In this training webinar, we will walk you through the basics of InfluxDB – the purpose-built time series database. InfluxDB has everything you need from a time series platform in a single binary – a multi-tenanted time series database, UI and dashboarding tools, background processing and monitoring agent. This one-hour session will include the training and time for live Q&A.
What you will learn
Core concepts of time series databases
An overview of the InfluxDB platform
How to ingesting and query data in InfluxDB
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Intro to InfluxDB
1. I N F L U X D B U N I V E R S I T Y
Intro to InfluxDB
Getting Started Training Series
2. Brought to you by InfluxDB University
InfluxDB University offers free live and self-paced training on:
• InfluxDB
• Telegraf
• Flux
• Kapacitor
• and more
influxdbu.com
3. Agenda
• Why do I need a Time Series Database?
• The InfluxDB Platform
• Basic Concepts
• Demo
4. The age of instrumentation
Instrumentation
of the virtual world
(DevOps)
Sensors
in the physical world
(IoT)
5. Characteristics of
the data
• Time-stamped
• Generated in regular
(metric) and
irregular (event) time
periods
• Huge volumes
• Real time and time
sensitive
6. Time series in every application
Infrastructure & data sources
Consumer & Industrial IoT Software Infrastructure
Renewable
& alternative
energy
systems
Manufact
uring &
industrial
platforms
Fleet
management
& telematics
Real-time Applications
Developer
Tools
& APIs
Kubernetes
(K8s)
DevOps
Monitoring
Gaming
Applications
Fintech
Applications
Network
Monitoring
TIME SERIES DATA
7. Rise of time series as a category
TIME SERIES
RELATIONAL DOCUMENT SEARCH
• Distributed
search
• Logs
• Geo
• High
throughput
• Large
document
• Orders
• Customers
• Records
• Events, metrics, time stamped
• for IoT, analytics, cloud native
Time series is fastest growing
data category by far
Time series
All others
source: DB Engines
10. InfluxDB is 3 things
API &
Toolset
POWERFUL
for real-time apps
HIGH PERFORMANCE MASSIVE
for real-time data
workloads
of cloud & open source
developers
1 2 3
Time Series
Engine
Community &
Ecosystem
11. Flux Language
Functional data scripting language for query, analysis, action
1. Transform data at storage level
instead of application level
2. Use one language across entire
InfluxDB platform
1
12. Time Series Engine
Run and grow large data workloads at high volume globally
High cardinality
High Throughput
Batch & stream inputs
High & low fidelity
storage
Global service on 3
clouds
Clustered option for
on-premises
PERFORMANCE FLEXIBILITY RELIABILITY
2
15. • Designed for time series
analysis
• Easy to share, easy to extend
• Multi data source
• Open Source (MIT license)
• Easy to get started, powerful
to scale
InfluxDB – Time Series Platform
Empowers developers to build IoT, analytics, & monitoring software
16. Core focus: Developers and Builders
• Developer happiness
• Time to awesome
• Ease of scale-out &
deployment
InfluxDB – Time Series Platform
17. A powerful api & tool set for building real-time apps
Collect using hundreds
of integrations & OSS
tools
Write/Query in multiple
languages built for
real-time data
Abstract using client
libraries for your
preferred language
Manage applications &
account via the
developer console
INFLUXDATA API & TOOLS
DEVELOPER APPLICATIONS
IoT Transactions Analytics
Get started quickly
with more tools
and less code.
• Rest API
• OSS integrations
• Cloud delivery
19. Reference Architecture
Data Sources
Application
Workflows
Infrastructure
Insights
Telegraf
Client Libraries
HTTP
Syslog
Kubernetes
Apache Kafka
Python
Arduino
Node.js
JavaScript
Go
Data Systems
Mobile apps
Web apps
Cloud Services
Devices
Sensors
Databases
Networks
Message Queues
APIs
IoT Platforms
CRMs
InfluxDB Platform
IoT
Actions
InfluxDB
Purpose-Built Time Series Database
Visualization, Query & Task Engine
Collect
Downsample
Trigger
Alert
Transform
…
200+ Plugins
20+ Languages
…
New Square
Native Ecosystems
JMeter
NiFi
AWS Kinesis
Azure Event Hubs
GCP PubSub
Java
.NET/C#
PHP
Ruby
Vector
Fluentd
20. Concepts: Data Model
Bucket
• All InfluxDB data is stored in a bucket. A bucket combines the concept of a database
and a retention period (the duration of time that each data point persists).
