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Saturday
Jun272009

Scaling Twitter: Making Twitter 10000 Percent Faster

Update 6: Some interesting changes from Twitter's Evan Weaver: everything in RAM now, database is a backup; peaks at 300 tweets/second; every tweet followed by average 126 people; vector cache of tweet IDs; row cache; fragment cache; page cache; keep separate caches; GC makes Ruby optimization resistant so went with Scala; Thrift and HTTP are used internally; 100s internal requests for every external request; rewrote MQ but kept interface the same; 3 queues are used to load balance requests; extensive A/B testing for backwards capability; switched to C memcached client for speed; optimize critical path; faster to get the cached results from the network memory than recompute them locally.
Update 5: Twitter on Scala. A Conversation with Steve Jenson, Alex Payne, and Robey Pointer by Bill Venners. A fascinating discussion of why Twitter moved to the Java JVM for their server infrastructure (long lived processes) and why they moved to Scala to program against it (high level language, static typing, functional). Ruby is used on the front-end but wasn't performant or reliable enough for the back-end.
Update 4: Improving Running Components at Twitter by Evan Weaver. Tells how Twitter changed their infrastructure to go from handling 3 requests to 139 requests a second. They moved to a messaging model, asynchronous process, 3 levels of cache, and moved their middleware to a mixture C and Scala/JVM.
Update 3: Upgrading Twitter without service disruptions by Gojko Adzic. Lots of good updates on the new Twitter architecture.
Update 2: a commenter in Twitter Fails Macworld Keynote Test said this entry needs to be updated. LOL. My uneducated guess is it's not a language or architecture problem, but more a problem of not being able to add hardware fast enough into their data center. The predictability of this problem is debatable, but once you have it, it's hard to fix.
Update: Twitter releases Starling - light-weight persistent queue server that speaks the MemCache protocol. It was built to drive Twitter's backend, and is in production across Twitter's cluster.

Twitter started as a side project and blew up fast, going from 0 to millions of page views within a few terrifying months. Early design decisions that worked well in the small melted under the crush of new users chirping tweets to all their friends. Web darling Ruby on Rails was fingered early for the scaling problems, but Blaine Cook, Twitter's lead architect, held Ruby blameless:

For us, it’s really about scaling horizontally - to that end, Rails and Ruby haven’t been stumbling blocks, compared to any other language or framework. The performance boosts associated with a “faster” language would give us a 10-20% improvement, but thanks to architectural changes that Ruby and Rails happily accommodated, Twitter is 10000% faster than it was in January.

If Ruby on Rails wasn't to blame, how did Twitter learn to scale ever higher and higher?

Update: added slides Small Talk on Getting Big. Scaling a Rails App & all that Jazz

Site: http://twitter.com

Information Sources

  • Scaling Twitter Video by Blaine Cook.
  • Scaling Twitter Slides
  • Good News blog post by Rick Denatale
  • Scaling Twitter blog post Patrick Joyce.
  • Twitter API Traffic is 10x Twitter’s Site.
  • A Small Talk on Getting Big. Scaling a Rails App & all that Jazz - really cute dog picks

    The Platform

  • Ruby on Rails
  • Erlang
  • MySQL
  • Mongrel - hybrid Ruby/C HTTP server designed to be small, fast, and secure
  • Munin
  • Nagios
  • Google Analytics
  • AWStats - real-time logfile analyzer to get advanced statistics
  • Memcached

    The Stats

  • Over 350,000 users. The actual numbers are as always, very super super top secret.
  • 600 requests per second.
  • Average 200-300 connections per second. Spiking to 800 connections per second.
  • MySQL handled 2,400 requests per second.
  • 180 Rails instances. Uses Mongrel as the "web" server.
  • 1 MySQL Server (one big 8 core box) and 1 slave. Slave is read only for statistics and reporting.
  • 30+ processes for handling odd jobs.
  • 8 Sun X4100s.
  • Process a request in 200 milliseconds in Rails.
  • Average time spent in the database is 50-100 milliseconds.
  • Over 16 GB of memcached.

