Elasticsearch vs PostGIS

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Elasticsearch

26.7K
20.4K
+ 1
1.6K
PostGIS

319
320
+ 1
29
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Elasticsearch vs PostGIS: What are the differences?

Elasticsearch: Open Source, Distributed, RESTful Search Engine. 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); PostGIS: Open source spatial database. 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.

Elasticsearch belongs to "Search as a Service" category of the tech stack, while PostGIS can be primarily classified under "Database Tools".

Some of the features offered by Elasticsearch are:

  • Distributed and Highly Available Search Engine.
  • Multi Tenant with Multi Types.
  • Various set of APIs including RESTful

On the other hand, PostGIS provides the following key features:

  • Processing and analytic functions for both vector and raster data for splicing, dicing, morphing, reclassifying, and collecting/unioning with the power of SQL
  • raster map algebra for fine-grained raster processing
  • Spatial reprojection SQL callable functions for both vector and raster data

"Powerful api" is the primary reason why developers consider Elasticsearch over the competitors, whereas "De facto GIS in SQL" was stated as the key factor in picking PostGIS.

Elasticsearch and PostGIS are both open source tools. Elasticsearch with 42.4K GitHub stars and 14.2K forks on GitHub appears to be more popular than PostGIS with 645 GitHub stars and 246 GitHub forks.

According to the StackShare community, Elasticsearch has a broader approval, being mentioned in 2003 company stacks & 979 developers stacks; compared to PostGIS, which is listed in 53 company stacks and 15 developer stacks.

Advice on Elasticsearch and PostGIS
Rana Usman Shahid
Chief Technology Officer at TechAvanza · | 5 upvotes · 182.3K views
Needs advice
on
FirebaseFirebaseElasticsearchElasticsearch
and
AlgoliaAlgolia

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 · | 7 upvotes · 138.4K views
Recommends
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
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 · 1.8K 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
  • 321
    Powerful api
  • 311
    Great search engine
  • 230
    Open source
  • 213
    Restful
  • 199
    Near real-time search
  • 96
    Free
  • 83
    Search everything
  • 54
    Easy to get started
  • 45
    Analytics
  • 26
    Distributed
  • 6
    Fast search
  • 5
    More than a search engine
  • 3
    Great docs
  • 3
    Easy to scale
  • 3
    Awesome, great tool
  • 2
    Document Store
  • 2
    Nosql DB
  • 2
    Great piece of software
  • 2
    Potato
  • 2
    Great customer support
  • 2
    Intuitive API
  • 2
    Fast
  • 2
    Easy setup
  • 2
    Highly Available
  • 1
    Open
  • 1
    Scalability
  • 1
    Easy to get hot data
  • 1
    Github
  • 1
    Elaticsearch
  • 1
    Actively developing
  • 1
    Responsive maintainers on GitHub
  • 1
    Ecosystem
  • 1
    Not stable
  • 1
    Reliable
  • 0
    Community
  • 24
    De facto GIS in SQL
  • 5
    Good Documentation

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Cons of Elasticsearch
Cons of PostGIS
  • 6
    Resource hungry
  • 6
    Diffecult to get started
  • 5
    Expensive
  • 4
    Hard to keep stable at large scale
    Be the first to leave a con

    Sign up to add or upvote consMake informed product decisions

    - 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.

    Need advice about which tool to choose?Ask the StackShare community!

    Jobs that mention Elasticsearch and PostGIS as a desired skillset
    Pinterest
    San Francisco, CA, US; Palo Alto, CA, US
    Pinterest
    San Francisco, CA, US; Palo Alto, CA, US
    Pinterest
    San Francisco, CA, US; Palo Alto, CA, US
    Pinterest
    San Francisco, CA, US; Palo Alto, CA, US
    CBRE
    Narva, Ida-Virumaa, Estonia
    Pinterest
    San Francisco, CA, US; Palo Alto, CA, US
    What companies use Elasticsearch?
    What companies use PostGIS?
    See which teams inside your own company are using Elasticsearch or PostGIS.
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    What tools integrate with Elasticsearch?
    What tools integrate with PostGIS?

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    Blog Posts

    May 21 2019 at 12:20AM

    Elastic

    ElasticsearchKibanaLogstash+4
    12
    3499
    GitHubPythonReact+42
    47
    39555
    GitHubPythonNode.js+47
    52
    69836
    What are some alternatives to Elasticsearch and PostGIS?
    Datadog
    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!
    Solr
    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.
    Lucene
    Lucene Core, our flagship sub-project, provides Java-based indexing and search technology, as well as spellchecking, hit highlighting and advanced analysis/tokenization capabilities.
    MongoDB
    MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
    Algolia
    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.
    See all alternatives