An Introduction to Elastic Search.
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Talk given for the #phpbenelux user group, March 27th in Gent (BE), with the goal of convincing developers that are used to build php/mysql apps to broaden their horizon when adding search to their site. Be sure to also have a look at the notes for the slides; they explain some of the screenshots, etc. An accompanying blog post about this subject can be found at http://www.jurriaanpersyn.com/archives/2013/11/18/introduction-to-elasticsearch/
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An Introduction to Elastic Search.
- 3. Jurriaan Persyn @oemebamo – CTO Engagor
- 7. SELECT * FROM myauwesomewebsite WHERE `text` LIKE „%shizzle%‟
- 10. TEXT SEARCH WITH PHP/MYSQL • FULLTEXT index • Only for CHAR, VARCHAR & TEXT columns • For MyISAM & InnoDB tables • Configurable Stop Words • Types: • Natural Language • Natural Language with Query Expansion • Boolean Full Text
- 11. MYSQL FULLTEXT BOOLEAN MODE Operators: •+ AND •- NOT • OR implied •() Nesting •* Wildcard •“ Literal matches
- 12. TEXT SEARCH WITH PHP/MYSQL (CONT‟D) • Typical columns for search table: • Type • Id • Text • Priority • Process: • Blog posts, comments, … • Save (filtered) duplicate of text in search table. • When searching … • Search table and translate to original data via type/id This is how most php/mysql sites implement their search, right?
- 13. SELECT * FROM mysearchtable WHERE MATCH(text) AGAINST („shizzle‟)
- 14. SELECT * FROM mysearchtable WHERE MATCH(text) AGAINST („+shizzle –”ma nizzle”‟ IN BOOLEAN MODE)
- 15. SELECT * FROM jobs WHERE role = „DEVELOPER‟ AND MATCH(job_description) AGAINST („node.js‟)
- 16. SELECT * FROM jobs j JOIN jobs_benefits jb ON j.id = jb.job_id WHERE j.role = „DEVELOPER‟ AND (MATCH(job_description) AGAINST („node.js -asp‟ IN BOOLEAN MODE) AND jb.free_espresso = TRUE
- 18. WHAT IS A SEARCH ENGINE? • Efficient indexing of data • On all fields / combination of fields • Analyzing data • Text Search • Tokenizing • Stemming • Filtering • Understanding locations • Date parsing • Relevance scoring
- 19. TOKENIZING • Finding word boundaries • Not just explode(„ „, $text); • Chinese has no spaces. (Not every single character is a word.) • Understand patterns: • URLs • Emails • #hashtags • Twitter @mentions • Currencies (EUR, €, …)
- 20. STEMMING • “Stemming is the process for reducing inflected (or sometimes derived) words to their stem, base or root form.” • Conjugations • Plurals • Example: • Fishing, Fished, Fish, Fisher > Fish • Better > Good • Several ways to find the stem: • Lookup tables • Suffix-stripping • Lemmatization •… • Different stemmers for every language.
- 21. FILTERING • Remove stop words • Different for every language • HTML • If you‟re indexing web content, not every character is meaningful.
- 22. UNDERSTANDING LOCATIONS • Reverse geocoding of locations to longitude & latitude • Search on location: • Bounding box searches • Distance searches • Searching nearby • Geo Polygons • Searching a country (Note: MySQL also has geospatial indeces.)
- 23. RELEVANCE SCORING • From the matched documents, which ones do you show first? • Several strategies: • How many matches in document? • How many matches in document as percentage of length? • Custom scoring algorithms • At index time • At search time • … A combination Think of Google PageRank.
- 24. “There‟s an app software for that.”
- 26. APACHE LUCENE • “Information retrieval software library” • Free/open source • Supported by Apache Foundation • Created by Doug Cutting • Written in 1999
- 27. “There‟s software a Java library for that.”
