In the world of monitoring and observability, Prometheus has grown into the de-facto standard for monitoring in cloud-native environments because of its robust data collection mechanism, flexible querying capabilities, and integration with other tools for rich dashboarding and visualization.
Prometheus is primarily built for short-term metric storage, typically retaining data in-memory or on local disk storage, with a focus on real-time monitoring and alerting rather than historical analysis. While it offers valuable insights into current metric values and trends, it may pose economic challenges and fall short of the robust functionalities and capabilities necessary for in-depth historical analysis, long-term trend detection, and forecasting. This is particularly evident in large environments with a substantial number of targets or high data ingestion rates, where metric data accumulates rapidly.
Numerous organizations assess their unique needs and explore avenues to augment their Prometheus monitoring and observability capabilities. One effective approach is integrating Prometheus with Elastic®. In this blog post, we will showcase the integration of Prometheus with Elastic, emphasizing how Elastic elevates metrics monitoring through extensive historical analytics, anomaly detection, and forecasting, all in a cost-effective manner.
Integrate Prometheus with Elastic seamlessly
Organizations that have configured their cloud-native applications to expose metrics in Prometheus format can seamlessly transmit the metrics to Elastic by using Prometheus integration. Elastic enables organizations to monitor their metrics in conjunction with all other data gathered through Elastic's extensive integrations.
Go to Integrations and find the Prometheus integration.
To gather metrics from Prometheus servers, the Elastic Agent is employed, with central management of Elastic agents handled through the Fleet server.
After enrolling the Elastic Agent in the Fleet, users can choose from the following methods to ingest Prometheus metrics into Elastic.
1. Prometheus collectors
The Prometheus collectors connect to the Prometheus server and pull metrics or scrape metrics from a Prometheus exporter.
2. Prometheus queries
The Prometheus queries execute specific Prometheus queries against Prometheus Query API.
3. Prometheus remote-write
The Prometheus remote_write can receive metrics from a Prometheus server that has configured the remote_write setting.
After your Prometheus metrics are ingested, you have the option to visualize your data graphically within the Metrics Explorer and further segment it based on labels, such as hosts, containers, and more.
You can also query your metrics data in Discover and explore the fields of your individual documents within the details panel.
Storing historical metrics with Elastic’s data tiering mechanism
By exporting Prometheus metrics to Elasticsearch, organizations can extend the retention period and gain the ability to analyze metrics historically. Elastic optimizes data storage and access based on the frequency of data usage and the performance requirements of different data sets. The goal is to efficiently manage and store data, ensuring that it remains accessible when needed while keeping storage costs in check.
After ingesting Prometheus metrics data, you have various retention options. You can set the duration for data to reside in the hot tier, which utilizes high IO hardware (SSD) and is more expensive. Alternatively, you can move the Prometheus metrics to the warm tier, employing cost-effective hardware like spinning disks (HDD) while maintaining consistent and efficient search performance. The cold tier mirrors the infrastructure of the warm tier for primary data but utilizes S3 for replica storage. Elastic automatically recovers replica indices from S3 in case of node or disk failure, ensuring search performance comparable to the warm tier while reducing disk cost.
The frozen tier allows direct searching of data stored in S3 or an object store, without the need for rehydration. The purpose is to further reduce storage costs for Prometheus metrics data that is less frequently accessed. By moving historical data into the frozen tier, organizations can optimize their storage infrastructure, ensuring that the recent, critical data remains in higher-performance tiers while less frequently accessed data is stored economically in the frozen tier. This way, organizations can perform historical analysis and trend detection, identify patterns and make informed decisions, and maintain compliance with regulatory standards in a cost-effective manner.
An alternative way to store your cloud-native metrics more efficiently is to use Elastic Time Series Data Stream (TSDS). TSDS can store your metrics data more efficiently with ~70% less disk space than a regular data stream. The downsampling functionality will further reduce the storage required by rolling up metrics within a fixed time interval into a single summary metric. This not only assists organizations in cutting down on storage expenses for metric data but also simplifies the metric infrastructure, making it easier for users to correlate metrics with logs and traces through a unified interface.
