These days I was in need of a full text search. The cool
kids teenagers in this
block are Elastic Search and Sorl: they are fast, flexible, heavy on resource consumptions and
require java, almost all the things that I want for a pet project running in a $5 digital
After discarding those options I was left with Xapian and postgres full-text search, while xapian seems to be richer in features, I decided to start with postgres due it native integration with django and my modest requirement for this particular project.
As you may have noticed, I'm running a job board. Voorjob is basically aggregating jobs from lever.co and letting the user search through it. Currently I have around 25k jobs in the database, this number grows slow, for each 2 or 3 jobs added another job is closed. So yah, if I have taken the elastic search path on this one, It would have been a text-book over-engineering situation.
To start using this new(ish) feature, I basically needed a
SearchVectorField in my model
and some method to update this field with the vectorized job description:
from django.contrib.postgres.search import SearchVectorField, SearchVector class Job(models.Model): title = models.CharField(max_length=200, blank=True) location = models.CharField(max_length=50, blank=True) body = models.TextField(null=True) body_vector = SearchVectorField(null=True) def make_search_vector(): self.body_vector=SearchVector('body') def save(self, *args, **kwargs): self.make_search_vector() super(Model, self).save(*args, **kwargs)
This approach works fine for something with few updates, like a job board, but if your application have frequently updates, you should avoid this strategy and have some task that periodically populate the vector:
or even better, you can do it directly with a postgres trigger by reading this documentation.
Now that you have your database ready it's time for querying it, let's see a didactic version of voorjob search view:
from django.contrib.postgres.search import SearchQuery class Index(ListView): model = Job paginate_by = 30 def get_queryset(self): search = self.request.GET.get("search", None) queryset = Job.objects.all() if search: if '"' in search: query = SearchQuery(search.replace('"', ''), search_type='phrase') else: query = SearchQuery(search) queryset = queryset.filter(body_vector=query) else: queryset = queryset return queryset
I'm basically considering two kinds of queries here: words presence and "exact expressions". Yes, there are a few flaws in that logic, go ahead and sue me :D
There are much more things that can be improved, django have support for weighted queries:
vector = SearchVector('title', weight='A') + SearchVector('body', weight='B') Job.objects.all().update(body_vector=vector)
This would ultimately return results in a better order, where a match in the title would give more weight than a match in the body.
The query system is also more flexible allowing logical operations OR/AND and NOT. In a near future, I will improve the search of my job board and update this post to describe the changes.
During development I used an I5 with 16GB of ram and a nice NVMe. Running queries against the 25k jobs in my local machine is basically instantaneous.
When I moved the project to production (in the $5 droplet) things get waaaayyy more slow.
Running the mississippi benchmark I got the following result:
Not the best performance but works for now. This article will be updated to reflect any performance improvements.
Considering that my search needs are modest - 25k entries with an overage word count not much bigger than this article - using postgres as backend for my full text search is working just fine for this early stage MVP. Right now, I'm more interested in trying things and grow the board audience than giving my 20 daily user the fastest experience in the world.
Good news! I learned that I can add an index to my
from django.contrib.postgres.indexes import GinIndex class Job(models.Model): class Meta: indexes = (GinIndex(fields=["body_vector"]),) title = models.CharField(max_length=200, blank=True) location = models.CharField(max_length=50, blank=True) body = models.TextField(null=True) body_vector = SearchVectorField(null=True) def make_search_vector(): self.body_vector=SearchVector('body') def save(self, *args, **kwargs): self.make_search_vector() super(Model, self).save(*args, **kwargs)
Now the search time is down to 1 mississippi for all cases. Since my data is small, the amount of memory used for this index is negligible.