Realtime Data

How To Power Your App Using a Realtime Data CDN

Combining Fastly (high scale pull) and Fanout (high scale push) to power realtime messaging at the edge

CDN — Content Delivery Network

Let’s start with defining a CDN. A content delivery network (CDN) is a system of distributed servers that traditionally delivers web content to a user, based on the geographic locations of the user, the origin of the webpage and the content delivery server. I use the term traditionally because we’re entering an era where CDNs are doing more than just delivering web content.

An example would be Cloudflare Workers, which lets you use their CDN to run code at the edge, rather than just serve web pages / cached content. You are basically able to deploy and run JavaScript away from the origin server — allowing you to decouple code from a user’s device. According to Cloudflare, “these Workers also enable programmatic functionality for routing, filtering and responding to HTTP requests that would otherwise need to be run on a customer’s server at the origin.”

The main point is that CDNs and edge computing are continuously evolving — whereby the two are starting to meld together in an era where high scalability is paramount.

Melding Realtime Data Push with Realtime Data Pull

Many realtime applications need to work with data that is both pushed and pulled (i.e live sports scores, auctions, chat). Separately, data push and data pull are fairly straightforward as independent entities. At initialization time, past content could be retrieved from a pull CDN and new/future updates could be pushed from a separate service.

But, what if you could chain these mechanisms together?

Proxy Chaining with Fastly and Fanout

Fastly is an edge cloud platform that enables applications to process, serve, and secure data at the edge of a network. It is essentially high scalable data pull and response, using a platform that can listen and respond to users’ needs in realtime. Similar to a traditional CDN, Fastly does allow you cache content, but it also lets you deliver application logic at the edge.

On the other hand, Fanout is high scalable data push — serving as a reverse proxy that handles long-lived client connections and pushes data as it becomes available.

Both Fastly and Fanout work as reverse proxies, so it is possible to have Fanout proxy traffic through Fastly — rather than sending that traffic directly to your origin server. Together, this coupled system has some interesting benefits:

  1. High availability — If your origin server goes down, Fastly can serve cached data and instructions to Fanout. This means clients could connect to your API endpoint, receive historical data, and activate a streaming connection, all without needing access to the origin server.
  2. Cached initial data — Fanout lets you build API endpoints that serve both historical and future content, for example an HTTP streaming connection that returns some initial data before switching into push mode. Fastly can provide that initial data, reducing load on your origin server.
  3. Cached Fanout instructions — Fanout’s behavior (e.g. transport mode, channels to subscribe to, etc.) is determined by instructions provided in origin server responses (using a system of special headers called Grip). Fastly can subsequently cache these instructions and headers.
  4. High scalability — By caching Fanout instructions and headers, Fastly can further reduce the load on your origin server — bringing that processing logic closer to the edge.

Mapping the Network Flow

Using Fanout and Fastly, let’s map the network flow to see how these push and pull mechanisms could work together.

Let’s suppose there’s an API endpoint /stream that returns some initial data and then stays open until there is a new update to push. With Fanout, this can be implemented by having the origin server respond with instructions:

HTTP/1.1 200 OK
Content-Type: text/plain
Content-Length: 29
Grip-Hold: stream
Grip-Channel: updates

{"data": "current value"}

When Fanout receives this response from the origin server, it converts it into a streaming response to the client:

HTTP/1.1 200 OK
Content-Type: text/plain
Transfer-Encoding: chunked
Connection: Transfer-Encoding

{"data": "current value"}

The request between Fanout and the origin server is now finished, but the request between the client and Fanout remains open. Here’s a sequence diagram of the process:

Since the request to the origin server is just a normal short-lived request/response interaction, it can alternatively be served through a caching server such as Fastly.

Here’s what the process looks like with Fastly in the mix:

Now, when the next client makes a request to the /stream endpoint, the origin server isn’t involved at all:

In other words, Fastly serves the same response to Fanout, with those special HTTP headers and initial data, and Fanout sets up a streaming connection with the client.

