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lastic the missing tutorial lastic Erik Rose & Laura Thomson Mozilla earch earch

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lastic the missing tutorial lastic Erik Rose & Laura Thomson Mozilla earch earch

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housekeeping • Make sure ES is installed. If you haven’t installed it yet and you’re on a Mac, just install 1.1.x. • Exercise code: clone the git repo at (or just visit)
 https://github.com/erikrose/oscon-elasticsearch/ • Make faces.

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• Full-text search • Big data • Faceting • Geographical queries what it’s good for

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Shay Banon, Heavy Lifter

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the rest of us ?

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characteristics

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• Elasticsearch wraps Lucene. • Read/write/admin via REST • Native format is JSON (vs XML). lucene++ JSON HTTP on port 9200

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• CAP: consistency, availability, partition tolerance • “pick any two” ! • “When it comes to CAP, in a very high level, elasticsearch gives up on partition tolerance” (2010) CAP

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• …it’s not that simple ! • Consistency is mostly eventual. • Availability is variable. • Partition tolerant it’s not. ! • Read http://aphyr.com/posts/317-call-me-maybe-elasticsearch (and despair). CAP

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• Generally not suitable as a primary data store. • It’s a distributed search engine ! • Easy to get started • Easy to integrate with your existing web app • Easy to configure it not-too-terribly • Enables fast search with cool features what it’s good for, redux

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definitions

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• node — a machine in your cluster • cluster — the set of nodes running ES • master node — Elected by the cluster. If the master fails, another node will take over. nodes and clusters

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• shard — A Lucene index. Each piece of data you store is written to a primary shard. Primary shards are distributed over the cluster. ! • replica — Each shard has a set of distributed replicas (copies). Data written to a primary shard is copied to replicas on different nodes. shards and replicas

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self-defense

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# Unicast discovery allows to explicitly control which nodes will be used # to discover the cluster. It can be used when multicast is not present, # or to restrict the cluster communication-wise. # # 1. Disable multicast discovery (enabled by default): # discovery.zen.ping.multicast.enabled: false exercise: fix clustering and listening # Elasticsearch, by default, binds itself to the 0.0.0.0 address, and listens # on port [9200-9300] for HTTP traffic and on port [9300-9400] for node-to-node # communication. (the range means that if the port is busy, it will automatically # try the next port). ! # Set the bind address specifically (IPv4 or IPv6): # network.bind_host: 127.0.0.1

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% cd elasticsearch-1.2.2 ! % bin/elasticsearch ! # On the Mac: % JAVA_HOME=$(/usr/libexec/java_home -v 1.7) bin/elasticsearch exercise: start up and check % curl -s -XGET 'http://127.0.0.1:9200/_cluster/health?pretty' { "cluster_name" : "grinchertoo", "status" : "yellow", "timed_out" : false, "number_of_nodes" : 1, "number_of_data_nodes" : 1, "active_primary_shards" : 19, "active_shards" : 19, "relocating_shards" : 0, "initializing_shards" : 0, "unassigned_shards" : 13 }

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exercise: tool up curl

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exercise: tool up BBEdit’s shell worksheets:
 http://pine.barebones.com/files/BBEdit_10.5.11.dmg

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exercise: tool up Marvel/Sense: http://www.elasticsearch.org/overview/marvel/download/

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data structure basics

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index doctype another doctype {… }

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curl -s -XPUT 'http://localhost:9200/test/' exercise: make an index

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IDs 6a8ca01c-7896-48e9-! 81cc-9f70661fcb32

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# Make a doc:
 curl -s XPUT 'http://127.0.0.1:9200/test/book/1' -d '{
 "title": "All About Fish",
 "author": "Fishy McFishstein",
 "pages": 3015
 }' ! # Make sure it's there:
 curl -s -XGET 'http://127.0.0.1:9200/test/book/1?pretty' {
 "_index" : "test",
 "_type" : "book",
 "_id" : "1",
 "_version" : 2,
 "found" : true,
 "_source" : {
 "title": "All About Fish",
 "author": "Fishy McFishstein",
 "pages": 3015
 }
 } exercise: make a doc

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# Delete the doc: curl -s -XDELETE 'http://localhost:9200/test/book/1' exercise: make a doc

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diplodocus …………………………… 333 duodenum …………………………… 201 dwaal …………………………… 500, 119

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row → 0,1,3 boat → 0,1 chicken → 2 row row row your boat row the row boat chicken chicken chicken the front row 0 1 2 3

