Monday, 11 February 2019

Meet StormCrawler users: Q&A with Pixray (Germany)

We are opening a series of Q&A blogs with Maik Piel telling us about the use of StormCrawler at Pixray.  

Q: What do you guys do at Pixray? Why do you need web crawling?

We are experts in image tracking on the web. We work for image rights holders to protect their pictures on the web as well as brands and manufacturers to monitor sales channels. Our customers range from news agencies and picture agencies, individual photographers, e-commerce companies to luxury brands. Web crawling is one of the core buildings blocks of our platform - next to a massive picture matching platform, various APIs and our customer portals.

Q: What sort of crawls do you do? How big are they?

We do three kinds of scans: broad scans across complete regions of the web (like the EU or North America), deep scans on single domains and also near-realtime discovery scans on thousands of selected domains. For all of these different scans, we employ customized versions of StormCrawler to match the very distinct requirements in crawling patterns. Obviously, the biggest crawls are the broad regional scans, including more than 10 billion URLs and tens of millions of different domains.

Q: What software stack do you use? e.g. SC + ES + Grafana? Hardware used?

Adapted and extended versions of StormCrawler as well as Elasticsearch and Kibana. We couple our crawling infrastructure with the rest of our platform through RabbitMQ. Our crawler is built on Ubuntu servers, with 32 GB of RAM and Intel Core I7 and 4 TB of disk space. Each runs Apache Storm and Elasticsearch. In the future, we will split the storage (Elasticsearch) and the computation (Storm) layers to separate hardware. We are also looking at options to employ container and service orchestration frameworks to scale our crawler infrastructure dynamically. 

Q: Why did you choose StormCrawler?

We initially built our crawler on Apache Nutch. Needless to say that Nutch is a great and robust platform.  But once you grow beyond a certain point you start to see limitations. The biggest limitation is the low responsiveness to changes and the uneven system utilization due to the long generate/crawl/update cycles. It sometimes took us 24 hours or more till we could see the effects of a change we made to the software. Furthermore, we found that it is a bit troublesome to get valid statistics data from Nutch in real time. StormCrawler solves all that for us. Every config or code change that we commit shows its effect immediately and you get statistics very, very easily. There is no long-cycle batching anymore in StormCrawler which gives us a very even and continuous crawling, reducing our need for massive queuing of results to ensure an even utilization of down-stream infrastructure.  Kibana gives us great real-time insights into the crawl database. With Nutch, we had to run analysis jobs of around 4 hours, even if we just needed the status of a single url. 

Q: What do you like the most / least in StormCrawler?

Besides the points mentioned above, we have to praise StormCrawlers extensibility. In our different setups we have both made changes to existing code in the StormCrawler project but also wrote large amounts of own code. The structure Apache Storm imposes is great. Components are very cleanly decoupled and it is easy to introduce custom functionality by just writing new Spouts and Bolts and linking them into the topology. For our use case we, of course, had to deal with pictures - which StormCrawler itself does not do. We just created our own Bolts for that. For our near-realtime discovery crawler, we needed an engine that calculates the revisit date for a URL based on various factors instead of a static value, again we could just create a specific spout for that. 

Q: Anything in particular you'd like to have in a future release?

It would be great to have a built-in way to prioritize different TLDs within the StormCrawler spouts. We have built a custom solution for that but a solid built-in way would be good to have.