Of course, this is only the connection setup. To send updates to connected clients, the data must be published to Fanout.

Purging the Fastly Cache

If an event that triggers a publish causes the origin server response to change, then we may also need to purge the Fastly cache.

For example, suppose the “value” that the /stream endpoint serves has been changed. The new value could be published to all current connections, but we’d also want any new connections that arrive afterwards to receive this latest value as well, rather than the older cached value. This can be solved by purging from Fastly and publishing to Fanout at the same time.

This sequence diagram illustrates a client connecting, receiving an update, and then another client connecting:

Effectively Handling Rate-Limiting

If your publishing data rate is relatively high, then this can negate the caching benefit of using Fastly.

The ideal data rate to effectively harness Fastly’s cache would be data that is:

  • Accessed frequently — many new vistors per second
  • Changed frequently — updates ever few seconds or minutes
  • Delivered instantly — in milliseconds

An example of this would be a live blog, whereby most requests can be served and handled from cache.

However, if your data changes multiple times per second (or has the potential to change that fast during peak moments), and you expect frequent access, you really don’t want to be purging your cache multiple times per second.

The workaround is to rate-limit your purges. For example, during periods of high throughput, you might purge and publish at a maximum rate of once per second or so. This way, the majority of new visitors can be served from cache, and the data will be updated shortly after.


You can reference the Github source code for the Fastly/Fanout high scale Live Counter demo. Requests first go to Fanout, then to Fastly, then to a Django backend server which manages the counter API logic. Whenever a counter is incremented, the Fastly cache is purged and the data is published through Fanout. The purge and publish process is also rate-limited to maximize caching benefit.

Final Thoughts: The Emergence of a Messaging CDN?

Broadly speaking, we could define a messaging content delivery network as a geographically distributed group of servers which work together to provide near realtime delivery of dynamic data and web content.

This new genre of CDN could allow data processing to take place at the edge, away from an app’s origin — thereby ushering in a new era of realtime computing that is both affordable and scalable.

Realtime on the Edge

Edge Computing —A Beginner’s Guide

Learn the basics of edge computing and how it is transforming the realtime landscape

Machine Pulse

The Edge

The ‘edge’ refers to computing infrastructure that exists close to the origin sources of data. It is distributed IT architecture and infrastructure where data is processed at the periphery of the network, as close to the originating source as possible.

Edge computing is a method of optimizing cloudcomputing systems by performing data processing at theedge of the network, near the source of the data.


Living on the Edge

A series of gateway servers sit outside your primary cloud environment, allowing for more localized data processing.

Examples of edge computing can be found throughout our everyday lives — we just may not notice them.

Industrial Internet of Things (IIoT)

  • Wind turbines
  • Magnetic resonance (MR) scanner
  • Undersea blowout preventers
  • Industrial controllers such as SCADA systems
  • Automated industrial machines
  • Smart power grid technology
  • Smart streetlights

Internet of Things (IoT)

  • Motor vehicles (Cars and trucks)
  • Mobile devices
  • Traffic lights
  • Thermostats
  • Home appliances

Edge Computing Benefits

Edge computing allows for the clear scoping of computing resources for optimal processing.

  1. Time-sensitive data can be processed at the point of origin by a localized processor (a device that has its own computing ability).
  2. Intermediary servers can be used to process data in close geographical proximity to the source (this assumes that intermediate latency is okay, though realtime decisions should be made as close to the origin as possible).
  3. Cloud servers can be used to process less time sensitive data or to store data for the longterm. With IoT, you’ll see this manifest in analytics dashboards.
  4. Edge application services significantly decrease the volumes of data that must be moved, the consequent traffic, and the distance the data must travel, thereby reducing transmission costs, shrinking latency, and improving quality of service(QoS) (source).
  5. Edge computing removes a major bottleneck and potential point of failure by de-emphasizing the dependency on the core computing environment.
  6. Security improves as encrypted data is checked as it passes through protected firewalls and other security points, where viruses, compromised data, and active hackers can be caught early on (source).
  7. The edge augments scalability by logically grouping CPU capabilities as needed, saving costs on realtime data transmission.

