--- sourceDocument: Australia IT Operations Management sourceDocumentLink: https://servicenow-prod.fluidtopics.net/r/de-DE/it-operations-management Release : - australia ft:locale : - de-DE ft:publication_title : - Australia IT Operations Management ft:clusterId : - itom bundleId : - itom workflow : - Technology --- # Exploring # Exploring Health Log Analytics {#ariaid-title1} * Freigeben Version: Australia * * Aktualisiert 12. März 2026 * * ![](https://www.servicenow.com/docs/portal-asset/ico-clock) 4 Minuten Lesedauer ServiceNow Health Log Analytics (HLA) predicts IT issues before they affect your users by collecting, analyzing, and correlating machine-generated log data in real time. It discovers anomalies and alerts you to potential issues. ## Health Log Analytics overview {#hla-exploring__section_nqx_vvd_d2c} Health Log Analytics typically receives and processes log data and sends events to ServiceNow Event Management. The application discovers anomalies as they happen and helps you identify the root cause of an issue by enabling you to triage related logs and analyze the raw data. Health Log Analytics can handle any kind of machine-generated textual log data. It can process application, infrastructure, and network logs, as well as other types of textual log data. Although a Configuration Management Database (CMDB) can be helpful to generate high-quality events and alerts, it is not necessary. Hinweis: * Health Log Analytics supports only UTF-8 logs. It does not support binary logs. * If you are sending logs in a language other than English, additional configuration may be required. {#hla-exploring__ul_urs_bk4_4bc} For a brief explanation of key terms and concepts used in HLA, see the [Health Log Analytics terminology](https://servicenow-prod.fluidtopics.net/kZCbgPiSSUEbsL6nAZIO2A "Before getting started with Health Log Analytics, it's important to familiarize yourself with some key concepts used in the application."). ## Health Log Analytics users {#hla-exploring__section_lxb_zcw_c2c} {#hla-exploring__table_km4_gdw_c2c__entry__3} | User | Description | Role | |-|-|-| | Administrator | Configures the Health Log Analytics application to make it ready for use by Operators. Performs administration tasks to keep the system running efficiently. | evt_mgmt_admin, admin | | Operator | Analyzes Log Analytics alerts and takes action to help resolve the underlying issue. | evt_mgmt_operator | [Tabelle : 1. Health Log Analytics Users] {#hla-exploring__table_km4_gdw_c2c} ## Health Log Analytics workflow {#hla-exploring__section_p3w_j54_xmb} Health Log Analytics collects and processes log data automatically. It structures the data logically for operators to analyze, and generates meaningful alerts and suggestions that display in Event Management. The diagram shows the Health Log Analytics workflow from collecting the data through sending an event or alert to Event Management. Abbildung : 1. Health Log Analytics workflow Ingestion : This layer connects your environment to Health Log Analytics. You can stream your logs directly from servers and endpoints or from log repositories. The optional guided setup helps you create data input connectors for common data sources, such as: : * Rsyslog * Beats * Splunk * Elasticsearch * MID Server * TCP {#hla-exploring__ul_wqh_m54_xmb} Structuring : This layer deals with structuring log data and auto-mapping it to logical silos, called Components. Data structuring can be done automatically or manually. : The system auto-structures log data by extracting the following properties from incoming log messages: Message, Timestamp, Host, Severity, and External-IDs. It extracts explicit values, like "property-name" and "value is IP." and semantic ones such as length, number of English words, and variance. : Auto-mapping assigns log samples and metadata to the appropriate tags automatically. The system tries to map log lines by analyzing the source that streams the data. The mapping is based on agent hints and common transport header fields. Enrichment : This layer handles identifying the variable parts of a log message. : Abbildung : 2. Health Log Analytics workflow - Enrichment : It also identifies keywords and contextual properties. In the image, "WARN" and "Failed" are the keywords to track. "User," "source IP," and "port" are the contextual properties. Analysis : In this layer, each log line is indexed. Health Log Analytics extracts properties from the inner log message that contribute to models of behavior that the system learns to expect. Anomalous behavior departs from this expected behavior. You can search for an event and its most significant properties for manual triaging. Machine Learning (ML) and Artificial Intelligence (AI) : Health Log Analytics uses advanced unsupervised machine-learning algorithms to discover patterns within logs and learn their unique data behavior. It then sets dynamic thresholds based on the data signature in real time to detect issues when they first occur. When the system detects a deviation from the typical pattern, it sends an event to Event Management. Alert in Event Management : Health Log Analytics sends events to Event Management. In Event Management, Health Log Analytics alerts appear in the All alerts list. This list enables operators to see alerts from the event and the Health Log Analytics alert type in a single location. ## Health Log Analytics benefits {#hla-exploring__section_wyw_kwv_c2c} {#hla-exploring__table_s14_mdw_c2c__entry__3} | Benefit | Feature | User | |-|-|-| | Simplify data input setup using the guided setup. | [Guided setup](https://servicenow-prod.fluidtopics.net/Dm_YnE5Ex0xZ3vULRTv62Q "The Health Log Analytics guided setup provides a sequence of tasks to