DataEngConf: Building Satori, a Hadoop toll for Data Extraction at LinkedIn
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By Nikolai Avteniev (Sr Software Engineer, LinkedIn) LinkedIn is the professional profile of record for our 370M+ members globally, but many people don't realize the full potential of their LinkedIn profile – especially on mobile. Adding blogs, photos and other rich content to your profile on a small screen device can get tedious. That's why LinkedIn created Satori, a Hadoop tool that crawls the web and extracts data to discover members' professional content online. Satori uses machine learning techniques and leverages other open source tools like Nutch and Gobblin in order to help match members with relevant content in order to maximize their professional profile. In this talk, Nikolai will share his experience in building the product and discuss the challenges and opportunities encountered along the way.
![• Avg. HTML Document is 6K
37% < 10K
• Samza can handle 1.2M
messages per node [2]
• There is a limit of how much
data is retained between 7
and 30 days.
• Most of the data is filtered out
• Need to bootstrap Samza
stores
12
Not a perfect fit
1. HTML Document Transfer size http://httparchive.org/interesting.php?a=All&l=Oct%2015%202015#bytesHtmlDoc
2. Feng, Tao “Benchmarking Apache Samza: 1.2 million messages per second on a single node”
https://engineering.linkedin.com/performance/benchmarking-apache-samza-12-million-messages-second-single-
node](https://image.slidesharecdn.com/dmvpqfcpt2qobadxhcbl-signature-d0adae91f6629531190eb37736690d3d0b8a61dbb686abf9a67bbbe68a8fd502-poli-151215053641/85/DataEngConf-Building-Satori-a-Hadoop-toll-for-Data-Extraction-at-LinkedIn-12-320.jpg)
![Induce wrappers based on data [4]
Build wrappers that are robust. [5]
Cluster similar pages by URL [6]
The web is huge and there are
interesting things in the long tale[7]
17
Industrial Web Data Extraction
4. Dalvi, Nilesh, Ravi Kumar, and Mohamed Soliman. "Automatic wrappers for large scale web extraction." Proceedings of the VLDB
Endowment 4.4 (2011): 219-230.
5. Dalvi, Nilesh, Philip Bohannon, and Fei Sha. "Robust web extraction: an approach based on a probabilistic tree-edit model." Proceedings of
the 2009 ACM SIGMOD International Conference on Management of data. ACM, 2009.
6. Blanco, Lorenzo, Nilesh Dalvi, and Ashwin Machanavajjhala. "Highly efficient algorithms for structural clustering of large websites."
Proceedings of the 20th international conference on World wide web. ACM, 2011.
7. Dalvi, Nilesh, Ashwin Machanavajjhala, and Bo Pang. "An analysis of structured data on the web." Proceedings of the VLDB Endowment
5.7 (2012): 680-691.](https://image.slidesharecdn.com/dmvpqfcpt2qobadxhcbl-signature-d0adae91f6629531190eb37736690d3d0b8a61dbb686abf9a67bbbe68a8fd502-poli-151215053641/85/DataEngConf-Building-Satori-a-Hadoop-toll-for-Data-Extraction-at-LinkedIn-17-320.jpg)
![Match[in]](https://image.slidesharecdn.com/dmvpqfcpt2qobadxhcbl-signature-d0adae91f6629531190eb37736690d3d0b8a61dbb686abf9a67bbbe68a8fd502-poli-151215053641/85/DataEngConf-Building-Satori-a-Hadoop-toll-for-Data-Extraction-at-LinkedIn-28-320.jpg)
![Narrow the candidates with
LSH[1]
Use the simple model to
generate the ground truth
Train using a simple algorithm
and a few hundred features
32
Keep It Simple
1. https://en.wikipedia.org/wiki/Locality-sensitive_hashing](https://image.slidesharecdn.com/dmvpqfcpt2qobadxhcbl-signature-d0adae91f6629531190eb37736690d3d0b8a61dbb686abf9a67bbbe68a8fd502-poli-151215053641/85/DataEngConf-Building-Satori-a-Hadoop-toll-for-Data-Extraction-at-LinkedIn-32-320.jpg)
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DataEngConf: Building Satori, a Hadoop toll for Data Extraction at LinkedIn
- 1. Building Satori: Web Data Extraction On Hadoop Nikolai Avteniev Sr. Staff Software Engineer LinkedIn
- 2. Building Opportunity from the Empire State Building 2 LinkedIn NYC
- 3. 3 The Team Nikita Lytkin Staff Software Engineer Pi-Chuan Chang Sr. Software Engineer David Astle Sr. Software Engineer Nikolai Avteniev Sr. Staff Software Engineer Eran Leshem Sr. Staff Software Engineer
- 5. Connecting talent with opportunity at massive scale
- 6. What we thought we needed 6 The BIG Idea Inspired by Hsieh, Jonathan M., Steven D. Gribble, and Henry M. Levy. "The Architecture and Implementation of an Extensible Web Crawler." NSDI. 2010.
- 7. Questions we wanted to answer 7 Focused our Vision Who would use this tool? Do we need to crawl the entire web? Do we need to process the pages near line? Where would we store this data? How would we correct mistakes in the flow?
