Typesense is a fast, typo-tolerant search engine for building delightful search experiences.
✨ Here are a couple of live demos that show Typesense in action on large datasets:
- Search a 32M songs dataset from MusicBrainz: songs-search.typesense.org
- Search a 28M books dataset from OpenLibrary: books-search.typesense.org
- Search a 2M recipe dataset from RecipeNLG: recipe-search.typesense.org
- Spellchecker with type-ahead, with 333K English words: spellcheck.typesense.org
🗣️ 🎥 If you prefer watching videos, here's one where we introduce Typesense and show a walk-through: https://youtu.be/F4mB0x_B1AE?t=144
- Features
- Who's using this
- Install
- Quick Start
- Step-by-step Walk-through
- API Documentation
- API Clients
- Search UI Components
- Benchmarks
- FAQ
- Support
- Contributing
- Getting Latest Updates
- Build from Source
- Typo Tolerance: Handles typographical errors elegantly, out-of-the-box.
- Simple and Delightful: Simple to set-up, integrate with, operate and scale.
- ⚡ Blazing Fast: Built in C++. Meticulously architected from the ground-up for low-latency (<50ms) instant searches.
- Tunable Ranking: Easy to tailor your search results to perfection.
- Sorting: Sort results based on a particular field at query time (helpful for features like "Sort by Price (asc)").
- Faceting & Filtering: Drill down and refine results.
- Grouping & Distinct: Group similar results together to show more variety.
- Federated Search: Search across multiple collections (indices) in a single HTTP request.
- Scoped API Keys: Generate API keys that only allow access to certain records, for multi-tenant applications.
- Synonyms: Define words as equivalents of each other, so searching for a word will also return results for the synonyms defined.
- Curation & Merchandizing: Boost particular records to a fixed position in the search results, to feature them.
- Raft-based Clustering: Setup a distributed cluster that is highly available.
- Seamless Version Upgrades: As new versions of Typesense come out, upgrading is as simple as swapping out the binary and restarting Typesense.
Don't see a feature on this list? Search our issue tracker if someone has already requested it and upvote it, or open a new issue if not. We prioritize our roadmap based on user feedback, so we'd love to hear from you.
Typesense is used by a range of users across different industries. We've only recently started documenting who's using it in our Showcase.
If you'd like to be included in the list, please feel free to edit SHOWCASE.md and send us a PR.
Option 1: You can download the binary packages that we publish for Linux (x86-64) and Mac.
Option 2: You can also run Typesense from our official Docker image.
Option 3: Spin up a managed cluster with Typesense Cloud:
Here's a quick example showcasing how you can create a collection, index a document and search it on Typesense.
Let's begin by starting the Typesense server via Docker:
docker run -p 8108:8108 -v/tmp/data:/data typesense/typesense:0.19.0 --data-dir /data --api-key=Hu52dwsas2AdxdE
We have API Clients in a couple of languages, but let's use the Python client for this example.
Install the Python client for Typesense:
pip install typesense
We can now initialize the client and create a companies collection:
import typesense
client = typesense.Client({
'api_key': 'Hu52dwsas2AdxdE',
'nodes': [{
'host': 'localhost',
'port': '8108',
'protocol': 'http'
}],
'connection_timeout_seconds': 2
})
create_response = client.collections.create({
"name": "companies",
"fields": [
{"name": "company_name", "type": "string" },
{"name": "num_employees", "type": "int32" },
{"name": "country", "type": "string", "facet": True }
],
"default_sorting_field": "num_employees"
})Now, let's add a document to the collection we just created:
document = {
"id": "124",
"company_name": "Stark Industries",
"num_employees": 5215,
"country": "USA"
}
client.collections['companies'].documents.create(document)Finally, let's search for the document we just indexed:
search_parameters = {
'q' : 'stork',
'query_by' : 'company_name',
'filter_by' : 'num_employees:>100',
'sort_by' : 'num_employees:desc'
}
client.collections['companies'].documents.search(search_parameters)Did you notice the typo in the query text? No big deal. Typesense handles typographic errors out-of-the-box!
A step-by-step walk-through is available on our website here.
