DataSift Architecture: Realtime Datamining at 120,000 Tweets Per Second
Tuesday, November 29, 2011 at 9:12AM
Todd Hoff in BigData, Example, Twitter

I remember the excitement of when Twitter first opened up their firehose. As an early adopter of the Twitter API I could easily imagine some of the cool things you could do with all that data. I also remember the disappointment of learning that in the land of BigData, data has a price, and that price would be too high for little fish like me. It was like learning for the first time there would be no BigData Santa Clause.

For a while though I had the pleasure of pondering just how I would handle all that data. It's a fascinating problem. You have to be able to reliably consume it, normalize it, merge it with other data, apply functions on it, store it, query it, distribute it, and oh yah, monetize it. Most of that in realish-time. And if you are trying to create a platform for allowing the entire Internet do to the same thing to the firehose, the challenge is exponentially harder.

DataSift is in the exciting position of creating just such a firehose eating, data chomping machine. You see, DataSift has bought multi-year re-syndication rights from Twitter, which grants them access to the full Twitter firehose with the ability resell subsets of it to other parties, which could be anyone, but the primary target is of course businesses. Gnip is the only other company to have these rights.

DataSift was created out of Nick Halstead's, Founder and CTO of DataSift, experience with TweetMeme, a popular real-time Twitter news aggregator, which at one time handled 1.1 billion page views per day. TweetMeme is famous for inventing the social signaling mechanism, better known as the retweet, with their retweet button, an idea that came out of an even earlier startup called fav.or.it (favorite). Imagine if you will a time before like buttons were plastered all over the virtual place.

So processing the TweetMeme at scale is nothing new for the folks at DataSift, what has been the challenge is turning that experience into an Internet-scale platform so that everyone else can do the same thing. That has been a multi-year odyssey. 

DataSift is position themselves as a realtime datamining platform. The platform angle here is really the key take home message. They are pursuing a true platform strategy for processing real-time streams. TweetMeme while successful, could not be a billion dollar company, but a BigData platform could grow that large, so that’s the direction they are headed. A money quote by Nick highlights the logic in neon: "There's no money in buttons, there's money in data."

Part of the strategy behind a platform play is to become the incumbent player by building a giant technological moat around your core value proposition. When others come a knockin they can't cross over your moat because of your towering technological barrier to entry. That's what DataSift is trying to do. The drawbridge on the moat is favored access to Twitter's firehose, but the real power is in the Google quality real-time data processing platform infrastructure that they are trying to create. 

DataSift's real innovation is in creating an Internet scale filtering system that can quickly evaluate very large filters (think Lady Gaga follower size) combined with the virtuous economics of virtualization, where the more customers you have the more money you make because they are sharing resources.

How are they making all this magic happen? Let's see...

Site: http://DataSift.com

Information Sources

Stats

Platform 

Languages Used

Data Stores

Message Queues

  • 0mq (custom build from latest alpha branch, with some stability fixes, to use publisher-side filtering), used in different configurations:
    •  PUB-SUB for replication / message broadcasting;
    •  PUSH-PULL for round-robin workload distribution;
    •  REQ-REP for health checks of different components.
  • Kafka (LinkedIN's persistent and distributed message queue) for high-performance persistent queues. 
  • In both cases they're working with the developers and contributing bug reports / traces / fixes / client libraries.

CI / Deployments

Monitoring

Architecture in a Picture

DataSift has created an awesome picture of their overall architecture. Here's a small version, for the full sized version please go here.

Basic Ideas 

Key Ideas

Use Cases

Given how silly most tweets seem individually, it's almost hard to imagine they have value collectively, so it's sobering to take a look what people may want to use these systems for:

The usual mix of the venal plus profound. How this power is used is up to you grasshopper.

Real-time Only has Far Reaching Consequences

The nature of DataSift as a real-time filtering system has far reaching consequences. Keep in mind that it has no memory. It has no history. You are not seeing an ordered stream that you can iterate over in any direction. You are getting data live from the firehose. The tweets get directed to filters, pass through the filters, and then they are gone, like the night.

This is a sampling model. You can’t think you are consuming the entire stream. Any application that expects to see every tweet from an account, in order, will not work. The type of applications this is targeted at are applications that can be accurate sampling highly targeted data.

For example, if you wanted to build a real-time control system with DataSift using Twitter as a message bus, it would not work. You could not guarantee that you would see every command over an account or that those commands would be ordered properly.

The type of problem you can solve is to create a filter that fires when there's 100 mentions of the word "earthquake" in 10 seconds. Then you could use the geolocation information to figure out where an earthquake has occurred. There's no tweet order required and it doesn't matter if you see every tweet. It's a different mindset.

Filter Only has Far Reaching Consequences

A surprise for me was that DataSift is a real-time filtering engine only (hence the word "sift" in their name). Higher level services are to be provided by partners.

I was expecting DataSift to be more of a stateful segmentation platform, enabling the answering of questions like "What is the count of males, 18-24, that live in USA?" DataSift doesn't count, so it can't have the sliding window counting features of a segmentation platform. 

That's a consequence of it being stateless. And technically speaking DataSift can't identify an age segment either, mostly because Twitter has a pretty anemic profile feature. DataSift is storing the firehose in HBase/Hadoop for offline analytics, but that is not real-time.

So you have to wrap your head around the filter model. No application logic can be executed on DataSifts's platform. There are no stored procedures. Tweets can be augmented in-line via value added services, like sentiment analysis, but that's a highly structured and constrained process, that's not your application. And if you are expecting a sort of pipe model, nope, that's not it either. Streams can't be piped and weaved through a series of transducers.

What DataSift is, it is for a reason. DataSift is a highly sophisticated and scalable filter system. It filters out tweets from the firehose to create a highly targeted stream of tweets for you to process in a separate application. Their job is to get the data down to the cross section of tweets that are needed to perform analysis. Data comes in and is matched against rules, if it doesn't match it's tossed, if it does match, it's put in your bucket. Filters are like a gauntlet, only the most deserving pass.

The drivers for a filter only model are: firehose scale and low latency. The firehose produces events at a very high rate. How would you make an Internet scale platform that folds in the application logic for potentially millions of customers? How do you do that and build an end-to-end system that maintains a low latency guarantee? You can't.

What can you do? Evaluate large filters. Fast.  More on this in the next section.

Filtering Engine

Augmentation Pipeline for Each Tweet

Tweets are augmented with 3rd party datasets. Making these augmentations low latency was a major pain point.

No Cloud for Them

Platform Means Open

Billing

The Output Side

Testing

Pooling the Stream into a Lake

DataSift also supports non Real-time processing. There are two major collections of tweets stored in HBase:

How Do You Make Money?

As a developer I'm always interested in how platforms and services can be used to make money for developers, not just the creators. My angle is if you build a service on top of a service that is making money by charging you enough to make money, can you build a profitable service, or will your costs be too high, or will you really have to target high value high margin products to justify the cost? In other words, could TweetMeme be profitably built on DataSift, for example? The build or buy question is  always a tough one. 

Lessons Learned

From my interview, reading, and viewing a dozen or so other interviews,  I've been very impressed with the quality of thought and effort that has gone into DataSift. I don’t know if it works of course, or if it will succeed, but if it fails my guess is it won't because of a lack of professionalism. Mark Suster, entrepreneur turned VC, explains his investment in DataSift as doubling down on the Twitter ecosystem. I think that's the wrong way to look at it. DataSift can quickly and easily work with any stream. Maybe a better way to look at is doubling down on DataSift.

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