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Distributed Stream Processing on Fluentd / #fluentd

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SATOSHI TAGOMORI
SATOSHI TAGOMORI

This document discusses NHN Japan's use of Fluentd for log collection, conversion, and analysis. It summarizes their architecture which involves collecting logs from servers using Scribeline, delivering them to Fluentd processes, converting the logs to a structured format using Fluentd plugins, writing the structured data to HDFS, and performing analysis and reporting with Hive. Key aspects discussed include the scaling of Fluentd processes, performance improvements over their previous Hadoop Streaming-based approach, and implementation details of plugins used.

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12 2 4
12 2 4
Working at NHN Japan we are hiring! 12 2 4
What we are doing about logs with fluentd data mining reporting page views, unique users, traffic amount per page, ... 12 2 4
What we are doing about logs with fluentd super large scale 'sed | grep | wc' like processes 12 2 4
What fluentd? (not Storm, Kafka or Flume?) Ruby, Ruby, Ruby! (NOT Java!) we are working in lightweight language culture easy to try, easy to patch Plugin model architecture Builtin TimeSlicedOutput mechanism 12 2 4
What I talk today What we are trying with fluentd How we did, and how we are doing now What is distributed stream process topologies like? What is important about stream processing Implementation details (appendix) 12 2 4
Architecture in last week's presentation archive deliver server(scribed) archive server (scribed) deliver server RAID server (scribed) (scribed) Large volume RAID server send data both archive servers and Fluentd workers (as stream) import past logs and convert on demand (as batch) Hadoop Hadoop Hadoop Hadoop Hadoop Hadoop Shib Cluster Hadoop Cluster Hadoop Cluster Hadoop Cluster Fluentd Cluster Hadoop Cluster Hadoop Hadoop Hive Cluster Cluster Cluster Cluster Cluster Cluster Web Client aggregation queries convert logs as structured data on demand and write HDFS (as stream) 12 2 4
Now archive deliver server(scribed) archive server (scribed) deliver server RAID server (scribed) (Fluentd) Large volume RAID server Hadoop Hadoop Hadoop Hadoop Hadoop Hadoop Shib Cluster Hadoop Cluster Hadoop Cluster Hadoop Cluster Fluentd Cluster Hadoop Cluster Hadoop Hadoop Hive Cluster Cluster Cluster Cluster Cluster Cluster Web Client Fluentd Watcher 12 2 4
Fluentd in production service 10 days 12 2 4
Scale of Fluentd processes from 127 Web Servers 146 log streams 12 2 4
Scale of Fluentd processes 70,000 messages/sec 120 Mbps (at peak time) 12 2 4
Scale of Fluentd processes 650 GB/day (non-blog: 100GB) 12 2 4
Scale of Fluentd processes 89 fluentd instances on 12 nodes (4Core HT) 12 2 4
We can't go back. crouton by kbysmnr 12 2 4
What we are trying with fluentd log conversion from: raw log (apache combined like format) to: structured and query-friendly log (TAB separated, masked some fields, many flags added) 12 2 4
What we are trying with fluentd log conversion 99.999.999.99 - - [03/Feb/2012:10:59:48 +0900] "GET /article/detail/6246245/ HTTP/1.1" 200 17509 "http://news.livedoor.com/topics/detail/6246245/" "Mozilla/4.0 (compatible; MSIE 8.0; Windows NT 6.1; Trident/4.0; SLCC2; .NET CLR 2.0.50727; .NET CLR 3.5.30729; .NET CLR 3.0.30729; Media Center PC 6.0; InfoPath.1; .NET4.0C)" "news.livedoor.com" "xxxxxxx.xx.xxxxxxx.xxx" "-" 163266 152930 news.livedoor.com /topics/detail/6242972/ GET 302 210 226 - 99.999.999.99 TQmljv9QtXkpNtCSuWVGGg Mozilla/5.0 (iPhone; CPU iPhone OS 5_0_1 like Mac OS X) AppleWebKit/534.46 (KHTML, like Gecko) Version/5.1 Mobile/9A406 Safari/7534.48.3 TRUE TRUE FALSE FALSE FALSE FALSE FALSE hhmmdd vhost path method status bytes duration referer rhost userlabel agent FLAG [FLAGS] FLAGS: status_redirection status_errors rhost_internal suffix_miscfile suffix_imagefile agent_bot FLAG: logical OR of FLAGS userlabel: hash of (tracking cookie / terminal id (mobile phone) / rhost+agent) 12 2 4
