There’s been a lot of buzz about time-series data for the past few years. No matter what you’re monitoring—financial data, server performance, social media activity or something else entirely—time-series data can give you valuable insights into trends and patterns.
But what exactly is a time-series database, and why do you need one? This article will look closely at time-series databases and how they can help you make sense of your data.
What is a Time-Series Database?
A time-series database (TSDB) is a database management system optimized for storing and querying data that changes over time. Time-series data is often used in monitoring applications where it’s important to quickly retrieve information about a system’s current state and trends and patterns over time.
In most definitions, time-series data, often referred to as time-stamped data, is a sequence of values indexed in time order. Time stamping refers to data collected at various times, where each value is time stamped. These data points are usually gathered from the same source and are used to measure progress over time.
Important Notes:
You can use relational or NoSQL databases to crunch time-series data, but purpose-built time-series databases are tailored to exploit the unique features of time-series data. This implies that time-series databases ingest at a faster rate, query more quickly and compress data more efficiently. Furthermore, time-series databases include special analytical capabilities and management features that are not found in most relational or NoSQL databases.
Some Advantages
This list is not exhaustive, but here are some of the advantages that you might get from using a time-series database:
1.) Time-series databases can handle high-velocity data very well.
2.) Time-series databases are purpose-built for storing time-series data, making them more efficient in storage and querying.
3.) Time-series databases often have built-in analytics and management features designed specifically for time-series data.
4.) Many time-series databases are open source, which means they’re free to use.
Some Disadvantages
There are also some disadvantages that you should be aware of before using a time-series database:
1.) Time-series databases are often more complex to set up and manage than relational or NoSQL databases.
2.) There are relatively few time-series databases to choose from, so you might not have as much flexibility in choosing a platform that meets your specific needs.
3.) Time-series data can be very large, so you’ll need enough storage capacity to accommodate your data.
What Are the Characteristics of Time-Series Data?
There are several characteristics of time-series data that make it unique and require special handling. From a database standpoint, the most essential ones are as follows:
- Timestamp: Every data point has its timestamp. The timestamp is crucial for calculating or analyzing the information.
- Structure: Metrics from devices or monitoring are almost always structured, unlike those produced by Internet applications. They have predetermined data types and lengths, and the structure will not alter until the device’s firmware is updated.
- Stream-like: Data sources, such as audio or video programs generate data at a set or constant rate. These data streams are completely unrelated to one another.
- Stable flow: Time-series data traffic is constant over time, and it may be calculated and predicted if enough data sources are used within a certain sampling period.
- Immutability: The source of this data is a time-series data store. Each data point is created only once and never updated or corrected. Time-series data, like log data, is typically append-only.
How Is Time-Series Data Used?
Many of you may be wondering why time-series data is so important. The answer is that it allows us to track changes over time, which can be incredibly valuable for monitoring and troubleshooting purposes.
For example, let’s say you’re a system administrator tasked with keeping an eye on server performance. By monitoring various metrics—such as CPU usage, memory usage and network traffic—you can quickly identify when a server is starting to experience issues.
Time-series data can also be used for predictive purposes. For example, if you notice that CPU usage spikes at certain times of day, you can use that information to plan for future capacity needs.
Of course, time-series data isn’t just limited to server performance. It can be used for any application where it’s important to track changes over time. This includes everything from weather and financial data to social media and website analytics.
Let’s Get Specific
Here’s what you should know about time-series data: It’s typically used to seek insights into operations, create alerts based on real-time analysis and forecast future trends.
The following characteristics are found in time-series data applications:
- High write-read ratio: Twitter and LinkedIn are internet applications with single articles that millions of people read, but raw time-series data is primarily scanned and evaluated by apps and algorithms.
- Retention policy: In general, time-series data is not kept permanently. Organizations have a retention policy that specifies when and how their data is destroyed.
- Real-time analytics and computing: Time-series data must be calculated in real-time to identify out-of-the-ordinary activity and sound alarms based on the acquired information or aggregate findings.
- Query scope: Time-series data is generally requested over a period or a set of data sources, and filters are used to prevent all historical information from being requested. Furthermore, all or a portion of the data sources with a filter condition are always aggregated.
- Trends: Single data points are typically not significant in time-series data. The emphasis is on how data evolves, such as fluctuations in the previous hour or day.