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row → 0,1,3 boat → 0,1 chicken → 2 row row row your boat row the row boat chicken chicken chicken the front row 0 1 2 3

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row → 0,1,3 boat → 0,1 chicken → 2 row row row your boat row the row boat chicken chicken chicken the front row 0 1 2 3

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row → 0,1,3 boat → 0,1 chicken → 2 row row row your boat row the row boat chicken chicken chicken the front row 0 1 2 3

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row → 0,1,3 boat → 0,1 chicken → 2 row row row your boat row the row boat chicken chicken chicken the front row 0 1 2 3

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doc row → 0 [0,1,2] 1 [0,2] 3 [2] boat → 0 [4] 1 [3] chicken → 2 [0,1,2] row row row your boat row the row boat chicken chicken chicken the front row 0 1 2 3 positions

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doc positions row → 0 [0,1,2] 1 [0,2] 3 [2] boat → 0 [4] 1 [3] chicken → 2 [0,1,2] row row row your boat row the row boat 0 1 chicken chicken chicken the front row 2 3

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doc positions row → 0 [0,1,2] 1 [0,2] 3 [2] boat → 0 [4] 1 [3] chicken → 2 [0,1,2] row row row your boat row the row boat 0 1 chicken chicken chicken the front row 2 3 ?

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doc positions row → 0 [0,1,2] 1 [0,2] 3 [2] boat → 0 [4] 1 [3] chicken → 2 [0,1,2] row row row your boat row the row boat 0 1 chicken chicken chicken the front row 2 3 ?

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doc row 232 → 0 [0,1,2] 1 [0,2] 3 [2] boat 78 → 0 [4] 1 [3] chicken 91 → 2 [0,1,2] row row row your boat row the row boat chicken chicken chicken the front row 0 1 2 3 positions

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indices on properties "title": "All About Fish", "author": "Fishy McFishstein", "pages": 3015 "title": "Nothing About Pigs", "author": "Nopiggy Nopigman", "pages": 0 "title": "All About Everything", "author": "Everybody", "pages": 4294967295

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inner objects curl -s -XPUT 'http://localhost:9200/test/book/1' -d '{ "title": "All About Fish", "author": { "name": "Fisher McFishstein", "birthday": "1980-02-22", "favorite_color": "green" } }' title: All About Fish author.name: Fisher McFishstein author.birthday: 1980-02-22 author.favorite_color: green curl -s -XGET 'http://127.0.0.1:9200/test/book/1?pretty' { "_index" : "test", "_type" : "book", "_id" : "1", "_version" : 1, "found" : true, "_source" : { "title": "All About Fish", "author": { "name": "Fisher McFishstein", "birthday": "1980-02-22", "favorite_color": "green" } }

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arrays # Insert a doc containing an array: curl -s XPUT 'http://127.0.0.1:9200/test/book/1' -d '{ "title": "All About Fish", "tag": ["one", "two", "red", "blue"] }' doc one → 1 two → 1 red → 1 blue → 1 ["one", "two", "red", "blue"] doc 1

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# Insert a bunch of different docs by changing the things in bold: % curl -s XPUT 'http://127.0.0.1:9200/test/book/1' -d '{ "title": "All About Fish", "tag": ["one", "two", "red", "blue"] }' exercise: array play # A sample query--try changing the bold things: % curl -s -XGET 'http://127.0.0.1:9200/test/book/_search?pretty' -d '{ "query": { "match_all": {} }, "filter": { "term": {"tag": ["two", "three"]} } }' "red" ["blue"] ["one", "red"] "two"

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mappings

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# Make a new album doc:
 curl -s XPUT 'http://127.0.0.1:9200/test/ album/1' -d '{ "title": "Fish Sounds", "gapless_playback": true, "length_seconds": 210000, "weight": 1.22, "released": "2013-01-23"
 }' ! # See what kind of mapping ES guessed:
 curl -s -XGET 'http://127.0.0.1:9200/test/ album/_mapping?pretty'
 implicit mappings { "test" : { "mappings" : { "album" : { "properties" : { "title" : { "type" : "string" }, "gapless_playback" : { "type" : "boolean" }, "length_seconds" : { "type" : "long" }, "weight" : { "type" : "double" }, "released" : { "type" : "date", "format" : "dateOptionalTime" } } } } }

