Health Log Analytics

Health Log Analytics

Overview

This skill provides a structured approach to managing Health Log Analytics (HLA) in ServiceNow ITOM. It covers:

• Configuring log sources for metric collection and log ingestion
• Defining and tuning metric thresholds for proactive alerting
• Detecting anomalies in CI performance patterns using ML-driven baselines
• Performing capacity planning analysis based on historical metric trends
• Building performance trending reports for infrastructure and services
• Managing threshold policies and alert suppression rules

When to use: When operations teams need to proactively monitor infrastructure health, detect performance degradation before it impacts services, plan for capacity needs, or tune alerting thresholds to reduce noise.

Plugin required: com.snc.sa.health-log-analytics

Prerequisites

• Roles: sa_admin, evt_mgmt_admin, or itil
• Access: Read/write to sa_metric_definition, sa_threshold, sa_anomaly, sa_log_source, em_alert
• Knowledge: Familiarity with ITOM metric types, time-series data concepts, and CI performance baselines
• Plugins: com.snc.sa.health-log-analytics and com.snc.em must be activated
• Data: MID Server collecting metrics from monitored CIs, or observability platform connectors feeding metric data

Procedure

Step 1: Review Configured Log Sources

Inventory active log sources to understand current data collection scope.

Using MCP:

Tool: SN-Query-Table
Parameters:
  table_name: sa_log_source
  query: active=true^ORDERBYname
  fields: sys_id,name,type,source_type,ci,status,last_collection_time,collection_interval,mid_server
  limit: 50

Using REST API:

GET /api/now/table/sa_log_source?sysparm_query=active=true^ORDERBYname&sysparm_fields=sys_id,name,type,source_type,ci,status,last_collection_time,collection_interval,mid_server&sysparm_display_value=true&sysparm_limit=50

Log Source Types:

Step 2: Review Metric Definitions

Examine which metrics are being tracked and their configurations.

Using MCP:

Tool: SN-Query-Table
Parameters:
  table_name: sa_metric_definition
  query: active=true^ORDERBYci_type,name
  fields: sys_id,name,description,ci_type,metric_type,unit,source,aggregation_type,collection_interval
  limit: 50

Using REST API:

GET /api/now/table/sa_metric_definition?sysparm_query=active=true^ORDERBYci_type,name&sysparm_fields=sys_id,name,description,ci_type,metric_type,unit,source,aggregation_type,collection_interval&sysparm_display_value=true&sysparm_limit=50

Common Metric Types:

Step 3: Manage Thresholds

Review and configure metric thresholds for proactive alerting.

Using MCP:

Tool: SN-Query-Table
Parameters:
  table_name: sa_threshold
  query: active=true^ORDERBYmetric_definition.name
  fields: sys_id,name,metric_definition,ci,ci_type,warning_value,critical_value,operator,duration,active
  limit: 50

Using REST API:

GET /api/now/table/sa_threshold?sysparm_query=active=true^ORDERBYmetric_definition.name&sysparm_fields=sys_id,name,metric_definition,ci,ci_type,warning_value,critical_value,operator,duration,active&sysparm_display_value=true&sysparm_limit=50

Create a new threshold:

Tool: SN-Create-Record
Parameters:
  table_name: sa_threshold
  data:
    name: High CPU Alert - Production Servers
    metric_definition: [metric_def_sys_id]
    ci_type: cmdb_ci_server
    warning_value: 80
    critical_value: 95
    operator: ">="
    duration: 300
    active: true

Step 4: Detect Anomalies

Query for detected anomalies to identify unexpected performance patterns.

Using MCP:

Tool: SN-Query-Table
Parameters:
  table_name: sa_anomaly
  query: state=active^ORDERBYDESCdetection_time
  fields: sys_id,ci,metric_definition,severity,state,detection_time,expected_value,actual_value,deviation,description
  limit: 30

Using REST API:

GET /api/now/table/sa_anomaly?sysparm_query=state=active^ORDERBYDESCdetection_time&sysparm_fields=sys_id,ci,metric_definition,severity,state,detection_time,expected_value,actual_value,deviation,description&sysparm_display_value=true&sysparm_limit=30

Step 5: Capacity Planning Analysis

Analyze historical metric trends to predict capacity needs.

