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Displaying posts with tag: cluster (reset)

MySQL Cluster Performance Best Practices: Q & A
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With its distributed, shared-nothing, real-time design, MySQL Cluster (http://www.mysql.com/products/cluster/) has attracted a lot of attention from developers who need to scale both read and write traffic with ultra-low latency and fault-tolerance, using commodity hardware. With many proven deployments (http://www.mysql.com/customers/cluster/) in web, gaming, telecoms and mobile use-cases, MySQL Cluster is certainly able to meet these sorts of requirements.

But, as a distributed database, developers do need to think a little differently about data access patterns along with schema and query optimisations in order to get the best possible performance.

Sharing best practices developed by working with MySQL Cluster's largest users, we recently ran a Performance Essentials webinar

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MySQL Cluster Powers El Chavo from Playful Play, Latin America’s Most Popular Facebook Game
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Introduction

Attracting over 2m subscribers in just 4 months and growing by 30,000 new users per day, Playful Play needed a database that was able to

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NoSQL Java API for MySQL Cluster: Questions & Answers
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The MySQL Cluster engineering team recently ran a live webinar, available now on-demand (http://www.mysql.com/news-and-events/on-demand-webinars/display-od-716.html) demonstrating the ClusterJ and ClusterJPA NoSQL APIs for MySQL Cluster (http://www.mysql.com/products/cluster/), and how these can be used in building real-time, high scale Java-based services that require continuous availability.

Attendees asked a number of great questions during the webinar, and I thought it would be useful to share those here, so others are also able to learn more about the Java NoSQL APIs.

First, a little bit about why we developed these APIs and why they are interesting to Java developers.

ClusterJ and Cluster JPA

ClusterJ is a Java interface to MySQL Cluster that provides either a static or dynamic domain object model, similar to the data model used

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ARM based data center. Inspiring.
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In a previous post I wrote ARM based servers. Since then, and thanks to all the comments and responses I got, I looked more into this ARM thing and it's absolutely fascinating...

Look at this beauty (taken from the site of Calxeda, the manufacturer):

What is it? A chip? A server? No, it's a cluster of 4 servers...

And this:







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The catch-22 of read/write splitting
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In my previous post I covered the shard-disk paradigm's pros and cons, but the conclusion that is that it cannot really qualify as a scale-out solution, when it comes to massive OLTP, big-data, big-sessions-count and mixture of reads and writes.

Read/Write splitting is achieved when numerous replicated database servers are used for reads. This way the system can scale to cope with increase in concurrent load. This solution qualifies as a scale-out solution as it allow expansion beyond the boundaries of one DB, DB machines are shared-nothing, can be added as a slave to the replication "group" when required.



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New MySQL & MariaDB Instructional Videos from SkySQL
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Are you looking to expand your knowledge about MySQL and MariaDB database solutions?

Well, you’re in luck! SkySQL is introducing an exclusive collection of educational videos featuring some of the industry’s leading experts on the MySQL database and related technologies. View informative, technical talks on a variety of topics, from the experts at SkySQL, MariaDB, Calpont InfiniDB, Continuent, ScaleDB, Severalnines, Sphinx, Webyog, and others.

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Why shared-storage DB clusters don't scale
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Yesterday I was asked by a customer for the reason why he had failed to achieve scale with a state-of-the-art "shared-storage" cluster. "It's a scale-out to 4 servers, but with a shared disk. And I got, after tons of work and efforts, 130% throughput, not even close to the expected 400%" he said.

Well, scale-out cannot be achieved with a shared storage and the word "shared" is the key. Scale-out is done with absolutely nothing shared or a "shared-nothing" architecture. This what makes it linear and unlimited. Any shared resource, creates a tremendous burden on each and every database server in the cluster.

In a previous post, I identified database engine activities such as buffer management, locking, thread locks/semaphores,



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MySQL Cluster on Raspberry Pi
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Earlier this week, Andrew Morgan wrote a piece on running MySQL Cluster on Raspberry Pi. Since the term “Cluster” is hideously overloaded, I’ll note that we’re talking about the NDB cluster storage engine here, a very specific architecture originally acquired by MySQL AB from Ericsson (telco).

Raspberry Pi is a new single-board computer based on the ARM processor series (same stuff that powers most mobile phones these days), and it can run Linux without any fuss. Interfaces include Ethernet, USB, and HDMI video, and the cost is $25-50. I’m looking

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Dynarr256 for DBACC -or- The death of MAX_ROWS
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Back in 2006 we became aware of problems storing large numbers of rows in a single table in cluster. Johan Andersson and Yves Trudeau have each blogged about the problem and the common workaround here and here.  We've since then done some cleanup to provide a more proper "Table is full" error message when running into this problem.

As explained in the referenced blog posts, the problem is the result of a limitation on the size of the hash index of each partition. The hash index for each partition would allow at most ~49 million records. By default an ndbd or ndbmtd node have only 1 local query handler (LQH) block and thus 1 partition per node.  The ndbmtd nodes having

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MySQL Cluster 7.3 Labs Release – Foreign Keys Are In!
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Summary (aka TL/DR):

Support for Foreign Key constraints has been one of the most requested feature enhancements for MySQL Cluster

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10 Newer Entries Showing entries 61 to 70 of 246 10 Older Entries

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