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Update readme with information about Typesense Cloud

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Jason Bosco 2020-09-23 20:23:00 -07:00
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@ -12,6 +12,7 @@ Typesense is a fast, typo-tolerant search engine for building delightful search
- [Install](#install)
- [Quick Start](#quick-start)
- [Detailed Guide](#detailed-guide)
- [Search UIs](#search-uis)
- [Build from Source](#build-from-source)
- [FAQ](#faq)
- [Help](#help)
@ -19,16 +20,18 @@ Typesense is a fast, typo-tolerant search engine for building delightful search
## Features
- **Typo tolerant:** Handles typographical errors elegantly.
- **Simple and delightful:** Simple to set-up and manage.
- **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
You can download the [binary packages](https://typesense.org/downloads) that we publish for
**Option 1:** You can download the [binary packages](https://typesense.org/downloads) that we publish for
Linux (x86-64) and Mac.
You can also run Typesense from our [official Docker image](https://hub.docker.com/r/typesense/typesense):
**Option 2:** You can also run Typesense from our [official Docker image](https://hub.docker.com/r/typesense/typesense).
**Option 3:** Spin up a cluster with [Typesense Cloud](https://cloud.typesense.org).
## Quick Start
@ -104,6 +107,13 @@ client.collections['companies'].documents.search(search_parameters)
A detailed guide is available on [Typesense website](https://typesense.org/guide).
## Search UIs
You can use our [InstantSearch.js adapter](https://github.com/typesense/typesense-instantsearch-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](https://typesense.org/docs/0.15.0/guide/#search-ui)
## Build from source
**Building with Docker**
@ -137,13 +147,41 @@ The first build will take some time since other third-party libraries are pulled
**How does this differ from using Elasticsearch?**
Elasticsearch is better suited for larger teams who have the bandwidth to administer, scale and fine-tune it and
especially when have a need to store billions of documents and scale horizontally.
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
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](https://cloud.typesense.org).
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