Why the Edge

Transmitting massive amounts of data is expensive and taxing on network resources. Edge computing allows you to process data near the source and only send relevant data over the network to an intermediate data processor.

For example, a smart refrigerator does not need to continually send internal temperature data back to a cloud analytics dashboard. Rather, it can be configured to only send data when the temperature has changed beyond a particular point; or, it could be polled to send data only when the dashboard is loaded. Similarly, an IoT security camera could only need to send data back to your device when it detects motion or when you explicitly toggle a live data feed.

Devise Relationship Management (DRM)

To manage edge devices, device relationship management (DRM) refers to the monitoring and maintenance of complex, intelligent, and interconnected equipment over the internet. DRM is specifically designed to interface with the microprocessors and local software in IoT devices.

Device relationship management (DRM) is enterprise software that enables the monitoring, managing, and servicing of intelligent devices over the Internet.


The Fog

Between the edge and cloud is the fog layer, which helps bridge the connections between edge devices and cloud data centers. According to Matt Newton of Opto 22:

Fog computing pushes intelligence down to the local area network level of network architecture, processing data in a fog node or IoT gateway.

Edge computing pushes the intelligence, processing power and communication capabilities of an edge gateway or appliance directly into devices like programmable automation controllers (PACs).


Edge and Realtime

Sensors and remotely deployed devices demand realtime processing. A centralized cloud system is often too slow for this, especially when decisions need to be made in microseconds. This is especially true for IoT devices in regions or locations with poor connectivity.

However, sometimes realtime capabilities demand cloud processing. For example, lets say data consumed by remote tornado weather monitors needs to be sent in realtime to massive supercomputers.

This is where realtime infrastructure comes into play to help enable those data transactions.

Getting Started with Building Realtime API Infrastructure

How companies are adding realtime capabilities to their products and building realtime APIs

Mirroring the rise of API-driven applications, realtime is becoming an emerging, omnipresent force in modern application development. It is powering instant messaging, live sports feeds, geolocation, big data, and social feeds. But, what is realtime and what does it really mean? What types of software and technology are powering this industry? Let’s dive into it.

What Is Realtime?

For the more technical audience, realtime traditionally describes realtime computing, whereby “hardware and software systems are subject to a realtime constraint, for example from event to system response” (Source). For this article, we’re framing realtime from the perspective of an end-user: the perception that an event or action happens sufficiently quickly to be perceived as nearly instantaneous.

Moreover, realtime could be defined in a more relative temporal sense. It could mean that a change in A synchronizes with a change in B. Or, it could mean that a change in A immediately triggers a change in B. Or… it could mean that A tells B that something changed, yet B does nothing. Or… does it mean that A tells everyone something changed, but doesn’t care who listens?

Let’s dig a bit deeper. Realtime does not necessarily mean that something is updated instantly (in fact, there’s no singular definition of “instantly”). So, let’s not focus on the effect, but rather the mechanism. Realtime is about pushing data as fast as possible — it is automated, synchronous, and bi-directional communication between endpoints at a speed within a few hundred milliseconds. 

  • Synchronous means that both endpoints have access to data at the same time.
  • Bi-directional means that data can be sent in either direction.
  • Endpoints are senders or receivers of data (phone, tablet, server).
  • A few hundred milliseconds is a somewhat arbitrary metric since data cannot be delivered instantly, but it most closely aligns to what humans perceive as realtime (Robert Miller proved this in 1986).

With this definition and its caveats in mind, let’s explore the concept of pushing data.

Data Push

We’ll start by contrasting data push with “request-response.” Request-response is the most fundamental way that computer systems communicate. Computer A sends a request for something from Computer B, and Computer B responds with an answer. In other words, you can open up a browser and type “” The browser sends a request to Reddit’s servers and they respond with the web page.

