Finding the Needle (DjangoCon US 2013)

The ability to find content on a site is important to users, and there are some great tools that make a sometimes tricky problem a lot simpler. This talk will address the search problem, introduce a few of the tools at your disposal, and provide a way to get started using Django Haystack.

The goals is for developers new to search to understand what's different about using a search engine as an additional service, to be aware of some of the "gotchas", and to know not just what's possible but how to get started.

Transcript

1. n Hendrik 05 work Synopsis Pla es' Linkia levis was in fact P. lanc n's name in his 1810 work Prodromus Florae diae et Insulae Van Diemen, and echoed Persoon's vanilles' original name and specimen. In the 1995 ing revision of Finding the Needle, reviewed the m erial of Linkia levis, and found that Cavanilles had m erial from both Search and Django. He set one spe ee, which was clearly P. levis, as the lectotype, whi aterial with the description. Common names include eebung, willow geebung and smooth synonyms. Th derived from the language word. Like most other onia levis has seven chromosomes th her Proteaceae. In 1870, G gement of Pe th DjangoCon 2013 - Ben Lopatin
2. ➜    ~  whoami Ben  Lopatin  (bennylope) ➜    ~  echo  $HOME Richmond,  VA  /  Washington,  DC Principal and developer @ Wellfire Interactive
3. 1.Understand the search problem 2.Role of the search engine 3.Nifty search features 4.Adding search with Haystack 5.Implementation strategies 6.Limitations and options
4. Search text content
5. Searching for & Looking for
6. Use Cases
10. Search Engines
11. A queryable inverted index of documents based on filtered tokens.
12. Tokenized “These” “are” “not” “the” “droids” “you” “are” “looking” “for”
13. Filtered “these” “droids” “you” “look” “These” “are” “not” “the” “droids” “you” “are” “looking” “for”
14. Indexed
15. Documents
16. •ElasticSearch (Java/Lucene) •Solr (Java/Lucene) •Whoosh (Python) •Xapian (C++) •Sphinx (C++)
17. Sounds like Mongo! Sounds like Mongo!
18. What about SQL full-text indexing?
19. Data in Data out
20. ANALYZERS ANALYZERS ANALYZERS ANALYZERS ANALYZERS ANALYZERS ANALYZERS ANALYZERS ANALYZERS ANALYZERS ANALYZERS ANALYZERS
21. Tokenizer + Filters
22. Whitespace N-grams Word delimiters Snowball
23. ASCII Stemming Lowercase Stop words Synonyms
24. Language specific
25. Querying
26. Faceting
27. Spell chexking
28. Geospatial search
29. Auto complete compile zone
30. Highlighting
31. Documents
32. Let’s talk Django
33. Haystack
34. Just Pythonic abstraction
35. ORM oriented
36. Haystack components
37. SearchIndex SearchQuerySet SearchForm SearchView
38. SearchIndex SearchQuerySet SearchForm SearchView
39. SearchIndex = data mapping Take Away
40. SearchIndex SearchQuerySet SearchForm SearchView
41. SearchIndex SearchQuerySet SearchForm SearchView
42. SearchIndex SearchQuerySet SearchForm SearchView
43. ElasticSearch
44. Mac: brew install elasticsearch http://mxcl.github.com/homebrew/ Ubuntu/Debian Download .deb http://www.elasticsearch.org/download/
45. Let searching guide index modeling
46. Insert, update, remove
47. Haystack management commands
48. clear_index update_index rebuild_index flags: remove, age, batch-size, using, and more
49. Indexing Strategies One time Real time Real time-ish (queued) Periodic
50. Indexing Strategies One time Real time Real time-ish (queued) Periodic
51. Indexing Strategies One time Real time Real time-ish (queued) Periodic
52. Indexing Strategies One time Real time Real time-ish (queued) Periodic
53. Indexing Strategies One time Real time Real time-ish (queued) Periodic
54. Building indexed content
