Apache Solr - An Experience Report
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The document discusses the use of Apache Solr, a powerful and customizable full-text search engine built on Apache Lucene, for intranet search solutions. It highlights various performance considerations, configuration challenges, and optimization techniques, as well as several lessons learned from real-world experience, particularly regarding disk space, memory usage, and indexing strategies. Additionally, it mentions the capacity for horizontal and vertical scaling through replication and sharding, emphasizing the importance of continuous testing and monitoring in maintaining effective search performance.
Apache Solr - An Experience Report
- 1. Apache Solr An experience report 2013-10-23 - Corsin Decurtins
- 3. Apache Solr Full-Text Search Engine Apache Lucene Project based on Apache Lucene Apache Solr: http://lucene.apache.org/solr/ Notes Fast Proven and Well-Known Technology Java based Open APIs Customizable Clustering Features
- 6. Plaza Search Full-Text Search Engine for the Intranet of Netcetera Integrates Various Data Sources Needs to be fast Ranking is crucial Simple Searching Relevant Filtering Options Desktop, Tables and Phones Notes
- 7. Warum Intranet-Suchmaschinen unbrauchbar sind …und was dagegen getan werden kann 2013-07-03 – Corsin Decurtins http://www.slideshare.net/netceteragroup/20130703-intranet-searchintranetkonferenz
- 9. Some Numbers Live since Data since 1996 05/2012 ~ 275 Users ~ 3'000'000 Documents ~ 500 – 2'000 Searches per day Index Size ~ 75 GByte ~ 40 Releases
- 10. Some Numbers Notes Very small load (only a few hundred requests per day) The indexer agents actually produce a lot more load than the actual end users Medium size index (at least I think) Not that many objects, but relatively big documents Load performance is not a big topic for us When we talk about performance, we actually usually mean response time
- 12. File System Plaza Search UI Wiki Indexer Plaza Search Indexer Rest API Apache Solr Indexer Email Archive Apache Tika Issue System Index CRM
- 13. Architecture Based on Apache Solr (and other components) Apache Solr takes care of the text-search aspect We certainly do not want to build this ourselves Apache Tika is used for analyzing (file) contents Also here, we certainly do not want to build this ourselves Notes
- 15. Magic
- 17. Apache Solr Notes Apache Solr is a very complex system with a lot of knobs and dials Most things just seem like magic at the beginning … or they just do not work Apache Solr is extremely powerful with a lot of features You have to know how to configure the features Most features need a bit more configuration than just a check box for activating it Configuration options seem very confusing at the beginning You do not need to understand everything from the start Defaults are relatively sensible and the example applications are good starting point
- 19. Development Process In our experience, Apache Solr works best with a very iterative process Definition of Done is very difficult to specify for search use cases Iterate through: - Researching - Thinking / Designing - Implementation / Configuration / Testing - Observing / Analyzing / Debugging Notes
- 20. Research
- 21. Observe Debug
- 22. Solr Admin Interface Notes Apache Solr has a pretty good admin interface Very helpful for analysis and (manual) monitoring If you are not familiar with the Solr Admin interface, you should be Other tools like profilers, memory analyzers, monitoring tools etc. are also useful
- 23. Our Requirements Correctness Results that match query Relevance Results that matter Speed "Instant" results Intelligence Do you know what I mean?
