- The document summarizes a meetup about RedisTimeSeries, a time-series data structure for Redis. - RedisTimeSeries allows ingesting large amounts of time-series data at high speeds, performing fast queries with aggregation, and scaling resource efficiency for more users and richer metrics. - Example use cases discussed are infrastructure and services monitoring, caching time-series data to improve performance and reduce costs, and industrial IoT, energy/utilities, and fraud detection applications.

1. Silicon Valley Redis Meetup
“RedisTimeSeries – a Time Series data structure for Redis”
Speaker: @RoshanKumar Meetup: #SVRedis
Wi-Fi: RedisLabs-Guest Password: welcome2redislabs

2. Stack Overflow Survey:
Redis is “Most Loved Database” … 3nd Year in a row!

3. Redis University
university.redislabs.com

4. We’re hiring:

5. RedisTimeSeries

6. RedisTimeSeries is Purpose Built for Time Series Data Model
6
Ingest UI App

7. RedisTimeSeries Delivers
7
Raw Data Down Sampling & Aggregation
Store and analyze billions of
metrics by time-intervals
Run high speed, low latency queries with
time-bucketed aggregation, reverse lookups
by labels and properties
Down sample and evict detailed data
with data retention policy
EFFORTLESS SCALE RESOURCE EFFICIENCY EASY, FAST QUERIES

8. • Client and server: AWS c5.18xl
• Redis Enterprise version 5.4.4-7
• A Redis Enterprise database with 16 shards and 48 proxy
threads
• A data set with:
–4,000 devices, 10 metrics per device
–Each metric is a CPU measurement
–10,800 samples per metric (every 10 seconds for 30 hours)
–43,200,000 samples in total
RedisTimeSeries Benchmark Results
8

9. RedisTimeSeries Benefits
9
More users; richer metrics
No choke points
Save on infrastructure

10. RedisTimeSeries Helps You Scale With Reduced Investment
10
You get more than what you want
Foster innovation High performance, more data-driven apps
Save cost More performance with fewer resources
You get more than what you want
Ingest speed Over 500K per second per shard
Query speed and latency Queries run with sub-millisecond latency
Scaling to more end users Linear scaling
Address your business challenges
Meet your technical needs

11. Ready-made Connectors
11
RedisTimeSeries has connectors that offer out-of-the-box integration with
popular sources of time series data
SQLRedis Streams

12. Client Libraries in Popular Languages
12
You can integrate your apps with RedisTimeSeries using Redis client libraries

13. 13
RedisTimeSeries Use Cases

14. Use Case 1. Infrastructure and Services Monitoring
14

15. Drawbacks of Prometheus’s native storage
and disk-based databases:
• Slow data ingest
• Queries are expensive
• Databases aren’t designed for time series model
• Limits the number of dashboards in Grafana
• Cannot scale for more end-users
Infrastructure and Services Monitoring
15
Current Popular Solution: Grafana + Prometheus + (Optional Backend Database)
SQL

16. Benefits and Advantages of RedisTimeSeries
• ~10X faster than Prometheus’s native disk-based data
store
• Enables ingesting 500K data points per second per shard
• Serves more end-users, more dashboards
• Delivers sub-millisecond latency for data driven
decisions and alerts in Prometheus
Infrastructure and Services Monitoring
16
Integration: Grafana + Prometheus + RedisTimeSeries
RedisTimeSeries

17. Use Case 2. Caching Time Series Data
17

18. • Slow user experience
• Heavy load on backend databases
• Leads to more investment towards
compute resources
• Hinders innovation
Disadvantages of Storing Time Series Data in SQL and Not Caching
18
SQL
Custom App
About 80% enterprises surveyed by Redis Labs
store time series data in a relational database

19. Advantages of Caching Time Series Data Using RedisTimeSeries
19
• RedisTimeSeries is designed for Time Series Data
• More aggregation options than Sorted Sets
• Designed for low cardinality of values; allows reverse lookups
by values
Benefits
• Delivers better user experience
• Scales to more end-users
• Saves cost by limiting investment on backend databases
• Reduces the load on operational and transactional databases
SQL
Custom App
RedisTimeSeries

20. More Use Cases
20
Industrial IoT Energy and Utilities
Data Analysis: Forensics
and Fraud Detection
• Real-time analysis of
sensor data
• Anomaly detection
• Tracking and
monitoring
• Optimize power
consumption and
distribution
• Monitor meters and
control systems
• Point-in-time analysis
of data
• Real-time fraud
detection
And many other time series related solutions

21. Thank you!