What we are trying with fluentd TimeSlicedOutput of fluentd Traditional 'log rotation' is important, but troublesome We want: 2/3 23:59:59 log in access.0203_23.log 2/4 00:00:00 log in access.0204_00.log 12 2 4
How we did, and how we are doing now collect archive convert aggregate show 12 2 4
How we did in past (2011) collect (scribed) stream stream store to hdfs archive (scribed) HIGH LATENCY hourly/daily time to flush + hourly invocation + convert (Hadoop Streaming) running time 20-25mins on demand aggregate (Hive) on demand show 12 2 4
How we are doing now collect (Fluentd) stream stream archive (scribed) stream convert stream VELY LOW LATENCY convert (Fluentd) 2-3 minutes (only time to wait flush) store to hdfs (over Cloudera's Hoop) on demand aggregate (Hive) on demand show 12 2 4
crouton by kbysmnr break. 12 2 4
What is important about stream processing reasonable efficiency (compared with batch throughput) ease to re-run same conversion as batch None SPOF ease to add/remove nodes 12 2 4
Stream processing and batch How to re-run conversion as batch when we got troubles? We want to use 'just one' converter program for both stream processes and batch processes! 12 2 4
out_exec_filter (fluentd built-in plugin) 1. fork and exec 'command' program 2. write data to child process stdin as TAB separated fields specified by 'out_keys' (for tag, remove_prefix available) 3. read data from child process stdout as TAB separated fields named by 'in_keys' (for tag, add_prefix available) 4. set message's timestamp by 'time_key' value in parsed data as format specified by 'time_format' 12 2 4
'out_exec_filter' and 'Hadoop Streaming' read from stdin / write to stdout TAB separated values as input/output WOW!!!!!!! difference: 'tag' may be needed with out_exec_filter simple solution: if not exists, ignore. 12 2 4
What is important about stream processing reasonable efficiency (compared with batch throughput) ease to re-run same conversion as batch None SPOF ease to add/remove nodes 12 2 4
What is distributed stream process toplogies like? deliver archiver backup servers deliver servers servers worker worker worker worker worker worker worker worker worker servers servers serializer serializer Redundancy and load balancing HDFS MUST be guaranteed anywhere. (Hoop Server) 12 2 4
Deliver nodes deliver archiver backup servers deliver servers Accept connections from web servers, servers Copy messages and send to: worker worker worker worker worker worker worker worker worker servers 1. archiver (and its backup) 2. convert workers (w/ load balancing) servers serializer and ... 3. serializer useful for casual worker append/remove HDFS (Hoop Server) 12 2 4
Worker nodes deliver archiver backup servers Under load balancing, deliver workers as many as you want servers servers worker worker worker worker worker worker worker worker worker servers servers serializer serializer HDFS (Hoop Server) 12 2 4
Serializer nodes deliver archiver backup Receive converted data stream from workers, servers deliver aggregate by services, and : servers 1. write to storage(hfds/hoop) servers 2. and... worker worker worker worker useful to reduce overhead of storage from many worker worker worker worker worker servers concurrent write operations servers serializer serializer HDFS (Hoop Server) 12 2 4
Watcher nodes deliver archiver backup servers deliver Watching data for servers real-time workload repotings and trouble notifications servers worker worker worker worker worker 1. for raw data from delivers worker worker worker worker servers 2. for structured data from serializers servers serializer serializer watcher HDFS watcher (Hoop Server) 12 2 4