    The Architecture

  • Ran into very public scaling problems. The little bird of failure popped up a lot for a while.
  • Originally they had no monitoring, no graphs, no statistics, which makes it hard to pinpoint and solve problems. Added Munin and Nagios. There were difficulties using tools on Solaris. Had Google analytics but the pages weren't loading so it wasn't that helpful :-)
  • Use caching with memcached a lot.
    - For example, if getting a count is slow, you can memoize the count into memcache in a millisecond.
    - Getting your friends status is complicated. There are security and other issues. So rather than doing a query, a friend's status is updated in cache instead. It never touches the database. This gives a predictable response time frame (upper bound 20 msecs).
    - ActiveRecord objects are huge so that's why they aren't cached. So they want to store critical attributes in a hash and lazy load the other attributes on access.
    - 90% of requests are API requests. So don't do any page/fragment caching on the front-end. The pages are so time sensitive it doesn't do any good. But they cache API requests.
  • Messaging
    - Use message a lot. Producers produce messages, which are queued, and then are distributed to consumers. Twitter's main functionality is to act as a messaging bridge between different formats (SMS, web, IM, etc).
    - Send message to invalidate friend's cache in the background instead of doing all individually, synchronously.
    - Started with DRb, which stands for distributed Ruby. A library that allows you to send and receive messages from remote Ruby objects via TCP/IP. But it was a little flaky and single point of failure.
    - Moved to Rinda, which a shared queue that uses a tuplespace model, along the lines of Linda. But the queues are persistent and the messages are lost on failure.
    - Tried Erlang. Problem: How do you get a broken server running at Sunday Monday with 20,000 users waiting? The developer didn't know. Not a lot of documentation. So it violates the use what you know rule.
    - Moved to Starling, a distributed queue written in Ruby.
    - Distributed queues were made to survive system crashes by writing them to disk. Other big websites take this simple approach as well.
  • SMS is handled using an API supplied by third party gateway's. It's very expensive.
  • Deployment
    - They do a review and push out new mongrel servers. No graceful way yet.
    - An internal server error is given to the user if their mongrel server is replaced.
    - All servers are killed at once. A rolling blackout isn't used because the message queue state is in the mongrels and a rolling approach would cause all the queues in the remaining mongrels to fill up.
  • Abuse
    - A lot of down time because people crawl the site and add everyone as friends. 9000 friends in 24 hours. It would take down the site.
    - Build tools to detect these problems so you can pinpoint when and where they are happening.
    - Be ruthless. Delete them as users.
  • Partitioning
    - Plan to partition in the future. Currently they don't. These changes have been enough so far.
    - The partition scheme will be based on time, not users, because most requests are very temporally local.
    - Partitioning will be difficult because of automatic memoization. They can't guarantee read-only operations will really be read-only. May write to a read-only slave, which is really bad.
  • Twitter's API Traffic is 10x Twitter’s Site
    - Their API is the most important thing Twitter has done.
    - Keeping the service simple allowed developers to build on top of their infrastructure and come up with ideas that are way better than Twitter could come up with. For example, Twitterrific, which is a beautiful way to use Twitter that a small team with different priorities could create.
  • Monit is used to kill process if they get too big.

    Lessons Learned

  • Talk to the community. Don't hide and try to solve all problems yourself. Many brilliant people are willing to help if you ask.
  • Treat your scaling plan like a business plan. Assemble a board of advisers to help you.
  • Build it yourself. Twitter spent a lot of time trying other people's solutions that just almost seemed to work, but not quite. It's better to build some things yourself so you at least have some control and you can build in the features you need.
  • Build in user limits. People will try to bust your system. Put in reasonable limits and detection mechanisms to protect your system from being killed.
  • Don't make the database the central bottleneck of doom. Not everything needs to require a gigantic join. Cache data. Think of other creative ways to get the same result. A good example is talked about in Twitter, Rails, Hammers, and 11,000 Nails per Second.
  • Make your application easily partitionable from the start. Then you always have a way to scale your system.
  • Realize your site is slow. Immediately add reporting to track problems.
  • Optimize the database.
    - Index everything. Rails won't do this for you.
    - Use explain to how your queries are running. Indexes may not be being as you expect.
    - Denormalize a lot. Single handedly saved them. For example, they store all a user IDs friend IDs together, which prevented a lot of costly joins.
    - Avoid complex joins.
    - Avoid scanning large sets of data.
  • Cache the hell out of everything. Individual active records are not cached, yet. The queries are fast enough for now.
  • Test everything.
    - You want to know when you deploy an application that it will render correctly.
    - They have a full test suite now. So when the caching broke they were able to find the problem before going live.
  • Long running processes should be abstracted to daemons.
  • Use exception notifier and exception logger to get immediate notification of problems so you can address the right away.
  • Don't do stupid things.
    - Scale changes what can be stupid.
    - Trying to load 3000 friends at once into memory can bring a server down, but when there were only 4 friends it works great.
  • Most performance comes not from the language, but from application design.
  • Turn your website into an open service by creating an API. Their API is a huge reason for Twitter's success. It allows user's to create an ever expanding and ecosystem around Twitter that is difficult to compete with. You can never do all the work your user's can do and you probably won't be as creative. So open you application up and make it easy for others to integrate your application with theirs.

