Posts Tagged distribution
CAP Theorem
Posted by Jonathan Gray in Distributed Systems, Web Scale on August 24th, 2009
If you’re talking or thinking about distributed data systems these days, you are almost certain to come across some discussion of the CAP theorem. This is one of those beautifully simplistic ideas that helps explain something extraordinarily complex.
So, what does it mean?
CAP stands for Consistency, Availability, and Partition tolerance. The theorem simply states that any shared-data system can only achieve two of these three.
Consistency
Consistency describes how and whether a system is left in a consistent state after an operation. In a distributed data system, this usually means that once a writer has written, all readers will see that write.
A distributed data system is either strongly consistent or has some form of weak consistency. The most well known example of strong consistency in databases is ACID (Atomicity Consistency Isolation Durability), used in most relational databases. On the other end of the spectrum is BASE (Basically Available Soft-state Eventual consistency).
Most often, weak consistency comes in the form of eventual consistency which means the database eventually reaches a consistent state. Weak consistency systems are usually ones where data is replicated; the latest version of something is sitting on some node in the cluster, but older versions are still out there on other nodes, but eventually all nodes will see the latest version.
If you are interested in learning more, Werner Vogels, CTO of Amazon, has two good blog posts on eventual consistency here and here.
Availability
High-Availability refers to the design and implementation of a system such that it is ensured to remain operational over some period of time.
In this context, it generally means a system that is tolerant of node failures and can also remain available during software and hardware upgrades. This is perhaps the simplest to understand and most commonly desired property, yet can be quite difficult to achieve to any level of certainty.
Partition Tolerance
Partition tolerance refers to the ability for a system to continue to operate in the presence of a network partitions. For example, if I have a database running on 80 nodes across 2 racks and the interconnect between the racks is lost, my database is now partitioned. If the system is tolerant of it, then the database will still be able to perform read and write operations while partitioned. If not, often times the cluster is completely unusable or is read-only.
Who came up with it?
In July 2000, Dr. Eric Brewer of Berkeley gave a talk, Toward Robust Distributed Systems.
In it, he talks about the many trade-offs between ACID and BASE systems. He explains that it should be thought of not as one or the other, but rather as a continuous spectrum. As a useful principle, he then introduced the CAP Theorem.
Why now?
Distributed data systems are increasingly becoming a hot area of research and development. Before the Internet and the web, there were not many companies dealing with terabyte or petabyte datasets. With the explosion of content and information from websites, blogs, and social networks, more and more businesses are now trying to store, analyze, and serve massive amounts of data. And they need to be able to perform massive batch operations on it while also serving it up to clients in near real-time.
These companies each have their own requirements: performance, reliability, durability; ACID, BASE, or somewhere in between.
Real World Example
In November 2006, Google released a paper, BigTable: A Distributed Storage System for Structured Data describing a distributed, column-oriented database that sat on top of the distributed Google File System.
In October 2007, Amazon released their own paper, Dynamo: Amazon’s highly available Key-value Store describing a distributed key-value database designed and in-use at Amazon.
What makes these two products a great example is that they are modern designs and implementations of distributed, shared data systems but with two different philosophies regarding CAP.
BigTable is a CA system; it is strongly consistent and highly available, but can be unavailable under network partitions. BigTable has no replication at the database level, rather replication is handled underneath by GFS.
Dynamo is an AP system; it is highly available, even under network partitions, but eventually consistent. Data is replicated within a single cluster, so even under partitions most data is available, however one node’s latest version might not match that of another, so every reader is only guaranteed to see every write eventually.
CAP at Streamy
First and foremost, Streamy is backed by HBase, an open-source implementation of Google’s BigTable. As such, the core of our database systems are strongly consistent and highly available. We are not overly concerned with network partitions as our clusters are all within a single data center and connected via local gigabit switches. Once we expand to additional data centers, we plan to employ inter-cluster replication, with each cluster located in a single DC. Remote replication will introduce some eventual consistency into the system, but each cluster will continue to be strongly consistent.
In addition to HBase, we also have a number of additional data systems that are responsible for indexing, sorting, merging, aggregating, and joining our data. Some of these systems could be considered distributed or replicated, meaning there are multiple instances on multiple nodes and they talk to each other. Since none are actually persistent (we do not rely on their state or their ability to save data under faults, that’s what HBase is for), we are most heavily focused on high availability and ensuring a read-your-writes consistency (a special form of eventual consistency).
Without going into more detail, it can be said that we employ both CA and AP systems here at Streamy. The focus is not on fundamentally which is “better” but rather what the requirements are for that particular application and what the expectation is for our users. The most important thing to avoid is a user who performs an action but then is unable to see that action immediately, which is why we often enforce a read-your-write consistency when we do need to relax our constraints.
Web Scale
Posted by Jonathan Gray in Web Scale on April 14th, 2009
Development at Streamy is always done with the mindset of “will it scale” in the back of our minds.
