An Intro to Elasticsearch and Kibana

Oct 16, 2017Download as PPTX, PDF1 like3,250 views

Visualize some of Austin's open source data using Elasticsearch with Kibana. ObjectRocket's Steve Croce presented this talk on 10/13/17 at the DBaaS event in Austin, TX.

www.objectrocket.com
An Intro to Elasticsearch
and Kibana
Visualizing Austin’s Data

www.objectrocket.com
You Know, for Search
What is Elasticsearch: A distributed, real-time, document store / search engine
It easily scales to
hundreds of servers
and TBs of data
Data is searchable as
it is added
Less strict schema
or SQL-like rigidity.
Very JSONified
Best full text search
capabilities of any
technology in this space
● In Top 10 Databases by DB-Engines
● Used by customers across industries: Facebook, Uber, Tinder,
Yelp, ebay, Dell, Cisco, The Home Depot, etc.
● WHY?
○ Easy to Bootstrap
○ Multiple Use Cases
○ Community / Ecosystem
○ Compatibility / JSON RESTful

www.objectrocket.com
X-Pack ($$$)
Security
Alerting
Monitoring
Graph
Machine Learning
Kibana (FOSS)
GUI
Data Visualization
Dashboards
Elasticsearch (FOSS)
Storage
Indexing
Search
Cluster Management
Logstash (FOSS)
Extract / Transform
/ Load - and More
The Elastic Stack / Ecosystem
Beats (FOSS)
Lightweights apps
to ship data
FOSS = Free Open Source Software
FileBeat
MetricBeat
HeartBeat
PacketBeat
WinlogBeat
Curator (FOSS)
Index maintenance

www.objectrocket.com
Where can you use it?
● Mongo / CouchDB / MySQL / PostgreSQL / Kafka shop?
○ Use Elasticsearch/Kibana to visualize your data
○ Logstash + jdbc input plugin can sync from any compatible DB
○ Try our MongoDB->Elasticsearch connector (Beta)
● Hadoop shop?
○ Use Elasticsearch to add search and viz to your Hadoop data
○ Elasticsearch-Hadoop and other tools can make this easier
● Has servers of any kind shop?
○ Use Elasticsearch for log and metrics analysis and visualization
○ Filebeat / Winlogbeat make log ingest easy
○ Metricbeat / Packetbeat / Heartbeat for metrics management

www.objectrocket.com
Sample Datasets for today
Austin Restaurant Inspections (2013-2017)
Sourced from data.austintexas.gov
Rideaustin data (June 2016 - April 2017)
Sourced from data.world

www.objectrocket.com
Our Stack
FileBeat Elasticsearch &
Ingest Pipelines
.csv files
Kibana
Using open source
map tiles from
maps.stamen.com
This flow is mostly documented in the following blog:
https://www.objectrocket.com/blog/how-to/elasticsearch-ingest-csv

www.objectrocket.com
Document Example
Meta-Fields
● _index : What index is it in
● _type: document type
● _id: Unique document identifier
● _score: Computed relevance to
search
Document “Body”
● Holds whatever fields you create
● Data type is defined by mapping
● Data types include
○ Simple: booleans, numerics, dates
○ Geographic: points and shapes
○ Text: analyzed and non-analyzed
○ Special: IPs, range, percolator

www.objectrocket.com
8
API
Query
Structure
API Endpoint
Query
Filter
Bucket Aggregation
Metric Aggregation

www.objectrocket.com
Inspections Dashboard

www.objectrocket.com
Rideaustin Dashboard

www.objectrocket.com
Rideaustin - ABIA Pick-ups/Drop-offs
<Pickups
Dropoffs>

www.objectrocket.com
Rideaustin - Driver Metrics

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This document provides an overview of Elasticsearch, including: - It is a NoSQL database that indexes and searches JSON documents in real-time. Documents are distributed across a cluster of servers for high performance and availability. - Elasticsearch uses Lucene under the hood for indexing and search. It is part of the ELK (Elasticsearch, Logstash, Kibana) stack and is open source. - Documents are organized into indexes and types, similar to databases and tables. Documents can be created, updated, and deleted via a RESTful API.