Measurement
• A name to a group of data at a high level
Tag set
• A set of key-value pairs to group data at a low level (values are strings)
Field set
• A set of key-value pairs to represent data (values are numerical & strings)
Timestamp
• Time of the data with nanosecond precision
Series
• A unique combination of measure+tags
21. Line Protocol: Simple but powerful
• Points are written to InfluxDB using the Line Protocol, which
follows the following format:
<measurement>[,<tag-key>=<tag-value>]
[<field-key>=<field-value>]
[unix-nano-timestamp]
Reference: https://docs.influxdata.com/influxdb/cloud/reference/syntax/line-protocol/
Tag Set
hostname=server02, us_west=az
Measurement
cpu_load
Field Set
temp=24.5, volts=7
Timestamp
1234567890000000
22. Quickly Map your data for ingestion
• Our client libraries include a Point object
• Simply build a point from your data and call write
28. Zeppelin Notebooks + InfluxData
• A completely open web-based notebook that enables
interactive data analytics.
• Multi-purpose Notebook enables:
• Data Ingestion
• Data Discovery
• Data Analytics
• Data Viz and Collaboration
• Zeppelin InfluxDB interpreter makes querying data even easier
• Built in Apache Spark integration
29. Wherever your data is, InfluxDB
Cloud has tools to help you
ingest it quickly
30. Analyzing your data
• As simple as comparing ingested metrics across
hosts/containers
• As complex as your application needs it to be
• Keys to successful Analytics
• Performance - Run calculations close to the data for the best
performance
• Flexibility - Do not hit the limits of your language
32. What can you do with Flux?
• Custom aggregations
• Custom functions
• Source/Destinations functions
• Joins
• Math across measurements
• Pivot
• Histograms
• Covariance
• Double and Triple Exponential Smoothing
33. Examples of Anomaly Detection with Flux
• MAD (median absolute deviation) across multiple series to
detect a series that is “deviating from the pack”
• Writing a Naive Bayes classifier from scratch.
34. Other Analytics Capabilities
• Supports existing InfluxQL users (simple SQL-like syntax)
• Background processing for custom or pre-calculated metrics
• APIs to build custom analytics
35. Ingesting data is only valuable if
you can analyze that data
at scale in real-time
36. Acting on that Data
• Once your data is analyzed,
act on it
• Serve it to your application’s
users
• Alert on your data
38. Enable organizations to make
cost–disruptive decisions
on high volumes
of time-sensitive data
Influxdb cloud: the time series platform for your data applications
S U M M A R Y
39. Learn more at influxdata.com
Come hang out with us!
Slack Community
41. Run apps in
production with
absolute confidence
on the only
purpose-built time
series engine.
High Speed Ingest
via both batch &
streaming
Flexible Schema
learns & adapts
as it goes
High & Low Fidelity
retention &
storage
Managed Functions
hosted in
the cloud
A HIGH-PERFORMANCE ENGINE TO HANDLE REAL-TIME
DATA WORKLOADS
INFLUXDATA API & TOOLS
INFLUXDATA REAL-TIME ENGINE
TIME SERIES
DATABASE
42. INFLUXDB ENTERPRISE
Self Managed, High Availability
& Secure
priced per node
INFLUXDB OSS
Open source
time series database.
High performing, Schemaless,
Smart extraction of data (raw,
sliding, aggregates)
INFLUXDB CLOUD
Elastic Serverless
Time Series as a Service
pay per use
PRODUCT OFFERINGS
•INFLUXDB PLATFORM