Getting to Slack faster with incremental boot

At Slack, we’re on a mission to make people’s working lives simpler, more pleasant, and more productive — improving the performance of our products falls into the “more productive” part of that mission. Today, we’d like to tell you about a change we’ve made to Slack’s web app to speed up initial load times — that is, how quickly…

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Mark Christian
•10 min read•beginner•
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Overview

The article discusses Slack's implementation of incremental boot to enhance the web app's initial load times, transitioning from a complete model loading to a more efficient, phased approach. This change significantly improves user experience by reducing the time users wait to access their channels and messages.

What You'll Learn

1

How to implement incremental boot for web applications

2

Why client-side caching is crucial for performance in web apps

3

How to measure performance metrics like 'content visible' and 'fully loaded'

Prerequisites & Requirements

  • Understanding of web application architecture and performance metrics
  • Experience with JavaScript and API integrations

Key Questions Answered

How does incremental boot improve Slack's web app performance?
Incremental boot allows Slack to load only the necessary data for the initial channel, significantly reducing the time to display messages. This method contrasts with the previous approach, which required loading a complete model, thus enhancing user experience by making content visible faster.
What are the key performance metrics Slack uses to measure loading times?
Slack uses two key metrics: 'content visible', which measures when messages first appear on screen, and 'fully loaded', which indicates when the app is fully interactive. The incremental boot process improved 'content visible' from around 7 seconds to under 5 seconds.
What challenges did Slack face with large teams during the initial load?
As Slack teams grow, the amount of data required for a complete model increases, making initial loads slower. The previous method of loading all data at once was inefficient, prompting the need for the incremental boot approach to enhance performance.
How does Slack ensure responsiveness during loading?
Slack maintains responsiveness by using client-side caching, which allows the app to access previously loaded data quickly. This strategy minimizes the need for API calls during initial loading, ensuring a smoother user experience even with large datasets.

Key Statistics & Figures

'content visible'
improved from around 7 seconds to under 5 seconds
This metric indicates the time taken for messages to first appear on the screen after initiating the app.
'fully loaded'
decreased from around 7 seconds to around 6 seconds
This metric measures the total time taken for the app to become fully interactive.
Average daily loads of Slack's web app
4.2 million times
This statistic highlights the scale at which performance improvements can impact user experience.

Technologies & Tools

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Backend
Websocket
Used for real-time updates and maintaining a connection with the server after the initial load.
Frontend
Javascript
Utilized for building the client-side application and managing interactions.
Backend
API
The 'rtm.start' API is essential for fetching the necessary data to boot Slack.

Key Actionable Insights

1
Implement an incremental loading strategy to enhance user experience in web applications.
By loading only essential data first, users can start interacting with the application sooner, which is crucial for maintaining engagement and satisfaction.
2
Utilize client-side caching to minimize data transfer and improve load times.
Caching previously loaded data allows applications to respond faster, especially in environments with slow connections or large datasets.
3
Regularly measure and analyze performance metrics to identify areas for improvement.
Understanding metrics like 'content visible' and 'fully loaded' can guide development efforts and help prioritize performance enhancements.

Common Pitfalls

1
Relying solely on complete data models can lead to slow load times, especially for large teams.
This approach can frustrate users who expect immediate access to their channels and messages. Incremental loading strategies can mitigate this issue.
2
Neglecting performance metrics can result in unnoticed slowdowns in user experience.
Regular monitoring of key metrics is essential to ensure that performance remains optimal and to identify areas needing improvement.