Docker Logging and analysing with Elastic Stack

1. Docker Logging and analysing with Elastic Stack JAKUB HAJEK, jakub.hajek@cometari.com, @_jakubhajek November 2019, Warsaw www.devopsdays.pl

2. Introduction • I am the owner and technical consultant working for Cometari • I have been system admin since 1998. • Cometari is a solution company implementing DevOps culture, providing consultancy, workshops and software services. • Our areas of expertise are DevOps, Elastic Stack (log analysis), Cloud Computing. • We are very deeply involved in the travel tech industry, however our solutions go much further than just integrating travel API’s.

3. — “I strongly believe that implementing DevOps culture, across the entire organisation, should provide measurable value and solve the real issue rather than generate a new one.”

4. Agenda • A little bit of the theory about logs. • The major difference with old fashioned approach comparing to container world. • Distributed logging with Elasticsearch and Fluentd • Demo of logging based on live demos: • A simple example sending logs from container to Fluentd • Fully ﬂedged environment running on Docker Swarm with deployed: Elasticsearch Cluster, Kibana and Fluentd • Deployed application stack contains multi tier application stack including Traeﬁk frontend and backend application

5. For what for do we need to collect logs?

6. What are logs?

7. What are logs? • Logs are the stream of aggregated, time ordered events collected from the output stream • The output stream can be generated by processes and backing services • Raw logs are typically a text format with one event per line • Backtraces from exceptions are usually multiline • Logs have no beginning or end but ﬂows continuously as long as the app is operating.

8. Logging considerations • Logging is not cheap. Requires lots of computing: storage, cpu, memory. • Logging can be even expensive if you want to search against logs and correlate data. • Having “LIVE” data accessible immediately can be even more expensive. • Don’t log everything, consider which data you are interested in (it’s not for free) • Logging retention time have to be considered (Curator if you store logs in Elasticsearch) • I recommend Elasticsearch to keep logs as a time based data. It requires some experience with Elasticsearch to provide reliable environment for logs. • Logging is a mess ; Logging is not fun but we have to deal with it and build logging solution

9. Logging in production • Service logs • Web access logs • Transaction Logs • Distributed tracing • System Logs • Syslog, system and other logs • Audit logs • Basic operating system metrics (CPU, memory, load …) Logs for Business KPI Machine Learning Predctive analytics … Logs for Service System monitoring Bottleneck Troubleshooting …

10. Logging is not the same as Monitoring • Logging is recording to diagnose a system • Monitoring is an observation, checking and than recording • A Notification ( usually called alerts) can be send out to any notification channels for both: logging and monitoring • The notification can be triggered when specific criteria is met. e.g. Http_requests_response_code is 500 in the last 60 seconds A plugin had an unrecoverable error. Will restart this plugin. Pipeline_id:main_dlq Plugin: <LogStash::Inputs::DeadLetterQueue pipeline_id=>"main", path=>"/usr/share/logstash/data/dead_letter_queue", id=>"830027210528f50ad1234fe96f0ccc5f8a6989bb0b2d944881373ec56e555357", commit_offsets=>true, enable_metric=>true, codec=><LogStash::Codecs::Plain id=>"plain_32044710-aeb5-4303-ba0e-2feb2dd851e9", enable_metric=>true, charset=>"UTF-8">> Error: Exception: Java::JavaNio::BufferOverflowException Stack: java.nio.HeapByteBuffer.put(java/nio/HeapByteBuffer.java:189) eas_errors{errorType=“CONTENT”,provider=“HRS",requestName="HotelAvailability", errorId=“1234",errorSeverity="2",startDate="2019-11-20T22:00:00",endDate="2019-11-21T21:59:59",} 10.0

11. Standard approach of logging to ﬁle system

12. Writing message to a log ﬁle Application LOGS Logging} Files -> Multiple places (usually /var/log)

13. Application STDOUT 1 STDERR 2STDIN Standard I/O Streams { echo "stdout"; echo "stderr" 1>&2; } | grep -v std STDOUT 1 STDERR 2 Logging in container World!

14. The container world Bare metal Container world Service architecture Monolithic Microservices System image Mutable Immutable Local data Persistent Ephemeral Network Physical Address No ﬁxed address Environment Manually / Automation Orchestration tools Logging syslogd/rsync ? *There is nothing wrong with monolithic system unless you can distinguish boundaries in the system and move that domain to the service on demand !

15. What are the challenges with logs in container world?

16. Logging challenges with Containers • No permanent storage (Container are stateless and storage is Ephemeral • No ﬁxed physical address. • No ﬁxed mapping between server and roles • Lots of various application types • Transfer logs immediately to distributed logging infrastructure • Push logs from containers • Labels logs with service name or use tags • Need to handle various logs with regexp, GROK

17. Logging and Docker containers

18. Logging and Docker container strategy • Application should writes a message to the STDOUT STDOUT APPLICATION running in Docker container Hello World!

