NRQL reference

SELECT attribute ...

SELECT function(attribute) ...

The SELECT specifies what portion of a data type you want to query by specifying an attribute or a function. It's followed by one or more arguments separated by commas. In each argument you can:

• Get the values of all available attributes by using * as a wildcard. For example: SELECT * from Transaction.
• Get values associated with a specified attribute or multiple attributes specified in a comma separated list.
• Get aggregated values from specified attributes by selecting an aggregator function.
• Label the results returned in each argument with the AS clause.

You can also use SELECT with basic math functions.

SELECT average(duration) FROM PageView SINCE 1 week ago

SELECT ...
FROM data type
...

Use the FROM clause to specify the data type you wish to query. You can start your query with FROM or with SELECT. You can merge values for the same attributes across multiple data types in a comma separated list.

SELECT count(*) FROM Transaction SINCE 7 days ago

SELECT count(*) FROM Transaction, PageView SINCE 3 days ago

SELECT ...
AS 'label'
...

Use the AS clause to label an attribute, aggregator, step in a funnel, or the result of a math function with a string delimited by single quotes. The label is used in the resulting chart. Note that AS clause labels in time series charts will not be displayed if a FACET clause is used.

SELECT count(*)/uniqueCount(session) AS 'Pageviews per Session'
FROM PageView

SELECT funnel(SESSION,
  WHERE name='Controller/about/main' AS 'Step 1',
  WHERE name = 'Controller/about/careers' AS 'Step 2')
FROM PageView SINCE 1 week ago

SELECT ... 
(SINCE or UNTIL) (integer units) AGO
COMPARE WITH (integer units) AGO
...

Use the COMPARE WITH clause to compare the values for two different time ranges.

COMPARE WITH requires a SINCE or UNTIL statement. The time specified by COMPARE WITH is relative to the time specified by SINCE or UNTIL. For example, SINCE 1 day ago COMPARE WITH 1 day ago compares yesterday with the day before.

The time range for theCOMPARE WITH value is always the same as that specified by SINCE or UNTIL. For example, SINCE 2 hours ago COMPARE WITH 4 hours ago might compare 3:00pm through 5:00pm against 11:00am through 1:00pm.

COMPARE WITH can be formatted as either a line chart or a billboard:

• With TIMESERIES, COMPARE WITH creates a line chart with the comparison mapped over time.
• Without TIMESERIES, COMPARE WITH generates a billboard with the current value and the percent change from the COMPARE WITH value.

Example: This query returns data as a line chart showing the 95th percentile for the past week compared to the same range one week ago. First as a single value, then as a line chart.

SELECT percentile(duration, 95) FROM PageView
SINCE 1 week ago COMPARE WITH 1 week AGO

SELECT percentile(duration, 95) FROM PageView
SINCE 1 week ago COMPARE WITH 1 week AGO TIMESERIES AUTO

You can use this clause with these data types:

• Transaction

• TransactionError

• Custom events reported via APM agent APIs

  The purpose of EXTRAPOLATE is to mathematically compensate for the effects of APM agent sampling of event data so that query results more closely represent the total activity in your system.

  This clause will be useful when a APM agent reports so many events that it often passes its harvest cycle reporting limits. When that occurs, the agent begins to sample events.

  When EXTRAPOLATE is used in a NRQL query that supports its use, the ratio between the reported events and the total events is used to extrapolate a close approximation of the total unsampled data. When it is used in a NRQL query that doesn’t support its use or that hasn’t used sampled data, it has no effect.

  重要

  Note that EXTRAPOLATE is most useful for homogenous data (like throughput or error rate). It's not effective when attempting to extrapolate a count of distinct things (like uniqueCount() or uniques()).

  This clause works only with NRQL queries that use one of the following aggregator functions:

• apdex

• average

• count

• histogram

• sum

• percentage (if function it takes as an argument supports EXTRAPOLATE)

• rate (if function it takes as an argument supports EXTRAPOLATE)

• stddev

  Example of extrapolating throughput

  A query that will show the extrapolated throughput of a service named interestingApplication.

  SELECT count(*) FROM Transaction WHERE appName='interestingApplication' SINCE 60 minutes ago EXTRAPOLATE

  コピー

  Example of extrapolating throughput as a time series

  A query that will show the extrapolated throughput of a service named interestingApplication by transaction name, displayed as a time series.

  SELECT count(*) FROM Transaction WHERE appName='interestingApplication'
  SINCE 60 minutes ago FACET name TIMESERIES 1 minute EXTRAPOLATE

  コピー

SELECT ...
FACET attribute
...

Use FACET to separate and group your results by attribute values. For example, you could FACET your PageView data by deviceType to figure out what percentage of your traffic comes from mobile, tablet, and desktop devices.

Use the LIMIT clause to specify how many facets appear (default is 10). For more complex grouping, use FACET CASES. FACET clauses support up to five attributes, separated by commas.

The facets are sorted in descending order by the first field you provide in the SELECT clause. If you are faceting on attributes with more than 5,000 unique values, a subset of facet values is selected and sorted according to the query type. Note that if a time series chart returns no data (NRQL matches no matching data, invalid NRQL, etc.) then it will only show a flat line with the label matching the first table in the FROM clause.

When selecting min(), max(), percentile(), average() or count(), FACET uses those functions to determine how facets are picked and sorted. When selecting any other function, FACET uses the frequency of the attribute you are faceting on to determine how facets are picked and sorted.

SELECT count(*) FROM PageView FACET city

SELECT uniqueCount(pageUrl) FROM PageView FACET city

FROM Event SELECT average(attribute) FACET name

FROM Event SELECT attribute, name WHERE attribute IS NOT NULL

Use FACET ... AS to name facets using the AS keyword in queries. This clause is helpful for adding clearer or simplified names for facets in your results. It can also be used to rename facets in nested aggregation queries.

FACET ... AS queries will change the facet names in results (when they appear as headers in tables, for example), but not the actual facet names themselves.

FROM Transaction SELECT count(*) FACET response.headers.contentType AS 'content type'

SELECT ...
FACET CASES 
(
  WHERE attribute operator value, 
  WHERE attribute operator value, 
  ...
)
...

Use FACET CASES to break out your data by more complex conditions than possible with FACET. Separate multiple conditions with a comma ,. For example, you could query your PageView data and FACET CASES into categories like less than 1 second, from 1 to 10 seconds, and greater than 10 seconds. You can combine multiple attributes within your cases, and label the cases with the AS selector. Data points will be added to at most one facet case, the first facet case that they match.

You may also use a time function with your attribute, and you can use the OR operator to facet results that don't match any of your specified cases.

