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.
In this blog post BJ Clark overviews the available key-value stores that aren't SQL based.
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 ?
one database: easier to make changes to database layout
multiple databases:more scalable, easier to backup/restore
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!
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:
From a system perspective PNUTS offers a lot of the good things: hosted, reliability, lowish latency, automation, scalability, supports many application models, and there's a lot of room to improvement that all applications will be able to take advantage of when available.
From a programmer perspective the are also a lot of good things: it's hosted so fewer worries, notifications, flexible schemas, ordered records, secondary indexes, lowish latency, strong consistency on a single record, scalability, high write rates, reliability, and range queries over a small set of records.
Unfortunately Goldilocks still needs to keep searching for just right, though she may be getting closer. From a system perspective Yahoo!'s ideas are good, but they don't help you as the system isn't available for you to use. From a programmer perspective the programmer's job is still way too hard. To be just right programmer's need low latency aggregate operators, complex transactions, scalable counters, automatic relationship management, and all the other features that will help them just buy instant porridge and be done with it.
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?
No Expire Solution
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.
Stale Date Solution
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:
Alexey describes a different two key approach:
I dislike embedding meta data with data so I like Alexey's approach a bit better, even though it doubles the key space.
None of these options prevent the problem for ever happening, but they do greatly reduce the failure window for relatively little cost.
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:
- Document processing pipelines – convert hundreds of thousands of documents from Microsoft Word to PDF, OCR millions of pages/images into raw searchable text
- Image processing pipelines – create thumbnails or low resolution variants of an image, resize millions of images
- Video transcoding pipelines – transcode AVI to MPEG movies
- Indexing – create an index of web crawl data
- Data mining – perform search over millions of records
- Back-office applications (in financial, insurance or retail sectors)
- Log analysis – analyze and generate daily/weekly reports
- Nightly builds – perform nightly automated builds of source code repository every night in parallel
- Automated Unit Testing and Deployment Testing – Test and deploy and perform automated unit testing (functional, load, quality) on different deployment configurations every night
- Websites that “sleep” at night and auto-scale during the day
- Instant Websites – websites for conferences or events (Super Bowl, sports tournaments)
- Promotion websites
- “Seasonal Websites” - websites that only run during the tax season or the holiday season (“Black Friday” or Christmas)
A good list, but after having worked on a seasonal website for taxes AWS is a horrible match. AWS only works on the instance level, so you need a whole instance turned on all the time even when there's no demand. This is a complete waste of money. An AWS model truly based on use combined with an SLA driven dashboard would be very convenient. But on to cases where AWS is a good fit.
SmugMug's Cloud ArchitectureAWS pioneer Don MacAskill of SmugMug details how they process high-resolution photos and high-definition video use a cloud hosted queuing architecture in SkyNet Lives! (aka EC2 @ SmugMug).
SkyNet, as you might expect, operates completely without human minders and automatically scales up and down in relation to the work load. Their system has several components:
mechanisms for automatically configuring and running VMs.
Don shares a lot of practical detailia on how to efficiently use AWS, how their queue service works, and how their controller manages to balances minimizing cost while still being responsive to users. Achieving fairness and balance in a queue system can be difficult, but SmugMug appears to have done a good job of that.
What rocks about queuing architectures is that they are just so damn robust. Work is safe in the queues. A random reboot won't cause a loss. If one component is producing events too fast the queue will buffer up events until they can be processed. New components can be cleanly added and removed from the system at any time. Timing isn't critical. Work is processed when someone gets around to it. Timeouts and retries are unnecessary. Programs are simple loops that block on the queue, do something, persist results, and feed back more parallelizable work requests back into the queue. Very hard to screw up. Compare and contrast to complex multi-threaded system with shared-state.
Building GrepTheWeb in the CloudAmazon has published a great couple of articles on building a canonical Cloud Architecture: Building GrepTheWeb in the Cloud, Part 1: Cloud Architectures and Building GrepTheWeb in the Cloud, Part 2: Best Practices.
These are really tight and well written articles so I'll just hit certain high points. The example used is an application called GrepTheWeb. GrepTheWeb searches using a regular expression across millions of web documents. So it's a grep for the web, ah got it now. The idea is to take an unpredictable but possibly large number of search requests, apply the search expression to hundreds of terabytes of documents, and return the results in a reasonable period of time.
How exactly would you do such a thing? Here's how you do it in the cloud:
Clearly these are all (except for Hadoop) built on Amazon services, but the general ideas apply anywhere. For storing large amounts of data and accessing it efficiently in parallel you need a distributed file system like S3. To coordinate and dispatch work you need a queuing service like SQS. For keeping intermediate state you need a scalable database store like SimpleDB, though you could also imagine using S3. For dynamically scaling processing nodes something like EC2 is necessary. And for actually carrying out the document search a framework like Hadoop provides a lot of features, though you can imagine using other compute grid products.
