Paul Dix [InfluxData] | InfluxDays Opening Keynote | InfluxDays EMEA 2021

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InfluxData

In this session, InfluxData Founder and CTO Paul Dix will open the conference with an update on InfluxDB IOx.

Technologie

Paul Dix
InﬂuxData – CTO & co-founder
@pauldix
InﬂuxDB IOx
overview and
updates

© 2021 InﬂuxData. All rights reserved. 10
| Project Goals
• Time series & events
– Metrics (sensor or server)
– Tracing (or any event, really)
– Logs (semi-structured events)
• Dashboards/monitoring AND Analytics
• Unlimited cardinality
• Decoupled compute & storage
• Bulk import/export
• Replication & Subscriptions
• Embeddable scripting
• Broader ecosystem integration

© 2021 InﬂuxData. All rights reserved. 15
| Operational Environments
• Public Clouds (AWS, GCP, Azure)
• Kubernetes
• Other data center
• Edge devices

© 2021 InﬂuxData. All rights reserved. 20
| InﬂuxDB IOx Highlights
• In-memory columnar database
• Object store for persistence
• Supports SQL natively
• Flux & InﬂuxQL support through API

© 2021 InﬂuxData. All rights reserved. 21

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© 2021 InﬂuxData. All rights reserved. 27
| arrow-rs
• RecordBatch & compute kernels
• Parquet reader/writer
• Arrow Flight RPC layer
• DataFusion SQL parser, planner, executor

© 2021 InﬂuxData. All rights reserved. 28
&

How data is organized
telegrafdb
2021-05-14T10:00 2021-05-14T11:00 2021-05-14T12:00
cpu mem cpu mem
0 1 0 1
Database (bucket)
Partition
Table (measurement)
Chunk

© 2021 InﬂuxData. All rights reserved. 35
| Mapping InﬂuxDB to IOx & SQL
• Bucket = database
• Measurement = table
• Tag = column (dictionary encoded)
• Field = column
• Time = column

© 2021 InﬂuxData. All rights reserved. 36

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| Backing Object Stores
• Memory
• Locally attached disk
• S3
• Azure blob store
• Google Cloud Storage (objects)
• Anything compatible

© 2020 InﬂuxData. All rights reserved.
40
2815898179/
my_db/
data/
2020-01/
0/
sensors.parquet
stocks.parquet
1/
sensors.parquet
2020-02/
0/
sensors.parquet
1/
sensors.parquet

© 2020 InﬂuxData. All rights reserved.
41
2815898179/
my_db/
data/
2020-01/
0/
sensors.parquet
stocks.parquet
1/
sensors.parquet
2020-02/
0/
sensors.parquet
1/
sensors.parquet
Writer ID

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42
2815898179/
my_db/
data/
2020-01/
0/
sensors.parquet
stocks.parquet
1/
sensors.parquet
2020-02/
0/
sensors.parquet
1/
sensors.parquet
Database

© 2020 InﬂuxData. All rights reserved.
43
2815898179/
my_db/
data/
2020-01/
0/
sensors.parquet
stocks.parquet
1/
sensors.parquet
2020-02/
0/
sensors.parquet
1/
sensors.parquet
Partition

© 2020 InﬂuxData. All rights reserved.
44
2815898179/
my_db/
data/
2020-01/
0/
sensors.parquet
stocks.parquet
1/
sensors.parquet
2020-02/
0/
sensors.parquet
1/
sensors.parquet
Chunk

© 2020 InﬂuxData. All rights reserved.
45
2815898179/
my_db/
data/
2020-01/
0/
sensors.parquet
stocks.parquet
1/
sensors.parquet
2020-02/
0/
sensors.parquet
1/
sensors.parquet
Table

© 2021 InﬂuxData. All rights reserved. 51
| IOx Components
• Write Buﬀer
• MUB (Mutable Buﬀer)
• RUB (Read Buﬀer)
• Catalog

© 2021 InﬂuxData. All rights reserved. 52
Query Engine
SQL Frontend
Flux Frontend
InﬂuxQL
Frontend
Mutable
Buﬀer
Read Buﬀer
Object
Storage
Reader
| IOx Read Path