SELECT count(*) FROM PageView 
FACET CASES 
(
  WHERE duration < 1, 
  WHERE duration > 1 AND duration < 10, 
  WHERE duration > 10
)

SELECT count(*) FROM Transaction 
FACET CASES 
(
  WHERE name LIKE '%login%', 
  WHERE name LIKE '%feature%' AND customer_type = 'Paid'
)

SELECT count(*) FROM Transaction 
FACET CASES 
(
  WHERE name LIKE '%login%' AS 'Total Logins', 
  WHERE name LIKE '%feature%' AND customer_type='Paid' AS 'Feature Visits from Paid Users'
)

SELECT count(*) FROM Transaction 
FACET CASES 
(
  WHERE name LIKE '%login%', 
  WHERE name LIKE '%feature%' AND customer_type='Paid'
) 
OR name

In NRQL, the default is for the first aggregation in the SELECT clause to guide the selection of facets in a query. FACET ... ORDER BY allows you to override this default behavior by adding an aggregate function with the ORDER BY modifier to specify how facets are selected. Specifically, the clause will override the priority by which facets are chosen to be in the final result before being limited by the LIMIT clause. This clause can be used in querying but not for alerts or streaming.

This example shows how to use FACET ... ORDER BY to find the average durations of app transactions, showing the top 10 (default limit) highest durations by apps which have the highest response size. In this case, if FACET ... ORDER BY is not used, the query results will instead show the top 10 by highest durations, with response size being irrelevant to the app selection.

FROM Transaction SELECT average(duration) TIMESERIES 
FACET appName ORDER BY max(responseSize)

Keep in mind that if you use the FACET ... ORDER BY clause, you can't change the sort order by adding the ASC and DESC modifiers. By default, this clause uses DESC.

Use the JOIN clause to combine data from one event type with the results of a subquery based on a common attribute or key.

FROM Event [INNER|LEFT] JOIN (SELECT... FROM...) ON [key =] key SELECT ...

There are a few simple rules for subquery joins:

• The JOIN clause must always follow immediately after the FROM clause.
• Prefixing a join type (INNER or LEFT) is optional. When omitted, the join type defaults to INNER.
• Parenthesis containing a subquery must immediately follow JOIN.
• The ON clause must immediately follow the subquery.

Restrictions and limitations to consider:

• The joined subquery will continue to have a default LIMIT of 10, with a maximum LIMIT of 5,000. Note that the outer query's LIMIT does not affect the inner query.
• The use of TIMESERIES in the joined subquery is not supported. If your outer query uses TIMESERIES, keep in mind that the joined subquery will provide a single result for the full query timespan.
• Like all subqueries, joined subqueries cannot be used in alert conditions.
• While SELECT * is supported in the parent query, it is not supported in the joined subquery.
• The cardinality of the join is limited to 1:100, meaning a single join key cannot map to more than one hundred rows in the subquery result.

For an in depth look at the JOIN clause, please see the NRQL subquery joins tutorial.

INNER JOIN with a faceted subquery

This query finds the count of events faceted by browserTransactionName from the PageView event type, and then by currentUrl from the PageAction event type. This joins the two event types based on common session attribute values.

FROM PageView
JOIN 
(
  FROM PageAction 
  SELECT count(*) 
  FACET session, currentUrl
) 
ON session
SELECT count(*) FACET browserTransactionName, currentUrl

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LEFT JOIN with a faceted subquery

This example queries the same data as the faceted INNER JOIN example, but as a LEFT JOIN query, the results include items in the PageView table that do not have matching session values in the results of the PageAction subquery.

FROM PageView
LEFT JOIN 
(
  FROM PageAction 
  SELECT count(*) 
  FACET session, currentUrl
) 
ON session
SELECT count(*) FACET browserTransactionName, currentUrl

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INNER JOIN with an unaggregated subquery

Here we are performing an unaggregated, row-wise subquery, with the outer query finding the count of events faceted by currentUrl from the PageAction event type, and then by browserTransactionNamed from the PageView event type. This joins the two event types based on common session attribute values.

Note that the session value 34d5ce6acf4c60be has two browserTransactionName values from the subquery's PageView event type, adding additional rows to the result.

FROM PageAction
LEFT JOIN 
(
  FROM PageView 
  SELECT session, browserTransactionName 
  LIMIT MAX
) 
ON session
SELECT count(*) FACET session, currentUrl, browserTransactionName LIMIT MAX

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SELECT ...
LIMIT count
...

Use the LIMIT clause to control the maximum number of facet values returned by FACET queries or the maximum number of items returned by SELECT * queries. This clause takes a single integer value as an argument. If the LIMIT clause is not specified, or no value is provided, the limit defaults to 10 for FACET queries and 100 in the case of SELECT * queries.

The maximum allowed value for the LIMIT clause is 5,000. Queries can use the LIMIT MAX clause instead of a specific value, which automatically defaults to the current maximum value. You can use this if you always want to post the maximum number of results, even if it changes in the future. If you want your query's behavior to remain unchanged, specify an explicit value instead of using LIMIT MAX.

SELECT uniqueCount(session), percentile(duration, 95)
FROM PageView WHERE userAgentOS = 'Windows'
FACET countryCode LIMIT 20 SINCE YESTERDAY

SELECT ...
LIMIT count OFFSET count
...

Use the OFFSET clause with LIMIT to control the portion of rows returned by SELECT * or SELECT column queries. Like the LIMIT clause, OFFSET takes a single integer value as an argument. OFFSET sets the number of rows to be skipped before the selected rows of your query are returned. This is constrained by LIMIT.

OFFSET rows are skipped starting from the most recent record.

For example, the query SELECT interestingValue FROM Minute_Report LIMIT 5 OFFSET 1 returns the last 5 values from Minute_Report except for the most recent one.

The ORDER BY clause allows you to specify how you want to sort your query results in queries that select event attributes by row.

This query orders two specific transaction attributes by duration.

FROM Transaction SELECT appName, duration ORDER BY duration

The default sort order is ascending, but this can be changed by adding the ASC or DESC modifiers.

This query orders all transaction attributes by duration in descending order.

FROM Transaction SELECT * ORDER BY duration DESC

SHOW EVENT TYPES...

SHOW EVENT TYPES will return a list of all the data types present in your account for a specific time range. It is used as the first clause in a query instead of SELECT.

SHOW EVENT TYPES SINCE 1 day ago

SELECT ...
SINCE [numerical units AGO | phrase]
...

The default value is 1 hour ago.

Use the SINCE clause to define the inclusive beginning of a time range for the returned data. You can specify a time zone for the query but not for the results. NRQL results are based on your system time.

When using NRQL, you can set a UTC timestamp, a relative time, or a DateTime string. See Specifying a time.