Here's their fabulous picture of what the system looks like:
All the parts and linkages are described in the paper. What's important to note is that even though there are a lot of independently moving parts all the boundaries are clear and well described. In your typical program few will have any idea how it works. Using Cloud Architecture principles it's possible to create a system which both scales and easy to understand and explain.
The paper makes several key architectural recommendations:
All good stuff which is why I like this paper so much. There's a big conceptual shift here, especially of you are used to relatively simple client-server and N-tier systems. It's like simulating in your mind how to keep an army of ants all working independently while still communicating, coordinating, and making progress on a goal. We implemented similar architecture in datacenters long before the cloud, it was just a lot harder as everything was roll your own. The cloud makes all the necessary components standard, featureful, and relatively inexpensive. This opens any application to completley different ways of structuring their backends than they did in the past.
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?
- Unorthodox approach to database design Part1:History
- Unorthodox approach to database design Part 2:Friendster
What is sharding?
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:
How is sharding different than traditional architectures?Sharding is different than traditional database architecture in several important ways:
This doesn't mean you don't also segregate data by type. You can keep a user's profile data separate from their comments, blogs, email, media, etc, but the user profile data would be stored and retrieved as a whole. This is a very fast approach. You just get a blob and store a blob. No joins are needed and it can be written with one disk write.
Smaller sets of data are also easier to backup, restore, and manage.
Obviously the master becomes the write bottleneck and a single point of failure. And as load increases the cost of replication increases. Replication costs in CPU, network bandwidth, and disk IO. The slaves fall behind and have stale data. The folks at YouTube had a big problem with replication overhead as they scaled.
Sharding cleanly and elegantly solves the problems with replication.
Some Problems With ShardingSharding isn't perfect. It does have a few problems.
On some platforms I've worked on this is a killer problem. You had to build out the data center correctly from the start because moving data from shard to shard required a lot of downtime.
Rebalancing has to be built in from the start. Google's shards automatically rebalance. For this to work data references must go through some sort of naming service so they can be relocated. This is what Flickr does. And your references must be invalidateable so the underlying data can be moved while you are using it.
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.
- 2 x Lenovo ThinkServer RS110 1U
- 4 cores, 2.83 Ghz, 12 MB L2 cache
- 500 GB datacenter hard drives, mirrored
- 8 GB RAM
- 500 GB RAID 1 mirror array
- 1 x Lenovo ThinkServer RD120 2U
- 8 cores, 2.5 Ghz, 24 MB L2 cache
- 48 GB RAM
Lessons LearnedThis is a mix of lessons taken from Jeff and Joel and comments from their posts.
It's true there's not much about their architecture here. We know about their machines, their tool chain, and that they use a two-tier architecture where they access the database directly from the web server code. We don't know how they implement tags, etc. If interested you'll be able to glean some of this information from an explanation of their schema.
DiscussionAs an architecture profile candidate Stack Overflow has earned two important HighScalability badges: the Microsoft Stack Badge and and the Scale Up Badge. Both controversial and interesting topics of discussion.
Microsoft Stack BadgeThe Microsoft Stack Badge was earned because Stack Overflow uses the entire Microsoft Stack: OS, database, C#, Visual Studio, and ASP .NET. People are always interested in how MS compares to LAMP, but I don't have many case studies to show them.
Markus Frind of Plenty of Fish fame is often used as a Microsoft stack poster child, but since he explicitly uses as little of the stack as possible he's not really a good example. Stack Overflow on the other hand is brash in proclaiming their love for MS, even when that love is occasionally spurned.
It's hard to separate out the Microsoft stack and the scale up approach because for licensing reasons they tend to go together. If you find yourself in the position of transitioning from scale up to scale out by adding dozens of cores, MS licensing will bite you.
Licensing aside I personally find C#, Visual Studio, and .Net a very productive environment. C#/.Net is at least as good as Java/JVM. ASP .NET has always been a confusing mess to me. The knock against SQL Server is you have to pay for it and if that doesn't bother you then it's a solid choice. The Windows OS may not be as solid as other alternatives but it works well enough.
So for a scale up solution a Microsoft stack works, especially if you are already Windows centric.
Scale Up BadgeThis won't be a reenactment of the scale out vs scale up vs rent vs buy wars. For a thorough discussion of these issues please take a look at Scaling Up vs. Scaling Out and Server Hosting — Rent vs. Buy?. If you aren't confused and if your head doesn't hurt after reading all that then you haven't properly understood the material :-)
The Scale Up Badge was awarded because Stack Overflow uses a scale up strategy to meet their scaling requirements. When they reach a limit they scale vertically by buying a bigger machine and adding more memory.
Stack Overflow is in the sweet spot for scale up. It's not too large, but with an Alexa ranking of 1,666 and 16 million page views a month it's still a substantial site. Not Google scale, and probably will never have to be, but those are numbers many sites would be thrilled to have. Yet they aren't uploading large amounts of media. They aren't dealing with billions of tweets across complex social networks with millions of users. Their number of users is self limiting. And there are still directions they can take if they need to scale (caching, more web servers, faster disks, more denormalization, more memory, some partitioning, etc). All-in-all it's a well done and very useful two-tier CRUD application.