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explicit mappings { "test" : { "mappings" : { "album" : { "properties" : { "title" : { "type" : "string" }, "gapless_playback" : { "type" : "boolean" }, "length_seconds" : { "type" : "long" }, "weight" : { "type" : "double" }, "released" : { "type" : "date", "format" : "dateOptionalTime" } } } } } curl -s XPUT 'http://127.0.0.1:9200/test/ _mapping/album' -d '{ "properties" : { "title" : { "type" : "string" }, "gapless_playback" : { "type" : "boolean" }, "length_seconds" : { "type" : "long" }, "weight" : { "type" : "double" }, "released" : { "type" : "date", "format" : "dateOptionalTime" } } }' { curl -s -XDELETE 'http://127.0.0.1:9200/ test/album'

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1. Delete the “album” doctype, if you’ve made one by following along. 2. Think of an album which would prompt ES to guess a wrong type. 3. Insert it, and GET the _mapping to show the wrong guess. 4. Delete all “album” docs again so you can change the mapping. 5. Set a mapping explicitly so you can’t fool ES anymore. exercise: use explicit mappings

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Lurking Horrors

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queries

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• Query ES via HTTP/REST • Possible to do with query string • DSL is better ! • Let’s write some queries. • But first, let’s get some data in our cluster to query. queries

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exercise 1 • Bulk load a small test data set to use for querying. • This is exercise_1 in the queries/ directory of the git repo, so you can cut and paste, or execute it directly. ! % curl -XPOST localhost:9200/_bulk --data-binary @data.bulk

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! ! % curl -s -XGET 'http://127.0.0.1:9200/test/book/1?pretty' exercise 2 • Let’s check we can pull that data, by grabbing a single document. ! • This is exercise_2 in the queries/ directory of the repo, so you can cut and paste.

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exercise 3 • We’ll begin by using a URI search (sometimes called, a little fuzzily, a query string query). ! • (This is exercise_3) ! % curl -s -XGET 'http://127.0.0.1:9200/test/book/_search?q=title:Python'

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• Passes searches via GET in the query string • This is fine for running simple queries, basic “is it working” type tests and so on. • Once you have any level of complexity in your query, you’re going to need the query DSL. ! limited appeal

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• DSL == Domain Specific Language • DSL is an AST (abstract syntax tree) of queries. ! • What does that actually mean? • Write your queries in JSON, which can be arbitrarily complex. query DSL

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{ "query" : { "match" : { "title" : "Python" } } } simple DSL term query

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• Run this query (exercise 4). ! % curl -XGET 'http://localhost:9200/test/book/_search' -d '{ "query" : { "match" : { "title" : "Python" } } }' ! (What do you notice about the results?) exercise 4

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• Filters: • Boolean: document matches or it does not • Order of magnitude faster than queries • Use for exact values • Cacheable queries vs. filters

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• Queries: • Use for full text searches • Relevance scored ! ! ! Filter when you can; query when you must. queries vs. filters

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curl -XGET -s 'http://localhost:9200/test/book/_search?pretty=true' -d \ ! '{ ! "query": { ! "filtered": { ! "filter": { ! "term": { ! "category": "Web Development" ! } ! }, ! "query": { ! "bool": { ! "should": [ ! { ! "match": { ! "title": "Python" ! } ! }, ! { ! "match": { ! "summary": "Python" ! } ! } ! ] ! } ! } ! } ! } ! }' use them together!

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exercise 5 • Let’s run that query. ! • (This is exercise_5)

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exercise 5 results • Where are my results???

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exercise 6 • Similar to many relational databases, ElasticSearch supports an explainer. Let’s run it on this query. • (This is exercise_6) ! curl -XGET -s 'http://localhost:9200/test/book/4/_explain?pretty=true' -d \ ! '{ ! "query": { ! "filtered": { ! "filter": { ! "term": { ! "category": "Web Development" ! } ! }, ! "query": { … ! ! !

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exercise 6 results {! "_index" : "test",! "_type" : "book",! "_id" : "4",! "matched" : false,! "explanation" : {! "value" : 0.0,! "description" : "failure to match filter: cache(category:Web Development)",! "details" : [ {! ! …! ! ! !

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• This is a classic beginner gotcha. • Using the standard analyzer, applied to all fields (by default) “Web Development” will be broken into the terms “web” and “development” and those will be indexed. ! • The term “Web Development” is not indexed anywhere. analyze that!