    Related Articles

  • For a discussion of partitioning take a look at Amazon Architecture, An Unorthodox Approach to Database Design : The Coming of the Shard, Flickr Architecture
  • The Mailinator Architecture has good strategies for abuse protection.
  • GoogleTalk Architecture addresses some interesting issues when scaling social networking sites.
  • Friday
    Jun262009

    PlentyOfFish Architecture

    Update 5: PlentyOfFish Update - 6 Billion Pageviews And 32 Billion Images A Month
    Update 4: Jeff Atwood costs out Markus' scale up approach against a scale out approach and finds scale up wanting. The discussion in the comments is as interesting as the article. My guess is Markus doesn't want to rewrite his software to work across a scale out cluster so even if it's more expensive scale up works better for his needs.
    Update 3: POF now has 200 million images and serves 10,000 images served per second. They'll be moving to a 250,000 IOPS RamSan to handle the load. Also upgraded to a core database machine with 512 GB of RAM, 32 CPU’s, SQLServer 2008 and Windows 2008.
    Update 2: This seems to be a POF Peer1 love fest infomercial. It's pretty content free, but the production values are high. Lots of quirky sounds and fish swimming on the screen.
    Update: by Facebook standards Read/WriteWeb says POF is worth a cool one billion dollars. It helps to talk like Dr. Evil when saying it out loud.

    PlentyOfFish is a hugely popular on-line dating system slammed by over 45 million visitors a month and 30+ million hits a day (500 - 600 pages per second). But that's not the most interesting part of the story. All this is handled by one person, using a handful of servers, working a few hours a day, while making $6 million a year from Google ads. Jealous? I know I am. How are all these love connections made using so few resources?

    Site: http://www.plentyoffish.com/

    Information Sources

  • Channel9 Interview with Markus Frind
  • Blog of Markus Frind
  • Plentyoffish: 1-Man Company May Be Worth $1Billion

    The Platform

  • Microsoft Windows
  • ASP.NET
  • IIS
  • Akamai CDN
  • Foundry ServerIron Load Balancer

    The Stats

  • PlentyOfFish (POF) gets 1.2 billion page views/month, and 500,000 average unique logins per day. The peak season is January, when it will grow 30 percent.
  • POF has one single employee: the founder and CEO Markus Frind.
  • Makes up to $10 million a year on Google ads working only two hours a day.
  • 30+ Million Hits a Day (500 - 600 pages per second).
  • 1.1 billion page views and 45 million visitors a month.
  • Has 5-10 times the click through rate of Facebook.
  • A top 30 site in the US based on Competes Attention metric, top 10 in Canada and top 30 in the UK.
  • 2 load balanced web servers with 2 Quad Core Intel Xeon X5355 @ 2.66Ghz), 8 Gigs of RAM (using about 800 MBs), 2 hard drives, runs Windows x64 Server 2003.
  • 3 DB servers. No data on their configuration.
  • Approaching 64,000 simultaneous connections and 2 million page views per hour.
  • Internet connection is a 1Gbps line of which 200Mbps is used.
  • 1 TB/day serving 171 million images through Akamai.
  • 6TB storage array to handle millions of full sized images being uploaded every month to the site.

