New Relic Group By: Creating Effective Queries for Data Segmentation

Creating effective queries for data segmentation is crucial to getting the most out of New Relic Group By. This involves selecting the right attributes to group by, which can be a mix of numeric and string attributes.

For example, grouping by a numeric attribute like response time can help identify which transactions are taking the longest to complete. This can be especially useful for identifying performance bottlenecks.

To create effective queries, it's essential to understand how New Relic handles data types and aggregation. New Relic automatically converts numeric attributes to a data type that supports aggregation, making it easy to perform calculations on these attributes.

For instance, the average response time can be calculated by grouping by a numeric attribute like response time. This can be particularly helpful for identifying trends and patterns in performance data.

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Automatic grouping behavior is essential for organizing your data in a meaningful way. New Relic analyzes requests and splits URLs into segments on separator characters like dots for domains and slashes for paths.

This logic preserves useful terms and uses them in automated grouping, so you can easily identify patterns and trends. For example, domain information is separated by port number automatically, so HTTP and HTTPS endpoints are listed separately.

To group your data by month, use the monthOf function in an NRQL query. This will return a table of results by month, allowing you to visualize your data over time.

Automatic behavior is a key feature of New Relic's analysis. It helps you understand how your application behaves by automatically grouping similar requests together.

New Relic analyzes requests and splits URLs into segments on separator characters, such as dots for domains and slashes for paths. This allows for more accurate grouping and analysis.

The domain information is separated by port number automatically, so HTTP and HTTPS endpoints are listed separately. This is useful for identifying and troubleshooting differences in behavior between these two protocols.

New Relic's automated grouping logic preserves useful terms and uses them to categorize and analyze requests. This helps you identify patterns and trends in your application's behavior.

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You can use the monthOf function to group all results based on the month. This is a great way to see how your data changes over time.

To create a time series chart with a specific end date, you need to be aware of the UNTIL clause. The default value of UNTIL is NOW, so if you don't specify anything, the values of the time functions may be separated or combined.

You can specify UNTIL today to create a chart that ends at 12:00 AM on the same day. This can be really helpful for visualizing data from a specific time period.

To construct a bucket query, you need to create a NRQL statement with a SELECT statement for an attribute, such as average(duration). Then, add a FACET clause that facets on buckets().

Here's an example of how to use the buckets() function to create a bucketed query:

This query returns the average duration for each bucket, with the bottom bucket including outliers. You can adjust the number of buckets and the upper limit to suit your needs.

You can query unique combinations of facets without relying on COUNT or SUM. This can be achieved by using a different approach to group values together.

The current method of using COUNT is not ideal when you don't care about the counted sum, as it still performs the additional calculation.

For analysis reasons, you may want to see the different combinations of facets a certain record has. This can be done by formulating a better query than the one currently in use.

The query SELECT COUNT(myRecord) FROM Metric FACET foo, bar does the job, but it's not the most efficient way to achieve the desired result.

Creating Queries is a crucial part of working with New Relic. You can create bucketed NRQL queries to return bucketed results.

To return bucketed results, use the FACET buckets() clause in a NRQL query. A bucketed query has the following structure.

You can use bucketed NRQL queries with any attribute stored as a numerical value in the New Relic database. This allows you to segment your data into buckets for easier analysis.

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To construct a bucket query, create a NRQL statement with a SELECT statement for an attribute, such as SELECT average(duration). Then add a FACET clause that facets on buckets(), like this: buckets(duration, 40, 10).

Here's a step-by-step guide to constructing a bucket query:

This query computes the average duration for a specific page path across 10 buckets, with an upper limit of 40. All values above our upper limit of 40 are grouped in the last bucket, >=36.0.