Elasticsearch is a powerful open-source search and analytics engine that makes data easy to explore. It powers search for many of the world’s largest organizations, including Wikipedia, Netflix, and The Guardian.
Elasticsearch is built on top of the Apache Lucene search library, offering powerful features like distributed search, real-time analytics, and support for multiple data types.
Despite its power, Elasticsearch is easy to use. It has a simple REST API that makes it easy to index and search data. It also has a web-based console that makes it easy to manage your cluster.
✔️ Elasticsearch is a distributed, RESTful search and analytics engine built on top of Apache Lucene.
✔️ Elasticsearch is easy to use and scalable.
✔️ Elasticsearch is suitable for many use cases, from personal to enterprise search.
✔️ Elasticsearch has a rich set of features, including full-text search, aggregations, and geolocation.
✔️ Elasticsearch is open source and available under the Apache license.
In this, we will understand the installation procedure of Elasticsearch in detail.
To install Elasticsearch on your local computer, you will have to follow the steps given below −
🟠 Step 1 − Check the version of Java installed on your computer. It should be Java 7 or higher. You can check by doing the following −
In Windows Operating System (OS) (using command prompt)−
> java -version
In UNIX OS (Using Terminal) −
$ echo $JAVA_HOME
🟠 Step 2 − Depending on your operating system, download Elasticsearch from www.elastic.co as mentioned below −
➡️ For Windows OS, download the ZIP file.
➡️ For UNIX OS, download the TAR file.
➡️ For Debian OS, download the DEB file.
➡️ For Red Hat and other Linux distributions, download the RPM file.
➡️ APT and Yum utilities can also be used to install Elasticsearch in many Linux distributions.
🟠 Step 3 − The installation process for Elasticsearch is simple and is described below for different OS −
➡️ Windows OS− Unzip the zip package and Elasticsearch is installed.
➡️ UNIX OS− Extract tar file in any location and Elasticsearch is installed.
➡️ Linux OS− For Linux refer to the link.
Django Elasticsearch DSL is a package that allows the indexing of Django models in Elasticsearch. It is built as a thin wrapper around elasticsearch-dsl-py so you can use all the features developed by the elasticsearch-dsl-py team.
You can view the full documentation here.
➡️ Based on elasticsearch-dsl-py you can make queries with the Search class.
➡️ Django signal receivers on save and delete for keeping Elasticsearch in sync.
➡️ Management commands for creating, deleting, rebuilding, and populating indices.
➡️ Elasticsearch auto mapping from Django models fields.
➡️ Complex field type support (ObjectField, NestedField).
➡️ Index fast using parallel indexing.
➡️ Requirements
Install Django Elasticsearch DSL:
pip install django-elasticsearch-dsl
Then add django_elasticsearch_dsl to the INSTALLED_APPS
You must define ELASTICSEARCH_DSL in your Django settings.
For example:
ELASTICSEARCH_DSL={
'default': {
'hosts': 'localhost:9200'
},
}
Then for a model:
# models.py
class Car(models.Model):
name = models.CharField()
color = models.CharField()
manufacturer = models.ForeignKey('Manufacturer')
class Manufacturer(models.Model):
name = models.CharField()
country_code = models.CharField(max_length=2)
created = models.DateField()
class Ad(models.Model):
title = models.CharField()
description = models.TextField()
created = models.DateField(auto_now_add=True)
modified = models.DateField(auto_now=True)
url = models.URLField()
car = models.ForeignKey('Car', related_name='ads')
To make this model work with Elasticsearch, create a subclass of django_elasticsearch_dsl. Document, create a class Index inside the Document class to define your Elasticsearch indices, names, settings, etc, and at last register the class using the registry.register_document decorator. It required defining the Document class in documents.py in your app directory.
