This document presents RedisTimeSeries, which provides time series functionality for Redis. It defines a time series as a series of data points indexed in time order. RedisTimeSeries allows ingesting, querying, aggregating, and downsampling of time series data stored in Redis in a more optimized way than traditional Redis methods. It provides features like labeling of time series for enhanced queries, aggregation functions, and integration with tools like Prometheus and Grafana. The presentation includes demos of using RedisTimeSeries to store and analyze weather station and web page view count data.

1. PRESENTED BY
RedisTimeSeries
Danni Moiseyev (@dannidevmo), Pieter Cailliau (@cailliaup)
RedisLabs, SW Team leader, Modules Product Manager

2. PRESENTED BY
What is a Time Series?
A time series is a series of data points indexed (or listed or graphed)
in time order. Most commonly, a time series is a sequence taken at
successive equally spaced points in time. Thus it is a sequence
of discrete-time data.
What you wrote, Wikipedia
*time series data is append-only

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TimeSeries Use Cases

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Time Series
Trends in the last 24 months

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Why TimeSeries DBMS?

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Why TimeSeries DBMS?

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Time-Series in Redis – the traditional way
Sorted Sets
Streams
{ eventid1: timestamp1, eventId2: timestamp2,…}
{ timestamp1: f/v-pair1, timestamp2:f/v-pair2,…}
1 Hashes
{ eventid1: f/v-pair1, eventId2: f/v-pair2,…}
+
2

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• Downsampling (compaction)
• Enhanced queries: Aggregation
(e.g.: avg, sum, min, max, range, count,
first, last)
• Enhanced queries: Labeling
(e.g.: sensor_id:2, cpu:8, area:5)
What is missing ?

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Why
+
TimeSeries DBMS

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Deep Dive

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• Each Key is a time series
• Each Key can have multiple downsampling rules
– Each downsampling rule will write to a different key
• Each time series can have any set of labels
• Sharding is simple - just shard by the key name
RedisTimeSeries Basics

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Downsampling
Raw Data Downsampled and aggregated using average

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Chunk 1
---------------------
Sample 1
Sample 2
Label
fvp(field=value)
Label
fvp2
Labels search
index
Timestamp indexing using RAX
Key
Chunk 2
---------------------
Sample 1
Sample 2
Chunk 3
---------------------
Sample 1
Sample 2
key [Labels..]: (timestamp,numeric value), (timestamp,numeric value), (timestamp,numeric value).
Aggregat
edKey
Chunk 1
---------------------
Sample 1
Sample 2
Chunk 2
---------------------
Sample 1
Sample 2
RedisTimeSeries architecture

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Labels
• A label is a key value pair
– Example: sensor=12
• Each Key can have multiple labels
• Indexed internally as a secondary index based on Redis hashes (aka RAX)
• Used for multi time-series queries
– Filtering operator including: equals (=), not equals(!=), contains key(=“”), does not contain a
key(!=“”)
– Example: TS.MRANGE 1553973212 1553973656 FILTER region=us-west-1 app=frontend

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Query
• Single Key:
TS.RANGE KEY START_TS END_TS AGGREGATION avg 60
• Results:
– 1554320779 123
– 1554320780 122
• Multi Key:
TS.MRANGE KEY START_TS END_TS AGGREGATION avg 60 FILTER region=us-west-1
– Results:
– Key 1
• 1554320779 123
• 1554320780 122
– Key 2
• 1554320779 123
• 1554320780 122

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Ecosystem Integration
• Prometheus
– RedisTimeSeries Adapter – read and write
• Grafana
– Run any PromQL query!
• Influx Telegraf

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Demo #1
Weather Station

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Toolset
• Docker-compose running
– Redis + RedisTimeSeries with automatic downsmapled keys max,min
and average for 1 min
– Prometheus
– Grafana
• Python
• Web Browser

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Data model
• Simple weather station
– Each station reports Temperature and Humidity every second
• Goals
– Monitor 2 metrics from multiple sensors
– Create dashboard

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How do I ingest data?
• TS.ADD KEY 1553828843 123
• TS.ADD KEY * 123
• TS.ADD KEY * 123 LABELS sensor 1 room 21
• TS.ADD KEY 1553828843 123 LABELS sensor 1 room 21

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Demo #2
Page Views

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Data model
• Keep track of page views
– Each visitor is counted on every page
• Goals
– Be able to query:
• Latest view count
• History of page views
• History of page views per region
– Dashboard

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How do I ingest data?
• TS.INCRBY KEY INCER VALUE [LAEBLS key value ]
– Each visitor is counted on every page
• Goals
– Be able to query:
• Latest view count
• History of page views
• History of page views per region
– Dashboard

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Time-Series benchmark
RedisTimeSeries TimescaleDB InfluxDB
DS1
100 devices x 1 metric
DS2
100 devices x 10 metric
DS3
4,000 devices x 10 metric
1
1
1
2.22
3.17
2.83
3.13
4.43
1.99
DS1
DS2
DS3
Query 1 Single GroupBy 1-1-1
1
1
1
3.24
7.61
9.64
4.79
10.34
2.83
DS1
DS2
DS3
Query 2 Single GroupBy 1-1-12
1
1
1
19.43
31.27
7.84
10.78
13.24
4.54
DS1
DS2
DS3
Query 9 Double GroupBy - 1
1
1
2.91
1.64
7.70
5.18
DS2
DS3
Query 11 Double GroupBy - All
x4+
~x8 x31+
x10+

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Next steps
• Double delta compression
• Redis search integration
• Support all common timeseries use cases
• A more complete benchmark
– Based on TSBS

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Get RedisTimeSeries
• https://oss.redislabs.com/redistimeseries/
• https://github.com/RedisLabsModules/RedisTimeSeries/
• Demos:
https://github.com/danni-m/redisconf19-demo

27. Thank you!

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