© 2021 InﬂuxData. All rights reserved. 53
Query Engine
SQL Frontend
Flux Frontend
InﬂuxQL
Frontend
Mutable
Buﬀer
Read Buﬀer
Object
Storage
Reader
In-memory
| IOx Read Path

© 2021 InﬂuxData. All rights reserved. 59
| RUB techniques
• Dictionary Encoding
• RLE
• Byte-trimming (ints and natural ﬂoats)
• Frame of reference encoding
• Handling nulls

© 2021 InﬂuxData. All rights reserved. 60

© 2021 InﬂuxData. All rights reserved. 61

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© 2021 InﬂuxData. All rights reserved. 68
| Write
• InﬂuxDB 2.x compatible (HTTP & Line Protocol)
• Protobuf
• /write
• gRPC service deﬁnition
• Bulk via Parquet
• IOx direct
• Object Store

© 2021 InﬂuxData. All rights reserved. 71
| Read
• HTTP GET
/iox/api/v1/databases/:name/query?q=..&format=..
• SQL query
• Format: pretty, csv, json
• Arrow Flight

© 2021 InﬂuxData. All rights reserved. 77
| Manage
• gRPC
• CLI
• Create database
• Manage lifecycle rules
• Persist to object store
• Evict from memory
• Load from object store (soon)
• Subscriptions (soon)

© 2021 InﬂuxData. All rights reserved. 83
| Road to Release
• Dogfooding in production (in process)
• Optimization
• Memory footprint
• Query execution
• Recovery from object store
• Available in InﬂuxDB Cloud (Q3)
• Migration tooling
• Docs, Docker builds and Cargo crate (later)

© 2021 InﬂuxData. All rights reserved. 84
| Find out more
• #inﬂuxdb-iox in community Slack
• Tech talk every 2nd Wednesday at 8:00 AM PT (15:00 GMT)
• github.com/inﬂuxdata/inﬂuxdb_iox

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Kubernetes Operators are the next phase of the journey towards automating complex applications in containers. Many Operators that exist today handle initial deployment, but they don’t provide automation for tasks like binary upgrades, configuration updates, and failure recovery. Implementing a production-grade controller for a complex workload typically requires thousands of lines of code and many months of development. As a result, the quality of operators that are available today varies. The goal of KUDO is to help solve some of these problems by providing a comprehensive toolkit for building Kubernetes operators declaratively using YAML, with a focus on distributed stateful applications.

KUDO - Kubernetes Operators, the easy way

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Kafka & InfluxDB: BFFs for Enterprise Data Applications | Russ Savage, Influx...

KUDO - Kubernetes Operators, the easy way

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InfluxData is excited to announce InfluxDB Clustered, the self-managed version of InfluxDB 3.0 with unparalleled flexibility, speed, performance, and scale. The evolution of InfluxDB Enterprise, InfluxDB Clustered is delivered as a collection of Kubernetes-based containers and services, which enables you to run and operate InfluxDB 3.0 where you need it, whether that's on-premises or in a private cloud environment. With this new enterprise offering, we’re excited to provide our customers with real-time queries, low-cost object storage, unlimited cardinality, and SQL language support – all with improved data access, support, and security! The newest version of InfluxDB was built on Apache Arrow, and through the open source ecosystem and integrations, extends the value of your time-stamped data. Join this webinar to learn more about InfluxDB Clustered, and how to manage your large mission-critical workloads in the highly available database service offering! In this webinar, Balaji Palani and Gunnar Aasen will dive into: Key features of the new InfluxDB Clustered solution Use cases for using the newest version of the purpose-built time series database Live demo During this 1-hour technical webinar, you’ll also get a chance to ask your questions live.

