The basics of fluentd
•
37 likes•29,345 views
This document discusses Fluentd, an open source log collector. It provides a pluggable architecture that allows data to be collected, filtered, and forwarded to various outputs. Fluentd uses JSON format for log messages and MessagePack internally. It is reliable, scalable, and extensible through plugins. Common use cases include log aggregation, monitoring, and analytics across multiple servers and applications.
Report
Share
The basics of fluentd
- 1. Structured logging ! Reliable forwarding ! Pluggable architecturehttp://fluentd.org/
- 2. Agenda > Background > Overview > Product Comparison > Use cases
- 3. Background
- 4. Data Processing Collect Store Process Visualize Data source Reporting Monitoring
- 5. Related Products Store Process Cloudera Horton Works Treasure Data Collect Visualize Tableau Excel R easier & shorter time ???
- 7. Before Fluentd Application ･･･ Server2 Application ･･･ Server3 Application ･･･ Server1 FluentLog High Latency! must wait for a day...
- 8. After Fluentd Application ･･･ Server2 Application ･･･ Server3 Application ･･･ Server1 In streaming! Fluentd Fluentd Fluentd Fluentd Fluentd
- 9. Overview
- 10. > Open sourced log collector written in Ruby > Reliable, scalable and easy to extend > Using rubygems ecosystem for plugins ! ! In short It’s like syslogd, but uses JSON for log messages
- 11. tail insert event buffering 127.0.0.1 - - [11/Dec/2012:07:26:27] "GET / ... 127.0.0.1 - - [11/Dec/2012:07:26:30] "GET / ... 127.0.0.1 - - [11/Dec/2012:07:26:32] "GET / ... 127.0.0.1 - - [11/Dec/2012:07:26:40] "GET / ... 127.0.0.1 - - [11/Dec/2012:07:27:01] "GET / ... ... Fluentd Web Server 2012-02-04 01:33:51 apache.log { "host": "127.0.0.1", "method": "GET", ... } Example (apache to mongo)
- 12. > default second unit > from data source or adding parsed time Event structure(log message) ✓ Time > for message routing ✓ Tag > JSON format > MessagePack internally > non-unstructured ✓ Record
- 13. Pluggable Architecture Buffer Output Input > Forward > HTTP > File tail > dstat > ... > Forward > File > MongoDB > ... > File > Memory Engine Output > rewrite > ... Pluggable Pluggable
- 14. Fluentd # Ruby! Fluent.open(“myapp”)! Fluent.event(“login”, {“user” => 38})! #=> 2012-12-11 07:56:01 myapp.login {“user”:38} > Ruby > Java > Perl > PHP > Python > D > Scala > ... Application Time:Tag:Record Client libraries
- 15. Configuration and operation > No central / master node > HTTP include helps configuration sharing > Operation depends on your environment > Use your deamon management > Use Chef in Treasure Data > Apache like syntax and Ruby DSL
- 16. # receive events via HTTP <source> type http port 8888 </source> ! # read logs from a file <source> type tail path /var/log/httpd.log format apache tag apache.access </source> ! # save access logs to MongoDB <match apache.access> type mongo database apache collection log </match> # save alerts to a file <match alert.**> type file path /var/log/fluent/alerts </match> ! # forward other logs to servers <match **> type forward <server> host 192.168.0.11 weight 20 </server> <server> host 192.168.0.12 weight 60 </server> </match> ! include http://example.com/conf
- 17. Reliability (core + plugin) > Buffering > Use file buffer for persistent data > buffer chunk has ID for idempotent > Retrying > Error handling > transaction, failover, etc on forward plugin > secondary for backup
- 18. Plugins - use rubygems $ fluent-gem search -rd fluent-plugin! ! $ fluent-gem search -rd fluent-mixin! ! $ fluent-gem install fluent-plugin-mongo
- 20. in_tail ✓ read a log file! ✓ read log files in directory! ✓ custom regexp! ✓ custom parser in Ruby FluentdApache access.log > apache > apache2 > syslog > nginx > json > csv > tsv > ltsv Supported format: > none > multiline
- 21. Fluentd out_mongo Apache bufferaccess.log ✓ retry automatically! ✓ exponential retry wait! ✓ persistent on a file
- 22. Fluentd out_webhdfs buffer ✓ retry automatically! ✓ exponential retry wait! ✓ persistent on a file ✓ slice files based on time 2013-01-01/01/access.log.gz! 2013-01-01/02/access.log.gz! 2013-01-01/03/access.log.gz! ... HDFS ✓ custom text formatter Apache access.log
- 23. out_copy + other plugins ✓ routing based on tags! ✓ copy to multiple storages Amazon S3 Hadoop Fluentd buffer Apache access.log
- 24. out_forward apache ✓ automatic fail-over! ✓ load balancing FluentdApache bufferaccess.log ✓ retry automatically! ✓ exponential retry wait! ✓ persistent on a file Fluentd Fluentd Fluentd
- 26. Nagios MongoDB Hadoop Alerting Amazon S3 Analysis Archiving MySQL Apache Frontend Access logs syslogd App logs System logs Backend Databases filter / buffer / routing
- 27. Nagios MongoDB Hadoop Alerting Amazon S3 Analysis Archiving MySQL Apache Frontend Access logs syslogd App logs System logs Backend Databases filter / buffer / routing
- 28. Nagios MongoDB Hadoop Alerting Amazon S3 Analysis Archiving MySQL Apache Frontend Access logs syslogd App logs System logs Backend Databases filter / buffer / routing
- 29. td-agent > Open sourced distribution package of fluentd > ETL part of Treasure Data > deb, rpm, dmg (since td-agent 2.0) > Including useful components > ruby, jemalloc, fluentd > 3rd party gems: td, mongo, webhdfs, etc… > http://packages.treasure-data.com/
- 30. v1 > New features without breaking compatibility > Filter, Label and better error handling > Serverengine based: multi-process, signal, etc. > New configuration and DSL format > JRuby and Windows support > github issue: Plan for v1 release #251
- 31. Use cases
- 32. Treasure Data Frontend Job Queue Worker Hadoop Hadoop Fluentd Applications push metrics to Fluentd (via local Fluentd) Librato Metrics for realtime analysis Treasure Data for historical analysis Fluentd sums up data minutes (partial aggregation)
- 33. hundreds of app servers sends event logs sends event logs sends event logs Rails app td-agent td-agent td-agent Google Spreadsheet Treasure Data MySQL Logs are available after several mins. Daily/Hourly Batch KPI visualizationFeedback rankings Rails app Rails app Unlimited scalability Flexible schema Realtime Less performance impact Cookpad ✓ Over 100 RoR servers (2012/2/4)
- 34. http://www.slideshare.net/tagomoris/log-analysis-with-hadoop-in-livedoor-2013 NHN Japan by @tagomoris ✓ 16 nodes! ✓ 120,000+ lines/sec! ✓ 400Mbps at peak! ✓ 1.5+ TB/day (raw) Web Servers Fluentd Cluster Archive Storage (scribed) Fluentd Watchers Graph Tools Notifications (IRC) Hadoop Cluster CDH4 (HDFS, YARN) webhdfs Huahin Manager hive server STREAM Shib ShibUI BATCH SCHEDULED BATCH
- 35. Other usecases > Collect censor logs > Embedded devise, Rapsberry Pi, etc > Integrated with Elasticsearch and Kibana > Integrated with Norikra CEP engine http://www.fluentd.org/guides
- 37. > Fluentd is a widely-used log collector > There are many use cases > Many contributors and plugins > Keep it simple > Easy to integrate your environment Conclusion