Submit Search
Upload
Redis trouble shooting
โข
104 likes
โข
6,104 views
DaeMyung Kang
Follow
Redis Trouble Shooting
Read less
Read more
Technology
Report
Share
Report
Share
1 of 48
Download now
Download to read offline
Recommended
In this session, we will explore key challenges with function interactions and coordination, addressing these problems using Enterprise Integration Patterns (EIP) and modern approaches with the latest innovations from the Apache Camel community: Apache Camel is the Swiss army knife of integration, and the most powerful integration framework. In this session you will hear about the latest features in the brand new 3rd generation. Camel K, is a lightweight integration platform that enables Enterprise Integration Patterns to be used natively on any Kubernetes cluster. When used in combination with Knative, a framework that adds serverless building blocks to Kubernetes, and the subatomic execution environment of Quarkus, Camel K can mix serverless features such as auto-scaling, scaling to zero, and event-based communication with the outstanding integration capabilities of Apache Camel. - Apache Camel 3 - Camel K - Camel Quarkus We will show how Camel K works. Weโll also use examples to demonstrate how Camel K makes it easier to connect to cloud services or enterprise applications using some of the 300 components that Camel provides.
Apache Camel v3, Camel K and Camel Quarkus
Apache Camel v3, Camel K and Camel Quarkus
Claus Ibsen
ย
ใฏใใใฆใฎใใใใฉๆญป
ใปใใใใAthenaใฆใๆญปใฌใพใฆใ
ใปใใใใAthenaใฆใๆญปใฌใพใฆใ
Shinichi Takahashi
ย
"This is a technical architect's case study of how Loggly has employed the latest social-media-scale technologies as the backbone ingestion processing for our multi-tenant, geo-distributed, and real-time log management system. This presentation describes design details of how we built a second-generation system fully leveraging AWS services including Amazon Route 53 DNS with heartbeat and latency-based routing, multi-region VPCs, Elastic Load Balancing, Amazon Relational Database Service, and a number of pro-active and re-active approaches to scaling computational and indexing capacity. The talk includes lessons learned in our first generation release, validated by thousands of customers; speed bumps and the mistakes we made along the way; various data models and architectures previously considered; and success at scale: speeds, feeds, and an unmeltable log processing engine."
Infrastructure at Scale: Apache Kafka, Twitter Storm & Elastic Search (ARC303...
Infrastructure at Scale: Apache Kafka, Twitter Storm & Elastic Search (ARC303...
Amazon Web Services
ย
์ 3ํ ๋ค์ด๋ฒ ์คํ์์ค ์ธ๋ฏธ๋ 2018.09.04
[๋ค์ด๋ฒ์คํ์์ค์ธ๋ฏธ๋] Contribution, ์ ์์ ์๋ง : Apache OpenWhisk ์ฑ๋ฅ ๊ฐ์ - ๊น๋๊ฒฝ
[๋ค์ด๋ฒ์คํ์์ค์ธ๋ฏธ๋] Contribution, ์ ์์ ์๋ง : Apache OpenWhisk ์ฑ๋ฅ ๊ฐ์ - ๊น๋๊ฒฝ
NAVER Engineering
ย
์์ฆ Hadoop ๋ณด๋ค ๋ ๋จ๊ณ ์๋ Spark. ๊ทธ Spark์ ํต์ฌ์ ์ดํดํ๊ธฐ ์ํด์๋ ํต์ฌ ์๋ฃ๊ตฌ์กฐ์ธ Resilient Distributed Datasets (RDD)๋ฅผ ์ดํดํ๋ ๊ฒ์ด ํ์ํฉ๋๋ค. RDD๊ฐ ์ด๋ป๊ฒ ๋์ํ๋์ง, ์ ๋ ผ๋ฌธ์ ๋ฆฌ๋ทฐํ๋ฉฐ ์ดํด๋ณด๋๋ก ํฉ์๋ค. http://www.cs.berkeley.edu/~matei/papers/2012/sigmod_shark_demo.pdf
Spark ์ ํต์ฌ์ ๋ฌด์์ธ๊ฐ? RDD! (RDD paper review)
Spark ์ ํต์ฌ์ ๋ฌด์์ธ๊ฐ? RDD! (RDD paper review)
Yongho Ha
ย
Redis
Redis
knight1128
ย
ๆฅๆฌJavaใฆใผใถใผใฐใซใผใ JJUG ใใคใใปใใใผ 2 ๆ 27 ๆฅ(ๆฐด) http://www.java-users.jp/?p=309 ๆณจ๏ผใตใณใใซใฝใผในใฏใใใพใงใไพใงใใใใใฎไฟฎๆญฃไพใๅฎๅ จใชใใฎใงใฏใใใพใใใ
Javaใฏใฉใฎใใใซๅใใฎใ๏ฝในใฉใคใใงใใใJVMใฎไป็ตใฟ
Javaใฏใฉใฎใใใซๅใใฎใ๏ฝในใฉใคใใงใใใJVMใฎไป็ตใฟ
Chihiro Ito
ย
์ด๋ ํด์ปค์์ ์ฐธ์ฌํ ๋ฐฑ์๋ ๊ฐ๋ฐ์๋ค์ ์ํ ๊ต์ก์๋ฃ ์ฝ๊ฒ ๋ง๋ ๋ค๊ณ ํ๋๋ฐ๋, ๋ง์ด ์ด๋ ค์ ๋๋ด ๋๋ค. ์ ์์ฌ์ด ๊ณผํ๋ ๊ฒ ๊ฐ์์. ๋ด๋ฒ์ ์ข ๋ ์ฝ๊ฒ ! - ๋ ์ : ๋ฐฑ์๋ ๊ฐ๋ฐ์๋ฅผ ํฌ๋งํ๋ ์ฌ๋ (์ทจ์ค์, ์ด์ง ํฌ๋ง์), 5๋ ์ฐจ ์ดํ - ์ฃผ์ ๋ด์ฉ : ๋ฐฑ์๋ ๊ฐ๋ฐ์ ํ ๋ ์ผ์ด๋๋ ์ผ๋ค(๊ฐ๋ฐํ์ ์ผ) - ๋น์์ ์ ๋ชฉ์ ์ผ๋ก ์ธ์ฉ์ ๊ฐ๋ฅํฉ๋๋ค. (์ถ์ฒ ๋ช ๊ธฐ ํ์)
์๋ฒํ๊ฐ๋ก (๋ฐฑ์๋ ์๋ฒ ๊ฐ๋ฐ์๋ฅผ ์ํ)
์๋ฒํ๊ฐ๋ก (๋ฐฑ์๋ ์๋ฒ ๊ฐ๋ฐ์๋ฅผ ์ํ)
์๋ณด ๊น
ย
Recommended
In this session, we will explore key challenges with function interactions and coordination, addressing these problems using Enterprise Integration Patterns (EIP) and modern approaches with the latest innovations from the Apache Camel community: Apache Camel is the Swiss army knife of integration, and the most powerful integration framework. In this session you will hear about the latest features in the brand new 3rd generation. Camel K, is a lightweight integration platform that enables Enterprise Integration Patterns to be used natively on any Kubernetes cluster. When used in combination with Knative, a framework that adds serverless building blocks to Kubernetes, and the subatomic execution environment of Quarkus, Camel K can mix serverless features such as auto-scaling, scaling to zero, and event-based communication with the outstanding integration capabilities of Apache Camel. - Apache Camel 3 - Camel K - Camel Quarkus We will show how Camel K works. Weโll also use examples to demonstrate how Camel K makes it easier to connect to cloud services or enterprise applications using some of the 300 components that Camel provides.
Apache Camel v3, Camel K and Camel Quarkus
Apache Camel v3, Camel K and Camel Quarkus
Claus Ibsen
ย
ใฏใใใฆใฎใใใใฉๆญป
ใปใใใใAthenaใฆใๆญปใฌใพใฆใ
ใปใใใใAthenaใฆใๆญปใฌใพใฆใ
Shinichi Takahashi
ย
"This is a technical architect's case study of how Loggly has employed the latest social-media-scale technologies as the backbone ingestion processing for our multi-tenant, geo-distributed, and real-time log management system. This presentation describes design details of how we built a second-generation system fully leveraging AWS services including Amazon Route 53 DNS with heartbeat and latency-based routing, multi-region VPCs, Elastic Load Balancing, Amazon Relational Database Service, and a number of pro-active and re-active approaches to scaling computational and indexing capacity. The talk includes lessons learned in our first generation release, validated by thousands of customers; speed bumps and the mistakes we made along the way; various data models and architectures previously considered; and success at scale: speeds, feeds, and an unmeltable log processing engine."
Infrastructure at Scale: Apache Kafka, Twitter Storm & Elastic Search (ARC303...
Infrastructure at Scale: Apache Kafka, Twitter Storm & Elastic Search (ARC303...