See also:

• Setting a query's time range
• UNTIL

The SLIDE BY clause supports a feature known as sliding windows. With sliding windows,SLIDE BY data is gathered into "windows" of time that overlap with each other. These windows can help to smooth out line graphs with a lot of variation in cases where the rolling aggregate (such as a rolling mean) is more important than aggregates from narrow windows of time.

To use SLIDE BY, place it in a query after the TIMESERIES clause. For example, this query pulls data in 5-minute windows with a 1-minute SLIDE BY interval, meaning that each window lasts 5 minutes, but window 1 starts at 0 minutes, window 2 starts at 1 minute, window 3 starts at 2 minutes, and so on.

SELECT average(duration) FROM Transaction TIMESERIES 5 minutes SLIDE BY 1 minute

To learn more about how and when you can use SLIDE BY, see Create smoother charts with sliding windows. Or, watch this short video (approx. 3:20 minutes).

SELECT average(duration) FROM Transaction TIMESERIES 5 minutes SLIDE BY AUTO

SELECT average(duration) FROM Transaction TIMESERIES 5 minutes SLIDE BY MAX

SELECT ...
TIMESERIES integer units
...

Use the TIMESERIES clause to return data as a time series broken out by a specified period of time. Since TIMESERIES is used to trigger certain charts, there is no default value.

To indicate the time range, use integer units. For example:

• TIMESERIES 1 minute
• TIMESERIES 30 minutes
• TIMESERIES 1 hour
• TIMESERIES 30 seconds

TIMESERIES can be combined with arguments such as MAX, AUTO, and SLIDE BY to further tailor query results, as shown in the examples below.

SELECT ... SINCE 1 day AGO TIMESERIES 30 minutes

SELECT average(duration), percentile(duration, 50, 90)
FROM PageView SINCE 1 week AGO TIMESERIES AUTO

SELECT average(duration) FROM Transaction SINCE 1 day ago TIMESERIES MAX

SELECT ...
UNTIL integer units AGO
...

Use the UNTIL clause to define the end of the time range to query. The value is exclusive, meaning the time range will go to the specified instant in time, but not include it.

The default value is NOW. Only use UNTIL to specify an end point other than the default.

See also:

• Setting a query's time range
• SINCE

Use the WHERE clause to filter results. NRQL returns the results that fulfill the condition(s) you specify in the clause.

SELECT function(attribute) ...
WHERE attribute [operator 'value' | IN ('value' [, 'value']) | IS [NOT] NULL ]
[AND|OR ...]
...

• If you specify more than one condition, separate the conditions by the operators AND or OR.

animalType IN ('cat', 'dog', 'fish')

SELECT * FROM PageView WHERE countryCode NOT IN ('CA', 'WA')

SELECT histogram(duration, 50, 20) FROM PageView
WHERE countryCode IN ('CA', 'US') AND userAgentName='Safari' AND pageUrl LIKE '%checkout%'
SINCE 1 day ago

For information on querying metric data, see Query metrics.

FROM ...
WITH function(attribute) AS var
SELECT var
...

Use the WITH ... AS clause to define NRQL variables to store values as variables that can be referenced anywhere in the query. Some rules and tips:

• The WITH ... AS clause can go before, in between, or directly after the FROM or SELECT clause
• Only row-wise functions (for example, capture()) can be set as a variable. Aggregator functions, such as average(), are not supported.
• Only one WITH can be used, but you can use multiple NRQL variables as long as they're separated by a comma.
• If a defined NRQL variable uses the same name as an existing attribute, the variable will take precedence.
• Variable names cannot include the % symbol.

Here are some example queries:

FROM Transaction
WITH duration * 1000 AS millisec
SELECT millisec

FROM Log
WITH aparse(message, '%itemId":"*","unitPrice":*}%') AS (itemId, unitPrice)
SELECT itemId, unitPrice

FROM Log
WITH aparse(message, '%itemId":"*","unitPrice":*}%') AS (itemId, unitPrice),
  numeric(unitPrice) AS unitPriceNum
SELECT sum(unitPriceNum)
FACET itemId
WHERE unitPriceNum < 100

SELECT ... WITH TIMEZONE (selected zone)
...

Use the WITH TIMEZONE clause to select a time zone for a date or time in the query that hasn't already had a time zone specified for it.

If you include the WITH TIMEZONE clause without specifying a time zone in a date time, the since and until clauses keep in the provided time zone.

If you don't include the WITH TIMEZONE clause, but you include a time zone in a date time string, your date time string time zone keeps.

For example, the query clause SINCE Monday UNTIL Tuesday WITH TIMEZONE 'America/New_York' returns data recorded from Monday at midnight, America/New York time, until midnight Tuesday, America/New York time.

Here are some examples of query timespan clauses:

• No time zone in date time string using the WITH TIMEZONE clause:

  SINCE today UNTIL '2022-05-19T12:00' WITH TIMEZONE 'America/Los_Angeles'

  コピー

  This resolves as "beginTime": "2022-05-19T07:00:00Z" and "endTime": "2022-05-19T19:00:00Z".

• Time zone in date time string, not using the WITH TIMEZONE clause:

  SINCE today UNTIL '2022-05-19T12:00-0500'

  コピー

  This resolves as "beginTime": "2022-05-19T00:00:00Z" and "endTime": "2022-05-19T17:00:00Z".

• Time zone in date time string, using the WITH TIMEZONE clause America/Los Angeles, which is -0700 during daylight saving time:

  SINCE today UNTIL '2022-05-19T12:00-0500' WITH TIMEZONE 'America/Los_Angeles'

  コピー

  This resolves as "beginTime": "2022-05-19T07:00:00Z" and "endTime": "2022-05-19T19:00:00Z".

See the available Zone IDs list.

See Set time range on dashboards and charts for detailed information and examples.

Use the aggregationendtime() function to return the time of the relevant aggregation. More specifically, for a given aggregate, the aggregationendtime() function provides the timestamp of the end of the time period of that aggregation. For example, in a time series query, for a data point that encompasses an hour’s worth of data, the function would return the timestamp of the end of that hour period.

Use the apdex function to return an Apdex score for a single transaction or for all your transactions. The default Apdex score is 0.5 seconds. The attribute can be any attribute based on response time, such as duration or backendDuration. The t: argument defines an Apdex T threshold in the same unit of time as the chosen attribute. For instance, if the attribute is measured in seconds, t will be a threshold in seconds.

The Apdex score returned by the apdex() function is based only on execution time. It does not account for APM errors. If a transaction includes an error but completes in Apdex T or less, that transaction will be rated satisfying by the apdex () function.

