Sunday, August 9, 2009 at 2:01AM In this blog post BJ Clark overviews the available key-value stores that aren't SQL based.
On some projects he yields interesting experiences. Make sure to check out the comments as well.
The post has spread throughout social tiny text services so there is a chance you've already read it, but still worthwhile to put here.
Saturday, August 8, 2009 at 9:22AM Me and my partner are making a blueprint for an online webshop service. The purpose of this project is to make webshops available for small company's/ individuals automatically just by creating an account with us. Our webapp can be used to add products/pages/... to the store and we'll handle secure checkout by paypal.
Our app should be scalable and manageable. Because we also want to offer free webshops, the amount of webshops could be +10.000 within a few years. We are building on the Zend framework and are using mysql for database.
From the start we want to build our application for optimal and easy scalability in the future, to avoid a lot changes to our app/database in the future.
Now our questions are:
Should we use?:
* one database for all shops (or limited to X shops );
* one database for each new shop (each having products, orders... tables);
I think both approaches have PRO/CONS. What do you think ? Does anyone has experience with this kind of structure ?
PRO:
one database: easier to make changes to database layout
multiple databases:more scalable, easier to backup/restore
CON:
one database: harder to code because of extra keyfields in tables , slower or more difficult to backup/restore.
multiple databases: Takes longer to push changes to database layout
We are totally not clear on what hosting we should use. Would it be a solution to use a cloud service such as mosso/amazon/gogrid? Or is it better to just start with one dedicated server and expand 'manually' later?
Thanks in advance for your help!
Saturday, August 8, 2009 at 2:06AM 
So far every massively scalable database is a bundle of compromises. For some the weak guarantees of Amazon's eventual consistency model are too cold. For many the strong guarantees of standard RDBMS distributed transactions are too hot. Google App Engine tries to get it just right with entity groups. Yahoo! is also trying to get is just right by offering per-record timeline consistency, which hopes to serve up a heaping bowl of rich database functionality and low latency at massive scale:
We describe PNUTS [Platform for Nimble Universal Table Storage], a massively parallel and geographically distributed database system for Yahoo!’s web applications. PNUTS provides data storage organized as hashed or ordered tables, low latency for large numbers of con-current requests including updates and queries, and novel per-record consistency guarantees. It is a hosted, centrally managed, and geographically distributed service, and utilizes automated load-balancing and failover to reduce operational complexity. The first version of the system is currently serving in production. We describe the motivation for PNUTS and the design and implementation of its table storage and replication layers, and then present experimental results.
Some of the cool things about PNUTS are:
Friday, August 7, 2009 at 1:44AM Update: Asynchronous HTTP cache validations. A proposed HTTP caching extension: if your application can afford to show slightly out of date content, then stale-while-revalidate can guarantee that the user will always be served directly from the cache, hence guaranteeing a consistent response-time user-experience.
Caching is like aspirin for headaches. Head hurts: pop a 'sprin. Slow site: add caching. Facebook must have a lot of headaches because they popped 805 memcached servers between 10,000 web servers and 1,800 MySQL servers and they reportedly have a 99% cache hit rate. But what's the best way for you to cache for your application? It's a remarkably complex and rich topic. Alexey Kovyrin talks about one common caching problem called the Dog Pile Effect in Dog-pile Effect and How to Avoid it with Ruby on Rails. Glenn Franxman also has a Django solution in MintCache.
Data is usually cached because it's too expensive to calculate for every hit. Maybe it's a gnarly SQL query you want to avoid and a little stale data is OK. Or maybe the amount of data you have is simply larger than physical memory on any one machine. Or maybe you have the temerity to write to your database and cause its cache to flush so database caching isn't sufficient at a certain level of scale.
Typical examples are for caching article vote counts, comment threads, and event streams. One familiar example that bit me hard is displaying the the top N blog articles. Do you want to scan through your entire access log table for every page display? Absolutely not. Especially when the nightly backups are going on and the network is very slow. Not good :-) Yet you still want to update the results every X minutes so the stats stay fresh.
Data freshness requires a refrigeration truck or an expiry time on your cache entry that causes stats to be periodically recalculated. Now, what happens when your cached data expires and a 1000 requests simultaneously try to recalculate the expensive to calculate data? Database load spikes and the world nearly ends. And since memcached operations are not atomic it's possible stale data could be cached and you'll serve stale data. Which kind of defeats of the purpose of taking load off the data while providing accurate data. So, how do you unpile the dogs?