    What's Inside

  • Revenue model has been to use Google ads. Match.com, in comparison, generates $300 million a year, primarily from subscriptions. POF's revenue model is about to change so it can capture more revenue from all those users. The plan is to hire more employees, hire sales people, and sell ads directly instead of relying solely on AdSense.
  • With 30 million page views a day you can make good money on advertising, even a 5 - 10 cents a CPM.
  • Akamai is used to serve 100 million plus image requests a day. If you have 8 images and each takes 100 msecs you are talking a second load just for the images. So distributing the images makes sense.
  • 10’s of millions of image requests are served directly from their servers, but the majority of these images are less than 2KB and are mostly cached in RAM.
  • Everything is dynamic. Nothing is static.
  • All outbound Data is Gzipped at a cost of only 30% CPU usage. This implies a lot of processing power on those servers, but it really cuts bandwidth usage.
  • No caching functionality in ASP.NET is used. It is not used because as soon as the data is put in the cache it's already expired.
  • No built in components from ASP are used. Everything is written from scratch. Nothing is more complex than a simple if then and for loops. Keep it simple.
  • Load balancing
    - IIS arbitrarily limits the total connections to 64,000 so a load balancer was added to handle the large number of simultaneous connections. Adding a second IP address and then using a round robin DNS was considered, but the load balancer was considered more redundant and allowed easier swap in of more web servers. And using ServerIron allowed advanced functionality like bot blocking and load balancing based on passed on cookies, session data, and IP data.
    - The Windows Network Load Balancing (NLB) feature was not used because it doesn't do sticky sessions. A way around this would be to store session state in a database or in a shared file system.
    - 8-12 NLB servers can be put in a farm and there can be an unlimited number of farms. A DNS round-robin scheme can be used between farms. Such an architecture has been used to enable 70 front end web servers to support over 300,000 concurrent users.
    - NLB has an affinity option so a user always maps to a certain server, thus no external storage is used for session state and if the server fails the user loses their state and must relogin. If this state includes a shopping cart or other important data, this solution may be poor, but for a dating site it seems reasonable.
    - It was thought that the cost of storing and fetching session data in software was too expensive. Hardware load balancing is simpler. Just map users to specific servers and if a server fails have the user log in again.
    - The cost of a ServerIron was cheaper and simpler than using NLB. Many major sites use them for TCP connection pooling, automated bot detection, etc. ServerIron can do a lot more than load balancing and these features are attractive for the cost.
  • Has a big problem picking an ad server. Ad server firms want several hundred thousand a year plus they want multi-year contracts.
  • In the process of getting rid of ASP.NET repeaters and instead uses the append string thing or response.write. If you are doing over a million page views a day just write out the code to spit it out to the screen.
  • Most of the build out costs went towards a SAN. Redundancy at any cost.
  • Growth was through word of mouth. Went nuts in Canada, spread to UK, Australia, and then to the US.
  • Database
    - One database is the main database.
    - Two databases are for search. Load balanced between search servers based on the type of search performed.
    - Monitors performance using task manager. When spikes show up he investigates. Problems were usually blocking in the database. It's always database issues. Rarely any problems in .net. Because POF doesn't use the .net library it's relatively easy to track down performance problems. When you are using many layers of frameworks finding out where problems are hiding is frustrating and hard.
    - If you call the database 20 times per page view you are screwed no matter what you do.
    - Separate database reads from writes. If you don't have a lot of RAM and you do reads and writes you get paging involved which can hang your system for seconds.
    - Try and make a read only database if you can.
    - Denormalize data. If you have to fetch stuff from 20 different tables try and make one table that is just used for reading.
    - One day it will work, but when your database doubles in size it won't work anymore.
    - If you only do one thing in a system it will do it really really well. Just do writes and that's good. Just do reads and that's good. Mix them up and it messes things up. You run into locking and blocking issues.
    - If you are maxing the CPU you've either done something wrong or it's really really optimized. If you can fit the database in RAM do it.
  • The development process is: come up with an idea. Throw it up within 24 hours. It kind of half works. See what user response is by looking at what they actually do on the site. Do messages per user increase? Do session times increase? If people don't like it then take it down.
  • System failures are rare and short lived. Biggest issues are DNS issues where some ISP says POF doesn't exist anymore. But because the site is free, people accept a little down time. People often don't notice sites down because they think it's their problem.
  • Going from one million to 12 million users was a big jump. He could scale to 60 million users with two web servers.
  • Will often look at competitors for ideas for new features.
  • Will consider something like S3 when it becomes geographically load balanced.

