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 contains links to MongoDB documentation pages about sharding, databases, collections, inserting, querying, updating, indexing, replication, and backups. It includes a link to a slideshare presentation on MongoDB sharding and links to pages explaining replica set internals and operations.
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 contains links to MongoDB documentation pages about sharding, databases, collections, inserting, querying, updating, indexing, replication, and backups. It includes a link to a slideshare presentation on MongoDB sharding and links to pages explaining replica set internals and operations.
This document discusses using MongoDB to analyze user data from a game platform. It includes examples of queries on collections like user_charge, daily_charge, user_trace, and daily_trace to retrieve user activity and purchase data for a given date range. It also shows an example of retrieving user registration information and calculating metrics for user retention and engagement. The document demonstrates how to store and retrieve analytics data from different collections in MongoDB.
David Mytton is a MongoDB master and the founder of Server Density. In this presentation David delves deeper into what's discussed in our how to monitor MongoDB tutorial (https://blog.serverdensity.com/monitor-mongodb/), with the aim of taking you through:
Key MongoDB metrics to monitor.
Non-critical MongoDB metrics to monitor.
Alerts to set for MongoDB on production.
Tools for monitoring MongoDB.
The document provides tips and explanations for various MongoDB commands and operations including explain, hint, setProfilingLevel, currentOp, and mongostat. It discusses using indexes to optimize queries, setting profiling levels to log slow queries, using currentOp to view currently running operations, and using mongostat to view MongoDB server statistics.
goa is a Go library for designing and implementing REST microservices. It includes a DSL for describing APIs, a code generation tool called goagen, and runtime support libraries. goagen takes the API description and generates Go code including a controller scaffold, validation logic, documentation, and more. The generated code is organized across multiple packages for clear separation of concerns between the auto-generated and custom code.
18. MMS の仕組み
MMS
(on AWS)
your server #1
MMS Agent
your server #2
pymongo
Internet
mongod
mongod
push data
(over HTTP/SSL)
gather data
view data
your client
Web browser
your server #3
mongod
gather data
app
key