Discovering python search engines

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Discovering python search engine José Manuel Ortega - Pycones 2017

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Agenda ● Introduction to search engines ● ElasticSearch,whoosh,django-hystack ● ElasticSearch example ● Other solutions & tools ● Conclusions

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Search engines

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Search engines ● Document based ● A document is the unit of searching in a full text search system. ● A document can be a json or python dictionary

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Core concepts

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Core concepts ● Index: Named collection of documents that have similar characteristics(like a database) ● Type:Logical partition of an index that contains documents with common fields(like a table) ● Document:basic unit of information(like a row) ● Mapping:field properties(datatype,token extraction). Includes information about how fields are stored in the index

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Core concepts ● Relevance are the algorithms used to rank the results based on the query ● Corpus is the collection of all documents in the index ● Segments:Sharded data storing the inverted index.Allow searching in the index in a efficient way

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Inverted index ● Is the heart of the search engine ● Each inverted index stores position and document IDs

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ElasticSearch

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● Open source search server based on Apache Lucene ● Written in Java ● Cross-platform ● Communications with the search server is done through HTTP REST API ● curl -X http://localthost:9200///id

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● You can add a document without creating an index ● ElasticSearch will create the index,mapping type and fields automatically ● ElasticSearch will infer the data types based on the document’s data

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Metadata Fields ● Each document has metadata associated with it ● _index:Allows matching documents based on their indexes. ● _type:Type of the document ● _id:Document id(not indexed) ● _uid:_type + _id(indexed) ● _source:contains the json passed in creation time of the index or document(not indexed) ● _version

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ElasticSearch vs Relational DB

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Creating an Index curl -XPUT ‘localhost:9200/myindex’-d { “settings”:{..} “mappings”:{..} }

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Searching a document curl -XGET ‘localhost:9200/myindex/mydocument/_search?q=elasticSearch’ curl -XGET ‘localhost:9200/myindex/mydocument/_search?pretty’ -d{ “query”:{ “match”:{ “_all”:”elasticSearch” } } } Query DSL

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Searching a document ● Search can get much more complex ○ Multiple terms ○ Multi-match(math query on specific fields) ○ Bool(true,false) ○ Range ○ RegExp ○ GeoPoint,GeoShapes

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ElasticSearch python client ● The official low-level client is elasticsearch-py ○ pip install elasticsearch

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ElasticSearch-py API

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ElasticSearch-py API

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Geo queries ● Elastic search supports two types of geo fields ○ geo_point(lat,lon) ○ geo_shapes(points,lines,polygons) ● Perform geographical searches ○ Finding points of interest and GPS coordinates

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Whoosh

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● Pure-python full-text indexing and searching library ● Library of classes and functions for indexing text and then searching the index. ● It allows you to develop custom search engines for your content. ● Mainly focused on index and search definition using schemas ● Python 2.5 and Python 3

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Schema

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Create index and insert document

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Searching single field

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Searching multiple field

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Django-haystack

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● Multiple backends (you have a Solr & a Whoosh index, or a master Solr & a slave Solr, etc.) ● An Elasticsearch backend ● Big query improvements ● Geospatial search (Solr & Elasticsearch only) ● The addition of Signal Processors for better control ● Input types for improved control over queries ● Rich Content Extraction in Solr

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● Create the index ○ Run ./manage.py rebuild_index to create the new search index. ● Update the index ○ ./manage.py update_index will add new entries to the index. ○ ./manage.py rebuild_index will recreate the index from scratch.

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● Pros: ○ Easy to setup ○ Looks like Django ORM but for searches ○ Search engine independent ○ Support 4 engines (Elastic, Solr, Xapian, Whoosh) ● Cons: ○ Poor SearchQuerySet API ○ Difficult to manage stop words ○ Loose performance, because extra layer ○ Django Model based

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Other solutions ● https://xapian.org ● https://docs.djangoproject.com/en/1.11/ref/contrib/pos tgres/search/ ● https://www.postgresql.org/docs/9.6/static/textsearch. html

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pysolr

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Other tools

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References ● http://elasticsearch-py.readthedocs.io/en/master/ ● https://whoosh.readthedocs.io/en/latest ● http://django-haystack.readthedocs.io/en/master/ ● http://solr-vs-elasticsearch.com/ ● https://wiki.apache.org/solr/SolPython ● https://github.com/django-haystack/pysolr