Introduction
For any project there may be a need to use a database full-text search. We expect high speed and relevant results from this search. When we face such problem, we usually think about Solr, ElasticSearch, Sphinx, AWS CloudSearch, etc. But in this article we will talk about PostgreSQL. Starting from version 8.3, a full-text search support in PostgreSQL is available. Let's look at how it is implemented in the DBMS itself.
The basic idea of a full-text search in PostgreSQL is that you create an index using a document, which contains the words you would like to use for search. For storing search data in PostgreSQL, new data type has been added - tsvector. Tsvector is a storage for the tokens from a document.
postgres=# SELECT to_tsvector('All cats love fish but hate to get their paws wet');
to_tsvector
--------------------------------------------------------------
'cat':2 'fish':4 'get':8 'hate':6 'love':3 'paw':10 'wet':11
(1 row)to_tsvector - is a support function that has been implemented to convert a document into the tsvector data type.
We can use logical operators & (AND), | (OR), and ! (NOT) for our queries. We need to use tsquery for this:
postgres=# SELECT to_tsquery('fishes | dog);
to_tsquery
----------------
'fish' | 'dog'
(1 row)When we want to check the search for a match, we have to use a full-text operator @@:
postgres=# SELECT to_tsvector('All cats love fish but hate to get their paws wet') @@ to_tsquery('fishes | dog');
?column?
----------
t
(1 row)Excellent! The match is found. Let's write a query that uses a logical operator AND:
postgres=# SELECT to_tsvector('All cats love fish but hate to get their paws wet') @@ to_tsquery('fishes & cat');
?column?
----------
t
(1 row)Match is found again. Now let's modify the query so that it could return a negative result:
postgres=# SELECT to_tsvector('All cats love fish but hate to get their paws wet') @@ to_tsquery('cat & dog');
?column?
----------
f
(1 row)More information can be found in the PostgreSQL documentation. So at this point we will stop talking about full-text search in PostgreSQL and move on to using this in Django.
Using a full text search django
A full-text search with PostgreSQL support in Django appeared with version 1.10. In previous Django versions the full-text search in PostgreSQL was possible with djorm-ext-pgfulltext library.
Let's look at what kind of interface gives for us django postgres full text search. The easiest way to do a full-text search using search. Let’s consider the following example:
Post.objects.filter(body__search='programming') # [, ]In our Post model 2 matches with the word Programming have been found. This record creates to_tsvector in the database for the body field and plainto_tsquery for Programming.
SearchVector
search lookup is great when we need to search by one field. If we want to search by two or more fields, SearchVector should be used, for example:
from django.contrib.postgres.search import SearchVector
Post.objects.annotate(search=SearchVector('body', 'category__title')).filter(search='programming') # [, , ]As you can see, this time we were searching by body and category__title fields, and as a result, found 3 matches. SearchVector objects can be grouped together for the further reuse (the DRY principles are followed):
from django.contrib.postgres.search import SearchVector
body_vector = SearchVector('body')
category_title_vector = SearchVector('category__title')
Post.objects.annotate(search=body_search + category_title_search).filter(search='programming')SearchQuery
If we need to convert the word into the search query object, we can use SearchQuery:
from django.contrib.postgres.search import SearchQuery
SearchQuery('python') & SearchQuery('java') # python AND java
SearchQuery('python') | SearchQuery('java') # python OR java
~SearchQuery('java') # NOT javaSearchRank
Now let's assume that we need to sort the search results by relevance. PostgreSQL provides a ranging function for these purposes, which determines how often the words are found in the document, how these words are close to each other in the document and how important that part of the document, where these words are situated, is. Ranking interface in Django full text search is provided by SearchRank. Let's look at an example:
from django.contrib.postgres.search import SearchQuery, SearchRank, SearchVector
body_vector = SearchVector('body')
term_query = SearchQuery('programming')
Post.objects.annotate(rank=SearchRank(body_vector, term_query)).order_by('-rank')The most relevant records are at the top.
Search configuration
SearchQuery and SearchVector accept the config as a parameter. It is necessary for using different search settings, for example, if you want to change the language parser or dictionary.
from django.contrib.postgres.search import SearchQuery, SearchVector
Post.objects.annotate(search=SearchVector('body', config='german')).filter(search=SearchQuery('programmierung', config='german'))We can also control the relevance, by setting different weight to those fields, which we plan to search by. For this SearchVector accepts a weight parameter. As the weight value there can be one of these letters: D, C, B, A. The default values for these letters are 0.1, 0.2, 0.4 and 1.0 respectively.
from django.contrib.postgres.search import SearchQuery, SearchRank, SearchVector
search_vector = SearchVector('body', weight='A') + SearchVector('category__title', weight='B')
term_query = SearchQuery('programming')
Post.objects.annotate(rank=SearchRank(search_vector, term_query)).filter(rank__gte=0.3).order_by('rank')Optimization
Search in your project may be one of the most resource-intensive operations, especially if it is a search in a few hundred or even thousands of records. And this is the point when the optimization problem arises. Let's look at the tools for optimization which PostgreSQL gives us and how it is implemented in Django.
Indexes
To speed up the search, PostgreSQL offers the use of indexes. It is worth noting that the index is not required for a full-text search. But if the search takes place in specific (constant) columns, the presence of the index is desirable.
There are 2 types of indexes:
GiST (Generalized Search Tree) - a bit signature is assigned for each document, which contains information about all the tokens that are in this document. It is created with a command:
CREATE INDEX name ON table USING GIST (column);GIN (Generalized Inverted Index) - in this type of index the key is a token and the value is an organized list of document identifiers which contain the token. It is created with a command:
CREATE INDEX name ON table USING GIN (column);There is a big difference in performance between these two types of indexes. Before you choose which one to use, I recommend you to study the documentation on them in detail. I would like to add that:
- the creation of GIN index is 3 times faster than GiST
- GIN index is 2-3 times bigger than GiST-index
- Search for GIN index is 3 times faster than GiST index
- GiN index updates 10 times slower
It is best to use GiST index for updated data, and GIN index suits well for static data.
SearchVectorField
Another way to speed up the search is to use SearchVectorField in the model. You will need to use triggers to fill in this field, for example, as described in the Triggers for Automatic Updates documentation. After that, you can query the field, so if it is annotated SearchVector.
Post.objects.update(search_vector=SearchVector('body_text'))
Post.objects.filter(search_vector='programming') # [, ]Conclusion
When you need a full-text search, and you do not want to connect third-party libraries to the project, you can easily manage database resources using a full-text search in PostgreSQL and the api, which provides Django for work with the search.
Subscribe for the news and updates
We’ll tell you how to create a video timelapse from a sequence of snapshots and provide customers with video playlists optimized for browser playback.
One of our recent projects includes the functionality of an inline code editor. This code editor needed to be highly extensible and have custom features. To address this, we chose Codemirror v6 due to its peculiar architecture - it is highly customizable, and all the additional features are provided into codemirror engine as Extension objects.
In this article, we have prepared a quick tutorial on how to use MongoDB in Python and listed top ORM.
In our article, you will find the best JavaScript framework comparison so that you know for sure how to choose the right one for your project.
There are two most popular web frameworks in Python. There is the Django with lots of intelligent defaults and the Flask micro framework with complete freedom in the choice of modules. Let’s see, what django vs flask is in 2017.
When dealing with one of our projects (LookSMI media monitoring platform) we have to handle the huge volume of data – and its quantity is constantly growing. At the same time, we must run quick searches with smart rules. In this article I'll explain how we have achieved required performance.
