Percona ServerをMySQL 5.6と5.7用に作るエンジニアリング(そしてMongoDBのヒント)Colin Charles
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Engineering that goes into making Percona Server for MySQL 5.6 & 5.7 different (and a hint of MongoDB) for dbtechshowcase 2017 - the slides also have some Japanese in it. This should help a Japanese audience to read it. If there are questions due to poor translation, do not hesitate to drop me an email (byte@bytebot.net) or tweet: @bytebot
[db tech showcase Tokyo 2017] D21: ついに Red Hat Enterprise Linuxで SQL Serverが使...Insight Technology, Inc.
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いよいよリリースが間近に迫ったSQL Server 2017 Linux版。SQL Serverの第一人者 Dr. Kこと熊澤 幸生がリリース版を待ちきれずにRed Hat Enterprise Linux上で検証してみました。
Windows版と Linux版で果たしてSQL Serverの処理性能に差があるのか?注目の検証結果をいち早くお知らせします。
[db tech showcase Tokyo 2017] E26: 窓は開かれた! SQL Server on Linux で拡がる可能性 by 日本マ...Insight Technology, Inc.
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近日中にリリースが予定されているSQL Server 2017は、ついにその活用の場を Linux プラットフォームへ拡げます。
2016 年 11 月のプレビュー版リリース以降、すでに非常に多くの皆さまに体験いただき、その注目度の高さをズッシリと感じています。
これまで皆さまからの多数のフィードバックをもとに品質向上や機能改善が繰り返され、現時点ではリリース候補版RC2が公開されています。
本セッションでは、SQL Server on Linux が Windows プラットフォーム上と変わらぬ動作を実現するための「アーキテクチャ紹介」から、Linux プラットフォーム上での具体的なユースケースを想定した「機能紹介」までを解説します。
[db tech showcase Tokyo 2017] E26: 窓は開かれた! SQL Server on Linux で拡がる可能性 by 日本マ...Insight Technology, Inc.
?
近日中にリリースが予定されているSQL Server 2017は、ついにその活用の場を Linux プラットフォームへ拡げます。
2016 年 11 月のプレビュー版リリース以降、すでに非常に多くの皆さまに体験いただき、その注目度の高さをズッシリと感じています。
これまで皆さまからの多数のフィードバックをもとに品質向上や機能改善が繰り返され、現時点ではリリース候補版RC2が公開されています。
本セッションでは、SQL Server on Linux が Windows プラットフォーム上と変わらぬ動作を実現するための「アーキテクチャ紹介」から、Linux プラットフォーム上での具体的なユースケースを想定した「機能紹介」までを解説します。
The document discusses various methods for efficiently serializing and deserializing data in C# using MessagePack, including:
- Methods for reading primitive data types like integers and floats from bytes
- Representing float values as individual bytes for efficient serialization
- Using lookup tables and decoder interfaces to quickly determine MessagePack types and decode values
- Discussing faster alternatives like using direct memory copying instead of serialization delegates
- Mentioning how to extend MessagePack specifications while maintaining compatibility for faster serialization
Semi-Supervised Classification with Graph Convolutional Networks @ICLR2017読み会Eiji Sekiya
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This document describes research on semi-supervised learning on graph-structured data using graph convolutional networks. It proposes a layer-wise propagation model for graph convolutions that is more efficient than previous methods. The model is tested on several datasets, achieving state-of-the-art results for semi-supervised node classification while training faster than alternative methods. Future work to address limitations regarding memory requirements, directed graphs, and locality assumptions is also discussed.
The document describes reinforcement learning algorithms. It defines equations for the policy, reward, and value functions in a reinforcement learning problem. It then derives the policy gradient theorem, which gives an expression for the gradient of expected returns with respect to the policy parameters that can be used to optimize the policy via gradient ascent. Subsequent equations adjust the policy gradient derivation for use in actor-critic methods.
This document summarizes an internship project using deep reinforcement learning to develop an agent that can automatically park a car simulator. The agent takes input from virtual cameras mounted on the car and uses a DQN network to learn which actions to take to reach a parking goal. Several agent configurations were tested, with the three-camera subjective view agent showing the most success after modifications to the reward function and task difficulty via curriculum learning. While the agent could sometimes learn to park, the learning was not always stable, indicating further refinement is needed to the deep RL approach for this automatic parking task.
Now a days, thousands of database are supporting many kind of Rakuten's services. and it is hard to manage many databases well. especially, backup and restore.
so, we are progressing new backup system for our databases.
I am going to share some know-hows and experiences that have been acquired with you