Before you Begin

Oracle Logging Analytics service is a highly scalable, reliable, and real-time log analysis solution. Logging Analytics automates the collection of historic and real-time logs from any on-premises or cloud resource. For more information on this service, see About Logging Analytics.

This guide provides a very simple walk-through to get started with Logging Analytics service. In about 10 minutes, you will set up Logging Analytics, ingest OCI Audit Logs by automatically creating a service connector, review aggregated data in a dashboard and explore the available logs in the Log Explorer.

For a quick start guide to get started with Logging Analytics and set up continuous log collection by installing Management Agent on your host, see Tutorial - OCI Logging Analytics: Set Up Continuous Log Collection.


    A log source is the built-in definition of where log files are located and how to collect, mask, parse, extract and enrich the collected log data.

    An entity refers to a real asset on your on-premises host where a Management Agent is installed. Each entity has an entity type, of over 100 pre-defined or any custom-created types.

    Each log is assigned to a log group and this property is used to define who has access to query the logs.

    A user of Logging Analytics associates a log source to an entity to initiate the continuous log collection process through the OCI Management Agents. The concept of source-entity association only applies to continuous log collection through the agent.

    For more information on these concepts, see Logging Analytics Terms and Concepts.

What Do You Need?

    Log in to an Oracle Cloud account where Logging Analytics is not yet enabled. This user will be set up with the default access to the OCI Audit Logs in your environment.

Enable Logging Analytics

  1. The Logging Analytics service is available from the top level OCI console menu. Navigate to Observability & Management and click Logging Analytics.
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  2. If this is the first time you're using the service in this region, review the on-boarding page that will give you some high level details of the service and an option to start using Logging Analytics. Click Start Using Logging Analytics.
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  3. Review the policies that are automatically created. A log group Default is created if it does not already exist. After Logging Analytics service is enabled successfully, click Set Up Ingestion to continue.
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  4. Select Configure OCI audit log analysis in this region, and click Next.
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  5. After reviewing the statements of the policy logging_analytics_automatic_ingestion_policies, click Set Up Ingestion.

    The policy withthe listed policy statements is created.

  6. After the OCI Audit Logs Analysis is enabled successfully, click Go to OCI Audit Logs Dashboard.
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Dashboards Overview

Take a look at the example of a Dashboard, which is based on the data automatically collected from the OCI Audit Logs during the Logging Analytics enabling process. Depending on the cloud account you used, this data will vary.

Note that this environment has 58 active users and over 3 million OCI Audit Logs collected in the last 14 days. You can see the data by compartments, examine the Trend, and Active Users Per Hour.

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Lower on the same page, note some further analysis of the data: correlation and grouping of information to make it easy to identify issues.

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Visualize and Explore Log Data

Learn About Logging Analytics User Interface

The interactive data visualizations in Oracle Cloud Logging Analytics enable you to get deeper insights into your log data. Depending on the data you want to filter, group, and compare, you can choose various visualization types, from a rich set of options. This section is an introduction to log exploring and data visualization.

Navigate to Logging Analytics and click Log Explorer. The following image presents the main parts of the Log Explorer user interface:

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  1. Query bar, with Clear, Search Help, and Run buttons at the right end of the bar

  2. Time range menu, and Actions menu where you can find actions such as, Open, Save, and Save as

  3. Fields panel, where you can select sources and fields to filter your data.

  4. Visualization panel, where you can select the way to present search data in a form that helps you.

  5. Main panel, where the visualization outputs appear above the results of the query


Use the browser Back button to return to a previous page. Do not use the Refresh button.

Explore Logs

  1. In Log Explorer, click the Filter icon to open the Scope Filter.
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    If the filter is not set with the Log Group Compartment that was created earlier while setting up ingestion, then select logging_analytics_ociaudit.

    The Compartment selector lets you choose which log groups will be included in the search based on which compartment those log groups are in. When you select a compartment here, this compartment plus all child compartments are all automatically included. By using the root compartment, you will be searching across all logs that your user has access to, based on your user's compartment access policy and the log groups in those compartments.

