Leveraging Time Series Databases for Cutting-Edge Analytics: Specialized Software for Providing Timely Insights at Scale

Editor's Note: The following is an article written for and published in DZone's 2024 Trend Report, Database Systems: Modernization for Data-Driven Architectures.

Time series data has become an essential part of data collection in various fields due to its ability to capture trends, patterns, and anomalies. Through continuous or periodic observation, organizations are able to track how key metrics are changing over time. This simple abstraction powers a broad range of use cases.

The widespread adoption of time series data stems from its versatility and applicability across numerous domains. For example: 

• Financial institutions analyze market trends and predict future movements. 
• IoT devices continuously generate time-stamped data to monitor the telemetry of everything from industrial equipment to home appliances.
• IT infrastructure relies on temporal data to track system performance, detect issues, and ensure optimal operation.

As the volume and velocity of time series data have surged, traditional databases have struggled to keep pace with the unique demands placed by such workloads. This has led to the development of specialized databases, known as time series databases (TSDBs). TSDBs are purpose built to handle the specific needs of ingesting, storing, and querying temporal data.

Core Features and Advantages of Time Series Databases

TSDBs work with efficient data ingestion and storage capabilities, optimized querying, and analytics to manage large volumes of real-time data. 

Data Ingestion and Storage

TSDBs utilize a number of optimizations to ensure scalable and performant loading of high-volume data. There are several of these optimizations that stand out as key differentiators:

Table 1. Ingestion and storage optimizations

Optimized Querying and Analytics

To ensure fast data retrieval at query time, several optimizations are essential. These include specialized time-based indexing, time-based sharding/partitioning, and precomputed aggregates. These techniques take advantage of the time-based, sequential nature of the data to minimize the amount of data scanned and reduce the computation required during queries. An overview of these techniques are highlighted below.

Indexing

Various indexing strategies are employed across TSDBs to optimize data retrieval. Some TSDBs use an adapted form of the inverted index, which allows for rapid indexing into relevant time series by mapping metrics or series names to their locations within the dataset. Others implement hierarchical structures, such as trees, to efficiently index time ranges, enabling quick access to specific time intervals.

Additionally, some TSDBs utilize hash-based indexing to distribute data evenly and ensure fast lookups, while others may employ bitmap indexing for compact storage and swift access. These diverse strategies enhance the performance of TSDBs, making them capable of handling large volumes of time-stamped data with speed and precision.

Partitioning

Partitioning consists of separating logic units of time into separate structures so that they can be accessed independently.

Figure 1. Data partitioning to reduce data scan volume

Pre-Computed Aggregates

A simplified version of pre-computation is shown below. In practice, advanced statistical structures (e.g., sketches) may be used so that more complex calculations (e.g., percentiles) can be performed over the segments.

Figure 2. Visualizing pre-computation of aggregates

Scalability and Performance

Several tactics and features ensure TSDBs remain reliable and performant as data velocity and volume increase. These are summarized in the table below:

Table 2. Scalability tactics and features

Time Series Databases vs. Time Series in OLAP Engines

Due to the ubiquity of time series data within businesses, many databases have co-opted the features of TSDBs in order to provide at least some baseline of the capabilities that a specialized TSDB would offer. And in some cases, this may satisfy the use cases of a particular organization. However, outlined below are some key considerations and differentiating features to evaluate when choosing whether an existing OLAP store or a time-series-optimized platform best fit a given problem.

Key Considerations

An organization's specific requirements will drive which approach makes the most sense. Understanding the three topics below will provide the necessary context for an organization to determine if bringing in a TSDB can provide a high return on investment.

Data Volume and Ingestion Velocity

TSDBs are designed to handle large volumes of continuously arriving data, and they may be a better fit in cases where the loading volumes are high and the business needs require low latency from event generation to insight.

Typical Query Patterns

It is important to consider whether the typical queries are fetching specific time ranges of data, aggregating over time ranges, performing real-time analytics, or frequently downsampling. If they are, the benefits of a TSDB will be worth introducing a new data framework into the ecosystem.

Existing Infrastructure and Process

When considering introducing a TSDB into an analytic environment, it is worthwhile to first survey the existing tooling since many query engines now support a subset of temporal features. Determine where any functionality gaps exist within the existing toolset and use that as a starting point for assessing fit for the introduction of a specialized back end such as TSDB.

Differentiating Features

There are many differences in implementation, and the specific feature differences will vary depending on the platforms being considered. However, generally, the two feature sets are emphasized broadly in TSDBs: time-based indexing and data management constructs. This emphasis stems from the fact that both feature sets are tightly coupled with time-based abstractions. Use of a TSDB will be most successful when these features can be best leveraged.

Time-Based Indexing

Efficient data access is achieved through constructs that leverage the sequential nature of time series data, allowing for fast retrieval while maintaining low ingest latency. This critical feature allows TSDBs to excel in use cases where traditional databases struggle to scale effectively.

Data Management Constructs

Time-based retention policies, efficient compression, and downsampling simplify the administration of large datasets by reducing the manual work required to manage time series data. These specialized primitives are purposefully designed to manage and analyze time series data, and they include functionality that traditional databases typically lack.

