[B! Kafka] krrrrã®ãƒ–ãƒƒã‚¯ãƒžãƒ¼ã‚¯

krrrr id:krrrr

Kafkaã«é–¢ã™ã‚‹krrrrã®ãƒ–ãƒƒã‚¯ãƒžãƒ¼ã‚¯ (22)

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ãƒžã‚¤ã‚¯ãƒã‚µãƒ¼ãƒ“ã‚¹ã¨ãƒ¡ãƒƒã‚»ãƒ¼ã‚¸ãƒ³ã‚°ã®ãªãœ [ç–‘å•ç·¨] - èµ¤å¸½ã‚¨ãƒ³ã‚¸ãƒ‹ã‚¢ãƒ–ãƒã‚°
ã€Œãƒžã‚¤ã‚¯ãƒã‚µãƒ¼ãƒ“ã‚¹ã¨ãƒ¡ãƒƒã‚»ãƒ¼ã‚¸ãƒ³ã‚°ã®ãªãœ [æ¦‚è¦ç·¨]ã€ã¯ã“ã¡ã‚‰ã§ã™ã€‚ ãƒ¬ãƒƒãƒ‰ãƒãƒƒãƒˆã§ã‚¤ãƒ³ãƒ†ã‚°ãƒ¬ãƒ¼ã‚·ãƒ§ãƒ³ã®ãŸã‚ã®ãƒŸãƒ‰ãƒ«ã‚¦ã‚§ã‚¢è£½å“ã®ãƒ†ã‚¯ãƒ‹ã‚«ãƒ«ã‚µãƒãƒ¼ãƒˆã‚’æ‹…å½“ã—ã¦ã„ã‚‹å±±ä¸‹ã§ã™ã€‚ æ¦‚è¦ç·¨ã§ã¯ãƒ¡ãƒƒã‚»ãƒ¼ã‚¸ãƒ³ã‚°ã®è‰¯ã„é¢ã°ã‹ã‚Šã«ç„¦ç‚¹ã‚’å½“ã¦ã¦ãã¾ã—ãŸãŒã€ä»Šå›žã®ç–‘å•ç·¨ã§ã¯ãƒ¡ãƒƒã‚»ãƒ¼ã‚¸ãƒ³ã‚°ã‚’æ¤œè¨Žã—å§‹ã‚ãŸã¨ãã«ç–‘å•ã«æ€ã£ãŸã‚Šå›°ã‚ŠãŒã¡ãªã“ã¨ã‚’èª¬æ˜Žã—ãŸã„ã¨æ€ã„ã¾ã™ã€‚æ¦‚è¦ç·¨ã¨ã¯ç•°ãªã‚Šã€ç´°ã‹ãªæŠ€è¡“çš„å†…å®¹ã‚‚å«ã¾ã‚Œã¾ã™ã®ã§ã€ãã®æ™‚ã€…ã§å¿…è¦ãªéƒ¨åˆ†ã‚„èˆˆå‘³ã‚ã‚‹éƒ¨åˆ†ã ã‘èªã‚“ã§ã„ãŸã ã‘ã‚Œã°ã¨æ€ã„ã¾ã™ã€‚ (ã¨ã“ã‚ã§ã€å½“åˆã¯å‰å›žã‚’å‰ç·¨ã€ãã—ã¦ä»Šå›žã‚’å¾Œç·¨ã«ã—ã¦çµ‚ã‚ã‚‰ã›ã‚ˆã†ã¨æ€ã£ã¦ã„ãŸã®ã§ã™ãŒã€ä»Šå›žã‚‚ã‚ã¾ã‚Šã«é•·ããªã£ã¦ã—ã¾ã£ãŸã®ã§ã€æ§‹æˆã‚’å¤‰ãˆãŸã®ã§ã—ãŸã€‚ ã“ã®ãŸã‚å½“åˆã®å‰ç·¨ã¯æ¦‚è¦ç·¨ã¨åå‰ã‚’å¤‰æ›´ã—ã¦ã„ã¾ã™ã€‚) ã§ã¯ã¾ãšä¸»ã«ç–‘å•ã¨ã•ã‚Œã‚‹ã“ã¨ã‚’ç¢ºèªã—ã¦ã€ãã®å¾Œã«å¯¾å‡¦æ³•ã‚’è¦‹ã¦ã„ãã¾ã—ã‚‡ã†ã€‚ ãƒ¡ãƒƒã‚»ãƒ¼ã‚¸ãƒ³ã‚°ã‚’åˆ©ç”¨ã™ã‚‹ã“ã¨ã«ã‚ˆã‚‹ä¸»ãªç–‘å• å¯¾å‡¦æ–¹æ³• Q1:
krrrr 2020/03/09
Transactional outbox ãƒ‘ã‚¿ãƒ¼ãƒ³