    Lessons Learned

  • You don't need millions in funding, a sprawling infrastructure, and a building full of employees to create a world class website that handles a torrent of users while making good money. All you need is an idea that appeals to a lot of people, a site that takes off by word of mouth, and the experience and vision to build a site without falling into the typical traps of the trade. That's all you need :-)
  • Necessity is the mother of all change.
  • When you grow quickly, but not too quickly you have a chance grow, modify, and adapt.
  • RAM solves all problems. After that it's just growing using bigger machines.
  • When starting out keep everything as simple as possible. Nearly everyone gives this same advice and Markus makes a noticeable point of saying everything he does is just obvious common sense. But clearly what is simple isn't merely common sense. Creating simple things is the result of years of practical experience.
  • Keep database access fast and you have no issues.
  • A big reason POF can get away with so few people and so little equipment is they use a CDN for serving large heavily used content. Using a CDN may be the secret sauce in a lot of large websites. Markus thinks there isn't a single site in the top 100 that doesn’t use a CDN. Without a CDN he thinks load time in Australia would go to 3 or 4 seconds because of all the images.
  • Advertising on Facebook yielded poor results. With 2000 clicks only 1 signed up. With a CTR of 0.04% Facebook gets 0.4 clicks per 1000 ad impressions, or .4 clicks per CPM. At 5 cent/CPM = 12.5 cents a click, 50 cent/CPM = $1.25 a click. $1.00/CPM = $2.50 a click. $15.00/CPM = $37.50 a click.
  • It's easy to sell a few million page views at high CPM’s. It's a LOT harder to sell billions of page views at high CPM’s, as shown by Myspace and Facebook.
  • The ad-supported model limits your revenues. You have to go to a paid model to grow larger. To generate 100 million a year as a free site is virtually impossible as you need too big a market.
  • Growing page views via Facebook for a dating site won't work. Having a visitor on you site is much more profitable. Most of Facebook's page views are outside the US and you have to split 5 cent CPM’s with Facebook.
  • Co-req is a potential large source of income. This is where you offer in your site's sign up to send the user more information about mortgages are some other product.
  • You can't always listen to user responses. Some users will always love new features and others will hate it. Only a fraction will complain. Instead, look at what features people are actually using by watching your site.

    Related Articles

  • MySpace also uses Windows to run their site.
  • Markus Frind's posts on Webmaster World.
  • And the Money Comes Rolling In by Max Chafkin
  • How I started A Dating Empire by Markus Frind

    Thanks to Erik Osterman for recommending profiling PlentyOfFish.
  • Wednesday
    Jun242009

    Habits of Highly Scalable Web Applications 

    Nick Belhomme wrote up a excellent summary of a talk given by Eli White on building scalable web applications. Eli worked at digg.com and is now the PHP Community Manager & DevZone Editor-in-Chief at Zend Technologies. Eli takes us on a grand tour through various proven scaling strategies. On the trip you'll visit:

  • What is scalable application design
  • Tip 1: load balancing the webserver
  • Tip 2: scaling from a single DB server to a Master-Slave setup
  • Tip 3: Partitioning, Vertical DB Scaling
  • Tip 4: Partitioning, horizontal DB Scaling
  • Tip 5: Application Level Partitioning
  • Tip 6: Caching to get around your database
  • Resources
  • Tuesday
    Jun232009

    Learn How to Exploit Multiple Cores for Better Performance and Scalability

    InfoQueue has this excellent talk by Brian Goetz on the new features being added to Java SE 7 that will allow programmers to fully exploit our massively multi-processor future. While the talk is about Java it's really more general than that and there's a lot to learn here for everyone.

    Brian starts with a short, coherent, and compelling explanation of why programmers can't expect to be saved by ever faster CPUs and why we must learn to exploit the strengths of multiple core computers to make our software go faster.

    Some techniques for exploiting multiple cores are given in an equally short, coherent, and compelling explanation of why divide and conquer as the secret to multi-core bliss, fork-join, how the Java approach differs from map-reduce, and lots of other juicy topics.