- 9. Virtually All Member Value Relies On Identity Data Susan Kaplan Sr. Marketing Manager at Weblo SEARCH Research & Contact AD TARGETING Market Products & Services PMYK Build Your Network RECRUITER Recruit & Hire FEED Get Daily News NETWORK Keep in Touch RECOMMENDATIONS Get a Job/Gig WVMP Establish Yourself as Expert
- 10. Identity Use Case A smarter way to build your profile • Suggest 1-click profile updates to members • Using this, we can help members easily fill in profile gaps & get credit for certificates, patents, publications…
- 11. Kafka/Samza Team
- 12. • Avg. HTML Document is 6K 37% < 10K • Samza can handle 1.2M messages per node [2] • There is a limit of how much data is retained between 7 and 30 days. • Most of the data is filtered out • Need to bootstrap Samza stores 12 Not a perfect fit 1. HTML Document Transfer size http://httparchive.org/interesting.php?a=All&l=Oct%2015%202015#bytesHtmlDoc 2. Feng, Tao “Benchmarking Apache Samza: 1.2 million messages per second on a single node” https://engineering.linkedin.com/performance/benchmarking-apache-samza-12-million-messages-second-single- node
- 13. Help 400M members fully realize their professional identity on LinkedIn. Find sources of professional content on the public internet. Fetch the content, extract structured data and match it to member profiles 13 The Project: Satori
- 14. Web Data Extraction HOW TO:
- 15. • Enterprise VS Social Web use cases • Web Sources • Wrappers 15 Web Data Extraction System 3. Ferrara, Emilio, et al. "Web data extraction, applications and techniques: A survey." Knowledge-Based Systems 70 (2014): 301-323.
- 16. 16 What is a Wrapper?
- 17. Induce wrappers based on data [4] Build wrappers that are robust. [5] Cluster similar pages by URL [6] The web is huge and there are interesting things in the long tale[7] 17 Industrial Web Data Extraction 4. Dalvi, Nilesh, Ravi Kumar, and Mohamed Soliman. "Automatic wrappers for large scale web extraction." Proceedings of the VLDB Endowment 4.4 (2011): 219-230. 5. Dalvi, Nilesh, Philip Bohannon, and Fei Sha. "Robust web extraction: an approach based on a probabilistic tree-edit model." Proceedings of the 2009 ACM SIGMOD International Conference on Management of data. ACM, 2009. 6. Blanco, Lorenzo, Nilesh Dalvi, and Ashwin Machanavajjhala. "Highly efficient algorithms for structural clustering of large websites." Proceedings of the 20th international conference on World wide web. ACM, 2011. 7. Dalvi, Nilesh, Ashwin Machanavajjhala, and Bo Pang. "An analysis of structured data on the web." Proceedings of the VLDB Endowment 5.7 (2012): 680-691.
- 19. HERITRIX powers archive.org NUTCH powers common crawl BUbinNG part of LAW Scrapy used with in LinkedIn 19 The Contestants 8. Web crawling, C Olston, M Najork - Foundations and Trends in Information Retrieval, 2010 9. An Introduction to Heritrix: An Open Source Archival Quality Web Crawler, A Dan, K Michele – 2004 10.BUbiNG: massive crawling for the masses, P Boldi, A Marino, M Santini, S Vigna -, 2014 11.Nutch: A Flexible and Scalable Open-Source Web Search Engine. CommerceNet Labs, R Khare, D Cutting, K Sitaker, A Rifkin - 2004 - CN-TR-04-04, November
- 20. 20 And the winner is …
- 21. Satori
- 22. • Built on Nutch 1.9 • Runs on Hadoop 2.3 • Scheduled to run every 5 hours • Respects robots.txt • Default crawl delay of 5 seconds 22 Crawl Flow
- 23. • Output into target schema • Apply XPATH wrappers • Wrappers are hierarchical mapping of Schema field to XPath expression • Indexed by data domain and data source 23 Extract Flow
- 24. Crawl rate is bound by the number of sites and the site crawl delay
- 25. Common Crawl Great Source https://commoncrawl.org/ Gobblin Great Ingestion Framework https://github.com/linkedin/gobblinn 25 Bootstrap From Bulk Sources
- 26. XPath extractors can be challenging on sites with rich data
- 27. It is easy to exceed the Hadoop quota
- 28. Match[in]
- 29. Matching authors and publications to members to power profile edit experiences
- 30. 30 Overview
- 31. Match using global identifiers, email or full name. The data might not be clean after extraction Start with a small set of data and get it to the users quickly 31 Start Simple
- 32. Narrow the candidates with LSH[1] Use the simple model to generate the ground truth Train using a simple algorithm and a few hundred features 32 Keep It Simple 1. https://en.wikipedia.org/wiki/Locality-sensitive_hashing
- 33. 5.3 2.3 3.9 0.6 Publications Companies Extractor Objects Total Processed 33 Current Status 56 2 5.6 2.5 1.2 0.1 Publication Company Crawler Objects Unfetched Fetched Gone
- 34. Target a data source which has data that will be easy to fetch, extract and match.
- 35. Add tracking to the entire flow
- 36. Do it all offline if you can
- 37. Get the product to the customers early to validate the process and value proposition
- 38. Most important of all write it all down and share it with everyone
- 39. ©2014 LinkedIn Corporation. All Rights Reserved.