Amazon Web Services
ย
์ 3ํ ๋ค์ด๋ฒ ์คํ์์ค ์ธ๋ฏธ๋ 2018.09.04
[๋ค์ด๋ฒ์คํ์์ค์ธ๋ฏธ๋] Contribution, ์ ์์ ์๋ง : Apache OpenWhisk ์ฑ๋ฅ ๊ฐ์ - ๊น๋๊ฒฝ
[๋ค์ด๋ฒ์คํ์์ค์ธ๋ฏธ๋] Contribution, ์ ์์ ์๋ง : Apache OpenWhisk ์ฑ๋ฅ ๊ฐ์ - ๊น๋๊ฒฝ
NAVER Engineering
ย
์์ฆ Hadoop ๋ณด๋ค ๋ ๋จ๊ณ ์๋ Spark. ๊ทธ Spark์ ํต์ฌ์ ์ดํดํ๊ธฐ ์ํด์๋ ํต์ฌ ์๋ฃ๊ตฌ์กฐ์ธ Resilient Distributed Datasets (RDD)๋ฅผ ์ดํดํ๋ ๊ฒ์ด ํ์ํฉ๋๋ค. RDD๊ฐ ์ด๋ป๊ฒ ๋์ํ๋์ง, ์ ๋ ผ๋ฌธ์ ๋ฆฌ๋ทฐํ๋ฉฐ ์ดํด๋ณด๋๋ก ํฉ์๋ค. http://www.cs.berkeley.edu/~matei/papers/2012/sigmod_shark_demo.pdf
Spark ์ ํต์ฌ์ ๋ฌด์์ธ๊ฐ? RDD! (RDD paper review)
Spark ์ ํต์ฌ์ ๋ฌด์์ธ๊ฐ? RDD! (RDD paper review)
Yongho Ha
ย
Redis
Redis
knight1128
ย
ๆฅๆฌJavaใฆใผใถใผใฐใซใผใ JJUG ใใคใใปใใใผ 2 ๆ 27 ๆฅ(ๆฐด) http://www.java-users.jp/?p=309 ๆณจ๏ผใตใณใใซใฝใผในใฏใใใพใงใไพใงใใใใใฎไฟฎๆญฃไพใๅฎๅ จใชใใฎใงใฏใใใพใใใ
Javaใฏใฉใฎใใใซๅใใฎใ๏ฝในใฉใคใใงใใใJVMใฎไป็ตใฟ
Javaใฏใฉใฎใใใซๅใใฎใ๏ฝในใฉใคใใงใใใJVMใฎไป็ตใฟ
Chihiro Ito
ย
์ด๋ ํด์ปค์์ ์ฐธ์ฌํ ๋ฐฑ์๋ ๊ฐ๋ฐ์๋ค์ ์ํ ๊ต์ก์๋ฃ ์ฝ๊ฒ ๋ง๋ ๋ค๊ณ ํ๋๋ฐ๋, ๋ง์ด ์ด๋ ค์ ๋๋ด ๋๋ค. ์ ์์ฌ์ด ๊ณผํ๋ ๊ฒ ๊ฐ์์. ๋ด๋ฒ์ ์ข ๋ ์ฝ๊ฒ ! - ๋ ์ : ๋ฐฑ์๋ ๊ฐ๋ฐ์๋ฅผ ํฌ๋งํ๋ ์ฌ๋ (์ทจ์ค์, ์ด์ง ํฌ๋ง์), 5๋ ์ฐจ ์ดํ - ์ฃผ์ ๋ด์ฉ : ๋ฐฑ์๋ ๊ฐ๋ฐ์ ํ ๋ ์ผ์ด๋๋ ์ผ๋ค(๊ฐ๋ฐํ์ ์ผ) - ๋น์์ ์ ๋ชฉ์ ์ผ๋ก ์ธ์ฉ์ ๊ฐ๋ฅํฉ๋๋ค. (์ถ์ฒ ๋ช ๊ธฐ ํ์)
์๋ฒํ๊ฐ๋ก (๋ฐฑ์๋ ์๋ฒ ๊ฐ๋ฐ์๋ฅผ ์ํ)
์๋ฒํ๊ฐ๋ก (๋ฐฑ์๋ ์๋ฒ ๊ฐ๋ฐ์๋ฅผ ์ํ)
์๋ณด ๊น
ย
How to build massive internet service well.
Massive service basic
Massive service basic
DaeMyung Kang
ย
MySQL ใใใฉใผใใณในใ็ฃ่ฆใใ Cacti ใฐใฉใใฎ่ฆๆนใInnoDB ใฎ I/O ใฎไป็ตใฟใฎ่ชฌๆ
Osc2015ๅๆตท้ ๆญๅนmy sqlๅๅผทไผ_ๆณขๅค้_r3
Osc2015ๅๆตท้ ๆญๅนmy sqlๅๅผทไผ_ๆณขๅค้_r3
Nobuhiro Hatano
ย
Spring Boot makes creating small Java application easy - and also facilitates operations and deployment. But for Microservices need more: Because Microservices are a distributed systems issues like Service Discovery or Load Balancing must be solved. Spring Cloud adds those capabilities to Spring Boot using e.g. the Netflix stack. This talks covers Spring Boot and Spring Cloud and shows how these technologies can be used to create a complete Microservices environment.
Microservices with Java, Spring Boot and Spring Cloud
Microservices with Java, Spring Boot and Spring Cloud
Eberhard Wolff
ย
ใในใใปใฉใ ใใขใผใญใใฏใใฃใฎๅใๆญ? Apache Hudi ๏ผNTTใใผใฟ ใใฏใใญใธใผใซใณใใกใฌใณใน 2020 ็บ่กจ่ณๆ๏ผ 2020ๅนด10ๆ16ๆฅ๏ผ้๏ผ NTT ใฝใใใฆใงใขใคใใใผใทใงใณใปใณใฟ Zhai Hongjie ่ฌๆผๅ็ปใฏใYouTubeใใฃใณใใซใNTT DATA Techใใซใฆๅ ฌ้ไธญ๏ผ https://www.youtube.com/watch?v=qMmJUjpff-8
ใในใใปใฉใ ใใขใผใญใใฏใใฃใฎๅใๆญ? Apache Hudi๏ผNTTใใผใฟ ใใฏใใญใธใผใซใณใใกใฌใณใน 2020 ็บ่กจ่ณๆ๏ผ
ใในใใปใฉใ ใใขใผใญใใฏใใฃใฎๅใๆญ? Apache Hudi๏ผNTTใใผใฟ ใใฏใใญใธใผใซใณใใกใฌใณใน 2020 ็บ่กจ่ณๆ๏ผ
NTT DATA Technology & Innovation
ย
ใAzure ใฏ็ใพใใๆใใPaaSใงใใใใใใฆใPaaSใจใใ่จ่ใใใ้็บใใฉใใใใฉใผใ ใจใใฆใฎ ใๅพๆใฎ่ฉฑใใใใใจใใใงใใใใใใใ ใขใใช้็บใฎPaaSๆฉ่ฝ ใจใใฆใฎๅด้ขใฏใ ใใถ่ชใใคใใใใฆใใฆใพใใฎใงใไปๅใฏใใใฆใใใผใฟใใผในในใใขใจใใฆใฎ PaaSๆฉ่ฝใซ็ตใฃใฆใ่ฉฑใใใฆใใใ ใใพใใใใ#ๅๅผทไผ #paasjp #PaaSๅๅผทไผใ ๅๅผทไผใตใคใใhttps://paas.connpass.com/ใใ ใคใใณใใฎๅฎๆณใพใจใtg https://togetter.com/li/1128578?page=2
Azureไธใฎ ใใผใฟใใผใน ๆฉ่ฝใฎ้ธใณๆนใKVSใใDWHใพใง
Azureไธใฎ ใใผใฟใใผใน ๆฉ่ฝใฎ้ธใณๆนใKVSใใDWHใพใง
Daisuke Masubuchi
ย
REST is an alternate and simpler approach for implementing WebServices. It is based on the HTTP protocol and hence leverages a lot of existing infrastructures. It uses an uniform interface thus making it easy to build client applications. In this session we will look at the fundamental concepts behind REST (Resource, URI, Stateless Conversation ..) and how to apply it in the context of a real applcation. We will also discuss the pros & cons of RESTful vs Soap based webservices. We will discuss the design of RESTful application and then look at how to implement it using Spring MVC.
Building RESTful applications using Spring MVC
Building RESTful applications using Spring MVC
IndicThreads
ย
ไปๅคฉๅ ฌๅธๅ ง่จ็็ฐกๅ ฑๆ้ใ ็ ็ฉถ Swoole ๅคๅนด๏ผไน็ขบๅฏฆๅจไธ็ท็ฐๅขๆญฃๅผ้่กๅพไน ไธ็ฉฉๅฎ๏ผๆไปฅ่ๅไบๅไบซ้ปๆปด่ๆๅทงใ ้คไบไธ้ป็ฏไพ็จๅผๅค๏ผๅไบซ่้็ๆฏใๆถๆงใๅทฎ็ฐ๏ผๅปถไผธๅบๅปๅ่ Nginx/PHP, Node ๅ Go ้ฒ่กไธไบๆฏ่ผใ ไธป่ฆๆฏๆๅบๅนพๅๅ้ก๏ผๅธๆๅผ็ผๆ่๏ผ โ ๅพๅคไบบ้ฝ่ชช Swoole ๅฟซ๏ผๆฏๅช่ฃกๅฟซ๏ผ(ๅ ถๅฏฆๆ่ฆบๅพ็ถฒ่ทฏไธ็่ณ่จไธๅค ๅ จ้ข๏ผ่ฌๅพไนไธไธๅฎ็บ็) โ ็ๆณ Nginx/PHP, Swoole, Node, Go ๅบ็คๆ่ฝๆฏ่ผ๏ผ้่ฝๆไฝณๅๅ๏ผไฝ่่ๆ๏ผ โ ๅ ถไปใ
Swoole Love PHP
Swoole Love PHP
Yi-Feng Tzeng
ย
This presentation cover Adobe AEM Dispatcher security and CDN and browser caching. This presentation is the second part of a webinar on AEM Dispatcher: http://dev.day.com/content/ddc/en/gems/dispatcher-caching---new-features-and-optimizations.html Visit url above to view the whole presentation. Domique Pfister the primary engineer developing AEM Dispatcher covers the first part on new features.
AEM (CQ) Dispatcher Security and CDN+Browser Caching
AEM (CQ) Dispatcher Security and CDN+Browser Caching
Andrew Khoury
ย
The distributed cache is becoming a popular technique to improve performance and simplify the data access layer when dealing with databases. Bringing the data as close as possible to the CPU allows unparalleled execution speed as well as horizontal scalability. This approach is often successful when used in a microservices design in which the cache is accessed only by a single API. However, it becomes more challenging if multiple applications are involved and changes are made to the database directly by other applications. The data held in the cache eventually becomes stale and no longer consistent with its underlying database. When consistency problems arise, the Engineering team must address that through additional coding โ which directly jeopardizes the teamโs ability to be agile between releases. This talk presents a set of patterns for cache-based architectures that aim to keep the caches always hot; by using Apache Kafka and its connectors to accomplish that goal. It will be shown how to set up these patterns across different IMDGs such as Hazelcast, Apache Ignite or Coherence. These patterns can be used in conjunction with different cache topologies such as cache-aside, read-through, write-behind, and refresh-ahead, making it reusable enough to be used as a framework to achieve data consistency in any architecture that relies on distributed caches.
