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Elasticsearch vs PostGIS: What are the differences?

Introduction

Elasticsearch and PostGIS are both popular tools used for managing and analyzing large amounts of data. However, they have some key differences in terms of functionality and purpose. In this article, we will explore and compare these differences.

  1. Data Structure: Elasticsearch is a search engine that uses a flexible and schema-less JSON-based data structure. It is designed to handle unstructured and semi-structured data efficiently. PostGIS, on the other hand, is an extension of PostgreSQL that adds support for geographic objects. It allows storing and querying spatial data using various geometric and geographic data types.

  2. Querying Capabilities: Elasticsearch offers advanced full-text search capabilities, supporting complex queries including fuzzy search, wildcard search, and phrase matching. It also provides relevance scoring to rank search results based on relevance. PostGIS, however, focuses on spatial queries and offers a wide range of spatial operators and functions to perform spatial analysis and calculations.

  3. Scalability and Distribution: Elasticsearch is built from the ground up to be distributed and scalable. It allows horizontal scaling by adding more nodes to the cluster, enabling data to be distributed across multiple machines. It also provides built-in replication and sharding mechanisms for high availability and performance. PostGIS, on the other hand, is primarily designed as a single-node database system and does not offer native support for distributed architectures.

  4. Indexing and Data Storage: Elasticsearch uses an inverted index for efficient full-text search. It automatically indexes every field in a document, making it suitable for fast text-based searches. PostGIS uses a B-tree index for efficient querying of spatial data. It supports both spatial and non-spatial indexing, allowing for efficient retrieval of specific spatial objects or attribute values.

  5. Data Modeling: Elasticsearch allows for dynamic data modeling, meaning that fields and data types can be modified on-the-fly without requiring a predefined schema. This flexibility is suitable for scenarios where the data structure is not known in advance or frequently changes. PostGIS, however, requires a predefined table schema and is more suitable for scenarios with a fixed or predictable data structure.

  6. Integration and Ecosystem: Elasticsearch has a rich ecosystem with various plugins and integrations available. It seamlessly integrates with other tools like Logstash and Kibana for log analysis and visualization. It also provides APIs and libraries for easy integration with different programming languages. PostGIS, being an extension of PostgreSQL, benefits from the vast ecosystem and features of the PostgreSQL database, including transaction support, user management, and support for other data types.

In summary, Elasticsearch is a search engine designed for full-text search and scalable, distributed data processing, while PostGIS is an extension of PostgreSQL specifically tailored for spatial data management and analysis.

Advice on Elasticsearch and PostGIS
Rana Usman Shahid
Chief Technology Officer at TechAvanza · | 6 upvotes · 391K views
Needs advice
on
AlgoliaAlgoliaElasticsearchElasticsearch
and
FirebaseFirebase

Hey everybody! (1) I am developing an android application. I have data of around 3 million record (less than a TB). I want to save that data in the cloud. Which company provides the best cloud database services that would suit my scenario? It should be secured, long term useable, and provide better services. I decided to use Firebase Realtime database. Should I stick with Firebase or are there any other companies that provide a better service?

(2) I have the functionality of searching data in my app. Same data (less than a TB). Which search solution should I use in this case? I found Elasticsearch and Algolia search. It should be secure and fast. If any other company provides better services than these, please feel free to suggest them.

Thank you!

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Replies (2)
Josh Dzielak
Co-Founder & CTO at Orbit · | 8 upvotes · 293.5K views
Recommends
on
AlgoliaAlgolia

Hi Rana, good question! From my Firebase experience, 3 million records is not too big at all, as long as the cost is within reason for you. With Firebase you will be able to access the data from anywhere, including an android app, and implement fine-grained security with JSON rules. The real-time-ness works perfectly. As a fully managed database, Firebase really takes care of everything. The only thing to watch out for is if you need complex query patterns - Firestore (also in the Firebase family) can be a better fit there.

To answer question 2: the right answer will depend on what's most important to you. Algolia is like Firebase is that it is fully-managed, very easy to set up, and has great SDKs for Android. Algolia is really a full-stack search solution in this case, and it is easy to connect with your Firebase data. Bear in mind that Algolia does cost money, so you'll want to make sure the cost is okay for you, but you will save a lot of engineering time and never have to worry about scale. The search-as-you-type performance with Algolia is flawless, as that is a primary aspect of its design. Elasticsearch can store tons of data and has all the flexibility, is hosted for cheap by many cloud services, and has many users. If you haven't done a lot with search before, the learning curve is higher than Algolia for getting the results ranked properly, and there is another learning curve if you want to do the DevOps part yourself. Both are very good platforms for search, Algolia shines when buliding your app is the most important and you don't want to spend many engineering hours, Elasticsearch shines when you have a lot of data and don't mind learning how to run and optimize it.

