![]() ![]() New Relic recommends against using predictLinear in TIMESERIES queries.Any dataset which grows exponentially, logarithmically, or by other nonlinear means will likely only be successful in very short-term predictions.Since predictLinear() is a linear regression, familiarity with the dataset being queried helps to ensure accurate long-term predictions.Generally, predictLinear() is helpful for continuously growing values like disk space, or predictions on large trends.For example, predictLinear(attributeName, 1 hour) is a linear prediction 1 hour into the future of the query time window. The time interval is how far the query will look into the future.It uses a similar method of least-squares linear regression to predict the future values for a dataset. PredictLinear() is an extension of the derivative() function. If you're using an aggregator function multiple times in the same query (for example, SELECT median(one_metric), median(another_metric)), it can cause problems in displaying results.Or, go to the full online course Writing NRQL queries. See New Relic University tutorials for Filter queries, Apdex queries, and Percentile queries.You can use aggregator functions to filter and aggregate data. In this section we explain NRQL functions, both aggregator functions and non-aggregator functions. Query metric timeslice data, which is our original metric data type reported by our APM, mobile monitoring, and browser monitoring.įor more details about how we report metric data, see Metric data types. ![]() Query dimensional metrics, which are reported by our Metric API and by some of our solutions that use that API (for example, our Dropwizard integration or Micrometer integration).We have two types of metric data, each with their own query guidelines: There are specific tips for querying it well. Metric data is more complex than other types of data. See Set time range on dashboards and charts for detailed information and examples. Than one hundred rows in the subquery result.įor an in depth look at the JOIN clause, please see the The cardinality of the join is limited to 1:100, meaning a single join key cannot map to more.While SELECT * is supported in the parent query, it is not supported in the joined subquery.Like all subqueries, joined subqueries cannot be used in alert conditions.TIMESERIES, keep in mind that the joined subquery will provide a single result for the full The use of TIMESERIES in the joined subquery is not supported.Note that the outer query's LIMIT does not affect the inner query. The joined subquery will continue to have a default LIMIT of 10, with a.Restrictions and limitations to consider: This is an abbreviated syntax for when the key identifier is the same in both contexts. The right-hand side is used for the subquery key value, and must be an identifier.The left-hand side is always the key used in the parent query and may be an attribute or.First as a single value, then as a line chart.ĭefines the key values to compare in the subquery and the outer query. Without TIMESERIES, COMPARE WITH generates a billboard with the current value and the percent change from the COMPARE WITH value.Įxample: This query returns data as a line chart showing the 95th percentile for the past week compared to the same range one week ago.With TIMESERIES, COMPARE WITH creates a line chart with the comparison mapped over time.For example, SINCE 2 hours ago COMPARE WITH 4 hours ago might compare 3:00pm through 5:00pm against 11:00am through 1:00pm.ĬOMPARE WITH can be formatted as either a line chart or a billboard: The time range for the COMPARE WITH value is always the same as that specified by SINCE or UNTIL. For example, SINCE 1 day ago COMPARE WITH 1 day ago compares yesterday with the day before. The time specified by COMPARE WITH is relative to the time specified by SINCE or UNTIL. Use the COMPARE WITH clause to compare the values for two different time ranges.ĬOMPARE WITH requires a SINCE or UNTIL statement. ![]()
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