Typesense is a fast, typo-tolerant search engine for building delightful search experiences.
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Features
- Typo tolerant: Handles typographical errors elegantly.
- Simple and delightful: Simple to set-up, manage and scale.
- Tunable ranking: Easy to tailor your search results to perfection.
- Fast: Meticulously designed and optimized for speed.
Install
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.
Quick Start
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:0.15.0 --data-dir /data --api-key=Hu52dwsas2AdxdE
Install the Python client for Typesense (we have clients for other languages too):
pip install typesense
We can now initialize the client and create a companies collection:
import typesense
client = typesense.Client({
'nodes': [{
'host': 'localhost',
'port': '8108',
'protocol': 'http',
'api_key': 'Hu52dwsas2AdxdE'
}],
'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!
Detailed Guide
A detailed guide is available on Typesense website.
Search UIs
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/0.15.0/guide/#search-ui
Build from source
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.
FAQ
How does this differ from using Elasticsearch?
Elasticsearch is better suited for large teams who have the bandwidth to administer, scale and fine-tune it and especially when they have a need to store billions of documents and scale horizontally.
Typesense is built specifically for decreasing the "time to market" for a delightful search experience. This means focusing on Developer Productivity and Experience with a clean API, clear semantics and smart defaults so that it just works without turning many knobs.
How does this differ from using Algolia?
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: synonyms, 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.
Speed is great, but what about the memory footprint?
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. We hope to add better benchmarks on a variety of different data sets soon. In the mean time, if you have any numbers from your own datasets, please send us a PR!
Help
If you've any questions or run into any problems, please create a Github issue and we'll try our best to help.
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