crouton by kbysmnr break. 12 2 4
Implementation details log agents on servers (scribeline) deliver (copy, in_scribe, out_scribe, out_forward) worker (in/out_forward, out_exec_filter) serializer/hooper (in/out_forward, out_hoop) watcher (in_forward, out_flowcounter, out_growthforecast) 12 2 4
log agent: scribeline log delivery agent tool, python 2.4, scribe/thrift easy to setup and start/stop works with any httpd configuration updates works with logrotate-ed log files automatic delivery target failover/takeback (NEW) Cluster support (random select from server list) https://github.com/tagomoris/scribe_line 12 2 4
From scribeline To deliver deliver server (primary) category: blog message: RAW LOG fluentd (Apache combined + α) in_scribe scribeline scribe servers in_scribe fluentd deliver server (secondary) 12 2 4
deliver 01 (primary) From scribeline To deliver deliver 02 (secondary) xNN servers x8 fluentd per node deliver 03 (primary for high throughput nodes) 12 2 4
From scribeline To deliver deliver server (primary) category: blog message: RAW LOG fluentd (Apache combined + α) in_scribe scribeline servers in_scribe fluentd deliver server (secondary) 12 2 4
deliver node internal routing deliver server (primary) x8 fluentd instances deliver fluentd copy scribe.* in_scribe out_scribe category: blog host archive.server.local message: RAW LOG add_prefix scribe remove_prefix scribe add_newline true remove_newline true out_flowcounter (see later..) time: received_at tag: scribe.blog roundrobin (see next) message: RAW LOG out_forward (see later with out_flowcounter..) 12 2 4
deliver node: roundrobin strategy to workers roundrobin x56 substore configurations (7workers x 8instances) out_forward server: worker01 port 24211 secondary server: worker02 port 24211 out_forward time: received_at server: worker01 port 24212 secondary tag: scribe.blog server: worker03 port 24212 message: RAW LOG out_forward server: worker01 port 24213 secondary server: worker04 port 24213 out_forward server: worker01 port 24214 secondary server: worker05 port 24214 12 2 4
From deliver To worker deliver server worker server X deliver fluentd worker fluentd Xn1 copy scribe.* in_forward roundrobin time: received_at tag: scribe.blog out_forward message: RAW LOG in_forward time: received_at tag: scribe.blog message: RAW LOG worker fluentd Yn2 worker server Y 12 2 4
worker node internal routing worker server x8 worker instances, x1 serializer instance worker fluentd serializer fluentd in_forward out_exec_filter scribe.* in_forward command: convert.sh in_keys: tag,message remove_prefix scribe out_hoop converted.blog out_keys: ....... hoop_server servername.local add_prefix: converted username time_key: timefield path /on_hdfs/%Y%m%d/blog-%H.log time_format: %Y%m%d%H%M%S time:received_at out_hoop converted.news tag: scribe.blog out_forward converted.* path /on_hdfs/%Y%m%d/news-%H.log message: RAW LOG time:written_time TAB separated tag: converted.blog text data [many data fields] 12 2 4
out_exec_filter (review.) 1. fork and exec 'command' program 2. write data to child process stdin as TAB separated fields specified by 'out_keys' (for tag, remove_prefix available) 3. read data from child process stdout as TAB separated fields named by 'in_keys' (for tag, add_prefix available) 4. set message's timestamp by 'time_key' value in parsed data as format specified by 'time_format' 12 2 4
out_exec_filter behavior details time: 2012/02/04 17:50:35 tag: converted.blog path:... agent:... worker fluentd referer:... flag1:TRUE out_exec_filter scribe.* command: convert.sh in_keys: tag,message remove_prefix: scribe out_keys: ....... add_prefix: converted time_key: timefield time_format: %Y%m%d%H%M%S time: received_at tag: scribe.blog message: RAW LOG blog RAWLOG blog 20120204175035 field1 field2..... stdin stdout Forked Process (convert.sh -> perl convert.pl) 12 2 4