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• term queries or filters look for an exact match, so find nothing ! • But {“match” : “Web Development”} does work. Why? • match queries or filters use analysis: they break this down into searches for “web” or “development” but match works!

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exercise 7 • Let’s make it work. ! • One solution is in exercise_7. • Take a couple minutes before peeking. • TMTOWTDI ! !

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• Term queries look for the whole term and are not analyzed. • Match queries are analyzed, and look for matches to the analyzed parts of the query. summary: term vs. match

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curl -XGET -s 'http://localhost:9200/test/book/_search?pretty=true' -d \ '{ "query": { "match_phrase": { "summary": { "query": "old versions of browsers", "slop": 2 } } } }' match_phrase

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• Where are my favorites, AND, OR, and NOT? • Tortured syntax of the bool query: • must: everything in the must clause is AND • should: everything in the should clause is OR • should not: you guessed it. • Nest them as much as you like boolean queries

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• minimum_should_match is the number of should clauses that have to match. boolean bonuses

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"query": {! "bool": {! "must": { ! "bool": {! "should": [ ! {! "match": {! "category": "development"! }! },! {! "match": { ! "category": "programming" ! }! }! ]! }! },! "should": [! {! "match": {…!

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exercise 8 • Run this query - it’s in exercise_8 ! • Can you modify it to find books for intermediate or above level programmers? ! !

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• We’re actually not going to cover faceting - deprecated in favor of aggregations. faceting

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• Aggregations let you put returned documents into buckets and run metrics over those buckets. • Useful for drill down navigation of data. aggregations

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exercise 9 curl -XGET -s 'http://localhost:9200/test/book/_search?pretty=true' -d \! '{! "size" : 0,! "aggs" : {! "category" : {! "terms" : {! "field" : "category"! }! }! }! }' • Run a sample aggregation - exercise_9

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• You can affect the way ES calculates relevance scores for results. For example: • Boost: weigh one part of a query more heavily than others • Custom function-scoring queries: e.g. weighting more complete user profiles • Constant score queries: pre-set a score for part of a query (useful for filters!) scoring

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boosting "query": { "bool": { "should": [ { "term": { "title": { "value": "python", "boost": 2.0 } } }, { "term": { "summary": "python" } } ] } }

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function scoring curl -XGET -s 'http://localhost:9200/test/book/_search?pretty=true' -d \ '{ "query": { "function_score": { "query": { "match": { "title": "Python" } }, "script_score": { "script": "_score * doc[\"rating\"].value" } } } }'

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• You have various options for writing your functions: • Default has been mvel but is now Groovy • Plugins for: • JS • Python • Clojure • mvel scripting languages

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analysis

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stock analyzers original: Red-orange gerbils live at #43A Franklin St. ! whitespace: Red-orange gerbils live at #43A Franklin St. standard: red orange gerbils live 43a franklin st simple: red orange gerbils live at a franklin st stop: red orange gerbils live franklin st snowball: red orang gerbil live 43a franklin st • stopwords • stemming • punctuation • case-folding

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curl -XGET -s 'http://localhost:9200/_analyze? analyzer=whitespace&pretty=true' -d 'Red-orange gerbils live at #43A Franklin St.' { "tokens" : [ { "token" : "Red-orange", "start_offset" : 0, "end_offset" : 10, "type" : "word", "position" : 1 }, { "token" : "gerbils", "start_offset" : 11, "end_offset" : 18, "type" : "word", "position" : 2 }, ...

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exercise: find 10 stopwords curl -XGET -s 'http://localhost:9200/_analyze? analyzer=stop&pretty=true' -d 'The word "an" is a stopword.' Hint: Run the above and see what happens.

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solution: find 10 stopwords curl -XGET -s 'http://localhost:9200/_analyze? analyzer=stop&pretty=true' -d 'The an is a with that be for to and snookums' { "tokens" : [ { "token" : "snookums", "start_offset" : 36, "end_offset" : 44, "type" : "word", "position" : 11 } ] } [0,1,2] [0,2] [2] [4] [3] [0,1,2] positions

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applying mappings to properties curl -s XPUT 'http://127.0.0.1:9200/test/_mapping/album' -d '{ "properties": { "title": { "type": "string" }, "description": { "type": "string", "analyzer": "snowball" }, ... } }'

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analyzer internals name_analyzer CharFilter Tokenizer Token Filter terms O Brien ’