Use Cases of Time Series Databases in Analytics

There are various uses for time series data across all industries. Furthermore, emerging trends such as edge computing are putting the power of real-time time series analytics as close to the source of data generation as possible, thereby reducing the time to insight and removing the need for continuous connectivity to centralized platforms. This opens up a host of applications that were previously difficult or impossible to implement until recently. A few curated use cases are described below to demonstrate the value that can be derived from effectively leveraging temporal data.

Telemetry Analysis and Anomaly Detection

One of the most common use cases for TSDBs is the observation and analytics on real-time metrics. These metrics come from a variety of sources, and a few of the most prominent sources are described below.

IT and Infrastructure Monitoring

TSDBs enable real-time monitoring of servers, networks, and application performance, allowing for immediate detection and response to issues. This real-time capability supports performance optimization by identifying bottlenecks, determining capacity needs, and detecting security intrusions. Additionally, TSDBs enhance alert systems by identifying anomalous patterns and breaches of predefined thresholds, proactively informing staff to prevent potential problems. They also support custom dashboards and visualizations for quick and effective data interpretation, making them an invaluable tool for modern IT operations.

IoT and Sensor Data

TSDBs are vital for telemetry analysis and anomaly detection in IoT and sensor data applications, particularly when aligned with edge computing. They efficiently handle the large volumes of temporal data generated by IoT devices and sensors, enabling real-time monitoring and analysis at the edge of the network. This proximity allows for immediate detection of anomalies, such as irregular patterns or deviations from expected behavior, which is crucial for maintaining the health and performance of IoT systems. By processing data locally, TSDBs reduce latency and bandwidth usage, enhancing the responsiveness and reliability of IoT operations.

Smart Cities and Utilities

Extreme weather and the need for quick time to action has driven a growth in the usage of temporal data within city and utility infrastructures. Quickly deriving insights from deviations in normal operations can make a significant impact in these applications. TSDBs enable this through both the ability to ingest large volumes of data quickly as well as natively providing highly performant real-time analytic capabilities. For instance, it can mean the difference between high winds causing live wire breakages, which increase fire risk, and an automated shutdown that significantly reduces such risks.

Furthermore, better information about energy generation and demand can be used to improve the efficiency of such systems by ensuring that supply and demand are being appropriately matched. This is particularly important during times when there is heavy strain on the energy grid, such as periods of unusual heat or cold, when effective operation can save lives.

Trend Analysis

The usefulness of TSDBs is not limited to real-time analytics; they are also used for performing long-term trend analysis and often provide the most value when identifying real-time deviations from longer term trends. The optimizations mentioned above, such as pre-computation and partitioning, allow TSDBs to maintain high performance, even if data volumes grow dramatically.

Financial Analytics

In the realm of financial analytics, TSDBs are indispensable for trend analysis. Analysts can identify patterns and trends over time, helping to forecast market movements and inform investment strategies. The ability to process and analyze this data in real time allows for timely decision making, reducing the risk of losses and capitalizing on market opportunities. Additionally, TSDBs support the integration of various data sources, providing a comprehensive view of financial markets and enhancing the accuracy of trend analysis.

Healthcare and Biometric Data

Medical devices and wearables generate vast amounts of time-stamped data, including heart rates, glucose levels, and activity patterns. TSDBs facilitate the storage and real-time analysis of this data, allowing healthcare providers to monitor patients continuously and detect any deviations from normal health parameters promptly. Trend analysis using TSDBs can also help in predicting the onset of diseases, monitoring the effectiveness of treatments, and tailoring personalized healthcare plans. This proactive approach not only improves patient outcomes but also enhances the efficiency of healthcare delivery.

Industrial Predictive Maintenance

Industries deploy numerous sensors on equipment to monitor parameters such as vibration, temperature, and pressure. By collecting and analyzing time-stamped data, TSDBs enable the identification of patterns that indicate potential equipment failures. This trend analysis allows maintenance teams to predict when machinery is likely to fail and schedule timely maintenance, thereby preventing costly unplanned downtimes. Moreover, TSDBs support the optimization of maintenance schedules based on actual usage and performance data, enhancing overall operational efficiency and extending the lifespan of industrial equipment.

Conclusion

Time series databases offer tools that simplify working with temporal data, thereby enabling businesses to improve operational efficiency, predict failures, and enhance security.

The expanding capabilities of TSDBs highlight the value of real-time analytics and edge processing. Features like time-based partitioning, fast ingestion, and automated data retention — now found in traditional databases — encourage TSDB adoption by allowing proof of concepts on existing infrastructure. This demonstrates where investing in TSDBs can yield significant benefits, pushing the boundaries of temporal data management and optimizing analytics ecosystems.

Integration with machine learning and AI for advanced analytics, enhanced scalability, and adoption of cloud-native solutions for flexibility are driving forces ensuring future adoption. TSDBs will support edge computing and IoT for real-time processing, strengthen security and compliance, and improve data retention management. Interoperability with other tools and support for open standards will create a cohesive data ecosystem, while real-time analytics and advanced visualization tools will enhance data interpretation and decision making. Together, these factors will ensure that TSDBs continue to be an essential piece of data infrastructure for years to come.

This is an excerpt from DZone's 2024 Trend Report, Database Systems: Modernization for Data-Driven Architectures.

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