    After a minute, you should start seeing logs coming in for your sources.

    Set the Time range to Last 14 Days.

    Click OCI Audit Logs, then click Drill Down, as shown in the image below:

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    By default, your log data is displayed as Records with histogram to help reduce the size of the data set:

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    You can further click a specific segment in the histogram to drill down to the corresponding set of log records and to view the original log content.

  2. Clustering uses machine learning to identify a pattern of log records and then groups the logs that have similar patterns. You can see in the search screen above that 6,501 log entries (the number of logs can vary) were collected for the last 14 days. This is a very large number of logs to inspect manually. In larger production environments, you may have billions of log entries in a 14 day period.

    Change the Visualization option to Cluster to take a look at the Cluster Analysis options.

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    The screen changes to show clusters of log entries. Here, you can see that 6k log entries are reduced to only 14 clusters, and we have identified 1 of those clusters that indicate a potential problem and 2 clusters that appear to be outliers. With a larger data set over a longer period of time, the cluster capabilities get better as there is a recurring pattern of data to compare against.

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  3. Save a Search.

    Saving a search is important for a couple of reasons. First, you may want to regularly use a search without having to rewrite it. You may also create searches that multiple people across your organization use. This provides a consistent view of important data. Second, a saved search can be used as a widget for a dashboard as you will see later in this walk-through.

    Change your visualization to Horizontal Bar Chart.

    1. Select the compartment to save the search.
    2. Give a name and description to the search.
    3. Click the Add to Dashboard checkbox.
    4. To add the saved search a widget to a new dashboard, select New Dashboard. Alternatively, you can add it to an Existing Dashboard.
    5. Select the compartment to save the dashboard.
    6. In case of a new dashboard, specify a name and description for the new dashboard. Otherwise, select the existing dashboard name.
    7. Give a name and description to the new dashboard.
    8. Click Save.
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    You will now see that the Log Explorer title has changed to include the name of the saved search you are working with. If you make changes here, navigate to Action and click Save to update the saved search.

Get Started with Queries

Using queries and searches is a more advanced way of searching and analyzing your logs. A search is a series of commands delimited by a pipe ( | ) character. The result from the prior command is used as input for the next command. Some commands search for data and other commands aggregate the results. The first command in a query is the search command containing:

  • keywords or phrases
  • boolean expressions
  • wildcards
  • field name/value pairs

Here is an example query with a search and aggregate command that would search your logs and show how many distinct load balancers are monitored. A command like this can be broken into four separate sections as follows:

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  1. Search all logs from the source OCI Load Balancer Access Logs
  2. Aggregate results from the previous subquery
  3. Count distinct occurrences of a field
  4. Save the aggregate results in a new temporary field


Click Search Help, at the right side of the search bar, to open a panel with more information about search queries. The help wizard provides the format and syntax of the queries you can compose. Run the example queries starting from the very basic search to advanced analysis and familiarize yourself with the query reference. The wizard gives you some tips and shortcuts to make your search efficient. A view of the typical use cases of the common command results is available.

In this section, you can try a few simple search commands to get an idea of how the query search works.

Navigate to Logging Analytics and click Log Explorer.

Copy the following queries and paste them in the query bar, then press Run.

  • failed

    This simple query shows you all log records containing the keyword failed.

    Sample Output:

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  • (error or failure) NOT success

    Simple queries can be combined with logical operators to build more complex ones. This query shows you all the log records containing the keywords failed or error.

    Sample Output:

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  • fail*

    Wildcard characters ( * ) can be used in queries to substitute one or more characters in a string. This query shows you all logs containing the wildcard expression (a string that begins with fail).

    Sample Output:

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  • * | stats count

    This simple query calculates the total number of log records.

    Sample Output:

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  • * | timestats count by 'Log Source'

    This query shows the trend of the number of log records for different log sources.

    Sample Output:

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  • * | cluster

    This query clusters log events by the shape of the log records and analyzes large data sets in a structured way.

    Sample Output:

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Learn More