Using MCP:

Tool: SN-Execute-Background-Script
Parameters:
  description: Analyze capacity trends for servers approaching resource limits
  script: |
    gs.info('=== CAPACITY PLANNING ANALYSIS ===');

    // Find CIs with recent high-utilization alerts
    var ga = new GlideAggregate('em_alert');
    ga.addQuery('source', 'Health Log Analytics');
    ga.addQuery('severity', '<=', '2'); // Critical or Major
    ga.addQuery('sys_created_on', '>=', gs.daysAgo(30));
    ga.addAggregate('COUNT');
    ga.groupBy('ci');
    ga.orderByAggregate('COUNT', 'DESC');
    ga.setLimit(20);
    ga.query();

    gs.info('\n--- CIs with Most HLA Alerts (Last 30 Days) ---');
    while (ga.next()) {
      var ciId = ga.ci.toString();
      var count = ga.getAggregate('COUNT');
      var ci = new GlideRecord('cmdb_ci');
      if (ci.get(ciId)) {
        gs.info('CI: ' + ci.name + ' (' + ci.sys_class_name + ') | Alerts: ' + count + ' | Status: ' + ci.operational_status.getDisplayValue());
      }
    }

    // Identify anomalies with upward trends (capacity risk)
    gs.info('\n--- Active Anomalies Indicating Growth ---');
    var anomaly = new GlideRecord('sa_anomaly');
    anomaly.addQuery('state', 'active');
    anomaly.addQuery('actual_value', '>', 'expected_value');
    anomaly.orderByDesc('deviation');
    anomaly.setLimit(15);
    anomaly.query();

    while (anomaly.next()) {
      gs.info('CI: ' + anomaly.ci.getDisplayValue() + ' | Metric: ' + anomaly.metric_definition.getDisplayValue() + ' | Expected: ' + anomaly.expected_value + ' | Actual: ' + anomaly.actual_value + ' | Deviation: ' + anomaly.deviation + '%');
    }

Step 6: Performance Trending Report

Generate a performance summary for critical infrastructure.

Using MCP:

Tool: SN-Execute-Background-Script
Parameters:
  description: Generate performance trending summary for critical services
  script: |
    gs.info('=== PERFORMANCE TRENDING REPORT ===');

    // Alerts by severity over time
    gs.info('\n--- Alert Trend (Last 7 Days) ---');
    for (var d = 6; d >= 0; d--) {
      var dayStart = gs.daysAgo(d + 1);
      var dayEnd = gs.daysAgo(d);

      var ga = new GlideAggregate('em_alert');
      ga.addQuery('source', 'Health Log Analytics');
      ga.addQuery('sys_created_on', '>=', dayStart);
      ga.addQuery('sys_created_on', '<', dayEnd);
      ga.addAggregate('COUNT');
      ga.query();
      ga.next();
      var count = ga.getAggregate('COUNT');

      var dt = new GlideDateTime(dayStart);
      gs.info(dt.getDate().getByFormat('yyyy-MM-dd') + ': ' + count + ' alerts');
    }

    // Top alerting CIs
    gs.info('\n--- Top 10 Alerting CIs (Last 7 Days) ---');
    var topCI = new GlideAggregate('em_alert');
    topCI.addQuery('source', 'Health Log Analytics');
    topCI.addQuery('sys_created_on', '>=', gs.daysAgo(7));
    topCI.addAggregate('COUNT');
    topCI.groupBy('ci');
    topCI.orderByAggregate('COUNT', 'DESC');
    topCI.setLimit(10);
    topCI.query();

    while (topCI.next()) {
      gs.info('CI: ' + topCI.ci.getDisplayValue() + ' | Alerts: ' + topCI.getAggregate('COUNT'));
    }

    // Threshold breach distribution
    gs.info('\n--- Threshold Breaches by Metric ---');
    var thr = new GlideAggregate('em_alert');
    thr.addQuery('source', 'Health Log Analytics');
    thr.addQuery('sys_created_on', '>=', gs.daysAgo(7));
    thr.addAggregate('COUNT');
    thr.groupBy('metric_name');
    thr.orderByAggregate('COUNT', 'DESC');
    thr.setLimit(10);
    thr.query();

    while (thr.next()) {
      gs.info('Metric: ' + thr.metric_name + ' | Breaches: ' + thr.getAggregate('COUNT'));
    }

Step 7: Tune Threshold Policies

Adjust thresholds based on trending data to reduce false positives.

