Introduction
In today’s world, if someone asks you what the most important thing companies require to succeed, some might say investment or any other item. Still, the answer is data and proper analysis at the appropriate timestamp. If the data is essential to the company, the developer is working in the company or any other freelancer.
So to analyze the data, the data need to be related to the previous data for better comparison. There are many ways to store data in that order, and among all the other time series is one of the most suitable methods. We will learn the reason for the same in this blog. So without wasting any further time, let's get on with our topic.
Redis Time Series
As the name suggests, the redis time series is a sequence of timestamps and values corresponding to different time stamps. In this, we store the importance of data from different periods in a connected way to compare and analyze the data over periods.
In time series, the data is stored in a connected manner like LinkedList, with different nodes representing the different timestamps.
Redis Time Series has many uses like stock prices, IoT, and telemetry. Redis Time Series is advisable over regular time series as with this, and you can query and analyze millions of samples with the speed of redis. It also provides some advanced tooling such as aggregation and downloading. It uses a variety of queries for monitoring and visualization with built-in connectors to popular tools.
Reasons for Using Redis Time Series
Recently, time-series databases have grown in popularity. We believe there are two critical technological causes for this trend the ever-growing volumes of data and new time series use cases for algorithmic trading, online retail, self-driving cars, smart homes, and more.
There are mainly two reasons for using the redis time series:
The first is that most existing databases can handle more read requests than write requests, but in the time-series database, we can handle an extensive volume of data, write requests and read requests. Redis is capable of delivering high write throughput with low latency.
The second reason is the toolset that we require to use time series. We cannot use the regular databases toolsets, and redis provide a toolset for easy use of time series.
Use Cases of Redis Time Series
There are multiple uses of the Redis Time Series. We will discuss some of them in this blog section.
- Anomaly detection: We can process millions of transactions or requests within seconds with low latency. SO we can react to enemies in real-time.
- Telemetry: We can collect data from any remote devices in the cloud or on edge for analysis and redirection to IoT devices.
- Application Monitoring: Gain deep insights into application health and infrastructure with integrations into Telegraf and Grafana.
Main Capabilities of Redis Time Series
In this blog section, we will discuss some of the main capabilities of the Redis Time Series.
- Retention and DownSampling: Redis Time Series automatically executes retention and downsampling rules to store the extensive data of telemetry applications using double delta compression in a space-efficient manner.
- Fast data ingest with infinite scale: Supports millions of operations with low latency. It can achieve linear horizontal scalability. It allows fast operations no matter the number of data points in time series because of cluster architecture.
- Visualization with Grafana: It is integrated with popular analytics, data collection, and monitoring libraries, including grafana for analytics and telegraf for data, ingest.
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