From serializer To HDFS (Hoop) worker server Hadoop NameNode serializer fluentd Hoop Server HTTP in_forward out_hoop converted.blog hoop_server servername.local username path /on_hdfs/%Y%m%d/blog-%H.log time:written_time tag: converted.blog [many data fields] out_hoop converted.news path /on_hdfs/%Y%m%d/news-%H.log TAB separated text data HDFS 12 2 4
Overview deliver node cluster deliver archiver backup servers deliver servers servers worker worker worker worker worker worker worker worker worker servers servers serializer serializer worker node cluster HDFS (Hoop Server) 12 2 4
crouton by kbysmnr 12 2 4
Traffics: Bytes/sec (on deliver 2/3-4) • bytes 12 2 4
Traffics: Messages/sec (on deliver 2/3-4) • counts 12 2 4
Traffic/CPU/Load/Memory: deliver nodes (2/3-4) 12 2 4
Traffics: workers network traffics total • total network traffics 12 2 4
Traffic/CPU/Load/Memory: a worker (2/3-4) 12 2 4
Fluentd stream processing Finally, works fine, now Log conversion latency dramatically reduced Many useful plugins for monitoring are waiting shipped Hundreds of cool features to implement are also waiting for us! 12 2 4
Thank you! crouton by kbysmnr 12 2 4
crouton by kbysmnr Appendix 12 2 4
input traffics: by fluent-plugin-flowcounter deliver server (primary) x8 fluentd instances deliver fluentd copy scribe.* in_scribe out_scribe category: blog host archive.server.local message: RAW LOG add_prefix scribe remove_prefix scribe add_newline true remove_newline true out_flowcounter (see later..) time: received_at tag: scribe.blog roundrobin (see next) message: RAW LOG out_forward (see later with out_flowcounter..) 12 2 4
bytes/messages counting on fluentd 1. 'out_flowcounter' counts input message and its size (specified fields) and its rate (/sec) 2. Counting results emitted per minute/hour/day 3. Worker fluentd sends results to 'Watcher' node over out_forward 4. Watcher receives counting results, and pass to 'out_growthforecast'. 'GrowthForecast' is graph drawing tool with REST API for data registration, by kazeburo 12 2 4
Why not out_forward roundrobin in deliver? out_forward roundrobin is per buffer flushing ! (per buffer size, or flush_interval) For high throughput stream, this unit is too large. We needs roundrobin per 'emit'. 12 2 4
deliver node: roundrobin strategy to workers roundrobin x56 substore configurations (7workers x 8instances) out_forward server: worker01 port 24211 secondary server: worker02 port 24211 out_forward time: received_at server: worker01 port 24212 secondary tag: scribe.blog server: worker03 port 24212 message: RAW LOG out_forward server: worker01 port 24213 secondary server: worker04 port 24213 out_forward server: worker01 port 24214 secondary server: worker05 port 24214 12 2 4
Why not out_forward roundrobin in deliver? out_forward roundrobin is per buffer flushing ! (per buffer size, or flush_interval) For high throughput stream, this unit is too large. We needs roundrobin per 'emit'. 12 2 4
From worker To serializer: details worker server x8 worker instances, x1 serializer instance worker fluentd serializer fluentd out_forward converted.* in_forward server: localhost secondary: worker1, worker2, worker3, worker4 worker5, worker6, worker7 normally send to localhost in trouble, balance all traffic to all other worker's serializers 12 2 4
Software list: scribed: github.com/facebook/scribe/ scribeline: github.com/tagomoris/scribe_line fluent-plugin-scribe: github.com/fluent/fluent-plugin-scribe Hoop: http://cloudera.github.com/hoop/docs/latest/ServerSetup.html fluent-plugin-hoop: github.com/fluent/fluent-plugin-hoop GrowthForecast: github.com/kazeburo/growthforecast fluent-plugin-growthforecast: github.com/tagomoris/fluent-plugin-growthforecast fluent-plugin-flowcounter: github.com/tagomoris/fluent-plugin-flowcounter 12 2 4