microservices

ãƒžã‚¤ã‚¯ãƒã‚µãƒ¼ãƒ“ã‚¹

Apache Kafka

Messaging

kafka
ãƒªãƒ³ã‚¯
Apache Kafkaã§ç™ºç”Ÿã—ãŸåŽŸå› ä¸æ˜Žã®ãƒ‘ãƒ•ã‚©ãƒ¼ãƒžãƒ³ã‚¹ä½Žä¸‹ã¨ã€ãã‚Œã‚’è§£æ±ºã™ã‚‹ãŸã‚ã«ã‚„ã£ãŸã“ã¨
2019å¹´7æœˆ17æ—¥ã€kafka.apache.jpãŒä¸»å‚¬ã™ã‚‹ã‚¤ãƒ™ãƒ³ãƒˆã€ŒApache Kafka Meetup Japan #7ã€ãŒLINEæ ªå¼ä¼šç¤¾ã«ã¦é–‹å‚¬ã•ã‚Œã¾ã—ãŸã€‚åˆ†æ•£ã‚¹ãƒˆãƒªãƒ¼ãƒŸãƒ³ã‚°ãƒ—ãƒ©ãƒƒãƒˆãƒ•ã‚©ãƒ¼ãƒ ã€ŒApache Kafkaã€ã«é–¢ã™ã‚‹ãƒŠãƒ¬ãƒƒã‚¸ã‚„æœ€æ–°æƒ…å ±ã‚’å…±æœ‰ã™ã‚‹æœ¬ã‚¤ãƒ™ãƒ³ãƒˆã€‚ä»Šå›žã¯4äººã®ã‚¨ãƒ³ã‚¸ãƒ‹ã‚¢ãŒã€è‡ªèº«ã‚„è‡ªç¤¾ã«ãŠã‘ã‚‹çŸ¥è¦‹ã‚’èªžã‚Šã¾ã—ãŸã€‚ãƒ—ãƒ¬ã‚¼ãƒ³ãƒ†ãƒ¼ã‚·ãƒ§ãƒ³ã€ŒKafka Broker performance degradation by mysterious JVM pauseã€ã«ç™»å£‡ã—ãŸã®ã¯ã€LINEæ ªå¼ä¼šç¤¾ã®æ²³æ‘å‹‡äººæ°ã€‚ã‚ã‚‹æ—¥Kafkaã«èµ·ã“ã£ãŸçªç„¶ã®ãƒ‘ãƒ•ã‚©ãƒ¼ãƒžãƒ³ã‚¹ä½Žä¸‹ã¨ãã®åŽŸå› ã«ã¤ã„ã¦ã€è§£æ±ºã¾ã§ã®è»Œè·¡ã‚’èªžã‚Šã¾ã—ãŸã€‚è¬›æ¼”è³‡æ–™ã¯ã“ã¡ã‚‰ Apache Kafkaã®ãƒ‘ãƒ•ã‚©ãƒ¼ãƒžãƒ³ã‚¹ä½Žä¸‹ã¨ãã®åŽŸå› æ²³æ‘å‹‡äººæ°ï¼šã‚ˆã‚ã—ããŠé¡˜ã„ã—ã¾ã™ã€‚æœ€åˆã«è‡ªå·±ç´¹ä»‹ã‚’ã—ã¾ã™ã€‚æ²³æ‘å‹‡äººã¨ã„ã„ã¾ã™ã€‚ LINEã§å…¨ç¤¾å‘ã‘ã®
krrrr 2019/08/08
Java

Kafka
ãƒªãƒ³ã‚¯
Kafka Broker performance degradation by mysterious JVM pause
"Kafka Broker performance degradation by mysterious JVM pause" Yuto Kawamura (LINE Corporation) Presentation at Apache Kafka Meetup Japan #7 @LINE (â€¦
krrrr 2019/07/18
https://gist.github.com/kawamuray/a27aaa2d0668d0050537651978735321