    The multi-core "problem" is only going to get worse. Tilera founder Anant Agarwal estimates by 2017 embedded processors could have 4,096 cores, server CPUs might have 512 cores and desktop chips could use 128 cores. Some disagree saying this is too optimistic, but Agarwal maintains the number of cores will double every 18 months.

    An abstract of the talk follows though I would highly recommend watching the whole thing. Brian does a great job.

    Why is Parallelism More Important Now?

  • Coarse grain concurrency was all the rage for Java 5. The hardware reality has changed. The number of cores is increasing so applications must now search for fine grain parallelism (fork-join)
  • As hardware becomes more parallel, more and more cores, software has to look for techniques to find more and more parallelism to keep the hardware busy.
  • Clock rates have been increasing exponentially over the last 30 years or so. Allowed programmers to be lazy because a faster processor would be released that saved your butt. There wasn't a need to tune programs.
  • That wait for faster processor game is up. Around 2003 clock rates stopped increasing. Hit the power wall. Faster processors require more power. Thinner chip conductor lines were required and the thinner lines can't dissipate the increased power without causing overheating which effects the resistance characteristics of the conductors. So you can't keep increasing clock rate.
  • Fastest Intel CPU 4 or 5 years ago was 3.2 Ghz. Today it's about the same or even slower.
  • Easier to build 2.6 Ghz or 2.8 Ghz chips. Moore's law wasn't repealed so we can cram more transistors on each wafer. So more processing power could be put on a chip which leads to putting more and more processing cores on a chip. This is multicore.
  • Multicore systems are the trend. The number of cores will grow at exponential rate for the next 10 years. 4 cores at the low end. The high end 256 (Sun) and 800 (Azul) core systems.
  • More cores per chip instead of faster chips. Moore's law has been redirected to multicore.
  • The problem is it's harder to make a program go faster on a multicore system. A faster chip will run your program faster. If you have a 100 cores you program won't go faster unless you explicitly design it to take advantage of those chips.
  • No free lunch anymore. Must now be able to partition your program so it can run faster by running on multiple cores. And you must be able keep doing that as the number of cores keeps improving.
  • We need a way to specify programs so they can be made parallel as topologies change by adding more cores.
  • As hardware evolves platforms must evolve to take advantage of the new hardware. Started off with course grain tasks which was sufficient given the number of cores. This approach won't work as the number cores increase.
  • Must find finer-grained parallelism. Example sorting and searching data. Opportunities around data. The data can for sorting can be chunked and sorted and the brought together with a merge sort. Searching can be done in parallel by searching subregions of the data and merging the results.
  • Parallel solutions use more CPU in aggregate because of the coordination needed and that data needs to be handled more than once (merge). But the result is faster because it's done in parallel. This adds business value. Faster is better for humans.

    What has Java 7 Added to Support Parallelism?

  • Example problem is to find the max number from a list.
  • The course grained threading approach is to use a thread pool, divide up the numbers, and let the task pool compute the sub problems. A shared task pool is slow as the number increases which forces the work to be more course grained. No way to load balance. Code is ugly. Doesn't match the problem well. The runtime is dominated by how long it takes the longest subtask to run. Had to decide up front how many pieces to divide the problem into.
  • Solution using divide and conquer. Divide set into pieces recursively until the problem is so small the sequential solution is more efficient. Sort the pieces. Merge the results. 0(n log n), but problem is parallelizable. Scales well and can keep many CPUs busy.
  • Divide and conquer uses fork-join to fork off subtasks and wait for them to complete and then join the results. A typical thread pool solution is not efficient. Creates too many threads and creating threads are expensive and use a lot of memory.
  • This approach portable because it's abstract. It doesn't know how many processors are available It's independent of the topology.
  • The fork-join pool is optimized for fine grained operations whereas the thread pool is optimized for course grained operations. Best used for problems without IO. Just computations using CPU that tend to fork off sub problems. Allows data to be shared read-only and used across different computations without copying.
  • This approach scales nearly linearly with the number of hardware threads.
  • The goal for fork-join: Avoid context switches; Have as many threads as hardware threads and keep them all busy; Minimize queue lock contention for data structures. Avoid common task queue.
  • Implementation uses Work-Stealing. Each thread has a work queue that is a double ended queue. Each thread pulls work from the head of queue and processes it. When there's nothing do it steals work from the tail of another queue. No contention for the head because only one thread access it. Rare contention on tail because stealing is infrequent as the stolen work is large which takes them time to process. Process starts with one task. It breaks up the work. Other tasks steal work and start the same process. Load balances without central coordination, few context switches, little coordination.
  • The same approach also works for graph traversal, matrix operations, linear algebra, modeling, generate moves and evaluate the result. Latent parallelism can be found in a lot of places once you start looking.
  • Support higher level operations like ParallelArray. Can specify filtering, transformation, and aggregation options. Not a generalized in-memory database, but has a very transparent cost model. It's clear how many parallel operations are happening. Can look at the code and quickly know what's a parallel operation so you will know the cost.
  • Looks like map reduce except this is scaling across a multicore system, one single JVM, whereas map reduce is across a cluster. The strategy is the same: divide and conquer.
  • Idea is to make specifying parallel operations so easy you wouldn't even think of the serial approach.