More Related Content

Distributed Stream Processing on Fluentd / #fluentd

  • 1. 12 2 4
  • 2. 12 2 4
  • 3. Working at NHN Japan we are hiring! 12 2 4
  • 4. What we are doing about logs with fluentd data mining reporting page views, unique users, traffic amount per page, ... 12 2 4
  • 5. What we are doing about logs with fluentd super large scale 'sed | grep | wc' like processes 12 2 4
  • 6. What fluentd? (not Storm, Kafka or Flume?) Ruby, Ruby, Ruby! (NOT Java!) we are working in lightweight language culture easy to try, easy to patch Plugin model architecture Builtin TimeSlicedOutput mechanism 12 2 4
  • 7. What I talk today What we are trying with fluentd How we did, and how we are doing now What is distributed stream process topologies like? What is important about stream processing Implementation details (appendix) 12 2 4
  • 8. Architecture in last week's presentation archive deliver server(scribed) archive server (scribed) deliver server RAID server (scribed) (scribed) Large volume RAID server send data both archive servers and Fluentd workers (as stream) import past logs and convert on demand (as batch) Hadoop Hadoop Hadoop Hadoop Hadoop Hadoop Shib Cluster Hadoop Cluster Hadoop Cluster Hadoop Cluster Fluentd Cluster Hadoop Cluster Hadoop Hadoop Hive Cluster Cluster Cluster Cluster Cluster Cluster Web Client aggregation queries convert logs as structured data on demand and write HDFS (as stream) 12 2 4
  • 9. Now archive deliver server(scribed) archive server (scribed) deliver server RAID server (scribed) (Fluentd) Large volume RAID server Hadoop Hadoop Hadoop Hadoop Hadoop Hadoop Shib Cluster Hadoop Cluster Hadoop Cluster Hadoop Cluster Fluentd Cluster Hadoop Cluster Hadoop Hadoop Hive Cluster Cluster Cluster Cluster Cluster Cluster Web Client Fluentd Watcher 12 2 4
  • 10. Fluentd in production service 10 days 12 2 4
  • 11. Scale of Fluentd processes from 127 Web Servers 146 log streams 12 2 4
  • 12. Scale of Fluentd processes 70,000 messages/sec 120 Mbps (at peak time) 12 2 4
  • 13. Scale of Fluentd processes 650 GB/day (non-blog: 100GB) 12 2 4
  • 14. Scale of Fluentd processes 89 fluentd instances on 12 nodes (4Core HT) 12 2 4
  • 15. We can't go back. crouton by kbysmnr 12 2 4
  • 16. What we are trying with fluentd log conversion from: raw log (apache combined like format) to: structured and query-friendly log (TAB separated, masked some fields, many flags added) 12 2 4
  • 17. What we are trying with fluentd log conversion 99.999.999.99 - - [03/Feb/2012:10:59:48 +0900] "GET /article/detail/6246245/ HTTP/1.1" 200 17509 "http://news.livedoor.com/topics/detail/6246245/" "Mozilla/4.0 (compatible; MSIE 8.0; Windows NT 6.1; Trident/4.0; SLCC2; .NET CLR 2.0.50727; .NET CLR 3.5.30729; .NET CLR 3.0.30729; Media Center PC 6.0; InfoPath.1; .NET4.0C)" "news.livedoor.com" "xxxxxxx.xx.xxxxxxx.xxx" "-" 163266 152930 news.livedoor.com /topics/detail/6242972/ GET 302 210 226 - 99.999.999.99 TQmljv9QtXkpNtCSuWVGGg Mozilla/5.0 (iPhone; CPU iPhone OS 5_0_1 like Mac OS X) AppleWebKit/534.46 (KHTML, like Gecko) Version/5.1 Mobile/9A406 Safari/7534.48.3 TRUE TRUE FALSE FALSE FALSE FALSE FALSE hhmmdd vhost path method status bytes duration referer rhost userlabel agent FLAG [FLAGS] FLAGS: status_redirection status_errors rhost_internal suffix_miscfile suffix_imagefile agent_bot FLAG: logical OR of FLAGS userlabel: hash of (tracking cookie / terminal id (mobile phone) / rhost+agent) 12 2 4
  • 18. What we are trying with fluentd TimeSlicedOutput of fluentd Traditional 'log rotation' is important, but troublesome We want: 2/3 23:59:59 log in access.0203_23.log 2/4 00:00:00 log in access.0204_00.log 12 2 4