This will guide you through the process of starting up a Typesense server, indexing data in it and querying the data set.
Here's our official API documentation, available on our website: https://typesense.org/api.
If you notice any issues with the documentation or walk-through, please let us know or send us a PR here: https://github.com/typesense/typesense-website.
While you can definitely use CURL to interact with Typesense Server directly, we offer official API clients to simplify using Typesense from your language of choice. The API Clients come built-in with a smart retry strategy to ensure that API calls made via them are resilient, especially in an HA setup.
If we don't offer an API client in your language, you can still use any popular HTTP client library to access Typesense's APIs directly.
Here are some community-contributed clients and integrations:
We welcome community contributions to add more official client libraries and integrations. Please reach out to us at [email protected] or open an issue on Github to collaborate with us on the architecture. 🙏
You can use our InstantSearch.js adapter to quickly build powerful search experiences, complete with filtering, sorting, pagination and more.
Here's how: https://typesense.org/docs/0.19.0/guide/#search-ui
- A dataset containing 28M records (book titles, authors and categories):
- Took up about 14GB of RAM when indexed in Typesense
- Took 78mins to index all 28M records
- On a server with 4vCPUs, we were able to get a concurrency of 46 concurrent search queries per second, with an average search processing time of 28ms.
- A dataset containing 2.2M records (recipe names and ingredients):
- Took up about 900MB of RAM when indexed in Typesense
- Took 3.6mins to index all 2.2M records
- On a server with 4vCPUs, we were able to get a concurrency of 104 concurrent search queries per second, with an average search processing time of 11ms.
- A dataset containing ~3 million records (Amazon product data) and ~13GB on disk, we were able to achieve a throughput of 250 concurrent search queries per second on a 16GB RAM, 8-vCPU 3-node Typesense cluster.
We'd love to benchmark with larger datasets, if we can find large ones in the public domain. If you have any suggestions for structured datasets that are open, please let us know by opening an issue.
We'd also be delighted if you're able to share benchmarks from your own large datasets. Please send us a PR!
Elasticsearch is a large piece of software, that takes non-trivial amount of effort to setup, administer, scale and fine-tune. It offers you a few thousand configuration parameters to get to your ideal configuration. So it's better suited for large teams who have the bandwidth to get it production-ready, regularly monitor it and scale it, especially when they have a need to store billions of documents and petabytes of data (eg: logs).
Typesense is built specifically for decreasing the "time to market" for a delightful search experience. It is a light-weight yet powerful & scaleable alternative that focuses on Developer Happiness and Experience with a clean well-documented API, clear semantics and smart defaults so it just works well out-of-the-box, without you having to turn many knobs.
Elasticsearch also runs on the JVM, which by itself can be quite an effort to tune to run optimally. Typesense, on the other hand, is a single light-weight self-contained native binary, so it's simple to setup and operate.
See a side-by-side feature comparison here.
Algolia is a proprietary, hosted, search-as-a-service product that works well, when cost is not an issue. From our experience, fast growing sites and apps quickly run into search & indexing limits, accompanied by expensive plan upgrades as they scale.
Typesense on the other hand is an open-source product that you can run on your own infrastructure or use our managed SaaS offering - Typesense Cloud. The open source version is free to use (besides of course your own infra costs). With Typesense Cloud we do not charge by records or search operations. Instead, you get a dedicated cluster and you can throw as much data and traffic at it as it can handle. You only pay a fixed hourly cost & bandwidth charges for it, depending on the configuration your choose, similar to most modern cloud platforms.
From a product perspective, Typesense is closer in spirit to Algolia than Elasticsearch. However, we've addressed some important limitations with Algolia:
Algolia requires separate indices for each sort order, which counts towards your plan limits. Most of the index settings like fields to search, fields to facet, fields to group by, ranking settings, etc are defined upfront when the index is created vs being able to set them on the fly at query time.
With Typesense, these settings can be configured at search time via query parameters which makes it very flexible and unlocks new use cases. Typesense is also able to give you sorted results with a single index, vs having to create multiple. This helps reduce memory consumption.
