Building a Search Engine for the Cuban Web
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1 Building a Search Engine for the Cuban Web Jorge Luis Betancourt Search/Crawl Engineer NOVEMBER 16-18, 2016 SEVILLE, SPAIN
2 Who am I 01 Jorge Luis Betancourt González Search/Crawl Engineer Apache Nutch Committer & PMC Apache Solr/ES enthusiast 2
3 Agenda Introduction & motivation Technologies used Customizations Conclusions and future work 3
4 Introduction / Motivation Cuba Internet Intranet Global search engines can t access documents hosted the Cuban Intranet 4
5 Writing your own web search engine from scratch? or 5
6 Common search engine features 1 Web search: HTML & documents (PDF, DOC) highlighting filters (facets) suggestions autocorrection 2 Image search (size, format, color, objects) thumbnails filters (facets) show metadata match text with images 3 News search (alerting, notifications) near real time , push, SMS 6
7 How to fulfill these requirements? store query At the core a search engine: stores some information a retrieve this information when a question is received 7
8 Open Source to the rescue crawler 1 Index Server 2 web interface 3 8
9 Apache Nutch Nutch is a well matured, production ready Web crawler. Enables fine grained configuration, relying on Apache Hadoop data structures, which are great for batch processing. 9
10 Apache Nutch Highly scalable Highly extensible Pluggable parsing protocols, storage, indexing, scoring, Active community Apache License 10
11 Apache Solr TOTAL DOWNLOADS 8M+ MONTHLY DOWNLOADS 250,000+ Apache License Great community Highly modular Stability / Scalability Based on Lucene Battle tested 11
12 Back to the list of features 1 Web search: HTML & documents (PDF, DOC) highlighting filters (facets) suggestions autocorrection 2 Image search (size, format, color, objects) thumbnails show metadata filters (facets) match text with images 3 News search (alerting, notifications) near real time , push, SMS 12
13 Image search and thumbnails Custom parser & indexer to store the image thumbnail h1 Custom parser & indexer & scoring identify and store the text img p related with an image 13
14 How does it work? 2 img 1 h1 3 img img p 14
15 News search (NRT & alerting) Nutch is really not suited for this task: Batch nature of the Hadoop Jobs doesn t fit well in this scenario 15
16 Our topology index RSS fetch parse flaxsearch/luwak parse the RSS feed and outputs the news links to be processed according to SC protocol. monit or 16
17 Querying the data 1 Web search: HTML & documents (PDF, DOC) highlighting filters (facets) suggestions autocorrection 2 Image search (size, format, color, objects) thumbnails show metadata filters (facets) match text with images 3 News search (alerting, notifications) near real time , push, SMS 17
18 Querying the data 1 Web search: HTML & documents (PDF, DOC) highlighting filters (facets) suggestions autocorrection 2 Image search (size, format, color, objects) thumbnails show metadata filters (facets) match text with images 3 News search (alerting, notifications) near real time , push, SMS 18
19 Apache Solr Solr has full support for highlighting (3 impl) powerful faceting capabilities (even more on recent releases) autocorrection support based on the index content awesome scalability (SolrCloud, classic master-slave replication) 19
20 The features, once again 1 Web search: HTML & documents (PDF, DOC) highlighting filters (facets) suggestions autocorrection 2 Image search (size, format, color, objects) thumbnails show metadata filters (facets) match text with images 3 News search (alerting, notifications) near real time , push, SMS 20
21 The features, once again 1 Web search: HTML & documents (PDF, DOC) highlighting filters (facets) suggestions autocorrection 2 Image search (size, format, color, objects) thumbnails show metadata filters (facets) match text with images 3 News search (alerting, notifications) near real time , push, SMS 21
22 Other features - monitoring We needed a way of monitoring our infrastructure without a great Internet connection you can t send GB of logs to a cloud environment, so (and metrics) (and logs) time series store analytical tool (and facets) 22
23 Other features - monitoring (and logs) parsing & aggregation (and metrics) (and logs) time series store analytical tool (and facets) 23
24 Banana (Kibana port) for visualizations 24
25 Infrastructure HTTP HTTP HTTP WEB 2 Solr Replicador HTTP Solr Master JAVABIN Crawlers Nutch 1 25
26 Some usage stats less than visits around 600 unique visitors 26
27 Future work Apply deep learning techniques to process the raw images and mix with current approach Increase the number of signals that we get from our crawlers (correlate even more crawl related events) 27
28 Thanks M!
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