    Related Articles

  • The Free Lunch Is Over - A Fundamental Turn Toward Concurrency in Software By Herb Sutter
  • Intuition, Performance, and Scale by Dan Pritchett
  • "Multi-core Mania": A Rebuttal by Ted Neward
  • CPU designers debate multi-core future by Rick Merritt
  • Multicore puts screws to parallel-programming models by Rick Merritt
  • Challenges in Multi-Core Era – Part 1 and Part 2 by Gaston Hillar.
  • Running multiple processes to understand multicore CPUs power.
  • Learning to Program all Over Again by Vineet Gupta
  • Monday
    Jun222009

    Improving performance and scalability with DDD

    Distributed systems are not typically a place domain driven design is applied. Distributed processing projects often start with an overall architecture vision and the idea about a processing model which basically drives the whole thing, including object design if it exists at all. Elaborate object designs are thought of as something that just gets in the way of distribution and performance, so the idea of spending time to apply DDD principles gets rejected in favour of raw throughput and processing power. However, from my experience, some more advanced DDD concepts can significantly improve the performance, scalability and throughput of distributed systems when applied correctly.

    This article a summary of the presentation titled "DDD in a distributed world" from the DDD Exchange 09 in London.

    Saturday
    Jun202009

    Building a data cycle at LinkedIn with Hadoop and Project Voldemort

    Update: Building Voldemort read-only stores with Hadoop.

    A write up on what LinkedIn is doing to integrate large offline Hadoop data processing jobs with a fast, distributed online key-value storage system, Project Voldemort.

    Friday
    Jun192009

    GemFire 6.0: New innovations in data management

    GemStone has unveiled GemFire 6.0 which is the culmination of several years of development and the continuous solving of the hardest data management problems in the world. With this release GemFire touts some of the latest innovative features in data management.

    In this release:

    - GemFire introduces a resource manager to continuously monitor and protect cache instances from running out of memory, triggering rebalancing to migrate data to less loaded nodes or allow dynamic increase/decrease in the number of nodes hosting data for linear scalability without impeding ongoing operations (no contention points).

    - GemFire provides explicit control over when rebalancing can be triggered, on what class of data and even allows the administrator to simulate a "rebalance" operation to quantify the benefits before actually doing it.

    - With built in instrumentation that captures throughput and latency metrics, GemFire now enables applications to sense changing performance patterns and proactively provision extra resources and trigger rebalancing. The end result is predictable data access throughput and latency without the need to overprovision capacity.

    - We continue down the path of making the product more resilient than ever before - dealing with complex membership issues when operating in large clusters and allowing thresholds to be set in terms of consumption of memory in any server JVM that significantly reduces the probability of "stop the world" garbage collection cycles.

    - Advanced Data Partitioning: Applications are no longer restricted by the memory available across the cluster to manage partitioned data. Applications can pool available memory as well as disk and stripe the data across memory and disk across the cluster. When the data fabric is configured as a cache, partitioned data can be expired or evicted so that only the most frequently used data is managed.

