Introduction
For a company storing the data and analyzing it at an appropriate timestamp is necessary to compete. They need to understand the different needs of people with changing times, but if they don't, they will soon be out of competition and lose a significant amount of money and time.
To store and analyze the data at different timestamps, we can use time series and redis data structure, but we don’t know how to model redis core data structure with time series. Don't worry. You will learn everything about merging redis data structures with time series and references of time series while moving further with the blog, so without wasting any time further, let's get on with our topic.
Redis Time Series
In time series, the data is stored in a connected manner like LinkedList, with different nodes representing the different timestamps.
Time Series With Redis Data Structures
There are mainly two ways to use time series in redis while using its data structures: streams and sorted sets. We will discuss both of them in this blog section:
Streams
Redis Streams are the most recently added data structure, and because of this, they are not frequently used for time series by the users. It consumes comparatively less memory than another method, i.e., sorted sets. It is implemented using a different method of radix trees known as Rax. Generally, Redis streams enhance the performance of reads and interactions compared to sorted sets. It is designed as a generic data structure, so it misses a toolset specific to the time series.
Downsides of this approach:
- It only has one downside, i.e., it misses a time series toolset.
Sorted Sets
In sorted sets, the values are stored by their scores. In this, the score refers to the timestamp at which data is stored. We store the value of the timestamp followed by a colon and then the actual measurement, e.g.,, “ <timestamp>:<measurement>”. As it is set, every value stored is unique. They store the data in the form of a hash table, i.e., key-value pair.
Downsides of this approach:
- These are not memory efficient.
- It’s missing a time series toolset.
- It takes more time to insert.
Capabilities of Redis Time series
The data is stored in memory chunks and uses radix tree implementation as a redis stream. Streams limit the number of messages by count.
- Integrations: With the redis time series, you can integrate into several existing time series tools. We can have all the data inside monitoring metrics. With its remote data adapter, we can operate it from a remote distance.
- Aggregation: While reading the data, you can aggregate it according to your requirement. During aggregation, it makes sure the data is traversal with low latency.
- Secondary Indexing: You can only retrieve a time series using Redis' fundamental data structures if you know the exact key that holds the time series. Unfortunately, your application won't know the precise key it's looking for in many time series use cases. To extract the knowledge you need, these use cases often desire to query a number of time series related to each other in a few dimensions. To help with this, you could design your secondary index using core Redis data structures, but it would be expensive to develop and require you to manage edge cases to ensure the index is right.
- Downsampling: Your data set will grow linearly over time if you want to keep all of your raw data points indefinitely. Downsampling can be used if your use case enables you to have less fine-grained data further back in time. Using a given aggregation function, you can keep fewer historical data points by aggregating raw data for a specific time range. RedisTimeSeries support the following aggregations: average, sum, min, max, capacity, count, first, and last.
Benefits of Redis Time Series
There are many benefits of using the redis tie series, and we will discuss some of them in this blog section:
- Easy and Efficient: The most efficient and easiest way to store time series is redis. Downsampling, retention rules, and multi-key queries can be possible using some simple commands.
- Tight integrations with popular tools: It rapidly integrates with the tools like Prometheus, Grafana, Telegraf, and StatsD for visualization and data integration in monitoring.
- Tight Coupling with other Modules: Redis Time Series can work well with Redis Gears and RedisAI, enabling advanced use cases such as predictive maintenance and anomaly detection.
Features of Redis Time Series
The redis time series has many features, discussing some of them in this blog section.
- Low latency reads High Volume inserts.
- Secondary indexing for time-series indexing.
- Configurable maximum retention period.
- Aggregate queries for any time bucket.
- Query by end-time and start-time.
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