  • 19. How we did, and how we are doing now collect archive convert aggregate show 12 2 4
  • 20. How we did in past (2011) collect (scribed) stream stream store to hdfs archive (scribed) HIGH LATENCY hourly/daily time to flush + hourly invocation + convert (Hadoop Streaming) running time 20-25mins on demand aggregate (Hive) on demand show 12 2 4
  • 21. How we are doing now collect (Fluentd) stream stream archive (scribed) stream convert stream VELY LOW LATENCY convert (Fluentd) 2-3 minutes (only time to wait flush) store to hdfs (over Cloudera's Hoop) on demand aggregate (Hive) on demand show 12 2 4
  • 22. crouton by kbysmnr break. 12 2 4
  • 23. What is important about stream processing reasonable efficiency (compared with batch throughput) ease to re-run same conversion as batch None SPOF ease to add/remove nodes 12 2 4
  • 24. Stream processing and batch How to re-run conversion as batch when we got troubles? We want to use 'just one' converter program for both stream processes and batch processes! 12 2 4
  • 25. out_exec_filter (fluentd built-in plugin) 1. fork and exec 'command' program 2. write data to child process stdin as TAB separated fields specified by 'out_keys' (for tag, remove_prefix available) 3. read data from child process stdout as TAB separated fields named by 'in_keys' (for tag, add_prefix available) 4. set message's timestamp by 'time_key' value in parsed data as format specified by 'time_format' 12 2 4
  • 26. 'out_exec_filter' and 'Hadoop Streaming' read from stdin / write to stdout TAB separated values as input/output WOW!!!!!!! difference: 'tag' may be needed with out_exec_filter simple solution: if not exists, ignore. 12 2 4
  • 27. What is important about stream processing reasonable efficiency (compared with batch throughput) ease to re-run same conversion as batch None SPOF ease to add/remove nodes 12 2 4
  • 28. What is distributed stream process toplogies like? deliver archiver backup servers deliver servers servers worker worker worker worker worker worker worker worker worker servers servers serializer serializer Redundancy and load balancing HDFS MUST be guaranteed anywhere. (Hoop Server) 12 2 4
  • 29. Deliver nodes deliver archiver backup servers deliver servers Accept connections from web servers, servers Copy messages and send to: worker worker worker worker worker worker worker worker worker servers 1. archiver (and its backup) 2. convert workers (w/ load balancing) servers serializer and ... 3. serializer useful for casual worker append/remove HDFS (Hoop Server) 12 2 4
  • 30. Worker nodes deliver archiver backup servers Under load balancing, deliver workers as many as you want servers servers worker worker worker worker worker worker worker worker worker servers servers serializer serializer HDFS (Hoop Server) 12 2 4
  • 31. Serializer nodes deliver archiver backup Receive converted data stream from workers, servers deliver aggregate by services, and : servers 1. write to storage(hfds/hoop) servers 2. and... worker worker worker worker useful to reduce overhead of storage from many worker worker worker worker worker servers concurrent write operations servers serializer serializer HDFS (Hoop Server) 12 2 4
  • 32. Watcher nodes deliver archiver backup servers deliver Watching data for servers real-time workload repotings and trouble notifications servers worker worker worker worker worker 1. for raw data from delivers worker worker worker worker servers 2. for structured data from serializers servers serializer serializer watcher HDFS watcher (Hoop Server) 12 2 4
  • 33. crouton by kbysmnr break. 12 2 4