Elastic - ELK, Logstash & KibanaSpringPeople

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Elk - An introductionHossein Shemshadi

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This document provides an overview and introduction to Elasticsearch. It discusses the speaker's experience and community involvement. It then covers how to set up Elasticsearch and Kibana locally. The rest of the document describes various Elasticsearch concepts and features like clusters, nodes, indexes, documents, shards, replicas, and building search-based applications. It also discusses using Elasticsearch for big data, different search capabilities, and text analysis.

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An Intro to Elasticsearch and Kibana

1. www.objectrocket.com An Intro to Elasticsearch and Kibana Visualizing Austin’s Data

2. www.objectrocket.com You Know, for Search What is Elasticsearch: A distributed, real-time, document store / search engine It easily scales to hundreds of servers and TBs of data Data is searchable as it is added Less strict schema or SQL-like rigidity. Very JSONified Best full text search capabilities of any technology in this space ● In Top 10 Databases by DB-Engines ● Used by customers across industries: Facebook, Uber, Tinder, Yelp, ebay, Dell, Cisco, The Home Depot, etc. ● WHY? ○ Easy to Bootstrap ○ Multiple Use Cases ○ Community / Ecosystem ○ Compatibility / JSON RESTful

3. www.objectrocket.com X-Pack ($$$) Security Alerting Monitoring Graph Machine Learning Kibana (FOSS) GUI Data Visualization Dashboards Elasticsearch (FOSS) Storage Indexing Search Cluster Management Logstash (FOSS) Extract / Transform / Load - and More The Elastic Stack / Ecosystem Beats (FOSS) Lightweights apps to ship data FOSS = Free Open Source Software FileBeat MetricBeat HeartBeat PacketBeat WinlogBeat Curator (FOSS) Index maintenance

4. www.objectrocket.com Where can you use it? ● Mongo / CouchDB / MySQL / PostgreSQL / Kafka shop? ○ Use Elasticsearch/Kibana to visualize your data ○ Logstash + jdbc input plugin can sync from any compatible DB ○ Try our MongoDB->Elasticsearch connector (Beta) ● Hadoop shop? ○ Use Elasticsearch to add search and viz to your Hadoop data ○ Elasticsearch-Hadoop and other tools can make this easier ● Has servers of any kind shop? ○ Use Elasticsearch for log and metrics analysis and visualization ○ Filebeat / Winlogbeat make log ingest easy ○ Metricbeat / Packetbeat / Heartbeat for metrics management

5. www.objectrocket.com Sample Datasets for today Austin Restaurant Inspections (2013-2017) Sourced from data.austintexas.gov Rideaustin data (June 2016 - April 2017) Sourced from data.world

6. www.objectrocket.com Our Stack FileBeat Elasticsearch & Ingest Pipelines .csv files Kibana Using open source map tiles from maps.stamen.com This flow is mostly documented in the following blog: https://www.objectrocket.com/blog/how-to/elasticsearch-ingest-csv

7. www.objectrocket.com Document Example Meta-Fields ● _index : What index is it in ● _type: document type ● _id: Unique document identifier ● _score: Computed relevance to search Document “Body” ● Holds whatever fields you create ● Data type is defined by mapping ● Data types include ○ Simple: booleans, numerics, dates ○ Geographic: points and shapes ○ Text: analyzed and non-analyzed ○ Special: IPs, range, percolator

8. www.objectrocket.com 8 API Query Structure API Endpoint Query Filter Bucket Aggregation Metric Aggregation

9. www.objectrocket.com Demo

10. www.objectrocket.com Discover

11. www.objectrocket.com Visualization

12. www.objectrocket.com Inspections Dashboard

13. www.objectrocket.com Rideaustin Dashboard

14. www.objectrocket.com Rideaustin - ABIA Pick-ups/Drop-offs <Pickups Dropoffs>

15. www.objectrocket.com Rideaustin - Driver Metrics

An Intro to Elasticsearch and Kibana

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