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Displaying posts with tag: 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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MariaDB Galera Cluster is a unique technology worth testing
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As alway with new technology there is always different approaches regarding the adoption. You can try to use the bleeding edge features or start with a very standard configuration. My personal advise to new users is to start with the most basic configuration.

This allow you to get familiar with the fundamentals : - how [...]

How scalable is your database?
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Most of the time, when people say “scalability” they mean any of dozens of things. Most of the time, when I say it I mean exactly one precisely defined thing. However, I don’t claim that’s the only correct use of “scalability.” There is another, in particular, that I think is very important to understand: the inherent limitations of the system. This second one doesn’t have a single mathematical definition, but it’s vital nonetheless.

I’ll frame the discussion by asking this: how scalable is your database?

Using the two definitions I like to use the most, I answer the question in this way.

  • Scalability in terms of the Universal Scalability Law is the degree to which you can add more workers (or units of hardware) and get equal
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    Forthcoming webinar: Strategies for scaling MySQL
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    On February 28 at 1pm EST I’ll be taking part in a webinar, sponsored by ScaleBase, on strategies for scaling MySQL.

    Scalability is one of the primary drivers we’ve seen for database users considering alternatives to traditional relational databases. That could mean adopting an entirely new database for new projects or – more likely for existing applications – looking at various strategies for improving the scalability of an existing database.

    During the webinar I will be joined by Doron Levari and Paul Campaniello, both from ScaleBase, which enables applications to scale without disruption to the existing infrastructure. We’ll be discussing, amongst other things:

    • Scaling-out your MySQL databases
    • New
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    Achieve the Highest Levels of MySQL Scalability, Security & Uptime
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    Oracle's MySQL Enterprise Edition includes the most comprehensive set of advanced features, management tools and technical support to help you reduce the cost, risk & time to deploy and manage your MySQL applications.

    Access our Resource Kit to discover:

    Normal 0 false false false EN-US X-NONE X-NONE

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    A close look at New Relic’s scalability chart
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    I’ve written a lot about modeling MySQL with the USL, and I like it best of all the scalability models I’ve seen, but it’s not the only way to think about scalability. I was aware that New Relic supports a scalability chart, so I decided to take a peek at that. Here’s a screenshot of the chart, from their blog:

    Here’s how it works. It plots response time (or database time, or CPU) as the dependent variable, versus throughput as the independent variable. There’s a line through it to indicate the general shape. Samples are charted as points in a scatter plot. The points are color-coded by the time of day. Outliers

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    Modeling scalability with the USL at concurrencies less than 1
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    Last time I said that you can set a starting value for the USL’s coefficient of performance and let your modeling software (R, gnuplot, etc) manipulate this as part of the regression to find the best fit. However, there is a subtlety in the USL model that you need to be aware of. Here is a picture of the low-end of the curve:

    The graph shows the USL model as the blue curve and linear scalability as the black line. Notice that at concurrencies less than 1, the value of the USL function is actually greater than the linear scalability function. This deserves some thought and explanation, because it can cause problems.

    If you think about

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    Determining the USL’s coefficient of performance, part 2
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    Last time I said that the USL has a forgotten third coefficient, the coefficient of performance. This is the same thing as the system’s throughput at concurrency=1, or C(1). How do you determine this coefficient? There are at least three ways.

    Neil Gunther’s writings, or at least those that I’ve read and remember, say that you should set it equal to your measurement of C(1). Most of his writing discusses a handful of measurements of the system: one at concurrency 1, and at least 4 to 6 at higher concurrencies. I can’t remember a time when he’s discussed taking more than one measurement of throughput at each level of concurrency, so I think the assumption is that you’re going to take a single

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    Why your cloud is speeding for a scalability cliff
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    Read the original article at Why your cloud is speeding for a scalability cliff

    Also find Sean Hull’s ramblings on twitter @hullsean. Don’t believe me that you’re headed for the cliff? A startup scales up to no avail Towards the end of 2012 I worked with an internet startup in the online education space. Their web application was not unusual, built in PHP and using Linux, Apache & Mysql [...]

    For more articles like these go to Sean Hull's Scalable Startups

    Related posts:
  • 3 Ways to Boost Cloud Scalability
  • Cloud Operations Interview
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    Determining the Universal Scalability Law’s coefficient of performance
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    If you’re familiar with Neil Gunther’s Universal Scalability Law, you may have heard it said that there are two coefficients, variously called alpha and beta or sigma and kappa. There are actually three coefficients, though. See?

    No, you don’t see it — but it’s actually there, as a hidden “1″ multiplied by N in the numerator on the right-hand side. When you’re using the USL to model a system’s scalability, you need to use the C(1), the “capacity at one,” as a multiplier. I call this the coefficient of performance. It’s rarely 1; it’s usually thousands.