  • 34. Implementation details log agents on servers (scribeline) deliver (copy, in_scribe, out_scribe, out_forward) worker (in/out_forward, out_exec_filter) serializer/hooper (in/out_forward, out_hoop) watcher (in_forward, out_flowcounter, out_growthforecast) 12 2 4
  • 35. log agent: scribeline log delivery agent tool, python 2.4, scribe/thrift easy to setup and start/stop works with any httpd configuration updates works with logrotate-ed log files automatic delivery target failover/takeback (NEW) Cluster support (random select from server list) https://github.com/tagomoris/scribe_line 12 2 4
  • 36. From scribeline To deliver deliver server (primary) category: blog message: RAW LOG fluentd (Apache combined + α) in_scribe scribeline scribe servers in_scribe fluentd deliver server (secondary) 12 2 4
  • 37. deliver 01 (primary) From scribeline To deliver deliver 02 (secondary) xNN servers x8 fluentd per node deliver 03 (primary for high throughput nodes) 12 2 4
  • 38. From scribeline To deliver deliver server (primary) category: blog message: RAW LOG fluentd (Apache combined + α) in_scribe scribeline servers in_scribe fluentd deliver server (secondary) 12 2 4
  • 39. deliver node internal routing deliver server (primary) x8 fluentd instances deliver fluentd copy scribe.* in_scribe out_scribe category: blog host archive.server.local message: RAW LOG add_prefix scribe remove_prefix scribe add_newline true remove_newline true out_flowcounter (see later..) time: received_at tag: scribe.blog roundrobin (see next) message: RAW LOG out_forward (see later with out_flowcounter..) 12 2 4
  • 40. deliver node: roundrobin strategy to workers roundrobin x56 substore configurations (7workers x 8instances) out_forward server: worker01 port 24211 secondary server: worker02 port 24211 out_forward time: received_at server: worker01 port 24212 secondary tag: scribe.blog server: worker03 port 24212 message: RAW LOG out_forward server: worker01 port 24213 secondary server: worker04 port 24213 out_forward server: worker01 port 24214 secondary server: worker05 port 24214 12 2 4
  • 41. From deliver To worker deliver server worker server X deliver fluentd worker fluentd Xn1 copy scribe.* in_forward roundrobin time: received_at tag: scribe.blog out_forward message: RAW LOG in_forward time: received_at tag: scribe.blog message: RAW LOG worker fluentd Yn2 worker server Y 12 2 4
  • 42. worker node internal routing worker server x8 worker instances, x1 serializer instance worker fluentd serializer fluentd in_forward out_exec_filter scribe.* in_forward command: convert.sh in_keys: tag,message remove_prefix scribe out_hoop converted.blog out_keys: ....... hoop_server servername.local add_prefix: converted username time_key: timefield path /on_hdfs/%Y%m%d/blog-%H.log time_format: %Y%m%d%H%M%S time:received_at out_hoop converted.news tag: scribe.blog out_forward converted.* path /on_hdfs/%Y%m%d/news-%H.log message: RAW LOG time:written_time TAB separated tag: converted.blog text data [many data fields] 12 2 4
  • 43. out_exec_filter (review.) 1. fork and exec 'command' program 2. write data to child process stdin as TAB separated fields specified by 'out_keys' (for tag, remove_prefix available) 3. read data from child process stdout as TAB separated fields named by 'in_keys' (for tag, add_prefix available) 4. set message's timestamp by 'time_key' value in parsed data as format specified by 'time_format' 12 2 4
  • 44. out_exec_filter behavior details time: 2012/02/04 17:50:35 tag: converted.blog path:... agent:... worker fluentd referer:... flag1:TRUE out_exec_filter scribe.* command: convert.sh in_keys: tag,message remove_prefix: scribe out_keys: ....... add_prefix: converted time_key: timefield time_format: %Y%m%d%H%M%S time: received_at tag: scribe.blog message: RAW LOG blog RAWLOG blog 20120204175035 field1 field2..... stdin stdout Forked Process (convert.sh -> perl convert.pl) 12 2 4
