What Is a Time Series Database and When Should You Use One?
Ethan ClarkeWhen you need to analyze data every minute, every day, or other date range, a time series database is an ideal choice. This type of database is specifically designed to store data points and their corresponding timestamps so that trends over a certain period of time can be displayed to users, and is usually used for time-based information visualization and analysis.
What is a time series database?
Each record in a time series database contains a timestamp. This timestamp can be used to display a single data point or for charting and analysis. Time series databases are primarily used for information that needs to be tracked by date ranges, such as weather changes or monitoring records of specific events.
What is a time series collection?
A time series collection is a slice of data extracted from a time series database and returned to an application. This collection contains data points within a given date range. The user or application passes a time range as input to the database, and the database returns a collection of all data points within that range.
How a time series database works
Time series databases are often created to capture large amounts of data for future analysis. Users set a date range in the application, and the database returns the corresponding data points. These databases work by capturing data at regular intervals. For example, a stock ticker might update stock prices every minute, and the database would store the stock name, price, and timestamp for analysis and historical backtracking.
Data retrieved from a time series database is sorted in chronological order, which allows developers to build visualizations more efficiently. Because time series databases are typically powerful servers, they are able to sort data sets faster than front-end applications. The database determines how the data is sorted based on the application's input, allowing developers to present analytical results to users. For example, a user might request stock price data for a specific date range and want the data sorted in ascending order.
Comparison of common time series databases
Each time series database has a backend engine that is responsible for storing and retrieving data. This engine must process large amounts of data quickly and efficiently while returning results with extremely low latency. Although traditional databases can store time-related data, there are some time series databases on the market that are specifically designed to handle such data and can better meet query and storage needs.
Why InfluxDB: An Open Source Time Series Database
Many developers prefer open source databases because they can freely fork the codebase and customize the base product. InfluxDB is an open source time series database capable of storing thousands of data points per second. If you need to monitor IoT devices or other industrial applications, InfluxDB is an excellent choice.
Prometheus VS InfluxDB
The main difference between InfluxDB and Prometheus is the way data is retrieved. InfluxDB allows applications to continuously send data to the database for storage and retrieval. In contrast, Prometheus works through an API to which applications push data, and then Prometheus uses polling to obtain data from storage. For large-scale enterprises, Prometheus's cloud infrastructure enables developers to upload data from multiple locations and view it uniformly in a central dashboard.
TimescaleDB VS InfluxDB
InfluxDB is a NoSQL database, while TimescaleDB is a database based on the relational model. There are significant differences between the two in how data is stored and retrieved. Relational databases connect stored data through tables and keys, while NoSQL databases use more flexible data structures. If you know exactly what type of data you want to store, and can organize it into tabular form, TimescaleDB may be a suitable choice.
Elasticsearch VS InfluxDB
Elasticsearch is a commonly used engine in enterprise applications, often for performance. It has an advantage over InfluxDB when processing large data sets because it can shard indexes to optimize data retrieval efficiency. Elasticsearch is often used in scenarios where large amounts of data need to be analyzed, such as monitoring log files for suspicious user activity in large enterprise networks. It can handle complex queries across millions of data points, helping users quickly obtain key information.
When to use a time series database
Time series databases are often used to monitor hardware or software to identify specific events by analyzing large amounts of data. To get a comprehensive view of events in your environment, you need to collect a large amount of data from multiple sources. For example, IoT sensors can collect temperature data from multiple devices, and a time series database would store temperature information every minute of the day so that engineers can identify any anomalies and take timely action before the machine fails.
Relational Databases vs. Time Series Databases
Most time series databases use NoSQL documents to store data, which is a common way to handle unstructured data. Unstructured data allows developers to freely store information such as timestamps and names without forcing the data to be organized into a predefined table structure. In contrast, relational databases require data to follow a specific structure, which makes them unsuitable for handling unknown values and data types that may exist in time series data. For example, developers cannot store string values in a timestamp column in a relational database, but can easily do so with a time series NoSQL database.
NoSQL vs. Time Series Databases
NoSQL databases are often the preferred solution for handling unknown values. You should choose a database that supports NoSQL, such as InfluxDB or Elasticsearch. These time series databases provide efficient batch data storage and fast performance when querying, and are particularly suitable for large enterprise applications. However, their deployment and configuration are relatively complex, and incorrect configuration may affect the performance of query processing.
Advantages of time series databases
Time series databases are designed for time-based analysis and can efficiently store large amounts of data with each data point containing a timestamp. These databases are built with large data sets in mind, so they are generally faster and more efficient than traditional databases when inserting new records and retrieving large amounts of data. In addition, time series databases provide higher accuracy for queries involving dates and times and are more efficient when storing time series data. Any organization that needs to monitor data can benefit from this, and applications can use time series databases for data analysis, visualization, financial trend analysis, and monitoring environmental changes.
Disadvantages of time series databases
However, time series databases are more complex to deploy and configure. Most time series databases employ NoSQL architectures, which can significantly degrade performance if not configured properly or optimized. Therefore, there is a need for professionals within the organization who understand database optimization. In addition, when enterprises want to store large amounts of time series data, they may need to invest corresponding resources. Although data can be stored in the cloud, this increases IT costs and the infrastructure to support data storage and time series database processing can become expensive.
Conclusion
If you are looking for a better solution to handle time-based data, a time series database is an option worth considering. When choosing a database engine, compare the different types, consider the cost, and find a solution that can scale with business growth and data volume. At the same time, make sure to check the configuration and optimization options to ensure that the database runs efficiently.
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