- 30. ELASTICSEARCH • “You know, for Search” • Also Free & Open Source • Built on top of Lucene • Created by Shay Banon @kimchy • Versions • First public release, v0.4 in February 2010 • A rewrite of earlier “Compass” project, now with scalability built-in from the very core • Now stable version at 0.20.6 • Beta branch at 0.90 (working towards 1.0 release) • In Java, so inherently cross-platform
- 31. WHAT DOES IT ADD TO LUCENE? • RESTfull Service • JSON API over HTTP • Want to use it from PHP? • CURL Requests, as if you‟d do requests to the Facebook Graph API. • High Availability & Performance • Clustering • Long Term Persistency • Write through to persistent storage system.
- 32. $ cd ~/Downloads $ wget https://github.com/…/elasticsearch-0.20.5.tar.gz $ tar –xzf elasticsearch-0.20.5.tar.gz $ cd elasticsearch-0.20.5/ $ ./bin/elasticsearch
- 33. $ cd ~/Downloads $ wget https://github.com/…/elasticsearch-0.20.5.tar.gz $ tar –xzf elasticsearch-0.20.5.tar.gz $ git clone https://github.com/elasticsearch/elasticsearch- servicewrapper.git elasticsearch-servicewrapper $ sudo mv elasticsearch-0.20.5 /usr/local/share $ cd elasticsearch-servicewrapper $ sudo mv service /usr/local/share/elasticsearch-0.20.5/bin $ cd /usr/local/share $ sudo ln -s elasticsearch-0.20.5 elasticsearch $ sudo chown -R root:wheel elasticsearch $ cd /usr/local/share/elasticsearch $ sudo bin/service/elasticsearch start
- 34. $ sudo bin/service/elasticsearch start Starting ElasticSearch... Waiting for ElasticSearch... . . . running: PID:83071 $
- 36. $ curl -XPUT http://localhost:9200/test/stupid-hypes/planking -d '{"name":"Planking", "stupidity_level":"5"}' {"ok":true,"_index":"test","_type":"stupid- hypes","_id":"planking","_version":1}
- 37. $ curl -XPUT http://localhost:9200/test/stupid-hypes/gallon- smashing -d '{"name":"Gallon Smashing", "stupidity_level":"5"}' {"ok":true,"_index":"test","_type":"stupid- hypes","_id":"gallon-smashing","_version":1}
- 38. $ curl -XPUT http://localhost:9200/test/stupid-hypes/gallon- smashing -d '{"name":"Gallon Smashing", "stupidity_level":"10"}' {"ok":true,"_index":"test","_type":"stupid- hypes","_id":"gallon-smashing","_version":2}
- 40. $ curl -XPUT http://localhost:9200/test/stupid-hypes/gallon- smashing -d '{"name":"Gallon Smashing", "stupidity_level":"10", "lifetime":30}’ {"ok":true,"_index":"test","_type":"stupid- hypes","_id":"gallon-smashing","_version":3}
- 42. SCHEMALESS, DOCUMENT ORIENTED • No need to configure schema upfront • No need for slow ALTER TABLE –like operations • You can define a mapping (schema) to customize the indexing process • Require fields to be of certain type • If you want text fields that should not be analyzed (stemming, …)
- 43. “Ok, so it‟s a NoSQL store?”
- 46. TERMINOLOGY MySQL Elastic Search Database Index Table Type Row Document Column Field Schema Mapping Index Everything is indexed SQL Query DSL SELECT * FROM table … GET http://… UPDATE table SET … PUT http://…
- 47. DISTRIBUTED & HIGHLY AVAILABLE • Multiple servers (nodes) running in a cluster • Acting as single service • Nodes in cluster that store data or nodes that just help in speeding up search queries. • Sharding • Indeces are sharded (# shards is configurable) • Each shard can have zero or more replicas • Replicas on different servers (server pools) for failover • One in the cluster goes down? No problem. • Master • Automatic Master detection + failover • Responsible for distribution/balancing of shards
- 49. SCALING ISSUES? • No need for an external load balancer • Since cluster does it‟s own routing. • Ask any server in the cluster, it will delegate to correct node. • What if … • More data > More shards. • More availability > More replicas per shard.
