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Understanding InfluxDB’s New Storage Engine

Learn more about InfluxDB’s new storage engine! The team developed a cloud-native, real-time, columnar database optimized for time series data. We built it all in Rust and it sits on top of Apache Arrow and DataFusion. We chose Apache Parquet as the persistent format, which is an open source columnar data file format. This new storage engine provides InfluxDB Cloud users with new functionality, including the removal of cardinality limits, so developers can bring in massive amounts of time series data at scale. In this webinar, Anais Dotis-Georgiou will dive into: Requirements for rebuilding InfluxDB’s core Key product features and timeline How Apache Arrow’s ecosystem is used to meet those requirements Stick around for a demo and live Q&A

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| © Copyright 2023, InfluxData
Understanding the New
InfluxDB Storage Engine
Anais Dotis-Georgiou
January 2023
| © Copyright 2023, InfluxData
2
Anais Dotis-Georgiou
Developer Advocate
LinkedIn
| © Copyright 2023, InfluxData
| © Copyright 2023, InfluxData
Agenda
● What is the new InfluxDB Engine?
● What requirements does the new InfluxDB Engine meet?
○ Understanding the Apache Ecosystem
● Offerings and Release Timeline
● New InfluxDB Cloud Features
● SQL support
● Interoperability plans
● Survey
● Resources
| © Copyright 2023, InfluxData
The new storage engine that
powers InfluxDB Cloud
4
| © Copyright 2023, InfluxData
| © Copyright 2023, InfluxData
InfluxDB’s new storage engine is built on
● Rust
● Apache Arrow
● Apache Parquet
● Arrow Flight
● DataFusion
| © Copyright 2023, InfluxData
| © Copyright 2023, InfluxData
Release Details
● Release Jan 31st
● Available in InfluxDB Cloud on AWS in:
○ AWS us-east-1
○ AWS eu-central-1
6

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Understanding InfluxDB’s New Storage Engine

  • 1. | © Copyright 2023, InfluxData Understanding the New InfluxDB Storage Engine Anais Dotis-Georgiou January 2023
  • 2. | © Copyright 2023, InfluxData 2 Anais Dotis-Georgiou Developer Advocate LinkedIn
  • 3. | © Copyright 2023, InfluxData | © Copyright 2023, InfluxData Agenda ● What is the new InfluxDB Engine? ● What requirements does the new InfluxDB Engine meet? ○ Understanding the Apache Ecosystem ● Offerings and Release Timeline ● New InfluxDB Cloud Features ● SQL support ● Interoperability plans ● Survey ● Resources
  • 4. | © Copyright 2023, InfluxData The new storage engine that powers InfluxDB Cloud 4
  • 5. | © Copyright 2023, InfluxData | © Copyright 2023, InfluxData InfluxDB’s new storage engine is built on ● Rust ● Apache Arrow ● Apache Parquet ● Arrow Flight ● DataFusion
  • 6. | © Copyright 2023, InfluxData | © Copyright 2023, InfluxData Release Details ● Release Jan 31st ● Available in InfluxDB Cloud on AWS in: ○ AWS us-east-1 ○ AWS eu-central-1 6
  • 7. | © Copyright 2023, InfluxData Requirements for the new storage engine Requirement/Feature Rust Arrow DataFusion Parquet 1. No limits on cardinality. Write any kind of event data and don't worry about what a tag or field is. X X X X 2. Best-in-class performance on analytics queries in addition to our already well-served metrics queries. X X X X 3. Separate compute from storage and tiered data storage. The DB should use cheaper object storage as its long-term durable store. X X 4. Operator control over memory usage. The operator should be able to define how much memory is used for each buffering, caching, and query processing. X 5. Bulk data export and import. X 6. Broader ecosystem compatibility. Where possible, we should aim to use and embrace emerging standards in the data and analytics ecosystem. X X X X 7. Run at the edge and in the datacenter. Federated by design. X X
  • 8. | © Copyright 2023, InfluxData Rust and InfluxDB Requirements 8
  • 9. | © Copyright 2023, InfluxData Rust and Requirement 1. Requirement: No limits on cardinality. Write any kind of event data and don't worry about what a tag or field is. Rust Contributions: • InfluxDB’s new storage engine is built on the Rust implementation of Apache Arrow which contributes heavily to meeting this requirement. • Handling unlimited cardinality use cases requires non-trivial CPU during query processing. Rust supports optimizing resources for increased performance.
