How does Message DB compare to Apache Kafka?

Message DB is a minimalist, database-native event store ideal for PostgreSQL-based systems, offering ACID compliance and simplicity, while Kafka is a distributed streaming platform better for high-throughput, scalable messaging across clusters with built-in replication.

Can I use Message DB with Python or Java?

Yes, since Message DB exposes PostgreSQL functions, you can use any language with a PostgreSQL driver to interact with it, but you'll need to implement the client logic yourself, as pre-built libraries are limited to Ruby and JavaScript.

How to set up consumer groups in Message DB?

Use the get_category_messages function with consumer_group_member and consumer_group_size parameters to distribute message processing across instances, ensuring each handles a subset based on global position, as detailed in the API overview.

What are the performance limits of Message DB?

Performance depends on PostgreSQL configuration; it can handle thousands of messages per second on typical hardware, but for millions per second, specialized systems like Kafka are more suitable due to PostgreSQL's inherent scaling constraints.

How to backup and restore a Message DB database?

Use standard PostgreSQL backup tools like pg_dump, focusing on the message_store schema and functions, as Message DB is just a schema within PostgreSQL, requiring no special backup procedures.

Is there a GUI for managing Message DB?

No, Message DB lacks a built-in graphical interface; administration is done via SQL commands or scripts, which might be a drawback for teams preferring visual tools over command-line management.

Open-Awesome

Message DB

MITShellv1.3.0

A PostgreSQL-based event store and message store for pub/sub, event sourcing, and microservices applications.

Visit Website GitHub

1.7k stars63 forks0 contributors

What is Message DB?

Message DB is a microservice-native event store and message store implemented in PostgreSQL. It provides a complete solution for pub/sub messaging, event sourcing, and event-driven microservices by storing messages as JSON in dedicated database tables with functions for writing and reading streams.

Target Audience

Developers and architects building event-driven microservices, event-sourced systems, or pub/sub messaging infrastructures who want a database-native solution without external dependencies.

Value Proposition

It offers a minimalist, self-contained alternative to complex systems like Kafka or Event Store, leveraging PostgreSQL's reliability and JSON capabilities while providing essential messaging patterns and easy self-hosting.

Overview

Microservice native message and event store for Postgres

Use Cases

Best For

Implementing event sourcing in microservices architectures
Building pub/sub messaging systems within PostgreSQL
Creating event-driven applications without external message brokers
Developing consumer group patterns for scalable message processing
Storing and querying event streams with JSON metadata
Self-hosting a lightweight alternative to Kafka or Event Store

Not Ideal For

Applications requiring massive-scale, high-throughput messaging beyond PostgreSQL's capabilities (e.g., IoT data ingestion at millions of events per second)
Projects not using PostgreSQL that would incur significant overhead from adopting it solely for messaging
Cloud-native environments where managed messaging services (e.g., AWS SQS, Google Pub/Sub) offer better operational simplicity and integration

Pros & Cons

Pros

Database-Native Simplicity

Implements messaging directly in PostgreSQL, eliminating external dependencies and leveraging existing database infrastructure for deployment ease.

Robust Event Sourcing

Ensures gapless stream positions in the messages table, which is critical for reliable event replay and consistency in event-sourced systems.

Flexible JSON Handling

Uses PostgreSQL's JSONB type for message data and metadata, allowing efficient storage, querying, and schema-less flexibility as shown in the API examples.

Built-in Consumer Groups

Supports cooperative message processing via functions like get_category_messages with consumer_group parameters, enabling scalable microservice architectures without extra tooling.

Cons

PostgreSQL Lock-in

The entire system is tightly coupled to PostgreSQL's functions and schema, making migration to other databases nearly impossible and limiting technology flexibility.

Scalability Constraints

Relies on PostgreSQL's single-instance scalability, which may bottleneck under ultra-high message volumes compared to distributed systems like Kafka, as admitted in the rationale.

Custom Client Development

Requires developers to write their own clients for each language, since the API is based on PostgreSQL functions with limited pre-built libraries beyond Ruby and NPM packages.

Open Source Alternative To

Message DB is an open-source alternative to the following products:

Apache Kafka

Event Store

Event Store is a database built for event sourcing, designed to store events as an immutable sequence over time. It provides a high-performance storage engine for event-driven architectures and supports the Event Sourcing pattern.

Frequently Asked Questions

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Message DB

MITShellv1.3.0

A PostgreSQL-based event store and message store for pub/sub, event sourcing, and microservices applications.

Visit Website GitHub

1.7k stars63 forks0 contributors

What is Message DB?

Target Audience

Developers and architects building event-driven microservices, event-sourced systems, or pub/sub messaging infrastructures who want a database-native solution without external dependencies.

Value Proposition

Overview

Microservice native message and event store for Postgres

Use Cases

Best For

Implementing event sourcing in microservices architectures
Building pub/sub messaging systems within PostgreSQL
Creating event-driven applications without external message brokers
Developing consumer group patterns for scalable message processing
Storing and querying event streams with JSON metadata
Self-hosting a lightweight alternative to Kafka or Event Store

Not Ideal For

Applications requiring massive-scale, high-throughput messaging beyond PostgreSQL's capabilities (e.g., IoT data ingestion at millions of events per second)
Projects not using PostgreSQL that would incur significant overhead from adopting it solely for messaging
Cloud-native environments where managed messaging services (e.g., AWS SQS, Google Pub/Sub) offer better operational simplicity and integration

Pros & Cons

Pros

Database-Native Simplicity

Implements messaging directly in PostgreSQL, eliminating external dependencies and leveraging existing database infrastructure for deployment ease.

Robust Event Sourcing

Ensures gapless stream positions in the messages table, which is critical for reliable event replay and consistency in event-sourced systems.

Flexible JSON Handling

Uses PostgreSQL's JSONB type for message data and metadata, allowing efficient storage, querying, and schema-less flexibility as shown in the API examples.

Built-in Consumer Groups

Supports cooperative message processing via functions like get_category_messages with consumer_group parameters, enabling scalable microservice architectures without extra tooling.

Cons

PostgreSQL Lock-in

The entire system is tightly coupled to PostgreSQL's functions and schema, making migration to other databases nearly impossible and limiting technology flexibility.

Scalability Constraints

Relies on PostgreSQL's single-instance scalability, which may bottleneck under ultra-high message volumes compared to distributed systems like Kafka, as admitted in the rationale.

Custom Client Development

Requires developers to write their own clients for each language, since the API is based on PostgreSQL functions with limited pre-built libraries beyond Ruby and NPM packages.