Large Scale Log Analytics with Solr (from Lucene Revolution 2015)

1. OCTOBER 13-16, 2016 • AUSTIN, TX

2. Large Scale Log Analytics with Solr Rafał Kuć and Radu Gheorghe Sematext Group

3. 3 01 About Us RaduRafał Logsene

4. 4 02 Agenda Logstash + Solr rsyslog + Solr rsyslog + Redis + Logstash + Solr Solr

5. 5 01 Flow in Logstash /var/log/apache.log redis https://cdn2.iconfinder.com/data/icons/gconstruct/2118/gconstruct1-14.png input

6. 6 01 Flow in Logstash /var/log/apache.log redis https://cdn2.iconfinder.com/data/icons/gconstruct/2118/gconstruct1-14.png plain {json} input codec

7. 7 01 Flow in Logstash /var/log/apache.log redis Rafał @kucrafal grok { "user": "Rafał", "twitter": "@kucrafal" } - w $numberOfWorkers https://cdn2.iconfinder.com/data/icons/gconstruct/2118/gconstruct1-14.png plain {json} input codec filter

8. 8 01 Flow in Logstash /var/log/apache.log redis Rafał @kucrafal grok { "user": "Rafał", "twitter": "@kucrafal" } - w $numberOfWorkers https://cdn2.iconfinder.com/data/icons/gconstruct/2118/gconstruct1-14.png workers => 2 plain {json} input codec filter output

9. 9 01 Simple Config https://github.com/sematext/lucene-revolution-samples/tree/master/2015 input { file { path => "/opt/logs/example.log" start_position => "beginning" } } output { solr_http { solr_url => "http://localhost:8983/solr/gettingstarted" flush_size => 5000 workers => 4 } } bin/plugin install logstash-output-solr_http apache combined logs

10. 10 01 Base Result

11. 11 01 Parse JSON input { file { path => "/opt/logs/example.log.parsed" start_position => "beginning" … filter { json { source => "message" } } output { solr_http { … apache combined logs in JSON bin/logstash -f logstash.conf -w 4 # filterWorkers=4

12. 12 01 JSON Result

13. input { file { path => "/opt/logs/example.log" start_position => "beginning" … filter { grok { match => [ "message", "%{COMBINEDAPACHELOG}" ] } } output { solr_http { … 13 01 Grok

14. 14 01 Grok Result

15. 15 01 Flow Options https://upload.wikimedia.org/wikipedia/commons/thumb/b/bb/Gorilla-server.svg/2000px-Gorilla-server.svg.png https://www.elastic.co/assets/blt69f6410148efbab8/logstash.png

16. 16 01 Flow Options (cont.) http://www.hanselman.com/blog/content/binary/Windows-Live-Writer/ef572a4c3e50_13F7B/redis_logo_a83f44f3-708d-4fad-aa6e-6eb0d6f82001.png https://upload.wikimedia.org/wikipedia/commons/thumb/f/f8/Question_mark_alternate.svg/2000px-Question_mark_alternate.svg.png or Kafka or *MQ or... something light here rsyslog rsyslog rsyslog

17. 17 01 Flow in rsyslog /var/log/apache.log syslog socket input

18. 18 01 Flow in rsyslog /var/log/apache.log syslog socket main queue (RAM+Disk) input queue.type queue.size ...

19. 19 01 Flow in rsyslog /var/log/apache.log syslog socket main queue (RAM+Disk) input queue.type queue.size ... queue.workerThreads (filter, parse and send events)

20. 20 01 Flow in rsyslog /var/log/apache.log syslog socket main queue (RAM+Disk) input queue.type queue.size ... queue.workerThreads (filter, parse and send events) queue.dequeueBatchSize

21. 21 01 Flow in rsyslog /var/log/apache.log syslog socket main queue (RAM+Disk) input queue.type queue.size ... queue.workerThreads (filter, parse and send events) queue.dequeueBatchSize rsyslog_solr.py rsyslog_solr.py rsyslog_solr.py action template {JSON}

22. 22 01 Flow in rsyslog /var/log/apache.log syslog socket main queue (RAM+Disk) input queue.type queue.size ... queue.workerThreads (filter, parse and send events) queue.dequeueBatchSize rsyslog_solr.py rsyslog_solr.py rsyslog_solr.py action template {JSON}

23. 23 01 Simple Config (1/2) https://github.com/sematext/lucene-revolution-samples/tree/master/2015 module(load="imfile") module(load="omprog") input(type="imfile" File="/opt/logs/example.log" Tag="apache:") main_queue( queue.highWatermark="100000" queue.lowWatermark="50000" queue.maxDiskSpace="5g" queue.fileName="solr_action" queue.spoolDirectory="/opt/rsyslog/queues" queue.saveOnShutdown="on" queue.workerThreads="4" queue.dequeueBatchSize="500" ) apache combined logs