  • 10. | © Copyright 2023, InfluxData Rust and Requirement 2. Requirement: Best-in-class performance on analytics queries in addition to our already well-served metrics queries. Rust Contributions: • Arrow and DataFusion are built on Rust.
  • 11. | © Copyright 2023, InfluxData Rust and Requirement 4. Requirement: Operator control over memory usage. The operator should be able to define how much memory is used for each buffering, caching, and query processing. Rust Contributions: • Rust is used for memory control.
  • 12. | © Copyright 2023, InfluxData Rust and Requirement 6. Requirement: Broader ecosystem compatibility. Where possible, we should aim to use and embrace emerging standards in the data and analytics ecosystem. Rust Contributions: • Rust helps support the implementation of Arrow, DataFusion, and Parquet.
  • 13. | © Copyright 2023, InfluxData Rust and Requirement 7. Requirement: Run at the edge and in the datacenter. Federated by design. Rust Contributions: • Optimizing your memory usage with Rust, means that InfluxDB Cloud will also contain these memory optimizations at the edge or in the datacenter.
  • 14. | © Copyright 2023, InfluxData Rust and Requirement 7. Requirement: Run at the edge and in the datacenter. Federated by design. Rust Contributions: • Optimizing your memory usage with Rust, means that InfluxDB Cloud will also contain these memory optimizations at the edge or in the datacenter.
  • 15. | © Copyright 2023, InfluxData Arrow and InfluxDB Requirements 15
  • 16. | © Copyright 2023, InfluxData Arrow and Requirement 1. Requirement: No limits on cardinality. Write any kind of event data and don't worry about what a tag or field is. Arrow Contributions: • Apache Arrow overcomes memory challenges associated with large-cardinality use cases by providing efficient columnar data exchange.
  • 17. | © Copyright 2023, InfluxData Advantages of Columnar Data Storage (sidebar) 17
  • 18. | © Copyright 2023, InfluxData | © Copyright 2023, InfluxData Sidebar–Advantages of Columnar Data Storage 18 measurement1,tag1=tagvalue1 field1=1i timestamp1 measurement1,tag1=tagvalue2 field1=2i timestamp2 measurement1,tag2=tagvalue3 field1=3i timestamp3 measurement1,tag1=tagvalue1,tag2=tagvalue3 field1=4i,field2=true timestamp4 measurement1, field1=1i timestamp5
  • 19. | © Copyright 2023, InfluxData | © Copyright 2023, InfluxData Sidebar–Advantages of Columnar Data Storage 19 Name: measurement1 field1 field2 tag1 tag2 tag3 time 1i null tagvalue1 null null timestamp1 2i null tagvalue2 null null timestamp2 3i null null tagvalue3 null timestamp3 4i true tagvalue1 tagvalue3 tagvalue4 timestamp4 1i null null null null timestamp5
  • 20. | © Copyright 2023, InfluxData | © Copyright 2023, InfluxData Sidebar–Advantages of Columnar Data Storage 20 1i 2i 3i 4i 1i null null null true null tagvalue1 tagvalue2 null tagvalue1 null null null tagvalue3 tagvalue3 null null null null tagvalue4 null timestamp1 timestamp2 timestamp3 timestamp4 timestamp5 1i, 2i, 3i, 4i, 1i; null, null, null, true, null; tagvalue1, tagvalue2, null, tagvalue1, null; null, null, null, tagvalue3, tagvalue3, null; null, null, null, tagvalue4, null; timestamp1, timestamp2, timestamp3, timestamp4, timestamp5.