If cache items never expire then there can never be a recalculation storm. Then how do you update the data? Use cron to periodically run the calculation and populate the cache. Take the responsibility for cache maintenance out of the application space. This approach can also be used to pre-warm the the cache so a newly brought up system doesn't peg the database.
The problem is the solution doesn't always work. Memcached can still evict your cache item when it starts running out of memory. It uses a LRU (least recently used) policy so your cache item may not be around when a program needs it which means it will have to go without, use a local cache, or recalculate. And if we recalculate we still have the same piling on issues.
This approach also doesn't work well for item specific caching. It works for globally calculated items like top N posts, but it doesn't really make sense to periodically cache items for user data when the user isn't even active. I suppose you could keep an active list to get around this limitation though.
This solution introduces a stale date in addition to the expiration date. Glen describes it as:
The first client to request data past the stale date is asked to refresh the data,
while subsequent requests are given the stale but not-yet-expired data as if it
were fresh, with the understanding that it will get refreshed in a 'reasonable'
amount of time by that initial request
In the memcached FAQ a one key approach is described:
Friday, August 7, 2009 at 12:06AM Update 2: Elastic Load Balancer and EC2 instance bandwidth. It turns out we are limited by bandwidth and not by CPU. Solution: use DNS Round Robin for two to three HighCPU medium instances.
Update: The Skinny Straw: Cloud Computing's Bottleneck and How to Address It. For cloud computing, bandwidth to and from the cloud provider is a bottleneck. Solution: Evaluate application architecture and consider application partitioning.
I'm writing this post as a sort of penance. My sin was getting involved in another mutli-threaded mess of a program that was rife with strange pauses and unexpected errors. I really should have known better. But when APIs choose to make callbacks from some mystery thread pool it's hard to keep things straight. I eventually sobered up and posted all events to a queue so I could make sure the program would work correctly. Doh. I may never know why the .Net console output stopped working, but I'll live with it.
And that reminded me that I've been meaning to write a post on the standard Cloud Architecture. I've tried to hit all the common architectures at one time or another, but there have been some excellent sources lately on structuring programs in a cloud that people may "know" in the same way I knew what not to do, but when the code hits the editor those thoughts may have hidden like a kid next to a broken cookie jar.
The easiest way to create a scalable service is to compose the service from other scalable services. This is how Google AppEngine works and is largely how AWS works as well (EC2, S3, SQS, SimpleDB, etc), though AWS also functions as a blank canvas on which you can draw your own designs.
The canonical cloud architecture that has evolved revolves around dynamically scalable CPUs consuming asynchronous, persistently queued events. We talked about this idea already in Flickr - Do the Essential Work Up-front and Queue the Rest. The cloud is just another way of implementing the same idea.
Amazon suggests a few applications of the Cloud Architecture as:
Thursday, August 6, 2009 at 3:24PM Update 4: Why you don’t want to shard. by Morgon on the MySQL Performance Blog. Optimize everything else first, and then if performance still isn’t good enough, it’s time to take a very bitter medicine.
Update 3: Building Scalable Databases: Pros and Cons of Various Database Sharding Schemes by Dare Obasanjo. Excellent discussion of why and when you would choose a sharding architecture, how to shard, and problems with sharding.
Update 2: Mr. Moore gets to punt on sharding by Alan Rimm-Kaufman of 37signals. Insightful article on design tradeoffs and the evils of premature optimization. With more memory, more CPU, and new tech like SSD, problems can be avoided before more exotic architectures like sharding are needed. Add features not infrastructure. Jeremy Zawodny says he's wrong wrong wrong. we're running multi-core CPUs at slower clock speeds. Moore won't save you.
Update: Dan Pritchett shares some excellent Sharding Lessons: Size Your Shards, Use Math on Shard Counts, Carefully Consider the Spread, Plan for Exceeding Your Shards
Once upon a time we scaled databases by buying ever bigger, faster, and more expensive machines. While this arrangement is great for big iron profit margins, it doesn't work so well for the bank accounts of our heroic system builders who need to scale well past what they can afford to spend on giant database servers. In a extraordinary two article series, Dathan Pattishall, explains his motivation for a revolutionary new database architecture--sharding--that he began thinking about even before he worked at Friendster, and fully implemented at Flickr. Flickr now handles more than 1 billion transactions per day, responding in less then a few seconds and can scale linearly at a low cost.
What is sharding and how has it come to be the answer to large website scaling problems?
While working at Auction Watch, Dathan got the idea to solve their scaling problems by creating a database server for a group of users and running those servers on cheap Linux boxes. In this scheme the data for User A is stored on one server and the data for User B is stored on another server. It's a federated model. Groups of 500K users are stored together in what are called shards.