Advanced analytics
Besides Metrics Explorer and Discover, Elasticsearch® provides more advanced analytics capabilities and empowers organizations to gain deeper, more valuable insights into their Prometheus metrics data.
Out of the box, Prometheus integration provides a default overview dashboard.
From Metrics Explorer or Discover, users can also easily edit their Prometheus metrics visualization in Elastic Lens or create new visualizations from Lens.
Elastic Lens enables users to explore and visualize data intuitively through dynamic visualizations. This user-friendly interface eliminates the need for complex query languages, making data analysis accessible to a broader audience. Elasticsearch also offers other powerful visualization methods with aggregations and filters, enabling users to perform advanced analytics on their Prometheus metrics data, including short-term and historical data. To learn more, check out the how-to series: Kibana.
Anomaly detection and forecasting
When analyzing data, maintaining a constant watch on the screen is simply not feasible, especially when dealing with millions of time series of Prometheus metrics. Engineers frequently encounter the challenge of differentiating normal from abnormal data points, which involves analyzing historical data patterns — a process that can be exceedingly time consuming and often exceeds human capabilities. Thus, there is a pressing need for a more intelligent approach to detect anomalies efficiently.
Setting up alerts may seem like an obvious solution, but relying solely on rule-based alerts with static thresholds can be problematic. What's normal on a Wednesday at 9:00 a.m. might be entirely different from a Sunday at 2:00 a.m. This often leads to complex and hard-to-maintain rules or wide alert ranges that end up missing crucial issues. Moreover, as your business, infrastructure, users, and products evolve, these fixed rules don't keep up, resulting in lots of false positives or, even worse, important issues slipping through the cracks without detection. A more intelligent and adaptable approach is needed to ensure accurate and timely anomaly detection.
Elastic's machine learning anomaly detection excels in such scenarios. It automatically models the normal behavior of your Prometheus data, learning trends, and identifying anomalies, thereby reducing false positives and improving mean time to resolution (MTTR). With over 13 years of development experience in this field, Elastic has emerged as a trusted industry leader.
The key advantage of Elastic's machine learning anomaly detection lies in its unsupervised learning approach. By continuously observing real-time data, it acquires an understanding of the data's behavior over time. This includes grasping daily and weekly patterns, enabling it to establish a normalcy range of expected behavior. Behind the scenes, it constructs statistical models that allow accurate predictions, promptly identifying any unexpected variations. In cases where emerging data exhibits unusual trends, you can seamlessly integrate with alerting systems, operationalizing this valuable insight.
Machine learning's ability to project into the future, forecasting data trends one day, a week, or even a month ahead, equips engineers not only with reporting capabilities but also with pattern recognition and failure prediction based on historical Prometheus data. This plays a crucial role in maintaining mission-critical workloads, offering organizations a proactive monitoring approach. By foreseeing and addressing issues before they escalate, organizations can avert downtime, cut costs, optimize resource utilization, and ensure uninterrupted availability of their vital applications and services.
Creating a machine learning job for your Prometheus data is a straightforward task with a few simple steps. Simply specify the data index and set the desired time range in the single metric view. The machine learning job will then automatically process the historical data, building statistical models behind the scenes. These models will enable the system to predict trends and identify anomalies effectively, providing valuable and actionable insights for your monitoring needs.
In essence, Elastic machine learning empowers us to harness the capabilities of data scientists and effectively apply them in monitoring Prometheus metrics. By seamlessly detecting anomalies and predicting potential issues in advance, Elastic machine learning bridges the gap and enables IT professionals to benefit from the insights derived from advanced data analysis. This practical and accessible approach to anomaly detection equips organizations with a proactive stance toward maintaining the reliability of their systems.
Try it out
Start a free trial on Elastic Cloud and ingest your Prometheus metrics into Elastic. Enhance your Prometheus monitoring with Elastic Observability. Stay ahead of potential issues with advanced AI/ML anomaly detection and prediction capabilities. Eliminate data silos, reduce costs, and enhance overall response efficiency.
Elevate your monitoring capabilities with Elastic today!
The release and timing of any features or functionality described in this post remain at Elastic's sole discretion. Any features or functionality not currently available may not be delivered on time or at all.