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After watching this awesome talk by Martin Klepmann about how Kafka can be used to stream events so that we can get rid of 2-phase-commits, I have a couple of questions related to updating a cache only when the database is updated properly.

Problem Statement

Lets say you have a Redis cache which stores the user's profile pic and a Postgres database which is used for all the User related operations(creating, updation, deletion, etc)

I want to update my Redis cache only and only when a new user has been successfully added to my database.

How can I do that using Kafka ?

If I am to take the example given in the video then the workflow would follow something like this:

  • User registers
  • Request is handled by User Registration Micro service
  • User Registration Microservice inserts a new entry into the User's table.
  • Then generates an User Creation Event in the user_created topic.
  • Cache population microservice consumes the newly created User Creation Event
  • Cache population microservice updates the redis cache.

The problem starts what would happen if the User Registration Microservice crashed just after writing to the database, but failed to send the event to Kafka ?

What would be the correct way of handling this ?

  • Does the User Registration Microservice maintain the last event it published ? How can it reliably do that ? Does it write to a DB ? Then the problem starts all over again, what if it published the event to Kafka but failed before it could update its last known offset.
ng.newbie
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1 Answers1

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There are three broad approaches one can take for this:

  • There's the transactional outbox pattern, wherein, in the same transaction as inserting the new entry into the user table, a corresponding user creation event is inserted into an outbox table. Some process then eventually queries that outbox table, publishes the events in that table to Kafka, and deletes the events in the table. Since the inserts are in the same transaction, they either both occur or neither occurs; barring a bug in the process which publishes the outbox to Kafka, this guarantees that every user insert eventually has an associated event published (at least once) to Kafka.

  • There's a more event-sourcingish pattern, where you publish the user creation event to Kafka and then some consuming process inserts into the user table based on the event. Since this happens with a delay, this strongly suggests that the user registration service needs to keep state of which users it has published creation events for (with the combination of Kafka and Postgres being the source of truth for this). Since Kafka allows a message to be consumed by arbitrarily many consumers, a different consumer can then update Redis.

  • Change data capture (e.g. Debezium) can be used to tie into Postgres' write-ahead log (as Postgres actually event sources under the hood...) and publish an event that essentially says "this row was inserted into the user table" to Kafka. A consumer of that event can then translate that into a user created event.

CDC in some sense moves the transactional outbox into the infrastructure, at the cost of requiring that the context it inherently throws away be reconstructed later (which is not always possible).

That said, I'd strongly advise against having ____ creation be a microservice and I'd likewise strongly advise against a RInK store like Redis. Both of these smell like attempts to paper over architectural deficiencies by adding microservices and caches.

The one-foot-on-the-way-to-event-sourcing approach isn't one I'd recommend, but if one starts there, the requirement to make the registration service stateful suddenly opens up possibilities which may remove the need for Redis, limit the need for a Kafka-like thing, and allow you to treat the existence of a DB as an implementation detail.

Levi Ramsey
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