Navigate all your data visually using the Data Explorer without needing to know how to write NRQL-format queries. The Data Explorer is part of the Query Console, accessible through the query drawer at the bottom of the New Relic platform.
Need to perform a more detailed search? Read how to query your data. If you haven't already, create your free New Relic account below to start monitoring your data today.
Why it matters
Do you research the state of your systems? Do you need to plan resources, identify and respond to incidents, or troubleshoot faulty behaviors? The Data Explorer makes it easy to identify, fetch and visualize the data you are looking for through visual menus, without ever using NRQL or building queries.
With the Data Explorer you can access all data stored in the New Relic database (NRDB) in a quick, intuitive way. You can then select Dimensions to facet by specific dimensions of that data.
Other things the Data Explorer helps you do:
See data from different points of view: from raw data to different visualizations that provide insights on evolution, distribution, and more.
Drill down into data using filters.
Add predictions to line and area charts (Available with the public preview of NRQL Predictions).
Metric timeslice data: agents, mobile agents, and the agent report this data type. To explore this type of data, you must choose an entity monitored by one of those agents, and then you'll see those options.
Logs: the Log data type (which you can also explore via our logs UI).
Lookup tables: Data that isn't present in your New Relic account, by uploading CSV files (which you can also explore via our lookup tables UI).
Explore your data
To access the Data Explorer, click the Query your data button in the bottom drawer of any New Relic page, then select the Data Explorer option.
The Data Explorer consists of these:
Scoping section on the top. You can select the account and choose from multiple data types: events, metrics, timeslices, logs, and lookups. If you select metrics, you can filter by entity.
Data browsing area on the left. If you choose metrics, the options are metric and dimensions. If you choose events, the options are event type, plot, and dimensions.
Workspace. This area shows you the result of your choices.
Access the Data Explorer through the Query your data drawer: Data Explorer view.
To use the Data Explorer:
Define the scope. Select the data type (metric or event), the account, and the entities.
Use the blocks on the left to browse the available data and build your search. You can only select one element per block. Blocks are searchable.
Event type
Lists all available events for the selected account. By default, the sort of events is by Name.
Plot
Lists all the numeric attributes of the event previously selected.
By default, the sort of attributes is by Name.
The first item on the list is count(*), which is not an attribute. It calculates the count of the selected event.
Select the function that you want to plot. By default each attribute is set to the function Average.
Dimensions
Lists all the dimensions of the event and plot previously selected.
By default, the sort of dimensions is by Name.
Dimensions are string values that provide information on the event.
They represent the cardinality, that is, the uniqueCount of the different values of that attribute in the selected time range. If there is only one element it shows the value of the attribute.
To explore APM timeslice data, select AppID, AppName, or EntityGuid as dimensions (or group by those dimensions). Otherwise, you'll get aggregated data for all entities.
Metric
Lists all the metrics available for the selected account. By default, the sort of metrics is by Name.
Dimensions
Lists all the dimensions of the metric previously selected.
By default, the sort of dimensions is by Name.
Dimensions are string values that provide information on the metric.
They represent the cardinality, that is, the uniqueCount of the different values of that attribute in the selected time range. If there is only one element it shows the value of the attribute.
중요
We use the metric system (including metric SI prefixes) to display our units.
Visualize and refine your exploration
The working space on the right displays the result of your exploration.
At the working space you can see:
The querying area breaks down the query into its different constituents. The auto-complete feature helps you build your query by suggesting fields and functions as you type.
Here, you can easily read the result of your exploration as an NRQL query, and check the exact data being plotted.
By default, you see the data on a line chart. Change the view easily to Area chart, Pie chart, and Bar chart using the chart picker. You can also choose to see your results' raw data as a table, or as in JSON format.
If you have selected a dimension, the chart is updated with the different facets. Below the chart you can see the facets' table with the list of facets and the value for each one.
Use the facet table to drill down data. By clicking on a facet, it is applied as a filter. The table stays visible so you can easily select another facet to continue your exploration.
You can get the chart as an image, share it as a link, or add it to a dashboard using the Options menu on the top right corner.
You can also copy the URL and share your whole exploration with other New Relic users.
preview
We're still working on this feature, but we'd love for you to try it out!
This feature is currently provided as part of a preview program pursuant to our pre-release policies.
If you have enrolled in NRQL Predictions, you can add predictions to line and area charts, which use historical data to project future trends.
To add a prediction, select Predict trend from the menu of the chart.
The prediction appears in dotted lines, with the default range set to 20% of the query window, highlighted in gray.
You can further refine your query for the prediction by adjusting the range as needed. For more information, refer to NRQL Predictions.
Access the Data Explorer through the Query your data drawer: Predicted view.
Use cases
See the following examples to learn how and when to use the Data Explorer.
I've just connected new instrumentation and want to see if new data is available.
Select the account and event or metric that's generating the new data.
Use the different tools in the Data Explorer to explore the new data that has become available: have a look at the raw data of that event or metric as a table, shape it as a list, or click to see it plotted as a chart.
After selecting an event or metric, discover the shape of the data in its dimensions. Guided by cardinality, you can see the different points of view of any data.
Found anything relevant? Save it to a dashboard or share it with a colleague.
I changed a custom event/metric and need to check if this change has been successful.
In the Data Explorer, select the account, data type and event/metric you made changes to.
Verify the entity is reporting data, and that all the attributes are being plotted.
Find the attribute you made changes to and check the update was successful.
preview
We're still working on this feature, but we'd love for you to try it out!
This feature is currently provided as part of a preview program pursuant to our pre-release policies.
I want to see the future trend of a line or area chart to take proactive measures based on the predicted data.
Access the Data Explorer by clicking the Query your data button in the bottom drawer of any New Relic page.
Select a line or area chart where you want to add prediction.
From the Options menu, select Predict trend. The chart with the prediction opens in the Data Explorer.
Save it to a dashboard.
I know there's something off with an event/metric from an alert or dashboard. I need to know the root cause about the event/metric/attribute behavior.
In the Data Explorer, use the menus to select the event or metric that's not behaving as expected and let the Data Explorer plot that chart.
From there, you can drill down in the dimensions of that data and filter by those attributes that are relevant.
You can also see that data from different perspectives: its distribution, ranking of values, or evolution over time.
Found anything relevant? Save it to a dashboard or share it with a colleague.