  • 21. | © Copyright 2023, InfluxData Arrow and Requirement 2. Requirement: Best-in-class performance on analytics queries in addition to our already well-served metrics queries. Arrow Contributions: • Arrow offers best in class performance on analytics through the memory optimizations and efficient data exchange.
  • 22. | © Copyright 2023, InfluxData Arrow and Requirement 3. Requirement: Separate compute from storage and tiered data storage. The DB should use cheaper object storage as its long-term durable store. Arrow Contributions: • Arrow provides the in-memory columnar storage while Parquet will provide the column-oriented data file format on disk.
  • 23. | © Copyright 2023, InfluxData Arrow and Requirement 4. Requirement: Operator control over memory usage. The operator should be able to define how much memory is used for each buffering, caching, and query processing Arrow Contributions: • The Rust implementation of Apache Arrow provides fine grained memory control.
  • 24. | © Copyright 2023, InfluxData Arrow and Requirement 6. Requirement: Broader ecosystem compatibility. Where possible, we should aim to use and embrace emerging standards in the data and analytics ecosystem. Arrow Contributions: • Leveraging Arrow is easier with the 12 libraries it supports for C, C++, Java, JavaScript, Python, Ruby, and more.
  • 25. | © Copyright 2023, InfluxData DataFusion and InfluxDB Requirements 25
  • 26. | © Copyright 2023, InfluxData DataFusion and Requirement 1. Requirement: No limits on cardinality. Write any kind of event data and don't worry about what a tag or field is. DataFusion Contributions: • What use is unlimited cardinality data if you can’t query it? DataFusion provides the query, processing, and transformation of this data.
  • 27. | © Copyright 2023, InfluxData DataFusion and Requirement 2. Requirement: Best-in-class performance on analytics queries in addition to our already well-served metrics queries. DataFusion Contributions: • What use is unlimited cardinality data if you can’t query it? DataFusion provides the query, processing, and transformation of this data.
  • 28. | © Copyright 2023, InfluxData DataFusion and Requirement 3. Requirement: Separate compute from storage and tiered data storage. The DB should use cheaper object storage as its long-term durable store. DataFusion Contributions: • DataFusion enables fast query against data stored on cheaper object store and separate compute.
  • 29. | © Copyright 2023, InfluxData DataFusion and Requirement 6. Requirement: Broader ecosystem compatibility. Where possible, we should aim to use and embrace emerging standards in the data and analytics ecosystem. DataFusion Contributions: • DataFusion supports both a postgres compatible SQL and DataFrame API.
  • 30. | © Copyright 2023, InfluxData Parquet and InfluxDB Requirements 30
  • 31. | © Copyright 2023, InfluxData Parquet and Requirement 2. Requirement: Best-in-class performance on analytics queries in addition to our already well-served metrics queries. Parquet Contributions: • Efficient compression and interoperability with ML and analytics tooling.
  • 32. | © Copyright 2023, InfluxData Parquet and Requirement 3. Requirement: Separate compute from storage and tiered data storage. The DB should use cheaper object storage as its long-term durable store. Parquet Contributions: • Parquet files take up little disk space and are fast to scan.
  • 33. | © Copyright 2023, InfluxData Parquet and Requirement 5. Requirement: Bulk data export and import. Parquet Contributions: • Parquet files enable bulk data export and import.
  • 34. | © Copyright 2023, InfluxData Parquet and Requirement 5. Requirement: Broader ecosystem compatibility. Where possible, we should aim to use and embrace emerging standards in the data and analytics ecosystem. Parquet Contributions: • Parquet offers interoperability with modern ML and analytics tools.
  • 35. | © Copyright 2023, InfluxData Parquet and Requirement 5. Requirement: Run at the edge and in the datacenter. Federated by design. Parquet Contributions: • Because Parquet files are so efficient, they will facilitate and increase the capacity for data storage at the edge.
