2. InfluxDB Storage
Overview
Brian Gilmore - Director of IoT and Emerging
Technologies, InfluxData

3. Connect Learn Build
Hear from and meet developers
from the InfluxDB Community
Be inspired by use cases from
our partners and InfluxDB engineers
Learn best practices that will
help you build great experiences
for your projects

4. This session explores the evolution of data storage over
the decades. It then dives into how InfluxData has taken
a unique approach and how that ultimately benefits the
users of InfluxDB.
Brian Gilmore
Director of IoT and Emerging
Technologies, InfluxData
Brian Gilmore is Director of IoT and Emerging
Technology at InfluxData, the creators of InfluxDB. He
has focused the last decade of his career on working
with organizations around the world to drive the
unification of industrial and enterprise IoT with machine
learning, cloud, and other truly transformational
technology trends.
InfluxDB Storage Overview

5. Agenda
1. The Evolution of Data Storage
2. InfluxDB Approach to Data Storage
3. Benefits to Customers and Community

6. The Evolution of Storage

7. 1956
5MB
$30,000
2022
2TB
$200
400M times more storage for less than 1% of the price.

8. Pivoting from Disks to Data

9. 1993 2054
You Are
Here

10. The InfluxDB Storage Engine

11. InfluxDB Design Principles
1. Time-ordered
2. CR-ud: Create/Read prioritized
3. Schemaless for ephemeral series
4. Optimized for organization and aggregates
5. Assumptive deduplication
6. Eventual consistency

12. Components of the InfluxDB Storage Engine
Write-ahead Log
(WAL)
In-memory Cache Time-Structured
Merge Tree (TSM)
Time Series Index
(TSI)
New data
Now
Then

13. Lower Storage Costs
Only tested technology to successfully
compress all datasets more than 1:1
Independent Performance Study performed by DAIICT
B. Shah, PM Jat, K. Sashidhar. PERFORMANCE STUDY OF TIME SERIES DATABASES. arXiv preprint arXiv:2208.13982, 2022

14. Faster Time-to-Query
InfluxDB saw faster ingest performance for large datasets
compared to SQL (TimescaleDB) & NoSQL (Cassandra)
InfluxDB 4x faster than TimescaleDB by 4x for IoT data
Independent Performance Study performed by DAIICT
B. Shah, PM Jat, K. Sashidhar. PERFORMANCE STUDY OF TIME SERIES DATABASES. arXiv preprint arXiv:2208.13982, 2022

15. Spend Less Time Waiting for Results
Faster query
performance than
than SQL
(TimescaleDB)
5x
for IoT data for financial data
56x
Independent Performance Study performed by DAIICT
B. Shah, PM Jat, K. Sashidhar. PERFORMANCE STUDY OF TIME SERIES DATABASES. arXiv preprint arXiv:2208.13982, 2022

16. Key Takeaways
• Existing TSM/TSI architecture provides fast ingestion, efficient
compression, and fast insights.
• Innovation in physical (NVMe) and virtual storage (S3) have
opened opportunities to make InfluxDB faster and even MORE
scalable.
• As you will see later in Paul Dix’s talk, we aren’t stopping at
great.

17. Additional Resources
Free InfluxDB: OSS or Cloud - influxdata.com/cloud
Forums: community.influxdata.com
Slack: influxcommunity.slack.com
Reddit: r/InfluxData
Influx Community (GH): github.com/InfluxCommunity
Book: awesome.influxdata.com
Docs: docs.influxdata.com
Blogs: influxdata.com/blog
InfluxDB University: influxdata.com/university
How to guides: docs.influxdata.com/resources/how-to-guides/

18. T H A N K Y O U