Algolia offers the following features that Typesense does not have currently: geo spatial searches, personalization & server-based search analytics. With Typesense, we intend to bridge this gap, but in the meantime, please let us know if any of these are a show stopper for your use case by creating a feature request in our issue tracker.
See a side-by-side feature comparison here.
A fresh Typesense server will consume about 30 MB of memory. As you start indexing documents, the memory use will increase correspondingly. How much it increases depends on the number and type of fields you index.
We've strived to keep the in-memory data structures lean. To give you a rough idea: when 1 million Hacker News titles are indexed along with their points, Typesense consumes 165 MB of memory. The same size of that data on disk in JSON format is 88 MB. If you have any numbers from your own datasets that we can add to this section, please send us a PR!
From our experience companies are generally concerned when libraries they use are GPL licensed, since library code is directly integrated into their code and will lead to derivative work and trigger GPL compliance. However, Typesense Server is server software and we expect users to typically run it as a separate daemon, and not integrate it with their own code. GPL covers and allows for this use case generously (eg: Linux is GPL licensed). Now, AGPL is what makes server software accessed over a network result in derivative work and not GPL. And for that reason we’ve opted to not use AGPL for Typesense.
Now, if someone makes modifications to Typesense server, GPL actually allows you to still keep the modifications to yourself as long as you don't distribute the modified code. So a company can for example modify Typesense server and run the modified code internally and still not have to open source their modifications, as long as they make the modified code available to everyone who has access to the modified software.
Now, if someone makes modifications to Typesense server and distributes the modifications, that's where GPL kicks in. Given that we’ve published our work to the community, we'd like for others' modifications to also be made open to the community in the spirit of open source. We use GPL for this purpose. Other licenses would allow our open source work to be modified, made closed source and distributed, which we want to avoid with Typesense for the project’s long term sustainability.
Here's more background on why GPL, as described by Discourse: https://meta.discourse.org/t/why-gnu-license/2531. Many of the points mentioned there resonate with us.
Now, all of the above only apply to Typesense Server. Our client libraries are indeed meant to be integrated into our users’ code and so they use Apache license.
So in summary, AGPL is what is usually problematic for server software and we’ve opted not to use it. We believe GPL for Typesense Server captures the essence of what we want for this open source project. GPL has a long history of successfully being used by popular open source projects. Our libraries are still Apache licensed.
If you have specifics that prevent you from using Typesense due to a licensing issue, we're happy to explore this topic further with you. Please reach out to us.
👋 🌐 New: If you have general questions about Typesense, want to say hello or just follow along, we'd like to invite you to join our Slack Community.
We also do virtual office hours every Friday. Reserve a time slot here.
If you run into any problems or issues, please create a Github issue and we'll try our best to help.
We strive to provide good support through our issue trackers on Github. However, if you'd like to receive private & prioritized support with:
- Guaranteed SLAs
- Phone / video calls to discuss your specific use case and get recommendations on best practices
- Private discussions over Slack
- Guidance around deployment, ops and scaling best practices
- Prioritized feature requests
We do offer Paid Support options. Please reach out to us at [email protected] to sign up.
We are a lean team on a mission to democratize search and we'll take all the help we can get! If you'd like to get involved, here's information on where we could use your help: Contributing.md
If you'd like to get updates when we release new versions, click on the "Watch" button on the top and select "Releases only". Github will then send you notifications along with a changelog with each new release.
We also post updates to our Twitter account about releases and additional topics related to Typesense. Follow us here: @typesense.
👋 🌐 New: We'll also post updates on our Slack Community.
Building with Docker
The docker build script takes care of all required dependencies, so it's the easiest way to build Typesense:
TYPESENSE_VERSION=nightly ./docker-build.sh --build-deploy-image --create-binary [--clean] [--depclean]
Building on your machine
Typesense requires the following dependencies:
- C++11 compatible compiler (GCC >= 4.9.0, Apple Clang >= 8.0, Clang >= 3.9.0)
- Snappy
- zlib
- OpenSSL (>=1.0.2)
- curl
- ICU
- brpc
- braft
./build.sh --create-binary [--clean] [--depclean]
The first build will take some time since other third-party libraries are pulled and built as part of the build process.
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