Request-Response vs Evented APIs

In a data push model, data is pushed to a user’s device rather than pulled (requested) by the user. For example, modern push email allows users to receive email messages without having to check manually. Similarly, we can examine data push in a more continuous sense, whereby data is continuously broadcasted. Anyone who has access to a particular channel or frequency can receive that data and decide what to do with it.

Moreover, there are a few ways that data push/streaming is currently achieved:

HTTP Streaming

HTTP Streaming

HTTP streaming provides a long-lived connection for instant and continuous data push. You get the familiarity of HTTP with the performance of WebSockets. The client sends a request to the server and the server holds the response open for an indefinite length. This connection will stay open until a client closes it or a server side-side event occurs. If there is no new data to push, the application will send a series of keep-alive ticks so the connection doesn’t close.


HTTP Web Sockets

WebSockets provide a long-lived connection for exchanging messages between client and server. Messages may flow in either direction for full-duplex communication. This bi-directional connection is established through a WebSocket handshake. Just like in HTTP Streaming and HTTP Long-Polling, the client sends a regular HTTP request to the server first. If the server agrees to the connection, the HTTP connection is replaced with a WebSocket connection.



Webhooks are a simple way of sending data between servers. No long-lived connections are needed. The sender makes an HTTP request to the receiver when there is data to push. A WebHook registers or “hooks” to a callback URL and will notify you anytime an event has occurred. You register this URL in advance and when an event happens, the server sends a HTTP POST request with an Event Object to the callback URL. This event object contains the new data that will be pushed to the callback URL. You might use a WebHook if you want to receive notifications about certain topics. It could also be used to notify you whenever a user changes or updates their profile.

HTTP Long-Polling

HTTP Long Polling

HTTP long-polling provides a long-lived connection for instant data push. It is the easiest mechanism to consume and also the easiest to make reliable. This technique provides a long-lived connection for instant data push. The server holds the request open until new data or a timeout occurs. Most send a timeout after 30 to 120 seconds, it depends on how the API was setup. After the client receives a response (whether that be from new data or a timeout), the client will send another request and this is repeated continuously.

Is pushing data hard? Yes, it is, especially at scale (ex. pushing updates to millions of phones simultaneously). To meet this demand, an entire realtime industry has emerged, which we’ll define as Realtime Infrastructure as Service (Realtime IaaS).

Realtime Libraries

Here is a compilation of resources that are available for developers to build realtime applications based on specific languages / frameworks:

Realtime Infrastructure as a Service

According to Gartner, “Infrastructure as a service (IaaS) is a standardized, highly automated offering, where compute resources, complemented by storage and networking capabilities are owned and hosted by a service provider and offered to customers on-demand. Customers are able to self-provision this infrastructure, using a Web-based graphical user interface that serves as an IT operations management console for the overall environment. API access to the infrastructure may also be offered as an option.”

We often here PaaS (Platform as a Service) and SaaS (Software as a Service), so how are they different than IaaS?

  • Infrastructure as a Service (IaaS): hardware is provided by an external provider and managed for you.
  • Platform as a Service (PaaS): both hardware and your operating system layer are managed for you.
  • Software as a Service (SaaS): an application layer is provided for the platform and infrastructure (which is managed for you).

To power realtime, applications require a carefully architected system of servers, APIs, load balancers, etc. Instead of building these systems in-house, organizations are finding it more cost-effective and resource-efficient to purchase much of this systemic infrastructure and then drive it in-house. These systems, therefore, are not just IaaS, but typically provide both a platform and software layer to help with management. Foundationally speaking, their core benefit is that they provide realtime infrastructure, whether you host it internally or rely on managed instance

It all comes down to the simple truth that realtime is hard for a number of reasons:

  • Customer Uptime Demand – Customers that depend on realtime updates will immediately notice when your network is not performant.
  • Horizontal Scalability – You must be able to handle volatile and massive loads on your system or risk downtime. This is typically achieved through clever horizontal scalability and systems that are able to manage millions of simultaneous connections.
  • Architectural Complexity – Maintaining a performant realtime system is not only complex, but it requires extensive experience and expertise. This is expensive to buy, especially in today’s high demand engineering market.
  • Contingencies – Inevitably, your system will experience some downtime, whether due to an anticipated load spike or a newly released feature. It is important, therefore, to have multiple uptime contingencies in place to make sure that the system knows what to do, should your primary realtime mechanism fail to perform.
  • Queuing – When you’re sending a lot of data, then you likely need an intermediate queuing mechanism to ensure that your backend processes are not overburdened with increased message loads.

Realtime Application IaaS

Realtime app infrastructure sends data to browsers and clients. It typically uses pub/sub messaging, webhooks, and/or websockets — and is separate from an application or service’s main API. These solutions are best for organizations that are looking for realtime messaging without the need to build their own realtime APIs.

Pub-Subscribe PubSub Pattern for Realtime API

These systems also have more well-built platform/software management tools on top of their infrastructure offerings. For instance, the leading providers have built-in configuration tools like access controls, event delegation, debugging tools, and channel configuration.

Benefits of Realtime App IaaS

  • Speed – typically explicitly designed to deliver data with low latencies to end-user devices, including smartphones, tablets, browsers, and laptops.
  • Multiple SDKs for easier integration.
  • Uses globally distributed realtime data delivery platforms.
  • Multiple protocol adapters.
  • Well-tested in production environments.
  • Keeps internal configuration to a minimum.

Use Cases

While some of the platforms out there function differently, here are some of the most typical use cases:

  • Realtime Chat – In a microservice environment, a realtime API proxy makes it easy to listen for instant updates from other microservices without the need for a centralized message broker. Each microservice gets its own proxy instance, and microservices communicate with each other via your organization’s own API contracts rather than a vendor-specific mechanism.
  • IoT Device Control – Securely monitor, control, provision and stream data between Internet-connected devices.
  • Geotracking / Mapping Realtime Updates – Integrates with other realtime APIs like (Google Maps) to construct rich realtime updates.
  • Multiplayer Game Synchronization – Synchronize communications amongst multiple simultaneous players to keep play fluid.


Here are some realtime application IaaS providers (managed) to check out for further learning: PubNubPusher, and Ably.

Realtime API IaaS for API Development

Realtime API infrastructure specifically allows developers to build realtime data push into their existing APIs. Typically, you would not need to modify your existing API contracts, as the streaming server would serve as a proxy. The proxy design allows these services to fit nicely within an API stack. This means it can inherit other facilities from your REST API, such as authentication, logging, throttling, etc and, consequently, it can be easily combined with an API management system. In the case of WebSocket messages being proxied out as HTTP requests, the messages may be handled statelessly by the backend. Messages from a single connection can even be load balanced across a set of backend instances.

Realtime API Infrastructure as a service IaaS Proxy

All in all, realtime API IaaS is used for API development, specifically geared for organizations that need to build highly-performant realtime APIs like Slack, Instagram, Google, etc. All of these orgs build and manage their infrastructure internally, so the IaaS offering can be thought of as a way to extend these capabilities to organizations that lack the resources and technical expertise to build a realtime API from scratch.

Benefits of Realtime API IaaS

  • Custom build an internal API.
  • Works with existing API management systems.
  • Does not lock you into a particular tech stack.
  • Provides realtime capabilities throughout entire stack.
  • Usually proxy-based, with pub/sub or polling.
  • Add realtime to any API, no matter what backend language or database.
  • Cloud or self-hosted API infrastructure.
  • It can inherit facilities from your REST API, such as authentication, logging, throttling.