Announcing InfluxDB Clustered

InfluxData

Apache Arrow is an open source project intended to provide a standardized columnar memory format for flat and hierarchical data. It enables more efficient analytics workloads for modern CPU and GPU hardware, which makes working with large data sets easier and cheaper. InfluxData and Dremio are both members of the Apache Software Foundation (ASF). Dremio is a data lakehouse management service known for its scalability and capacity for direct querying across diverse data sources. InfluxDB is the purpose-built time series database, and InfluxDB 3.0 has a new columnar storage engine and uses the Arrow format for representing data and moving data to and from Parquet. Discover how InfluxDB and Dremio have advanced their solutions by relying on the Apache Arrow framework. Join this live panel as Alex Merced and Anais Dotis-Georgiou dive into: Advantages to utilizing the Apache Arrow ecosystem Tips and tricks for implementing the columnar data structure How developers can best utilize the ASF to innovate and contribute to new industry standards

Best Practices for Leveraging the Apache Arrow Ecosystem

InfluxData

Bevi are the creators of smart water dispensers which empower people to choose their desired beverage — flat or sparkling, their desired flavor and temperature. Since 2014, Bevi users have saved more than 350 million bottles and cans. Their "smart" water coolers have prevented the extraction of 1.4 trillion oz of oil from Earth and have saved 21.7 billion grams of CO2 from the atmosphere. Discover how Bevi uses a time series database to enable better predictive maintenance and alerting of their entire ecosystem — including the hardware and software. They are using InfluxDB to collect sensor data in real-time remotely from their internet-connected machines about their status and activity — i.e., flavor and CO2 levels, water temp, filter status, etc. They a7re using these metrics to improve their customer experience and continuously improve their sustainability practices. Gain tips and tricks on how to best utilize InfluxDB's schema-less design. Join this webinar as Spencer Gagnon dives into: Bevi's approach to reducing organizations' carbon footprint — they are saving 50K+ bottles and cans annually Their entire system architecture — including InfluxDB Cloud, Grafana, Kafka, and DigitalOcean The importance of using time-stamped data to extend the life of their machines

How Bevi Uses InfluxDB and Grafana to Improve Predictive Maintenance and Redu...

InfluxData

If you're using InfluxDB to store and manage your time series data, you're already off to a great start. But why stop there? In our upcoming webinar, we'll show you how to take your data analysis to the next level by building predictive analytics using a variety of tools and techniques. We will demonstrate how to use Quix to create custom dashboards and visualizations that allow you to monitor your data in real-time. We'll also introduce you to Hugging Face, a powerful tool for building models that can predict future trends and identify anomalies. With these tools at your disposal, you'll be able to extract valuable insights from your data and make more informed decisions about the future. Don't miss out on this opportunity to improve your data analysis skills and take your business to the next level! What you will learn: Use InfluxDB to store and manage time series data Utilize Quix and Hugging Face to build models, visualize trends, and identify anomalies Extract valuable insights from your data Improve your data analysis skills to make informed decision

Power Your Predictive Analytics with InfluxDB

InfluxData

Are you considering replacing your legacy data historian and moving your OT data to the cloud? Join this technical webinar to learn how to adopt InfluxDB and IO Base - a digital platform used to improve operational efficiencies! Teréga Solutions are the creators of digital solutions used to improve energy efficiencies and to address decarbonization challenges. Their network includes 5,000+ km of gas pipelines within France; they aim to help France attain carbon neutrality by 2050. With these impressive goals in mind, Teréga has created IO-Base — the digital platform to improve industrial performance, and increase profitability. Creating digital twins for their clients allows them to collect data from all production sites and view it in real time, from anywhere and at any time. Discover how Teréga uses InfluxDB, Docker, and AWS to monitor its gas and hydrogen pipeline infrastructure. They chose to replace their legacy data historian with InfluxDB — the purpose built time series database. They are collecting more than 100K different metrics at various frequencies — some are collected every 5 seconds to only every 1-2 minutes. THey have reduced overall IT spend by 50% and collect 2x the amount of data at 20x frequency! By using various industrial protocols (Modbus, OPC-UA, etc.), Teréga improved output, reduced the TCO, and is now able to create added-value services: forecast, monitoring, predictive maintenance. Join this webinar as Thomas Delquié dives into: Teréga's approach to modernizing fossil fuel pipelines IT systems while improving yields and safety Their centralized methodology to collecting sensor, hardware, and network metrics The importance of time series data and why they chose InfluxDB

