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Vegas ES
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- 6. Index / Type -An index is a collection of documents that should be grouped together for a common reason. - A type is a collection of documents all share an identical (or very similar) schema
- 7. Sharding PUT http:localhost:9200/reviews { "settings" : "index": { "number_of_shards" : 5 "number_of_replicas" : 1 } } }'
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- 17. Relationships ● Application SideJoins ● Parent-Child ● Nested objects
- 18. Relationships ● Application SideJoins ● Parent-Child ● Nested objects ● ● ● ●
- 19. Relationships ● Application SideJoins ● Parent-Child ● Nested objects ● Parent-child queries can be 5 to 10 times slower than the equivalent nested query! ● ● ●
- 20. Relationships ● Application SideJoins ● Parent-Child ● Nested objects ● ● ● ● ● ●
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- 23. Searching A filter asksa yes|no question of every document and is used for fields that contain exact values - Is a date within the range 2012 to 2015 ? - Is the status “Approved” ? - Is the language code “DE” ? STRUCTURED SEARCH A query calculates how relevant each document is to the query, and assigns it a relevance _score, which is later used to sort matching documents by relevance. - Containing the word run, but maybe also matching runs, running, jog, or sprint UNSTRUCTURED SEARCH
- 24. Searching A filter asksa yes|no question of every document and is used for fields that contain exact values - Is a date within the range 2012 to 2015 ? - Is the status “Approved” ? - Is the language code “DE” ? STRUCTURED SEARCH A query calculates how relevant each document is to the query, and assigns it a relevance _score, which is later used to sort matching documents by relevance. - Containing the word run, but maybe also matching runs, running, jog, or sprint UNSTRUCTURED SEARCH
- 25. Terms Query Example POST/{index}/{type}/_search
- 26. Unstructured Search (FullText) Quick brown foxes leap over lazy dogs in summer Quick, brown, foxes, leap, over, lazy, dogs, in, summer Quick, brown, foxes, leap, lazy, dogs, summer Quick, brown, fox, leap, lazy, dog, summer fast, brown, fox, jump, lazy, dog, summer tsar -> star TERM | DOC1 | DOC2 ----------------------- Quick | | X The | X | brown | X | X dog | X | dogs | | X fox | X | foxes | | X in | | X jumped | X | lazy | X | X leap | | X over | X | X quick | X | summer | | X the | X | ----------------------- Inverted Index
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- 28. Scoring & Relevancein Full-Text Search Relevance is the algorithm to calculate how similar the contents of a field to a query. TF/IDF Term Frequency How often does the term appear in the field? Inverse Document Frequency How often does each term appear in the index? Field Length Norm How long is the field?
- 29. Vector Space Model Thevector space model provides a way of comparing a multiterm query against a document. - The model represents both the document and the query as vectors.
- 30. Vector Space Model 1.I am happy in summer. 2. After Christmas I’m a hippopotamus. 3. The happy hippopotamus helped Harry. - By measuring the angle between the query vector and the document vector, it is possible to assign a relevance score to each document. - If The angle between a document and the query is large, so it is of low relevance.
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- 38. Aggregation Search Analytics Business Requirement“Help me find the best documents ?” “What do theses documents tell me about my business ?” Enablers Matching, Relevance, Filtering, Auto-completion,... Summaries, Patterns, Trends, Outliers, Predictions, Visualization - Aggregations help build complex summaries & analytics of the indexed data.
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- 42. Metrics Aggregations ● ExtendedStats Aggregation ● Geo Bounds Aggregation ● Geo Centroid Aggregation ● Percentiles Aggregation ● Stats Aggregation ● Value Count Aggregation ● Avg, Sum, Min, Max Aggregations
- 43. Metrics Aggregations ● ExtendedStats Aggregation ● Geo Bounds Aggregation ● Geo Centroid Aggregation ● Percentiles Aggregation ● Stats Aggregation ● Value Count Aggregation ● Avg, Sum, Min, Max Aggregations
- 44. Metrics Aggregations ● ExtendedStats Aggregation ● Geo Bounds Aggregation ● Geo Centroid Aggregation ● Percentiles Aggregation ● Stats Aggregation ● Value Count Aggregation ● Avg, Sum, Min, Max Aggregations
- 45. Metrics Aggregations ● ExtendedStats Aggregation ● Geo Bounds Aggregation ● Geo Centroid Aggregation ● Percentiles Aggregation ● Stats Aggregation ● Value Count Aggregation ● Avg, Sum, Min, Max Aggregations
- 46. Metrics Aggregations ● ExtendedStats Aggregation ● Geo Bounds Aggregation ● Geo Centroid Aggregation ● Percentiles Aggregation ● Stats Aggregation ● Value Count Aggregation ● Avg, Sum, Min, Max Aggregations
- 47. Metrics Aggregations ● ExtendedStats Aggregation ● Geo Bounds Aggregation ● Geo Centroid Aggregation ● Percentiles Aggregation ● Stats Aggregation ● Value Count Aggregation ● Avg, Sum, Min, Max Aggregations
- 48. Metrics Aggregations ● ExtendedStats Aggregation ● Geo Bounds Aggregation ● Geo Centroid Aggregation ● Percentiles Aggregation ● Stats Aggregation ● Value Count Aggregation ● Avg, Sum, Min, Max Aggregations
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- 56. What’s uncommonly common aboutthis sub-group ?
- 57. Significant Terms - Significant_termsanalyzes your data and finds terms that appear with a frequency that is statistically anomalous compared to the background data. - It can uncover surprisingly sophisticated trends and correlation in your data. - Used in discovering anomalies
- 58. Significant Terms Summarisehow theirstyle differ from everyone else Find all people who like these products
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- 62. ElasticSearch in Pdp(Vegas)
- 63. The New PDP ReviewsCatwalk
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