    To illustrate why this matters, consider two systems’ throughput as load increases:

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    Typical “Big” Data Architecture
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    Here is the typical “Big” data architecture, that covers most components involved in the data pipeline. More or less, we have the same architecture in production in number of places[...]
    Why do people leave consulting?
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    Read the original article at Why do people leave consulting?

    As a long time freelancer, it’s a question that’s intrigued me for some time. I do have some theories… First, definitions… I’m not talking about working for a large consulting firm. Although this role may be called “consultant”, my meaning is consultant as sole proprietor, entrepreneur, gun for hire or lone wolf. 1. Make more [...]

    For more articles like these go to Sean Hull's Scalable Startups

    Related posts:
  • Consulting essentials: Getting the business
  • Hiring is a numbers
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    Anatomy of a Performance Review
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    Read the original article at Anatomy of a Performance Review

    A lot of firms come to us with a specific scalability problem. “Our user base is growing rapidly and the website is falling over!” Or they’re selling more widgets, “Our shopping cart is slowing down and we’re seeing users abandon their purchases”. These are real startup growing pains, so what to do?

    We like to take a measured approach with these types of challenges, so we thought it would be helpful to run through a hypothetical scenario and see how we work.

    Having trouble with scalability? Check out our

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    Why you should attend Percona Live 2012
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    Read the original article at Why you should attend Percona Live 2012

    What I loved about Percona Live 2011 Last year I was excited to go to Percona Live for the first time in NYC. I arrived just in time to hear Harrison Fisk from Facebook speak about some of the awesome tweaks they’re running with MySQL there. It’s not everyday that you get to hear from [...]

    For more articles like these go to Sean Hull's Scalable Startups

    No related posts.
    Upcoming for Scalable Startups
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    Read the original article at Upcoming for Scalable Startups

    Just back from the Labor Day holiday, and ready to dive back in.

    I thought this would be a great time to outline some of our upcoming topics so here goes…

    1. Why Oracle usability sucks

    - a rant about Oracle’s weak points

    In the meantime take a peek at our piece on why we wrote the book on Oracle & Open Source. We ruminate on trends in the datacenter and take a stab at Oracle’s future.

    2. Why relational databases don’t scale

    - Is there any such thing as automatic scalability?
    - What blocks scalability?
    - Are NoSQL databases magic?

    Also one of our articles that went viral –



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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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    31 Essential Blogs for Startups & Scalability
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    Read the original article at 31 Essential Blogs for Startups & Scalability

    So many blogs, so little time! Here’s our list of the best we’ve found. Currently our favorite reader is Pulse pictured left. Starting to play around with flipboard too.

    Nuts & Bolts Technical

    Slashdot
    One of the original tech blogs, that still covers lots of breaking news, and difficult topics. Very technical, with probing commentary. Beware the actual comments though, as they’re often full of immature

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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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    Best of Guide – Highlights of Our Popular Content
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    Read the original article at Best of Guide – Highlights of Our Popular Content

    We cherry pick the top 5 most popular posts of various topics we’ve covered in recent months.

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    MySQL 5.6 Replication: FAQ
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    On Wednesday May 16th, we ran a webinar to provide an overview of all of the new replication features and enhancements that are previewed in the MySQL 5.6 Development Release – including Global Transaction IDs, auto-failover

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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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    MariaDB-5.5 Thread Pool Performance
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    MariaDB-5.5.21-beta is the first MariaDB release featuring the new thread pool. Oracle offers a commercial thread pool plugin for MySQL Enterprise, but now MariaDB brings a thread pool implementation to the community!

    If you are not familiar with the term, please read the Knowledge Base article about it.

    The main design goal of the thread pool is to increase the scalability of the MariaDB server with many concurrent connections. In order to test and demonstrate this, I have run the sysbench OLTP RO benchmark with up to 4096 threads to compare the new pool-of-threads and the traditional thread-per-connection scheduler:

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    Black-Box Performance Analysis with TCP Traffic
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    This is a cross-post from the MySQL Performance Blog. I thought it would be interesting to users of PostgreSQL, Redis, Memcached, and $system-of-interest as well.

    For about the past year I’ve been formulating a series of tools and practices that can provide deep insight into system performance simply by looking at TCP packet headers, and when they arrive and depart from a system. This works for MySQL as well as a lot of other types of systems, because it doesn’t require any of the contents of the packet. Thus, it works without knowledge of what the server and client are conversing about. Packet headers contain only information that’s usually regarded as non-sensitive (IP address, port, TCP flags, etc), so it’s

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