    - Data-aware application behavior routing: There are several extensions added to the GemFire data-aware function execution service - a simple grid programming model that allows the application to synchronously or asynchronously execute application behavior on the data nodes. Applications invoke functions hinting the data they are dependent on and the service parallelizes the execution of the application function on all the grid nodes where the data is being managed. Applications can now define relationships between different classes of data to colocate all related data sets and application functions when routed to the data nodes can execute complex queries on in-process data. These and other features offered in the 'Function execution service' offers linear scalability for compute and data intensive applications. Simply add more nodes when demand spikes to rebalance data and behavior to increase the overall throughput for your application.

    - API additions for C++, C#: Support for continuous querying, client side connection pooling and dynamic load balancing and ability to invoke server side functions.

    - Cost based Query optimization: A new compact index to conserve memory utilizaton and enhanced query processor design with cost-based optimization has been introduced as part of this release.

    - Developer productivity tools: It can be daunting when developers have to quickly develop and test their clustered application. Developers need the capability to browse the distributed data using ad-hoc queries, apply corrections or monitor resource utilization and performance metrics. A new graphical Data browser permits browsing and editing of data across the entire cluster, execution of ad-hoc queries and even create real-time table views that are continuously kept up-to-date through continuous queries. The GemFire Monitor tool (GFMon) also has several enhancements making the tool much more developer friendly.

    For more information on GemFire, view our newly rewritten technical white paper at:
    http://community.gemstone.com/download/attachments/4752318/GemFire+Data+Fabric+-+Technical+White+paper.pdf?version=1

    Monday
    Jun152009

    Large-scale Graph Computing at Google

    To continue the graph theme Google has got into the act and released information on Pregel. Pregel does not appear to be a new type of potato chip. Pregel is instead a scalable infrastructure...

    ...to mine a wide range of graphs. In Pregel, programs are expressed as a sequence of iterations. In each iteration, a vertex can, independently of other vertices, receive messages sent to it in the previous iteration, send messages to other vertices, modify its own and its outgoing edges' states, and mutate the graph's topology.

    Currently, Pregel scales to billions of vertices and edges, but this limit will keep expanding. Pregel's applicability is harder to quantify, but so far we haven't come across a type of graph or a practical graph computing problem which is not solvable with Pregel. It computes over large graphs much faster than alternatives, and the application programming interface is easy to use. Implementing PageRank, for example, takes only about 15 lines of code. Developers of dozens of Pregel applications within Google have found that "thinking like a vertex," which is the essence of programming in Pregel, is intuitive.

    Pregel does not appear to be publicly available, so it's not clear what the purpose of the announcement could be. Maybe it will be a new gmail extension :-)

    Monday
    Jun152009

    starting small with growth in mind

    Hello all,

    I'm working on a web site that might totally flop or it might explode to be the next facebook/flickr/digg/etc. Since I really don't know how popular the site will be I don't want to spend a ton of money on the hardware/hosting right away but I want to be able to scale it easily if it does grow rapidly. With this in mind, what would be the best approach to launch the site?

    Thanks,
    Dan

    Sunday
    Jun142009

    CLOUD & GRID EVENT BY THE ONLINE GAMING HIGH SCALABILITY SIG

    The first meeting of this Online Gaming High Scalability SIG will be on the 9th of July 2009 in central London, starting at 10 AM and finishing around 5PM.

    The main topic of this meeting will be potentials for using cloud and grid technologies in online gaming systems. In addition to experience reports from the community, we have invited some of the leading cloud experts in the UK to discuss the benefits such as resource elasticity and challenges such as storage and security that companies from other industries have experienced. We will have a track for IT managers focused on business opportunities and issues and a track for architects and developers more focused on implementation issues.

    The event is free but up-front registration is required for capacity planning, so please let us know in advance, if you are planning to attend by completing the registration form on this page

    To propose a talk or for programme enquiries, contact meetings [at] gamingscalability [dot] org.

    Note: The event is planned to finish around 5 PM so that people can make their way to Victoria on time for CloudCamp London. CloudCamp is a meeting of the cloud computing community with short talks, is also free but you will have to register for it separately

    PROGRAMME: http://skillsmatter.com/event/cloud-grid/online-gaming-high-scalability-sig/wd-99