Kafka
ãƒªãƒ³ã‚¯
Twitterâ€™s Kafka adoption story
The real-time nature of Twitter poses unique and challenging issues for engineering teams at Twitter. We need to quickly surface breaking news, serve relevant advertisements to users, and address many other real-time use cases. Twitterâ€™s Pub/Sub system provides the infrastructure for Twitter teams to handle this workload. The Messaging team at Twitter has been running an in-house Pub/Sub system, E
krrrr 2018/11/30
twitter

kafka
ãƒªãƒ³ã‚¯
How to Choose the Number of Topics/Partitions in a Kafka Cluster? | Confluent
[Webinar] Donâ€™t Get Left Behind: Unlock the Secrets of Shifting Left | Register Now
krrrr 2017/09/12
Kafka
ãƒªãƒ³ã‚¯
Kafkaã®ãƒ‘ãƒ¼ãƒ†ã‚£ãƒ³ã‚·ãƒ§ãƒ³æ•°ã‚’æ±ºã‚ã‚‹æ™‚ã«è€ƒæ…®ã™ã‚‹ã“ã¨ - The Dabsong Conshirtoe
kafkaã®ãƒ‘ãƒ¼ãƒ†ã‚£ã‚·ãƒ§ãƒ³æ•°ã‚’é¸æŠžã™ã‚‹éš›ã«è€ƒæ…®ã™ã‚‹ç‚¹ã«ã¤ã„ã¦ã€kafkaä½œè€…ã®ãƒ–ãƒã‚°ã«ã¾ã¨ã‚ã‚‰ã‚Œã¦ã„ã¾ã—ãŸã€‚ blog.confluent.io è‡ªèº«ã®ç†è§£ã®ãŸã‚ã«ã€ã¡ã‚‡ã£ã¨ã¾ã¨ã‚ã¦ã¿ã‚ˆã†ã¨æ€ã„ã¾ã™ã€‚ â€»ã“ã®è¨˜äº‹ã¯ã‚ãã¾ã§ã‚‚å€‹äººçš„ãªã¾ã¨ã‚ã§ã™ã®ã§ã€ä½œè€…ã®åŽŸæ–‡ã«ä¸€åº¦ç›®ã‚’é€šã™ã“ã¨ã‚’ã‚ªã‚¹ã‚¹ãƒ¡ã—ã¾ã™ã€‚ æ¦‚è¦ è€ƒæ…®ã™ã‚‹ç‚¹ã¯ã€ã–ã£ãã‚Šã¾ã¨ã‚ã‚‹ã¨ä»¥ä¸‹ãŒã‚ã‚Šã¾ã™ã€‚ å¿…è¦ã¨ã•ã‚Œã‚‹ã‚¹ãƒ«ãƒ¼ãƒ—ãƒƒãƒˆ å°†æ¥çš„ã«å¿…è¦ã¨ãªã‚Šãã†ãªãƒ‘ãƒ¼ãƒ†ã‚£ã‚·ãƒ§ãƒ³æ•° ãƒ•ã‚¡ã‚¤ãƒ«ãƒ‡ã‚£ã‚¹ã‚¯ãƒªãƒ—ã‚¿ã®æ•° ãƒŽãƒ¼ãƒ‰éšœå®³ã‹ã‚‰ã®å¾©æ—§æ™‚é–“ ãƒ¬ãƒ—ãƒªã‚±ãƒ¼ã‚·ãƒ§ãƒ³ã«ä¼´ã†è² è· Producerã®ãƒãƒƒãƒ•ã‚¡ãƒ¡ãƒ¢ãƒª å¿…è¦ã¨ã•ã‚Œã‚‹ã‚¹ãƒ«ãƒ¼ãƒ—ãƒƒãƒˆ ãƒ‘ãƒ¼ãƒ†ã‚£ã‚·ãƒ§ãƒ³ã‚’è¿½åŠ ã™ã‚‹ã“ã¨ã¯ã€ãƒ¡ãƒƒã‚»ãƒ¼ã‚¸ã®producerã¨consumerã®ä¸¦åˆ—åŒ–ã«ã¤ãªãŒã‚‹ã®ã§ã‚¹ãƒ«ãƒ¼ãƒ—ãƒƒãƒˆå‘ä¸ŠãŒæœŸå¾…ã§ãã¾ã™ã€‚ 1ãƒ‘ãƒ¼ãƒ†ã‚£ã‚·ãƒ§ãƒ³ã‚ãŸã‚Šã®producerã®ã‚¹ãƒ«ãƒ¼ãƒ—ãƒƒãƒˆãŒp 1ãƒ‘ãƒ¼ãƒ†ã‚£ã‚·ãƒ§ãƒ³ã‚ãŸã‚Šã®consumerã®ã‚¹
krrrr 2017/09/01
Kafka
ãƒªãƒ³ã‚¯
Kafka meetup JP #3 - Engineering Apache Kafka at LINE
This document summarizes a presentation about engineering Apache Kafka at LINE. Some key points: - LINE uses Apache Kafka as a central data hub to pass data between services, handling over 140 billion messages per day. - Data stored in Kafka includes application logs, data mutations, and task requests. This data is used for tasks like data replication, analytics, and asynchronous processing. - Per
