fstore-sql (event-store, based on postgres)

This project offers a seamless SQL model for efficiently prototyping event-sourcing and event-streaming by using Postgres database.

Check the schema.sql and extensions.sql! It is all there! No additional tools, frameworks, or programming languages are required at this level.

Table of contents

• Run Postgres
  • Requirements
• Examples of usage
  • Event Sourcing
  • 1. Registering a simple decider decider1 with two event types it can publish: 'event1', 'event2'
  • 2. Appending two events for the decider f156a3c4-9bd8-11ed-a8fc-0242ac120002.
  • 3. Get/List events for the decider f156a3c4-9bd8-11ed-a8fc-0242ac120002
  • Event Streaming
  • 4. Registering a (materialized) view view1 with 1 second pooling frequency, starting from 28th Jan.
  • 5. Appending two events for another decider 2ac37f68-9d66-11ed-a8fc-0242ac120002.
  • 6a. Stream the events to concurrent consumers/views
  • 6b. Stream the events to concurrent consumers / edge-functions (views)
• Design
• fmodel
  • fmodel-kotlin | fmodel-ts | fmodel-rust | fmodel-java
  • FModel Demo Applications
• Try YugabyteDB
• References and further reading

This model is enabling and supporting:

• event-sourcing data pattern (by using Postgres database) to durably store events
  • Append events to the ordered, append-only log, using entity id/decider id and decider type as a key
  • Load all the events for a single entity/decider, in an ordered sequence, using the entity id/decider id and decider type as a key
  • Support optimistic locking/concurrency
• event-streaming to concurrently coordinate read over a stream of messages from multiple consumer instances/views
  • Support real-time concurrent consumers to project events to view/query models
  • Acknowledge that event with decider_id and offset is successfully processed by the view / ACK
  • Acknowledge that event with decider_id is NOT processed by the view, and the view will process it again automatically / NACK
  • (Optionally) Acknowledge that event with decider_id is NOT processed by the view, and the view will process it again after delay / SCHEDULE NACK

Every decider/entity stream of events represents an independent kafka-like partition. The events within a partition are ordered. There is no ordering guarantee across different partitions.

The API is a set of SQL functions that you can use to interact with the database. You can use them in your application. The API is what you would expect from a typical event-sourcing and event-streaming database.

Run Postgres

It is a Supabase Docker image of Postgres, with extensions installed:

• pg_cron and
• pg_net.

Requirements

Notice that we only need these two extensions to publish events to edge-functions/HTTP endpoints/serverless applications, as explained in section 6b below. If you do not need to publish events directly to your serverless applications, vanilla Postgres will work just fine!

You can run the following command to start Postgres in a Docker container:

docker compose up -d

Examples of usage

These examples are using SQL to interact with the database. Hopefully, you will find them useful, and you can use them in your application.

Import the schema.sql (imported by default) and extensions.sql (not imported!) into your database.

Event Sourcing

1. Registering a simple decider decider1 with two event types it can publish: 'event1', 'event2'

The deciders table controls the decider and event names/types that can be used in the events table itself through composite foreign keys. It must be populated before events can be appended to the main table called events.

SELECT *
from register_decider_event('decider1', 'event1', 'description1', 1);
SELECT *
from register_decider_event('decider1', 'event2', 'description2', 1);

2. Appending two events for the decider f156a3c4-9bd8-11ed-a8fc-0242ac120002.

Multiple constraints are applied to events table to ensure bad events do not make their way into the system. This includes duplicated events, incorrect naming (event and decider names cannot be misspelled, and the client cannot insert an event from the wrong decider), ensured sequential events, disallowed delete, and disallowed update.

Notice how previous_id of the second event points to event_id of the first event (effectively implementing optimistic locking).

SELECT *
from append_event('event1', '21e19516-9bda-11ed-a8fc-0242ac120002', 'decider1', 'f156a3c4-9bd8-11ed-a8fc-0242ac120002',
                  '{}', 'f156a3c4-9bd8-11ed-a8fc-0242ac120002', null, 1);
SELECT *
from append_event('event2', 'eb411c34-9d64-11ed-a8fc-0242ac120002', 'decider1', 'f156a3c4-9bd8-11ed-a8fc-0242ac120002',
                  '{}', 'f156a3c4-9bd8-11ed-a8fc-0242ac120002', '21e19516-9bda-11ed-a8fc-0242ac120002', 1);

3. Get/List events for the decider f156a3c4-9bd8-11ed-a8fc-0242ac120002

SELECT *
from get_events('f156a3c4-9bd8-11ed-a8fc-0242ac120002', 'decider1');

Event Streaming

4. Registering a (materialized) view view1 with 1 second pooling frequency, starting from 28th Jan.

The View must be registered before events can be streamed to it. This streaming is kafka-like, in that it is modeling the concept of partitions and offsets. Every unique stream of events for the one deciderId/entityId is a partition. Lock table is used to prevent concurrent access/reading to the same partition, guaranteeing that only one consumer can read from a partition at a time / guaranteeing the ordering within the partition on the reading side.

You can configure the view to publish event(s) every 1 second, starting from 28th Jan, 2023 with lock/ACK timeout of 300 seconds (if you dont acknowledge that you processed the event in 300 sec, the lock will be released and event will be published again, automatically).

Notice how lock for the two events with decider_id=f156a3c4-9bd8-11ed-a8fc-0242ac120002 is created in the background (using triggers).

