Full Text search in Django with Postgres

Post on 07-Nov-2014

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There are number of players that provide full text search feature, starting from embedded search to dedicated search servers [solr, sphinx, elasticsearch etc], but setting up and configuring them is a time consuming process and requires considerable knowledge of the tools. What if we could get comparable search results using full text search capabilities of Postgres. Developers already have the working knowledge of the database, so this should come natural. In addition to that, it will be one less tool to manage. Code: https://github.com/Syerram/postgres_search

Transcript of Full Text search in Django with Postgres

Full Text SearchDjango + Postgres

Search is everywhere

Search expectations● FAST● Full Text search● Linguistic support (“craziness | crazy”)● Ranking● Fuzzy Searching● More like this

Django

● SLOW● `icontains` is dumbed down version of

search● Searching across tables is pain● No relevancy, ranking or similar words

unless done manually● No easy way for fuzzy searching

Other Alternatives

● Solr● ElasticSearch● AWS CloudSearch● Sphinx● etc*

If you’re using any of the above, use Haystack

Postgres Search

● FAST● Simple to implement● Supports Search features like Full Text,

Ranking, Boosting, Fuzzy etc..

Django

Live Example● Search Students by name or by course● Use South migration to create tsvector

column● Store title in Search table● Update Search table via Celery on Save of

Student data

https://github.com/Syerram/postgres_search

GIN, GIST

● GIST is Hash based, GIN is B-trees● GINs = GISTs * 3 , s = Speed● GINu = GISTu * 3 , u = update time● GINkb = GISTkb * 3, kb = sizeA gin indexCREATE INDEX student_index ON students USING gin(to_tsvector('english'name));

Source http://www.postgresql.org/docs/9.2/static/textsearch-indexes.html

Full Text Search● All text should be preprocessed using tsvector and queried using tsquery

● Both reduce the text to lexemesSELECT to_tsvector('How much wood would a woodchuck chuck If a woodchuck could chuck wood?')"'chuck':7,12 'could':11 'much':2 'wood':3,13 'woodchuck':6,10 'would':4"

● Both are required for searching to work on normal text

SELECT to_tsvector('How much wood would a woodchucks chucks If a woodchucks could chucks woods?') @@ 'chucks' -- False

SELECT to_tsvector('How much wood would a woodchucks chucks If a woodchucks could chucks woods?') @@ to_tsquery('chucks') -- True

Full Text Search (Contd.)

● Technically you don’t need index, but for large tables it will be slow

SELECT * FROM students where to_tsvector('english', name) @@ to_tsquery('english', 'Kirk')

● GIN or GIST IndexCREATE INDEX <index_name> ON <table_name> USING gin(<col_name>);

● Expression BasedCREATE INDEX <index_name> ON <table_name> USING gin(to_tsvector(COALESCE(col_name,'') || COALESCE(col_name,'')));

Boosting

● Boost certain results over others● Still matching● Use ts_rank to boost resultse.g.…ORDER BY ts_rank(document, to_tsquery('python')) DESC

Ranking● Importance of search term within documente.g.Search term found in title > description > tag

● Use setweight to assign importance to each field when preparing Document

e.g.setweight(to_tsvector(‘english’, post.title), 'A') || setweight(to_tsvector(‘english’, post.description), 'B') || setweight(to_tsvector('english', post.tags), 'C'))...--In search query use ‘ts_rank’ to order by ranking

Trigram

● Group of 3 consecutive chars from String● Similarity between strings is matched by # of

trigrams they sharee.g. "hello": "h", "he", "hel", "ell", "llo", "lo", and "o”

"hallo": "h", "ha", "hal", "all", "llo", "lo", and "o”Number of matches: 4

● Use similarity to find related terms. Returns value between 0 to 1 where 0 no match and 1 is exact match

Soundex/Metaphone

● Oldest and only good for English names● Converts to a String of Length 4. e.g. “Anthony == Anthoney” => “A535 == A535”

● Create index itself with Soundex or Metaphone

e.g. CREATE INDEX idx_name ON tb_name USING GIN(soundex(col_name));

SELECT ... FROM tb_name WHERE soundex(col_name) = soundex(‘...’)

Pro & Con

Pros● Quick implementation● Lot easier to change document format and call refresh index● Speed comparable to other search engines● Cost effective

Cons● Not as flexible as pure search engines, like Solr● Not as fast as Solr though pretty fast for humans● Tied to Postgres● Indexes can get pretty large, but so can search engine indexes

Django ORM

● Implements Full text Searchclass StudentCourse(models.Model): ... search_index = VectorField() objects = SearchManager( fields = ('student__user__name', 'course__name'), config = 'pg_catalog.english', # this is default search_field = 'search_index', # this is default auto_update_search_field = True )● StudentCourse.objects.search("David")

https://github.com/djangonauts/djorm-ext-pgfulltext

Next Steps

● Add Ranking, Boosting, Fuzzy Search to djorm pgfulltext

e.g. StudentCourse.objects.search("David & Python").rank("Python")StudentCourse.objects.fuzzy_search("Jython").rank("Python")StudentCourse.objects.soundex("Davad").rank("Java") & More

● Continue to add examples to postgres_search

Tips● Use separate DB if necessary or use

Materialized Views● Don’t index everything. Limit your

searchable data● Analyze using `Explain` and ts_stat● Create indexes on fly using concurrently● Don’t pull Foreign Key objects in search

Code

• https://github.com/Syerram/postgres_search

• Stack• AngularJS, Django, Celery, Postgres

• Feel free to Fork, Pull Request

@agileseeker, github/syerram, syerram.silvrback.com/

Sai