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ELK Stack
Introduction to Elasticsearch, Logstash and
Kibana
github.com/phucnguyenvn
Agenda
• The story
• ELK Stack
• Elasticseach
• Logstash
• Kibana
• Demo
The story
Log
Log
Log
Log
Log
Log
Log
Log
Log
The story
“Can you check the errors from
yesterday between 15.02 and 15.07 ?!”
The story
The story
“Can you check the errors from
yesterday between 15.02 and 15.07 ?!”
SOLUTION
Visualization > Plaintext
The story
The story
The story
ELK Stack
The ELK Stack is a collection of
three open-source products:
Elasticsearch, Logstash, and
Kibana — from Elastic.
ELK Stack
ELK Stack
ELK Stack
Elasticsearch
SELECT * FROM `liv-dev`
WHERE `name` LIKE ‘%phuc%’
Elasticsearch
Elasticsearch
Elasticsearch
SELECT * FROM `liv-dev` j
JOIN jobs_benefits jb ON j.id =
jb.job_id
WHERE j.role = ‘DEVELOPER’
AND (MATCH(job_description)
AGAINST (‘node.js -asp’ IN BOOLEAN
MODE)
AND jb.free_coffee = TRUE
Elasticsearch
Apache lucene
•“Information retrieval software library”
•Free/open source
•Supported by Apache Foundation
•Created by Doug Cutting
•Written in 1999
Elasticsearch
Elasticsearch
Elasticsearch
• Open source search server written in
Java
•Used to index any kind of
heterogeneous data
•Enables real-time ability to search
through index
•Has REST API web-interface with
JSON output
Elasticsearch
•RESTfull Service
•JSON API over HTTP
•Want to use it from PHP?
•CURL Requests, as if you’d do
requests to the Facebook Graph
API.
•High Availability & Performance
•Clustering
•Long Term Persistency
•Write through to persistent
storage system.
Elasticsearch
• Based on top Lucence
• Multi-language support
• Geolocation support
• Full-text search
• Did-you-mean suggestions
• Extremely speed
• Support RESTful
• Text and number
• Scoring
Elasticsearch
Elasticsearch
MySQL Elastic Search
Database Index
Table Type
Row Document
Column Field
Schema Mapping
Index Everything is indexed
SQL Query DSL
SELECT * FROM table … GET http://…
UPDATE table SET … PUT http://…
Terminology:
Logstash
•Framework for managing logs
•Founded by Jordan Sissel
•Mainly consists of 3 components:
● input : passing logs to process them
into machine understandable
format(file,lumberjack).
● filters: set of conditionals to perform
specific action on a event(grok,geoip).
● output: decision maker for processed
event/log(elasticsearch,file)
Logstash
• Multiple Input /MultipleOutput
• Centralize Logs
• Collect
• Parse
• Store /Forward
Logstash
Logstash
Understanding Grok
Why grok?
actual regex to parse apache logs
Logstash
Understanding Grok
•Understanding grok nomenclature.
•The syntax for a grok pattern is %{SYNTAX:SEMANTIC}
•SYNTAX is the name of the pattern that will match your text.
● E.g 1337 will be matched by the NUMBER pattern,
254.254.254 will be matched by the IP pattern.
•SEMANTIC is the identifier you give to the piece of text being
matched.
● E.g. 1337 could be the count and 254.254.254 could be a
client making a request
%{NUMBER:count} %{IP:client}
Logstash
Logstash
Processing example
127.0.0.1 - - [05/Feb/2014:17:11:55 +0000] "GET /css/main.css HTTP/1.1" 200 140
"http://www.onet.pl" "Mozilla/5.0 (Windows NT 6.0; WOW64; rv:2.0.1)
Gecko/20100101 Firefox/4.0.1"
{
"host" :"127.0.0.1",
"@timestamp" : "2014-02-05T17:11:55+0000",
...
"verb" : "GET"
}
Logstash
Kibana
•Powerful front-end dashboard for visualizing
indexed information from elastic cluster.
•Capable to providing historical data in form of
graphs,charts,etc.
•Enables real-time search of indexed information.
Kibana
Kibana
Kibana
Demo
AAIO Proxy logs
Demo
Demo
"[%{TIMESTAMP_ISO8601:date_time:date}](%{BAS
E10NUM:pid})(%{IP:ip:ip})[%{LOGLEVEL:log_level}
][%{WORD:class:keyword}::(?<method:keyword>[
a-zA-Z_]+)] response http
%{BASE10NUM:return_code}|(?<response_phrase>[
a-zA-Z ]+)|%{NUMBER:response_time:float}
[host:%{URI:base_url}|login:%{USER:user}|server:
%{HOSTNAME:server_name}|session.v6:(?<session_
v6>[a-zA-Z0-9=]+)|session.v7:(?<session_v7>[a-z0-
9]+)|session.asp:(?<session_asp>[a-z0-
9]+)|(?<verb_uri>(GET|POST|PUT|PATCH|DELETE)[a
-zA-Z0-9-/. ]+)]"
input {
file {
path => "/tmp/*.log"
start_position => "beginning"
sincedb_path => "/dev/null"
}
}
filter {
grok {
match => {
"message" => [“..GROK..”]
}
}
if "_grokparsefailure" in [tags] { drop {} }
#Predefined date_time
date {
match => [ "date_time", "ISO8601","YYYY-MM-dd HH:mm:ss" ]
target => "date_time"
locale => "en"
}
mutate
{
remove_field => [ "message","host","@version","path","tags","@timestamp" ]
}
}
output {
elasticsearch {
hosts => "https://elasticsearch.com"
ssl => true
index => "logstash-test-01"
}
stdout { codec => rubydebug }
}
Thank you!

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Hinweis der Redaktion

  1. But search can go deeper and more into detail too .. Here I’m using AND, OR, NOT Nesting Restrictions on fields
  2. And the library analogy for a MySQL index is this … An index card box.
  3. Problems arise when you have lots of data … To speed things up you add indeces to your MySQL tables.
  4. Or very difficult … Searching in a mixed set of data Profiles Photos Friend connections Searching in a graph …