Get Apdex for specific customers

If you have defined custom attributes, you can filter based on those attributes. For example, you could monitor the Apdex for a particularly important customer:

SELECT apdex(duration, t: 0.4) FROM Transaction
WHERE customerName='ReallyImportantCustomer' SINCE 1 day ago

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Get Apdex for specific transaction

Use the name attribute to return a score for a specific transaction, or return an overall Apdex by omitting name. This query returns an Apdex score for the Controller/notes/index transaction over the last hour:

SELECT apdex(duration, t: 0.5) from Transaction
WHERE name='Controller/notes/index' SINCE 1 hour ago

コピー

Get overall Apdex for your app

This example query returns an overall Apdex for the application over the last three weeks:

SELECT apdex(duration, t: 0.08) FROM Transaction SINCE 3 week ago

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Use the average() function to return the mean average value for an attribute. It takes a single attribute name as an argument. If a value of the attribute is not numeric, it will be ignored when aggregating. If data matching the query's conditions is not found, or there are no numeric values returned by the query, it will return a value of null.

The bucketPercentile() function is the NRQL equivalent of the histogram_quantile function in Prometheus. It is intended to be used with dimensional metric data. Instead of the quantile, New Relic returns the percentile, which is the quantile * 100.

Use the bucketPercentile() function to calculate the quantile from the histogram data in a Prometheus format.

It takes the bucket name as an argument and reports percentiles along the bucket's boundaries:

SELECT bucketPercentile(duration_bucket) FROM Metric SINCE 1 day ago

Optionally, you can add percentile specifications as an argument:

SELECT bucketPercentile(duration_bucket, 50, 75, 90) FROM Metric SINCE 1 day ago

Because multiple metrics are used to make up Prometheus histogram data, you must query for specific Prometheus metrics in terms of the associated <basename>.

For example, to compute percentiles from a Prometheus histogram, with the <basename> prometheus_http_request_duration_seconds using NRQL, use bucketPercentile(prometheus_http_request_duration_seconds_bucket, 50). Note how _bucket is added to the end of the <basename> as a suffix.

See the Prometheus.io documentation for more information.

Use the cardinality() function to obtain the number of combinations of all the dimensions (attributes) on a metric.

It takes three arguments, all optional:

• Metric name: if present, cardinality() only computes the metric specified.
• Include: if present, the include list restricts the cardinality computation to those attributes.
• Exclude: if present, the exclude list causes those attributes to be ignored in the cardinality computation.

cdfPercentage() is an implementation of the cumulative distribution function, returning percentages of attribute values whose value is less than or equal to one or more thresholds.

cdfPercentage() aggregates on its attribute argument, which can be either a numeric attribute or a distribution metric attribute. Mixed types in one query are accepted. Other types (such as string) are ignored. Up to 10 thresholds can be specified.

FROM PageView SELECT cdfPercentage(firstPaint, 0.5, 1.0)

Use the count() function to return a count of available records. It takes a single argument; either *, an attribute, or a constant value. Currently, it follows typical SQL behavior and counts all records that have values for its argument.

Since count(*) does not name a specific attribute, the results will be formatted in the default "humanize" format.

derivative() finds the rate of change for a given dataset. The rate of change is calculated using a linear least-squares regression to approximate the derivative. Since this calculation requires comparing more than one datapoint, if only one datapoint is included in the evaluation range, the calculation is indeterminate and won't work, resulting in a null value.

The time interval is the period for which the rate of change is calculated. For example, derivative(attributeName, 1 minute) will return the rate of change per minute.

Use the earliest() function to return the earliest value for an attribute over the specified time range.

It takes a single argument.

If used in conjunction with a FACET it will return the most recent value for an attribute for each of the resulting facets.

SELECT earliest(countryCode) FROM PageView FACET userAgentName

Use the filter() function to limit the results for one of the aggregator functions in your SELECT statement. You can use filter() in conjunction with FACET or TIMESERIES. Filter is only useful when selecting multiple different aggregations such as:

SELECT filter(sum(x), WHERE attribute='a') AS 'A',
  filter(sum(x), WHERE attribute='b') AS 'B' ...

Otherwise, it's better to just use the standard WHERE clause.

Analyze purchases that used offer codes

You could use filter() to compare the items bought in a set of transactions for those using an offer code versus those who aren't:

Use the funnel() function to generate a funnel chart. It takes an attribute as its first argument. You then specify steps as WHERE clauses (with optional AS clauses for labels) separated by commas.

For details and examples, see the funnels documentation.

Use the histogram() function to generate histograms, which are useful for visualizing the distribution of a dataset. It divides the dataset into a specified number of buckets and counts the number of data points that fall into each bucket.

Arguments:

• attribute The first argument is required and specifies the attribute for which to count values that fall within each histogram bucket range.

• width: Inidicates the width of the sample range. The maximum value of the range is the start argument value plus this width value.

  • When using positional (non-labeled) arguments, width is the second argument.
  • Default: 10

• buckets: Total number of buckets (between 1 and 500, inclusive).

  • When using positional (non-labeled) arguments, buckets is the third argument.
  • Default: 40

• start: The beginning of the histogram range.

  • When using positional (non-labeled) arguments, start is the fourth argument.
  • Default: 0

  Note

  Values that fall outside the defined histogram range are included in the first or last buckets. The first bucket count will include items that are smaller than the histogram range, and the last bucket count will include items that are larger than the histogram range. To exclude these values from the histogram results, include a filter in the query's where clause. (Example: WHERE attribute >= [start] AND attribute <= [start + width])

  Histogram of response times from PageView events

  This query results in a histogram of response times ranging up to 10 seconds over 40 buckets. This means each bucket covers a 0.25 second range of values. (10 / 40 = 0.25). Any duration values larger than 10 seconds are included in the last bucket. If duration could be less than zero, those values would be included in the first bucket.

  SELECT histogram(duration) FROM PageView SINCE 1 week ago

  コピー

  Histogram with a width: 5, buckets: 10

  These equivalent queries result in a histogram of response times ranging up to 5 seconds over 10 buckets.

  SELECT histogram(duration, 5, 10) FROM PageView SINCE 1 week ago

  コピー

  SELECT histogram(duration, width: 5, buckets: 10) FROM PageView SINCE 1 week ago

  コピー

  Histogram with width: 3, buckets: 3, start: 1

  These equivalent queries result in a histogram of response times ranging from 1 to 4 seconds over 3 buckets.

