There are no configuration steps for your Datadog destination.

The following fields are optional:

1. Enter the name of the Splunk index you want your data in. This has to be an allowed index for your HEC.
2. Select whether the timestamp should be auto-extracted. If set to true, Splunk extracts the timestamp from the message with the expected format of yyyy-mm-dd hh:mm:ss.
3. Set the sourcetype to override Splunk’s default value, which is httpevent for HEC data.

The following fields are optional:

1. In the Encoding dropdown menu, select whether you want to encode your pipeline’s output in JSON, Logfmt, or Raw text. If no decoding is selected, the decoding defaults to JSON.
2. Enter a source name to override the default name value configured for your Sumo Logic collector’s source.
3. Enter a host name to override the default host value configured for your Sumo Logic collector’s source.
4. Enter a category name to override the default category value configured for your Sumo Logic collector’s source.
5. Click Add Header to add any custom header fields and values.

The rsyslog and syslog-ng destinations match these log fields to the following Syslog fields:

The following destination settings are optional:

1. Toggle the switch to enable TLS. If you enable TLS, the following certificate and key files are required:
   • Server Certificate Path: The path to the certificate file that has been signed by your Certificate Authority (CA) Root File in DER or PEM (X.509).
   • CA Certificate Path: The path to the certificate file that is your Certificate Authority (CA) Root File in DER or PEM (X.509).
   • Private Key Path: The path to the .key private key file that belongs to your Server Certificate Path in DER or PEM (PKCS#8) format.
2. Enter the number of seconds to wait before sending TCP keepalive probes on an idle connection.

To authenticate the Observability Pipelines Worker for Google Chronicle, contact your Google Security Operations representative for a Google Developer Service Account Credential. This credential is a JSON file and must be placed under DD_OP_DATA_DIR/config. See Getting API authentication credential for more information.

Note: If you are installing the Worker in Kubernetes, see Referencing files in Kubernetes for information on how to reference the credentials file.

To set up the Worker’s Google Chronicle destination:

1. Enter the customer ID for your Google Chronicle instance.
2. Enter the path to the credentials JSON file you downloaded earlier.
3. Select JSON or Raw encoding in the dropdown menu.
4. Select the appropriate Log Type in the dropdown menu.

Note: Logs sent to the Google Chronicle destination must have ingestion labels. For example, if the logs are from a A10 load balancer, it must have the ingestion label A10_LOAD_BALANCER. See Google Cloud’s Support log types with a default parser for a list of available log types and their respective ingestion labels.

The following fields are optional:

1. Enter the name for the Elasticsearch index.
2. Enter the Elasticsearch version.

Optionally, enter the name of the OpenSearch index.

1. Optionally, enter the name of the Amazon OpenSearch index.
2. Select an authentication strategy, Basic or AWS. For AWS, enter the AWS region.

This processor filters for logs that match the specified filter query and drops all non-matching logs. If a log is dropped at this processor, then none of the processors below this one receives that log. This processor can filter out unnecessary logs, such as debug or warning logs.

To set up the filter processor:

• Define a filter query. The query you specify filters for and passes on only logs that match it, dropping all other logs.

The remap processor can add, drop, or rename fields within your individual log data. Use this processor to enrich your logs with additional context, remove low-value fields to reduce volume, and standardize naming across important attributes. Select add field, drop field, or rename field in the dropdown menu to get started.

Add field

Use add field to append a new key-value field to your log.

To set up the add field processor:

1. Define a filter query. Only logs that match the specified filter query are processed. All logs, regardless of whether they do or do not match the filter query, are sent to the next step in the pipeline.
2. Enter the field and value you want to add. To specify a nested field for your key, use the path notation: <OUTER_FIELD>.<INNER_FIELD>. All values are stored as strings. Note: If the field you want to add already exists, the Worker throws an error and the existing field remains unchanged.

Drop field

Use drop field to drop a field from logging data that matches the filter you specify below.

To set up the drop field processor:

1. Define a filter query. Only logs that match the specified filter query are processed. All logs, regardless of whether they do or do not match the filter query, are sent to the next step in the pipeline.
2. Enter the key of the field you want to drop. To specify a nested field for your specified key, use the path notation: <OUTER_FIELD>.<INNER_FIELD>. Note: If your specified key does not exist, your log will be unimpacted.

Rename field

Use rename field to rename a field within your log.

To set up the rename field processor:

1. Define a filter query. Only logs that match the specified filter query are processed. All logs, regardless of whether they do or do not match the filter query, are sent to the next step in the pipeline.
2. Enter the name of the field you want to rename in the Source field. To specify a nested field for your key, use the path notation: <OUTER_FIELD>.<INNER_FIELD>. Once renamed, your original field is deleted unless you enable the Preserve source tag checkbox described below.
   Note: If the source key you specify doesn’t exist, a default null value is applied to your target.
3. In the Target field, enter the name you want the source field to be renamed to. To specify a nested field for your specified key, use the path notation: <OUTER_FIELD>.<INNER_FIELD>.
   Note: If the target field you specify already exists, the Worker throws an error and does not overwrite the existing target field.
4. Optionally, check the Preserve source tag box if you want to retain the original source field and duplicate the information from your source key to your specified target key. If this box is not checked, the source key is dropped after it is renamed.

