This document discusses exactly once semantics in Apache Kafka 0.11. It provides an overview of how Kafka achieved exactly once delivery between producers and consumers. Key points include:
- Kafka 0.11 introduced exactly once semantics with changes to support transactions and deduplication.
- Producers can write in a transactional fashion and receive acknowledgments of committed writes from brokers.
- Brokers store commit markers to track the progress of transactions and ensure no data loss during failures.
- Consumers can read from brokers in a transactional mode and receive data only from committed transactions, guaranteeing no duplication of records.
- This allows reliable message delivery semantics between producers and consumers with Kafka acting as
This document discusses messaging queues and platforms. It begins with an introduction to messaging queues and their core components. It then provides a table comparing 8 popular open source messaging platforms: Apache Kafka, ActiveMQ, RabbitMQ, NATS, NSQ, Redis, ZeroMQ, and Nanomsg. The document discusses using Apache Kafka for streaming and integration with Google Pub/Sub, Dataflow, and BigQuery. It also covers benchmark testing of these platforms, comparing throughput and latency. Finally, it emphasizes that messaging queues can help applications by allowing producers and consumers to communicate asynchronously.
This document discusses messaging queues and platforms. It begins with an introduction to messaging queues and their core components. It then provides a table comparing 8 popular open source messaging platforms: Apache Kafka, ActiveMQ, RabbitMQ, NATS, NSQ, Redis, ZeroMQ, and Nanomsg. The document discusses using Apache Kafka for streaming and integration with Google Pub/Sub, Dataflow, and BigQuery. It also covers benchmark testing of these platforms, comparing throughput and latency. Finally, it emphasizes that messaging queues can help applications by allowing producers and consumers to communicate asynchronously.
Tech Deep Dive #2 in Osaka
https://techdeepdive.connpass.com/event/79096/
2018/03/17
アプリケーションを動かしてて、データベースが遅くなったり壊れてしまった際に、どのように対処したらいいのかと、お困りの方は少なくないのではないでしょうか。そんな時に備えて、データベースの設計方式や実装方法をご紹介します。
2022/3/24に開催した「オンプレML基盤 on Kubernetes」の資料です。機械学習モデルの開発者が、よりモデルの開発にのみ集中できるようにすることを目指して開発している「LakeTahoe(レイクタホ)」について紹介します。
https://ml-kubernetes.connpass.com/event/239859/