- 50. PERFORMANCE TWEAKING • Bulk Indexing • Multi-Get • Avoids network latency (HTTP Api) • Api with administrative & monitoring interface • Cluster‟s availability state • Health • Nodes‟ memory footprint • Alternatives voor HTTP Api? • Java library • PHP wrappers (Sherlock, Elastica, …) • But simplicity of HTTP Api is brilliant to work with, latency is hardly an issue.
- 51. Still with me?
- 52. Some Examples
- 57. Query DSL Example: (language:nl OR location.country:be OR location.country:aa) (tag:sentiment.negative) author.followers:[1000 TO *] (- sub_category:like) ((-status:857.assigned) (-status:857.done))
- 61. FACETS • Instead of returning the matching documents … • … return data about the distribution of values in the set of matching documents • Or a subset of the matching documents • Possibilities: • Totals per unique value • Averages of values • Distributions of values •…
- 62. TERMINOLOGY (CONT‟D) MySQL Elastic Search SELECT field, COUNT(*) Facet FROM table GROUP BY field
- 65. ADVANCED FEATURES • Nested documents (Child-Parent) • Like MySQL joins? • Percolation Index • Store queries in Elastic • Send it documents • Get returned which queries match • Index Warming • Register search queries that cause heavy load • New data added to index will be warmed • So next time query is executed: pre cached
- 66. WHAT ARE MY OTHER OPTIONS? • RDBMS • MySQL, … • NoSQL • MongoDB, … • Search Engines • Solr • Sphinx • Xapian • Lucene itself • SaaS • Amazon CloudSearch
- 67. … VS. SOLR •+ • Also built on Lucene • So similar feature set • Also exposes Lucene functionality, like Elastic Search, so easy to extend. • A part of Apache Lucene project • Perfect for Single Server search •- • Clustering is there. But it‟s definitely not as simple as ElasticSearch‟ • Fragmented code base. (Lots of branches.) Engagor used to run on Solr.
- 68. … VS. SPHINX •+ • Great for single server full text searches; • Has graceful integration with SQL database; • (Eg. for indexing data) • Faster than the others for simple searches; •- • No out of the box clustering; • Not built on Lucene; lacks some advanced features; Netlog & Twoo use Sphinx.
- 69. WANT TO USE IT? • In an existing project: • As an extra layer next to your data … • Send to both your database & elasticsearch; • Consistency problems?; • Or as replacement for database • Elastic is as persistent as MySQL; • If you don‟t need RDBMS features; • @Engagor: Our social messages are only in Elastic
- 70. “Users are incredibly bad at finding and researching things on the web.” Nielsen (March 2013) http://www.nngroup.com/articles/search-navigation/
- 71. “Pathetic and useless are words that come to mind after this year‟s user testing.” Nielsen (March 2013) http://www.nngroup.com/articles/search-navigation/
- 72. “I‟m searching for apples and pears.”
- 76. “It‟s too young. Is it even stable enough?” Your boss (Tomorrow Morning)
- 81. elasticsearch.org irc.freenode.net #elasticsearch
- 82. elasticsearch node.js socket.io real time notifications rabbitmq backbone.js gearman jobs@engagor.com
- 84. $ cd /usr/local/share/elasticsearch $ sudo bin/service/elasticsearch stop
- 86. Sources include: • http://www.elasticsearch.org/videos/2010/02/07/es-introduction.html • http://www.elasticsearchtutorial.com/ • http://www.slideshare.net/clintongormley/cool-bonsai-cool-an-introduction-to-elasticsearch • http://www.slideshare.net/medcl/elastic-search-quick-intro • http://www.slideshare.net/macrochen/elastic-search-apachesolr-10881377 • http://www.slideshare.net/cyber_jso/elastic-search-introduction • http://www.slideshare.net/infochimps/elasticsearch-v4 • http://engineering.foursquare.com/2012/08/09/foursquare-now-uses-elastic-search-and-on-a-related-note-slashem-also- works-with-elastic-search/ • http://stackoverflow.com/questions/10213009/solr-vs-elasticsearch • http://stackoverflow.com/questions/11115523/how-does-amazon-cloudsearch-compares-to-elasticsearch-solr-or-sphinx- in-terms-o • http://blog.socialcast.com/realtime-search-solr-vs-elasticsearch/
Hinweis der Redaktion
- This talk is about adding search to your own website.Implementing a search engine for your own content.First the bit about how it’s done in MySQL, and what problems that brings with it;Then about what a search engine should do for you;Then about how Elastic Search helps;And code of course, lots of code.