Keeping Your Data Close and Your Caches Hotter (Ricardo Ferreira, Confluent) ...
Keeping Your Data Close and Your Caches Hotter (Ricardo Ferreira, Confluent) ...
confluent
ย
Kafka Streams is a library for developing applications for processing records from topics in Apache Kafka. It provides high-level Streams DSL and low-level Processor API for describing fault-tolerant distributed streaming pipelines in Java or Scala programming languages. Kafka Streams also offers elaborate API for stateless and stateful stream processing. Thatโs a high-level view of Kafka Streams. Have you ever wondered how Kafka Streams does all this and what the relationship with Apache Kafka (brokers) is? Thatโs among the topics of the talk. During this talk we will look under the covers of Kafka Streams and deep dive into Kafka Streamsโ Fault-Tolerant Distributed Stream Processing Engine. You will know the role of StreamThreads, TaskManager, StreamTasks, StandbyTasks, StreamsPartitionAssignor, RebalanceListener and few others. The aim of this talk is to get you equipped with knowledge about the internals of Kafka Streams that should help you fine-tune your stream processing pipelines for better performance.
Deep Dive Into Kafka Streams (and the Distributed Stream Processing Engine) (...
Deep Dive Into Kafka Streams (and the Distributed Stream Processing Engine) (...
confluent
ย
[124] ํ์ด๋ธ๋ฆฌ๋ ์ฑ ๊ฐ๋ฐ๊ธฐ ๊นํ์
[124] แแ กแแ ตแแ ณแ แ ตแแ ณ แแ ขแธ แแ ขแแ กแฏแแ ต แแ ตแทแแ กแซแแ ฉแฏ
[124] แแ กแแ ตแแ ณแ แ ตแแ ณ แแ ขแธ แแ ขแแ กแฏแแ ต แแ ตแทแแ กแซแแ ฉแฏ
NAVER D2
ย
Simple Search Engine Theory
Soma search
Soma search
DaeMyung Kang
ย
Transactions and Concurrency Control Patterns by Vlad Mihalcea Transactions and Concurrency Control are very of paramount importance when it comes to enterprise systems data integrity. However, this topic is very tough since you have to understand the inner workings of the database system, its concurrency control design choices (e.g. 2PL, MVCC), transaction isolation levels and locking schemes. In this presentation, Iโm going to explain what data anomalies can happen depending on the transaction isolation level, with references to Oracle, SQL Server, PostgreSQL, and MySQL. I will also demonstrate that database transactions are not enough, especially for multi-request web flows. For this reason, Iโm going to present multiple application-level transaction patterns based on both optimistic and pessimistic locking mechanisms. Last, Iโm going to talk about Concurrency Control strategies used in the Hibernate second-level caching mechanism, which can boost performance without compromising strong consistency.
Transactions and Concurrency Control Patterns
Transactions and Concurrency Control Patterns
J On The Beach
ย
๋ง์ดํฌ๋ก์๋น์ค ์คํ์ผ๋ก ๋ง๋ค์ด์ง ์์คํ ์ ๋ชจ๋ ธ๋ฆฌํฑ ์คํ์ผ๋ก ์ด๊ดํ ์ฌ๋ก์ ํจ๊ป ์คํ๋ง์ ์ด์ฉํด ๋ชจ๋ํ ๋ชจ๋ ธ๋ฆฌ์ค(modular monoliths)๋ฅผ ๋ง๋ ๊ฒฝํ์ ๋ฐํ์ผ๋ก ๋ชจ๋ ธ๋ฆฌํฑ/๋ง์ดํฌ๋ก์๋น์ค ๋ณด๋ค ๋ณธ์ง์ ์ธ ๋ฌธ์ ๋ฅผ ์ ๊ธฐํ๊ณ , ๋ฌธ์ ํด๊ฒฐ์ ์ํ ์์ด๋์ด์ ์ฝ๋๋ฅผ ๊ณต์ ํฉ๋๋ค. https://github.com/arawn/building-modular-monoliths-using-spring ์ด ์๋ฃ๋ 2019๋ KSUG ์ธ๋ฏธ๋์์ ์งํํ "์ ํค์ด ๋ชจ๋ ธ๋ฆฌ์ค ํ๋ ์ด ๋ง์ดํฌ๋ก์๋น์ค ์ ๋ถ๋ฝ๋ค"๋ฅผ ๊ธฐ๋ฐ์ผ๋ก ๋ช๊ฐ์ง ๋ด์ฉ์ ์ถ๊ฐํ๊ณ , ์ ๊ฐ ๋ฐฉ์์ ๋ค๋ฌ์ด ์กฐ๊ธ ๋ ์น์ ํ๊ฒ ๋ง๋ค์ด์ก์ต๋๋ค.
์ฐ์ํ ๋ชจ๋ ธ๋ฆฌ์ค
์ฐ์ํ ๋ชจ๋ ธ๋ฆฌ์ค
Arawn Park
ย
Laravel Presentation| Spring 2021
Laravel Presentation
Laravel Presentation
REZAUL KARIM REFATH
ย
Agenda: * What is HAProxy? * SQL Load balancing for MySQL * Failure detection using MySQL health checks * High Availability with Keepalived and Virtual IP * Use cases: MySQL Cluster, Galera Cluster and MySQL Replication * Alternative methods: Database drivers with inbuilt cluster support, MySQL proxy, MaxScale, ProxySQL
Load Balancing MySQL with HAProxy - Slides
Load Balancing MySQL with HAProxy - Slides
Severalnines
ย
elastic, resiliency, sharding, service discovery
webservice scaling for newbie
webservice scaling for newbie
DaeMyung Kang
ย
A talk describing my experiences with implementing clean architecture on production projects.
Clean architecture
Clean architecture
Travis Frisinger
ย
ใใใใใฎ InnoDB ใฎใ่ฉฑใงใใ
ใใใใใฎ InnoDB Adaptive Flushing ๏ผไปฎ๏ผ
ใใใใใฎ InnoDB Adaptive Flushing ๏ผไปฎ๏ผ
Takanori Sejima
ย
a small introduction to laravel
laravel.pptx
laravel.pptx
asif290119
ย
Redis Internals and RoadMap from 2.8 to 3.2
Redis acc 2015
Redis acc 2015
DaeMyung Kang
ย
Redis basicandroadmap
Redis basicandroadmap
DaeMyung Kang
ย
More Related Content
What's hot
How to build massive internet service well.
Massive service basic
Massive service basic
DaeMyung Kang
ย
MySQL ใใใฉใผใใณในใ็ฃ่ฆใใ Cacti ใฐใฉใใฎ่ฆๆนใInnoDB ใฎ I/O ใฎไป็ตใฟใฎ่ชฌๆ
Osc2015ๅๆตท้ ๆญๅนmy sqlๅๅผทไผ_ๆณขๅค้_r3
Osc2015ๅๆตท้ ๆญๅนmy sqlๅๅผทไผ_ๆณขๅค้_r3
Nobuhiro Hatano
ย
Spring Boot makes creating small Java application easy - and also facilitates operations and deployment. But for Microservices need more: Because Microservices are a distributed systems issues like Service Discovery or Load Balancing must be solved. Spring Cloud adds those capabilities to Spring Boot using e.g. the Netflix stack. This talks covers Spring Boot and Spring Cloud and shows how these technologies can be used to create a complete Microservices environment.
Microservices with Java, Spring Boot and Spring Cloud
Microservices with Java, Spring Boot and Spring Cloud
Eberhard Wolff
ย
ใในใใปใฉใ ใใขใผใญใใฏใใฃใฎๅใๆญ? Apache Hudi ๏ผNTTใใผใฟ ใใฏใใญใธใผใซใณใใกใฌใณใน 2020 ็บ่กจ่ณๆ๏ผ 2020ๅนด10ๆ16ๆฅ๏ผ้๏ผ NTT ใฝใใใฆใงใขใคใใใผใทใงใณใปใณใฟ Zhai Hongjie ่ฌๆผๅ็ปใฏใYouTubeใใฃใณใใซใNTT DATA Techใใซใฆๅ ฌ้ไธญ๏ผ https://www.youtube.com/watch?v=qMmJUjpff-8
ใในใใปใฉใ ใใขใผใญใใฏใใฃใฎๅใๆญ? Apache Hudi๏ผNTTใใผใฟ ใใฏใใญใธใผใซใณใใกใฌใณใน 2020 ็บ่กจ่ณๆ๏ผ
ใในใใปใฉใ ใใขใผใญใใฏใใฃใฎๅใๆญ? Apache Hudi๏ผNTTใใผใฟ ใใฏใใญใธใผใซใณใใกใฌใณใน 2020 ็บ่กจ่ณๆ๏ผ
NTT DATA Technology & Innovation
ย
ใAzure ใฏ็ใพใใๆใใPaaSใงใใใใใใฆใPaaSใจใใ่จ่ใใใ้็บใใฉใใใใฉใผใ ใจใใฆใฎ ใๅพๆใฎ่ฉฑใใใใใจใใใงใใใใใใใ ใขใใช้็บใฎPaaSๆฉ่ฝ ใจใใฆใฎๅด้ขใฏใ ใใถ่ชใใคใใใใฆใใฆใพใใฎใงใไปๅใฏใใใฆใใใผใฟใใผในในใใขใจใใฆใฎ PaaSๆฉ่ฝใซ็ตใฃใฆใ่ฉฑใใใฆใใใ ใใพใใใใ#ๅๅผทไผ #paasjp #PaaSๅๅผทไผใ ๅๅผทไผใตใคใใhttps://paas.connpass.com/ใใ ใคใใณใใฎๅฎๆณใพใจใtg https://togetter.com/li/1128578?page=2
Azureไธใฎ ใใผใฟใใผใน ๆฉ่ฝใฎ้ธใณๆนใKVSใใDWHใพใง
Azureไธใฎ ใใผใฟใใผใน ๆฉ่ฝใฎ้ธใณๆนใKVSใใDWHใพใง
Daisuke Masubuchi
ย
REST is an alternate and simpler approach for implementing WebServices. It is based on the HTTP protocol and hence leverages a lot of existing infrastructures. It uses an uniform interface thus making it easy to build client applications. In this session we will look at the fundamental concepts behind REST (Resource, URI, Stateless Conversation ..) and how to apply it in the context of a real applcation. We will also discuss the pros & cons of RESTful vs Soap based webservices. We will discuss the design of RESTful application and then look at how to implement it using Spring MVC.