19. Logging and Docker container strategy • Message encapsulated in a JSON map (with JSON driver) structure via Docker. Hello World! { “log” : “hello World!”, “stream”: “stdout”, “time”: “timestamp" }

20. Logging and Docker container strategy /var/log/docker/containers/00fae94d9a721bec312dba411… f55f37e37/00fae94d9a721bec312dba41168231…6303f274f55f37e37-json.log $: docker run -d busybox echo -n “Hello World!" 00fae94d9a721bec312dba411682313a4ab8846f01f7b406303f274f55f37e37 > cat 00fae94d9a721bec312dba411682313a4ab8846f01f7b406303f274f55f37e37- json.log { “log”:”HelloWorld!", “stream":"stdout", “time":"2019-11-21T14:12:01.599413578Z" }

21. Application running in a cluster

22. Node1 Node10 Node2 Node3 Node…nNode20 Region A Region B Region C Region D

23. What is the approach to the logging in the container world?

24. Treat logs as an event stream

25. Treat logs as an event stream • Application should be stateless and does not store data / logs locally. • Logs should not attempt to write to local storage • Logs should not be managed locally, e.g. logrotate • All logs should be treated as an event streams • Each running process writes its event to STDOUT and STDERR • In container based environment logging should be sent to STDOUT

26. Logging in the context of distributed cluster

27. HOTELS INSURENCES RAILS CARS FLIGHTS INVOICESPAYMENTSPROFILESSEARCH API GATEWAY USERS PROFILES PAYMENTS INVOICESSEARCHFLIGHTS CARS HOTELS RAILS INSURANCE APP LOGS API GATEWAY Live aggregated logs KPI, Dashboards Analytics grep / awk / Perl :-)

28. Log collectors for Central logging • Logstash from Elastic Stack, Fluentd, Apache Flume and many more… LOGS LOG COLLECTOR STORAGE • Example storage options: • S3, MongoDB, Hadoop, Elasticsearch • ﬁle, forward, copy, stdout (useful for debugging)

29. Fluentd data collector • An extensible and reliable data collection. • Uniﬁed Logging Layer - treats logs as JSON • Pluggable Architecture • Supports memory and ﬁle based buffering to prevent internode data lost • Built-in HA and load balancing

30. CORE • Divide and conquer • Buffering and retries • Error Handling • Message routing • Parallelism PLUGINS • Read data • Parse data • Buffer data • Write data • Format data

31. Unifying logging layer Services Services Collector nodes Aggregator Nodes Elasticsearch Fluentd Application generates logs Convert raw log data in a structured data Aggregated structured data Structured Data Ready for analysis

32. An event in Fluentd TAG: myapp.access TIME: (current time) RECORD: {“event”: “data”}

33. INPUT PARSER FILTER BUFFER OUTPUT FORMATTER Internal architecture of plugins “input-ish” “output-ish”

34. TAG TIME RECORD ROUTER input - filter Output Chunk Chunk Chunk Metadata Metadata Metadata BUFFER Chunk QUEUE Chunk ChunkChunk Chunk Process Format Write Try _write OUTPUT EMIT ENQUEUE source: https://docs.ﬂuentd.org/output

35. Brief overview of configuration • <source> where all the data come from, routing engine • <match> Tell Fluentd what to do! • <filter> Event processing pipeline • INPUT -> filter 1 -> …. -> filter N -> OUTPUT • <system> - system directive • <label> use for grouping filter and output for internal routing • @include split config into multiple files and re-use configuration Source: https://docs.fluentd.org/configuration/config-file

36. <source> @type forward port 24223 bind 0.0.0.0 tag backend.invoice </source> <filter **> @type parser key_name log reserve_data true hash_value_field log …. <parse> @type multi_format <pattern> format json </pattern> … </parse> </source> <match **> @type Elasticsearch host “#{ENV[‘ES_HOST’]}” Port 9200 id_key hash remove_keys hash type_name doc logstash_format true logstash_dateformat %Y.%m logstash_prefix logs logstash_tag_key true tag_key serviceTagName … <buffer tag> @type memory flush_thread_count 2 </buffer> </match> <source> @type forward port 24224 bind 0.0.0.0 </source> <match backend.*> @type mongo Database fluent Collection test </match>

37. Docker ﬂuentd driver • The logging driver sends container logs to Fluentd in as structured log data • Metadata: container_id, container_name, source, logs • —log-driver fluentd —log-opt tag=docker.{{.ID} —log-opt fluentd- address=tcp://fluenthost • Messages are buffered until connection is established. • The data can be buffered before ﬂushing • Retry, max-retry, sub-second-precision…

38. Architecture of the demo environment

39. FLUENTD ELASTICSEARCH KIBANA

40. TRAEFIK Frontend BACKEND

41. Live demo & Examples of code

42. JAKUB HAJEK, JAKUB.HAJEK@COMETARI.COM, @_jakubhajek I'm waiting for your feedback! You can rate speakers and lectures using our official conference app

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