- I’m leading the development team at Engagor, a startup in the city centreof Gent, Belgium.We’re 2 years old.We build a social media monitoring & management product.Before that I worked for Massive Media for 5 years.As a lead developer I worked on the Netlog & Gatchaproducts.
- Search can be a “simple” textsearch.Here I’m searching Tumblr for funny gifs, because that’s what Tumblris for.
- But search can go deeper and more into detail too ..Here I’m usingAND, OR, NOTNestingRestrictionson fields
- Or very difficult …Searching in a mixed set of dataProfilesPhotosFriend connectionsSearching in a graph …
- My first thought when I’d have to add search to a php/mysql site … It sort of works …
- Problems arisewhen you have lots of data …To speed things up you add indecesto your MySQL tables.
- And the library analogy for a MySQL index is this …An index card box.
- MySQL has an index type esp. for full text search.Natural Language:case insensitive, accent incensitiveQuery Expansion:Search for “database” > returns results that has often has words like “mysql”, “oracle”, … A second search with extended query string happens to find related documents too.Boolean ModeAdds operators to Natural Language type
- Anyone using a similar system to this?Implemented yourself, or from a CMS?phpBB has/had a table like this.
- Example of FULLTEXT MySQL search query.
- Example of FULLTEXT MySQL search query in boolean mode. A bit more powerful.
- Now, we add restrictions on a certain fields. Now you need combined indeces to keep this fast.
- And even more restrictions.Indeces on all involved feeds? In all combinations? In all orders?Lots of indeces make WRITE operations on your tables slower.
- MySQL just isn’t built for complex search …So let’s look at what a system built for search needs …
- So it’s old.But still active.Used a lot.
- It is however a java library. It’s not a fully managed service.
- If you have lots of data; you need a search engine to be scalable & highly available.
- And that’s where ElasticSearch comes in.
- Download, unzip, start.
- This time we install it in the right place, and wrapped in a service.
- Now it runs on your localhost. As a HTTP service, so open your browser and surf to your Elastic Search server.
- HTTP Access, it’s brilliant!Do you want to secure it? Add firewalls …Do you want to cache it? Add Varnish …
- Example of adding something to ElasticSearch from your command shell via a HTTP PUT request done by curl.You can do this right after installation. No need to create an index or configure anything, just add data right away.
- Adding a second record (document).
- Updating an existing record. (Mind the new version number.)
- Want to see the record?Surf to it.The url consist ofindex, type & id.
- Want to add a new field (column)?Update your document with new field added.
- It’s right there.
- So it’s schemaless.And actually we did ZERO CONFIGURATION. We didn’t have to create indeces or tell Elastic Search what type of data we’ll be adding.Actually, you can configure a mapping/schema:To require certain fields to be of a certain typeTo avoid text fields of being analyzed (text analysis)Basically: to speed things up …
- What we’ve demoed so far is aNoSQL store. That’s cool. But not all.
- Here we do a GET request (in the browser) that searches our newly created index for the word “smashing”It returns the 2nd document.
- Curl in PHP is simple.Simplest example of how to do a search to elastic from PHP.
- I’ve mentioned a few Elastic Search specific terms.Here’s the full breakdown of terminology and how they related to MySQL concepts.
