The Spring Web model-view-controller (MVC) framework is designed around a DispatcherServlet that dispatches requests to handlers, with configurable handler mappings, view resolution, locale and theme resolution as well as support for uploading files.
This document discusses strategies for automating remote database backups across multiple data centers. It recommends scheduling backups automatically after a queue time to use underutilized backup servers. The backup manager would select the target backup server based on its service zone, data center location, and available quota to balance load. It would also avoid using the same backup server consecutively and start backups at different times each day to improve reliability in case of failures.
The Spring Web model-view-controller (MVC) framework is designed around a DispatcherServlet that dispatches requests to handlers, with configurable handler mappings, view resolution, locale and theme resolution as well as support for uploading files.
This document discusses strategies for automating remote database backups across multiple data centers. It recommends scheduling backups automatically after a queue time to use underutilized backup servers. The backup manager would select the target backup server based on its service zone, data center location, and available quota to balance load. It would also avoid using the same backup server consecutively and start backups at different times each day to improve reliability in case of failures.
This document discusses MySQL 5.7's JSON datatype. It introduces JSON and why it is useful for integrating relational and schemaless data. It covers creating JSON columns, inserting and selecting JSON data using functions like JSON_EXTRACT. It discusses indexing JSON columns using generated columns. Performance is addressed, showing JSON tables can be 40% larger with slower inserts and selects compared to equivalent relational tables without indexes. Options for stored vs virtual generated columns are presented.
Intro KaKao MRTE (MySQL Realtime Traffic Emulator)I Goo Lee
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The document describes the process of opening a TCP connection between a client and MySQL database, including the initial handshake and response packets. It then explains how the MRTE-Collector works by using message queues to capture and parse MySQL packets from the source database, and replay them to the target database using multiple SQL player threads. The MRTE-Collector publishes messages to RabbitMQ queues which routes the messages to the proper queues subscribed by MRTE-Player.
MySQL Slow Query log Monitoring using Beats & ELKI Goo Lee
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This document provides instructions for using Filebeat, Logstash, Elasticsearch, and Kibana to monitor MySQL slow query logs. It describes installing and configuring each component, with Filebeat installed on database servers to collect slow query logs, Logstash to parse and index the logs, Elasticsearch for storage, and Kibana for visualization and dashboards. Key steps include configuring Filebeat to ship logs to Logstash, using grok filters in Logstash to parse the log fields, outputting to Elasticsearch, and visualizing slow queries and creating sample dashboards in Kibana.
MySQL Audit using Percona audit plugin and ELKI Goo Lee
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This document discusses setting up MySQL auditing using the Percona Audit Plugin and ELK (Elasticsearch, Logstash, Kibana). It describes installing and configuring the Percona Audit Plugin on MySQL servers to generate JSON audit logs. It then covers using Rsyslog or Filebeat to ship the logs to the Logstash server, and configuring Logstash to parse, enrich, and index the logs into Elasticsearch. Finally, it discusses visualizing the audit data with Kibana dashboards containing graphs and searching. The architecture involves MySQL servers generating logs, Logstash collecting and processing them, and Elasticsearch and Kibana providing search and analytics.
7. 7
ROW_COUNT() (Workbench Connection vs MySQL Connector .NET)
affect_row vs matched_row
affect_row : CASE WHEN 譟郁唄 , れ Update 螳 殊企 row
matched_row : WHERE 譟郁唄 襷譟燕 row
8. 8
affect_row vs matched_row
SUCCESS Case
FAIL Case
襦 ROW_COUNT() 螳 affected_row 襦
讌螻, 豌危蟇伎 れ一危瑚唄螳 襷讌
蟆曙 IF 覓語 伎ろ 豌襴
(れ 2螳讌 rune 覈 一危 讌 )
on Workbench Connection
ROW_COUNT() (Workbench Connection vs MySQL Connector .NET)
9. 9
affect_row vs matched_row
蟆螻 襴,
IF 覓 豌危螳 讌 ,
Result 0 朱 襴企螻 れ gold 讀螳
on MySQL Connector .NET
ROW_COUNT() (Workbench Connection vs MySQL Connector .NET)
10. 10
Unexpected result on ROW_COUNT()
Connection String option 磯 MySQL .NET Connector ろ Procedure 企
ROW_COUNT() 螳 affected_row matched_row 襦 語
Default
Use Affected Rows=False
matched_row
企濠化一 UPDATE ROW_COUNT 豌危 譯殊伎狩.
Use Affected Rows=True
affected_row
ROW_COUNT() (Workbench Connection vs MySQL Connector .NET)