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Realtime is increasingly becoming table stakes for messaging, collaboration, or event apps. But this doesn’t mean there isn’t room for improvement – and there are all sorts of engaging ways to add realtime to apps that don’t have it yet. We’ll dive into some examples and best practices.
At Fanout we’re always interested in trends involving moving and processing data in realtime. A major shift is coming, driven by the rise of connected devices and the vast amount of data they are going to collect. According to a Gartner report, 8.4 billion connected “things” will be in use in 2017, representing a 31% increase from 2016 – and every one of these IoT devices is going to need to collect, process, and transmit data in order to be effective.
Software development teams are beginning to realize the benefits of continuous, test-driven delivery of new releases.
Instead of a single, milestone release (waterfall development) or multiple, internal test releases before major external ones (agile development), continuous delivery focuses on constant releasing of features to market throughout the development process.
The goal of continuous delivery is that code is always ready to deploy and features are constantly rolled out independent of ‘releases’ – and doing so properly requires realtime data.
Chat is one of the most popular uses of realtime data. In this article we’ll explain how to build a web chat app in Django, using Django EventStream and Fanout Cloud. The Django EventStream module makes it easy to push JSON events through Fanout Cloud to connected clients.
Today we’re pleased to introduce Django EventStream, a module that provides Server-Sent Events capability to your Django server application. It relies on Pushpin or Fanout Cloud to manage the client connections. Events can optionally be persisted to your database, for highly reliable delivery.
It’s becoming the new normal that messaging and collaboration apps and platforms are available across multiple devices.
Business tools like Slack and JIRA offer feature-rich mobile apps, and users increasingly consume content from social networks like Facebook on their mobile devices instead of a desktop or laptop.
This isn’t a surprise – and we’re here to share our perspective on how developers can use realtime data to provide cross-platform users with the best notification experience.
Many software developers are familiar with realtime, but we believe that realtime concepts and user experiences are becoming increasingly important for less technical individuals to understand.
At Fanout, we power realtime APIs to instantly push data to endpoints – which can range from the actual endpoints of an API (the technical term) to external businesses or end users. We use the word in this post loosely to refer to any destination for data.
We’re here to share our experience with realtime: we’ll provide a definition and current examples, peer into the future of realtime, and try and shed some light on the eternal realtime vs. real-time vs. real time semantic debate.
Earlier this month we discussed the challenges of pushing data reliably. Fanout products such as Pushpin (and Fanout Cloud, which runs Pushpin) do not entirely insulate developers from these challenges, as it is not possible to do so within our scope. However, we recently devised a way to reduce the pain involved.
Realtime APIs usually require receivers to juggle two data sources if they want to receive data reliably. For example, a client might listen for updates using a best-effort streaming API, and recover data using a REST API. So we thought, what if Pushpin could manage these two data sources, such that the client only needs to worry about one?
In push architectures, one of the main challenges is delivering data reliably to receivers. There are many reasons for this:
- Most push architectures (including those developed by our company) use the publish-subscribe messaging pattern, which is unreliable.
- TCP’s built-in reliability is not enough to ensure delivery, as modern network sessions span multiple connections.
- Receivers can’t tell the difference between data loss and intentional silence.
- There is no one size fits all answer.
The last point trips up developers new to this problem space, who may wish for push systems to provide “guaranteed delivery.” If only it were that simple. Like many challenges in computer science, there isn’t a best answer, just trade-offs you are willing to accept.
Below we’ll go over the various issues and recommended practices around reliable push.
If you’ve built a REST API that clients poll for updates, you’ve probably considered adding a realtime push mechanism. Maybe you’ve been putting it off due to the added complexity, or the impact it might have on your API contract. These are valid concerns, but push doesn’t have to be that complicated.
In this article we’ll discuss how to update an API to use long-polling. It assumes:
- You have an existing REST API.
- You have clients repeatedly polling this API.
Long-polling is not the same as “plain” polling. With long-polling, the server delays the response to the client if there is no new data yet. This enables the server to respond instantly whenever the data does change. Aside from providing actual realtime updates, what’s great about long-polling is that technically it’s still RESTful, requiring hardly any changes to your API contract or client code.
Of course, long-polling may not be as efficient as streaming mechanisms like Server-Sent Events or WebSockets, but it’s inarguably more efficient than plain-polling. Let’s compare:
Mechanism Latency Load Plain-polling As high as the polling interval (e.g. 5 second interval means updates will be up to 5 seconds late) High Long-polling Zero Order of magnitude reduction
Long-polling is a great way to dip your feet in the realtime waters without having to dramatically change your API contract and client code.