Upgrade to Pro — share decks privately, control downloads, hide ads and more …

Instant browsing for static sites with Algolia

Josh Dzielak
December 05, 2017

Instant browsing for static sites with Algolia

The "R" in Google's RAIL web performance model is "Response". A response to a user's action must take place in less than 100ms for the experience to feel immediate and natural. When content must be fetched from a traditional backend or API in response to a user's interaction, it can be very difficult to meet that requirement. Algolia can help. Designed to power low-latency search-as-you-type experiences, Algolia responds to content fetching requests as "fast as ping". Learn how Algolia works and see how you can speed up your static sites. Demo included.

Josh Dzielak

December 05, 2017

More Decks by Josh Dzielak

Other Decks in Technology


  1. Josh Dzielak Instant browsing for static sites with Algolia Developer

    Relations Lead, Algolia 12/05/2017 Serverless London Meetup @dzello github.com/dzello
  2. The multiple faces of search Instant search Autocomplete Faceting Full

    text search @dzello · @algolia · @ServerlessLDN
  3. Search doesn’t always need keywords > Browsing > Discovery >

    Navigation @dzello · @algolia · @ServerlessLDN
  4. Speed is crucial to browsing and navigation experiences that users

    enjoy @dzello · @algolia · @ServerlessLDN
  5. Source: https://blog.algolia.com/performant-web-animations/ “Developers often invest quite a bit of time

    to reduce first page loads by even a few milliseconds, but forget to consider the impact of the interactions that follow.” — Emily Hayman, Solutions Engineer, Algolia @dzello · @algolia · @ServerlessLDN
  6. Source: https://developers.google.com/web/fundamentals/performance/rail “The majority of time users spend in your

    site isn't waiting for it to load, but waiting for it to respond while using it.” — Google’s RAIL web performance model Especially true for static sites! @dzello · @algolia · @ServerlessLDN
  7. Algolia = fast like ping @dzello @algolia Data for millions

    of searches on a large dataset designed for low-latency search-as-you-type experiences