  Here is a breakdown of the buckets:

  	Bucket 1	Bucket 2	Bucket 3
  Bucket range	1 to 2	2 to 3	3 to 4
  Values counted	< 2	≥ 2 and < 3	≥ 3

  SELECT histogram(duration, 3, 3, 1) 
  FROM PageView SINCE 1 week ago

  コピー

  SELECT histogram(duration, width: 3, buckets: 3, start: 1) 
  FROM PageView SINCE 1 week ago

  コピー

  Prometheus histogram buckets

  histogram() accepts Prometheus histogram buckets:

  SELECT histogram(duration_bucket, 10, 20) 
  FROM Metric SINCE 1 week ago

  コピー

  New Relic distribution metric

  histogram() accepts Distribution metric as an input:

  SELECT histogram(myDistributionMetric, 10, 20) 
  FROM Metric SINCE 1 week ago

  コピー

  Histogram with a FACET clause

  Use histogram() with a FACET clause to generate a heatmap chart:

  SELECT histogram(duration) 
  FROM PageView FACET appName SINCE 1 week ago

  コピー

Using keyset() will allow you to see all of the attributes for a given data type over a given time range. It takes no arguments. It returns a JSON structure containing groups of string-typed keys, numeric-typed keys, boolean-typed keys, and all keys.

SELECT keyset() FROM PageView SINCE 1 day ago

Use the latest() function to return the most recent value for an attribute over a specified time range.

It takes a single argument.

If used in conjunction with a FACET it will return the most recent value for an attribute for each of the resulting facets.

SELECT latest(countryCode) FROM PageView FACET userAgentName

Use the latestrate() function to return the rate of change of a value based on the last 2 data points. It takes the attribute in question as the first argument and the unit of time for the resulting rate as the second argument. The function returns a result in units of change in attribute/time interval.

This function can be useful to provide the most recent rate of change for an attribute in order to see leading-edge trends.

SELECT latestrate(duration, 1 SECOND) FROM PageView

Use the max() function to return the maximum recorded value of a numeric attribute over the time range specified. It takes a single attribute name as an argument. If a value of the attribute is not numeric, it will be ignored when aggregating. If data matching the query's conditions is not found, or there are no numeric values returned by the query, it will return a value of null.

Use the median() function to return an attribute's median, or 50th percentile. For more information about percentile queries, see percentile().

SELECT median(duration) FROM PageView TIMESERIES AUTO

Median in a JOIN clause:

• Because median is simply a shortcut for percentile(attribute, 50), a median() result from a joined subquery is a compound data type, which maps the 50th percentile to its calculated value.

  To reference the actual median value in the outer query, you can use the getField() function. Note, the mapped key is a string representation of a double value, so for median() it is '50.0'.

  Joined median query

  FROM PageView
  JOIN (FROM PageAction SELECT median(timeSinceLoad) FACET session, currentUrl) ON session
  SELECT latest(getField(median, '50.0')) AS median
  FACET browserTransactionName, currentUrl

  コピー

  

Use the min() function to return the minimum recorded value of a numeric attribute over the time range specified. It takes a single attribute name as an argument. If a value of the attribute is not numeric, it will be ignored when aggregating. If data matching the query's conditions is not found, or there are no numeric values returned by the query, it will return a value of null.

Use the percentage() function to return the percentage of a target data set that matches some condition.

This function expects exactly two arguments (arguments after the first two are ignored). The first argument requires an aggregator function against the desired attribute. If the attribute is not numeric, this function returns a value of 100%. The second argument requires a WHERE clause.

FROM Transaction 
SELECT percentage(count(*), WHERE error is true ) AS 'Error Percent' 
WHERE host LIKE '%west%' EXTRAPOLATE

Use the percentile() function to return an attribute's approximate value at a given percentile. It requires an attribute and can take any number of arguments representing percentile points. The percentile() function enables percentiles to displays with up to three digits after the decimal point, providing greater precision. Percentile thresholds may be specified as decimal values, but be aware that for most data sets, percentiles closer than 0.1 from each other will not be resolved.

Use TIMESERIES to generate a line chart with percentiles mapped over time.

• Omit TIMESERIES to generate a billboard and attribute sheet showing aggregate values for the percentiles.

  If no percentiles are listed, the default is the 95th percentile. To return only the 50th percentile value, the median, you can also use median().

  Basic percentile query

  This query will generate a line chart with lines for the 5th, 50th, and 95th percentile.

  SELECT percentile(duration, 5, 50, 95) FROM PageView TIMESERIES AUTO

  コピー

  Percentile in a JOIN clause:

• When using percentiles in a joined subquery, please note the results from the subquery are a compound data type, which maps each percentile to its calculated value.

  To reference any of the individual percentile values in the outer query, you can use the getField() function. Note, the mapped key is a string representation of a double value, so you need to add .0 to integers. For example, the key for the 95th percentile is '95.0'.

  Joined percentile query

  FROM PageView
  JOIN 
  (
    FROM PageAction 
    SELECT percentile(timeSinceLoad, 95, 99.5) AS pctl
    FACET session, currentUrl
  ) 
  ON session
  SELECT latest(getField(pctl, '95.0')) AS `95th`, 
    latest(getField(pctl, '99.5')) AS `99.5th`
  FACET browserTransactionName, currentUrl

  コピー

  

predictLinear() is an extension of the derivative() function. It uses a similar method of least-squares linear regression to predict the future values for a dataset.

• The time interval is how far the query will look into the future. For example, predictLinear(attributeName, 1 hour) is a linear prediction 1 hour into the future of the query time window.
• Generally, predictLinear() is helpful for continuously growing values like disk space, or predictions on large trends.
• Since predictLinear() is a linear regression, familiarity with the dataset being queried helps to ensure accurate long-term predictions.
• Any dataset which grows exponentially, logarithmically, or by other nonlinear means will likely only be successful in very short-term predictions.
• New Relic recommends against using predictLinear in TIMESERIES queries. This is because each bucket will be making an individual prediction based on its relative timeframe within the query, meaning that such queries will not show predictions from the end of the time series forward.

Use the rate() function to visualize the frequency or rate of a given query per time interval. For example, you might want to know the number of pageviews per minute over an hour-long period or the count of unique sessions on your site per hour over a day-long period.

• Use TIMESERIES to generate a line chart with rates mapped over time.

• Omit TIMESERIES to generate a billboard showing a single rate value averaged over time.

  Here's a basic query that will generate a line chart showing the rate of throughput for APM transactions per 10 minutes over the past 6 hours:

  SELECT rate(count(*), 10 minute) FROM Transaction 
  SINCE 6 hours ago TIMESERIES

  コピー

  Here's a short video (3:21 minutes) explaining how to use rate to compare data across different time windows:

Use the stdvar() function to return the standard variance for a numeric attribute over the time range specified.

It takes a single argument. If the attribute is not numeric, it will return a value of zero.

Use the sum() function to return the sum recorded values of a numeric attribute over the time range specified.

It takes a single argument. If the attribute is not numeric, it will return a value of zero.

Use the uniqueCount() function to return the number of unique values recorded for an attribute over the time range specified.

