The source of

Where it all started - the challenges

When your application requires a message broker or a queue, Implementing one will require you to -
  • Build a dead-letter queue, create observability, and a retry mechanism
  • Build a scalable environment
  • Create client wrappers
  • Tag events to achieve multi-tenancy
  • Enforce schemas and handle transformations
  • Handle back pressure. Client or queue side
  • Configure monitoring and real-time alerts
  • Create a cloud-agnostic implementation
  • Create config alignment between production to a dev environment
  • Spent weeks and months learning the internals through archival documentation, ebooks, and courses
  • Onboard your developers
And the list continues...

So, What is is more than a broker. It's a new streaming stack. It significantly accelerates the development of real-time applications that require a streaming platform with high throughput, low latency, easy troubleshooting, fast time-to-value, minimal platform operations, and all the observability you can think of.

The four main components of Memphis

  1. 1.
    Memphis Broker. A distributed engine, which also acts as the primary storage layer for produced events or data.
  2. 2.
    Memphis Schemaverse. Schema management and enforcement tool built within Memphis to help users increase data quality and avoid upstream breaks.
  3. 3.
    Memphis Functions. Developer-first serverless stream processing to transform and enrich ingested events on-the-fly.
  4. 4.
    Memphis Connect. A modular framework to enable fast pull and push of data to and from different sources and destinations.

Core characteristics

  1. 1.
    Reliability - Queues and brokers are a mission-critical component in the modern application architecture and should be highly available and stable as possible.
  2. 2.
    Performance and Efficiency - Provide great performance while maintaining efficient resource consumption.
  3. 3.
    Developer Experience - Enable rapid development and ultra-short time-to-production.
  4. 4.
    Observability - Increase observability, integrations with 3rd-party monitoring tools, real-time notifications, stream lineage, and therefore troubleshooting time reduction.


High-level diagram

Common use cases

  • Async task management
  • Real-time streaming pipelines
  • Data ingestion
  • Async communication between services on k8s
  • Queuing
  • Multiple destinations to a single message
  • Ingest Grafana loki logs
  • Stream video frames