krrrr 2017/07/11
Kafka
ãƒªãƒ³ã‚¯
Exactly-once Support in Apache Kafka
On Thursday we released a new version of Apache Kafka that dramatically strengthens the semantic guarantees it provides. This release came at the tail end of several years of thinking through how to do reliable stream processing in a way that is fast, practical, and correct. The implementation effort itself was on the order of about a year, including an extended period in which about a hundred pag
krrrr 2017/07/04
Kafka
ãƒªãƒ³ã‚¯
Exactly-once Semantics is Possible: Here's How Apache Kafka Does it
Building a GenAI App? Learn Tips in This Webinar! | Register Now
krrrr 2017/07/01
Kafka
ãƒªãƒ³ã‚¯
Java Clientã§å…¥é–€ã™ã‚‹ Apache Kafka #jjug_ccc #ccc_e2
1. Copyrig ht Â© 2017 Yahoo Japan Corporation. All Rig hts Reserved. 2017å¹´5æœˆ20æ—¥ 1 ãƒ¤ãƒ•ãƒ¼æ ªå¼ä¼šç¤¾ ãƒ‡ãƒ¼ã‚¿ï¼†ã‚µã‚¤ã‚¨ãƒ³ã‚¹ã‚½ãƒªãƒ¥ãƒ¼ã‚·ãƒ§ãƒ³çµ±æ‹¬æœ¬éƒ¨ ãƒ‡ãƒ¼ã‚¿ãƒ—ãƒ©ãƒƒãƒˆãƒ•ã‚©ãƒ¼ãƒ æœ¬éƒ¨ é–‹ç™º4éƒ¨ ãƒ‘ã‚¤ãƒ—ãƒ©ã‚¤ãƒ³ æ£®è°· å¤§è¼” Java Clientã§å…¥é–€ã™ã‚‹ Apache Kafka JJUG CCC 2017 Spring 2. Copyrig ht Â© 2017 Yahoo Japan Corporation. All Rig hts Reserved. è‡ªå·±ç´¹ä»‹ â€¢ æ£®è°· å¤§è¼” â€¢ ã‚¤ãƒ³ãƒ•ãƒ©ã‚¨ãƒ³ã‚¸ãƒ‹ã‚¢ â€¢ ã©ã¡ã‚‰ã‹ã¨ã„ã†ã¨BEã‚¢ãƒ—ãƒªã‚±ãƒ¼ã‚·ãƒ§ãƒ³é–‹ç™º â€¢ ãƒ¤ãƒ•ãƒ¼ã®ãƒ‡ãƒ¼ã‚¿ãƒ‘ã‚¤ãƒ—ãƒ©ã‚¤ãƒ³ã‚’ã¤ãã£ãŸã‚Šé‹ç”¨ã—ãŸã‚Š â€¢ å¯¾å¤–ç™ºè¡¨ â€¢ ã‚¹ãƒˆãƒªãƒ¼ãƒ å‡¦ç†ãƒ—ãƒ©ãƒƒãƒˆãƒ•ã‚©ãƒ¼ãƒ ã«ãŠã‘ã‚‹Kafkaå°Žå…¥äº‹ä¾‹ (Apache Kafka Meetup Japan
krrrr 2017/05/23
Kafka
ãƒªãƒ³ã‚¯
Announcing Kafka Connect: Building large-scale low-latency data pipelines | Confluent
Announcing Kafka Connect: Building large-scale low-latency data pipelines For a long time, a substantial portion of data processing that companies did ran as big batch jobs â€” CSV files dumped out of databases, log files collected at the end of the day etc. But businesses operate in real-time and the software they run is catching up. Rather than processing data only at the end of the day, why not r