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Mike Endale
Recommends
on
Cloud FirestoreCloud Firestore

Rana - we use Cloud Firestore at our startup. It handles many million records without any issues. It provides you the same set of features that the Firebase Realtime Database provides on top of the indexing and security trims. The only thing to watch out for is to make sure your Cloud Functions have proper exception handling and there are no infinite loop in the code. This will be too costly if not caught quickly.

For search; Algolia is a great option, but cost is a real consideration. Indexing large number of records can be cost prohibitive for most projects. Elasticsearch is a solid alternative, but requires a little additional work to configure and maintain if you want to self-host.

Hope this helps.

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Decisions about Elasticsearch and PostGIS
Kyle Harrison
Web Application Developer at Fortinet · | 2 upvotes · 3.4K views

When I found out how powerful PostGIS was, I was gobsmacked. No matter how ridiculous that sample data I'd provide, the results would be fast and come back accurate and consistently.

The only other database engine that offered decent GIS indexing and searching, was ElasticSearch. But ES is not an ACID adhering engine, and is specifically designed to be a screaming fast fulltext search engine first, and everything else second. You never want ES to be your primary database engine (it's not designed for that anyways), it should always be a compliment to your more stable and consistent database solution.

Simply put, I could have stuck to a MySQL + ElasticSearch solution, but the operating costs around that get astronomical when you get down to ho HEAVY ElasticSearch is, and how expensive it is to operate in the any hosting solution.

PostGIS allows to me not need ES for geospatial indexing and querying, and to be really fast at it while doing it. A god send.

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Pros of Elasticsearch
Pros of PostGIS
  • 328
    Powerful api
  • 315
    Great search engine
  • 231
    Open source
  • 214
    Restful
  • 200
    Near real-time search
  • 98
    Free
  • 85
    Search everything
  • 54
    Easy to get started
  • 45
    Analytics
  • 26
    Distributed
  • 6
    Fast search
  • 5
    More than a search engine
  • 4
    Great docs
  • 4
    Awesome, great tool
  • 3
    Highly Available
  • 3
    Easy to scale
  • 2
    Potato
  • 2
    Document Store
  • 2
    Great customer support
  • 2
    Intuitive API
  • 2
    Nosql DB
  • 2
    Great piece of software
  • 2
    Reliable
  • 2
    Fast
  • 2
    Easy setup
  • 1
    Open
  • 1
    Easy to get hot data
  • 1
    Github
  • 1
    Elaticsearch
  • 1
    Actively developing
  • 1
    Responsive maintainers on GitHub
  • 1
    Ecosystem
  • 1
    Not stable
  • 1
    Scalability
  • 0
    Community
  • 25
    De facto GIS in SQL
  • 5
    Good Documentation

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Cons of Elasticsearch
Cons of PostGIS
  • 7
    Resource hungry
  • 6
    Diffecult to get started
  • 5
    Expensive
  • 4
    Hard to keep stable at large scale
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    - No public GitHub repository available -

    What is Elasticsearch?

    Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack).

    What is PostGIS?

    PostGIS is a spatial database extender for PostgreSQL object-relational database. It adds support for geographic objects allowing location queries to be run in SQL.

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    What companies use PostGIS?
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    May 21 2019 at 12:20AM

    Elastic

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    GitHubPythonNode.js+47
    55
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    What are some alternatives to Elasticsearch and PostGIS?
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    Datadog is the leading service for cloud-scale monitoring. It is used by IT, operations, and development teams who build and operate applications that run on dynamic or hybrid cloud infrastructure. Start monitoring in minutes with Datadog!
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    Solr is the popular, blazing fast open source enterprise search platform from the Apache Lucene project. Its major features include powerful full-text search, hit highlighting, faceted search, near real-time indexing, dynamic clustering, database integration, rich document (e.g., Word, PDF) handling, and geospatial search. Solr is highly reliable, scalable and fault tolerant, providing distributed indexing, replication and load-balanced querying, automated failover and recovery, centralized configuration and more. Solr powers the search and navigation features of many of the world's largest internet sites.
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    Our mission is to make you a search expert. Push data to our API to make it searchable in real time. Build your dream front end with one of our web or mobile UI libraries. Tune relevance and get analytics right from your dashboard.
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