Using MCP:

Tool: SN-Update-Record
Parameters:
  table_name: sa_threshold
  sys_id: [threshold_sys_id]
  data:
    warning_value: 85
    critical_value: 97
    duration: 600

Using REST API:

PATCH /api/now/table/sa_threshold/[threshold_sys_id]
Content-Type: application/json

{
  "warning_value": "85",
  "critical_value": "97",
  "duration": "600"
}

Tool Usage

MCP Tools Reference

REST API Reference

Best Practices

• Baseline Before Alerting: Allow HLA to build 2-4 weeks of baseline data before configuring aggressive thresholds
• Use Duration Windows: Set threshold duration to 5-10 minutes to avoid alerting on transient spikes
• Layer Thresholds: Create both warning and critical thresholds to provide early warning before critical impact
• Review Anomalies Weekly: Schedule weekly reviews of anomaly detections to identify emerging capacity trends
• Correlate with Changes: Cross-reference anomaly detections with recent change requests to distinguish expected from unexpected behavior
• Tune Iteratively: Start with conservative thresholds and tighten based on operational experience

Troubleshooting

No Metrics Being Collected

Cause: Log source not active, MID Server offline, or credentials invalid Solution: Check sa_log_source.status for errors. Verify MID Server connectivity. Test credentials from the MID Server host.

False Positive Anomalies

Cause: Insufficient baseline data or scheduled maintenance not excluded Solution: Extend the baseline learning period. Configure maintenance windows to exclude known scheduled activities from anomaly detection.

Threshold Alerts Not Firing

Cause: Threshold not linked to the correct metric definition or CI type, or duration window too long Solution: Verify metric_definition reference on the threshold. Check that ci_type matches the monitored CIs. Reduce duration if spikes are short-lived.

HLA Dashboard Shows Stale Data

Cause: Collection interval too long or log source polling stopped Solution: Check sa_log_source.last_collection_time. Verify collection schedule is active. Review MID Server logs for collection errors.

Examples

Example 1: Server Capacity Review

# 1. Find servers with high-severity anomalies
Tool: SN-Query-Table
Parameters:
  table_name: sa_anomaly
  query: state=active^ci.sys_class_name=cmdb_ci_server^severity<=2
  fields: ci,metric_definition,severity,actual_value,expected_value,deviation,detection_time
  limit: 20

# 2. Check threshold configuration for those servers
Tool: SN-Query-Table
Parameters:
  table_name: sa_threshold
  query: ci_type=cmdb_ci_server^active=true
  fields: name,metric_definition,warning_value,critical_value,duration
  limit: 20

Example 2: Log Source Health Check

Tool: SN-Execute-Background-Script
Parameters:
  description: Check log source collection health
  script: |
    var ga = new GlideAggregate('sa_log_source');
    ga.addQuery('active', true);
    ga.addAggregate('COUNT');
    ga.groupBy('status');
    ga.query();

    gs.info('=== LOG SOURCE STATUS SUMMARY ===');
    while (ga.next()) {
      gs.info('Status: ' + ga.status.getDisplayValue() + ' | Count: ' + ga.getAggregate('COUNT'));
    }

    // Find stale log sources (no collection in 24 hours)
    gs.info('\n--- Stale Log Sources ---');
    var stale = new GlideRecord('sa_log_source');
    stale.addQuery('active', true);
    stale.addQuery('last_collection_time', '<', gs.daysAgo(1));
    stale.query();
    while (stale.next()) {
      gs.info('Source: ' + stale.name + ' | Last Collected: ' + stale.last_collection_time + ' | CI: ' + stale.ci.getDisplayValue());
    }

Example 3: Weekly Performance Summary

Tool: SN-Query-Table
Parameters:
  table_name: em_alert
  query: severity=1^source=Health Log Analytics^sys_created_on>=javascript:gs.daysAgoStart(7)
  fields: number,ci,severity,metric_name,description,sys_created_on
  limit: 25

Related Skills

• itom/observability-integration - Feed external metrics into HLA
• itom/alert-analysis - Analyze HLA-generated alerts
• itom/service-mapping - Correlate HLA data with service topology
• reporting/trend-analysis - Build reports from HLA metric data

References

• ServiceNow Health Log Analytics
• Metric Definitions
• Anomaly Detection