Use the uniques() function to return a list of unique values recorded for an attribute over the time range specified. When used along with the facet clause, a list of unique attribute values will be returned per each facet value.

The limit parameter is optional. When it is not provided, the default limit of 1,000 unique attribute values per facet is applied. You may specify a different limit value, up to a maximum of 10,000. The uniques() function will return the first set of unique attribute values discovered, until the limit is reached. Therefore, if you have 5,000 unique attribute values in your data set, and the limit is set to 1,000, the operator will return the first 1,000 unique values that it discovers, regardless of their frequency.

The maximum number of values that can be returned in a query result is the product of the uniques() limit times the facet limit. In the following query, the theoretical maximum number of values that can be returned is 5 million (5,000 x 1,000).

Depending on the data set being queried, and the complexity of the query, memory protection limits may prevent a very large query from being executed.

From Transaction SELECT uniques(host,5000) FACET appName LIMIT 1000

FROM NodeStatus SELECT uniques(tuple(index, cellName), 5)

Use the accountId() function to return the account ID associated with queried data. This function takes no arguments. Here are some example queries:

SELECT accountId() FROM Transaction SINCE 1 day ago

SELECT count(*) FROM Transaction FACET accountId() SINCE 1 day ago

SELECT count(*) FROM Transaction WHERE accountId() IN (1,2,3) SINCE 1 day ago

Use the anchor parse function, aparse() to extract specific values from a string. This is an alternative to capture().

aparse() takes two arguments:

• A string attribute

• A pattern string with anchor strings and extract characters. For example, you could use www.*.com to extract the domain from a URL.

  When using aparse(), the pattern string should contain anchors, like www. and .com above, to identify the location of the intended extracted string, noted by *.

  aparse() uses the following characters in pattern strings:

• %: Non-capturing wildcard, as you'd see in the LIKE clause

• *: Capturing wildcard, similar to using regex capture

  In practice, the anchor strings often occur in the middle of a string attribute, and not at the beginning or end.

  In this case, use the % wildcard to ignore unwanted values: for example, %www.*.com%.

  Like capture(), all results from aparse() are strings. To use these results in math functions they must cast with the numeric() function.

  Note: aparse() is case-insensitive.

  Basic use

  FROM PageView
  SELECT aparse(browserTransactionName, 'website.com/*')

  コピー

  aparse() specific values

  To extract a value from the middle of a string, use the non-capturing wildcard, %, at the beginning and end of the pattern string. For example:

  FROM Log
  SELECT count(*)
  FACET aparse(string, '%"itemId":"*"%')

  コピー

  aparse() multiple values

  When extracting multiple values as variables, note that the order matters. For example:

  FROM Log
  WITH aparse(string, 'POST: * body: {"itemId":"*","unitPrice":*}\n') AS (url, itemId, unitPrice)
  SELECT url, itemId, unitPrice

  コピー

  For more on variables, see NRQL variables.

Use the blob() function on a blob type attribute to return a base-64 encoded string of that attribute.

This function has the following restrictions:

• Queries containing calls to blob() have a max LIMIT value of 20

• blob() cannot be called in the WHERE clause of a query

• blob() cannot be used in faceted queries or time series queries

  For more information about how this is used in Logging, see Find data in long logs (blobs).

  To decode a base-64 encoded blob, see the decode() function.

  Using blob() on extended logs

  SELECT message, blob(`newrelic.ext.message`) 
  FROM Log WHERE newrelic.ext.message IS NOT NULL

  コピー

Use the buckets() function to aggregate data split up by a FACET clause into buckets based on ranges. You can bucket by any attribute that is stored as a numerical value in the New Relic database.

It takes three arguments:

• Attribute name

• Maximum value of the sample range (any outliers will appear in the final bucket)

• Total number of buckets

  For more information and examples, see Split your data into buckets.

Use the concat() function to return the string resulting from concatenating its arguments.

Up to 20 arguments of numeric, boolean, tuple, or array types may be provided. Null arguments and arguments of unsupported types are ignored. If no arguments are provided, the result is the empty string.

The optional precision argument may be provided in order to limit the number of decimal places included when concatenating floating-point numeric values.

The resulting string may have a maximum length of 4096 characters.

FROM PageView 
SELECT concat('Backend Duration: ', backendDuration, ', Network Duration: ', networkDuration, precision: 2)

FROM PageView SELECT average(connectionSetupDuration) 
FACET concat(city, ', ', regionCode, ' ', countryCode) 
WHERE countryCode IN ('US', 'CA')

Use the convert() function to perform unit conversion between the provided units on the given input value.

Common units and abbreviations for time, length, weight, volume, and data are supported using the UCUM standards to align with OpenTelemetry specifications. For convenience, the standardized abbreviations are augmented by some natural language alternatives like ft in addition to ft_us, kilobytes, and µs.

The units are case sensitive. All units are lowercase, unless their specification requires them to be uppercase. For example, the data units 'bits' is valid for bits and 'By' must have a capital B for bytes.

The largest unit of time is the Julian year, which is always 365.25 days.

FROM Transaction SELECT convert(duration, 'ms', 'min') AS durationMin

FROM Product SELECT convert(sum(itemWeight), 'grams', 'lbs')

FROM Metric 
SELECT average(convert(apm.mobile.external.duration, unit, 's')) 
WHERE appName = 'my-application'

Use capture() to extract values from an attribute using a regular expression with RE2 syntax.

It takes two arguments:

• Attribute name

• Regular expression with capture syntax (regex expressions in NRQL use Python-like syntax, r'...')

  When capturing, use the RE2 named-capture syntax ...(?P<name> pattern )... to capture the contained pattern, given the specified name.

  Multiple values can be captured by specifying additional capture groups in a regular expression. For example: ...(?P<name1> pattern1)...(?P<name2> pattern2)...

  Note: When capturing multiple values, each capture statement can have up to 16 capture groups, and each NRQL query can have up to 5 capture statements.

  Read how to use regex capture to improve your query results.

  ヒント

  The regular expression must match its entire input. If a capture expression is not extracting the expected results, check whether it needs .* at the beginning or end, which is the pattern for a partial match regex. However, the partial match regex may cause a slower query execution.

  Here's a short video (3:05 minutes) showing how to use capture() to improve dashboard readability:

  For more information, see the examples below:

  capture() within a SELECT clause condition

  The following will select the domain name of the website, removing https:// and any paths following the .com

  SELECT capture(pageUrl, r'https://(?P<baseUrl>.*.com)/.+') 
  FROM PageView SINCE 1 day ago

  コピー

  The following will capture only the first word of the error message.

  SELECT capture(errorMessage, r'(?P<firstWord>\S+)\s.+') 
  FROM Transaction 
  WHERE errorMessage IS NOT NULL
  SINCE 1 hour ago

  コピー

  capture() within a FACET clause condition

  The following will facet by the captured HTTP method.