help you create data inputs on your ServiceNow instance. Data input configuration is an essential step in setting up the Health Log Analytics application. Using guided setup ensures that you have the minimum required setup for the data input process.") | Administrator | | Set up log data connector integrations quickly and conveniently from the Integrations Launchpad. | [Log data connector integrations](https://servicenow-prod.fluidtopics.net/YIHnVuAtC7n005I9~87dEg "Set up integrations from the Event Management Integrations Launchpad in Service Operations Workspace for ITOM.") | Administrator | | Shorten onboarding time by installing content packs. | [Content packs](https://servicenow-prod.fluidtopics.net/Qaq8YoUBpwMKcfpvBdI8bQ "Shorten onboarding time for the Health Log Analytics application by installing content packs. The packs contain default source types and mapping script templates that save you the time it takes to create them from scratch.") | Administrator | | Save time and reduce errors by migrating data input configurations between instances. | [Data input migration](https://servicenow-prod.fluidtopics.net/U4N~6bG_H~x7D~_iwjHIAg "Export a Health Log Analytics data input and source types configuration as an update set and import it to a different ServiceNow instance. In the target environment, you can use the migrated data input for streaming and processing log data. This functionality saves time and reduces possible errors by avoiding the need to configure the settings again on the target instance.") | Administrator | | Identify the root cause of an alert by analyzing the logs that surround the anomaly. | [Surrounding logs](https://servicenow-prod.fluidtopics.net/uW7dSm2MiHpmIH~Sxqr7aw "When Health Log Analytics identifies an anomaly, viewing the logs that surround the anomaly provides clues about the state of faulting systems. This information can help you narrow down the root cause of an alert.") | Operator | | Visualize anomalous log data on the Log viewer. | [Log viewer](https://servicenow-prod.fluidtopics.net/~KZgCI02ygNerk6YUa8png "The Log Viewer tab lets you browse the logs for an alert by timestamp or time range, and visualize anomaly frequency within a specific time period. Customizing the displayed data and adjusting time filters enables you to better understand the framework in which the anomaly occurred, helping you find the root cause faster.") | Operator | | Detect relationships in log data. | [Log correlators](https://servicenow-prod.fluidtopics.net/NQ1d6E9fJlxpioxlkY7pHg "Log correlators are keys or values in log data that detect correlations between alerts to help you determine whether an alert is part of a larger issue. For example, a log correlator could detect when the interface ID of a particular network device occurs simultaneously in multiple warnings across different service instances.") | Operator | | Assign higher or lower significance to alerts. | [Mute alert metrics](https://servicenow-prod.fluidtopics.net/GWEhrWJ5Dz7Z8kZm84yw3A "Label an alert in Health Log Analytics as meaningful or insignificant, or restore normal importance to the metric involved in generating it.") | Operator | | Reduce noise by creating log filters. | [Log alert filters](https://servicenow-prod.fluidtopics.net/zokVf2Sqn8uEzozJyuptRw "Use advanced log alert filters to determine whether to allow an alert or to drop it. These filters reduce noise by dropping alerts that don't indicate a significant issue.") | Operator | | Influence how Health Log Analytics finds anomalies by managing keywords it looks for in the log data. | [Lexical keywords](https://servicenow-prod.fluidtopics.net/E~BJE_mSsw4k6AFCNexMag "Influence how Health Log Analytics finds anomalies by managing keywords it looks for in your log data. When text in log data for a source matches a lexical keyword that exceeds a specified count threshold, the system identifies an anomaly and generates an alert.") | Operator | | Create alerts for specified metrics by adding, changing, or deleting rules. | [Custom alert rules](https://servicenow-prod.fluidtopics.net/XtlZr0V6VIMgumgHBXIA3Q "Alert rules enable you to set thresholds and conditions that determine when Health Log Analytics triggers an alert. When the conditions of an alert rule are met, the system generates a Log Analytics alert. You can add, change, or delete alert rules to create Log Analytics alerts for a specified metric.") | Operator | [Tabelle : 2. Health Log Analytics benefits] {#hla-exploring__table_s14_mdw_c2c} ## What to explore next {#hla-exploring__section_ynm_4nv_c2c} * [Health Log Analytics architecture](https://servicenow-prod.fluidtopics.net/jwdVw0uGVE4F3_eya_XPvQ "Health Log Analytics collects logs streaming into your ServiceNow instance from endpoints or data lakes, such as Splunk and Elasticsearch.") * [How Health Log Analytics generates alerts](https://servicenow-prod.fluidtopics.net/AEo46~B6PwdZOjwsulXPXw "Health Log Analytics identifies patterns in your log data and learns pattern behavior. When HLA's AI engine detects anomalous behavior, it sends an event to the ServiceNow Event Management application. As an operator, you can use these predictive alerts to handle emerging IT issues before they impact users.") * [Types of anomalous behavior in Health Log Analytics](https://servicenow-prod.fluidtopics.net/TWGIZ1frOJHt3EvK8SBxLQ "Anomalous behavior in a CI or a service can indicate an important issue. For example, a spike in the frequency or number of messages of a particular type can indicate a problem.") * [Types of Health Log Analytics alerts](https://servicenow-prod.fluidtopics.net/wyhSBPwW96jRO1m_dsi4BQ "Health Log Analytics generates several types of alerts.") {#hla-exploring__ul_n1q_pnv_c2c}