Building RESTful applications using Spring MVC
Building RESTful applications using Spring MVC
IndicThreads
ย
ไปๅคฉๅ ฌๅธๅ ง่จ็็ฐกๅ ฑๆ้ใ ็ ็ฉถ Swoole ๅคๅนด๏ผไน็ขบๅฏฆๅจไธ็ท็ฐๅขๆญฃๅผ้่กๅพไน ไธ็ฉฉๅฎ๏ผๆไปฅ่ๅไบๅไบซ้ปๆปด่ๆๅทงใ ้คไบไธ้ป็ฏไพ็จๅผๅค๏ผๅไบซ่้็ๆฏใๆถๆงใๅทฎ็ฐ๏ผๅปถไผธๅบๅปๅ่ Nginx/PHP, Node ๅ Go ้ฒ่กไธไบๆฏ่ผใ ไธป่ฆๆฏๆๅบๅนพๅๅ้ก๏ผๅธๆๅผ็ผๆ่๏ผ โ ๅพๅคไบบ้ฝ่ชช Swoole ๅฟซ๏ผๆฏๅช่ฃกๅฟซ๏ผ(ๅ ถๅฏฆๆ่ฆบๅพ็ถฒ่ทฏไธ็่ณ่จไธๅค ๅ จ้ข๏ผ่ฌๅพไนไธไธๅฎ็บ็) โ ็ๆณ Nginx/PHP, Swoole, Node, Go ๅบ็คๆ่ฝๆฏ่ผ๏ผ้่ฝๆไฝณๅๅ๏ผไฝ่่ๆ๏ผ โ ๅ ถไปใ
Swoole Love PHP
Swoole Love PHP
Yi-Feng Tzeng
ย
This presentation cover Adobe AEM Dispatcher security and CDN and browser caching. This presentation is the second part of a webinar on AEM Dispatcher: http://dev.day.com/content/ddc/en/gems/dispatcher-caching---new-features-and-optimizations.html Visit url above to view the whole presentation. Domique Pfister the primary engineer developing AEM Dispatcher covers the first part on new features.
AEM (CQ) Dispatcher Security and CDN+Browser Caching
AEM (CQ) Dispatcher Security and CDN+Browser Caching
Andrew Khoury
ย
The distributed cache is becoming a popular technique to improve performance and simplify the data access layer when dealing with databases. Bringing the data as close as possible to the CPU allows unparalleled execution speed as well as horizontal scalability. This approach is often successful when used in a microservices design in which the cache is accessed only by a single API. However, it becomes more challenging if multiple applications are involved and changes are made to the database directly by other applications. The data held in the cache eventually becomes stale and no longer consistent with its underlying database. When consistency problems arise, the Engineering team must address that through additional coding โ which directly jeopardizes the teamโs ability to be agile between releases. This talk presents a set of patterns for cache-based architectures that aim to keep the caches always hot; by using Apache Kafka and its connectors to accomplish that goal. It will be shown how to set up these patterns across different IMDGs such as Hazelcast, Apache Ignite or Coherence. These patterns can be used in conjunction with different cache topologies such as cache-aside, read-through, write-behind, and refresh-ahead, making it reusable enough to be used as a framework to achieve data consistency in any architecture that relies on distributed caches.
Keeping Your Data Close and Your Caches Hotter (Ricardo Ferreira, Confluent) ...
Keeping Your Data Close and Your Caches Hotter (Ricardo Ferreira, Confluent) ...
confluent
ย
Kafka Streams is a library for developing applications for processing records from topics in Apache Kafka. It provides high-level Streams DSL and low-level Processor API for describing fault-tolerant distributed streaming pipelines in Java or Scala programming languages. Kafka Streams also offers elaborate API for stateless and stateful stream processing. Thatโs a high-level view of Kafka Streams. Have you ever wondered how Kafka Streams does all this and what the relationship with Apache Kafka (brokers) is? Thatโs among the topics of the talk. During this talk we will look under the covers of Kafka Streams and deep dive into Kafka Streamsโ Fault-Tolerant Distributed Stream Processing Engine. You will know the role of StreamThreads, TaskManager, StreamTasks, StandbyTasks, StreamsPartitionAssignor, RebalanceListener and few others. The aim of this talk is to get you equipped with knowledge about the internals of Kafka Streams that should help you fine-tune your stream processing pipelines for better performance.
Deep Dive Into Kafka Streams (and the Distributed Stream Processing Engine) (...
Deep Dive Into Kafka Streams (and the Distributed Stream Processing Engine) (...
confluent
ย
[124] ํ์ด๋ธ๋ฆฌ๋ ์ฑ ๊ฐ๋ฐ๊ธฐ ๊นํ์
[124] แแ กแแ ตแแ ณแ แ ตแแ ณ แแ ขแธ แแ ขแแ กแฏแแ ต แแ ตแทแแ กแซแแ ฉแฏ
[124] แแ กแแ ตแแ ณแ แ ตแแ ณ แแ ขแธ แแ ขแแ กแฏแแ ต แแ ตแทแแ กแซแแ ฉแฏ
NAVER D2
ย
Simple Search Engine Theory
Soma search
Soma search
DaeMyung Kang
ย
Transactions and Concurrency Control Patterns by Vlad Mihalcea Transactions and Concurrency Control are very of paramount importance when it comes to enterprise systems data integrity. However, this topic is very tough since you have to understand the inner workings of the database system, its concurrency control design choices (e.g. 2PL, MVCC), transaction isolation levels and locking schemes. In this presentation, Iโm going to explain what data anomalies can happen depending on the transaction isolation level, with references to Oracle, SQL Server, PostgreSQL, and MySQL. I will also demonstrate that database transactions are not enough, especially for multi-request web flows. For this reason, Iโm going to present multiple application-level transaction patterns based on both optimistic and pessimistic locking mechanisms. Last, Iโm going to talk about Concurrency Control strategies used in the Hibernate second-level caching mechanism, which can boost performance without compromising strong consistency.
Transactions and Concurrency Control Patterns
Transactions and Concurrency Control Patterns
J On The Beach
ย
๋ง์ดํฌ๋ก์๋น์ค ์คํ์ผ๋ก ๋ง๋ค์ด์ง ์์คํ ์ ๋ชจ๋ ธ๋ฆฌํฑ ์คํ์ผ๋ก ์ด๊ดํ ์ฌ๋ก์ ํจ๊ป ์คํ๋ง์ ์ด์ฉํด ๋ชจ๋ํ ๋ชจ๋ ธ๋ฆฌ์ค(modular monoliths)๋ฅผ ๋ง๋ ๊ฒฝํ์ ๋ฐํ์ผ๋ก ๋ชจ๋ ธ๋ฆฌํฑ/๋ง์ดํฌ๋ก์๋น์ค ๋ณด๋ค ๋ณธ์ง์ ์ธ ๋ฌธ์ ๋ฅผ ์ ๊ธฐํ๊ณ , ๋ฌธ์ ํด๊ฒฐ์ ์ํ ์์ด๋์ด์ ์ฝ๋๋ฅผ ๊ณต์ ํฉ๋๋ค. https://github.com/arawn/building-modular-monoliths-using-spring ์ด ์๋ฃ๋ 2019๋ KSUG ์ธ๋ฏธ๋์์ ์งํํ "์ ํค์ด ๋ชจ๋ ธ๋ฆฌ์ค ํ๋ ์ด ๋ง์ดํฌ๋ก์๋น์ค ์ ๋ถ๋ฝ๋ค"๋ฅผ ๊ธฐ๋ฐ์ผ๋ก ๋ช๊ฐ์ง ๋ด์ฉ์ ์ถ๊ฐํ๊ณ , ์ ๊ฐ ๋ฐฉ์์ ๋ค๋ฌ์ด ์กฐ๊ธ ๋ ์น์ ํ๊ฒ ๋ง๋ค์ด์ก์ต๋๋ค.
์ฐ์ํ ๋ชจ๋ ธ๋ฆฌ์ค
์ฐ์ํ ๋ชจ๋ ธ๋ฆฌ์ค
Arawn Park
ย
Laravel Presentation| Spring 2021
Laravel Presentation
Laravel Presentation
REZAUL KARIM REFATH
ย
Agenda: * What is HAProxy? * SQL Load balancing for MySQL * Failure detection using MySQL health checks * High Availability with Keepalived and Virtual IP * Use cases: MySQL Cluster, Galera Cluster and MySQL Replication * Alternative methods: Database drivers with inbuilt cluster support, MySQL proxy, MaxScale, ProxySQL
Load Balancing MySQL with HAProxy - Slides
Load Balancing MySQL with HAProxy - Slides
Severalnines
ย
elastic, resiliency, sharding, service discovery
webservice scaling for newbie
webservice scaling for newbie
DaeMyung Kang
ย
A talk describing my experiences with implementing clean architecture on production projects.