  SELECT count(*) FROM Log 
  WHERE message LIKE '%HTTP%' 
  FACET capture(message, r'.* "(?P<httpMethod>[A-Z]+) .*')

  コピー

  capture() within a WHERE clause condition

  The following will filter the results based on Log events with message attribute that matches the regular expression where the captured job name is ExampleJob.

  SELECT message FROM Log 
  WHERE capture(message, r'.*Job Failed: (?P<jobName>[A-Za-z]+),.*') = 'ExampleJob' 
  SINCE 10 minutes ago

  コピー

  capture() with a numeric cast

  The following will capture sum of CPU Time from log lines. You must explicitly cast to numeric to do mathematical operations.

  SELECT sum(numeric(capture(message, r'.*CpuTime:\s(?P<cpuTime>\d+)'))) 
  FROM Log 
  WHERE message LIKE '%CpuTime:%' SINCE 1 hour ago

  コピー

  capture() multiple values with NRQL Variables

  In this example NRQL Variables are used to store multiple captured values from a log message.

  FROM Log
  WITH capture(message, r'POST to carts: (?P<URL>.*) body: {"itemId":"(?P<UUID>.*)","unitPrice":(?P<unitPrice>.*)}.*')
    AS (URL, UUID, unitPrice)
  SELECT URL, UUID, unitPrice
  WHERE URL IS NOT NULL

  コピー

  See more on NRQL Variables here.

Use decode() to perform base-64 conversions on strings and blobs. The input value (the first argument) will be decoded using the base-64 standard specified by the encoding (the second argument).

The following string values are supported encoding parameters:

• 'base64': Uses the RFC4648 base-64 standard

• 'base64mime': Uses the RFC2045 base-64 standard (MIME)

• 'base64url': Uses the RFC4648 base-64 standard with URL and filename safe alphabet

  As blob() is not allowed in WHERE or FACET clauses, decode() with blob types is not supported in the WHERE clause or for faceted queries.

  To encode strings, see the encode() function.

  Using decode() on a string attribute

  FROM Span SELECT entity.guid, decode(entity.guid, 'base64') 
  WHERE entity.guid IS NOT NULL

  コピー

  Using decode() in a FACET

  FROM Span SELECT count(*) 
  WHERE entity.guid IS NOT NULL 
  FACET entity.guid, decode(entity.guid, 'base64')

  コピー

  Using decode() in a WHERE clause

  FROM Span SELECT count(*) 
  WHERE entity.guid IS NOT NULL 
  AND decode(entity.guid, 'base64') NOT LIKE '%APM%'

  コピー

  Using decode() with a blob type attribute

  FROM Log
  WITH blob(`newrelic.ext.message`) AS encodedBlob,
  decode(encodedBlob, 'base64') AS decodedBlob
  SELECT encodedBlob, decodedBlob
  WHERE newrelic.ext.message IS NOT NULL
  LIMIT 10

  コピー

Use the dimensions() function to return all the dimensional values on a data type.

You can explicitly include or exclude specific attributes using the optional arguments:

• include: if present, the include list limits dimensions() to those attributes.

• exclude: if present, the dimensions() calculation ignores those attributes.

  FROM Metric SELECT count(node_filesystem_size) 
  TIMESERIES FACET dimensions()

  コピー

  When used with a FACET clause, dimensions() produces a unique time series for all facets available on the event type, similar to how Prometheus behaves with non-aggregated queries.

Use encode() to perform base-64 conversions on strings. The input value (the first argument) will be encoded using the base-64 standard specified by the encoding (the second argument).

The following string values are supported encoding parameters:

• 'base64': Uses the RFC4648 base-64 standard

• 'base64mime': Uses the RFC2045 base-64 standard (MIME)

• 'base64url': Uses the RFC4648 base-64 standard with URL and filename safe alphabet

  To decode strings or blobs, see the decode() function. encode() is not supported for blobs.

  Using encode() on an attribute

  FROM PageView SELECT session, encode(session, 'base64')

  コピー

Use the cidrAddress() function to obtain the base network address from a CIDR IP address.

cidrAddress() takes the following arguments:

• attribute - A string value that contains either an IP address on its own or with a prefix length in CIDR notation.

  • This can be a string attribute or a string literal in quotes.
  • The IP address must be an IPv4 address.

• number - An integer value which represents the prefix length.

  • This can be an integer attribute or an integer value.
  • If the attribute parameter is in CIDR notation this parameter is optional and takes precedence over the prefix length provided in the CIDR String.

• cidrFormat - An optional boolean value that is used to determine if the network address output should be formatted in CIDR notation. This will default to true.

  The cidrAddress() function will return a value as long as the attribute and number parameters contain a valid IP address and prefix length. If the parameter input is invalid, cidrAddress() will return null.

  Find which subnets are processing the most requests

  The following query returns the subnets which are processing the most requests from the SyntheticRequest event type.

  FROM SyntheticRequest SELECT count(*) FACET cidrAddress(serverIPAddress, 24)

  コピー

  This would return responses in a format like:

  Cidr Address of Server IPAddress	Count
  10.0.0.0/24	6k
  10.10.1.0/24	4k
  10.0.14.0/24	1k

  Query all IP addresses that exist in a CIDR range

  This query returns all IP addresses in the serverIPAddress attribute that exist within the CIDR range of 10.0.0.0 to 10.0.0.255.

  FROM SyntheticRequest SELECT uniques(serverIPAddress) 
  WHERE cidrAddress(serverIPAddress, 24) = '10.0.0.0/24'

  コピー

  Filter IP addresses that exist in a specific CIDR range from query results

  This query returns a count of all records while excluding records which contain an serverIPAddress value that falls into the CIDR range of 10.0.0.0/24 or 10.10.1.0/24.

  FROM SyntheticRequest SELECT count(*) 
  WHERE cidrAddress(serverIPAddress, 24) NOT IN ('10.0.0.0/24', '10.10.1.0/24')

  コピー

...WHERE eventType() = 'EventNameHere'...
...FACET eventType()...

Use the eventType() function in a FACET clause to break out results by the selected data type or in a WHERE clause to filter results to a specific data type. This is particularly useful for targeting specific data types with the filter() and percentage() functions.

SELECT 100 * filter(count(*), WHERE eventType() = 'TransactionError') / filter(count(*), where eventType() = 'Transaction') 
FROM Transaction, TransactionError 
WHERE appName = 'App.Prod' 
TIMESERIES 2 Minutes SINCE 6 hours ago

SELECT count(*) FROM Transaction, TransactionError FACET eventType() TIMESERIES

Use the getField() function to extract a field from compound data types, such as dimensional metric data.

