Creating and managing Falcon LogScale connections

About this task

Before creating the connection, you should be aware of the following information.

• Load: To prevent this connection placing an inordinate load on your data source and potentially impacting your logging operations, this connection only connects to one API with a default data frequency of 60 seconds. This is controlled by using the Sampling rate setting in the Procedure section.

• Access: Custom data sources are cloud-based REST APIs. Access is configured by using the authentication methods that are specified in the Authentication type setting in the Procedure section.

• Data volume: Data volume depends on the application, and is not a set value. Therefore, it does not appear in the settings.

Procedure

To create a custom connection from a specific source, step through the following sections:

• Adding a connection
• Specifying connection parameters
• Specifying field mapping
• Specifying how log data is collected for AI training

Adding a connection

1. Log in to IBM Cloud Pak Automation console.

2. Expand the navigation menu (four horizontal bars), then click Define > Data and tool connections.

3. On the Data and tool connections page, click Add connection.

4. From the list of available connections, find and click the Falcon LogScale tile.

   Note: If you do not immediately see the connection that you want to create, you can filter the tiles by type of connection. Click the type of connection that you want in the Category section.

5. On the side-panel, review the instructions and when ready to continue, click Connect.

6. On the Add connection page, define the general connection details:

   • Name: The display name of your connection.
   • Description: An optional description for the connection.
   • Falcon LogScale service URL: The fully qualified Falcon LogScale query API URL for your Falcon LogScale instance, for example: http://<Falcon LogScale_HOSTNAME>/api/v1/repositories/<REPO_NAME>/query
   • API Token: The connection token for your Falcon LogScale instance, which is available from the account settings page in Falcon LogScale.
   • Certificate: An optional certificate used to verify the SSL/TLS connection to the REST service.
   • Filters: Optional filters that define subsets of data that is pulled from Falcon LogScale.
   • Base parallelism: Select a value to specify the number of Flink jobs that can run in parallel. These jobs run to process and normalize the collected data. The default value is 1. However, it is recommended to use a higher value than 1 so that you can process data in parallel. This value cannot exceed the total available free Flink slots. In a small environment, the available flinks slots are 16, while in a large environment, the maximum available slots are 32. If you are collecting historical data with this connection, you can set this value to be equal to the source parallelism.
   • Sampling rate: The rate at which data is pulled from live source (in seconds). The default value is 60.
   • JSON processing option: Select a JSON processing option.
     • None: The default option. The JSON is not processed or modified.
     • Flatten: This option flattens the JSON object by removing the opening and closing braces.
     • Filter: This option extracts the JSON object and replaces it with an empty string.
     • For more information about the options, see Managing embedded JSON.

   Note: To improve data throughput, you can increase the base parallelism value incrementally. For more information about maximum base parallelism for starter and production deployment sizes, see Improving data streaming performance for log anomaly detection.

7. Test your connection by clicking Test connection.

8. Click Next to move to the next page.

Specifying field mapping

1. Enter Field Mapping information (Optional):

   The default mapping setting that is provided by UI can improve the search performance by mapping the fields from your implementation fields to IBM Cloud Pak for Watson AIOps's standard fields. For more information about how field mappings are defined, see Mapping data from incoming sources. For more information about the use of mappings to clean your data for use in IBM Cloud Pak for Watson AIOps, see Cleaning mapped data that use regular expressions. Consider the supported data schema when you create your field mapping:

   {
     "codec": "humio",
     "message_field": "@rawstring",
     "log_entity_types": "kubernetes.container_name",
     "instance_id_field": "kubernetes.container_name",
     "rolling_time": 10,
     "timestamp_field": "@timestamp",
     "resource_id": "kubernetes.pod_name"
   }
   

   But processing of logs with this format into log anomaly alerts can lead to false alarms in the Alert Viewer.

