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If you are automating your infrastructure using Puppet, then this blog is for you. We are glad to announce the availability of a Puppet module for ClusterControl. For those using Chef, we already published Chef cookbooks for Galera Cluster and ClusterControl some time back.
The ClusterControl module initial release is available on Puppet Forge, installing the[Read more...]
MongoDB is great at storing clickstream data, but using it to analyze millions of documents can be challenging. Hadoop provides a way of processing and analyzing data at large scale. Since it is a parallel system, workloads can be split on multiple nodes and computations on large datasets can be done in relatively short timeframes. MongoDB data can be moved into Hadoop using ETL tools like Talend or Pentaho Data Integration (Kettle).
In this blog, we’ll show you how to integrate your MongoDB and Hadoop datastores using Talend. We have a MongoDB database collecting clickstream data from several websites. We’ll create a job in Talend to extract the documents from MongoDB, transform and then[Read more...]
In my last post, I gave a technical explanation of the performance characteristics of partitioned collections in TokuMX 1.5 (which is right around the corner) and partitioned tables in relational databases. Given those performance characteristics, in this post, I will present some best practices when using this feature in TokuMX or TokuDB. Note that these best practices are designed for TokuMX and TokuDB only, which[Read more...]
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 your database of choice. For example, this Oracle related post states “Anyone with un-partitioned databases over 500 gigabytes is courting disaster.” That’s not true for TokuDB or TokuMX. Nevertheless,[Read more...]
Thanks to everyone who attended and participated last week’s joint webinar on ClusterControl 1.2.6! We had great questions from participants (thank you), most of which are transcribed below with our answers to them.
If you missed the sessions or would like to watch the webinar again & browse through the slides, they are now available online.
Webinar topics discussed:
Background: If you did not read my first blog post about why I am sharing my thoughts on the benchmarks published by Mark Callaghan on Small Datum you may want to skim through it now for a little context: “Thoughts on Small Datum – Part 1”
Last time, in “Thoughts on Small Datum – Part 2” I shared my cliff notes and a graph on Mark Callaghan’s (@markcallaghan) March 11th insertion rate benchmarks using flash storage media. In those tests he compares MySQL (http://www.mysql.com/) outfitted with the[Read more...]
“As the database gets used, shards can grow at an uneven rate and one shard might carry a majority of the load. MongoDB corrects this by balancing shards, but because of MongoDB’s lack of concurrency this operation can stall the database unacceptably.”–John Partridge.
I have interviewed John Partridge, President & CEO of Tokutek, Inc.
Q1. Tokutek recently announced to have eliminated performance issues of MongoDB sharding. What was the problem?
John Partridge: The problem occurs after a shard is created. As the database gets used, shards can grow at an uneven rate and one shard might carry a majority of the load. MongoDB corrects this by balancing[Read more...]
Following the release of ClusterControl 1.2.6 a couple of weeks ago, we are now looking forward to demonstrating this latest version of the product on Tuesday next week, May 13th.
This release contains key new features (along with performance improvements and bug fixes), which we will be demonstrating live during the webinar.
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 (http://www.mysql.com" target="_blank) and MongoDB (or, preferably, our high-performance distributions of them, TokuDB and TokuMX).
Why should Mr. Harford take that walk? Well, he recently[Read more...]
On March 11th, Mark, a former Google and now Facebook database guru, published an insertion rate benchmark comparing MySQL (http://www.mysql.com) outfitted with the InnoDB storage engine with two NoSQL alternatives — basic MongoDB and TokuMX (the Tokutek high-performance[Read more...]
ClusterControl 1.2.6 introduces integration with Active Directory and LDAP authentication. This allows users to log into ClusterControl by using their corporate credentials instead of a separate password. LDAP groups can be mapped onto ClusterControl user groups to apply roles to the entire group, so it is very convenient for larger organizations who have a centralized LDAP-compliant authentication system. This blog shows you how to configure LDAP authentication in ClusterControl, and allow users to use their Active Directory or LDAP username and password to log in to ClusterControl.[Read more...]
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[Read more...]
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