Path notation example

For the following message structure, use outer_key.inner_key.double_inner_key to refer to the key with the value double_inner_value.

{
    "outer_key": {
        "inner_key": "inner_value",
            "a": {
                    "double_inner_key": "double_inner_value",
                    "b": "b value"
                },
            "c": "c value"
        },
        "d": "d value"
    }

This processor samples your logging traffic for a representative subset at the rate that you define, dropping the remaining logs. As an example, you can use this processor to sample 20% of logs from a noisy non-critical service.

The sampling only applies to logs that match your filter query and does not impact other logs. If a log is dropped at this processor, none of the processors below receives that log.

To set up the sample processor:

1. Define a filter query. Only logs that match the specified filter query are sampled at the specified retention rate below. The sampled logs and the logs that do not match the filter query are sent to the next step in the pipeline.
2. Set the retain field with your desired sampling rate expressed as a percentage. For example, entering 2 means 2% of logs are retained out of all the logs that match the filter query.

This processor parses logs using the grok parsing rules that are available for a set of sources. The rules are automatically applied to logs based on the log source. Therefore, logs must have a source field with the source name. If this field is not added when the log is sent to the Observability Pipelines Worker, you can use the Add field processor to add it.

If the source field of a log matches one of the grok parsing rule sets, the log’s message field is checked against those rules. If a rule matches, the resulting parsed data is added in the message field as a JSON object, overwriting the original message.

If there isn’t a source field on the log, or no rule matches the log message, then no changes are made to the log and it is sent to the next step in the pipeline.

To set up the grok parser:

1. Define a filter query. Only logs that match the specified filter query are processed. All logs, regardless of whether they do or do not match the filter query, are sent to the next step in the pipeline.
2. Click the Preview Rules button.
3. Search or select a source in the dropdown menu to see the grok parsing rules for that source.

The quota processor measures the logging traffic for logs that match the filter you specify. When the configured daily quota is met inside the 24-hour rolling window, the processor can either drop additional logs or send an alert using a Datadog monitor. You can configure the processor to track the total volume or the total number of events.

As an example, you can configure this processor to drop new logs or trigger an alert without dropping logs after the processor has received 10 million events from a certain service in the last 24 hours.

To set up the quota processor:

1. Enter a name for the quota processor. The pipeline uses the name to identify the quota across multiple Remote Configuration deployments of the Worker.
2. Define a filter query. Only logs that match the specified filter query are counted towards the daily limit.
   • Logs that match the quota filter and are within the daily quota are sent to the next step in the pipeline.
   • Logs that do not match the quota filter are sent to the next step of the pipeline.
3. In the Unit for quota dropdown menu, select if you want to measure the quota by the number of Events or by the Volume in bytes.
4. Set the daily quota limit and select the unit of magnitude for your desired quota.
5. Check the Drop events checkbox if you want to drop all events when your quota is met. Leave it unchecked if you plan to set up a monitor that sends an alert when the quota is met.
   • If logs that match the quota filter are received after the daily quota has been met and the Drop events option is selected, then those logs are dropped. In this case, only logs that did not match the filter query are sent to the next step in the pipeline.
   • If logs that match the quota filter are received after the daily quota has been met and the Drop events option is not selected, then those logs and the logs that did not match the filter query are sent to the next step in the pipeline.

The reduce processor groups multiple log events into a single log, based on the fields specified and the merge strategies selected. Logs are grouped at 10-second intervals. After the interval has elapsed for the group, the reduced log for that group is sent to the next step in the pipeline.

To set up the reduce processor:

1. Define a filter query. Only logs that match the specified filter query are processed. Reduced logs and logs that do not match the filter query are sent to the next step in the pipeline.
2. In the Group By section, enter the field you want to group the logs by.
3. Click Add Group by Field to add additional fields.
4. In the Merge Strategy section:
   • In On Field, enter the name of the field you want to merge the logs on.
   • Select the merge strategy in the Apply dropdown menu. This is the strategy used to combine events. See the following Merge strategies section for descriptions of the available strategies.
   • Click Add Merge Strategy to add additional strategies.

Merge strategies

These are the available merge strategies for combining log events.

The deduplicate processor removes copies of data to reduce volume and noise. It caches 5,000 messages at a time and compares your incoming logs traffic against the cached messages. For example, this processor can be used to keep only unique warning logs in the case where multiple identical warning logs are sent in succession.

