Optimizing IT operations with Elasticsearch Machine Learning
In the 21st Century, data is the biggest commodity. The value you can rip out of data is immense. However, you need a sophisticated system to analyze data to get valuable insights. There are multiple sources of data in any IT operations. You can analyze data generated from applications, servers, networks, and infrastructure to detect potential issues. This operational data is primarily gathered through system-produced metrics and logs. Operations teams have always depended on manual human efforts to search and report on the operational data. But now, organizations are embracing automated systems with intelligent machine learning tools to alert IT teams concerning the issues, their root causes, and recommended solutions without any human intervention.
For efficient IT operations, teams need more intelligent tools to reduce mean time to repair (MTTR) and eliminate time spent on the inefficient search for a cause in the data heap that is not impacting an outage. One prominent name in this space is Elasticsearch. Elasticsearch’s machine learning implements unsupervised and supervised learning algorithms to sort through operations data. It facilitates more effective alerting and graphical visualizations of operational data by identifying unusual activity based on analysis. With the help of a user-friendly interface, the Elastic Stack enables real-time search, reporting, and analysis of streaming metrics and logs.
Elasticsearch machine learning for enhanced IT operations
Organizations with excellent IT operations tend to excel at every aspect of business performance, including profitability, decision-making, investments, and more. This is precisely why IT operations need more intelligent solutions to detect and resolve issues related to application and infrastructure. And if something is not right, the administrator must identify and flag the error in log data. Elasticsearch’s machine learning identifies the unusual errors by modeling the data and notifies the operational team about the anomalies to help them forecast future behaviors. It simply gives a comprehensive view of the incident by determining how the system has changed compared to the day before.
Elasticsearch acts as an essential understructure for monitoring applications metrics and logs. Once the metrics and logs are collected, we need to leverage the power of Elasticsearch to query our data. With the rising volume of data, you can’t possibly eyeball so many time charts to find out what’s going on. But Elasticsearch machine learning allows you to monitor and model multiple entities at one go and identify unusual behaviors mathematically. You have to put a threshold or a rules-based alert which are static in nature to find out issues using standard search techniques. Typically, one doesn’t know where to set those bounds, as they become too rigid for dynamically changing data. Optimizing these thresholds while avoiding false positives is a tedious process. Elasticsearch Machine Learning allows the alerts to be more dynamic by learning the normal behavior models and alerting when data doesn’t fit the model.
Getting started with Elasticsearch machine learning
Elastic offers different machine learning algorithms that allow you to model your data. Unsupervised Learning can deduce patterns in your data without training or intervention. There are two types of unsupervised analysis: anomaly detection and outlier detection. Whereas supervised learning requires training data sets: classification & regression. Let’s get started with a machine learning setup in Elasticsearch and anomaly detection.
Setting up machine learning features in Elasticsearch
It is mandatory to have at least one machine learning node in your cluster to set up the machine learning features in Elasticsearch. A machine learning node is a node that must have the following values:
- xpack.ml.enabled: true, allows machine learning APIs on the node.
- node.role: ml, identifies the node as a machine learning node.
Getting started with anomaly detection
To explain the process, I will be using sample log data from Kibana.
- From the Kibana Main Menu, select “Machine Learning.”
- From the Anomaly Detection tab, select “Create Job.”
Select your index pattern or saved search:
- Here, I have selected kibana_sample_data_logs.
- You can create different types of jobs. For instance, I am taking the basic single metric job…read more.