NoSQL is HardSo should Stack Overflow have scaled out instead of up, just in case?
What some don't realize is NoSQL is hard. Relational databases have many many faults, but they make a lot of common tasks simple while hiding both the cost and complexity. If you want to know how many black Prius cars are in inventory, for example, then that's pretty easy to do.
Not so with most NoSQL databases (I'll speak generally here, some NoSQL databases have more features than others). You would have program a counter of black Prius cars yourself, up front, in code. There are no aggregate operators. You must maintain secondary indexes. There's no searching. There are no distributed queries across partitions. There's no Group By or Order By. There are no cursors for easy paging through result sets. Returning even 100 large records at time may timeout. There may be quotas that are very restrictive because they must limit the amount of IO for any one operation. Query languages may lack expressive power.
The biggest problem of all is that transactions can not span arbitrary boundaries. There are no ACID guarantees beyond a single record or small entity group. Once you wrap your head around what this means for the programmer it's not a pleasant prospect at all. References must be manually maintained. Relationships must be manually maintained. There are no cascading deletes that act correctly during a failure. Every copy of denormalized data must be manually tracked and updated taking into account the possibility of partial failures and externally visible inconsistency.
All this functionality must be written manually by you in your code. While flexibility to write your own code is great in an OLAP/map-reduce situation, declarative approaches still cover a lot of ground and make for much less brittle code.
What you gain is the ability to write huge quantities of data. What you lose is complacency. The programmer must be very aware at all times that they are dealing with a system where it costs a lot to perform distribute operations and failure can occur at anytime.
All this may be the price of building a truly scalable and distributed system, but is this really the price you want to pay?
The Multitenancy ProblemWith StackExchange Stack Overflow has gone into the multi-tenancy business. They are offering StackExchange either self-hosted or as a hosted white label application.
It will be interesting to see if their architecture can scale to handle a large number of sites. Salesorce is the king of multitenancy and although it's true they use Oracle as their database, they basically use very little of Oracle and have written their own table structure, indexing and query processor on top of Oracle. All in order to support multitenancy.
Salesforce went extreme because supporting a lot of different customers is way more difficult than it seems, especially once you allow customization and support versioning.
Clearly all customers can't run in one server for security, customization, and scaling reasons.
You may think just create a database for each customer, share a server for a certain number of customers, and then add more servers as needed. As long as a customer doesn't need more than one server you are golden.
This doesn't seem to work well in practice. Oddly database managers aren't optimized for adding or updating databases. Creating databases is a heavyweight operation and can degrade performance for existing customers as system locks are taken. Upgrade issues are also problematic. Adding columns locks tables which causes problems in high traffic situations. Adding new indexes can also take a very long time and degrade performance. Plus each customer will likely have specializations that makes upgrading even more complicated.
To get around these problems Salesforce's Craig Weissman, Chief Architect, created an innovative approach where tables are not created for each customer. All data from all customers is mapped into the same data table, including indexes. The schema for that table looks something like orgid, oid, value0, value1...value500. "orgid" is the organization ID and is how data is never mixed up. It's a very wide and sparse table, which Oracle seems to handle well. Hundreds and hundreds of "tables" and custom fields are mapped into the data table.
With this approach Salesforce has no option other than to build their own infrastructure to interpret what's in that table. Oracle is left to handle transactions, concurrency, and deadlock detection. The advatange is because there's an interpreted layer handling versions and upgrades is relatively simple because the handling logic can be baked in. Strange but true.
Related ArticlesThis list includes a number of posts by Jeff as he chronicles their journey with Stack Overflow. Jeff is wonderful about being open about what they are doing and why. The comment threads are often tremendous. There's a lot to learn.
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.
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
These are from Laura Thomson of OmniTi:
- Profile early, profile often. Pick a profiling tool and learn it in and out.
- Dev-ops cooperation is essential. The most critical difference in organizations that handles crises well.
- Test on production data. Code behavior (especially performance) is often data driven.
- Track and trend. Understanding your historical performance characteristics is essential for spotting emerging problems.
- Assumptions will burn you. Systems are complex and often break in unexpected ways.
- Decouple. Isolate performance failures.
- Cache. Caching is the core of most optimizations.
- Federate. Data federation is taking a single data set and spreading it across multiple database/application servers.
- Replicate. Replication is making synchronized copies of data available in more than one place.
- Avoid straining hard-to-scale resources. Some resources are inherently hard to scale: Uncacheable’ data, Data with a very high read+write rate, Non-federatable data, Data in a black-box
- Use a compiler cache. A compiler cache sits inside the engine and caches the parsed optrees.
- Be mindful of using external data sources. External data (RDBMS, App Server, 3rd Party data feeds) are the number one cause of application bottlenecks.
- Avoid recursive or heavy looping code. Deeply recursive code is expensive in PHP.
- Don’t Outsmart Yourself . Don’t try to work around perceived inefficiencies in PHP (at least not in userspace code!)
- Build with caching in mind. Caching is the most important tool in your tool box.