Generally speaking, scalability deals with the ability for a software system to handle increasing load when given additional resources. Increased load could mean more concurrency, a larger data set, or increased complexity. Additional resources refers to hardware and either scaling vertically (upgrading a single node) or scaling horizontally (adding additional nodes). While vertical scale is important in terms of squeezing all the performance you can out of each node, and rapidly dropping hardware prices means even cheap nodes are powerful, the key to achieving true scalability is the ability to horizontally scale, or distribute.
Distributed systems are becoming more and more mainstream as the web has flourished. Search engines index billions of web pages and social networks support millions of concurrent users. Content continues to evolve from being static, to dynamic, to the current emphasis on personalization and customization. The adoption of AJAX and COMET have further increased requirements for concurrency. And all of this must be highly-available and low-latency (sub-second).
This is what I call Web Scale.
So what exactly is it?
It’s a new set of technical requirements borne out of Web 2.0, and the move towards distributed systems and cloud computing as the solution. It encompasses all the different aspects of today’s web applications: the data, the storage, the caches, the web servers, the communications, the realtime queries, the batch queries, and everything in between. It marks a departure from the vertical scaling of relational databases and web servers as the solution to scale towards a new world of horizontal scalability: distributed hash tables, consistent hashing, column-orientation, horizontal partitioning, eventual consistency, elastic computing, and every other buzzword you can think of.
Who has solved it?
Google. They deserve a great deal of credit for being the first to really achieve web scale. Long before web sites really considered their architecture as part of their competitive advantage, Google embraced the notion and invented their own solutions. They subsequently published a number of papers describing their efforts: The Google File System, MapReduce, and BigTable.
Amazon. The enormous amount of work that was done in order to achieve scalability for the world’s premier e-commerce site is obvious; one need only look at their extensive and fast-growing elastic storage and computing services like S3 and EC2. An e-commerce site becoming a service provider? There’s only so much cost-savings a good architecture can give an e-commerce site, so it only makes sense that they try to profit from their proprietary systems. The CTO of Amazon, Werner Vogels, has an excellent blog AllThingsDistributed. Read through some of his posts and you can quickly see that Amazon is a company that understands scaling, distribution, and the right way to go about design and engineering in today’s Web Scale world.
Who struggles with it?
Facebook. Today’s prime example for what happens when you don’t build for scale early on: the only short-term solution to rapid growth is to throw money at the problem. Utilizing both horizontal and vertical scale, Facebook has enormous clusters of MySQL and Memcached to deal with storing user data and serving user queries. As reported nearly a year ago, they already had close to 2,000 MySQL boxes and 1,000 Memcached boxes in addition to their 10,000 web servers.
They have been playing catch-up ever since, slowly developing (and open-sourcing in some cases) distributed systems to deal with their enormous scale. Though almost always plagued by the lack of community and direct support from Facebook engineers, they have some very interesting projects including Cassandra and Hive. More recently, they seem to finally have solved their photo storage cost issues with Haystack, saving them from needing to buy an additional $2M+ server every month just to keep up. The difference in architectures is well described in Niall Kennedy’s post.
Twitter. Considering how well known the fail whale is, it’s clear to most that Twitter has been plagued by slow responses and downtime, even to this day. Though extremely simple in its requirements, the personalized views, emphasis on search, and massive use of the API by developers creates huge amounts of load for the microblogging (or is it nanoblogging?) service.
FriendFeed. A company led by ex-Googlers, and to my knowledge without major technical issues, seems to be going down a path of scaling that seems clunky and backwards. Generally speaking, scaling a database means letting go of some of the traditional restrictions, first things like normalization and secondary indexes, and then more significantly by relaxing ACID-compliance or adding eventual consistency. As outlined in a blog post by one of their founders, Bret Taylor, their attempt at a schema-less storage system atop MySQL seems to be a good idea and good effort gone wrong.
When what you’re after is schema-less storage, and the need for partitioning/distribution, why would you base it on a system completely tied to schemas and full-blown transactions? You’re bringing with you all the things you don’t want, at the expense of performance and flexibility, because they “trust” MySQL and are already familiar with it. Good reasons, no doubt, but the whole thing appears misguided. In any case, I bet that it works and performance is acceptable. But it’s not always about finding a solution that works. Flexibility, simplicity, and of course, additional scalability, are also important and something so confusing to do something so simple just ask for you to not want to touch it once it works 
Note: This is not to say that these companies are “doing it wrong”. I point out these examples because they are cases of costs gone out of control, continued performance and uptime issues, approaches I personally would not recommend, etc. MySQL + Memcached is certainly part of the Web Scale tool box and in a great deal of use cases is satisfactory. For more information on relational databases and how they compare to something like HBase, check out my presentation on Hadoop and HBase vs RDBMS.
So, how does Streamy solve it?
Stay tuned! This will be the topic of an upcoming series of posts over the next few weeks.
