Intro to Time Series
1 like510 views
This document provides an introduction to time series data and InfluxDB. It defines time series data as measurements taken from the same source over time that can be plotted on a graph with one axis being time. Examples of time series data include weather, stock prices, and server metrics. Time series databases like InfluxDB are optimized for storing and processing huge volumes of time series data in a high performance manner. InfluxDB uses a simple data model where points consist of measurements, tags, fields, and timestamps.
![Data Ingestion Format
• Points are written to InfluxDB using the Line Protocol, which
follows the following format:
Reference: https://docs.influxdata.com/influxdb/cloud/reference/syntax/line-protocol/
<measurement>[,<tag-key>=<tag-value>] [<field-key>=<field-value>]
[unix-nano-timestamp]
cpu_load,hostname=server02,us_west=az temp=24.5,volts=7 1234567890000000
Measurement Tag Set Field Set Timestamp](https://image.slidesharecdn.com/intrototimeseries-20220509-220511202748-6d465e01/85/Intro-to-Time-Series-44-320.jpg)
Ad
More Related Content
What's hot (20)
Similar to Intro to Time Series (20)
![Alan Pope [InfluxData] | Data Collectors | InfluxDays 2022](https://cdn.slidesharecdn.com/ss_thumbnails/dem03datacollectorsdemoalanpope-221020204223-95e29bc9-thumbnail.jpg?width=560&fit=bounds)
Ad
More from InfluxData (20)
![Ward Bowman [PTC] | ThingWorx Long-Term Data Storage with InfluxDB | InfluxDa...](https://cdn.slidesharecdn.com/ss_thumbnails/influxdays-221027185325-5d2f430b-thumbnail.jpg?width=560&fit=bounds)
![Scott Anderson [InfluxData] | New & Upcoming Flux Features | InfluxDays 2022](https://cdn.slidesharecdn.com/ss_thumbnails/influxdays2022-fluxupdates-scott-221021210238-9d323cba-thumbnail.jpg?width=560&fit=bounds)
![Steinkamp, Clifford [InfluxData] | Closing Thoughts | InfluxDays 2022](https://cdn.slidesharecdn.com/ss_thumbnails/influxdays2022closingthoughtsday2-221020220104-abde55ea-thumbnail.jpg?width=560&fit=bounds)
![Steinkamp, Clifford [InfluxData] | Welcome to InfluxDays 2022 - Day 2 | Influ...](https://cdn.slidesharecdn.com/ss_thumbnails/influxdays2022welcometoday2-221020215815-c8463942-thumbnail.jpg?width=560&fit=bounds)
![Steinkamp, Clifford [InfluxData] | Closing Thoughts Day 1 | InfluxDays 2022](https://cdn.slidesharecdn.com/ss_thumbnails/influxdays2022closingthoughtsday1-221020215301-f8040e1f-thumbnail.jpg?width=560&fit=bounds)
Ad
Recently uploaded (20)
Intro to Time Series
- 1. I N F L U X D B U N I V E R S I T Y Introduction to Time Series 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. Why Time-Series for Monitoring, Metrics, Real-Time Analytics and IoT/Sensor Data • What is Time Series Data • Differences between Time Series Databases (TSDBs) • InfluxDB Data model
- 4. What is time series data?
- 5. Time series data is • A sequence of data points, typically consisting of successive measurements made from the same source over a time interval. • Plot the points on a graph and one of your axes would always be time.
- 6. Let’s start by example • Weather condition
- 7. Let’s start by example • Weather condition • Stock exchange
- 8. Let’s start by example • Weather condition • Stock exchange • Cluster monitoring
- 9. Let’s start by example • Weather condition • Stock exchange • Cluster monitoring • Healthcare
- 10. Now something different • Logs
- 11. Now something different • Logs • Traces
- 12. Let’s start by example - What’s different? VS.
- 13. Regular vs Irregular Time Series Metrics (Regular) Events (Irregular) Measurements gathered at regular time intervals Measurements gathered at irregular time intervals
- 14. Measurements gathered at regular time intervals Measurements gathered at irregular time intervals METRICS EVENTS Regular Time Series Irregular Time Series
- 15. 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
- 16. What is a time series database?