Popular time-series solutions like TDengine or InfluxDB, for example, create more efficient processing of time-series data and greater performance than general databases by taking advantage of these qualities.
Do Time-Series Databases Need Specialized Databases?
We live in a modern world where data is constantly being generated at an unprecedented rate. To keep up with the demand, businesses need to be able to store and process large amounts of data quickly and efficiently. This is where specialized time-series databases come in.
Everything is online, including meters, automobiles, lifts, assembly lines and even bicycles. And these items are sending out a never-ending stream of numbers and events that IoT and the cloud have unleashed an explosion in time-series data generation.
With this in mind, it’s not surprising that several specialized time-series databases have been created to deal with this type of data influx.
Time-series data sets are enormous and pose a significant problem for general database management systems, such as relational and NoSQL databases. Non-specialized databases have trouble with the following elements of time-series data:
- Data ingestion rate: In many time-series data scenarios, millions of data points are generated every second and must be ingested in real-time. Relational databases are not built to handle this quantity of data, and while NoSQL databases may be scaled to do so, the amount of resources required soon becomes prohibitive.
- Query latency: It is often the case that a time-series system must scan a massive number of data points to get an aggregate result, which might lead to sluggish performance. For example, it would take days for a basic database to compute the average response time of all Amazon.com clicks, by which time the overall conclusion may be incorrect.
- Storage cost: Data generated by internet-connected devices and apps is nonstop 24/7, with a single day producing as much as a terabyte of data. Because relational and NoSQL databases cannot compress this data effectively, storage costs can quickly skyrocket.
These problems are generally linked to efficiency in processing big data sets, although there are some places where general databases frequently do not meet even the fundamental demands of time-series applications:
- Data life cycle management: Time-series data is generally removed in bulk, not one data point at a time, as it ages out.
- Roll-up: In most cases, time-series data is collected and rolled up over a set period before being stored in the new table. Raw data and rolled-up data can have distinct life cycles and retention policies.
- Special analytic functions: Time-series applications need more specialized features than general databases, such as time-weighted average, moving average, cumulative sum, rate of change, elapsed time for a specific state and the delta between two consecutive data points.
- Interpolation: The database management system must be able to interpolate data based on the adjacent data points and rules to regularize data sets when applications or algorithms require.
- Continuous query: Therefore, if you hear a user or customer say they want to know when their data is updated, it’s reasonable to assume they want this functionality.
- Session and state windows: Aggregations and analytical procedures may be performed on a session or state window, not just time—for example, consider one that only calculates average power consumption when a machine is switched on.
With general databases, developers must write custom code to implement features specific to their data set. Different data workloads require different database solutions; one size does not fit all. For time-series data, no matter the size of your data set, a purpose-built time-series database is the best tool for the job.
Why Are Time-Series Databases Becoming Popular?
Two years ago, time-series databases were the most popular database type in the business, owing to its expanding number of use cases. It is especially useful if you’re executing sophisticated transactions like advertising, e-commerce, supply chain management and so on.
However, because of the rapid expansion of the IoT, they are becoming even more popular. As more devices become internet-connected and send data—time-series, of course—to the cloud, many industries are interested in purpose-built time-series databases.
Time-series data is proving to be an important tool, not just for decision-making and optimization but also for industrial applications. Finally, IT infrastructure has been growing rapidly, with everything from servers, containers, network devices, apps, and microservices being monitored to create massive quantities of time-series data.
Older time-series databases, on the other hand, are often closed systems employing antiquated structures that do not scale to handle the increasing amount of data. A million time-series data points used to seem like a lot, but now millions and even billions of data points are commonplace.
Finally, integrating old (previous) time-series solutions with popular data analysis tools like artificial intelligence and machine learning platforms is difficult, if not impossible. These legacy systems cannot be transferred to the cloud without a lot of work and their licensing terms are no longer adequate for current apps.
Final Thoughts
The expanding market and the constraints of previous time-series databases are making room for a new generation. These new time-series databases are built from the ground up to take advantage of the cloud, big data and AI/ML technologies.
If your business or application deals with time-series data, you need a purpose-built time-series database. The advantages of using such a database are too great to ignore, and the disadvantages of using an older, less capable database are becoming more and more apparent.