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"analysis": { "analyzer": { "name_analyzer": { "type": "custom", "tokenizer": "name_tokenizer", "filter": ["lowercase"] } }, "tokenizer": { "name_tokenizer": { "type": "pattern", "pattern": "[^a-zA-Z']+" } } } name_analyzer CharFilter Tokenizer Token Filter terms x O’Brien

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exercise: write a custom analyzer tags: "red, two-headed, striped, really dangerous" ! curl -XGET -s 'http://localhost:9200/_analyze?analyzer=whitespace&pretty=true' -d 'red, two-headed, striped, really dangerous' red two-headed striped really dangerous curl -s -XGET 'http://127.0.0.1:9200/test/ monster/_search?pretty' -d '{ "query": { "match_all": {} }, "filter": { "term": {"tags": "dangerous"} } } { "took" : 3, "timed_out" : false, "_shards" : { "total" : 5, "successful" : 5, "failed" : 0 }, "hits" : { "total" : 1, "max_score" : 1.0, "hits" : [ { "_index" : "test", "_type" : "monster", "_id" : "1", "_score" : 1.0, "_source" : { "title": "Scarlet Klackinblax", "tags": "red, two-headed, striped, really dangerous" } } ] } }

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exercise: write a custom analyzer # How to update the "test" index's analyzers: curl -s -XPUT 'http://localhost:9200/test/_settings?pretty' -d '{ "analysis": { "analyzer": { "whitespace_analyzer": { "filter": ["lowercase"], "tokenizer": "whitespace_tokenizer" } }, "tokenizer": { "whitespace_tokenizer": { "type": "pattern", "pattern": " +" } } } }' curl -XGET -s 'http://localhost:9200/test/_analyze? analyzer=whitespace_analyzer&pretty=true' -d 'all your base are belong to us, dude' { "error" : "ElasticsearchIllegalArgumentException[Can't update non dynamic settings[[index.analysis.analyzer.comma_delim.filter.0, index.analysis.tokenizer.comma_delim_tokenizer.type, index.analysis.tokenizer.comma_delim_tokenizer.pattern, index.analysis.analyzer.comma_delim.tokenizer]] for open indices[[test]]]", "status" : 400 } curl -s -XPOST 'http://localhost:9200/test/_close' curl -s -XPOST 'http://localhost:9200/test/_open'

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solution: write a custom analyzer curl -s -XPUT 'http://localhost:9200/test/_settings?pretty' -d '{ "analysis": { "analyzer": { "comma_delim": { "filter": ["lowercase"], "tokenizer": "comma_delim_tokenizer" } }, "tokenizer": { "comma_delim_tokenizer": { "type": "pattern", "pattern": ", +" } } } }' curl -XGET -s 'http://localhost:9200/test/_analyze?analyzer=comma_delim&pretty=true' -d 'red, two- headed, striped, really dangerous' "token": "red" ... "token": "two-headed" ... "token": "striped" ... "token": "really dangerous"

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ngrams 'analyzer': { # A lowercase trigram analyzer 'trigramalyzer': { 'filter': ['lowercase'], 'tokenizer': 'trigram_tokenizer' } }, 'tokenizer': { 'trigram_tokenizer': { 'type': 'nGram', 'min_gram': 3, 'max_gram': 3 # Keeps all kinds of chars by default. } “Chemieingenieurwesen ” …ing nge gen eni nie ieu eur…

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clustering

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shards curl -XPUT 'http://localhost:9200/twitter/' -d ' index: number_of_shards: 3 '

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replicas curl -XPUT 'http://localhost:9200/twitter/' -d ' index: number_of_shards: 3 number_of_replicas: 2 '

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exercise: provisioning How would you provision a cluster if we were doing lots of CPU- expensive queries on a large corpus, but only a small subset of the corpus was “hot”?

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extremer extremes

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• At least 1 replica • Plenty of shards—but not a million • At least 3 nodes. recommendations Avoid split-brain: discovery.zen.minimum_master_nodes: 2 • Get unlucky?
 Set fire to the data center and walk away. Or continually repopulate.

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real-life examples

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• Protect with a firewall, or try elasticsearch-jetty. • discovery.zen.ping.multicast.enabled: false • discovery.zen.ping.unicast.hosts:
 [“master1”, “master2”] • cluster.name: something_weird too friendly

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adding nodes without downtime • Puppet out new config file:
 discovery.zen.ping.unicast.hosts:
 ["old.example.com", ..., "new.example.com"] • Bring up the new node.