Clean architecture
Clean architecture
Travis Frisinger
ย
ใใใใใฎ InnoDB ใฎใ่ฉฑใงใใ
ใใใใใฎ InnoDB Adaptive Flushing ๏ผไปฎ๏ผ
ใใใใใฎ InnoDB Adaptive Flushing ๏ผไปฎ๏ผ
Takanori Sejima
ย
a small introduction to laravel
laravel.pptx
laravel.pptx
asif290119
ย
What's hot
(20)
Massive service basic
Massive service basic
ย
Osc2015ๅๆตท้ ๆญๅนmy sqlๅๅผทไผ_ๆณขๅค้_r3
Osc2015ๅๆตท้ ๆญๅนmy sqlๅๅผทไผ_ๆณขๅค้_r3
ย
Microservices with Java, Spring Boot and Spring Cloud
Microservices with Java, Spring Boot and Spring Cloud
ย
ใในใใปใฉใ ใใขใผใญใใฏใใฃใฎๅใๆญ? Apache Hudi๏ผNTTใใผใฟ ใใฏใใญใธใผใซใณใใกใฌใณใน 2020 ็บ่กจ่ณๆ๏ผ
ใในใใปใฉใ ใใขใผใญใใฏใใฃใฎๅใๆญ? Apache Hudi๏ผNTTใใผใฟ ใใฏใใญใธใผใซใณใใกใฌใณใน 2020 ็บ่กจ่ณๆ๏ผ
ย
Azureไธใฎ ใใผใฟใใผใน ๆฉ่ฝใฎ้ธใณๆนใKVSใใDWHใพใง
Azureไธใฎ ใใผใฟใใผใน ๆฉ่ฝใฎ้ธใณๆนใKVSใใDWHใพใง
ย
Building RESTful applications using Spring MVC
Building RESTful applications using Spring MVC
ย
Swoole Love PHP
Swoole Love PHP
ย
AEM (CQ) Dispatcher Security and CDN+Browser Caching
AEM (CQ) Dispatcher Security and CDN+Browser Caching
ย
Keeping Your Data Close and Your Caches Hotter (Ricardo Ferreira, Confluent) ...
Keeping Your Data Close and Your Caches Hotter (Ricardo Ferreira, Confluent) ...
ย
Deep Dive Into Kafka Streams (and the Distributed Stream Processing Engine) (...
Deep Dive Into Kafka Streams (and the Distributed Stream Processing Engine) (...
ย
[124] แแ กแแ ตแแ ณแ แ ตแแ ณ แแ ขแธ แแ ขแแ กแฏแแ ต แแ ตแทแแ กแซแแ ฉแฏ
[124] แแ กแแ ตแแ ณแ แ ตแแ ณ แแ ขแธ แแ ขแแ กแฏแแ ต แแ ตแทแแ กแซแแ ฉแฏ
ย
Soma search
Soma search
ย
Transactions and Concurrency Control Patterns
Transactions and Concurrency Control Patterns
ย
์ฐ์ํ ๋ชจ๋ ธ๋ฆฌ์ค
์ฐ์ํ ๋ชจ๋ ธ๋ฆฌ์ค
ย
Laravel Presentation
Laravel Presentation
ย
Load Balancing MySQL with HAProxy - Slides
Load Balancing MySQL with HAProxy - Slides
ย
webservice scaling for newbie
webservice scaling for newbie
ย
Clean architecture
Clean architecture
ย
ใใใใใฎ InnoDB Adaptive Flushing ๏ผไปฎ๏ผ
ใใใใใฎ InnoDB Adaptive Flushing ๏ผไปฎ๏ผ
ย
laravel.pptx
laravel.pptx
ย
Similar to Redis trouble shooting
Redis Internals and RoadMap from 2.8 to 3.2
Redis acc 2015
Redis acc 2015
DaeMyung Kang
ย
Redis basicandroadmap
Redis basicandroadmap
DaeMyung Kang
ย
mongo db ์๋ฒฝ ๊ฐ์ด๋ 9์ฅ ๋ณต์ ํํธ ์คํฐ๋ ์๋ฃ
Mongo db ๋ณต์ (Replication)
Mongo db ๋ณต์ (Replication)
Hyosung Jeon
ย
Redis Overview
Redis Overview
kalzas
ย
postgresql bdr
Pgday bdr gt1000
Pgday bdr gt1000
์ ๋ ์ฒ
ย
PostgreSQL ์ด์คํ ์๋ฃจ์ BDR ์ฌ์ฉ ํ๊ธฐ ์ ๋๋ค. ๋ฌธ์ ์ฌํญ์ gt1000@gmail.com ์ผ๋ก~
Pgday bdr ์ฒ์ ๋
Pgday bdr ์ฒ์ ๋
PgDay.Seoul
ย
DEVIEW 2014 [2B5]nBase-ARC Redis Cluster
[2B5]nBase-ARC Redis Cluster
[2B5]nBase-ARC Redis Cluster
NAVER D2
ย
Cassandra ๋ฉ๋ถ๊ธฐ | Devon 2012
Cassandra ๋ฉ๋ถ๊ธฐ | Devon 2012
Daum DNA
ย
๋ถ์ฐ ์๋ฒ ์ค๊ณ๋ ์๋ฆฌ ์ดํด๊ฐ ์ค์ํ๋ค. ์ด๋ฅผ ๋ชจ๋ฅด๊ณ ์๋ ์๋ชป๋ ์ค๊ณ๋ก ์ธํด ๊ณ ์์ ๊ณ ์๋๋ก ํ๊ณ ๊ฒฐ๊ณผ๋ ๊ฒฐ๊ณผ๋๋ก ๋์ ์ ์๋ค. ๋ณธ ๊ฐ์ฐ์์๋ ๋ถ์ฐ ๊ฒ์ ์๋ฒ ๊ตฌ์กฐ๋ฅผ ์ง๊ธฐ ์ ์ ๋ฐ๋์ ์ดํดํด์ผ ํ๋ ์๋ฆฌ๋ฅผ ์ค๋ช ํ๋ค.
KGC 2014: ๋ถ์ฐ ๊ฒ์ ์๋ฒ ๊ตฌ์กฐ๋ก
KGC 2014: ๋ถ์ฐ ๊ฒ์ ์๋ฒ ๊ตฌ์กฐ๋ก
Hyunjik Bae
ย
3.[d2 แแ ฉแแ ณแซแแ ฆแแ ตแแ ก]แแ ฎแซแแ กแซแแ ตแแ ณแแ ฆแท แแ ขแแ กแฏ แแ ตแพ แแ ญแแ ฎแซ n base arc
3.[d2 แแ ฉแแ ณแซแแ ฆแแ ตแแ ก]แแ ฎแซแแ กแซแแ ตแแ ณแแ ฆแท แแ ขแแ กแฏ แแ ตแพ แแ ญแแ ฎแซ n base arc
3.[d2 แแ ฉแแ ณแซแแ ฆแแ ตแแ ก]แแ ฎแซแแ กแซแแ ตแแ ณแแ ฆแท แแ ขแแ กแฏ แแ ตแพ แแ ญแแ ฎแซ n base arc
NAVER D2
ย
์๋ฒ ์ํคํ ์ฒ ์ดํด๋ฅผ ์ํ ํ๋ก์ธ์ค์ ์ฐ๋ ๋
์๋ฒ ์ํคํ ์ฒ ์ดํด๋ฅผ ์ํ ํ๋ก์ธ์ค์ ์ฐ๋ ๋
์๋ฒ ์ํคํ ์ฒ ์ดํด๋ฅผ ์ํ ํ๋ก์ธ์ค์ ์ฐ๋ ๋
KwangSeob Jeong
ย
CUDA๋ฅผ ๊ฒ์ ํ๋ก์ ํธ์ ์ ์ฉํ๊ธฐ
CUDA๋ฅผ ๊ฒ์ ํ๋ก์ ํธ์ ์ ์ฉํ๊ธฐ
CUDA๋ฅผ ๊ฒ์ ํ๋ก์ ํธ์ ์ ์ฉํ๊ธฐ
YEONG-CHEON YOU
ย
Cache Governance
Cache governance
Cache governance
DaeMyung Kang
ย
th
The nosql echossytem
The nosql echossytem
์ข ์ ๋ฐ
ย
Redis, MongoDB ๊ทธ๋ฆฌ๊ณ MySQL ๊ณผ ํจ๊ปํ๋ ๋ชจ๋ฐ์ผ ์ ํ๋ฆฌ์ผ์ด์ ์๋น์ค์์์ ๋ก๊ทธ ์์ง๊ณผ ๋ถ์
[์ค๋งํธ์คํฐ๋]๋ชจ๋ฐ์ผ ์ ํ๋ฆฌ์ผ์ด์ ์๋น์ค์์์ ๋ก๊ทธ ์์ง๊ณผ ๋ถ์
[์ค๋งํธ์คํฐ๋]๋ชจ๋ฐ์ผ ์ ํ๋ฆฌ์ผ์ด์ ์๋น์ค์์์ ๋ก๊ทธ ์์ง๊ณผ ๋ถ์
smartstudy_official
ย
MySQL PowerGroup Tech Seminar (2017.1) - 3.MySQL/MariaDB Proxy Software Test (by ์ด์ค์ ) - URL : cafe.naver.com/mysqlpg
MySQL/MariaDB Proxy Software Test
MySQL/MariaDB Proxy Software Test
I Goo Lee
ย
์์คํ ์ด์์์์ด์ธ / ๊ธฐ์ ํ Oralce Solaris cluster์ ๋ํ Basic 4.x Base
Osc4.x installation v1-upload
Osc4.x installation v1-upload
Dong-Hwa jung
ย
NDC 2012 ๋ฐํ์๋ฃ GPGPU(CUDA)๋ฅผ ์ด์ฉํ MMOG์บ๋ฆญํฐ ์ถฉ๋์ฒ๋ฆฌ
GPGPU(CUDA)๋ฅผ ์ด์ฉํ MMOG ์บ๋ฆญํฐ ์ถฉ๋์ฒ๋ฆฌ
GPGPU(CUDA)๋ฅผ ์ด์ฉํ MMOG ์บ๋ฆญํฐ ์ถฉ๋์ฒ๋ฆฌ
YEONG-CHEON YOU
ย
nGrinder ์ฌํ ๊ณผ์ (3์๊ฐ๋ถ๋)
Advanced nGrinder 2nd Edition
Advanced nGrinder 2nd Edition
JunHo Yoon
ย
2011.6.19 jco ๋ฐํ์๋ฃ by ๊นํ์ค
NoSQL
NoSQL
Gruter
ย
Similar to Redis trouble shooting
(20)
Redis acc 2015
Redis acc 2015
ย
Redis basicandroadmap
Redis basicandroadmap
ย
Mongo db ๋ณต์ (Replication)
Mongo db ๋ณต์ (Replication)
ย
Redis Overview
Redis Overview
ย
Pgday bdr gt1000
Pgday bdr gt1000
ย
Pgday bdr ์ฒ์ ๋
Pgday bdr ์ฒ์ ๋
ย
[2B5]nBase-ARC Redis Cluster
[2B5]nBase-ARC Redis Cluster
ย
Cassandra ๋ฉ๋ถ๊ธฐ | Devon 2012
Cassandra ๋ฉ๋ถ๊ธฐ | Devon 2012
ย
KGC 2014: ๋ถ์ฐ ๊ฒ์ ์๋ฒ ๊ตฌ์กฐ๋ก