24. 24 01 Simple Config (2/2) template(name="json_lines" type="list" option.json="on") { constant(value="{") constant(value=""timestamp":"") property(name="timereported" dateFormat="rfc3339") constant(value="","message":"") property(name="msg") ... constant(value="","syslog-tag":"") property(name="syslogtag") constant(value=""}n") } action( type="omprog" binary="/opt/rsyslog/rsyslog_solr.py" template="json_lines" ) get from https://github.com/rsyslog/rsyslog/tree/master/plugins/external/solr

25. 25 01 Base Result

26. 26 01 Base Result 15% rsyslog, 4x1% rsyslog_solr.py

27. 27 01 Base Result 15% rsyslog, 4x1% rsyslog_solr.py 125MB rsyslog, 4x15MB rsyslog_solr.py Depends on queue. Here up to 100K events in RAM

28. 28 01 JSON Config # same main queue settings and modules input(type="imfile" File="/opt/logs/example.log.parsed" Tag="apache:") module(load="mmnormalize") action(type="mmnormalize" rulebase="/opt/rsyslog/json.rb" ) template(name="json_lines" type="list") { property(name="$!root") constant(value="n") } action(type="omprog" ... apache combined logs already parsed in JSON version=2 rule=:%root:json%

29. 29 01 JSON Result

30. 30 01 Normalizing Config input(type="imfile" File="/opt/logs/example.log" Tag="apache") action(type="mmnormalize" rulebase="/opt/rsyslog/apache_combined.rb" ) template(name="json_lines" type="list") { property(name="$!all-json") constant(value="n") } version=2 rule=:%[ {"type": "word", "name": "clientip"}, {"type": "literal", "text": " "}, ... {"type": "char-to", "name": "agent", "extradata": """}, {"type": "literal", "text": """}, {"type": "rest", "name": "blob"} ]%

31. 31 01 Normalizing Result

32. 32 01 Normalizing “Should Scale”* sys tem log d -ng performance depends mostly on log length and not on the number of rules: http://blog.gerhards.net/2013/01/performance-of-liblognormrsyslog-parse.html

33. rule=apache_combined:%[ {"type": "word", "name": "clientip"}, ... {"type": "char-to", "name": "agent", "extradata": """}, {"type": "literal", "text": """}, {"type": "rest", "name": "blob"} ]% rule=apache_common:%[ {"type": "word", "name": "clientip"}, ... {"type": "number", "name": "bytes"}, {"type": "rest", "name": "blob", "priority": 65535} ]% ... 33 01 Normalizing with Five Rules input(type="imfile" File="/opt/logs/example*" Tag="apache") action(type="mmnormalize" rulebase="/opt/rsyslog/multiple_rules.rb" ) if $!root <> "" then { set $.final-json = $!root; } else { set $.final-json = $!all-json; } template(name="json_lines" type="list") { property(name="$.final-json") constant(value="n") }

34. 34 01 5 Rules Result

35. 35 01 OK, so this works: rsyslog rsyslog rsyslog

36. 36 01 How about this: rsyslog rsyslog rsyslog

37. 37 01 rsyslog.conf module(load="imfile") module(load="omhiredis") input(type="imfile" File="/opt/logs/example.log" Tag="apache:") template(name="json_lines" type="list" option.json="on") {...} main_queue(queue.workerthreads="1" queue.dequeueBatchSize="100" queue.size="10000") action(type="omhiredis" mode="publish" key="rsyslog_logstash" template="json_lines") ./configure --enable-omhiredis small&light queue

38. 38 01 logstash.conf input { redis { data_type => "channel" key => "rsyslog_logstash" batch_count => 100 } } output { solr_http { ... } } JSON codec is implied

39. 39 01 Combined Result rsyslog 1% Redis 2% Logstash 200% rsyslog 10MB (10K queue) Redis 1000MB (configurable) Logstash 380MB

40. 40 01 5-Rule Normalizing Result rsyslog 100% Redis 2% Logstash 200% rsyslog 30MB Redis 1000MB Logstash 450MB

41. 41 01 Shipper conclusions rsyslog rsyslog rsyslog rsyslog rsyslog rsyslog easy setup; flexible heavy light; fast less flexible&easy offloads buffers and Logstash processing; flexible and efficient setup and maintenance overhead

42. 42 01 Solr Tuning Agenda Schema and config adjustments Time-based collections Tiered cluster (e.g. hot vs cold nodes)

43. 43 01 Schema: Two Kinds of Fields message:failed "docValues": true "omitNorms": true, "omitTermFreqAndPositions": true

44. 44 01 Schema: Two Kinds of Fields message:failed "docValues": true "omitNorms": true, "omitTermFreqAndPositions": true +20 to 100% capacity* 10% faster indexing* * http://blog.sematext.com/2014/11/17/solr-presentations-lucene-solr-revolution/

