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Elasticsearch vs Typesense: What are the differences?

Elasticsearch and Typesense are both highly popular solutions for search and data retrieval. Let's explore the key differences between them.

  1. Scalability: Elasticsearch is designed to be highly scalable, allowing for horizontal scaling by adding more nodes to a cluster. It can handle large amounts of data and scale to thousands of servers if needed. On the other hand, Typesense is built to be lightweight and optimized for low resource consumption. It is ideal for smaller deployments or when resource efficiency is a priority.

  2. Querying: Elasticsearch offers a powerful and flexible query DSL (Domain Specific Language), which allows for complex queries and aggregations. It also supports full-text search, filtering, and sorting efficiently. Typesense, on the other hand, provides a simplified query syntax, making it easier to use and understand. It is optimized for simple search use cases and may not provide the same level of flexibility as Elasticsearch.

  3. Schema-less vs Schema-based: Elasticsearch is schema-less, meaning that it can handle varying structures of documents within the same index. This flexibility can be beneficial when dealing with unstructured data. In contrast, Typesense follows a schema-based approach, where documents must adhere to a pre-defined schema. This ensures data consistency and more efficient indexing, but can be limiting when dealing with dynamic or evolving data structures.

  4. Indexing Speed: Elasticsearch is optimized for fast indexing of data. It can handle high write loads and can index data in near real-time. This makes it suitable for use cases that require frequent updates to the index. Typesense, while it also has good indexing performance, may not be as fast as Elasticsearch in high-write scenarios.

  5. Built-in Features: Elasticsearch comes with various built-in features like geolocation searches, language analyzers, and support for parent-child relationships. It also has a strong ecosystem of plugins and integrations. Typesense, on the other hand, focuses on providing a lightweight and easy-to-use search engine, with fewer built-in features. It may require additional customization or integration with external libraries for certain functionalities.

  6. Community and Adoption: Elasticsearch has been around for a longer time and has a larger community and user base. It has been widely adopted by enterprises and has a more mature ecosystem. Typesense, being a newer player in the market, may have a smaller community and fewer resources available.

In summary, Elasticsearch offers scalability, powerful querying capabilities, schema-less structure, fast indexing, a wider range of built-in features, and a larger community. Typesense, on the other hand, focuses on being lightweight, resource-efficient, easy to use, schema-based, and tailored for simple search use cases.

Advice on Elasticsearch and Typesense
Rana Usman Shahid
Chief Technology Officer at TechAvanza | 6 upvotes 路 378.8K views
Needs advice

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 路 282.7K views

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
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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Pros of Elasticsearch
Pros of Typesense
  • 328
    Powerful api
  • 315
    Great search engine
  • 231
    Open source
  • 214
  • 200
    Near real-time search
  • 98
  • 85
    Search everything
  • 54
    Easy to get started
  • 45
  • 26
  • 6
    Fast search
  • 5
    More than a search engine
  • 4
    Great docs
  • 4
    Awesome, great tool
  • 3
    Highly Available
  • 3
    Easy to scale
  • 2
  • 2
    Document Store
  • 2
    Great customer support
  • 2
    Intuitive API
  • 2
    Nosql DB
  • 2
    Great piece of software
  • 2
  • 2
  • 2
    Easy setup
  • 1
  • 1
    Easy to get hot data
  • 1
  • 1
  • 1
    Actively developing
  • 1
    Responsive maintainers on GitHub
  • 1
  • 1
    Not stable
  • 1
  • 0
  • 5
  • 4
    Facet search
  • 4
    Easy to deploy
  • 3
    Out-of-the-box dev experience
  • 3
    Ultra fast
  • 3
    Search as you type
  • 3
    Typo handling
  • 3
    Open source
  • 2
    Near real-time search
  • 2
    Super easy to implement
  • 2
    InstantSearch integration
  • 2
    Modern search engine
  • 1
  • 1
    Great documentation
  • 1
    SaaS option

Sign up to add or upvote prosMake informed product decisions

Cons of Elasticsearch
Cons of Typesense
  • 7
    Resource hungry
  • 6
    Diffecult to get started
  • 5
  • 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 Typesense?

    It is an open source, typo tolerant search engine that delivers fast and relevant results out-of-the-box. has been built from scratch to offer a delightful, out-of-the-box search experience. From instant search to autosuggest, to faceted search, it has got you covered.

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    May 21 2019 at 12:20AM


    What are some alternatives to Elasticsearch and Typesense?
    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 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 Core, our flagship sub-project, provides Java-based indexing and search technology, as well as spellchecking, hit highlighting and advanced analysis/tokenization capabilities.
    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.
    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