This document summarizes a microservices meetup hosted by @mosa_siru. Key points include:
1. @mosa_siru is an engineer at DeNA and CTO of Gunosy.
2. The meetup covered Gunosy's architecture with over 45 GitHub repositories, 30 stacks, 10 Go APIs, and 10 Python batch processes using AWS services like Kinesis, Lambda, SQS and API Gateway.
3. Challenges discussed were managing 30 microservices, ensuring API latency below 50ms across availability zones, and handling 10 requests per second with nginx load balancing across 20 servers.
This document summarizes a microservices meetup hosted by @mosa_siru. Key points include:
1. @mosa_siru is an engineer at DeNA and CTO of Gunosy.
2. The meetup covered Gunosy's architecture with over 45 GitHub repositories, 30 stacks, 10 Go APIs, and 10 Python batch processes using AWS services like Kinesis, Lambda, SQS and API Gateway.
3. Challenges discussed were managing 30 microservices, ensuring API latency below 50ms across availability zones, and handling 10 requests per second with nginx load balancing across 20 servers.
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.
Spring Batch is a framework for batch processing in Java. It allows developers to process large volumes of records by dividing the work into small chunks called steps. The framework includes components like job launcher, job repository, step, item reader, item processor and item writer to process a batch job. Jobs are composed of steps, and steps use readers, processors and writers to read, process and write data.
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.
Spring Batch is a framework for batch processing in Java. It allows developers to process large volumes of records by dividing the work into small chunks called steps. The framework includes components like job launcher, job repository, step, item reader, item processor and item writer to process a batch job. Jobs are composed of steps, and steps use readers, processors and writers to read, process and write data.