KGC 2014: ๋ถ์ฐ ๊ฒ์ ์๋ฒ ๊ตฌ์กฐ๋ก
ย
3.[d2 แแ ฉแแ ณแซแแ ฆแแ ตแแ ก]แแ ฎแซแแ กแซแแ ตแแ ณแแ ฆแท แแ ขแแ กแฏ แแ ตแพ แแ ญแแ ฎแซ n base arc
3.[d2 แแ ฉแแ ณแซแแ ฆแแ ตแแ ก]แแ ฎแซแแ กแซแแ ตแแ ณแแ ฆแท แแ ขแแ กแฏ แแ ตแพ แแ ญแแ ฎแซ n base arc
ย
์๋ฒ ์ํคํ ์ฒ ์ดํด๋ฅผ ์ํ ํ๋ก์ธ์ค์ ์ฐ๋ ๋
์๋ฒ ์ํคํ ์ฒ ์ดํด๋ฅผ ์ํ ํ๋ก์ธ์ค์ ์ฐ๋ ๋
ย
CUDA๋ฅผ ๊ฒ์ ํ๋ก์ ํธ์ ์ ์ฉํ๊ธฐ
CUDA๋ฅผ ๊ฒ์ ํ๋ก์ ํธ์ ์ ์ฉํ๊ธฐ
ย
Cache governance
Cache governance
ย
The nosql echossytem
The nosql echossytem
ย
[์ค๋งํธ์คํฐ๋]๋ชจ๋ฐ์ผ ์ ํ๋ฆฌ์ผ์ด์ ์๋น์ค์์์ ๋ก๊ทธ ์์ง๊ณผ ๋ถ์
[์ค๋งํธ์คํฐ๋]๋ชจ๋ฐ์ผ ์ ํ๋ฆฌ์ผ์ด์ ์๋น์ค์์์ ๋ก๊ทธ ์์ง๊ณผ ๋ถ์
ย
MySQL/MariaDB Proxy Software Test
MySQL/MariaDB Proxy Software Test
ย
Osc4.x installation v1-upload
Osc4.x installation v1-upload
ย
GPGPU(CUDA)๋ฅผ ์ด์ฉํ MMOG ์บ๋ฆญํฐ ์ถฉ๋์ฒ๋ฆฌ
GPGPU(CUDA)๋ฅผ ์ด์ฉํ MMOG ์บ๋ฆญํฐ ์ถฉ๋์ฒ๋ฆฌ
ย
Advanced nGrinder 2nd Edition
Advanced nGrinder 2nd Edition
ย
NoSQL
NoSQL
ย
More from DaeMyung Kang
count min sketch
Count min sketch
Count min sketch
DaeMyung Kang
ย
Redis, Redis Failover, Consistent Hashing, Commands, Failures
Redis
Redis
DaeMyung Kang
ย
Ansible basic
Ansible
Ansible
DaeMyung Kang
ย
GUID
Why GUID is needed
Why GUID is needed
DaeMyung Kang
ย
Redis, Redis Failover, Redis Monitoring
How to use redis well
How to use redis well
DaeMyung Kang
ย
The easiest Consistent Hashing
The easiest consistent hashing
The easiest consistent hashing
DaeMyung Kang
ย
Cache key Naming
How to name a cache key
How to name a cache key
DaeMyung Kang
ย
filebeat, logstash
Integration between Filebeat and logstash
Integration between Filebeat and logstash
DaeMyung Kang
ย
Remove SPOF, Using coordinator, Using object storage, circuit breaker, blue/green, canary, feature flag
How to build massive service for advance
How to build massive service for advance
DaeMyung Kang
ย
Data Engineering 101
Data Engineering 101
Data Engineering 101
DaeMyung Kang
ย
How to become better engineer
How To Become Better Engineer
How To Become Better Engineer
DaeMyung Kang
ย
Kafka Timestamp offset Final
Kafka timestamp offset_final
Kafka timestamp offset_final
DaeMyung Kang
ย
Kafka Timestamp offset
Kafka timestamp offset
Kafka timestamp offset
DaeMyung Kang
ย
Data Pipeline Data Lake
Data pipeline and data lake
Data pipeline and data lake
DaeMyung Kang
ย
Redis ACL RCP1
Redis acl
Redis acl
DaeMyung Kang
ย
๊ฐ์ฒด์งํฅ ๊ธฐ์ด, ์๋ฐ
Coffee store
Coffee store
DaeMyung Kang
ย
How to build scalable webservice
Scalable webservice
Scalable webservice
DaeMyung Kang
ย
Number System 10 -> 2, 2 -> 16
Number system
Number system
DaeMyung Kang
ย
Internet Scale Service Arichitecture
Internet Scale Service Arichitecture
Internet Scale Service Arichitecture
DaeMyung Kang
ย
BloomFilter, space-efficient probabilistic data structure
Bloomfilter
Bloomfilter
DaeMyung Kang
ย
More from DaeMyung Kang
(20)
Count min sketch
Count min sketch
ย
Redis
Redis
ย
Ansible
Ansible
ย
Why GUID is needed
Why GUID is needed
ย
How to use redis well
How to use redis well
ย
The easiest consistent hashing
The easiest consistent hashing
ย
How to name a cache key
How to name a cache key
ย
Integration between Filebeat and logstash
Integration between Filebeat and logstash
ย
How to build massive service for advance
How to build massive service for advance
ย
Data Engineering 101
Data Engineering 101
ย
How To Become Better Engineer
How To Become Better Engineer
ย
Kafka timestamp offset_final
Kafka timestamp offset_final
ย
Kafka timestamp offset
Kafka timestamp offset
ย
Data pipeline and data lake
Data pipeline and data lake
ย
Redis acl
Redis acl
ย
Coffee store
Coffee store
ย
Scalable webservice
Scalable webservice
ย
Number system
Number system
ย
Internet Scale Service Arichitecture
Internet Scale Service Arichitecture
ย
Bloomfilter
Bloomfilter
ย
Redis trouble shooting
1.
์ธํ๋ผ ์ด์์๋ฅผ ์ํ Redis
ํธ๋ฌ๋ธ ์ํ Clark.kang charsyam@naver.com
2.
๋ชฉ์ฐจ โข Redis ํน์ฑ
์๊ฐ โ Single Threaded โข ์ฅ์ ์ฌ๋ก ๋ฐ ๋์๋ฒ โ G ์๋น์ค ์ฅ์ ์ฌ๋ก โ P ์๋น์ค ์ฅ์ ์ฌ๋ก โ S ์๋น์ค ์ฅ์ ์ฌ๋ก โข Redis ๋ณด์ ์ด์
3.
Single Threaded #1 Client
#1 Client #2 โฆโฆ Client #N Redis Event Loop I/O Multiplexing Process Command Packet #1 Packet #2
4.
Single Threaded #2 โข
ํ ๋ฒ์ ํ๋์ ๋ช ๋ น๋ง ์ฒ๋ฆฌ ๋จ. โข ๊ธด ์์ ์ ํธ์ถํ๋ฉด Redis ์ ๋ค๋ฅธ ๋ช ๋ น๋ค์ ์ ๋ถ Pending ๋จ โ Keys, flushall, flushdb, lua script, MULTI/EXEC โข ๋ด๋ถ์ ์ผ๋ก ๋ค๋ฅธ ์ค๋ ๋๊ฐ ์๊ธด ํ์ง๋ง fsync ์ฉ์.
5.
์ผ๋ง๋ ๋๋ฆฐ๊ฐ? Command Item
Count Time flashall 1,000,000 1000ms(1 second)
6.
์ถ์ฒ ๋ฒ์ #1 โข
๊ฐ๋ฅํ ์ต์ ๋ฒ์ โ 3.0.x ๋ ๊ด์ฐฎ์. โ ์๋๋ฉด 2.8.x ํ๋ฐ๋(์ต์ 2.8.13 ์ดํ๊ฐ ์ ๋ฆฌ) โข ๋ฒ์ ๋ง๋ค ์ฝ๊ฐ์ฉ์ ์ฐจ์ด๊ฐ ์์.(์ต์ ๋ฒ์ ์ด ์ ค ์ข์) โ 2.6.x ์์๋ config set client-output-buffer-limit ์ redis- cli ์์๋ง ๊ฐ๋ฅ โ 2.8.20์์๋ config set client-output-buffer-limit ์์ 1GB ์ด๋ฐ ํํ์ด ์๋จ
7.
๋ฉ๋ชจ๋ฆฌ ํํธํ #1 โข
Jemalloc ์ด 3.6.0 ์ด์ ๋ฒ์
8.
๋ฉ๋ชจ๋ฆฌ ํํธํ #2 โข
Jemalloc ์ด 3.6.0 ์ดํ๋ฒ์ โ ๊ทธ๋๋ ์ฃผ์๊ฐ ํ์.
9.
์ถ์ฒ ํด๋ผ์ด์ธํธ(๋งค๋์ง๋จผํธ์ฉ) โข Redis-cli
๋ฅผ ์ฐ์ธ์. โ ๊ถ์ฅ ์ถ์ฒ โข telnet ๋ ๊ฐ๋ฅ โ Inline command ๋ผ๊ณ ํด์ ํ์ค์ง๋ฆฌ๋ ๋ ๋์ค๊ฐ ์์์ ํด์ โ Inline command๋ twemproxy์์๋ ์ง์๋์ง ์์ โ ๊ตฌ๋ฒ์ ์ ๊ฒฝ์ฐ๋ ์๋จน๋ ์ปค๋งจ๋๊ฐ ์์ ์๋ ์์
10.