krrrr 2017/05/10
Kafka
ãƒªãƒ³ã‚¯
Trifecta by ldaniels528
Trifecta is a Command Line Interface (CLI) tool that enables users to quickly and easily inspect, publish and verify messages (or data) in Kafka, Storm and Zookeeper View project on GitHub Trifecta is a Command Line Interface (CLI) tool that enables users to quickly and easily inspect, verify and even query Kafka messages. In addition, Trifecta offers data import/export functions for transf erring
krrrr 2017/05/10
Kafka
ãƒªãƒ³ã‚¯
Apache Kafka æ¦‚è¦ï¼ˆDesignï¼‰å’Œè¨³ã¾ã¨ã‚ - å¤¢ã¨ã‚¬ãƒ©ã‚¯ã‚¿ã®é›†ç©å ´
ã“ã‚“ã«ã¡ã¯ã€‚ã‚ˆã†ã‚„ãï¼‘ãƒšãƒ¼ã‚¸å…¨ã¦ã¾ã¨ã‚çµ‚ã‚ã£ãŸã®ã§ã€ æœ€å¾Œã«ã¾ã¨ã‚æŠ•ç¨¿ã¨ã—ã¦æŠ•ç¨¿ã—ã¦ãŠãã¾ã™ã€‚ å°šã€ãƒšãƒ¼ã‚¸ã¯ä¸‹è¨˜ã§ã™ã€‚ http://kafka.apache.org/07/design.html ï¼ï¼ï¼ï¼ï¼ï¼ï¼ï¼ï¼ï¼ 1.ä½•æ•…Kafkaã¯ä½œã‚‰ã‚ŒãŸã®ã‹ï¼Ÿ å…ƒã€…ã¯LinkedInã®Activity Streamã¨Data Processingã‚’ãƒ‘ã‚¤ãƒ—ãƒ©ã‚¤ãƒ³å¼ã«ç¹‹ããŸã‚ã«é–‹ç™ºã•ã‚ŒãŸãƒ—ãƒãƒ€ã‚¯ãƒˆã€‚ æœ€è¿‘ã¯Tumblrã€DataSiftã¨ã„ã£ãŸä¼æ¥ã§ã‚‚ä½¿ç”¨ã•ã‚Œã¦ã„ã‚‹ã€‚ â†’ã€€SNSã‚„ã€è¤‡æ•°ã®ã‚µãƒ¼ãƒ“ã‚¹ã®æƒ…å ±ã‚’çµ±åˆã™ã‚‹ã‚ˆã†ãªã‚·ã‚¹ãƒ†ãƒ ã§ä½¿ã‚ã‚Œã¦ã„ã‚‹ã‚ˆã†ã§ã™ã€‚ ã“ã“ã§ã„ã†Activity Streamã¨ã¯ Webãƒšãƒ¼ã‚¸ã§é–²è¦§ã€æ¤œç´¢ã€ãƒªãƒ³ã‚¯è¨å®šãªã©ã‚’è¡Œã†æ´»å‹•å…¨èˆ¬ã‚’æŒ‡ã™ã€‚ ã“ã‚Œã‚‰ã®ãƒ‡ãƒ¼ã‚¿ã¯é€šå¸¸ã®ã‚·ã‚¹ãƒ†ãƒ ãªã‚‰ã°ã€ãƒã‚°ãƒ•ã‚¡ã‚¤ãƒ«ã¨ã—ã¦å‡ºåŠ›ã—ã€å¾Œã§åˆ¥é€”è§£æžã«ç”¨ã„ã‚‰ã‚Œã‚‹ã€‚ ã‚‚ã†ä¸€ã¤è¨€è‘‰ã‚’å®šç¾©ã™ã‚‹ã€‚ Operational
krrrr 2016/08/26
Kafka
ãƒªãƒ³ã‚¯
Kafka Inside Keystone Pipeline
In summary, the Keystone pipeline is a unified event publishing, collection, and routing infrastructure for both batch and stream processing. We have two sets of Kafka clusters in Keystone pipeline: Fronting Kafka and Consumer Kafka. Fronting Kafka clusters are responsible for getting the messages from the producers which are virtually every application instance in Netflix. Their roles are data co
krrrr 2016/08/19
Kafka
ãƒªãƒ³ã‚¯
Introducing Kafka Streams: Stream Processing Made Simple | Confluent
[Webinar] Donâ€™t Get Left Behind: Unlock the Secrets of Shifting Left | Register Now
krrrr 2016/08/19
