Book: Designing Event Driven Systems

I wrote a book: Designing Event Driven Systems PDF EPUB

Apr 27th, 2018

The Data Dichotomy

Data Systems are about exposing data, Services are about hiding it.

Dec 14th, 2016

Elements of Scale: Composing and Scaling Data Platforms

This transcribed talk explores a range of data platforms through a lens of basic hardware and software tradeoffs.

Apr 28th, 2015

Log Structured Merge Trees

A detailed look at the interesting LSM file organisation seen in BigTable, Cassandra and most recently MongoDB

Feb 14th, 2015

Blog/News



REST Request-Response Gateway
Jun 7th, 2018

This post outlines how you might create a Request-Response Gateway in Kafka using the good old correlation ID trick and a shared response topic. It’s just a sketch. I haven’t tried it out.

A Rest Gateway provides an efficient Request-Response bridge to Kafka. This is in some ways a logical extension of the REST Proxy, wrapping the concepts of both a request and a response.

What problem does it solve?

  • Allows you to contact a service, and get a response back, for example:
    • to display the contents of the user’s shopping basket
    • to validate and create a new order.
  • Access many different services, with their implementation abstracted behind a topic name.
  • Simple Restful interface removes the need for asynchronous programming front-side of the gateway.

So you may wonder: Why not simply expose a REST interface on a Service directly? The gateway lets you access many different services, and the topic abstraction provides a level of indirection in much the same way that service discovery does in a traditional request-response architecture. So backend services can be scaled out, instances taken down for maintenance etc, all behind the topic abstraction. In addition the Gateway can provide observability metrics etc in much the same way as a service mesh does.

You may also wonder: Do I really want to do request response in Kafka? For commands, which are typically business events that have a return value, there is a good argument for doing this in Kafka. The command is a business event and is typically something you want a record of. For queries it is different as there is little benefit to using a broker, there is no need for broadcast and there is no need for retention, so this offers little value over a point-to-point interface like a HTTP request. So the latter case we wouldn’t recommend this approach over say HTTP, but it is still useful for advocates who want a single transport and value that over the redundancy of using a broker for request response (and yes these people exist).

This pattern can be extended to be a sidecar rather than a gateway also (although the number of response topics could potentially become an issue in an architecture with many sidecars).

Implementation 

Above we have a gateway running three instances, there are three services: Orders, Customer and Basket. Each service has a dedicated request topic that maps to that entity. There is a single response topic dedicated to the Gateway.

The gateway is configured to support different services, each taking 1 request topic and 1 response topic.

Imagine we POST and Order and expect confirmation back from the Orders service that it was saved. This work as follows:

  • The HTTP request arrives at one node in the Gateway. It is assigned a correlation ID.
  • The correlation ID is derived so that it hashes to a partition of the response topic owned by this gateway node (we need this to route the request back to the correct instance). Alternatively a random correlation id could be assigned and the request forwarded to the gateway node that owns the corresponding partition of the response topic.
  • The request is tagged with a unique correlation ID and the name of the gateway response topic (each gateway has a dedicated response topic) then forwarded to the Orders Topic. The HTTP request is then parked in the webserver.
  • The Orders Service processes the request and replies on the supplied response topic (i.e. the response topic of the REST Gateway), including the correlation ID as the key of the response message. When the REST Gateway receives the response, it extracts the correlation ID key and uses it to unblock the outstanding request so it responds to the user HTTP request.

Exactly the same process can be used for GET requests, although providing streaming GETs will require some form of batch markers or similar, which would be awkward for services to implement probably necessitating a client-side API.

If partitions move, whist requests are outstanding, they will timeout. We could work around this but it is likely acceptable for an initial version.

This is very similar to the way the OrdersService works in the Microservice Examples

Event-Driven Variant

When using an event driven architecture via event collaboration, responses aren’t based on a correlation id they are based on the event state, so for example we might submit orders, then respond once they are in a state of VALIDATED. The most common way to implement this is with CQRS.

Websocket Variant

Some users might prefer a websocket so that the response can trigger action rather than polling the gateway. Implementing a websocket interface is slightly more complex as you can’t use the queryable state API to redirect requests in the same way that you can with REST. There needs to be some table that maps (RequestId->Websocket(Client-Server)) which is used to ‘discover’ which node in the gateway has the websocket connection for some particular response.

Posted at Jun 7th |Filed Under: Blog, Kafka/Confluent - read on


Slides from Craft Meetup
May 9th, 2018

The slides for the Craft Meetup can be found here.

Posted at May 9th |Filed Under: Blog, Uncategorized - read on


Book: Designing Event Driven Systems
Apr 27th, 2018

I wrote a book: Designing Event Driven Systems

PDF

EPUB

Posted at Apr 27th |Filed Under: Blog, Top4 - read on


Building Event Driven Services with Kafka Streams (Kafka Summit Edition)
Apr 23rd, 2018

The Kafka Summit version of this talk is more practical and includes code examples which walk though how to build a streaming application with Kafka Streams.

Posted at Apr 23rd |Filed Under: Blog, Uncategorized - read on


Slides fo NDC – The Data Dichotomy
Jan 19th, 2018

When building service-based systems, we don’t generally think too much about data. If we need data from another service, we ask for it. This pattern works well for whole swathes of use cases, particularly ones where datasets are small and requirements are simple. But real business services have to join and operate on datasets from many different sources. This can be slow and cumbersome in practice.

These problems stem from an underlying dichotomy. Data systems are built to make data as accessible as possible—a mindset that focuses on getting the job done. Services, instead, focus on encapsulation—a mindset that allows independence and autonomy as we evolve and grow. But these two forces inevitably compete in most serious service-based architectures.

Ben Stopford explains why understanding and accepting this dichotomy is an important part of designing service-based systems at any significant scale. Ben looks at how companies make use of a shared, immutable sequence of records to balance data that sits inside their services with data that is shared, an approach that allows the likes of Uber, Netflix, and LinkedIn to scale to millions of events per second.

Ben concludes by examining the potential of stream processors as a mechanism for joining significant, event-driven datasets across a whole host of services and explains why stream processing provides much of the benefits of data warehousing but without the same degree of centralization.

Posted at Jan 19th |Filed Under: Blog - read on


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