  • 45. From serializer To HDFS (Hoop) worker server Hadoop NameNode serializer fluentd Hoop Server HTTP in_forward out_hoop converted.blog hoop_server servername.local username path /on_hdfs/%Y%m%d/blog-%H.log time:written_time tag: converted.blog [many data fields] out_hoop converted.news path /on_hdfs/%Y%m%d/news-%H.log TAB separated text data HDFS 12 2 4
  • 46. Overview deliver node cluster deliver archiver backup servers deliver servers servers worker worker worker worker worker worker worker worker worker servers servers serializer serializer worker node cluster HDFS (Hoop Server) 12 2 4
  • 48. Traffics: Bytes/sec (on deliver 2/3-4) • bytes 12 2 4
  • 49. Traffics: Messages/sec (on deliver 2/3-4) • counts 12 2 4
  • 51. Traffics: workers network traffics total • total network traffics 12 2 4
  • 53. Fluentd stream processing Finally, works fine, now Log conversion latency dramatically reduced Many useful plugins for monitoring are waiting shipped Hundreds of cool features to implement are also waiting for us! 12 2 4
  • 54. Thank you! crouton by kbysmnr 12 2 4
  • 55. crouton by kbysmnr Appendix 12 2 4
  • 56. input traffics: by fluent-plugin-flowcounter deliver server (primary) x8 fluentd instances deliver fluentd copy scribe.* in_scribe out_scribe category: blog host archive.server.local message: RAW LOG add_prefix scribe remove_prefix scribe add_newline true remove_newline true out_flowcounter (see later..) time: received_at tag: scribe.blog roundrobin (see next) message: RAW LOG out_forward (see later with out_flowcounter..) 12 2 4
  • 57. bytes/messages counting on fluentd 1. 'out_flowcounter' counts input message and its size (specified fields) and its rate (/sec) 2. Counting results emitted per minute/hour/day 3. Worker fluentd sends results to 'Watcher' node over out_forward 4. Watcher receives counting results, and pass to 'out_growthforecast'. 'GrowthForecast' is graph drawing tool with REST API for data registration, by kazeburo 12 2 4
  • 58. Why not out_forward roundrobin in deliver? out_forward roundrobin is per buffer flushing ! (per buffer size, or flush_interval) For high throughput stream, this unit is too large. We needs roundrobin per 'emit'. 12 2 4
  • 59. deliver node: roundrobin strategy to workers roundrobin x56 substore configurations (7workers x 8instances) out_forward server: worker01 port 24211 secondary server: worker02 port 24211 out_forward time: received_at server: worker01 port 24212 secondary tag: scribe.blog server: worker03 port 24212 message: RAW LOG out_forward server: worker01 port 24213 secondary server: worker04 port 24213 out_forward server: worker01 port 24214 secondary server: worker05 port 24214 12 2 4
  • 60. Why not out_forward roundrobin in deliver? out_forward roundrobin is per buffer flushing ! (per buffer size, or flush_interval) For high throughput stream, this unit is too large. We needs roundrobin per 'emit'. 12 2 4
  • 61. From worker To serializer: details worker server x8 worker instances, x1 serializer instance worker fluentd serializer fluentd out_forward converted.* in_forward server: localhost secondary: worker1, worker2, worker3, worker4 worker5, worker6, worker7 normally send to localhost in trouble, balance all traffic to all other worker's serializers 12 2 4
  • 62. Software list: scribed: github.com/facebook/scribe/ scribeline: github.com/tagomoris/scribe_line fluent-plugin-scribe: github.com/fluent/fluent-plugin-scribe Hoop: http://cloudera.github.com/hoop/docs/latest/ServerSetup.html fluent-plugin-hoop: github.com/fluent/fluent-plugin-hoop GrowthForecast: github.com/kazeburo/growthforecast fluent-plugin-growthforecast: github.com/tagomoris/fluent-plugin-growthforecast fluent-plugin-flowcounter: github.com/tagomoris/fluent-plugin-flowcounter 12 2 4