View application performance analysis reports

The Background jobs analysis report helps you analyze the performance of jobs and other non-web transactions running in production that use frameworks instrumented by New Relic, such as DelayedJob and Resque. The report shows comparison information from the past 24 hours back to the past 7 days, including the amount of time spent in throughput (requests per minute or rpm), total time spent in the job, and average time to execute it.

View report in UI

To view the Background jobs analysis report for your app: Go to one.newrelic.com > All capabilities > APM & services > (select an app) > Reports > Performance > Non-web transactions tab. The default report compares the last 24 hours with the previous 24-hour period, 7 days ago, and averages over the last 7 days.

APM's capacity analysis report shows how many instances of your app are running and how busy they are. This information is useful to analyze whether your app has enough instances deployed to support request loads. You can then use this information to fine-tune your configuration for optimal performance.

The capacity analysis report is only available for Python and Ruby language agents.

View the capacity analysis report

To view the capacity analysis report for your app:

1. Go to one.newrelic.com > All capabilities > APM & services > (select an app) > Reports > Capacity.

2. Use any of New Relic's standard page functions to drill down into detailed information.

   

   The report includes three charts.

   Capacity chart	Description
   App instance busy	This chart shows how busy are your application's instances as the percentage of time to process requests. This includes the average, minimum, and maximum percentages for any time period on the chart. Use this information to determine if you have the right number of instances for your application. As application utilization approaches 100%, your application needs more instances to handle incoming requests.
   App instance restarts by host	This chart shows the number of instance restarts for each time interval. Use this information to determine if your application instances are restarting too frequently. To isolate activity to a specific host, select other hosts' titles on the legend below the chart to hide or return them to the chart. OR: Select your choice from the Hosts list on the APM Summary page.
   App instance analysis	This chart shows the total number of instances running, along with the concurrent instance load. The concurrent instance load is the number of fully busy instances that would be needed to handle the load on the app. New Relic bases this on the average app instances value from the App instance busy chart, multiplied by the number of instances you have running.

   Python single or multi-threaded apps

   For the Python agent, capacity analysis reporting only works for traditional single or multi-threaded applications. This is because the way New Relic calculates a system load does not translate to co-routine based systems.

   When using a traditional single or multi-threaded application, the metric information generated by the Python agent is captured and reported only if the optional C extension component can be compiled and installed. When you use co-routine based systems such as gevent or eventlet in conjunction with a WSGI server, the capacity analysis report will not show any data except for agent restarts.

APM's database analysis report allows you to analyze your application's performance from the database viewpoint, from the past 24 hours back to the past seven days. It shows comparison information for transactions, including the amount of time spent in throughput (operation requests per minute or rpm), total time spent in the operation, and average time to execute it.

View the database analysis report

To view the database analysis report for your app: Go to one.newrelic.com > All capabilities > APM & services > (select an app) > Reports > Performance > Databases tab. The default report compares the last 24 hours with the previous 24-hour period, 7 days ago, and averages over the last 7 days.

APM's scalability analysis report shows response time, database time, and CPU time in relation to the application load. This helps you evaluate performance and identify potential hardware requirements as your app grows.

View the Scalability analysis report

To view the report for your app:

Go to one.newrelic.com > All capabilities > APM & services > (select an app) > Reports > Scalability.

The Scalability analysis report includes three tabs: Response time, Database, and APM CPU. Each tab contains a billboard and a chart and represents a different aspect of performance.

The charts feature two lines: the primary line, which connects the data points, and the secondary line, which is labeled as "Smoothed" and shows a smoothed version of the primary line. The secondary line helps visualize the application's performance without being concerned about unexpected fluctuations observed in the primary line.

These charts are constructed a bit differently from other New Relic charts and do not have the default chart actions commonly available.

Select options for time periods

The Scalability report has a time picker that allows you to view data over different time periods. The minimum recommended time period is 30 minutes because when analyzing an application’s scalability, lower time ranges don’t give a full picture. The time picker uses the default value of the last 30 minutes.

View data for individual instances

Each tab includes an instance picker which allows you to select a specific instance and the chart and the billboard will filter to the data of that instance.

APM's web transactions analysis report provides comparison information for web transactions, including the amount of time spent in throughput (requests per minute or rpm), total time in the web transaction, average time to execute it, and Apdex score. You can compare the past 24 hours of data with the previous 24 hour period, 7 days ago, and averages over the last 7 days.

Use this information to analyze which web transactions consume the most time, have the most number of calls, have the greatest number of standard deviations, and more. This information can help you identify where to fine-tune your app.

View the Web transactions analysis report

To view the web transactions analysis report for your app: Go to one.newrelic.com > All capabilities > APM & services > (select an app) > Reports > Performance > Web transactions tab.

