23.3 enhancements and patches

Review the BMC Helix Log Analytics 23.3 enhancements for features that will benefit your organization and to understand changes that might impact your users.

Version	SaaS	Fixed issues	Updates and enhancements
23.3.02	✅️	Known-and-corrected-issues	Update
23.3.01	✅️	Known-and-corrected-issues	Update
23.3.00	✅️	Known-and-corrected-issues	23.3.00 enhancements

For updates and enhancements in the previous releases and its patches, see 23-2-enhancements-and-patches.

23.3.02

Support for CentOS 8 connector

Collect and analyze application logs from CentOS 8.x environments. A new connector type, Linux Connector (CentOS 8), is added to collect these logs.

For more information, see Installing-and-managing-CentOS-connector.

Enhanced root-cause analysis with host enrichment in log records

In the log records, you can see the log_source_host field instead of the host.name field. The log_source_host field provides information about the data source where the logs originated. With this information, you can perform accurate root-cause analysis because the logs are enriched with the host or server name that caused service degradation.

If you have existing log collection policies, edit and save them so that the log_source_host field value is added to the log records.

For more information, see Collecting-application-logs and Troubleshooting-log-collection-and-visualization-in-the-Log-explorer.

23.3.01

Support for RHEL 9 connector

Collect and analyze application logs from Linux 9.x environments. A new connector type, Linux Connector (RHEL 9), is added to collect these logs.

For more information, see Installing-and-managing-Linux-connector.

23.3.00

The following video (1:52) provides a high-level summary of the new features and enhancements in version 23.3 of BMC Helix Log Analytics.

Watch the YouTube video to learn more about what's new in BMC Helix Log Analytics 23.3.

Anomaly detection improvements

Machine learning-based model for anomaly detection is trained every 10 minutes to improve the accuracy and efficacy. Anomalous logs and events are reported faster as the model is generated and trained within 5-10 minutes of logs ingestion. For more information, see Analyzing-anomalous-logs.

View additional metrics on the Self Monitoring dashboard

The following metrics are added to the Self Monitoring dashboard:

Archival and restore log volume
Archival and restore days
Log ingestion details (volume of ingested logs and day-wise ingestion)

For more information, see Visualizing-logs.

Self_MonitoringDashboard23.3.png

Collect logs from the beginning or end of a log file

Collect logs either from the beginning of the log file or from the time when new logs are generated in the log file. This feature helps you ignore the older logs from collection and save storage space.

For more information, see Creating-collection-policies.

Add fields to logs

Add fields to your logs in the form of key-value pairs. Use these fields to search and analyze logs in Explorer.

For more information, see Creating-collection-policies.

Support for RHEL 7 connector

Collect and analyze application logs from Linux 7.x environments. A new connector type, Linux Connector (RHEL 7), is added to collect these logs.

For more information, see Installing-and-managing-Linux-connector.

Use interactive guides for configuring log collection

The following interactive guides are added to the Self-help widget in the Setting up and going live section:

Creating a parsing rule
Creating a filtering rule
Creating a collection policy

These guides provide step-by-step guidance in the product to learn a procedure.

Updates to the AI-ML model for anomaly detection

The following enhancements have been made to the model that detects anomalies in the logs:

It takes 5-10 minutes to generate the model.
The model is retrained and fine-tuned every 10 minutes by using the latest logs (upto 10,000).