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Simple search with elastic search

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Simple search with elastic search

  1. 1. SIMPLE SEARCH WITH ELASTIC SEARCH MARK STORY @MARK_STORY
  2. 2. WAT? Java based Lucene powered JSON driven Document orientated database All out super search solution Easy to setup, and use
  3. 3. INDEXES AND TYPES INDEXES Similar concept to databases. Contain multiple types.
  4. 4. TYPES Similar concept to tables. Defines datatypes and indexing rules.
  5. 5. DOCUMENT BASED REST API Simple to use and easy to understand.
  6. 6. CREATE A DOCUMENT cr -PS lclot90/otcspol - ' ul XOT oahs:20cnat/epe d { "ae:"akSoy, nm" Mr tr" "mi" "akmr-tr.o" eal: mr@aksoycm, "wte" "mr_tr" titr: @aksoy, "onr" "aaa, cuty: Cnd" "as:[ckpp,"aeet,"aaa] tg" "aeh" ckfs" cnd" }' #Rsos: epne {o"tu, "k:re "idx:cnat" _ne""otcs, "tp""epe, _ye:pol" "i""iMaiBDAWs5g, _d:9zCSQq1J8i7" "vrin:} _eso"1
  7. 7. READ IT BACK cr -GTlclot90/otcspol/i?rtytu ul XE oahs:20cnat/epe$dpet=re #Rsos: epne {_ne""otcs, "idx:cnat" "tp""epe, _ye:pol" "i""iMaiBDAWs5g, _d:9zCSQq1J8i7" "vrin:, _eso"1 "xss:re eit"tu, "suc":{ _ore "ae:"akSoy, nm" Mr tr" "mi" "akmr-tr.o" eal: mr@aksoycm, "wte" "mr_tr" titr: @aksoy, "onr" "aaa, cuty: Cnd" "as:[ckpp,"aeet,"aaa] tg" "aeh" ckfs" cnd" }}
  8. 8. DELETE IT! cr -DLT lclot90/otcspol/i ul XEEE oahs:20cnat/epe$d #Rsos: epne {o"tu, "k:re "on"tu, fud:re "idx:cnat" _ne""otcs, "tp""epe, _ye:pol" "i""iMaiBDAWs5g, _d:9zCSQq1J8i7" "vrin:} _eso"2
  9. 9. SIMPLE SEARCH! More on search to come cr -GTlclot90/otcspol/sac?=akpet=re ul XE oahs:20cnat/epe_erhqMr&rtytu
  10. 10. #Rsos: epne {to"1, "ok:4 "ie_u"fle tmdot:as, "sad"{ _hrs: "oa"5 ttl:, "ucsfl:, scesu"5 "ald: fie"0 }, "is: ht"{ "oa"1 ttl:, "a_cr"01746, mxsoe:.1424 "is: ht"[ {_ne""otcs, "idx:cnat" "tp""epe, _ye:pol" "i""llMTWqMZUlw, _d:sJyBSaAfU-D" "soe:.1424 _cr"01746, "suc":{ _ore "ae:"akSoy, nm" Mr tr" "mi" "akmr-tr.o" eal: mr@aksoycm, "wte" "mr_tr" titr: @aksoy, "onr" "aaa, cuty: Cnd" "as:[ckpp,"aeet,"aaa] tg" "aeh" ckfs" cnd" } } ] }}
  11. 11. THIS ALL SOUNDS TOO BADASS TO BE TRUE
  12. 12. DOCUMENT "DATABASE" IS A BIT LIMITED Partial updates are doable but painful No joins No map reduce Cannot replace all other datasources
  13. 13. BUT SEARCH IS AMAZZZING
  14. 14. SEARCH BETWEEN TYPES & INDEXES Search multiple types cr -GTlclot90/otcspol,opne/sac?=aeMr ul XE oahs:20cnat/epecmais_erhqnm:ak Search multiple indexes in your cluster cr -GTlclot90/alpol/sac?=aeMr ul XE oahs:20_l/epe_erhqnm:ak
  15. 15. FANCY SEARCH OPTIONS
  16. 16. SEARCH WITH TEXT EXPRESSIONS cr -GTlclot90/otcspol/sac?rtytu - ' ul XE oahs:20cnat/epe_erhpet=re d { "ur" { qey: "ur_tig:{ qeysrn" "ur" "akO wlo" qey: mr R edn } } }'
  17. 17. HIGHLIGHT SEARCH KEYWORDS Wrap search terms in highlighting text/markup/html. Great for larger documents, as you can extract fragments.
  18. 18. cr -GTlclot90/otcspol/sac?rtytu - ' ul XE oahs:20cnat/epe_erhpet=re d { "ur" { qey: "et:{ tx" "mi" "ak eal: mr" } }, "ihih" { hglgt: "ils:{ fed" "mi" {, eal: } "ae:{ nm" } } } }'
  19. 19. FACETS Facets provide aggregated data about a query. You can use this data to create drill down search, or histogram data. Term counts. Custom script values. Ranges - like price ranges. Geo distance facets - aggregate results by distance.
  20. 20. cr -GTlclot90/otcspol/sac?rtytu - ' ul XE oahs:20cnat/epe_erhpet=re d { "ur" { qey: "ur_tig:{ qeysrn" "ur" ".o" qey: *cm } }, "aes:{ fct" "agd:{trs:{fed:"as}} tge" "em" "il" tg" } }'
