2019/08/23 第21回 Tokyo Jazug Night
https://jazug.connpass.com/event/139300/
動画: https://www.youtube.com/watch?v=YMAV8aqb9pk
This document provides information about an AWS webinar on AWS Step Functions hosted by Yuta Imamura from Amazon Web Services Japan. The agenda includes an overview of Step Functions, state machines, data input and output, describing states, checking execution status, and additional details. Step Functions allows orchestrating distributed applications and microservices using state machines defined in Amazon States Language (ASL). States can pass data and parameters between each other to synchronize processes.
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 compares Apache Kafka and AWS Kinesis for message streaming. It outlines that Kafka is an open source publish-subscribe messaging system designed as a distributed commit log, while Kinesis provides streaming data services. It also notes some key differences like Kafka typically handling over 8000 messages/second while Kinesis can handle under 100 messages/second.
2017/9/7 db tech showcase Tokyo 2017(JPOUG in 15 minutes)にて発表した内容です。
SQL大量発行に伴う処理遅延は、ミッションクリティカルシステムでありがちな性能問題のひとつです。
SQLをまとめて発行したり、処理の多重度を上げることができれば高速化可能です。ですが・・・
AP設計に起因する性能問題のため、開発工程の終盤においては対処が難しいことが多々あります。
そのような状況において、どのような改善手段があるのか、Oracleを例に解説します。
[db tech showcase Tokyo 2018] Azure Cosmos DB Technical Deep Dive ~グローバル分散型マル...Naoki (Neo) SATO
[db tech showcase Tokyo 2018] Azure Cosmos DB Technical Deep Dive ~グローバル分散型マルチ モデル データベース サービスを使いこなそう~
https://satonaoki.wordpress.com/2018/09/21/dbts2018-azure-cosmos-db/
db tech showcase Tokyo 2018 (2018/09/19-21)
https://www.db-tech-showcase.com/dbts/tokyo
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 compares Apache Kafka and AWS Kinesis for message streaming. It outlines that Kafka is an open source publish-subscribe messaging system designed as a distributed commit log, while Kinesis provides streaming data services. It also notes some key differences like Kafka typically handling over 8000 messages/second while Kinesis can handle under 100 messages/second.
2017/9/7 db tech showcase Tokyo 2017(JPOUG in 15 minutes)にて発表した内容です。
SQL大量発行に伴う処理遅延は、ミッションクリティカルシステムでありがちな性能問題のひとつです。
SQLをまとめて発行したり、処理の多重度を上げることができれば高速化可能です。ですが・・・
AP設計に起因する性能問題のため、開発工程の終盤においては対処が難しいことが多々あります。
そのような状況において、どのような改善手段があるのか、Oracleを例に解説します。
[db tech showcase Tokyo 2018] Azure Cosmos DB Technical Deep Dive ~グローバル分散型マル...Naoki (Neo) SATO
[db tech showcase Tokyo 2018] Azure Cosmos DB Technical Deep Dive ~グローバル分散型マルチ モデル データベース サービスを使いこなそう~
https://satonaoki.wordpress.com/2018/09/21/dbts2018-azure-cosmos-db/
db tech showcase Tokyo 2018 (2018/09/19-21)
https://www.db-tech-showcase.com/dbts/tokyo
Cosmos DB 入門の multi model multi API編。
BUILD 2017 で突如現れた、Cosmos DB。基本的には、従来のDocumentDBの発展ですが、単純な機能拡張とは少し違います。
「Cosmos DB = DocumentDB + multi-model and multi-API」という目線で、ざっくりと理念と現在の実装を探ります。
7. SQL
MongoDB
Table API
Turnkey global
distribution
Elastic scale out
of storage & throughput
Guaranteed low latency
at the 99th percentile
Comprehensive
SLAs
Five well-defined
consistency models
Azure Cosmos DB
DocumentColumn-family
Key-value Graph
A globally distributed, massively scalable, multi-model database service
出典 https://channel9.msdn.com/Events/Build/2018/BRK3319
8.
﹣
﹣
﹣
参考: A technical overview of Azure Cosmos DB https://azure.microsoft.com/en-us/blog/a-technical-overview-of-azure-cosmos-db/
43. .NET
SQL Database
App
Service
Web App / API App /
Mobile App
(Windows)
ASP.NET アプリ
Cosmos
DB
SQL API
(Documet型)
マスタ系データ
(読み取り)
トランザクション系データ
(更新、読み取り)
Node.js
App
Service
Web App / API App /
(Linux)
Node.js アプリ
(Express)
Cosmos
DB
MongoDB API
(Documet型)
マスタ系データ
(読み取り)
トランザクション系データ
(更新、読み取り)
Azure Database for
MySQL
45. Easy out-of-the-box bulk operation functionality
Supports bulk import and update
Auto handles congestion control + transient errors
10x client-side performance improvement
Easily scale-out clients across more VMs
Available starting with .NET and Java
複数ドキュメントの入出力に有効。
移行シナリオ以外にも十分適用可能。
.NET SDKは.NET Coreには未対応。
46. Remove friction for OSS NoSQL APIs
Provision RU/sec shared across containers
Mix containers with dedicated throughput and
containers with shared throughput
Elastically scale provisioned throughput for a
set of containers at any time
「50,000RU」以上から利用可能のため大規
模データベース向き
47. Perfect for Intelligent Cloud
and Intelligent Edge Applications
Write scalability around the world
Low latency writes around the world
99.999% High Availability around the world
Well-defined consistency models
Comprehensive conflict management
新規にCosmos DBアカウントを作成すると
プレビュー申込ボタンが表示される
(2018年5月末時点)
48. Try Azure Cosmos DB for free!
https://azure.microsoft.com/ja-jp/try/cosmosdb/