Use Cases

While some of the platforms out there function differently, here are some of the most typical use cases:

  • API development – As we’ve discussed, you can build custom realtime APIs on top of your existing API infrastructure.
  • Microservices – In a microservice environment, a realtime API proxy makes it easy to listen for instant updates from other microservices without the need for a centralized message broker. Each microservice gets its own proxy instance, and microservices communicate with each other via your organization’s own API contracts rather than a vendor-specific mechanism.
  • Message queue – If you have a lot of data to push, you may want to introduce an intermediate message queue. This way, backend processes can publish data once to the message queue, and the queue can relay the data via an adapter to one or more proxy instances. The realtime proxy is able to forward subscription information to such adapters, so that messages can be sent only to the proxy instances that have subscribers for a given channel.
  • API management – It’s possible to combine an API management system with a realtime proxy. Most API management systems work as proxy servers as well, which means all you need to do is chain the proxies together. Place the realtime proxy in the front, so that the API management system isn’t subjected to long-lived connections. Also, the realtime proxy can typically translate WebSocket protocol to HTTP, allowing the API management system to operate on the translated data.
  • Large scale CDN performance – Since realtime proxy instances don’t talk to each other, and message delivery can be tiered, this means the realtime proxy instances can be geographically distributed to create a realtime push CDN. Clients can connect to the nearest regional edge server, and events can radiate out from a data source to the edges.


Here are some realtime API IaaS providers (managed/open source) to check out for further learning: Fanout/, and LiveResource.


Realtime is becoming an emerging, omnipresent force in modern application development. It is not only a product differentiator, but is often sufficient for product success. It has accelerated the proliferation of widely-used apps like Google Maps, Lyft, and Slack. Whether you’re looking to build your own API from scratch or build on top of an IaaS platform, realtime capabilities are increasingly becoming a requirement of the modern tech ecosystem.

Resource Spotlight: Spec by Sam Curren and Phillip J. Windley

This online resource is a unique way to frame a conceptual model for evented APIs.  Sam Curren and Phillip J. Windley discuss the fundamentals of evented APIs, how evented systems work, and a proposed protocol.

Events indicate something has happened. In this they differ from the request-response interaction style popular on the Web. Event-based systems are declarative whereas request-response systems are interrogatory. The difference between events (“this happened”) and requests (“will you do this?”) offers benefits in looser coupling of components as well as semantic encapsulation (see On Hierarchies and Networks for more detail).

APIs have become an economic imperative for many companies. But APIs based solely on request-response style interactions limit integrations to those where one system always knows what it wants from the other. The calling service must script the interaction and the APIs simply follow along.

We envision a world where applications integrate multiple products and services as equals based on event-driven interactions. Evented APIs, following the form described in this document, enable building such applications.

Article Spotlight: 5 Protocols For Event-Driven API Architectures by Kristopher Sandoval

In this article, Kristopher Sandoval highlights the five most common event-driven methods for data push.  These methods all have their pros and cons, and work best based on your particular use cases.

The internet is a system of communication, and as such, the relationship between client and server, as well as server to server, is one of the most oft-discussed and hotly contested concepts. event-driven architecture is a methodology of defining these relationships, and creating systems within a specific set of relationships that allow for extensive functionality.

In this piece, we’re going to discuss 5 common event-driven methods — WebSocketsWebHooksREST HooksPub-Sub, and Server Sent Events. We’ll define what they fundamentally are and do, and how API providers go about using them. Additionally, we’ll provide some pros and cons on each to make choosing a solution for your platform easy and intuitive.

Full Article

Spotlight Article: How to Describe, Publish & Consume Real-Time Data by Phil Leggetter

In this article, Phil Leggetter discusses techniques for analyzing and processing realtime data. He goes through an example using RethinkDB. Check out the full article here.

In the first post in the series we covered discovering real-time data within your systems and applications. In part two we went through the use cases for your real-time data. In this final section we’ll cover the how: how to describe, publish & consume real-time data from your systems and expose the data so that you can build real-time features.

The main steps we’re going to cover are doing the following with the real-time event data:




Consume and Use

Full Source