To set up the deduplicate processor:

1. Define a filter query. Only logs that match the specified filter query are processed. Deduped logs and logs that do not match the filter query are sent to the next step in the pipeline.
2. In the Type of deduplication dropdown menu, select whether you want to Match on or Ignore the fields specified below.
   • If Match is selected, then after a log passes through, future logs that have the same values for all of the fields you specify below are removed.
   • If Ignore is selected, then after a log passes through, future logs that have the same values for all of their fields, except the ones you specify below, are removed.
3. Enter the fields you want to match on, or ignore. At least one field is required, and you can specify a maximum of three fields.
   • Use the path notation <OUTER_FIELD>.<INNER_FIELD> to match subfields. See the Path notation example below.
4. Click Add field to add additional fields you want to filter on.

Path notation example

For the following message structure, use outer_key.inner_key.double_inner_key to refer to the key with the value double_inner_value.

{
    "outer_key": {
        "inner_key": "inner_value",
            "a": {
                    "double_inner_key": "double_inner_value",
                    "b": "b value"
                },
            "c": "c value"
        },
        "d": "d value"
    }

The Sensitive Data Scanner processor scans logs to detect and redact or hash sensitive information such as PII, PCI, and custom sensitive data. You can pick from our library of predefined rules, or input custom Regex rules to scan for sensitive data.

To set up the sensitive data scanner processor:

1. Define a filter query. Only logs that match the specified filter query are scanned and processed. All logs, regardless of whether they do or do not match the filter query, are sent to the next step in the pipeline.
2. Click Add Scanning Rule.
3. Name your scanning rule.
4. In the Select scanning rule type field, select whether you want to create a rule from the library or create a custom rule.
   • If you are creating a rule from the library, select the library pattern you want to use.
   • If you are creating a custom rule, enter the regex pattern to check against the data.
5. In the Scan entire or part of event section, select if you want to scan the Entire Event, Specific Attributes, or Exclude Attributes in the dropdown menu.
   • If you selected Specific Attributes, click Add Field and enter the specific attributes you want to scan. You can add up to three fields. Use path notation (outer_key.inner_key) to access nested keys. For specified attributes with nested data, all nested data is scanned.
   • If you selected Exclude Attributes, click Add Field and enter the specific attributes you want to exclude from scanning. You can add up to three fields. Use path notation (outer_key.inner_key) to access nested keys. For specified attributes with nested data, all nested data is excluded.
6. In the Define action on match section, select the action you want to take for the matched information. Redaction, partial redaction, and hashing are all irreversible actions.
   • If you are redacting the information, specify the text to replace the matched data.
   • If you are partially redacting the information, specify the number of characters you want to redact and whether to apply the partial redaction to the start or the end of your matched data.
   • Note: If you select hashing, the UTF-8 bytes of the match are hashed with the 64-bit fingerprint of FarmHash.
7. Optionally, add tags to all events that match the regex, so that you can filter, analyze, and alert on the events.

This processor adds a field with the name of the host that sent the log. For example, hostname: 613e197f3526. Note: If the hostname already exists, the Worker throws an error and does not overwrite the existing hostname.

To set up this processor:

• Define a filter query. Only logs that match the specified filter query are processed. All logs, regardless of whether they do or do not match the filter query, are sent to the next step in the pipeline.

This processor converts the specified field into JSON objects.

To set up this processor:

1. Define a filter query. Only logs that match the specified filter query are processed. All logs, regardless of whether they do or do not match the filter query, are sent to the next step in the pipeline.
2. Enter the name of the field you want to parse JSON on.
   Note: The parsed JSON overwrites what was originally contained in the field.

Use this processor to enrich your logs with information from a reference table, which could be a local file or database.

To set up the enrichment table processor:

1. Define a filter query. Only logs that match the specified filter query are processed. All logs, regardless of whether they do or do not match the filter query, are sent to the next step in the pipeline.
2. Enter the source attribute of the log. The source attribute’s value is what you want to find in the reference table.
3. Enter the target attribute. The target attribute’s value stores, as a JSON object, the information found in the reference table.
4. Select the type of reference table you want to use, File or GeoIP.
   • For the File type:
     1. Enter the file path.
     2. Enter the column name. The column name in the enrichment table is used for matching the source attribute value. See the Enrichment file example.
        Note: If you are installing the Worker in Kubernetes, see Referencing files in Kubernetes for information on how to reference the file.
   • For the GeoIP type, enter the GeoIP path.

Enrichment file example

For this example, merchant_id is used as the source attribute and merchant_info as the target attribute.

This is the example reference table that the enrichment processor uses:

merch_id is set as the column name the processor uses to find the source attribute’s value. Note: The source attribute’s value does not have to match the column name.

If the enrichment processor receives a log with "merchant_id":"536":

• The processor looks for the value 536 in the reference table’s merch_id column.
• After it finds the value, it adds the entire row of information from the reference table to the merchant_info attribute as a JSON object:

merchant_info {
    "merchant_name":"Cindy's Couches",
    "city":"Boulder",
    "state":"Colorado"
}