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beware inconsistent config

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be wary of upgrades

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monitoring curl -XGET -s 'http://localhost:9200/_cluster/health?pretty' { "cluster_name" : "grinchertoo", "status" : "yellow", "timed_out" : false, "number_of_nodes" : 1, "number_of_data_nodes" : 1, "active_primary_shards" : 29, "active_shards" : 29, "relocating_shards" : 0, "initializing_shards" : 0, "unassigned_shards" : 26 } curl -XGET -s 'http://localhost:9200/_cluster/state?pretty' { "cluster_name" : "elasticsearch", "version" : 3, "master_node" : "ACuIytIIQ7G7b_Rg_G7wnA",

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exercise: monitoring Why is just checking for cluster color insufficient? ! What could we check in addition? "cluster_name" : "grinchertoo", "status" : "yellow", "timed_out" : false, "number_of_nodes" : 1, "number_of_data_nodes" : 1, "active_primary_shards" : 29, "active_shards" : 29, "relocating_shards" : 0, "initializing_shards" : 0, "unassigned_shards" : 26

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monitoring: elasticsearch-paramedic http://karmi.github.com/elasticsearch-paramedic/

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monitoring: marvel http://www.elasticsearch.org/overview/marvel/

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optimization

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bootstrap.mlockall: true

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ES_HEAP_SIZE: half of RAM

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open files /etc/security/limits.conf:! es_user soft nofile 65535! es_user hard nofile 65535 /etc/init.d/elasticsearch:! ulimit -n 65535! ulimit -l unlimited ✚

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Use default stores.

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RAM & JVM tuning

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MySQL

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shrinking indices % vmstat -S m -a 2 procs -----------memory---------- ---swap-- -----io---- r b swpd free inact active si so bi bo 1 0 4 37 54 55 0 0 0 1 0 0 4 37 54 55 0 0 0 0 0 0 4 37 54 55 0 0 0 0 ! "some_doctype" : { "_source" : {"enabled" : false} } "some_doctype" : { "_all" : {"enabled" : false} } "some_doctype" : { "some_field" : {"include_in_all" : false} }

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filter caching "filter": { "terms": { "tags": ["red", "green"], "execution": "plain" } } "filter": { "terms": { "tags": ["red", "green"], "execution": "bool" } }

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dealing with the future

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mappings

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expensive updates

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• Use Bulk API. how to reindex • Turn off auto-refresh: curl -XPUT localhost:9200/test/_settings -d '{ "index" : { "refresh_interval" : "-1" } }' • index.merge.policy.merge_factor: 1000 • Remove replicas if you can. • Use multiple feeder processes. • Put everything back.

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• Backups used to be fairly cumbersome but now there’s an API for that! ! • Set it up: curl -XPUT 'http://localhost:9200/_snapshot/backups' -d '{! "type": "fs",! "settings": {! "location": "/somewhere/backups",! "compress": true! }! }'! ! • Run a backup: curl -XPUT "localhost:9200/_snapshot/backups/july20" backups

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fancy & advanced features

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synonyms "filter": { "synonym": { "type": "synonym", "synonyms": [ "albert => albert, al", "allan => allan, al" ] } } original query: Allan Smith after synonyms: [allan, al] smith original query: Albert Smith after synonyms: [albert, al] smith

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• You can set up synonyms at indexing or at query time. • For all that’s beautiful in this world, do it at query time. • At indexing explodes your data size. • You can store synonyms in a file, and reference that file in your mapping. • Many gotchas. • Undocumented limits on the file. • Needs to be uploaded to the config dir on each node. synonym gotchas

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• Use to suggest possible search terms, or complete queries • Types: • Term and Phrase - will do spelling corrections • Completion - for autocomplete • Context - limit suggestions to a subset suggesters

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• Why? Hook your query up to JS and query-as-they-type ! • Completion suggester (faster, newer, slightly cumbersome) • Prefix queries (slower, older, more reliable) ! • Both require mapping changes to work autocompletion

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! curl -X POST 'localhost:9200/test/books/_suggest?pretty' -d '{! "title-suggest" : {! "text" : "p",! "completion" : {! "field" : "suggest"! }! }! }'! suggester autocompletion

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curl -XGET -s 'http://localhost:9200/test/book/_search?pretty=true' -d \ '{ "query": { "prefix": { "title": "P" } } }' prefix autocompletion

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thank you @ErikRose erik@mozilla.com @lxt laura@mozilla.com