SELECT *
from register_view('view1', '2023-01-28 12:17:17.078384', 300, 1, 'https://localhost:3000/functions/v1/event-handler');

5. Appending two events for another decider 2ac37f68-9d66-11ed-a8fc-0242ac120002.

The alone existence of the View is changing how append_event works. It is now creating a new event, but also updating a lock table.

• offset / current offset of the event stream for decider_id
• offset_final / an indicator if the offset is final / offset will not grow anymore

Notice how previous_id of the second event is pointing to event_id of the first event.

Notice how additional lock for the registered view and two new events with decider_id=2ac37f68-9d66-11ed-a8fc-0242ac120002 created in the background (using triggers).

SELECT *
from append_event('event1', 'f7c370aa-9d65-11ed-a8fc-0242ac120002', 'decider1', '2ac37f68-9d66-11ed-a8fc-0242ac120002',
                  '{}', 'f156a3c4-9bd8-11ed-a8fc-0242ac120002', null, 1);
SELECT *
from append_event('event2', '42ee177e-9d66-11ed-a8fc-0242ac120002', 'decider1', '2ac37f68-9d66-11ed-a8fc-0242ac120002',
                  '{}', 'f156a3c4-9bd8-11ed-a8fc-0242ac120002', 'f7c370aa-9d65-11ed-a8fc-0242ac120002', 1);

6a. Stream the events to concurrent consumers/views

stream_events function is used to stream events to the view. On every event being read a lock table is updated to acquire a lock on that partition. You can:

• unlock the partition with ack-event function / acknowledge that the event with decider_id and offset is processed by the view
• unlock the partition with nack-event function / acknowledge that the event with decider_id is NOT processed by the view, and the view should try to process it again / offset is not updated
• schedule the partition for retry with schedule_nack_event function / acknowledge that the event with decider_id is NOT processed by the view, and the view should try to process it again after some time/offset is not updated

Notice that this query can run in a loop within your application.

-- Get first 100 events 
SELECT * from stream_events('view1', 100);

SELECT * from ack_event('view1', 'f156a3c4-9bd8-11ed-a8fc-0242ac120002', 1);

-- ACK other 99 events, and call `stream_events` again to get the next 100 events.
-- If you do not ACK the events in 300 seconds as configured on the `view` table, they will be processed again on the next call to `stream_events`.

6b. Stream the events to concurrent consumers / edge-functions (views)

Import the extensions.sql into your database.

It is very similar to the 6a case. The difference is that the cron job will run SELECT * from stream_events('view1'); for you, and publish event(s) to your edge-functions/http endpoints automatically. So, the database is doing all the job.

The cron job is managed(created/deleted) by triggers on the view table. So, whenever you register a new View, the cron job will be created automatically.

Design

The SQL functions and schema we provide will help you to persist, query, and stream events in a robust way, but the decision-making and view-handling logic would be something that you would have to implement on your own.

• The decision-making process is a command handler responsible for handling the command/intent and producing new events/facts that can be saved in the database by using append_event SQL function. Command handler can be implemented in any programming language, Kotlin, TypeScript, Rust, ...
  • We call this function a decide.
  • You can run it as an edge function on Supabase or Deno.

• The view-handling process is an event handler that is responsible for handling the event/fact and producing a new view/query model. Event handler uses stream_events SQL function from your application to fetch/pool events, or stream_events SQL function is triggered by the cron job on the DB side and event(s) are published/pushed to your event handlers/HTTP endpoints/edge functions.
  • We call this function an evolve.
  • You can run it as an edge function on Supabase or Deno.
  • pg_crone and pg_net extensions are used to schedule the event publishing process and send the HTTP request/event to the edge function (view).

fmodel

fmodel-kotlin | fmodel-ts | fmodel-rust | fmodel-java

'fmodel' is a set of libraries that aims to bring functional, algebraic, and reactive domain modeling to Kotlin / TypeScript / Rust / Java. It is inspired by DDD, EventSourcing, and Functional programming communities.

💙 Accelerate the development of compositional, ergonomic, data-driven, and safe applications 💙

Command	Event	State
An intent to change the state of the system	The state change itself, a fact. It represents a decision that has already happened	The current state of the system. It has evolved out of past events
-	-	-
Decide	Evolve	React
A pure function that takes command and current state as parameters, and returns the flow of new events	A pure function that takes event and current state as parameters, and returns the new state of the system	A pure function that takes event as parameter, and returns the flow of commands, deciding what to execute next

FModel Demo Applications

Try YugabyteDB

Alternatively, you can use YugabyteDB instead of Postgres. It is fully compatible with Postgres.

YugabyteDB is a high-performance, cloud-native distributed SQL database that aims to support all Postgres features. It is best fit for cloud-native OLTP (i.e. real-time, business-critical) applications that need absolute data correctness and require at least one of the following: scalability, high tolerance to failures, and globally distributed deployments.

You can download as ready-to-use packages or installers for various platforms.

./bin/yugabyted start --master_flags=ysql_sequence_cache_minval=0 --tserver_flags=ysql_sequence_cache_minval=0

Alternatively, you can run the following command to start YugabyteDB in a Docker container:

docker run -d --name yugabyte  -p7000:7000 -p9000:9000 -p5433:5433 -p9042:9042\
 yugabytedb/yugabyte:latest bin/yugabyted start\
 --daemon=false --master_flags=ysql_sequence_cache_minval=0 --tserver_flags=ysql_sequence_cache_minval=0

References and further reading

• (Marco Pegoraro) https://github.com/marcopeg/postgres-event-sourcing
• (Matt Bishop) https://github.com/mattbishop/sql-event-store
• FModel
• Supabase

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