The default report compares the last 24 hours with the previous 24-hour period, 7 days ago, and averages over the last 7 days. Apdex entries followed by an asterisk (*) indicate that more than one Apdex threshold was set for the given time period.

Every Monday, New Relic sends a weekly performance report via email to any of your account's users who have selected to receive this report. The email report contains summary performance metrics for the past week for a select group of your applications. The weekly performance report is different than APM's Service Level Agreement (SLA) reports.

View example report details

To see an example of a weekly performance report for your account:

1. From the user menu, select User preferences.

2. Go down to Weekly summary emails and select See a sample email.

   

   The performance report is split into sections:

   Section	Description
   Featured app	This provides both throughput pattern and performance metrics for the application with the highest call count in your account. Applications with linked ブラウザ監視 will be prioritized over those that don't, even if the call count is lower.
   All apps report	This section provides important performance metrics for the 20 applications in your account with the highest call count. Metrics in this list include uptime, Apdex, load time, and throughput.
   Recent events	This section contains any recent alerts, deployments you have made to your app, or other events that are recorded for applications in your account.

   Query your weekly report metrics with NRQL

   These are the queries New Relic uses to generate the numbers in your weekly performance report email. They rely on APM timeslice data exposed through newrelic.timeslice.value on the Metric event, so you can run them in the query builder or pin them to a dashboard to reproduce any value in the email.

   Replace the variables with values for your application before running a query:

   Variable	Description
   <app_name>	The appName of the APM application.
   <APP_GUID>	The entity.guid of the APM application.
   <monday> / <next_monday>	A pair of ISO 8601 timestamps marking the start and end of the reporting week. The weekly email uses Monday 00:00 UTC as the boundary. For example, '2026-04-20T00:00:00Z' for <monday> and '2026-04-27T00:00:00Z' for <next_monday>.
   {N}	The number of weeks to include in a time-series query. For example, 8 for the last 8 weeks.

   ヒント

   Two constants appear in several queries: 10080 is the number of minutes in a week (used to convert summed timeslice counts into a per-minute throughput average), and 604800 is the number of seconds in a week (used in the CPU % calculation).

   App server metrics

   Each query in this group reports a single weekly value for one application, using SINCE '<monday>' UNTIL '<next_monday>' to scope the week and appName = '<app_name>' to select the app.

   Requests (millions): Total HTTP requests handled during the week, expressed in millions.

   SELECT sum(newrelic.timeslice.value[count]) / 1e6 AS 'Requests millions'
   FROM Metric
   WHERE metricTimesliceName = 'HttpDispatcher' AND appName = '<app_name>'
   SINCE '<monday>' UNTIL '<next_monday>'

   コピー

   Response time (ms): Average response time in milliseconds across all HTTP transactions.

   SELECT sum(newrelic.timeslice.value) / sum(newrelic.timeslice.value[count]) * 1000 AS 'Resp time ms'
   FROM Metric
   WHERE metricTimesliceName = 'HttpDispatcher' AND appName = '<app_name>'
   SINCE '<monday>' UNTIL '<next_monday>'

   コピー

   Throughput (RPM): Average requests per minute over the week.

   SELECT sum(newrelic.timeslice.value[count]) / 10080 AS 'Throughput RPM'
   FROM Metric
   WHERE metricTimesliceName = 'HttpDispatcher' AND appName = '<app_name>'
   SINCE '<monday>' UNTIL '<next_monday>'

   コピー

   Error rate (%): Errors as a percentage of all web and non-web transactions.

   SELECT filter(sum(newrelic.timeslice.value[count]), WHERE metricTimesliceName = 'Errors/all') /
     (filter(sum(newrelic.timeslice.value[count]), WHERE metricTimesliceName = 'HttpDispatcher') +
      filter(sum(newrelic.timeslice.value[count]), WHERE metricTimesliceName = 'OtherTransaction/all')) * 100 AS 'Error rate'
   FROM Metric
   WHERE metricTimesliceName IN ('Errors/all', 'HttpDispatcher', 'OtherTransaction/all')
     AND appName = '<app_name>'
   SINCE '<monday>' UNTIL '<next_monday>'

   コピー

   Apdex: The weekly Apdex score for the application.

   SELECT (sum(newrelic.timeslice.value[count]) + sum(newrelic.timeslice.value[total]) / 2.0) /
     (sum(newrelic.timeslice.value[count]) + sum(newrelic.timeslice.value[total]) + sum(newrelic.timeslice.value[exclusiveTotal])) AS 'Apdex'
   FROM Metric
   WHERE metricTimesliceName = 'Apdex' AND appName = '<app_name>'
   SINCE '<monday>' UNTIL '<next_monday>'

   コピー

   % Satisfied / % Tolerating / % Frustrated: The three Apdex components as percentages. This complements the single Apdex score above by breaking it into its underlying request categories.

