1.
Introducing (B)ELK stackIntroducing (B)ELK stack
BBeatseats
EElasticSearchlasticSearch
LLogStashogStash
KKibanaibana
Bart Van Bos - 11/07/2016

2.
(B)ELK – General Terminology(B)ELK – General Terminology
● Beats - ElasticSearch – LogStash – Kibana

3.
(B)ELK – Functional Flow(B)ELK – Functional Flow
● Back pressure – buffer points (Kafka) !!!

4.
(B)ELK – Architecture(B)ELK – Architecture
● ELK Architecture @ LinkedIn
– Ref: http://www.slideshare.net/TinLe1/elk-atlinked-in

5.
Step 1 – BeatsStep 1 – Beats
● Beats are lightweight shippers for (log) data
● Packetbeats for analysing complex distributed
applications and troubleshooting
● Topbeats for shipping resource utilization
metrics
● Filebeats for shipping log files
● Community beats
– httpbeat, pingbeat, apachebeat, dockerbeat,
nginxbeat, uwsgibeat, phpfpmbeat

6.
Step 1 – Beats – PacketbeatsStep 1 – Beats – Packetbeats
● Packetbeat use cases (example demo here)
– REST API monitoring: response times, HTTP error
codes, …
– DB monitoring: 10 slowest SQL queries
● Protocol support: DNS, HTTP, MySQL, PgSQL,
MongoDB, Memcache, Redis, Thrift-RPC

7.
Step 1 – Beats – PacketbeatsStep 1 – Beats – Packetbeats
● Packetbeat caveat – performance impact
● Traffic capturing options
– pcap / af_packet / pf_ring: use af_packet on AWS!
– memory mapped sniffing
– 200k packets per second before dropping packets

8.
Step 1 – Beats – TopbeatsStep 1 – Beats – Topbeats
● Topbeat use cases
– System wide stats: hooked onto the Linux top
command for system load, used/idle times,
free/used memory
– Per process stats: Process name, PID, CPU time,
memory size
– File system stats: Device name, mount point,
available disk space, used disk space

9.
Step 1 – Beats – FilebeatsStep 1 – Beats – Filebeats
● Filebeat components

10.
Step 1 – Beats – FilebeatsStep 1 – Beats – Filebeats
● Filebeat properties
● Send at least once by confirmation
● Handles log rotation
● Last reading state in case you restart your
system of LogStash is not reachable => upon
revive it will send all missing logs
● By default send new log lines every 10
seconds

11.
Step 2 – LogStash – IntroductionStep 2 – LogStash – Introduction
● LogStash functional flow
– Inputs: beats, syslog, stdin, S3, Redis, Kafka, ...
– Filters: using GROK (regex templating)
– Outputs: ElasticSearch, eMail, exec, Redis, Kafka,
Zabbix, ...

12.
Step 2 – LogStash – TipsStep 2 – LogStash – Tips
● LogStash Tips
– Check predefined GROK patterns (don’t re-invent
the wheel)
● http://grokconstructor.appspot.com/groklib/grok-patterns
– Use online tool to test your GROK filters!
● http://grokconstructor.appspot.com/do/match
– Don’t forget the Kibana re-indexing feature before
making new visualizations!
● https://rafaelmt.net/en/2015/09/01/kibana-tutorial/#refresh-
fields
– Keep logstash configuration files (c)lean

13.
Step 2 – LogStash – ConfigurationStep 2 – LogStash – Configuration
● LogStash: configuration example

14.
Step 3 – ElasticSearchStep 3 – ElasticSearch
● ElasticSearch
– Distributed, open source search and analytics engine
– Uses JSON Documents, is schema-less and RESTful
– Based on Lucene (Java): reverse indexing
– Performance profile:
● Slow in write (re-indexing)
● Fast in read => analysis

15.
Step 4 – KibanaStep 4 – Kibana
● Kibana
– Open source data visualization platform
– Interact with your data through powerful graphics
– Ongoing battle against Apache Solr
● Kibana dashboards per client => a 4x win
– DevOps (ssh/grep/alerting)
– Developers (performance analysis, API optimization)
– PM (pro-active vs. fire extinguishing)
– Customers => new revenue streams!
● Technical SEO
● Business Intelligence

16.
DEMO TIMEDEMO TIME
Bart Van Bos - 11/07/2016