Elastic stack Presentation

Introduction to Elastic
Search, Logstash, Kibana
and Beats
ELK Stack

Why LogsAre Important?!
● Application Monitoring
● Security Analysis
● Troubleshooting

Problems
● Non-consistent Log Format
● Expert Knowledge Needed
● Number Of Resources
● Running Analysis On Different Formats.
“ Can You Get POST Requests To Login
Endpoint Last Night !! “

ELK Solution
The ELK Stack is a collection of four main components:
Elasticsearch, Logstash, Kibana and beats- From Elastic.

Beats
The Beats are open source data shippers that you install as agents on your servers to
send different types of operational data to Elasticsearch. Beats can send data directly to
Elasticsearch or send it to Elasticsearch via Logstash, which you can use to parse and
transform the data.

Logstash
Logstash is an open source, server-side data processing pipeline that ingests
data from a multitude of sources simultaneously, transforms it, and then sends it
to your favorite "stash."
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
02-beats-input.conf
input {
twitter {
consumer_key => "***********"
consumer_secret => "***********"
oauth_token => "***********"
oauth_token_secret => "***********"
keywords => ["Big Data", "Blockchain","AI", "ML"]
full_tweet => true
ignore_retweets => true
languages => ["en", "ar"]
}
}

Logstash
10-sys-filter.conf
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" ]
}
}

Logstash
30-elasticsearch-output.conf
output {
elasticsearch {
hosts => ["localhost:9200"]
index => "twitter-feed"
document_type => "tweets"
template => "/etc/elasticsearch/twitter_example/twitter.json"
template_name => "twitter_elastic_example"
template_overwrite => true
}
}

Elasticsearch
Elasticsearch is a distributed, RESTful search and analytics engine
capable of addressing a growing number of use cases. As the heart
of the Elastic Stack, it centrally stores your data so you can discover
the expected and uncover the unexpected.
NoSQL(Elastic) Index Type Field Document
MYSQL Database Table Column Row

Elasticsearch
Node Types
Master Data Coordinator Ingest ML

Elasticsearch
Master Node
Master
The master node is responsible for lightweight cluster-wide
actions such as creating or deleting an index, tracking which
nodes are part of the cluster, and deciding which shards to
allocate to which nodes. It is important for cluster health to have a
stable master node.

Elasticsearch
Data Node
Data
Data nodes hold the shards that contain the documents you
have indexed. Data nodes handle data related operations like
CRUD, search, and aggregations. These operations are I/O-,
memory-, and CPU-intensive. It is important to monitor
these resources and to add more data nodes if they are
overloaded.

Elasticsearch
Coordinating Only Node
Coordinator
If you take away the ability to be able to handle master
duties, to hold data, and pre-process documents, then
you are left with a coordinating node that can only
route requests, handle the search reduce phase, and
distribute bulk indexing. Essentially, coordinating only
nodes behave as smart load balancers.

Elasticsearch
Ingest Node
Ingest
Ingest nodes can execute pre-processing pipelines,
composed of one or more ingest processors. Depending
on the type of operations performed by the ingest
processors and the required resources, it may make
sense to have dedicated ingest nodes, that will only
perform this specific task.

Elasticsearch
elasticsearch.yml
# ======================== Elasticsearch Configuration =========================
# ---------------------------------- Cluster -----------------------------------
#cluster.name: my-application
# ------------------------------------ Node ------------------------------------
#node.name: node-1
#node.attr.rack: r1
# ----------------------------------- Paths ------------------------------------
# Path to directory where to store the data (separate multiple locations by comma):
path.data: /var/lib/elasticsearch
# Path to log files:
path.logs: /var/log/elasticsearch
#---------------------------------- Network -----------------------------------
# Set the bind address to a specific IP (IPv4 or IPv6):
network.host: localhost
http.port: 9200
# --------------------------------- Discovery ----------------------------------
# The default list of hosts is ["127.0.0.1", "[::1]"]
#discovery.seed_hosts: ["host1", "host2"]
#cluster.initial_master_nodes: ["node-1", "node-2"]
-------- another --------
node.master: true
node.data: false
node.ingest: false
node.ml: false
xpack.ml.enabled: true

Elasticsearch
Data Access Via REST
Create curl –XPOST “http://localhost:9200/<index>/<type>/<id>”
Read curl –XGET “http://localhost:9200/<index>/<type>/<id>”
Update curl –XPUT “http://localhost:9200/<index>/<type>/<id>”
Delete curl –XDELETE “http://localhost:9200/<index>/<type>/<id>”

Kibana
kibana lets you visualize your Elasticsearch data and navigate the
Elastic Stack so you can do anything from tracking query load to
understanding the way requests flow through your apps.

VS
● SPL
● Schema On Read
● Memory efficient
● Standalone
Implementation
● Pay As You Go
● Enterprise
● KPL
● Schema On Write
● Memory Inefficient
● 3 or 4 Independent
Components
● Infrastructure Cost
● Medium Application

Contacts
Email: Amr.Alaa@barqsystems.com
Linkedin: https://www.linkedin.com/in/amr-alaa-yassen-609785108/
Github: https://github.com/AmrAlaaYassen

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