- 24. Intelligence Do you know what I mean? synonyms.txt stopwords.txt protwords.txt
- 25. Solr Configuration Files Notes Solr has pretty much out-of-the-box support for stop words, protected works and synonyms These features look very simple, but they are very powerful Unless you have a very general search use case, the defaults are not enough Definitely worth developing a configuration specific to your domain Iterate; consider these features for ranking optimizations as well
- 26. Relevance Results that matter score match boosting term frequency inverse document frequency field weights boosting function index time boosting elevation
- 27. Ranking in Solr (simplified) Notes Solr determines a score for the results of a query Score can be used for sorting the results Score is the product of different factors: A query-specific part, let's call it the match value that is computed using term frequency (tf) inverse document frequency (idf) There are also other parameters that can influence it (term weights, field weights, …) The match basically says how well a document matches the query
- 28. Ranking in Solr (simplified) Notes A generic part (not query specific), let's call this a boosting value Basically represents the general importance that you assign to a document Includes a ranking function, e.g. based on the age of the document Includes a boosting value, that is determined at index time (index-time boosting) We calculate the boost value based on different attributes of the document, such as type of resource (people are more important than files) status of the project that a document is associated with (closed projects are less important than running projects) archive flag (archived resources are less important) …
- 31. Regression Ranking Testing assertRank("jira", "url", "https://extranet.netcetera.biz/jira/", 1); assertRank("jira", "url", "https://plaza.netcetera.com/.../themas/JIRA", 2);
- 32. Regression Testing for the Ranking Ranking is influenced by various factors We have continuously executed tests for the ranking Find ranking regressions as soon as possible Tests are executed every night, not just with code changes Notes
- 33. War Stories
- 34. War Story #1: Disk Space
- 35. Situation Search is often extremely slow, response times of 20-30s Situation improves without any intervention Problem shows up again very soon Other applications in the same Tomcat server are brought to a grinding halt No releases within the last 7 days No significant data changes in the last 7 days 2-3 weeks earlier a new data sources have been added Index had grown by a factor of 2, but everything worked fine since then Notes
- 36. Disk Usage (fake diagram) 100 80 60 40 20 0
- 37. Lucene Index – Disk Usage Notes Index needs optimzation from time to time when you update it continuously Index optimzation uses a lot of resources, i.e. CPU, memory and disk space Optimzation requires twice the disk space than the optimal index When your normal index uses > 50% of the available disk space, it's already too late It's difficult to get out of this situation (without adding disk space) Deleting stuff from the index does not help, as you need an optimization
- 38. Lessons Learned We need least 2-3 times as much space as the "ideal" index needs If your index has grown beyond 50%, it's already too late. Disk Usage Monitoring has to be improved Some problems take a long time to show themselves Testing long-term effects and continuous delivery clash to some extent
- 39. War Story #2: Free Memory
- 40. Situation Search is always extremely slow, response times of 20-30s Other applications in the same Tomcat server show normal performance No releases within the last few days No significant data changes in the last few days Notes
- 41. Memory Usage (fake diagram) 12 10 8 6 4 2 0
- 42. I/O Caching OS uses "free" memory for caching I/O caching has a HUGE impact on I/O heavy applications Solr (actually Lucene) is a I/O heavy application Notes
- 43. Lessons Learned Free memory != unused memory Increasing the heap size can slow down Solr OS does a better job at caching Solr data than Solr
- 44. War Story #3: Know Your Maths
- 45. Situation Search starts up very fine and is reasonably fast Out Of Memory Errors after a couple of hours Restart brings everything back to normal Out Of Memory Errors come back after a certain time (no obvious pattern) Notes
- 46. Analysis Analysis of the memory usage using heap dumps Solr Caches use up a lot of memory (not surprisingly) Document cache with up to 2048 entries Entries are dominated by the content field Content is limited to 50 KByte by the indexers (or so I thought) Content abbreviation had a bug Instead of the 50KByte limit of the indexer, the 2 MByte limit of Solr was used 2048 * 2 MByte = 4GByte for the document cache Heap size at that time = 4GByte Notes
- 47. Lessons Learned Heap dumps are your friends Study your heap from time to time, even if you do not have a problem (yet) Test your limiters
- 49. Situation Search has become slower and slower We added a lot of data, so that's not really surprising Analysis into different tuning parameters Analysis into the cost of different features Notes
- 50. Highlighting 70% of the response time
- 51. Lessons Learned Some features are cool, but also very expensive Think about what you need to index and what you need to store Consider loading stuff "offline" and asynchronously Consider loading stuff from other data sources
- 52. A few words on Scaling
- 53. Solr Cloud – Horizontal and Vertical Scaling Support for Replication and Sharding Added with Apache Solr 4 Based on Apache Zookeeper Replication Fault tolerance, failover Handling huge amounts of traffic Sharding Dealing with huge amounts of data
- 55. Geographical Replication Notes Load is not an issue for us, but response time is We have multiple geographically distribute sites Network latency is a big factor of the response time if you are at a "far away" location We have been thinking of setting up replicas of the search engine at the different locations
- 57. Relevance-Aware Sharding Notes Normal sharding distributes data on different, but equal nodes We have been thinking about creating deliberately different nodes for the distribution of the data: Node 1 - extremely fast - small index, i.e. small amount of data - lots of memory, CPU, really fast disks Node 2 - lots more data - big, but slower disks - less memory and CPU Frontend would send queries to both nodes and show results as they come in. Distribution of the data would be based on the (query independent) boosting value
- 58. Wrapping Up
- 59. Search rocks
- 61. Learning Curve
- 64. Get your hands dirty Ranking Optimizations
- 65. Continuous Testing and Monitoring for Ranking and Performance Issues
- 66. Verification of features can take a long time
- 67. Cool side projects rock
- 69. References Apache Solr http://lucene.apache.org/solr/ Apache Solr Wiki http://wiki.apache.org/solr/ Apache Solr on Stack Overflow http://stackoverflow.com/questions/tagged/solr Notes