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Showing entries 1 to 10 of 13 3 Older Entries

Displaying posts with tag: scale out (reset)

MySQL thread pool and scalability examples
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Nice article about SimCity outage and ways to defend databases: http://www.mysqlperformanceblog.com/2013/03/16/simcity-outages-traffic-control-and-thread-pool-for-mysql/

The graphs showing throughput with and without the thread pool are taken from the benchmark performed by Oracle and taken from here:
http://www.mysql.com/products/enterprise/scalability.html (http://www.mysql.com/products/enterprise/scalability.html)

The main take away is this graph (all rights reserved to Oracle, picture original URL (http://www.mysql.com/common/images/enterprise/MySQL_Threadpool_Benchmark_RW.png" target="_blank)):

Scalability is






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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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So now Hadoop's days are numbered?
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Earlier this week we all read GigaOM's article with this title:
"Why the days are numbered for Hadoop as we know it"
I know GigaOM like to provoke scandals sometimes, we all remember some other unforgettable piece, but there is something behind it...

Hadoop today (after SOA not so long ago) is one of the worst case of an abused buzzword ever known to men. It's everything, everywhere, can cure illnesses and do "big-data" at the same time! Wow! Actually Hadoop is a software framework that supports data-intensive distributed applications, derived from Google's MapReduce and Google File System (GFS) papers.

My take from the article is




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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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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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Scale-out your DB on ARM-based servers
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Today, I think we witnessed a small sign for a big revolution...

http://www.pcworld.com/businesscenter/article/256383/dell_reaches_for_the_cloud_with_new_prototype_arm_server.html
"Dell announced a prototype low-power server with ARM processors, following a growing demand by Web companies for custom-built servers that can scale performance while reducing financial overhead on data centers"
In short, ARM (see Wikipedia definition here) is an architecture standard for processors. ARM processors are slower compared to good old x86 processors from Intel and AMD, but have power-efficiency, density and price attributes that intrigue


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Scale differences between OLTP and Analytics
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In my previous post,http://database-scalability.blogspot.com/2012/05/oltp-vs-analytics.html, I reviewed the differences between OLTP and Analytics databases.

Scale challenges are different between those 2 worlds of databases.



Scale challenges in the Analytics world are with the growing amounts of data. Most solutions have been leveraging those 3 main aspects: Columnar storage, RAM and parallelism.
Columnar storage makes scans and data filtering more precise and focused. After that – it all goes down to







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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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Showing entries 1 to 10 of 13 3 Older Entries

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