์๋น์ค ํ์ ์ ๊ณต์
์ ์ ์ฌํญ โข ์บ์์ธ์ง ์ ์ฅ์ฉ์ธ์ง์ ๋ํด์ ํ์ธ์ด ํ์ โ ์บ์์ฉ์ด๋ผ๋ฉด, SAVE ์ต์ ์ ๋ฌด์กฐ๊ฑด ๋๊ณ ์ฃผ์ด์ผ ํฉ๋๋ค. โ ์ ์ฅ์ฉ์ผ๋๋, ํด๋น ์ต์ ์ ๋ํ ์กฐ์ ์ด ํ์ํฉ๋๋ค. โข ํ๋์ ๋ ๋์ค ์ธ์คํด์ค๊ฐ ์ ์ฒด ๋ฉ๋ชจ๋ฆฌ๋ฅผ ์ฌ์ฉํ๋ ๊ฒ ๋ณด๋ค๋ ์ ์ ํ ์ฌ๋ฌ ๊ฐ์ ์ธ์คํด์ค๋ฅผ ๋์ฐ๋๋ก ๊ฐ์ด๋ํฉ๋๋ค. โ 16G ๋ฉด 11G, 12G๋ฅผ ์ฐ๋๊ฒ๋ณด๋ค๋ 6G * 2 ๋ก ์ฌ์ฉํ๋๊ฒ ๋ ์ข์. โข ๊ธฐ๋ณธ์ ์ผ๋ก maxmemory๋ฅผ ์ค์ ํด ๋๋ ๊ฒ์ด ์ ๋ฆฌ.
11.
์๋น์ค ํ์ ์ ๊ณต์
์ ์ ์ฌํญ โข CPU 4 core 32G Memory Mem: 26G Mem: 8G Mem: 8G Mem: 8G
12.
Replication
13.
Redis Replication โข Redis๋
Single Thread โ Replication์ ์ํด์ fork๋ฅผ ํ๊ฒ ๋จ โข Chained replication์ ์ง์ โ Multi-Master ๋๋ ์๋ฐฉํฅ Replication์ ์ง์ ์ํจ โข Replication์ ํด์ผํ ์ํฉ์ด ๋ฐ์ํ ์ ์์ผ๋ฏ๋ก ๋ฉ๋ชจ๋ฆฌ์ ์ ๊ฒฝ์จ ์ผ ํจ.
14.
Replication โขSupport Chained Replication Master
1st Slave 2nd Slave 1st slave is master of 2nd slave
15.
Replication Master Slave replicationCron Health check
16.
Replication Master Slave replicationCron Health check
17.
Replication Master Slave replicationCron When master
reruns, Resync with Master
18.
Mistake: Replication Master Slave replicationCron Slave
will has no data after resyncing If master has no data.
19.
Persistent
20.
Persistent ๋ผ๊ณ ์ฐ๊ณ Hell
Gate๋ผ๊ณ ์ฝ์ผ์ธ์.
21.
RDB/AOF โข RDB/AOF๋ ์์ ํ
๋ณ๊ฐ์ ๊ธฐ๋ฅ(์๋ก ์ฐ๊ด์ฑ์ด ์์) โข RDB โ ํ์ฌ ํ๋ก์ธ์ค๋ฅผ Fork ํด์ ํ์ฌ์ ๋ฉ๋ชจ๋ฆฌ ์ํ๋ฅผ ๋์คํฌ๋ก ๋คํํจ. โ COW์ ์ํด์ ์ฝ๊ธฐ๊ฐ ๋ง์ ๋๋ ์ถ๊ฐ๋ก ์ฌ์ฉํ๋ ๋ฉ๋ชจ๋ฆฌ๊ฐ ์ ์ง๋ง, write ๊ฐ ๋ง์ผ๋ฉด ์ต๋ ๋ฉ๋ชจ๋ฆฌ 2๋ฐฐ๊น์ง ์ฌ์ฉ ๊ฐ๋ฅ. โข AOF โ ํ์ฌ ๋ ๋์ค์ write ์ปค๋งจ๋๋ฅผ ๋์คํฌ์ ์ ์ฅํจ. โ Disk Sync ์ต์ ์ ๋ฐ๋ผ์ ์ฑ๋ฅ์ ์ํฅ์ ์ค(default: everysec) โ AOF rewrite๊ฐ ์ผ์ด๋๊ธฐ ์ ๊น์ง๋ ๊ทธ๋๋ ๋ถํ๊ฐ ์ ์.
22.
ํผํด๊ฐ๊ณ ์ถ์ด๋ ๋ฌด์กฐ๊ฑด ๊ฒช๊ฒ
๋ฉ๋๋ค. (์ฅ๋น ์ด์ ์ด์โฆ)
23.
Migration
24.
Migration ๋ฐฉ๋ฒ 1. ํ์ฌ
์ฅ๋น์ ๋ํด์ ๋์ฒด ์ฅ๋น๋ฅผ ์ค๋น 2. ๋์ฒด ์ฅ๋น์์ slave of ํ์ฌ์ฅ๋น IP ํ์ฌ์ฅ๋น port 3. Replication ์๋ฃ๋ฅผ ๊ธฐ๋ค๋ฆผ 1. ์ด๋ ๋ฌด์กฐ๊ฑด fork ๊ฐ ๋ฐ์(๋ฉ๋ชจ๋ฆฌ ๋๋ฐฐ ์ด์ ๋ฐ์ ๊ฐ๋ฅ) 4. Replication์ด ๋๋๋ฉด Slave ์ฅ๋น์์ ์ฐ๊ธฐ ํ์ฑํ 1. Config set slave-read-only no 5. ํด๋ผ์ด์ธํธ๋ค์ด ๋์ฒด ์ฅ๋น๋ฅผ ๋ฐ๋ผ๋ณด๋๋ก ์ค์ 6. ๋์ฒด ์ฅ๋น์์ ๋ค์ ๋ช ๋ น ์ํ 1. slaveof no one
25.
Partial Sync
26.
Redis Replication ๊ณผ์ 1.
Slave ๊ฐ Master๋ก Sync ๋ช ๋ น์ ๋ณด๋ 2. Master๋ Fork ํ์ฌ RDB ์์ฑ 3. RDB ์์ฑ์ด ๋๋๋ฉด Master๋ RDB๋ฅผ Slave๋ก ์ ์ก 4. ํด๋น ์๊ฐ๋์ master๊ฐ ๋ฐ๋ ๋ช ๋ น์ด๋ memory์ ์ ์ฅ 5. RDB ์ ์ก์ด ๋๋๋ฉด Slave ๊ฐ ํด๋น RDB Load 6. Slave์ RDB ๋ก๋๊ฐ ๋๋๋ฉด Master๊ฐ ๋ฉ๋ชจ๋ฆฌ์ ์์ธ ๋ฐ์ดํฐ ์ ์ก
27.
Redis Repliaction์ ๋ฌธ์ ์ โข
Redis์ ๊ฒฝ์ฐ ๋ง์คํฐ ์ฌ๋ ์ด๋ธ ์ํฉ์์ Master๋ ์ฐ๊ฒฐ์ด ์ ์ ๋ผ๋ ๋์ด์ง๋ฉด, Slave๋ Master์ ๋ชจ๋ ๋ด์ฉ์ Full Sync ๋ฐ๋๋ค. โ Disk IO๋ ๋น์ โข Slave๋ Master ์ํ๋ฅผ ๊ณ์ ํด๋ง์ผ๋ก ์ฒดํฌํจ.
28.
Partial Sync โข Master์์
์ ์์ด ๋๊ธฐ๊ณ ๋ค์ ์ฐ๊ฒฐ๋ ๋, ๊ธฐ์กด๊ณผ ๋์ผํ master ๋ผ๋ฉด, ๊ทธ๋ฆฌ๊ณ master์ memory buffer ๋ฒ์ ๋ด๋ก ๋ฐ์ดํฐ๊ฐ ๋ณ ๊ฒฝ๋์๋ค๋ฉด ํด๋น ๋ฐ์ดํฐ๋ง ์ ์ก ๋ฐ์์ full sync๋ฅผ ํผํจ. โ ๋ฐ์ดํฐ ๋ณ๊ฒฝ์ด ์์ด๋, ์๊ฐ์ด ์ง๋๋ฉด PING ๋ฑ์ ๋ฉ์์ง๋ก ์ธํด์ ๋ฒํผ๊ฐ ์ฐฐ ์ ์๋ค๋๊ฒ ํจ์ . โข ๋ค๋ง ๋ง์คํฐ๊ฐ ๋ฐ๋๋ ๊ฒฝ์ฐ์๋ Partial Sync๋ ์๋จ โ ์ฆ, ์ฅ์ ๋ก ๋ง์คํฐ ์ฅ๋น ์์น๊ฐ ๋ฐ๋๋ฉดโฆ slave๋ ๋ฌด์กฐ๊ฑด full sync
29.
์ฅ์ ์ฌ๋ก
30.
T๋ชจ ์๋น์ค โข ์ํฉ โ
์บ์๋ก๋ง ์ฌ์ฉ โข Redis ์ค์ โ stop-writes-on-bgsave-error yes โข ์ฅ์ ํ์ โ RDB ์์ฑ ์คํจ ํ, write ๊ฐ ๊ณ์ ์คํจ. โ ์ฝ๊ธฐ๋ง ๊ฐ๋ฅ โข ์ฅ์ ์ฒ๋ฆฌ โ stop-writes-on-bgsave-error no ๋ก ์ค์ โ ํด๋น ๊ฐ์ด yes ๋ฉด RDB ์์ฑ์ ์คํจํ๋ฉด write ๋ช ๋ น์ ๋ชจ๋ ์๋ฌ๋.
31.
G๋ชจ ์๋น์ค โข ์ํฉ โ
์บ์๋ก๋ง ์ฌ์ฉ โข Redis ์ค์ โ SAVE ๊ฐ ๋ํดํธ ์ต์ โ SAVE 900 1 โ SAVE 300 10 โ SAVE 60 10000 โข ์ฅ์ ํ์ โ ์งง์ ์๊ฐ์ ๊ณ์ RDB๊ฐ ์์ฑ๋๋ฉด์ Disk IO๊ฐ ๋ง์ด ๋ฐ์ โข ์ฅ์ ์ฒ๋ฆฌ โ SAVE ๊ฐ์ ์ ๊ฑฐ(config set SAVE โโ)
32.
