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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.
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.
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.
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.
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.
Pros of Elasticsearch
- Powerful api326
- Great search engine315
- Open source230
- Near real-time search199
- Search everything83
- Easy to get started54
- Fast search6
- More than a search engine5
- Highly Available3
- Awesome, great tool3
- Great docs3
- Easy to scale3
- Document Store2
- Great customer support2
- Intuitive API2
- Easy setup2
- Nosql DB2
- Great piece of software2
- Not stable1
- Actively developing1
- Responsive maintainers on GitHub1
- Easy to get hot data1
Pros of PostGIS
- De facto GIS in SQL25
- Good Documentation5
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Cons of Elasticsearch
- Resource hungry7
- Diffecult to get started6
- Hard to keep stable at large scale4
Cons of PostGIS
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