- Back to the clustering features Elastic Search adds …
- Here you have a Engagor specific dashboard that shows the 12 servers in the Engagor cluster.You see:server12 is the master;That every node knows each other;There are 11K shards;Each with one replica.Health = green means every shard has a replica (on a different server).If one server goes down: no problem.
- Now let’s look at how Engagor uses Elastic Search.First, what do we do?From a technical point of view:Engagor = Huge database of social messages.Facebook, Twitter, Instagram, News sites …We save those that are clients are interested in to our application and offer:Statistics about the tracked dataWorkflow toolsAutomation tools
- This is the time to show a slide I stole from a presentation from our CEO Folke.We started 2 years ago.I joined after 6 months.Now a team of 16 people.7 of them technical profilesweb developersdata scientistsbackend developersfrontend developersOur customers includeMobistarTelenetMicrosoft EuropeEuropean CommisionAlproSeveral agenciesThey use Engagor for customer support (“call center” software for social media)marketing insightscrisis detection…
- Here you see an example of a search on our cluster for a certain twitter user’s handle.You see it returns 260 social messages.Each message has data like the:IdService it’s fromContentDateAuthor details
- Here we’re searching in a topic about Belgacom & Mobile Vikings.“All messages from users with at least 1000 followers, that are negative and from Belgium or in Dutch.”On 4 different fields, and nested … It just works.
- This is the Query SQL we’re sending to Elastic for the previous search.
- Here we are in a topic about Coca Cola, thus high volume.About 50k message per day. 28 days.That’s 1,4M messages we’re searching in.This is a graph of messages per day.
- This is the inbox. Showing the last 10 messages in that topic.Performance: about half a second.
- Same inbox, but now only showing messages with the word “thirsty”.Performance: again about half a second.(Only 1 sample, so this is not really a benchmark .)
- Now, there’s another feature of search engines you might not immediately think about.
- Think of it as an equivalent to MySQL GROUP BY query.
- The pie chart:Facet on sentiment field of a mention. Returns totals per value.Sentiment Per Day:Facet on combined fields: sentiment + day(dateadd).Returns data used for second chart.You can also use the filter like in the inbox, to see these facets for a filtered set of data. Eg. Sentiment per day for mentions coming from Belgium only.
- For the Telenet Twitter profile:Totals of messages per dayPer typeRetweetsMentionsOwn tweetsEvery “segment” (color) is facet with custom filter/search querySingle ElasticSearch call to get all this information.
- Percolation example:Use it to route documents …Eg. you have a stream of data coming in and need to decide what to do with those documents based on queries.Everything matching x is for client AEverything matching y is for client B
- There’s a good competition going on. Looks like Elastic Search has made Solr alert again; since they’re also focusing on clustering features now.
- It’s not because you have a great search engine, that you have great search experience on your site …Users aren’t very good at it …But searching for things is not that easy …
- Look at how natural language differs from search query language.
- This happens quite often at the Engagor office.Not that we blame our users …It’s just difficult to get it right.
- If you want to play with it, and are excited …But you’re boss is all #meh.
- ElasticSearch is a mature product.Soundcloud is using it.
- StumbleUpon is using it.
- Foursquare switched to it …When checking in somewhere, they show:Locations close to youLocations you previously checked inLocations that are popularThat’s a pretty nifty search query right there.
- There’s also a real company behind Elastic Search, backing it with:TutorialsTrainingSupportSLA’sConsulting
- Recently redesigned site. Documentation is a bit frightening at first, since it’s hard to know what to search for, but I hope this presentation solves that.IRC channel is very active; quick answers.
- If you’re interested in working with Elastic Search, or in fact, any of these other cool technologies …We can always use good profiles: junior or senior, backend or frontend … Send us your resumes ;).March 27th: Now we’re esp. searching for someone who’s good at Javascript.
- Already gave an overview of how it’s like to work with the technologies at Engagor.Go to http://startupshizzle.tumblr.com to find out how it’s like to work with the people of our team ;).
- So, that’s it.
- So, that’s it.