  21. 21. KNOBS & BUTTONS
  22. 22. MAPPINGS Allows fine-grained searching later on, and lets you configure custom mappings. Control the data types, and indexing used for JSON document types. Disable indexing on specific fields. Configure custom analyzers. For example, non-english stemming.
  23. 23. AVAILABLE MAPPING TYPES string, integer, float, boolean, null object - Standard type for nested objects. Allows Arrays are automatically handled as the above. properties to be defined.
  24. 24. multi_field - Allows a field to be handled multiple ways with different aliases. nested - Indexes sub objects, and works with nested filter/queries. ip - For ipv4 data. geo_point - For lat/lon values. Enables piles of search options. attachment - Store a blob. Can index many text based documents like PDF.
  25. 25. CREATE A MAPPING cr -PTlclot90/otcspol/mpig- ' ul XU oahs:20cnat/epe_apn d { "epe:{ pol" "rpris:{ poete" "ae:{tp" "tig} nm" "ye: srn", "mi" {tp" "tig} eal: "ye: srn", "wte" {tp" "tig} titr: "ye: srn", "onr" {tp" "tig} cuty: "ye: srn", "as:{tp" "tig} tg" "ye: srn" } } }'
  26. 26. DEFINE THE ANALYZER USED When defining a field you can use a a y e i d x a a y e , nlzr ne_nlzr and s a c _ n l z r customize the way data is stored, and or e r h a a y e to searched. You can also disable analyzing for specific fields.
  27. 27. DISABLE INDEXING { "ae:{ nm" "ye:"tig, tp" srn" "ne" "o_nlzd, idx: ntaaye" }, "oe:{ nn" "ye:"nee" tp" itgr, "ne" "o idx: n" } }
  28. 28. SHARDS & REPLICAS SHARDS Define how many nodes you want to split your data across. If a node goes down, you still have some of your data. You can use routing to control how data is sharded. More shards improves indexing performance, as work is distributed.
  29. 29. SIMPLE SHARDING
  30. 30. SHARD OVER MULTIPLE NODES
  31. 31. REPLICAS Define how many copies of your data you want. If several nodes go down, you might still have all your data. More replicas improves search performance and cluster availability.
  32. 32. REPLICAS
  33. 33. MULTI-TENANCY Multi-tenancy is a reasonably common requirement, and there are a few ways to do it.
  34. 34. ONE INDEX PER 'TENANT' Great for small number of tenants. Painful for larger number of tenants. As sharding and replicas can be harder to manage.
  35. 35. cr -GTlclot90/akcnat/sac?rtytu - ' ul XE oahs:20mr/otcs_erhpet=re d { "ur" { qey: "ur_tig:{ qeysrn" "ur" "ednO js" qey: wlo R oe } } }'
  36. 36. SPECIAL FILTER CONDITIONS More error prone as you have to include a filter condition. Easy to shard and setup replicas. Easily scales to many tenants. As shards/replicas are shared. Make sure tenant id is a non-analyzed value.
  37. 37. cr -GTlclot90/conigivie/sac?rtytu - ' ul XE oahs:20acutn/nocs_erhpet=re d { "ur" { qey: "itrd:{ flee" "itr:{ fle" "em:{acutd:1 tr" "coni" } }, "ur" { qey: "ur_tig:{ qeysrn" "ur" "upewf" qey: prl ii } } } } }'
  38. 38. OTHER BATTERIES INCLUDED Routing Define how documents are sharded. Rivers Pipe data in realtime from sources like RabbitMQ. Thrift Talk thirft to ElasticSearch.
  39. 39. INTEGRATION WITH CAKEPHP
  40. 40. HTTPSOCKET + JSON_ENCODE() Basic, can be hard to use. No magic.
  41. 41. ELASTICSEARCH DATASOURCE (David Kullman) Behavior to auto index on aftersave Datasource for searching elasticsearch Console app to index models
  42. 42. ELASTICSEARCH PLUGIN (Kevin von Zonneveld) Similar features to the previous plugin Offers more control on how data is indexed
  43. 43. QUESTIONS?

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