Using Time Series Data for Infrastructure Monitoring: Challenges and Advantages

By Jason Myers / Developer
Feb 16, 2024

Monitoring the performance and health of infrastructure is crucial for ensuring smooth operations. From data centers and cloud environments to networks and IoT devices, infrastructure monitoring plays a vital role in identifying issues, optimizing resource utilization, and maintaining high availability.

However, traditional monitoring approaches often struggle to handle the volume and velocity of data generated by modern infrastructures. This is where time series databases, like InfluxDB, come into play. Let’s explore what it looks like to use InfluxDB for infrastructure monitoring, addressing some common pain points faced by monitoring teams.

Pain points and their impact

Monitoring infrastructure poses several challenges that can hinder effective operations and decision-making. These processes involve a lot of time series data, so solutions that can’t handle the unique characteristics of time series workloads are more likely to run into some or all of these pain points.

• Data volume and velocity: The sheer volume and velocity of data generated by modern infrastructures can overwhelm traditional monitoring systems. As a result, monitoring teams may face difficulties in efficiently storing, processing, and analyzing data. This can lead to delays in identifying and resolving issues, impacting the overall performance and availability of the infrastructure.
• Real-time monitoring: Real-time monitoring is crucial for proactive issue detection and rapid responses and resolutions. Traditional monitoring systems struggle to ingest and analyze data in real-time. They can’t write or query data at scale as it hits the database, causing delays in identifying critical events or anomalies. Delays can subsequently result in extended downtime, reduced customer satisfaction, and potential revenue loss.
• Scalability: As infrastructures grow in size and complexity, monitoring systems must scale in lock-step to handle the increasing data load. Traditional databases—especially relational databases—often face scalability limitations, leading to performance bottlenecks and degraded monitoring capabilities. This affects the ability to monitor and manage large-scale infrastructure effectively.
• Complex analysis and visualization: Infrastructure monitoring requires advanced analysis and visualization capabilities to derive meaningful insights from the collected data. Traditional databases may lack the necessary querying and analytics features, making it challenging to perform complex analysis and visualize data effectively. This limitation can hinder identification of trends, patterns, and anomalies, impacting decision-making and troubleshooting efforts.
• Data retention and storage optimization: Users need to store monitoring data for historical analysis, compliance, and auditing purposes. However, storing large volumes of data for extended periods can be costly and inefficient. Too often, businesses need to choose between keeping data and cost management. Because traditional databases are less likely to offer efficient data retention and storage optimization techniques for time series data, the result is increased costs, resource utilization, and management overhead.

Using InfluxDB for infrastructure monitoring

Time series data has unique characteristics that require a purpose-built database to address the pain points listed above. Let’s look at InfluxDB and consider how it addresses these issues and creates advantages for infrastructure monitoring.

• Efficient storage and retrieval: InfluxDB 3.0 is a columnar database designed to handle time series workloads, offering efficient storage and retrieval capabilities. Its revamped underlying storage engine enables fast writes and optimized queries, making it ideal for storing large volumes of infrastructure monitoring data.
• High scalability: InfluxDB is built to accommodate high write and read loads, making it suitable for monitoring large-scale infrastructures with millions of data points. It can easily scale horizontally by adding more nodes to the cluster, ensuring seamless performance as your infrastructure grows.
• Fast data ingestion: Real-time monitoring is crucial for infrastructure management. InfluxDB excels in high-speed data ingestion, allowing you to capture and analyze data in real-time. Its write-optimized design quickly ingests data and makes it available for analysis within milliseconds. Version 3.0 makes significant performance gains over previous versions (check out the latest benchmarks).
• Advanced querying and analytics: InfluxDB 3.0 made SQL a first-class query language. It also supports InfluxQL, a powerful SQL-like query language that allows you to perform complex analyses of infrastructure data. It supports various functions, aggregations, downsampling, and filtering, enabling you to extract valuable insights from your monitoring data. Combining InfluxDB with Grafana gives you robust visualization options, allowing you to create custom, dynamic dashboards for your data.
• Data retention and compression: Infrastructure monitoring generates a vast amount of data over time. InfluxDB 3.0’s columnar structure allows it to compress data more efficiently. Combining it with Apache Parquet, which further compresses data, means that you can store more data using less space—up to 90%+ in cost savings for data storage. InfluxDB also gives you the flexibility to set your data retention policies, so you can control—with granular detail—what data you keep and for how long.

Why choose InfluxDB for infrastructure monitoring?

InfluxDB offers numerous advantages for infrastructure monitoring, addressing common pain points faced by monitoring teams. Its efficient storage and retrieval, high scalability, fast data ingestion, advanced querying capabilities, and flexible data retention make it a powerful solution for handling the challenges of monitoring modern infrastructures. Understanding what InfluxDB brings to the table puts you in a position to make ‌informed decisions when building a robust, next-generation monitoring system that ensures the performance and availability of your infrastructure.