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  • 👉Getting Started
    • Introduction
    • Quick start
    • Learn by example
    • Case studies
    • How to contribute?
  • ⭐Memphis Broker
    • Architecture
    • Key concepts
      • Message broker
      • Station
      • Producer API
      • Consumer API
      • Consumer Group
      • Storage and Redundancy
      • Security/Authentication
      • Scaling
      • Ordering
      • Dead-letter Station (DLS)
      • Delayed messages
      • Data exchange
      • Idempotency (Duplicate processing)
      • Failover Scenarios
      • Troubleshooting process
      • Connectors
    • Best practices
      • Producer optimization
      • Compression
    • Memphis configuration
    • Comparisons
      • NATS Jetstream vs Memphis
      • RabbitMQ vs Memphis
      • AWS SQS vs Memphis
      • Apache Kafka vs Memphis
      • Apache Pulsar vs Memphis
      • ZeroMQ vs Memphis
      • Apache NiFi vs Memphis
    • Privacy Policy
  • ⭐Memphis Schemaverse
    • Overview
    • Getting started
      • Management
      • Produce/Consume
        • Protobuf
        • JSON Schema
        • GraphQL
        • Avro
    • Comparison
    • KB
  • 📦Open-Source Installation
    • Kubernetes
      • 1 - Installation
      • 2 - Access
      • 3 - Upgrade
      • Terraform
        • Deploy on AWS
        • Deploy on GCP
        • Deploy on DigitalOcean
      • Guides
        • Deploy/Upgrade Memphis utilizing predefined secrets
        • Monitoring/Alerts Recommendations
        • Production Best Practices
        • NGINX Ingress Controller and Cloud-Agnostic Memphis Deployments
        • Migrate Memphis storage between storageClass's
        • Expanding Memphis Disk Storage
        • Scale-out Memphis cluster
        • TLS - Deploy Memphis with TLS Connection to Metadata Frontend
        • TLS - Memphis TLS websocket configuration
        • TLS - Securing Memphis Client with TLS
        • Installing Memphis with an External Metadata Database
    • Docker
      • 1 - Installation
      • 2 - Access
      • 3 - Upgrade
    • Open-source Support
  • Client Libraries
    • REST (Webhook)
    • Node.js / TypeScript / NestJS
    • Go
    • Python
    • Kotlin (Community)
    • .NET
    • Java
    • Rust (Community)
    • NATS
    • Scala
  • 🔌Integrations Center
    • Index
    • Processing
      • Zapier
    • Change data Capture (CDC)
      • Debezium
    • Monitoring
      • Datadog
      • Grafana
    • Notifications
      • Slack
    • Storage tiering
      • S3-Compatible Object Storage
    • Source code
      • GitHub
    • Other platforms
      • Argo
  • 🗒️Release notes
    • KB
    • Releases
      • v1.4.3 - latest/stable
      • v1.4.2
      • v1.4.1
      • v1.4.0
      • v1.3.1
      • v1.3.0
      • v1.2.0
      • v1.1.1
      • v1.1.0
      • v1.0.3
      • v1.0.2
      • v1.0.1
      • V1.0.0 - GA
      • v0.4.5 - beta
      • v0.4.4 - beta
      • v0.4.3 - beta
      • v0.4.2 - beta
      • v0.4.1 - beta
      • v0.4.0 - beta
      • v0.3.6 - beta
      • v0.3.5 - beta
      • v0.3.0 - beta
      • v0.2.2 - beta
      • v0.2.1 - beta
      • v0.2.0 - beta
      • v0.1.0 - beta
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All rights reserved to Memphis.dev 2023

On this page
  • The Origin of Challenges
  • So, What is Memphis.dev?
  • The Core Elements of Memphis:
  • Key Features of Memphis.dev
  • Walkthrough
  • High-level diagram
  • Typical Applications

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  1. Getting Started

Introduction

Please pay attention that Memphis.dev is no longer supported officially by the Superstream (formerly Memphis.dev. team) and was released to the public.

The Origin of Challenges

When your application demands a message broker or queue, embarking on this journey will entail:

  1. Constructing a dead-letter queue while implementing observability and a retry system.

  2. Establishing a scalable infrastructure.

  3. Crafting client wrappers for integration.

  4. Tagging events for multi-tenancy.

  5. Enforcing schemas and handling transformations.

  6. Addressing back pressure, whether on the client or queue side.

  7. Configuring monitoring and real-time alerts.

  8. Developing a cloud-agnostic solution.

  9. Aligning configurations between the production and development environments.

  10. Devoting weeks and months to mastering the intricacies via archived documentation, ebooks, and courses.

  11. Bringing your developers on board.

And the list continues...

So, What is Memphis.dev?

Memphis.dev is more than a broker. It's a new streaming stack.

Memphis.dev is a highly scalable event streaming and processing engine. Before Memphis came along, handling ingestion and processing of events on a large scale took months to adopt and was a capability reserved for the top 20% of mega-companies. Now, Memphis opens the door for the other 80% to unleash their event and data streaming superpowers quickly, easily, and with great cost-effectiveness.

The Core Elements of Memphis:

  1. Memphis Broker: A distributed engine that serves as the primary storage layer for produced events and data.

  2. Memphis Schemaverse: An integrated schema management and enforcement tool within Memphis, assisting users in enhancing data quality and preventing disruptions upstream.

  3. Memphis Functions: A developer-centric, serverless stream processing feature for real-time event transformation and enrichment.

  4. Memphis Connect: A modular framework designed to facilitate rapid data extraction and transfer between various sources and destinations.

Key Features of Memphis.dev

  1. Rock-Solid Reliability - Our queues and brokers are the backbone of today's applications, ensuring they are as available and stable as can be.

  2. Blazing Performance and Efficiency - We deliver top-notch performance without guzzling up your resources.

  3. Developer-Friendly - Memphis.dev makes development lightning-fast, getting your creations into production in no time.

  4. Crystal-Clear Insight - Boost your observability with seamless integrations into third-party monitoring tools, real-time notifications, stream lineage, and slash troubleshooting time.

Walkthrough

High-level diagram

Typical Applications

  1. Handling Background Tasks

  2. Real-Time Data Pipelines

  3. Collecting Data

  4. Asynchronous Communication for Microservices

  5. Task Queues

  6. Distributing Messages to Multiple Targets

  7. Ingesting Grafana Loki Logs

  8. Streaming Video Frames

Last updated 12 months ago

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