How Teréga Replaces Legacy Data Historians with InfluxDB, AWS and IO-Base

InfluxData

FlowForge enables organizations to reliably deliver Node-RED applications in a continuous, collaborative, and secure manner. Node-RED is the popular, low-code programming solution that makes it easy to connect different services using a visual programming environment. InfluxData is the creator of InfluxDB, the purpose-built time series database run by developers at scale and in any environment in the cloud, on-premises, or at the edge. Jump-start monitoring your industrial IoT devices and discover how to build an edge-to-cloud solution with the MING stack. The MING stack includes Mosquitto/MQTT, InfluxDB, Node-RED, and Grafana. This solution can be used to improve fleet management, enable predictive maintenance of industrial machines and power generation equipment (i.e. turbines and generators) and increase safety practices (i.e. buildings, construction sites). Join this webinar to learn best practices from industrial IoT SME's. In this webinar, Robert Marcer and Jay Clifford dive into: Best practices for monitoring sensor data collected by everyone — from the edge to the factory Tips and tricks for using Node-RED and InfluxDB together Demo — see Node-RED and InfluxDB live

Build an Edge-to-Cloud Solution with the MING Stack

InfluxData

Rust is a systems programming language designed for high performance, type safety, and concurrency. According to Stack Overflow’s annual survey in 2022, Rust is the most loved language with 87% of developers saying they want to continue using it. The same survey also reported that nearly 20% of developers aren’t currently using Rust, but want to start developing using it. Ockam’s suite of programming libraries, command line tools, and managed cloud services enable developers to orchestrate end-to-end encryption. InfluxDB is the purpose-built time series database developed to handle time series data for IoT, monitoring, and real-time analytics. Ockam was originally developed using C, and InfluxDB was originally written using Go; both solutions have been completely rewritten in Rust. Discover why two founders decided to rewrite their developer tools using Rust, and gain insight into the strategy beforehand and the entire process. Join this live panel as Mrinal Wadhwa and Paul Dix dive into: Their approach to rewriting a project in Rust How to build and train engineering teams Tips and tricks learned along the way - pitfalls to look out for! Join this webinar as there will be a live discussion with Q&A

Meet the Founders: An Open Discussion About Rewriting Using Rust

InfluxData

InfluxData is excited to announce the general availability of InfluxDB Cloud Dedicated! It is a fully managed time series database service running on cloud infrastructure resources that are dedicated to a single tenant. With this new offering, we’re excited to provide our customers with additional security options, and more custom configuration options to best suit customers’ workload requirements. Join this webinar to learn more about InfluxDB Cloud, and the new dedicated database service offering! In this webinar, Balaji Palani and Gary Fowler will dive into: Key features of the new InfluxDB Cloud Dedicated solution Use cases for using the newest version of the purpose-built time series database Live demo During this 1-hour technical webinar, you’ll also get a chance to ask your questions live.

Introducing InfluxDB Cloud Dedicated

InfluxData

American Metal Processing Company ("AMP") is the US' largest commercial rotary heat treat facility with customers in the automotive, construction, military, and agriculture industries. They use their atmosphere-protected rotary retort furnaces to provide their clients with three primary hardening services: neutral hardening (quench and temper), carburizing, and carbonitriding. This furnace style ensures consistent, uniform heat treatment process vs. traditional batch-or-belt-style furnaces; excels at processing high volumes of smaller parts with tight tolerances; and improves the strength and toughness of plain carbon steels. Discover why AMP’s use of Telegraf, InfluxDB, Node-RED, and Grafana allows them to gain 24/7 insights into their plant operations and metallurgical results. Learn how they use time-stamped data to gain accurate metrics about their consumables usage, furnace profiles, and machine status. Join this webinar as Grant Pinkos dives into: American Metal Processing's approach to heat treating in a digitized environment through connected systems Their approach to collecting and measuring sensor data to enable predictive maintenance and improve product quality Why they need a time series database for managing and analyzing vast amounts of time-stamped data

How a Heat Treating Plant Ensures Tight Process Control and Exceptional Quali...

