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Showing entries 1 to 20

Displaying posts with tag: database scalability (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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Top Two Signs your MySQL Database is Maxing Out
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One of the main responsibilities of any database administrator is to keep a close eye on how database performance is impacting size and storage. Decisions will have to be made on whether or not to make changes within the database structure or application itself, or to make the changes on the storage and resource side [...] Read More
How to Tune a MySQL Application Like a Piano
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A default installation of MySQL is easy to perform, but if you really want your databases to sing, you should tune them like you would tune a piano. In MySQL tuning pertains to either the application or the database system. In this post, we cover some common tuning techniques and best practices to increase your [...] Read More
Setting Shakespeare Straight. NoSQL, NewSQL or MySQL: THAT is the Question.
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Come Find the Answer Launching a next-gen app?  You need a next-gen database.  But figuring out which one is no walk-in-the-park. Tune in next Tuesday to a webinar where Matt Aslett, research manager for data management and analytics at 451 Research, Doron Levari, ScaleBase’s CTO, and I will discuss: The increasingly complex and ever-changing database market The benefits and [...] Read More
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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NetworkWorld: How Mozilla keeps its MySQL database tidy
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MySQL database management tool ScaleBase virtualizes MySQL database, spreading database load into smaller bite-size chunks As an open source company, Mozilla developers make a lot of different versions of software code each day, and part of Sheeri Cabral’s job to keep track of them all: which ones work, which don’t, how many times they’ve been downloaded, and which have a [...] Read More
The Boston Java Meetup – Java & Mysql: Scaling is everything! | September 18, 2012, 6:30pm – 8:00pm
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Tuesday, September 18, 2012, 6:30pm – 8:00pm  Cambridge Innovation Center,  1 Broadway, Cambridge, MA (map) http://www.meetup.com/boston-java/events/75357012/ Doron Levari Doron will be speaking at the Java Meetup.  Doron is a long-time veteran of the database industry and the publisher of the Database Scalability Blog, has extensive experience in building and scaling-out database systems as well as the organizations and infrastructure necessary to support them.  Please find a [...] Read More
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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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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Data Store, Software and Hardware – What is best
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Other day we had a small discussion about data stores and hardware; and which one drives the other when it comes to data storage solution, rather it is a hard discussion as both on its own are bigger entities; and one can not easily conclude as it depends on use cases and actually speaking data [...]
CAP Theorem, Eventual Consistency, NoSQL
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Very nice and interesting post from Michael Stonebraker explaining how errors dictate CAP Theorem (Consistency, Availability and Partition-tolerance); as only one objective from the CAP can be achieved during normal error conditions as NoSQL system seems to relax the consistency model as CAP theorem anyway proves that one can’t get all 3 at the same [...]
Showing entries 1 to 20

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