# documents.py
from django_elasticsearch_dsl import Document, fields
from .models import Car, Manufacturer, Ad
@registry.register_document
class CarDocument(Document):
manufacturer = fields.ObjectField(properties={
'name': fields.TextField(),
'country_code': fields.TextField(),
})
ads = fields.NestedField(properties={
'description': fields.TextField(analyzer=html_strip),
'title': fields.TextField(),
'pk': fields.IntegerField(),
})
class Index:
name = 'cars'
class Django:
model = Car
fields = [
'name',
'color',
]
related_models = [Manufacturer, Ad] # Optional: to ensure the Car will be re-saved when Manufacturer or Ad is updated
def get_queryset(self):
"""Not mandatory but to improve performance we can select related in one sql request"""
return super(CarDocument, self).get_queryset().select_related(
'manufacturer'
)
def get_instances_from_related(self, related_instance):
"""If related_models is set, define how to retrieve the Car instance(s) from the related model.
The related_models option should be used with caution because it can lead in the index
to the updating of a lot of items.
"""
if isinstance(related_instance, Manufacturer):
return related_instance.car_set.all()
elif isinstance(related_instance, Ad):
return related_instance.car
To create and populate the Elasticsearch index and mapping use the search_index command:
python manage.py search_index --rebuild
To get an elasticsearch-dsl-py Search instance, use:
s = CarDocument.search().filter("term", color="red")
# or
s = CarDocument.search().query("match", description="beautiful")
for hit in s:
print(
"Car name : {}, description {}".format(hit.name, hit.description)
)
The previous example returns a result specific to elasticsearch_dsl, but it is also possible to convert the elastic search result into a real Django query set, just be aware that this costs an SQL request to retrieve the model instances with the IDs returned by the elastic search query.
s = CarDocument.search().filter("term", color="blue")[:30]
qs = s.to_queryset()
# qs is just a django queryset and it is called with order_by to keep
# the same order as the elasticsearch result.
for car in qs:
print(car.name)
To summarize, Elasticsearch integration with Django offers developers a powerful and scalable solution for implementing advanced search functionalities. With Elasticsearch’s robust search engine and Django’s versatile framework, developers can create intelligent and efficient search experiences.
The simplicity of Elasticsearch’s REST API and the convenience of the web-based console make it easy to index, search, and manage data. Leveraging the capabilities of Django Elasticsearch DSL further enhances the indexing of Django models. By combining Elasticsearch and Django, developers can unleash the full potential of search capabilities, delivering high-performance and user-friendly search functionalities in their applications.
Elasticsearch stands upon the foundation of the Apache Lucene search library, delivering a robust array of capabilities including distributed search functionality, real-time analytics prowess, and comprehensive support for diverse data types.
Elasticsearch is a widely used open-source search and analytics engine that offers several advantages for various applications and use cases. Here are some of the key advantages of using Elasticsearch: High-Speed Searching and Indexing, Near Real-Time Data, Full-Text Search, Scalability, Horizontal and Vertical Scaling, Data Analysis and Visualization, Community and Support.
Django Elasticsearch DSL is a high-level Python library that serves as a bridge between the Django web framework and Elasticsearch. It provides a convenient and intuitive way to interact with Elasticsearch within Django applications, enabling developers to seamlessly integrate advanced search and querying capabilities.
Yes, you can store the data from a Django model in an Elasticsearch index using the Elasticsearch DSL library. Elasticsearch DSL allows you to define Elasticsearch document classes that map to your Django models, facilitating the indexing of your data into Elasticsearch indices.
How to Effectively Hire and Manage a Remote Team of Developers.
Enhance Your Epic EHR Expertise in Just 60 Minutes!
Register HereMindbowser played a crucial role in helping us bring everything together into a unified, cohesive product. Their commitment to industry-standard coding practices made an enormous difference, allowing developers to seamlessly transition in and out of the project without any confusion....
CEO, MarketsAI
I'm thrilled to be partnering with Mindbowser on our journey with TravelRite. The collaboration has been exceptional, and I’m truly grateful for the dedication and expertise the team has brought to the development process. Their commitment to our mission is...
