faststream
ApacheKafka
faststream | ApacheKafka | |
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13 | 104 | |
1,858 | 28 | |
9.8% | - | |
9.7 | 0.0 | |
1 day ago | 6 months ago | |
Python | ||
Apache License 2.0 | - |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
faststream
- FastStream v0.4.0: Introducing Confluent Kafka Integration with Async Support
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Show HN: Confluent Kafka support added to FastStream v0.4.0rc0
FastStream - https://github.com/airtai/faststream, a stream processing framework, already supports Kafka stream processing using the aiokafka library, as well as other brokers such as Redis, RabbitMQ, and NATS.
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Processing streaming messages from a Django service
FastStream is a powerful and easy-to-use FOSS framework for building asynchronous services interacting with event streams such as Apache Kafka, RabbitMQ and NATS. It simplifies the process of writing producers and consumers for message queues, handling all the parsing, networking and documentation generation automatically.
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FastStream: Python's framework for Efficient Message Queue Handling
Ready to join the FastStream revolution? Head over to our GitHub repository and show your support by starring it. By doing so, you'll stay in the loop with the latest developments, updates, and enhancements as we continue to refine and expand FastStream.
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How we deprecated two successful projects and joined forces to create an even more successful one
After two months of hard work, we presented the newly released FastStream framework at Infobip Shift conference and got featured at ShiftMag. The framework now supports both Apache Kafka and RabbitMQ, but also NATS protocol with the plan to add more protocols in the near future. The overall code is much cleaner and the implementation is streamlined with abstractions covering the common functionality across the protocols. We deprecated both FastKafka and Propan, but promised to fix bugs as long as needed. However, it seems like the community already decided to switch over to gain new functionalities.
- FastStream 0.2.0 adds NATS support in addition to Apache Kafka and RabbitMQ. It is the easiest way to add broker-agnostic support for streaming protocols to your microservices.
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Generating production-level streaming microservices using GPT
faststream-gen(https://github.com/airtai/faststream-gen/) uses GPT models to automatically generate microservices using the FastStream(https://github.com/airtai/faststream) framework for Apache Kafka, RabbitMQ and NATS. Simply describe your microservice in plain English, and it will generate a production-level FastStream application ready to deploy in a few minutes and under $1 cost, together with unit and integration tests, documentation and Docker images.
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Generating production-level streaming microservices using AI
faststream-gen is a Python library that uses generative AI to automatically generate FastStream applications. Simply describe your microservice in plain English, and faststream-gen will generate a production-level FastStream application ready to deploy in a few minutes and under $1 cost.
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Introducing FastStream: the easiest way to write microservices for Apache Kafka and RabbitMQ in Python
FastStream simplifies the process of writing producers and consumers for message queues, handling all the parsing, networking and documentation generation automatically. It is a new package based on the ideas and experiences gained from FastKafka and Propan. By joining our forces, we picked up the best from both packages and created a unified way to write services capable of processing streamed data regardless of the underlying protocol. We'll continue to maintain both packages, but new development will be in this project.
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FastStream: the easiest way to add Kafka and RabbitMQ support to FastAPI services
FastStream (https://github.com/airtai/faststream) is a new Python framework, born from Propan and FastKafka teams' collaboration (both are deprecated now). It extremely simplifies event-driven system development, handling all the parsing, networking, and documentation generation automatically. Now FastStream supports RabbitMQ and Kafka, but supported brokers are constantly growing (wait for NATS and Redis a bit). FastStream itself is a really great tool to build event-driven services. Also, it has a native FastAPI integration. Just create a StreamRouter (very close to APIRouter) and register event handlers the same with the regular HTTP-endpoints way:
ApacheKafka
- PubNubとIFTTTによるSMS通知システム
- PubNub 및 IFTTT를 사용한 SMS 알림 시스템
- Système de notification par SMS avec PubNub et IFTTT
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Wie man Ereignisse von PubNub zu RabbitMQ streamt
Senden an Kafka (d. h. Senden der Daten an Apache Kafka)
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Choosing Between a Streaming Database and a Stream Processing Framework in Python
Stream-processing platforms such as Apache Kafka, Apache Pulsar, or Redpanda are specifically engineered to foster event-driven communication in a distributed system and they can be a great choice for developing loosely coupled applications. Stream processing platforms analyze data in motion, offering near-zero latency advantages. For example, consider an alert system for monitoring factory equipment. If a machine's temperature exceeds a certain threshold, a streaming platform can instantly trigger an alert and engineers do timely maintenance.
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How to Use Reductstore as a Data Sink for Kafka
Apache Kafka is a distributed streaming platform capable of handling high throughput of data, while ReductStore is a databases for unstructured data optimized for storing and querying along time.
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🦿🛴Smarcity garbage reporting automation w/ ollama
*Push data *(original source image, GPS, timestamp) in a common place (Apache Kafka,...)
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How to Build & Deploy Scalable Microservices with NodeJS, TypeScript and Docker || A Comprehesive Guide
RabbitMQ comes with administrative tools to manage user permissions and broker security and is perfect for low latency message delivery and complex routing. In comparison, Apache Kafka architecture provides secure event streams with Transport Layer Security(TLS) and is best suited for big data use cases requiring the best throughput.
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Easy Guide to Integrating Kafka: Practical Solutions for Managing Blob Data
Apache Kafka is a distributed streaming platform to share data between applications and services in real-time.
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Go concurrency simplified. Part 4: Post office as a data pipeline
also, this knowledge applies to learning more about data engineering, as this field of software engineering relies heavily on the event-driven approach via tools like Spark, Flink, Kafka, etc.
What are some alternatives?
Propan - Propan is a powerful and easy-to-use Python framework for building event-driven applications that interact with any MQ Broker
dramatiq - A fast and reliable background task processing library for Python 3.
Faust - Python Stream Processing
outbox-inbox-patterns - Repository to support the article "Building a Knowledge Base Service With Neo4j, Kafka, and the Outbox Pattern"
aiorabbit - An AsyncIO RabbitMQ client for Python 3
Jenkins - Jenkins automation server
aiokafka - asyncio client for kafka
RabbitMQ - Open source RabbitMQ: core server and tier 1 (built-in) plugins
cookiecutter-faststream - Cookiecutter template for FastStream apps
istio - Connect, secure, control, and observe services.
faststream-gen - The faststream-gen library uses advanced AI to generate FastStream code from user descriptions, speeding up FastStream app development.
Grafana - The open and composable observability and data visualization platform. Visualize metrics, logs, and traces from multiple sources like Prometheus, Loki, Elasticsearch, InfluxDB, Postgres and many more.