45. 45 01 Commits "updateHandler.autoSoftCommit.maxTime": 5000 "updateHandler.autoCommit.maxTime": 60000 <ramBufferSizeMB>200</ramBufferSizeMB> 5s feels near- realtime while searching Flush to disk every minute or 200MB

46. 46 01 Commits "updateHandler.autoSoftCommit.maxTime": 5000 "updateHandler.autoCommit.maxTime": 60000 <ramBufferSizeMB>200</ramBufferSizeMB> 5s feels near- realtime while searching Flush to disk every minute of 200MB +10% capacity; 10% faster indexing*

47. 47 01 Time-Based Collections indexing, merges, most searches doesn’t change => cache friendly can be optimized delete without triggering merges

48. 48 01 Time-Based Collections indexing, merges, most searches doesn’t change => cache friendly => can be optimized delete without triggering merges 20-30x capacity; less indexing degradation* * http://www.slideshare.net/sematext/side-by-side-with-elasticsearch-solr-part-2

49. 49 01 Tiered Cluster hot1 hot2 cold1 cold2 cold3 cold4

51. 51 01 Tiered Cluster hot1 hot2 cold1 cold2 cold3 cold4 ADDREPLICA

55. 55 01 Tiered Cluster hot1 hot2 cold1 cold2 cold3 cold4 quick recent searches and indexing rare lengthy requests

56. 56 01 Tiered Cluster cold1 cold2 cold3 cold4 quick recent searches and indexing rare lengthy requests hot1 hot2 buffer for indexing spikes

57. 57 01 Tiered Cluster cold1 cold2 cold3 cold4 quick recent searches and indexing rare lengthy requests hot1 hot2 buffer for indexing spikes less shards per collection and the cluster is still balanced

58. 58 01 Tiered Cluster cold1 cold2 cold3 cold4 quick recent searches and indexing rare lengthy requests hot1 hot2 buffer for indexing spikes less shards per collection and the cluster is still balanced CPU++ RAM++ IO++

59. 59 01 Wrap-Up

60. 60 01 Wrap-Up DocValues commits

61. 61 01 Wrap-Up DocValues commits https://cdn0.iconfinder.com/data/icons/dance-fitness/72/13-512.png https://www.standardlife.co.uk/resources/custom/uk/images/heroes/illustration/easy-box.png

62. 62 01 Wrap-Up DocValues commits https://cdn0.iconfinder.com/data/icons/dance-fitness/72/13-512.png https://www.standardlife.co.uk/resources/custom/uk/images/heroes/illustration/easy-box.png

63. 63 01 Wrap-Up DocValues commits http://www.funnyshirts.net/media/catalog/product/cache/1/image/9df78eab33525d08d6e5fb8d27136e95/z/o/zombies-hate-fast-food-funny-tshirt-preview.png https://cdn0.iconfinder.com/data/icons/dance-fitness/72/13-512.png https://www.standardlife.co.uk/resources/custom/uk/images/heroes/illustration/easy-box.png

64. 64 01 Wrap-Up DocValues commits http://www.funnyshirts.net/media/catalog/product/cache/1/image/9df78eab33525d08d6e5fb8d27136e95/z/o/zombies-hate-fast-food-funny-tshirt-preview.png https://cdn0.iconfinder.com/data/icons/dance-fitness/72/13-512.png https://www.standardlife.co.uk/resources/custom/uk/images/heroes/illustration/easy-box.png rsyslog

65. 65 01 Wrap-Up DocValues commits http://www.funnyshirts.net/media/catalog/product/cache/1/image/9df78eab33525d08d6e5fb8d27136e95/z/o/zombies-hate-fast-food-funny-tshirt-preview.png https://cdn0.iconfinder.com/data/icons/dance-fitness/72/13-512.png https://www.standardlife.co.uk/resources/custom/uk/images/heroes/illustration/easy-box.png rsyslog rsyslog rsyslog rsyslog

66. 66 01 Questions? Rafał Kuć @kucrafal rafal.kuc@sematext.com Radu Gheorghe @radu0gheorghe radu.gheorghe@sematext.com Sematext @sematext http://sematext.com

67. 67 01 Questions? Rafał Kuć @kucrafal rafal.kuc@sematext.com Radu Gheorghe @radu0gheorghe radu.gheorghe@sematext.com Sematext @sematext http://sematext.com we’re hiring, too!

Large Scale Log Analytics with Solr (from Lucene Revolution 2015)

You are reading a preview.

Large Scale Log Analytics with Solr (from Lucene Revolution 2015)

More Related Content

Viewers also liked

Similar to Large Scale Log Analytics with Solr (from Lucene Revolution 2015)

More from Sematext Group, Inc.

Featured

Related Books

Related Audiobooks