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Itamar Syn-Hershko
http://code972.com
@synhershko
Elasticsearch
Distributed search & analytics on
BigData made easy
Me?
• Itamar Syn-Hershko / @synhershko
• Lucene.NET PMC and lead committer
• Freelance consultant and developer
• Elasticsearch consulting partner
• Microsoft MVP
• RavenDB
– X-Core developer
– “RavenDB in Action” author
Consulting
Partner
An index
Elasticsearch
• Powered by Apache Lucene
• Open-source
• Rapid growth
• High profile users world-wide
REST API
• Indexes
• Types
• IDs
$ curl -XPUT 'http://localhost:9200/twitter/tweet/1' -d '{
"user" : "synhershko",
"post_date" : "2013-05-30T14:12:12",
"message" : "trying out Elastic Search",
"followers": 3,
"registered": true
}'
Full-Text Search
DocumentsTerm
<6>and
<2> <3>big
<6>dark
<4>did
<2>gown
<3>had
<2> <3>house
<1> <2> <3> <5> <6>in
<1> <3> <5>keep
<1> <4> <5>keeper
<1> <5> <6>keeps
<6>light
<4>never
<1> <4> <5>night
<1> <2> <3> <4>old
<4>sleep
<6>sleeps
<1> <2> <3> <4> <5> <6>the
<1> <3>town
<4>where
The index:
Dictionary and
posting lists
6 documents to index
Example from:
Justin Zobel , Alistair Moffat,
Inverted files for text search engines,
ACM Computing Surveys (CSUR)
v.38 n.2, p.6-es, 2006
The old night keeper keeps the keep in the town1
In the big old house in the big old gown.2
The house in the town had the big old keep3
Where the old night keeper never did sleep.4
The night keeper keeps the keep in the night5
And keeps in the dark and sleeps in the light.6
Full-text Search 101:
The inverted index
Full-text Search 101:
The inverted index
DocumentsTerm
<6>and
<2> <3>big
<6>dark
<4>did
<2>gown
<3>had
<2> <3>house
<1> <2> <3> <5> <6>in
<1> <3> <5>keep
<1> <4> <5>keeper
<1> <5> <6>keeps
<6>light
<4>never
<1> <4> <5>night
<1> <2> <3> <4>old
<4>sleep
<6>sleeps
<1> <2> <3> <4> <5> <6>the
<1> <3>town
<4>where
The index:
Dictionary and
posting lists
6 documents to index
The old night keeper keeps the keep in the town1
In the big old house in the big old gown.2
The house in the town had the big old keep3
Where the old night keeper never did sleep.4
The night keeper keeps the keep in the night5
And keeps in the dark and sleeps in the light.6
User queries for “keeper”
Term Normalization DocumentsTerm
<6>and
<2> <3>big
<6>dark
<4>did
<2>gown
<3>had
<2> <3>house
<1> <2> <3> <5> <6>in
<1> <3> <5>keep
<1> <4> <5>keeper
<1> <5> <6>keeps
<6>light
<4>never
<1> <4> <5>night
<1> <2> <3> <4>old
<4>sleep
<6>sleeps
<1> <2> <3> <4> <5> <6>the
<1> <3>town
<4>where
• Lowercasing
• Stop words (grey)
• Not best practice anymore
• Stemming
• Porter stemmer
• s-stemmer
• Relevance++
• SizeOnDisk--
Full-Text Search
Your data
store
How hard is it to get search right,
anyway?
Relevance
• Precision
The fraction of the retrieved
documents that are relevant
• Recall
The fraction of the relevant
documents that are retrieved
• Order of results
Challenges with search
• Relevance
• Getting the tokens right
– Tokenization
– Stemming
• Multi-lingual content
– Or other cross-cutting search concerns
• Tolerance
Real-time Analytics
Real-time Analytics
Queue
(Redis)
“Shippers”
“Indexer”
Scaling out
Moar use cases!
#1: Real-Time Alerting System
Percolation
#2: Smarter query parsing
Matching inexact queries
• Phrase slop
– “Bridge of London” -> “London Bridge”
• Word-level edit distance with fuzzy queries
– ditsance -> distance
– color -> colour
#3: Offline Classification
Structuring the unstructured
• Record linkage
– Bag of words model
– “More Like This” functionality
• NLP
• Entity extraction
#4: Everything is searchable
Geo-spatial search
• Distance
• Shape interactions
• Multiple algorithms
Geo-spatial search
Image search
http://colors.qbox.io/
http://cs.stanford.edu/people/karpathy/deepimage
sent
Deep Visual-Semantic Alignments for
Generating Image Descriptions
#5: Anomaly detection
The Significant Terms Aggregation
Uncommonly common
Mark Harwood’s talk at
http://www.infoq.com/presentations/elasticsear
ch-revealing-uncommonly-common
#6: Debugging a distributed system
Queue
(Redis)
#6: Debugging a distributed system
127.0.0.1 - frank [10/Oct/2000:13:55:36 -0700] "GET /apache_pb.gif
HTTP/1.0" 200 2326 "http://www.example.com/start.html"
"Mozilla/4.08 [en] (Win98; I ;Nav)"
System.NullReferenceException: Object reference not set to an instance of an object.
at System.Collections.Generic.Dictionary`2.Insert(TKey key, TValue value, Boolean add)
at AjaxControlToolkit.ToolkitScriptManager.GetScriptCombineAttributes(Assembly assembly)
at AjaxControlToolkit.ToolkitScriptManager.IsScriptCombinable(ScriptEntry scriptEntry)
at AjaxControlToolkit.ToolkitScriptManager.OnResolveScriptReference(ScriptReferenceEventArgs e)
at System.Web.UI.ScriptManager.RegisterScripts()
at System.Web.UI.ScriptManager.OnPagePreRenderComplete(Object sender, EventArgs e)
at System.Web.UI.Page.OnPreRenderComplete(EventArgs e)
at System.Web.UI.Page.ProcessRequestMain(Boolean includeStagesBeforeAsyncPoint, Boolean
includeStagesAfterAsyncPoint)
#7: Distributed git storage
• PoC in C# using libgit2sharp
• https://github.com/synhershko/libgit2sharp.El
asticsearch
• Kudos @nulltoken
Thank you.
Questions?
Itamar Syn-Hershko
http://code972.com
@synhershko

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Elasticsearch Distributed search & analytics on BigData made easy

Editor's Notes

  1. “I want full-text search. Oh, Aggregations!” Kibana 4 Usually log analysis => logstash Aggregations Framework Live data analysis and reporting, logs as an example Kibana as ES dashboard
  2. You could use the same approach as before Or this logstash pattern Roll your own indexers
  3. Round robin
  4. As data comes in, fire alerts when / if it meets a criteria
  5. Can do aggregations on matching queries
  6. Build your own DSL. Recognizing entities. As-you-type feedback Span queries. Match phrase prefix. Suggesters. Significant terms facet for query expansion / suggestions
  7. There are some challenges Built-in suggesters
  8. Offline can be also real-time; just doesn’t happen on search Pre-processing of data to allow for faster, more exact search later Using the percolaor Significant terms aggregation
  9. Process that often happens offline “People who liked this also liked…” Significant terms
  10. Midtown NYC level 5 cell It is just a matter of figuring out how to represent the piece of data in an index
  11. What is an anomaly
  12. Logs can be exceptions Exceptions can be parsed – “grokked”
  13. Logs can be exceptions Exceptions can be parsed – “grokked”
  14. Summary