The advantages are:
Wednesday, August 5, 2009 at 9:36AM Update 2: Stack Overflow Architecture Update - Now At 95 Million Page Views A Month
Update: Startup – ASP.NET MVC, Cloud Scale & Deployment shows an interesting alternative approach for a Windows stack using ServerPath/GoGrid for a dedicated database machine, elastic VMs for the front end, and a free load balancer.
Stack Overflow is a much loved programmer question and answer site written by two guys nobody has ever heard of before. Well, not exactly. The site was created by top programmer and blog stars Jeff Atwood and Joel Spolsky. In that sense Stack Overflow is like a celebrity owned restaurant, only it should be around for a while. Joel estimates 1/3 of all the programmers in the world have used the site so they must be serving up something good.
I fell in deep like with Stack Overflow for purely selfish reasons, it helped me solve a few difficult problems that were jabbing my eyes out with pain. I also appreciate their no-apologies anthropologically based design philosophy. Use design to engineer in the behaviours you want to encourage and minimize the responses you want to discourage. It's the conscious awareness of the mechanisms that creates such a satisfying synergy.
What is key about the Stack Overflow story for me is the strong case they make for scale up as a viable solution for a certain potentially large class of problems. The publicity these days is all going scale out using NoSQL databases.
If you need to Google scale then you really have no choice but to go the NoSQL direction. But Stack Overflow is not Google and neither are most sites. When thinking about your design options keep Stack Overflow in mind. In this era of multi-core, large RAM machines and advances in parallel programming techniques, scale up is still a viable strategy and shouldn't be tossed aside just because it's not cool anymore. Maybe someday we'll have the best of both worlds, but for now there's a big painful choice to be made and that choice decides your fate.
Joel boasts that for 1/10 the hardware they have performance comparable to similarly size sites. He wonders if these other sites have good programmers. Let's see how they did it and you be the judge.
Site: http://stackoverflow.com
Wednesday, August 5, 2009 at 12:49AM Update 8: Introducing MongoDB by Eliot Horowit.
Update 7: The Future of Scalable Databases by Robin Mathew.
Update 6: NoSQL : If Only it Was that Easy. BJ Clark lays down the law on which databases are scalable: Tokyo - NO, Redis - NO, Voldemort - YES, MongoDB - Not Yet, Cassandra - Probably, Amazon S3 - YES * 2, MySQL - NO. The real thing to point out is that if you are being held back from making something super awesome because you can’t choose a database, you are doing it wrong.
Update 5: Exciting stuff happening in Japan at this Key-Value Storage meeting in Tokyo. Presentations on Groonga, Senna, Lux IO, Tokyo-Cabinet, Tx, repcached, Kai, Cagra, kumofs, ROMA, and Flare.
Update 4: NoSQL and the Relational Model: don’t throw the baby out with the bathwater by Matthew Willson. So my key point is, this kind of modelling is WORTH DOING, regardless of which database tool you end up using for physical storage.
Update 3: Choosing a non-relational database; why we migrated from MySQL to MongoDB. An illuminating article explaining why Boxed Ice move to MongoDB over MySQL and other NoSQL options: easy install, PHP support, replication and master-master support, good doc, auto sharding on the road map. They still use MySQL for billing.
Update 2: They are now called NoSQL databases. So keep up! Eric Lai wrote a good article in Computerworld No to SQL? Anti-database movement gains steam about the phenomena. There was even a NoSQL conference. It was unfortunately full by the time I wanted to sign up, but there are presentations by all the major players. Nice Hacker News thread too.
Update: Some Notes on Distributed Key Stores by Leonard Lin. What's the best way to handle a fast growing system with 100M items that requires low latency and lots of inserts? Leanord takes a trip through several competing systems. The winner was: Tokyo Cabinet.
Richard Jones has put together a very nice list of various key-value stores around the internets. The list includes: Project Voldemort, Ringo, Scalaris, Kai, Dynomite, MemcacheDB, ThruDB, CouchDB, Cassandra, HBase, and Hypertable. Richard also includes some commentary and their basic components (language, fault tolerance, persistence, client protocol, data model, docs, community).
There's an excellent discussion in the comments of Paxos vs Vector Clock techniques for synchronizing writes in the face of network failures.
Monday, August 3, 2009 at 11:18AM This tutorial will show you how to use Amazon EC2 and Cloudera's Distribution for Hadoop to run batch jobs for a data intensive web application.
During the tutorial, we will perform the following data processing steps.... read more on Cloudera website
Saturday, August 1, 2009 at 11:43PM These are from Laura Thomson of OmniTi:
