Log analysis tools are essential for monitoring system performance, detecting anomalies, troubleshooting issues, and ensuring security. Here are the top 21 log analysis tools in 2026 with their major features:

1. ELK Stack (Elasticsearch, Logstash, Kibana)

The ELK Stack is one of the most popular open-source log analysis tools. It combines Elasticsearch, Logstash, and Kibana to offer a comprehensive solution for collecting, storing, and visualizing log data. Elasticsearch indexes the log data, Logstash processes and filters the data, and Kibana provides powerful visualization capabilities. Together, the ELK Stack is used for centralized log management, real-time search, and data analysis across distributed systems.

2. Splunk

Splunk is a widely-used log analysis tool that provides real-time monitoring, data visualization, and powerful search capabilities. It helps organizations collect, index, and analyze machine-generated data, including logs, metrics, and events. Splunk’s advanced search and reporting capabilities allow users to quickly identify issues, generate insights, and troubleshoot applications and infrastructure. It also supports integration with various third-party tools and offers both cloud and on-premises deployment options.

3. Graylog

Graylog is an open-source log management platform that allows users to collect, index, and analyze log data in real time. It supports central log collection and provides powerful search, filtering, and visualization features. Graylog is designed to be highly scalable and supports integration with other monitoring and alerting tools. Its intuitive interface and alerting capabilities make it an excellent choice for organizations looking for a customizable and flexible log analysis solution.

4. Loggly

Loggly is a cloud-based log analysis tool that allows users to collect, search, and analyze log data from a variety of sources, including applications, servers, and network devices. Loggly provides real-time log monitoring, advanced search features, and customizable dashboards. It supports integration with popular DevOps tools and offers automated log parsing, anomaly detection, and alerting to help teams quickly detect and resolve issues.

5. Sumo Logic

Sumo Logic is a cloud-native log analysis and monitoring tool that enables organizations to collect, analyze, and visualize log data at scale. It provides real-time log aggregation, search, and analysis, along with advanced machine learning-powered analytics for anomaly detection. Sumo Logic supports a wide range of integrations and provides a unified platform for monitoring applications, infrastructure, and security logs.

6. Papertrail

Papertrail is a cloud-based log aggregation tool that helps users collect, search, and analyze log data from various sources, including servers, applications, and devices. It provides real-time log search, advanced filtering, and custom alerting. Papertrail’s simple and user-friendly interface makes it easy for teams to quickly troubleshoot issues and gain insights into system performance and security.

7. Logz.io

Logz.io is a cloud-based log management and analysis platform built on top of the ELK Stack. It offers a centralized platform for aggregating and analyzing logs, as well as advanced machine learning-driven insights for detecting anomalies. Logz.io integrates with other monitoring and observability tools and provides powerful features like log indexing, full-text search, and customizable visualizations, making it ideal for large-scale log analysis.

8. Fluentd

Fluentd is an open-source data collector that unifies the log collection and analysis process. It allows users to collect, process, and forward log data from various sources to different destinations, including Elasticsearch, Hadoop, and other analytics tools. Fluentd supports a wide range of input and output plugins, making it highly customizable for different log aggregation and processing needs.

9. Nagios Log Server

Nagios Log Server is a powerful log management and analysis tool designed for centralized log aggregation. It offers real-time log monitoring, advanced searching, and alerting capabilities. Nagios Log Server can collect logs from a variety of sources and provides users with customizable dashboards and visualizations to gain insights into system health and performance. It integrates seamlessly with Nagios XI for network and infrastructure monitoring.

10. Sematext

Sematext is a cloud-based log management and monitoring tool that provides real-time log aggregation, search, and analysis. It supports a wide range of integrations and provides advanced features such as automated log parsing, anomaly detection, and alerting. Sematext’s log analysis features are complemented by its infrastructure and application monitoring capabilities, making it a comprehensive observability platform.

11. AWS CloudWatch Logs

AWS CloudWatch Logs is a fully managed log monitoring and analysis service that enables users to collect, monitor, and analyze log data from AWS resources and applications. It provides real-time log analysis, metric generation, and custom alerts. CloudWatch Logs integrates seamlessly with other AWS services, allowing users to gain insights into application performance, system health, and security.

12. LogRhythm

LogRhythm is a log management and SIEM (Security Information and Event Management) platform that provides real-time log aggregation, monitoring, and analysis. It offers advanced features like machine learning-driven threat detection, anomaly detection, and automated response workflows. LogRhythm is used by security teams to identify and mitigate security threats and optimize IT operations.

13. Monitis

Monitis is a cloud-based monitoring tool that offers log aggregation, analysis, and real-time monitoring. It supports log collection from a variety of sources, including web applications, servers, and network devices. Monitis provides customizable dashboards, detailed reporting, and automated alerts to help teams quickly detect issues and optimize system performance.

14. Kibana

Kibana is an open-source data visualization tool that works with Elasticsearch to provide interactive log analysis and reporting capabilities. It allows users to create customized dashboards, visualizations, and reports based on their log data. Kibana is particularly useful for analyzing log data stored in Elasticsearch and gaining insights into system performance, security, and usage patterns.

15. Logwatch

Logwatch is an open-source log analysis tool designed for Unix-based systems. It parses log files, summarizes log entries, and generates reports based on user-defined criteria. Logwatch can be used to monitor various logs, including system logs, security logs, and application logs. It provides daily or customized reports, helping administrators monitor system health and troubleshoot issues efficiently.

16. Elastic Stack (formerly ELK Stack)

Elastic Stack (formerly known as ELK Stack) is a collection of open-source tools—Elasticsearch, Logstash, and Kibana—that enables powerful log aggregation, search, and visualization. It allows users to collect, process, and analyze large volumes of log data in real time, providing detailed insights into system performance, security events, and application behavior. Elastic Stack is highly scalable and is widely used for log management in large organizations.

17. Splunk Cloud

Splunk Cloud is the cloud-based version of the popular Splunk platform, offering scalable log aggregation, search, and analysis capabilities. It provides real-time visibility into system performance, application logs, and security events. Splunk Cloud offers advanced features like machine learning-driven insights, automated alerting, and customizable dashboards, making it a powerful tool for log analysis and troubleshooting in cloud-based environments.

18. Sumo Logic Cloud SIEM

Sumo Logic Cloud SIEM is a security information and event management (SIEM) tool that focuses on log analysis for security monitoring. It provides real-time log aggregation, search, and analysis, allowing security teams to detect and respond to threats quickly. Sumo Logic Cloud SIEM integrates with cloud services and infrastructure monitoring tools, providing comprehensive security insights across various environments.

19. Rsyslog

Rsyslog is a powerful open-source logging tool that provides advanced log collection, processing, and forwarding capabilities. It allows users to aggregate logs from a wide range of sources, process them with filters, and forward them to various destinations, such as Logstash or Elasticsearch. Rsyslog is highly scalable and is commonly used in large distributed environments for centralized log management.

20. Logstash

Logstash is an open-source tool for aggregating, processing, and forwarding log data. It provides powerful filtering and transformation capabilities, allowing users to collect logs from multiple sources and forward them to destinations like Elasticsearch or cloud-based services. Logstash is a key component of the Elastic Stack and is used for centralized log management, data processing, and analysis.

21. Datadog

Datadog is a comprehensive monitoring and log analysis platform that provides real-time log aggregation, search, and analysis. It integrates with a wide range of cloud services, containers, and databases, allowing users to analyze logs and monitor infrastructure performance from a single platform. Datadog provides advanced features like machine learning-powered anomaly detection, automated alerts, and customizable dashboards to help teams quickly diagnose issues and optimize system performance.

This list includes a variety of tools designed for log analysis, from cloud-based solutions to open-source tools, each offering unique features for different use cases. Let me know if you’d like further modifications or additional details!

The post Top 21 Tools for Log Analysis Tools in 2026 appeared first on scmGalaxy.

The post Audit Log appeared first on scmGalaxy.

• Extended functionality via plugins.
• Free and open source.
• Filters are code.
• Great integration with other Elastic products.

• Handles up to 50,000 messages per second at peak time
• Data filtering and alerting
• Apache 2.0 License project
• Simplify and scale data pipeline management with tags
• More than 200 community-contributed plugins
• Real-time monitoring and alerts system
• Store data in multiple systems
• Collect and correlate web server access logs and application error logs

• Text-based logs from any source
• Custom source groups
• Point-and-click trending graphs
• Automated filters and event parsing
• Full-system RESTful API to integrate with other applications
• Unlimited saved searches and users
• Adaptable interface with multiple views, pages and workspaces
• Unlimited custom dashboards based on any search
• Built-in customizable alerts with triggers

• Leverages Java, Scala and ElasticSearch technologies
• Central syslog monitoring
• Interactive API browser
• Application debugging
• Exception monitoring
• API analytics
• Intuitive search interface
• Comprehensive dashboard

5. Splunk

• Works with Hadoop & NoSQL
• Setup standard searches as real-time alerts
• Trigger automatic responses
• Scale from a single server to multiple data centers
• Deployable to on premise sites, hybrid-cloud, and/or private and public cloud based infrastructures
• Securely make data available
• Supports various use cases including log consolidation/retention, security, compliance reporting, and more
• Perform ad hoc queries

The post Top 5 Log Monitoring Tools | List of Log Monitoring Tools | scmGalaxy appeared first on scmGalaxy.

The post How to attach the jenkins build log as part of the email body? appeared first on scmGalaxy.

Way 1 – 

Put following lines as part of Default Content text box

${BUILD_LOG, maxLines=9999, escapeHtml=false}

this works for free style projects

Way 2 – If you want send only specific line using regex

${BUILD_LOG_REGEX, regex=”\\b(Buildfile|BUILD)\\b”, linesAfter=1}

${BUILD_LOG_REGEX, regex=”^.*?BUILD FAILED.*?$”, linesBefore=0, linesAfter=10, maxMatches=5, showTruncatedLines=false, escapeHtml=true}

${BUILD_LOG_REGEX, regex=”\\b(FC0)\\b”, linesAfter=1}

${BUILD_LOG_REGEX, regex=”^.*?BUILD FAILED.*?$”, linesBefore=0, linesAfter=10, maxMatches=5, showTruncatedLines=false, escapeHtml=true}

${BUILD_LOG_REGEX, regex=”^.*?FC0.*?$”, maxMatches=95, showTruncatedLines=false, escapeHtml=false, matchedLineHtmlStyle=true}

This will show exactly which is expected lines as part of the regEx.

More Info

http://wiki.hudson-ci.org/display/HUDSON/Email-ext+plugin

http://stackoverflow.com/questions/9439450/how-will-i-filter-out-only-errors-in-jenkins-email-ext-build-log-regex

http://champasoft.blogspot.nl/2012/11/jenkins-pattern-for-e-mailing-log.html

The post How to attach the jenkins build log as part of the email body? appeared first on scmGalaxy.

When writing JUnit tests developers often add log statements that can help provide information on test failures. During the initial attempt to find a failure a simple System.out.println() statement is usually the first resort of most developers.

Replacing these System.out.println() statements with log statements is the first improvement on this technique. Using SLF4J (Simple Logging Facade for Java) provides some neat improvements using parameterized messages. Combining SLF4J with JUnit 4 rule implementations can provide more efficient test class logging techniques.

Some examples will help to illustrate how SLF4J and JUnit 4 rule implementation offers improved test logging techniques. As mentioned the inital solution by developers is to use System.out.println() statements. The simple example code below shows this method.

01    import org.junit.Test;
02
03    public class LoggingTest {
04
05      @Test
06      public void testA() {
07        System.out.println(“testA being run…”);
08      }
09
10      @Test
11      public void testB() {
12        System.out.println(“testB being run…”);
13      }
14    }

The obvious improvement here is to use logging statements rather than the System.out.println() statements. Using SLF4J enables us to do this simply whilst allowing the end user to plug in their desired logging framework at deployment time. Replacing the System.out.println() statements with SLF4J log statements directly results in the following code.
view source

01    import org.junit.Test;
02    import org.slf4j.Logger;
03    import org.slf4j.LoggerFactory;
04
05    public class LoggingTest {
06
07      final Logger logger =
08        LoggerFactory.getLogger(LoggingTest.class);
09
10      @Test
11      public void testA() {
12        logger.info(“testA being run…”);
13      }
14
15      @Test
16      public void testB() {
17        logger.info(“testB being run…”);
18      }
19    }

Looking at the code it feels that the hard coded method name in the log statements would be better obtained using the @Rule TestName JUnit 4 class. This Rule makes the test name available inside method blocks. Replacing the hard coded string value with the TestName rule implementation results in the following updated code.

01    import org.junit.Rule;
02    import org.junit.Test;
03    import org.junit.rules.TestName;
04    import org.slf4j.Logger;
05    import org.slf4j.LoggerFactory;
06
07    public class LoggingTest {
08
09      @Rule public TestName name = new TestName();
10
11      final Logger logger =
12        LoggerFactory.getLogger(LoggingTest.class);
13
14      @Test
15      public void testA() {
16        logger.info(name.getMethodName() + ” being run…”);
17      }
18
19      @Test
20      public void testB() {
21        logger.info(name.getMethodName() + ” being run…”);
22      }
23    }

SLF4J offers an improved method to the log statement in the example above which provides faster logging. Use of parameterized messages enable SLF4J to evaluate whether or not to log the message at all. The message parameters will only be resolved if the message will be logged. According to the SLF4J manual this can provide an improvement of a factor of at least 30, in case of a disabled logging statement.

Updating the code to use SLF4J parameterized messages results in the following code.

01    import org.junit.Rule;
02    import org.junit.Test;
03    import org.junit.rules.TestName;
04    import org.slf4j.Logger;
05    import org.slf4j.LoggerFactory;
06
07    public class LoggingTest {
08
09      @Rule public TestName name = new TestName();
10
11      final Logger logger =
12        LoggerFactory.getLogger(LoggingTest.class);
13
14      @Test
15      public void testA() {
16        logger.info(“{} being run…”, name.getMethodName());
17      }
18
19      @Test
20      public void testB() {
21        logger.info(“{} being run…”, name.getMethodName());  }
22      }
23    }

Quite clearly the logging statements in this code don’t conform to the DRY principle.

Another JUnit 4 Rule implementation enables us to correct this issue. Using the TestWatchman we are able to create an implementation that overrides the starting(FrameworkMethod method) to provide the same functionality whilst maintaining the DRY principle. The TestWatchman Rule also enables developers to override methods invoked when the test finishes, fails or succeeds.

Using the TestWatchman Rule results in the following code.

01    import org.junit.Rule;
02    import org.junit.Test;
03    import org.junit.rules.MethodRule;
04    import org.junit.rules.TestWatchman;
05    import org.junit.runners.model.FrameworkMethod;
06    import org.slf4j.Logger;
07    import org.slf4j.LoggerFactory;
08
09    public class LoggingTest {
10
11      @Rule public MethodRule watchman = new TestWatchman() {
12        public void starting(FrameworkMethod method) {
13          logger.info(“{} being run…”, method.getName());
14        }
15      };
16
17      final Logger logger =
18        LoggerFactory.getLogger(LoggingTest.class);
19
20      @Test
21      public void testA() {
22
23      }
24
25      @Test
26      public void testB() {
27
28      }
29    }

And there you have it. A nice test code logging technique using JUnit 4 rules taking advantage of SLF4J parameterized messages.

I would be interested to hear from anyone using this or similar techniques based on JUnit 4 rules and SLF4J.

Reference: http://www.catosplace.net/

The post JUnit 4 Test Logging Tips using SLF4J appeared first on scmGalaxy.