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Displaying posts with tag: sharding (reset)

The Data Day, Two days: January 9/10 2013
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SAP on HANA. Funding for Guavus and ScaleArc. And more

It’s alive! @451research‘s 2013 Database survey is available now at bit.ly/451db13 #mysql #nosql #newsql #postgresql etc etc

— Matt Aslett (@maslett) January 9, 2013

#SAPonHANA is official. Read the press release for the SAP Business Suite powered by #SAP

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Some sharding support and cache locality optimization support for PHP MySQL driver
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It is time for christmas presents: some sharding support and cache locality optimizations are coming with PECL/mysqlnd_ms 1.5. PECL/mysqlnd_ms is a plugin for the mysqlnd library. The plugin adds replication and load balancing support to any PHP MySQL API (mysql, mysqli, PDO_MySQL) if compiled to use the mysqlnd library.

As a MySQL user you can choose between a wide variety of clustering solutions to scale-out. Your options range from eventual consistent solutions to strong consistent ones, from built-in (MySQL Replication, MySQL Cluster) to third party or home-grown. PECL/mysqlnd_ms is a client side load

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Being successful like Pinterest without its DB adventures...
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I just came across this: "Scaling Pinterest and adventures in database sharding"  (http://gigaom.com/data/scaling-pinterest-and-adventures-in-database-sharding/)
"Pinterest has learned about scaling the way most popular sites do — the architecture works until one day it doesn’t"
Pinterest found out that "the architecture" is not scalable and they turned to development of a Scale Out mechanism also called Sharding.

I find it amazing that sharding, or in other words, the idea of "scale out by splitting and parallelizing data across shared-nothing commodity-hardware" is not supplied "out of the box" by "the architecture" (such as database, load-balancer, any other IT stuff). I'm wondering who was the one that


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Facebook makes big data look... big!
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Oh I love these things: http://techcrunch.com/2012/08/22/how-big-is-facebooks-data-2-5-billion-pieces-of-content-and-500-terabytes-ingested-every-day/

Every day there are 2.5B content items shares, and 2.7B "Like"s. I care less about GiGo content itself, but metadata, connections, relations are kept transactionally in a relational database. The above 2 use-cases generate 5.2B transactions on the database, and since there are only 86400 seconds a day, we get over 60000 write transactions per second on the database, from these 2 use-cases alone, not to mention all other use-cases, such as new profiles, emails, queries...

And what's the



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Scale Up, Partitioning, Scale Out
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On the 8/16 I conducted a webinar titled: "Scale Up vs. Scale Out" (http://www.slideshare.net/ScaleBase/scalebase-webinar-816-scaleup-vs-scaleout):


ScaleBase Webinar 8.16: ScaleUp vs. ScaleOut from ScaleBase
The webinar was successful, we had many attendees and great participation in questions and answers throughout the session and in the end. Only after the webinar it only occurred to me that one specific graphic was missing from the webinar deck. It was occurred to me after answering



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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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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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Impressions from Amazon's AWS Summit in NYC
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Yesterday (4/19) I attended the AWS Summit in NYC (http://aws.amazon.com/aws-summit-2012/nyc).

I'm a big fan and also a heavy user of AWS especially S3, EC2, and naturally, RDS. In every point in time I have several dozens of AWS machines running for me out there in the East region, and in some cases when we do some special benchmarks and tests, number of EC2 and RDS machines can easily reach 3-digit. As I said, I'm a fan...

A few quotes I was able to catch and document on my laptop, on my laps...:
"When you develop an app for facebook, you must be prepared (and be afraid) that to your party, not noone will show up, but everybody will show up!"
So true! Simple and true. We all want to succeed, to have success with our app. We have to think about scaling




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So how can we scale databases?
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There are ways to scale databases, unfortunately some are limited, some introduce complexities, some are do not fit the cloud...

By scaling solution I mean a solutions that help me scale my existing environment, my existing RDBMS. Some magic or technology that will take my existing Oracle or MySQL for example, to the next level, without porting to a new DB engine/vendor and without completely recoding my app.

Let's try to organize things a bit in this very summarized table, just to get the hunch of it. I can't imagine to cover it all in 1 table or even 100 pages, but that should be a start of a meaningful discussion to continue in next posts:

Solution Scales reads? Scales writes? Scales data? Scales sessions? Cloud? Bottom line Scale-Up: faster HW, CPU, memory,





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Applications come and go. Databases are here to scale.
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In my heart, I'm a DBA, always was and always will be. People say I'm a database guy by the way I think, keep my car, and file my music and also bank statements... However I did great deal of development, design, architecture on the apps side. I (hope to) have some perspective.

Applications come and go. The second programming language I've ever learned and worked on was COBOL, some still say most of the world's lines of code are written in this language, maybe so, but anyway I since then have known and written in dozens of programming languages, from Assembly to Force.com, from Pascal to Delphi, from functional C to Object Oriented SmallTalk, C++, Java and , from compiled C/CGI to interpreted Perl, ASP and Ruby back to compiled node.js... My first applications ran on Main-Frame with green screen, later I created beautiful graphic

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10 Newer Entries Showing entries 31 to 40 of 68 10 Older Entries

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