   SELECT sum(newrelic.timeslice.value[count]) /
     (sum(newrelic.timeslice.value[count]) + sum(newrelic.timeslice.value[total]) + sum(newrelic.timeslice.value[exclusiveTotal])) * 100 AS '% Satisfied',
     sum(newrelic.timeslice.value[total]) /
     (sum(newrelic.timeslice.value[count]) + sum(newrelic.timeslice.value[total]) + sum(newrelic.timeslice.value[exclusiveTotal])) * 100 AS '% Tolerating',
     sum(newrelic.timeslice.value[exclusiveTotal]) /
     (sum(newrelic.timeslice.value[count]) + sum(newrelic.timeslice.value[total]) + sum(newrelic.timeslice.value[exclusiveTotal])) * 100 AS '% Frustrated'
   FROM Metric
   WHERE metricTimesliceName = 'Apdex' AND appName = '<app_name>'
   SINCE '<monday>' UNTIL '<next_monday>'

   コピー

   CPU (%): Average CPU user time for the week, expressed as a percentage.

   SELECT sum(newrelic.timeslice.value) / 604800 * 100 AS 'CPU %'
   FROM Metric
   WHERE metricTimesliceName = 'CPU/User Time' AND appName = '<app_name>'
   SINCE '<monday>' UNTIL '<next_monday>'

   コピー

   Memory (MB): Average physical memory use in megabytes.

   SELECT average(newrelic.timeslice.value) AS 'Memory MB'
   FROM Metric
   WHERE metricTimesliceName = 'Memory/Physical' AND appName = '<app_name>'
   SINCE '<monday>' UNTIL '<next_monday>'

   コピー

   DB (%): Time spent in web datastore calls as a percentage of total web transaction time.

   SELECT filter(sum(newrelic.timeslice.value), WHERE metricTimesliceName = 'Datastore/allWeb') /
     filter(sum(newrelic.timeslice.value), WHERE metricTimesliceName = 'HttpDispatcher') * 100 AS 'DB %'
   FROM Metric
   WHERE metricTimesliceName IN ('Datastore/allWeb', 'HttpDispatcher') AND appName = '<app_name>'
   SINCE '<monday>' UNTIL '<next_monday>'

   コピー

   Note

   Database percentage represents the ratio of database operation time to total web transaction response time. Formula: (database time / total response time) × 100. Percentages exceeding 100% indicate parallel or asynchronous database call execution.

   Traffic pattern

   These queries drive the two-month daily sparkline shown at the top of the report. They use TIMESERIES 1 day over SINCE 2 months ago. There are two variants depending on how the app is instrumented: app-server apps are keyed off HttpDispatcher timeslices (rpm), and browser-instrumented apps use EndUser timeslices (ppm).

   Throughput — app-server apps (rpm): Daily requests-per-minute rate for apps reporting HttpDispatcher timeslices.

   SELECT rate(sum(newrelic.timeslice.value), 1 minute) AS 'Throughput rpm'
   FROM Metric
   WHERE metricTimesliceName = 'HttpDispatcher' AND entity.guid = '<APP_GUID>'
   SINCE 2 months ago TIMESERIES 1 day

   コピー

   Throughput — browser-instrumented apps (ppm): Daily page-loads-per-minute rate for apps reporting EndUser timeslices.

   SELECT rate(sum(newrelic.timeslice.value), 1 minute) AS 'Throughput ppm'
   FROM Metric
   WHERE metricTimesliceName = 'EndUser' AND entity.guid = '<APP_GUID>'
   SINCE 2 months ago TIMESERIES 1 day

   コピー

   End-user browser metrics

   These queries power the browser section of the report for applications with browser monitoring attached. They return a point per week over the most recent {N} weeks using TIMESERIES 1 week.

   End-user response time (s): Average page load time from the browser agent.

   SELECT average(newrelic.timeslice.value) AS 'End User Resp Time (s)'
   FROM Metric
   WHERE metricTimesliceName = 'EndUser' AND entity.guid = '<APP_GUID>'
   SINCE {N} weeks ago TIMESERIES 1 week

   コピー

   End-user Apdex (page load): Apdex score for end-user page loads.

   SELECT apdex(newrelic.timeslice.value) AS 'End User Apdex'
   FROM Metric
   WHERE metricTimesliceName = 'EndUser/Apdex' AND entity.guid = '<APP_GUID>'
   SINCE {N} weeks ago TIMESERIES 1 week

   コピー

   Selected applications

   New Relic selects the top 20 apps by call count that meet these criteria:

• The app must have at least 25 requests per minute (rpm) throughput.

• The app must include web transactions.

  Applications with linked browser monitoring will be prioritized over those that don't, even if the call count is lower.

  Opt in or out of report emails

  To manage your weekly performance report emails, go to one.newrelic.com, click the user menu, and select User preferences. Note that you opt in/out separately for every New Relic account you have access to.