Sumo Logic Queries Examples and Usage

2 min read 06-03-2025

Sumo Logic's query language provides a powerful way to analyze your machine data. Understanding its syntax and capabilities is key to extracting valuable insights from your logs and metrics. This post will explore several Sumo Logic query examples, demonstrating practical applications and showcasing the versatility of the platform.

Understanding the Basics

Before diving into specific examples, let's briefly revisit the fundamental components of a Sumo Logic query:

_sourceCategory: Specifies the source of your data. This is crucial for filtering your search to specific applications or systems.
| (Pipe): Used to chain multiple queries together, allowing you to filter, aggregate, and transform your data in a sequential manner.
Functions: Sumo Logic offers a vast library of functions for data manipulation, aggregation, and visualization. Examples include count(), avg(), sum(), max(), min(), and many more.
Wildcards: * and ? are used for flexible pattern matching within your search criteria.

Practical Query Examples

Let's explore some practical examples, demonstrating how to use Sumo Logic queries for various analytical tasks.

1. Counting Error Messages

This query counts the number of error messages originating from a specific application:

_sourceCategory="ApplicationLogs" AND "Error" | count()

This simple query uses the _sourceCategory filter to focus on the "ApplicationLogs" category and then uses the AND operator to further refine the results to only include those messages containing the string "Error". Finally, the count() function provides the total number of matching events.

2. Calculating Average Response Time

This example calculates the average response time of a web server:

_sourceCategory="WebServerLogs" | parse regex "Response Time: (?<responseTime>\d+)ms" | avg(responseTime)

This query parses the log entries using a regular expression to extract the response time in milliseconds. Then, the avg() function computes the average response time across all matched events. Note that the regular expression should be adjusted based on the specific format of your log messages.

3. Identifying Top Frequent IP Addresses

This query identifies the top 10 most frequent IP addresses accessing a particular service:

_sourceCategory="AccessLogs" | count by _ip | limit 10

This query uses the count by _ip clause to count events based on unique IP addresses. The limit 10 clause restricts the output to the top 10 most frequent IP addresses.

4. Analyzing Specific Time Ranges

To analyze data within a specific time range, use the _time field and Sumo Logic's time range syntax:

_sourceCategory="SystemLogs" AND _time >= "2024-03-08T00:00:00" AND _time <= "2024-03-08T23:59:59" | count()

This query filters events to include only those occurring on March 8th, 2024.

Advanced Query Techniques

Sumo Logic supports numerous advanced features that enhance query capabilities. These include:

Nested Queries: Combining multiple queries within a single search.
Subqueries: Using queries within functions.
Custom Fields: Creating and using custom fields to enrich your data analysis.
Time-Series Analysis: Analyzing trends over time.

Conclusion

Sumo Logic's query language is a powerful tool for analyzing machine data. This post provided basic query examples; the platform offers extensive documentation and resources for exploring its advanced capabilities. Mastering these techniques empowers users to gain critical insights from their data, improving system performance, security, and overall operational efficiency. Remember to consult the official Sumo Logic documentation for a comprehensive overview of available functions and operators.