InfluxData

Delft University is the oldest and largest technical university in the Netherlands with 25,000+ students. Since 1999, they have had a team of students (undergraduate and graduate) designing, building, and racing cars, as part of the Formula Student worldwide competition. The competition has grown to include teams from 1K+ universities in 20+ countries. Students are responsible for all aspects of car manufacturing (research, construction, testing, developing, marketing, management, and fundraising). Delft University's team includes 90 students across disciplines. Discover how Delft University's team uses Marple and InfluxDB to collect telemetry and sensor metrics while they develop, test, and race their electrics cars. They collect sensor data about their EV's control systems using a time series platform. During races, they are collecting IoT data about their batteries, accelerometer, gyroscope, tires, etc. The engineers are able to share important car stats during races which help the drivers tweak their driving decisions — all with the goal of winning. After races, the entire team are able to analyze data in Marple to understand what to do better next time. By using Marple + InfluxDB, their team are able to collect, share and analyze high frequency car data used to make their car faster at competitions. Join this webinar as Robbin Baauw and Nero Vanbiervliet dive into: Marple's approach to empowering engineers to organize, analyze, and visualize their data Delft University's collaborative methodology to building and racing their Formula-style race car How InfluxDB is crucial to their collaborative engineering and racing process

How Delft University's Engineering Students Make Their EV Formula-Style Race ...

InfluxData

InfluxData is excited to announce the general availability of InfluxDB Cloud's new storage engine! It is a cloud-native, real-time, columnar database optimized for time series data. InfluxDB's rebuilt core was coded in Rust and sits on top of Apache Arrow and DataFusion. InfluxData's team picked Apache Parquet as the persistent format. In this webinar, Paul Dix and Balaji Palani will demonstrate key product features including the removal of cardinality limits! They will dive into: The next phase of the InfluxDB platform How using Apache Arrow's ecosystem has improved InfluxDB's performance and scalability Key features of InfluxDB Cloud's new core — including SQL native support

Introducing InfluxDB’s New Time Series Database Storage Engine

InfluxData

balena.io helps companies develop, deploy, update, and manage IoT devices. By using Linux containers and other cloud technologies, balena enables teams to quickly and easily build fleets of connected devices. Developers are able to use containers with the language of choice and pull IoT sensor data from 70+ different single board computers into balenaCloud. Discover how to use balena.io to automate your InfluxDB deployments at the edge! During this one-hour session, experts from balena and InfluxData will demonstrate how to build and deploy your own air quality IoT solution. You will learn: The fundamentals of IoT sensor deployment and management using balena. How to use a time series platform to collect and visualize metrics from edge devices. Tips and tricks to using balenaCloud to automate InfluxDB deployments and Telegraf configurations. How to use InfluxDB's Edge Data Replication feature to collect sensor data and push it to InfluxDB Cloud for analysis. No coding experience required, just a curiosity to start your own IoT adventure.

Start Automating InfluxDB Deployments at the Edge with balena

InfluxData

RudderStack — the creators of the leading open source Customer Data Platform (CDP) — needed a scalable way to collect and store metrics related to customer events and processing times (down to the nanosecond). They provide their clients with data pipelines that simplify data collection from applications, websites, and SaaS platforms. RudderStack's solution enables clients to stream customer data in real time — they quickly deploy flexible data pipelines that send the data to the customer's entire stack without engineering headaches. Customers are able to stream data from any tool using their 16+ SDK's, and they are able to transform the data in-transit using JavaScript or Python. How does RudderStack use a time series platform to provide their customers with real-time analytics? Join this webinar as Ryan McCrary dives into: RudderStack's approach to streamlining data pipelines with their 180+ out-of-the-box integrations Their data architecture including Kapacitor for alerting and Grafana for customized dashboards Why using InfluxDB was crucial for them for fast data collection and providing single-sources of truths for their customers

Streamline and Scale Out Data Pipelines with Kubernetes, Telegraf, and InfluxDB

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