Kafka
ãƒªãƒ³ã‚¯
Stormã¨Kafkaã«ã‚ˆã‚‹ãƒªã‚¢ãƒ«ã‚¿ã‚¤ãƒ ãƒ‡ãƒ¼ã‚¿å‡¦ç†
ãƒ¤ãƒ•ãƒ¼æ ªå¼ä¼šç¤¾ã¯ã€2023å¹´10æœˆ1æ—¥ã«LINEãƒ¤ãƒ•ãƒ¼æ ªå¼ä¼šç¤¾ã«ãªã‚Šã¾ã—ãŸã€‚LINEãƒ¤ãƒ•ãƒ¼æ ªå¼ä¼šç¤¾ã®æ–°ã—ã„ãƒ–ãƒã‚°ã¯ã“ã¡ã‚‰ã§ã™ã€‚LINEãƒ¤ãƒ•ãƒ¼ Tech Blog ã“ã‚“ã«ã¡ã¯ã€éˆ´æœ¨ã‚«ã‚ºã§ã™ã€‚ ç¤¾å†…å‘ã‘ã®ç›£è¦–ã‚·ã‚¹ãƒ†ãƒ æ§‹ç¯‰ã®ãŸã‚ã€Stormã‚„Kafkaã‚’åˆ©ç”¨ã—ã¦é–‹ç™ºã‚’è¡Œã£ã¦ã„ã¾ã—ãŸã€‚ ãã®ã¨ãã®çµŒé¨“ã‚’ã‚‚ã¨ã«ã€ã¾ãšStormã«ã‚ˆã‚‹å®Ÿéš›ã®ã‚·ã‚¹ãƒ†ãƒ ãŒã©ã‚“ãªã‚‚ã®ã‹ã¨ã„ã†ã“ã¨ã‚’ç´¹ä»‹ã—ã€KafkaSpoutã®å‡¦ç†å†…å®¹ã€ã‚«ã‚¹ã‚¿ãƒžã‚¤ã‚ºæ–¹æ³•ã€Stormã®ãƒ¡ãƒƒã‚»ãƒ¼ã‚¸å‡¦ç†ãªã©ã‚’èª¬æ˜Žã—ãŸã„ã¨æ€ã„ã¾ã™ã€‚ èªè€…ã¨ã—ã¦ã¯ã€Stormã‚„Kafkaã«ã¤ã„ã¦èˆˆå‘³ãŒã‚ã‚Šè¨˜äº‹ã‚’èªã‚“ã ã‚Šã—ãŸã“ã¨ãŒã‚ã‚‹ãŒã‚‚ã†å°‘ã—å…·ä½“çš„ãªè©±ã‚’çŸ¥ã‚ŠãŸã„æ–¹ã€ã“ã‚Œã‹ã‚‰é–‹ç™ºäºˆå®šãŒã‚ã‚‹ã‚ˆã†ãªæ–¹ã‚’æƒ³å®šã—ã¦ã„ã¾ã™ã€‚ Stormã¨Kafka Stormã¯ç°¡å˜ã«è¨€ã†ã¨ã€ãƒªã‚¢ãƒ«ã‚¿ã‚¤ãƒ ã«æµã‚Œã¦ãã‚‹å¤§é‡ã®ãƒ‡ãƒ¼ã‚¿ã‚’å‡¦ç†ã™ã‚‹ãŸã‚ã®åˆ†æ•£ã‚·ã‚¹ãƒ†ãƒ ã§ã™ã€‚Twitterã®ãƒ¡ãƒƒã‚»ãƒ¼ã‚¸ã®åˆ†æžãªã©
krrrr 2016/06/18
Kafka

storm
ãƒªãƒ³ã‚¯
Monitoring Kafka w/ Prometheus
This document discusses monitoring Apache Kafka clusters and applications with Prometheus. It provides an overview of the architecture used, including deploying Prometheus servers, Kafka and HBase exporters, and a JSON exporter for YARN applications. Specific exporters are discussed for Kafka brokers using JMX, Kafka clients using the Prometheus Java library, and exposing application metrics via H
krrrr 2016/06/15
prometheus

Kafka
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KafkaMeetup Japan#1 ã§ç™ºè¡¨ã—ã¾ã—ãŸ - ã ã„ãŸã„ã‚ˆãã‚ã‹ã‚‰ãªã„ãƒ–ãƒã‚°
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krrrr 2016/06/01
Kafka
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krrrr 2015/09/07
Kafka
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Putting Apache Kafka To Use: A Practical Guide To Building an Event Streaming Platform (Part 1) | Confluent
[Webinar] Donâ€™t Get Left Behind: Unlock the Secrets of Shifting Left | Register Now
krrrr 2015/02/26
Kafka
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