55. Model attribute Templates Field method
56. Model attribute Templates Field method
57. Model attribute Templates Field method
58. Model Queryset interfaces
59. SearchIndex def get_model(self): return MyModel Default manager
60. SearchIndex def get_queryset(self): return MyModel.objects.filter( active=True) Defined queryset
61. One method to rull them all: .indexable()
62. Geospatial querying
63. def make_point(point_string): # Returns a Point or None from coords string def spatial_search_view(request): # Clean form, build initial searchqueryset bottom_left = make_point(request.GET.get('bl', '')) top_right = make_point(request.GET.get('tr', '')) if bottom_left and top_right: queryset = queryset.within('coords', bottom_left, bottom_right)
64. queryset = queryset.distance('coords', center_point).order_by(‘distance’)
65. Improving results quality
66. Adjusting relevance
67. Field boosting Document boosting Term boosting
68. Field boosting Document boosting Term boosting somefield = indexes.CharField(boost=1.2)
69. Field boosting Document boosting Term boosting def prepare(self, obj): data = super(ThisIndex, self).prepare(obj) data['_boost'] = 1.2 return data
70. Field boosting Document boosting Term boosting sqs = SearchQuerySet().boost('banana', 1.1)
71. Log searches, results & success
72. Search engine as cache*
73. Doing More with Haystack
74. ElasticSearch analysis settings { "index" : { "analysis" : { "analyzer" : { "synonym" : { "tokenizer" : "whitespace", "filter" : ["synonyms"] } }, "filter" : { "synonyms" : { "type" : "synonym", "synonyms_path" : "analysis/synonym.txt" } } } } }
75. ElasticSearch analysis settings { "index" : { "analysis" : { "analyzer" : { "synonym" : { "tokenizer" : "whitespace", "filter" : ["synonyms"] } }, "filter" : { "synonyms" : { "type" : "synonym", "synonyms_path" : "analysis/synonym.txt" } } } } }
76. ElasticSearch analysis settings { "index" : { "analysis" : { "analyzer" : { "synonym" : { "tokenizer" : "whitespace", "filter" : ["synonyms"] } }, "filter" : { "synonyms" : { "type" : "synonym", "synonyms_path" : "analysis/synonym.txt" } } } } }
77. ElasticSearch analysis settings { "index" : { "analysis" : { "analyzer" : { "synonym" : { "tokenizer" : "whitespace", "filter" : ["synonyms"] } }, "filter" : { "synonyms" : { "type" : "synonym", "synonyms_path" : "analysis/synonym.txt" } } } } }
78. ElasticSearch analysis settings { "index" : { "analysis" : { "analyzer" : { "synonym" : { "tokenizer" : "whitespace", "filter" : ["synonyms"] } }, "filter" : { "synonyms" : { "type" : "synonym", "synonyms_path" : "analysis/synonym.txt" } } } } }
79. analysis/synonym.txt droid, robot, android => robot shag, carpet, rug, wookie => rug
80. ElasticSearch analysis settings { "index" : { "analysis" : { "analyzer" : { "synonym" : { "tokenizer" : "whitespace", "filter" : ["synonyms"] } }, "filter" : { "synonyms" : { "type" : "synonym", "synonyms_path" : "analysis/synonym.txt" } } } } }
81. ElasticSearch analysis settings { "index" : { "analysis" : { "analyzer" : { "synonym" : { "tokenizer" : "whitespace", "filter" : ["synonyms"] } }, "filter" : { "synonyms" : { "type" : "synonym", "synonyms_path" : "analysis/synonym.txt" } } } } }
82. ElasticSearch analysis settings { "index" : { "analysis" : { "analyzer" : { "synonym" : { "tokenizer" : "whitespace", "filter" : ["synonyms"] } }, "filter" : { "synonyms" : { "type" : "synonym", "synonyms_path" : "analysis/synonym.txt" } } } } }
83. ElasticSearch analysis settings { "index" : { "analysis" : { "analyzer" : { "synonym" : { "tokenizer" : "whitespace", "filter" : ["synonyms"] } }, "filter" : { "synonyms" : { "type" : "synonym", "synonyms_path" : "analysis/synonym.txt" } } } } }