  • 36. | © Copyright 2023, InfluxData New Data Explorer 36
  • 37. | © Copyright 2023, InfluxData New Data Explorer 37
  • 38. | © Copyright 2023, InfluxData iox.from() vs from() 38
  • 39. | © Copyright 2023, InfluxData iox.from() vs from() 39 from(bucket: "anais-iox") |> range(start: 2022-12-01T19:05:41.000Z, stop: now()) |> filter(fn: (r) => r._measurement == "airSensors") |> filter(fn: (r) => r._field == "temperature") |> filter(fn: (r) => r.sensor_id == "TLM0100")
  • 40. | © Copyright 2023, InfluxData iox.from() vs from() 40 import "experimental/iox" data = iox.from(bucket: "anais-iox", measurement: "airSensors") |> range(start: 2022-12-01T19:05:41.000Z, stop: now()) |> filter(fn: (r) => r.sensor_id == "TLM0100") |> yield()
  • 41. | © Copyright 2023, InfluxData SQL Support 41
  • 42. | © Copyright 2023, InfluxData SQL Functions Supported by Jan 31 • Supported Statements: SELECT, FROM, WHERE, GROUP BY, ORDER BY, JOIN (left and inner), WITH clause, HAVING, UNION, LIMIT, OVER • Subqueries: EXISTS, NOT EXISTS, IN, NOT IN • Agg Functions: COUNT(), AVG(), MEAN(), SUM(), MIN(), MAX() • Time Series Functions: TIME_BUCKET_GAPFILL(), DATEBIN(), NOW() • Other: EXPLAIN, EXPLAIN ANALYZE 42
  • 43. | © Copyright 2023, InfluxData Interoperability plans • FlightSQL plugins (Timeline TBD): • Apache Superset • Tableau • PowerBI • Grafana 43
  • 44. | © Copyright 2023, InfluxData Survey 44 ● Please take 5 minutes to fill out this anonymous survey. Your feedback is extremely valuable to us. ● https://bit.ly/3imWP7Y
  • 45. | © Copyright 2023, InfluxData Get Started 45 influxdata.com/influxdb-engine-beta/
  • 46. | © Copyright 2023, InfluxData InfluxDB Community Slack workspace Please join us in the InfluxDB Community Slack at www.influxdata.com/slack. To participate in conversations, join the #influxdb_iox channel.
  • 47. | © Copyright 2023, InfluxData 47 influxdata.com https://github.com/InfluxCommunity
  • 48. | © Copyright 2023, InfluxData 48 Try it yourself https://www.influxdata.com https://github.com/InfluxCommunity Get Started
  • 49. | © Copyright 2023, InfluxData Related Blogs • Understanding InfluxDB IOx and the Commitment to Open Source • Querying Data in InfluxDB using Flux and SQL • Intro to InfluxDB IOx • Welcome to InfluxDB IOx: InfluxData’s New Storage Engine • The Journey of InfluxDB by Paul Dix • InfluxData Deploys Next-Generation InfluxDB Time Series Engine with Unlimited Scale • Announcing InfluxDB IOx - The Future Core of InfluxDB Built with Rust and Arrow • Evolving InfluxDB into the Smart Data Platform for Time Series • InfluxData is Building a Fast Implementation of Apache Arrow in Go Using c2goasm and SIMD • On InfluxData’s New Storage Engine. Q&A with Andrew Lamb • Apache Arrow, Parquet, Flight and Their Ecosystem are a Game Changer for OLAP 49
  • 50. | © Copyright 2023, InfluxData Get Help + Resources! 50 Forums: community.influxdata.com Slack: influxcommunity.slack.com GH: github.com/InfluxCommunity Book: awesome.influxdata.com Docs: docs.influxdata.com Blogs: influxdata.com/blog InfluxDB University: influxdata.com/university
  • 51. | © Copyright 2023, InfluxData Questions? 51
  • 52. | © Copyright 2023, InfluxData T H A N K Y O U