It takes the following arguments:

Examples:

SELECT max(getField(mySummary, count)) FROM Metric

SELECT sum(mySummary) FROM Metric where getField(mySummary, count) > 10

getCdfCount() is an implementation of the cumulative distribution function, returning the number of values in attribute at or below threshold.

Only one threshold is allowed. Attribute may be either a numeric attribute or a distribution metric attribute. Mixed types in one query are accepted.

For a numeric type, it returns 1 if the attribute is less than or equal to threshold, otherwise it returns 0. For a distribution, it returns the count in the dataset represented by the distribution. For all other types, it returns 0.

FROM PageView SELECT sum(getCdfCount(firstPaint, 1.0))

Use if() to perform if-then-else control flow operations throughout a query.

if() takes 3 arguments:

• condition - an expression that can evaluate to true or false

• trueValue - this value is returned if boolean expression is true

• falseValue - this optional value is returned if boolean expression is false, or if not provided NULL, is returned

  Basic use

  FROM Log
  SELECT count(*)
  FACET if(level_name = 'ERROR', 'ERROR', 'NOT_ERROR')

  コピー

  Use with AND and OR

  FROM Log
  SELECT count(*)
  FACET if(level_name = 'INFO' OR level_name = 'WARNING', 'NOT_ERROR', 'ERROR')

  コピー

  Nested If()

  Use a nested if() function to add additional conditional logic.

  FROM Transaction SELECT count(*)
    FACET if(appName LIKE '%java%', 'Java',
    if(appName LIKE '%kafka%', 'Kafka', 'Other'))

  コピー

Use the JSON parse function, jparse(), to parse a string value and produce a map/list of values (or nested structures) which can be handled like any other first-class value type in NRQL.

jparse() takes two arguments:

• attribute - A JSON string value.

• path - An optional string value that is used to directly reference a particular piece of the JSON within the attribute parameter. See the JSON Parse Path Syntax Reference section below.

  The jparse() function follows the RFC 8259 format to parse JSON values. When the jparse() function is used without the path parameter, it will return the deserialized JSON value.

  You can use square brackets to pull out individual values from a jparse() result via a key/index, and map JSON keys directly to attributes using the WITH clause.

  Referencing a key/index using the square-bracket syntax

  Referencing a key

  The following query references the key userNames within the jsonString attribute and will return ['abc', 'xyz'].

  WITH '{"userNames": ["abc", "xyz"]}' AS jsonString SELECT jparse(jsonString)[userNames]

  コピー

  Referencing an index

  The following query references index 0 within the jsonString attribute and will return 'abc'.

  WITH '["abc", "xyz"]' AS jsonString SELECT jparse(jsonString)[0]

  コピー

  Mapping JSON keys to attributes

  The following query uses jparse() in the WITH clause to map the JSON keys userName and id into NRQL variables so they can be used in the rest of the query.

  WITH '{"userName": "test", "unused": null, "id": 100}' AS jsonString, jparse(jsonString) AS (userName, id) SELECT userName, id

  コピー

  To parse specific values from the JSON string, you can use the path parameter.

  JSON Parse Path Syntax Reference

  It's common for JSON data to be nested in several layers in non-trivial shapes. The path syntax allows you to directly reference a particular piece of the JSON data.

  Example data:

  {
    "valueA": "test",
    "valueB": {
      "nestedValue1": [1, 2, 3],
      "nestedValue2": 100
    },
    "valueC": [
      { "id": 1, "label": "A", "other": 7 },
      { "id": 2, "label": "B", "other": 9 },
      { "id": 3, "label": "C", "other": 13 }
    ]
  }

  コピー

  Path syntax examples using the data above:

  Path Syntax Example	Result Description	Result
  valueA	Returns value at key	"test"
  ["valueA"]	Returns value at key	"test"
  [valueA, valueC]	Returns list of key values	["test", [{"id": 1…}, {"id": 2…}], {"id": 3…}]]
  valueB.nestedValue2	Returns value at key	100
  valueC[0]	Returns the list value at index 0	{"id": 1…}
  valueC[0,2]	Returns the list values at index 0 and 2	[{"id": 1…}, {"id": 3…}]
  valueC[0:2]	Returns the range of list values from the first index to second, excluding the value at the second index. In this case, the list values at index 0 and 1 are returned.	[{"id": 1…}, {"id": 2…}]
  valueC[:2]	Returns the range of list values from the beginning to the second index, excluding the value at the second index. In this case, the list values at index 0 and 1 are returned.	[{"id": 1…}, {"id": 2…}]
  valueC[:-2]	Returns all list values except the last n, where n is a negative number after the colon(i.e [:-n]). In this case, the list value at index 0 will be returned.	[{"id": 1…}]
  valueC[1:]	Returns the range of list values from the index specified to the end of the list. In this case, the list values at index 1 and 2 are returned.	[{"id": 2…}, {"id": 3…}]
  valueC[-1:]	Returns the last n list values, where n is a negative number before the colon(for example, [-n:]). In this case the list value at index 2 will be returned.	[{"id": 3…}]
  valueC[*]	Returns all list values	[{"id": 1…}, {"id": 2…}, {"id": 3…}]
  valueC[*].id	Returns the specified key value from all list members. In this case, the id key value.	[1, 2, 3]
  valueC[*]["label", "other"]	Returns the specified keys from all list members. In this case, the label and other key values.	[["A", 7],…]

  Examples:

  Basic Example

  The following query parses the JSON string within the jsonString attribute.

  WITH '{"user": {"name": "John", "id": 5}}' AS jsonString SELECT jparse(jsonString)

  コピー

  This query will return the deserialized JSON string:

  { "user": { "name": "John", "id": 5 } }

  コピー

  Parse a specific value from a log message

  A common problem is having rich and structured data hiding within a log message. You can leverage aparse() and jparse() to trim away noise and find specific values.

  The following query:

  1. Calls aparse() to extract JSON data from the the logMessage attribute

  2. Parses the user.name field from the extracted JSON data using jparse() and the user.name path parameter.

     WITH '1693242121842: value=\'{"user": {"name": "John", "id": 5}}\', useless=stuff' AS logMessage, aparse(logMessage, '%: value=\'*\'%') AS jsonString SELECT jparse(jsonString, 'user.name')

     コピー

  Parse multiple nested values from a JSON string

  The following query parses each id field from the list of objects within the jsonString attribute and outputs these values as an array.

  WITH '{"users": [{"name": "A", "id": 5}, {"name": "B", "id": 10}]}' AS jsonString, jparse(jsonString, 'users[*].id') AS ids SELECT ids

  コピー

  The above query will return [5, 10].

  Related functions: mapKeys(), mapValues()