2. To resolve the false alarms issue in step 1, use regular expression to exclude parts of the log lines at the end of fields mapping (Optional):

   Logs ingested by Falcon LogScale usually have the following format:

   • _timestamp_ + stdout F + _real message_

   • _timestamp_ + stderr F + _real message_

   You can define the field mapping as follows:

   {
   "codec": "humio",
   "message_field": "@rawstring",
   "log_entity_types": "kubernetes.container_name",
   "instance_id_field": "kubernetes.container_name",
   "rolling_time": 10,
   "timestamp_field": "@timestamp",
      "custom_regex": [
      ".+?(stdout F )",
      ".+?(stderr F )"
      ]
   }
   

3. Click Next to move to the next page.

Specifying how log data is collected for AI training

1. Enter AI training and log data (Optional):

   Select how you want to manage collecting data for use in AI training and anomaly detection. Set the Data collection toggle to 'on', then select how you want to collect data:

   • Live data for continuous AI training and anomaly detection: A continuous collection of data from your connection is used to both train AI models and analyze your data for anomalous behavior.

     Note: After an initial installation, there is no data at all in the system. If you select this option, then the two different log anomaly detection algorithms behave in the following ways:

     • Natural language log anomaly detection does not initially detect anomalies as no model has been trained. You can retrieve historical data (select Historical data for initial AI training) to speed up the retrieval of data to train on, or you can leave the Live data for continuous AI training and anomaly detection setting on. In the latter case, the system gathers training data live and after a few days there is enough data to train a model. When this model is deployed, then it detects anomalies as normal.

     • Statistical baseline log anomaly detection does not detect anomalies for the first 30 minutes of data collection. This is because it does not have a baseline yet. After 30 minutes of live data collection the baseline is automatically created. After that it detects anomalies on an ongoing basis, while continuing to gather data and improve its model every 30 minutes.

   • Live data for initial AI training: A single set of training data used to define your AI model. Data collection takes place over a specified time period that starts when you create your connection.

     Note: Selecting this option causes the system to continue to collect data while the option is enabled; however, the data is collected for training only, and not for log anomaly detection. For more information about AI model training, including minimum and ideal data quantities, see Configuring AI training.

   • Historical data for initial AI training: A single set of training data used to define your AI model. You need to give Start and End dates, and specify the parallelism of your source data. Historical data is harvested from existing logs in your connection over a specified time period in the past.

     • Start date: Select a start date from the calendar and enter the time in hh:mm (hours and minutes) format.

       Note: The start date must not exceed 31 days from the present as the maximum time period for historical data collection is 31 days. The recommended time period is two weeks.

     • Time zone: Select your time zone from the dropdown list.

     • End date and time: Click Add end date and select an end date from the calendar and enter the time in hh:mm format.

       Note: If you do not specify the end date, then live data collection follows the historical data collection. If you do not want to set an end date, click Remove end date.

     • Source parallelism (1-50): Select a value to specify the number of requests that can run in parallel to collect data from the source. Generally, you can set the value to equal the number of days of datat that you want to collect. When you are setting this value, consider the number of requests that are allowed by the source in a minute. For example, if only 1-2 requests are allowed, set the value to be low.

       Note: To avoid issues with task manager memory, run no more than one Historical data for initial AI training run at a time.

   Important: Keep in mind the following considerations when you select your data collection type:

   • Anomaly detection for your connection occurs if you select Live data for continuous AI training and anomaly detection.

   • Different types of AI models have different requirements to properly train a model. Make sure that your settings satisfy minimum data requirements. For more information about how much data you need to train different AI models, see Configuring AI training.

2. Click Done.

You have created a Falcon LogScale connection in your instance. After you create your connection, you must enable the data collection to connect your connection with the AI of IBM Cloud Pak for Watson AIOps. For more information about enabling your connection, see Enabling Falcon LogScale connections.

To create more connections (such as a ChatOps connection), see Configuring data and tool connections.

For more information about working with the insights provided by your connections, see ChatOps insight management.