84. How does Haystack map the index?
85. if current_mapping != self.existing_mapping: try: # Make sure the index is there first. self.conn.create_index(self.index_name, self.DEFAULT_SETTINGS) self.conn.put_mapping(self.index_name, 'modelresult', current_mapping) self.existing_mapping = current_mapping except Exception: if not self.silently_fail: raise
86. if field_class.field_type in ['date', 'datetime']: field_mapping['type'] = 'date' elif field_class.field_type == 'integer': field_mapping['type'] = 'long' elif field_class.field_type == 'float': field_mapping['type'] = 'float' elif field_class.field_type == 'boolean': field_mapping['type'] = 'boolean' elif field_class.field_type == 'ngram': field_mapping['analyzer'] = "ngram_analyzer" elif field_class.field_type == 'edge_ngram': field_mapping['analyzer'] = "edgengram_analyzer" elif field_class.field_type == 'location': field_mapping['type'] = 'geo_point' # ... code skipped here if field_mapping['type'] == 'string' and field_class.indexed: field_mapping["term_vector"] = "with_positions_offsets" if not hasattr(field_class, 'facet_for') and not\ field_class.field_type in('ngram', 'edge_ngram'): field_mapping["analyzer"] = "snowball"
87. This page intentionally blank
88. Use Your Own Backend
89. New default analyzer
90. user_analyzer = getattr(settings,'ELASTICSEARCH_DEFAULT_ANALYZER') if user_analyzer: setattr(self, 'DEFAULT_ANALYZER', user_analyzer)
91. def build_schema(self, fields): content_field_name, mapping = super(ConfigurableElasticBackend, self).build_schema(fields) for field_name, field_class in fields.items(): field_mapping = mapping[field_class.index_fieldname] if field_mapping['type'] == 'string' and field_class.indexed: if not hasattr(field_class, 'facet_for') and not \ field_class.field_type in('ngram', 'edge_ngram'): field_mapping['analyzer'] = self.DEFAULT_ANALYZER) mapping.update({field_class.index_fieldname: field_mapping}) return (content_field_name, mapping)
92. New search mapping
93. class ConfigurableElasticBackend(ElasticsearchSearchBackend): def __init__(self, connection_alias, **connection_options): super(ConfigurableElasticBackend, self).__init__( connection_alias, **connection_options) user_settings = getattr(settings, 'ELASTICSEARCH_INDEX_SETTINGS') if user_settings: setattr(self, 'DEFAULT_SETTINGS', user_settings)
94. Pick analyzers by field
95. def build_schema(self, fields): content_field_name, mapping = super(ConfigurableElasticBackend, self).build_schema(fields) for field_name, field_class in fields.items(): field_mapping = mapping[field_class.index_fieldname] if field_mapping['type'] == 'string' and field_class.indexed: if not hasattr(field_class, 'facet_for') and not \ field_class.field_type in('ngram', 'edge_ngram'): field_mapping['analyzer'] = getattr(field_class, 'analyzer', self.DEFAULT_ANALYZER) mapping.update({field_class.index_fieldname: field_mapping}) return (content_field_name, mapping)
96. from haystack.fields import CharField as BaseCharField class ConfigurableFieldMixin(object): def __init__(self, **kwargs): self.analyzer = kwargs.pop('analyzer', None) super(ConfigurableFieldMixin, self).__init__(**kwargs) class CharField(ConfigurableFieldMixin, BaseCharField): pass
97. SearchView: Non-CBV CBV
98. Some gotchas
99. Debugging search issues
100. Search is hard
101. n Hendrik 05 work Synopsis Pla es' Linkia levis was in fact P. lanc n's name in his 1810 work Prodromus Florae diae et Insulae Van Diemen, and echoed Persoon's vanilles' original name and specimen. In the 1995 ing revision of THE END, reviewed the mounted m a levis, and found that Cavanilles had mounted ma h Search and Django. He set one specimen of the t s clearly P. levis, as the lectotype, which aligned th e description. Common names include broad-leave llow geebung and smooth synonyms. The term ge om the language word. Like most other members o vis has seven chromosomes that are la e. In 1870, George Benth rsoonia in Volu nu DjangoCon 2013 - Ben Lopatin ciafactbook.herokuapp.com tinyurl.com/finding-the-needle