Performance”. It reminded me of an interesting characteristic of updates in MongoDB. Because MongoDB’s main data store is a flat file and secondary indexes store offsets into the flat file (as I explain here), if the location of a document changes, corresponding entries in secondary indexes must also change. So, an update to an unindexed field that causes the document to move also causes modifications to every secondary index, which, as William points out, can be expensive. If a document has indexed an array, this problem is exacerbated.

What …

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Webinar Replay & Slides: Repair & Recovery for Your MySQL, MariaDB & MongoDB / TokuMX Clusters

January 23, 2014 By Severalnines


Thanks to everyone who attended this week’s webinar; if you missed the sessions or would like to watch the webinar again and browse through the slides, they are now available online.


Special thanks to Seppo Jaakola from Codership, the creators of Galera Cluster, for walking us through the various scenarios of Galera recovery. 


Webinar topics discussed: 

  • Redundancy models for Galera, NDB and MongoDB / TokuMX
  • Failover & Recovery (Automatic vs Manual)
  • Zooming into Galera recovery procedures
  • Split brains in multi-datacenter setups


Watch the replay:


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New Webinar: Repair and Recovery for your MySQL, MariaDB and MongoDB/TokuMX Clusters

December 19, 2013 By Severalnines

Database clusters are pretty sophisticated distributed systems with complex dependencies between nodes. The failure of a node will generally impact the overall cluster, as the remaining nodes need to reconfigure themselves to continue to operate without the failed node. Since re-introducing a node will also affect the existing cluster, the timing could therefore be dependent on the state of the other nodes in the cluster. Repair and restarts often needs to be performed in a particular order in compliance with the redundancy model of the cluster so as not to jeopardize the normal functioning of existing nodes.


Webinar: Repair and Recovery for your MySQL, MariaDB and MongoDB/TokuMX clusters


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December 17 Webinar: Use Your MySQL Knowledge to Become a MongoDB Guru

Use your MySQL expertise to analyze the strengths and weaknesses of MongoDB.

SPEAKER: Tim Callaghan, VP of Engineering at Tokutek
DATE: Tuesday, December 17th
TIME: 1pm ET
Register Now!

MongoDB is a popular NoSQL DBMS that shares the ease-of-use and quick setup that made MySQL famous. But is MongoDB really up to the job? Is it right for your applications? If you understand MySQL well, you know how database systems work.

Join Tim Callaghan, VP/Engineering at Tokutek as he recaps his and CEO of Continuent, Robert Hodges, session from 2013′s Percona Live London. Learn how to lean on your knowledge of topics like schema design, query optimization, indexing, sharding, and high availability to analyze the strengths and weaknesses of MongoDB. System design is all …

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