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Drop data using NerdGraph

One way to manage your data ingest is to set up data dropping rules. For data that supports data dropping, you can:

  • Filter out unimportant low-value data
  • Filter out potentially sensitive data


When you set up data dropping rules, only data from that point onward is affected. Existing data that has already been ingested cannot be edited or deleted. Learn more in this NerdBytes video (7:09 minutes):

Besides using NerdGraph, other ways to drop data include:


The ability to create and edit drop filter rules depends on which user model you're on:

  • Newer user model: you must be assigned a role with "NRQL drop rules" capabilities.
  • Original user model: you must have an Admin role.

The following data types can be targeted for data dropping:

Support for additional types are planned for the future.

Create drop data rule


Use caution when deciding to drop data. The data you drop is not recoverable. Before using this feature, please review caution information below.

To drop data, create a NerdGraph-format drop rule that includes:

  • A NRQL string that specifies what data types to drop
  • An action type specifying how to apply the NRQL string

You can form and make the call in the NerdGraph explorer.

The limit on nrql query length is 4096 characters. If it exceeds the length the nerdGraph will throw an error INVALID_NRQL_TOO_LONG.

There are two ways to drop data:

  • Drop entire data types or a data subset (with optional filter). This uses the DROP_DATA action type and uses NRQL of the form:


    For this type of drop rule, you cannot use anything other than * in the SELECT clause.

  • Drop attributes from data types (with optional filter). This uses the DROP_ATTRIBUTES action type and uses NRQL of the form:


    For this type of drop rule, you must pass in a non-empty list of raw attributes names.

NRQL restrictions

Not all NRQL clauses make sense for generating drop rules. You can provide a WHERE clause to select data with specific attributes. Other features such as LIMIT, TIMESERIES, COMPARE WITH, FACET, and other clauses cannot be used.

SINCE and UNTIL are not supported in drop rules. If you have time-specific rules (say, drop everything until a time in the future), use WHERE timestamp < (epoch milliseconds in the future). You also can't use SINCE to drop historical data: NRQL drop rules only apply to data reported after the drop rule was created. If you need to delete data that has already been reported, contact your New Relic representative.

The two action types have these restrictions:

  • DROP_DATA can use only SELECT *.
  • DROP_ATTRIBUTES requires use of SELECT with "raw" attributes (attributes with no aggregator function applied). This also means you cannot use SELECT *. Additionally, there are some attributes that are integral to their data type and cannot be dropped (such as timestamp on event data). If you include them, registration will fail.

Example drop rules

Here are some example drop rules:

Verify your drop rule works

After you create a drop rule, verify that it is working as expected. The rule should take effect quickly after a successful registration, so try running a TIMESERIES version of the query you registered to see that the data drops off.

Drop rule type



Drop rule NRQL:

SELECT * FROM MyEvent WHERE foo = bar

Validation NRQL:

SELECT count(*) FROM MyEvent WHERE foo = bar TIMESERIES

This should drop to 0. To verify that it did not affect any thing else, invert the WHERE clause.


Drop rule NRQL:

SELECT dropAttr1, dropAttr2 FROM MyEvent WHERE foo = bar

Validation NRQL:

SELECT count(dropAttr1), count(dropAttr2) FROM MyEvent WHERE foo = bar TIMESERIES

Both lines should drop to 0. To verify that it did not affect events that contained these attributes and still should, invert the WHERE clause.

View rules

Here is an example NerdGraph call that returns the drop rules set on an account:

    actor {
        account(id: YOUR_ACCOUNT_ID) {
            nrqlDropRules {
                list {
                    rules {
                    error { reason description } 

Delete drop rules

Here is an example NerdGraph call deleting two specific drop rules:

mutation {
    nrqlDropRulesDelete(accountId: YOUR_ACCOUNT_ID, ruleIds: ["48", "98"]) {
    successes {
    failures {
        error { reason description }
        submitted { ruleId accountId }

Audit drop rule history

To see who created and deleted drop rules, query your account audit logs. The list endpoint also includes the user ID of the person who created the rule.

Cautions when dropping data

When creating drop rules, you are responsible for ensuring that the rules accurately identify and discard the data that meets the conditions that you have established. You are also responsible for monitoring the rule, as well as the data you disclose to New Relic.

New Relic cannot guarantee that this functionality will completely resolve data disclosure concerns you may have. New Relic does not review or monitor how effective the rules you develop are.

Creating rules about sensitive data can leak information about what kinds of data you maintain, including the format of your data or systems (for example, through referencing email addresses or specific credit card numbers). Rules you create, including all information in those rules, can be viewed and edited by any user with the relevant role-based access control permissions.

Only new data will be dropped. Existing data cannot be edited or deleted.

Drop attributes on dimensional metric rollups

Dimensional metrics aggregate metrics into rollups for long term storage and as a way to optimize longer term queries. Metric cardinality limits are applied to this data.

You can use this feature to decide which attributes you don't need for long term storage and query, but would like to maintain for real time queries.

For example, adding containerId as an attribute can be useful for live troubleshooting or recent analysis, but may not be needed when querying over longer periods of time for larger trends. Due to how unique something like containerId can be, it can quickly drive you towards your metric cardinality limits which when hit stops the synthesis of rollups for the remainder of that UTC day.

This feature also allows you to keep the high cardinality attributes on the raw data and drop it from rollups which gives you more control over how quickly you approach your cardinaliity limits.


Drop attributes from dimensional metrics rollups (with optional filter). This uses DROP_ATTRIBUTES_FROM_METRIC_AGGREGATES action type and uses NRQL of the form:


Here is an example NerdGraph request:

mutation {
    nrqlDropRulesCreate(accountId: YOUR_ACCOUNT_ID, rules: [
            nrql: "SELECT containerId FROM Metric WHERE metricName = 'some.metric'"
            description: "Removes the containerId from long term querys."
        successes { id }
        failures {
            submitted { nrql }
            error { reason description }

To verify it's working, wait 3 to 5 minutes for the rule to be picked up and for aggregate data to be generated. Then assuming the example NRQL above is your drop rule, run the following queries:

SELECT count(containerId) FROM Metric WHERE metricName = 'some.metric' TIMESERIES SINCE 2 hours ago
SELECT count(containerId) FROM Metric WHERE metricName = 'some.metric' TIMESERIES SINCE 2 hours ago RAW

The first query retrieves metric rollups and should drop to 0 since containerId has been dropped per the new drop rule. The second query retrieves metric raws using the RAW keyword and should continue to hold steady since raw data is not impacted by the new drop rule. For more information on how to see the impact this will have on your cardinality, check out Understand and query high cardinality metrics.


All restrictions that apply to DROP_ATTRIBUTES apply to DROP_ATTRIBUTES_FROM_METRIC_AGGREGATES with the additional restriction that you can only target the Metric data type. They also do not work on Metric queries targeting data created by an events to metrics rule or on Metric queries targeting timeslice data.

Learn more

Recommendations for learning more:

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