We are always looking for ways to make our solutions work better and smarter. We accomplish this by tracking the performance of each of the components underlying our solution. All this critical performance data has a time stamp and a value - also known as time series data. If this important time-stamped data is at the heart of initiatives to keep things performant, why are we entrusting this data to an ordinary relational database? In this webinar, Daniella Pontes, Product Marketing Manager at InfluxData, will review why you should use a Time Series Database (TSDB) for your important Times Series Data and not one of the traditional datastore you may have used in the past, She will discuss how Time Series Databases are built with specific workloads and requirements in mind, including the ability to ingest millions of data points per second; to perform real-time queries across these large data sets in a non-blocking manner; to downsample and evict high-precision low-value data; to optimize data storage to reduce storage costs; and to perform complex time-bound queries to extract meaningful insight from the data. All capabilities you would have to build yourself when using a traditional database.

1. Daniella Pontes, Product Marketing
February 2019
Gain a competitive edge with an
Open Source, purpose-built time
series database

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Agenda
✓ Why collect Time-stamped data
✓ What is time series data
✓ Differences between Time Series
Databases (TSDBs)
✓ InfluxDB Data model

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Why collect time stamped data
Custom monitoring
solutions to track
servers, VMs,
applications, users or
events
Industrial settings:
factories, oil & gas,
agriculture, smart roads &
infrastructure
Consumer: wearables,
consumer devices & trackers
Apps that instrument
business, social or
development metrics in
real-time
IoT DevOps Real-Time Analytics

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2018 Database Technology Survey (Dzone)
Are you currently collecting data with timestamps (e.g. when
an event happened or when data was collected) associated
with it?
19.4%
No
Ye
s
80.6%

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Regular vs Irregular Time Series
Metrics (Regular)
Events (Irregular)
Measurements
gathered at regular
time intervals
Measurements
gathered at irregular
time intervals

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Regular Time Series Irregular Time Series
Measurements
gathered at regular
time intervals
Measurements
gathered at irregular
time intervals
Metrics Events

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Summarization of Events
Events become regular time intervals, for example
Summarizing the average
trade price of Apple stock
every 10 minutes over the
course of a day
Summarizing the average
response time for requests
in an application over 1
minute intervals

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Characteristics
of the Data
✓ All Time-stamped data
✓ Generated in regular (Metric) and
irregular (event) time periods
✓ Huge volumes of data
✓ Real-time and time sensitive

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What is a Time Series database?

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Time Series databases
are optimized for
collecting, storing,
retrieving & processing
of Time Series data
Compare this to
• Document databases
Optimized for storing JSON documents
• Search databases
Optimized for full-text searches
• Traditional relational
Databases optimized for the tabular
storage of related data in rows & columns

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Some Databases used for Time Series
Other DB
Types
Time Series DBs

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How do you persist Time Series data?
2018 Database Technology Survey (Dzone)
5%
12%
18%
65%
We have yet to discover an effective option
With a relational database
With a non-time-series-specific NoSQL
database
With a specialized time-series database
(e.g. InfluxDB, KDB, Druid, Prometheus)

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2018 Database Technology Survey (Dzone)
What DBMS are you considering?

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Time Series and InfluxData
Time Series fastest growing category InfluxData is the leading time-series database
Trend of the Last 24 Months DB Engine Scores of the Last 24 Months
Source: DB-Engines Source: DB-Engines

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Why InfluxData

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Platform Strategy:
Be The Platform of Choice for All Metrics and Event Workloads
Common Metrics
and Events
Platform
Application,
Custom Logs &
Traces
Business
Metrics
Infrastructure
& Application
Metrics
IoT
Sensor
Events

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InfluxData Platform Features
INSTRUMENT OBSERVE
AUTOMAT
E
LEARN
✓ Quickly ingest data from
everywhere
✓ Efficiently store (Compress) the
data at scale
✓ Support real-time query,
analysis and visualization of
large data sets
✓ Provide time-based functions
for “change over time” analysis
and control
✓ Provide automation and control
functions
✓ Evict and down-sample data
✓ Facilitate machine learning and
anomaly detection algorithms
✓ Provide streaming analytics for
data in motion
METRICS
EVENTS

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InfluxData Products and Offerings

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Open Source, InfluxEnterprise and InfluxCloud

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Why Choose
InfluxDB
• Easy to get started with
• Familiar query syntax
• No external dependencies
• Allows for regular and irregular time series
• Horizontally scalable
• Member of a cohesive time series platform

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InfluxDB Data Model

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Points in InfluxDB look like…
stock_price,ticker=A,market=NASDAQ price=177.03 1445299200000000000
stock_price,ticker=AA,market=NYSE price=32.10 1445299200000000000
stock_price,ticker=AAPL,market=NASDAQ price=45 1445299200000000000a

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The Line protocol
measurement,tagset fieldset timestamp
stock_price,ticker=A,market=NASDAQ
price=177.03 14452992000000000

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Concepts: Time Series Database Schema
Data Ingestion Format
• 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]
cpu_load,hostname=server02,az=us_west temp=24.5,volts=7
1234567890000000
Reference: https://docs.influxdata.com/influxdb/v1.4/write_protocols/line_protocol_reference/
Measurement Tag Set Field Set
Timestamp

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A Series in InfluxDB
measurement + tagset = the series as a whole
measurement + tagset + timestamp = single point

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Examples of points in Line Protocol
cpu,host=server1 value=100 1445299200000000000
temperature,zipcode=94107,country=usa
value=75,humidity=10
response_time,method=GET,precision=ms
value=12i 1445299200000000000

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Demo

28. Question?