Founder & CEO, TravelRite
The Mindbowser team's professionalism consistently impressed me. Their commitment to quality shone through in every aspect of the project. They truly went the extra mile, ensuring they understood our needs perfectly and were always willing to invest the time to...
CTO, New Day Therapeutics
I collaborated with Mindbowser for several years on a complex SaaS platform project. They took over a partially completed project and successfully transformed it into a fully functional and robust platform. Throughout the entire process, the quality of their work...
President, E.B. Carlson
Mindbowser and team are professional, talented and very responsive. They got us through a challenging situation with our IOT product successfully. They will be our go to dev team going forward.
Founder, Cascada
Amazing team to work with. Very responsive and very skilled in both front and backend engineering. Looking forward to our next project together.
Co-Founder, Emerge
The team is great to work with. Very professional, on task, and efficient.
Founder, PeriopMD
I can not express enough how pleased we are with the whole team. From the first call and meeting, they took our vision and ran with it. Communication was easy and everyone was flexible to our schedule. I’m excited to...
Founder, Seeke
Mindbowser has truly been foundational in my journey from concept to design and onto that final launch phase.
CEO, KickSnap
We had very close go live timeline and Mindbowser team got us live a month before.
CEO, BuyNow WorldWide
If you want a team of great developers, I recommend them for the next project.
Founder, Teach Reach
Mindbowser built both iOS and Android apps for Mindworks, that have stood the test of time. 5 years later they still function quite beautifully. Their team always met their objectives and I'm very happy with the end result. Thank you!
Founder, Mindworks
Mindbowser has delivered a much better quality product than our previous tech vendors. Our product is stable and passed Well Architected Framework Review from AWS.
CEO, PurpleAnt
I am happy to share that we got USD 10k in cloud credits courtesy of our friends at Mindbowser. Thank you Pravin and Ayush, this means a lot to us.
CTO, Shortlist
Mindbowser is one of the reasons that our app is successful. These guys have been a great team.
Founder & CEO, MangoMirror
Kudos for all your hard work and diligence on the Telehealth platform project. You made it possible.
CEO, ThriveHealth
Mindbowser helped us build an awesome iOS app to bring balance to people’s lives.
CEO, SMILINGMIND
They were a very responsive team! Extremely easy to communicate and work with!
Founder & CEO, TotTech
We’ve had very little-to-no hiccups at all—it’s been a really pleasurable experience.
Co-Founder, TEAM8s
Mindbowser was very helpful with explaining the development process and started quickly on the project.
Executive Director of Product Development, Innovation Lab
The greatest benefit we got from Mindbowser is the expertise. Their team has developed apps in all different industries with all types of social proofs.
Co-Founder, Vesica
Mindbowser is professional, efficient and thorough.
Consultant, XPRIZE
Very committed, they create beautiful apps and are very benevolent. They have brilliant Ideas.
Founder, S.T.A.R.S of Wellness
Mindbowser was great; they listened to us a lot and helped us hone in on the actual idea of the app. They had put together fantastic wireframes for us.
Co-Founder, Flat Earth
Ayush was responsive and paired me with the best team member possible, to complete my complex vision and project. Could not be happier.
Founder, Child Life On Call
The team from Mindbowser stayed on task, asked the right questions, and completed the required tasks in a timely fashion! Strong work team!
CEO, SDOH2Health LLC
Mindbowser was easy to work with and hit the ground running, immediately feeling like part of our team.
CEO, Stealth Startup
Mindbowser was an excellent partner in developing my fitness app. They were patient, attentive, & understood my business needs. The end product exceeded my expectations. Thrilled to share it globally.
Owner, Phalanx
Mindbowser's expertise in tech, process & mobile development made them our choice for our app. The team was dedicated to the process & delivered high-quality features on time. They also gave valuable industry advice. Highly recommend them for app development...
Co-Founder, Fox&Fork