- 17. Characteristics of the data • Time-stamped • Generated in regular (metric) and irregular (event) time periods • Huge volumes • Real time and time sensitive
- 18. 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
- 19. Other DB Types Time Series DBs Some Databases used for Time Series
- 20. Time Series fastest growing category InfluxDB is the leading time-series database Time series and InfluxDB
- 22. Primary Use Cases IoT Monitoring Developer Tools Real-time Analytics Industrial Enterprise Consumer DevOps Networks Security Cloud Applications APIs Gaming Renewable Energy FinTech Crypto
- 23. Primary Use Cases 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 Real-Time Analytics IoT DevOps
- 24. Key Uses Cases Amazon Timestream IoT DevOps Analytics
- 25. 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
- 26. API & Toolset Get started quickly with more capability and less code RESTful API Open Source Integrations Functional Query Language Cloud delivery 1
- 27. 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
- 28. 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
- 29. Community & Ecosystem Meet developers where they already build and operate 3 Cloud Language & Tool
- 30. POWERFUL ANALYTICS Open source flux language to see across time • Designed for time series analysis • Easy to share, easy to extend • Multi data source • Open Source (MIT license) • Easy to get started but powerful to scale
- 31. 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
- 32. Why Choose InfluxDB • Easy to get started with a familiar query syntax • No external dependencies • Allows for regular and irregular time series • Horizontally scalable • Member of a cohesive time series platform
- 34. A typical time series graph
- 35. The Label We call this the measurement
- 36. The Legend (metadata) We call these tags. Tags are indexed.
- 37. Collection of all tags We call this the tagset.
- 38. Y-Axis Values We call these fields. Note that the values that the field stores can be floats, ints, strings, or bools.
- 39. The Collection of Fields We call this the fieldset. Note that in this case, there's only one field. (There could be many.) price=177.03 price=32.10 price=35.52
- 40. X-Axis Value We call this the timestamp.
- 41. How do we represent points textually?
- 42. 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
- 43. The Line protocol • measurement,tagset fieldset timestamp • stock_price,ticker=A,market=NASDAQ price=177.03 14452992000000000
- 44. Data Ingestion Format • Points are written to InfluxDB using the Line Protocol, which follows the following format: Reference: https://docs.influxdata.com/influxdb/cloud/reference/syntax/line-protocol/ <measurement>[,<tag-key>=<tag-value>] [<field-key>=<field-value>] [unix-nano-timestamp] cpu_load,hostname=server02,us_west=az temp=24.5,volts=7 1234567890000000 Measurement Tag Set Field Set Timestamp
- 45. A Series in InfluxDB measurement + tagset = the series as a whole measurement + tagset + timestamp = single point
- 46. Examples of points in Line Protocol • cpu,host=server1 value=100 1445299200000000000 • temperature,zipcode=94107,country=usa value=75,humidity=10 1445299200000000000 • response_time,method=GET,precision=ms value=12i 1445299200000000000
- 47. Use Cases
- 48. Created a solution that • allows their business to be data-driven • uncovers new revenue streams • provides predictive maintenance by collecting over half a billion real-time data points nightly from solar panels and batteries with Use Case REAL-TIME ANALYTICS
- 51. Created a solution that • creates a better customer experience across their web and mobile properties • reduces downtime attributed to CICD • provides faster root–cause analysis by storing, analyzing and visualizing click stream data, container and application metrics across all web infrastructure services with Use Case REAL USER MONITORING
- 52. Why replace Graphite w/ InfluxDB Graphite limitations ✔ No clustering ✔ Storage infrastructure that is difficult to manage ✔ No out-of-the-box data pipeline solution ✔ Graphite maxed out its performance potential ✔ No shard relocation & backup/restore InfluxDB Advantages ✔ Rapid performance improvements & hardening ✔ Supports true clustering ✔ Written in Go, a language that fully exploits multi-core environments ✔ Built with an emphasis on efficient & scalable storage ✔ Active community & an ecosystem of related tools for building enterprise-wide installations ✔ Growing customer base from a diverse set of companies
- 53. Join Our Community Slack: http://influxdata.com/slack Community Forum: https://community.influxdata.com Reddit: https://reddit.com/r/influxdb Twitter: @influxdb
- 54. www.influxdbu.com