S๋ชจ ์๋น์ค โข ์ํฉ โ
์บ์์ ์ผ๋ถ ๋ณต๊ตฌ๊ฐ ํ์ํ ๋ฐ์ดํฐ. โ ๋ฌผ๋ฆฌ๋ฉ๋ชจ๋ฆฌ 32G ์์ 28G ์ฌ์ฉ โ ๋์คํฌ ์ฅ์ ๋ ์๋ ์ํฉ โข ์ฅ์ ํ์ โ Swap memory ์ฌ์ฉ์ผ๋ก ์๋ต์๊ฐ์ด ๋์ด๋จ. โ RDB ์์ฑ์ ์์ฒญ๋ ์๊ฐ์ด ๊ฑธ๋ฆผ(28G ๋ฉ๋ชจ๋ฆฌ dump์ 7์๊ฐ ์ด์ ๊ฑธ๋ฆผ) โข ์ฅ์ ์ฒ๋ฆฌ โ ๊ทธ๋ฅ ํฌ๊ธฐ(T.T)
33.
Copy on Write โข
๋ถ๋ชจ ํ๋ก์ธ์ค์ ๋ฉ๋ชจ๋ฆฌ Page ์ Write ๊ฐ ๋ฐ์ํ ๋ ๋ง๋ค, ํด๋น ๋ฉ๋ชจ๋ฆฌ Page๋ฅผ ๋ณต์ฌํ๊ฒ ๋๋ค. โข ์ฝ๊ธฐ์ ๊ฒฝ์ฐ์๋ ๋ฉ๋ชจ๋ฆฌ๋ฅผ ์ถ๊ฐ๋ก ๋ณต์ฌํ ํ์๊ฐ ์๋ค.
34.
Copy on Write
#1 Process - Parent Physical Memory Page A Page B Page C
35.
Copy on Write
#2 โ Fork(), ๋ฉ๋ชจ๋ฆฌ ์์ ์ Process - Parent Physical Memory Page A Page B Page C Process - Child
36.
Copy on Write
#2 โ Fork(), ๋ฉ๋ชจ๋ฆฌ ์์ ํ Process - Parent Physical Memory Page A Page B Page C Copy of Page C Process - Child
37.
Copy on Write
๊ฒฐ๋ก โข ์ต์ ์ ๊ฒฝ์ฐ์๋ ๋ฉ๋ชจ๋ฆฌ๋ฅผ ๋ ๋ฐฐ ๊น์ง ์ฌ์ฉํ ์ ์๋ค. โ ๋ชจ๋ Page ์ write๊ฐ ๋ฐ์ํ์ ๊ฒฝ์ฐ โข ๋ฉ๋ชจ๋ฆฌ๊ฐ ๋ถ์กฑํด์ ์ค์ ์์ญ์ ์ฌ์ฉํ๋ฉด ์์ฒญ๋๊ฒ ๋๋ ค์ง๊ฒ ๋๋ค.
38.
P๋ชจ ์๋น์ค(#1) โข ์ํฉ โ
AOF ์ฌ์ฉ โ 8๊ฐ์ ์ธ์คํด์ค๊ฐ 256G ์ฅ๋น์์ ๋์ โข ์ฅ์ ํ์ โ 8๊ฐ์ AOF๊ฐ ์ ๋ถ AOF Rewrite๊ฐ ๋ฐ์ โ ๊ณผ๋ํ ๋ฉ๋ชจ๋ฆฌ ์ฌ์ฉ๊ณผ Disk ์ฌ์ฉ์ผ๋ก ์ธํ ์ฅ์ โข ์ฅ์ ์ฒ๋ฆฌ โ AOF Rewrite๋ฅผ ์ค์ง
39.
P๋ชจ ์๋น์ค(#2) โ
์ฅ์ ๋ ์๋ โข ์ํฉ โ ๋คํธ์ Master/Slave Replication์ด ๋ชจ๋ ๋์ด์ง. โข ์ฅ์ ๊ฐ๋ฅ์ฑ โ ๋คํธ์์ด ๋ณต๊ตฌ๋๋ฉด ๋ชจ๋ ์๋ฒ๊ฐ Master/Slave ์ํ๋ฅผ ๋ณต๊ตฌํ๊ฒ ๋จ โ ๋ชจ๋ Master๊ฐ Fork ํ์ฌ ๋ฉ๋ชจ๋ฆฌ์ Disk๋ฅผ ์ฌ์ฉํ ๊ฐ๋ฅ์ฑ(์ ๋ฉด์ฅ์ ) โข ๋์ฒ โ Slaveof no one ๋ช ๋ น์ ์ด์ฉํ์ฌ ๋ชจ๋ M/S ๊ด๊ณ๋ฅผ ์ ๊ฑฐ โ ์์ฐจ์ ์ผ๋ก ํ๋์ฉ Replication์ ์ฌ ์ฐ๊ฒฐํจ.
40.
Replication ์คํจ โข ์ํฉ โ
๋ฉ๋ชจ๋ฆฌ ์ฌ์ฉ๋์ด 20G ์ ๋ โ ์ด๋์ ๋์ Write๊ฐ ๋ฐ์ํ๋ ๊ฒฝ์ฐ โข ์ฅ์ ํ์ โ Master/Slave Replication ์ฐ๊ฒฐ์ด ๊ณ์ ์คํจํจ โข ์ฅ์ ์ฒ๋ฆฌ #1 โ Redis๋ Replication ์ค์ fork ํ์ ๋ค์ด์ค๋ ๋ฐ์ดํฐ๋ฅผ ์ ์ฅํจ. โ ํด๋น ๊ฐ์ด ํน์ ๋ฉ๋ชจ๋ฆฌ ์ฌ์ด์ฆ๋ฅผ ๋์ด๊ฐ๋ฉด ์ ์์ด ๋์ด์ง โ client-output-buffer-limit slave 256mb 64mb 60 โ 20G ์ด์์ด๋ฉด ์ด ๊ฐ์ 512mb ์ด์์ผ๋ก ์ก์๋๋ ๊ฒ์ด ์ข์(๋ฉ๋ชจ๋ฆฌ ์ฌ์ด์ฆ์ ๋น๋ก) โข ๋ฐ์ดํฐ๊ฐ ๊ต์ฅํ ๋ง์ผ๋ฉด ๋ฏธ๋ฆฌ ์ฌ๋ ์ด๋ธ์ ๋ฐ์ดํฐ๋ฅผ ์ง์์ฃผ๋ ๊ฒ์ด ์ ๋ฆฌํจ. โ Loading ์ง์ ์, ๋ฐ์ดํฐ๋ฅผ ์ง์ฐ๋ฏ๋ก, ์ง์ฐ๋๋ฐ๋ ์๊ฐ์ด ๋ง์ด ๊ฑธ๋ฆด ์ ์์.
41.
THP Disable โข ์ํฉ โ
THP๊ฐ enable โ ๋ฉ๋ชจ๋ฆฌ ์ฌ์ฉ๋์ด ๋ง์ โข ์ฅ์ ํ์ โ Fork ์์ ๋ฉ๋ชจ๋ฆฌ ์ฌ์ฉ๋์ ๋ฐ๋ผ์ fork ์๊ฐ์ด ์ค๋ ๊ฑธ๋ฆผ โข ์ฅ์ ์ฒ๋ฆฌ โ THP Disable
42.
Diskless Replication โข RDB
๋คํ๋ฅผ ๋์คํฌ์ ์ ์ฅํ๋ฉด disk io๊ฐ ๋ฐ์ํ๋ฏ๋ก, ๋์คํฌ์ ์ฐ์ง ์๊ณ , ๋ฐ๋ก Slave๋ก ์ ์ก โ AWS์ EBS๋ ๋๋ฆฌ๋ค. โ ์ผ๋ฐ ์๋ฒ์ ๋์คํฌ๋ ๋ง์ด ์ฐ๋ฉด ๋๋ฆฌ๋ค. โข ๋ฉ๋ชจ๋ฆฌ ๋๋ฐฐ ์ด์๋ฅผ ํด๊ฒฐํ์ง๋ ๋ชปํ์ง๋ง, Disk IO๋ ์ค์ผ ์ ์์.
43.
Redis ๋ชจ๋ํฐ๋ง
44.
Redis Monitoring ํญ๋ชฉ ํญ๋ชฉ
์์ง ์์น(Host or Redis(info)) CPU Usage, Load Host Network inbound, outbound Host ํ์ฌ ํด๋ผ์ด์ธํธ ๊ฐ์, max client ์ค์ Redis ํค ๊ฐ์, ๋ช ๋ น์ด ์ฒ๋ฆฌ ์ Redis ๋ฉ๋ชจ๋ฆฌ ์ฌ์ฉ๋, RSS Redis Disk ์ฌ์ฉ๋, io Host Expired keys, Evicted keys Redis
45.
Redis ๋ณด์ ์ด์
46.
Redis ๋ณด์์ ๊ต์ฅํ
์ทจ์ฝํ๋ค. โข ACL โ ๊ธฐ๋ฅ ์์ โ ๋ด๋ถ๋ง์ด ์๋ ๊ฒฝ์ฐ ์ ๋๋ก ํด๋น ํฌํธ๊ฐ ์ด๋ ค์์ผ๋ฉด ์๋จ โ Root๋ก ๋์ฐ๋ ๊ฒฝ์ฐ๊ฐ ๋ง์๋ฐ, ๊ฐ๋ฅํ ํน์ user๋ก ๋์์ผ ํจ โข ๋ฐ์ดํฐ โ ํ๋ฌธ์ผ๋ก ๋ชจ๋ ํ์ธ ๊ฐ๋ฅ.
47.
Redis ํดํน ์ฌ๋ก โข
ํด๋น ํฌํธ๊ฐ ์ธ๋ถ์ ์ด๋ ค์์(์์ธํ ์ฝ๋๋ ์จ๊น๋๋ค.) โ Config set dir โ/root/.sshโ โ Config set dbfilename โauthorized_keysโ โ Save โข ์ด์ ๋ฃจํธ๋ก ์ ๊ทผ ๊ฐ๋ฅ.
48.
๊ฐ์ฌํฉ๋๋ค.
Download now