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Event Sourcing in Postgres: The Events

Event Sourcing seems to be a complicated topic for most people, but in fact is quite simple and I’m sure you’ve used it almost every day of your life ever since you were a 6-7 years old kid: books.

👉 A book is the perfect Event Sourcing analogy: each page og the book is an event, it has a page number that solves two problems:

  1. It lets the reader start reading from where they stopeed the previous day
  2. It tells which page to read first, second, and so forth

After the reader is done with page n.32, they move to page n.33. But page n.32 is still there, unchanged, so that another reader can go through the same book.

Meet the Events table:

Our first data model will be based on the concept of a book.
It just needs some content and a page number:

CREATE TABLE IF NOT EXISTS "events" (
  "offset" BIGSERIAL,
  "payload" JSONB DEFAULT '{}'
  PRIMARY KEY ("offset")
);

We use the offset to identify the event in an ordered and unique way, and a simple JSONB field to store the event’s data.

How to write a new Event:

Writing events is as simple as appending rows into the table.

Thanks to PostreSQL, this is already a thread-safe operation and concurrency will never be an issue:

INSERT INTO "events" 
("payload") VALUES
('{"name": "first event"}'),
('{"name": "second event"}')
RETURNING *

How to read from the Events table:

Reading from this data model is also thread-safe and pretty basic.

Each consumer will ask one event at the time, using the last known offset to move forward into the list of available events:

SELECT * FROM "events"
WHERE "offset" > 0
ORDER BY "offset" ASC
LIMIT 1;

If we are at the beginning of the event stream, we simply start from the first item, which has offset=0. Later on, we’ll keep running the same query using the latest processed offset in order to get the following item.

When our query will stop yielding results, it means we reached the end of the “book”.

Pros / Cons

👍 This solution is extremely simple: it’s just one table.

Still, it can easily store billions of records and still run efficiently as we will always and only append data to it, and query based on the primary index.

👎 With this simple data-model, each consumer must store the last consumed message.

This design choice makes it difficult to build software as each consumer must store that information somewhere, and coordinate it between multiple instances in case of horizontal scaling of the consumer itself.

Moving Forward

In the next article of the serie, we’ll learn how to overcome this limitation and store the information in PostgreSQL itself.

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