NRQL syntax, clauses, and functions

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.

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 hour compared to the same range one week ago. First as a single value, then as a line chart.

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

SELECT percentile(duration) 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.

  Important

  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

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  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

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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 2,000 unique values, a subset of facet values is selected and sorted according to the query type.

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

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)

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 2,000.

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 transactions 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.

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 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 or a relative time range:

• Timestamps use the format YYYY-MM-DD HH:MM:SS ZZZZ. For instance, FROM Transaction SELECT count(*) SINCE '2021-12-25 00:00:00 +0000' UNTIL '2021-12-25 23:59:59 +0000'.
• We support the following relative time ranges: YESTERDAY, TODAY, SUNDAY, MONDAY, TUESDAY, WEDNESDAY, THURSDAY, FRIDAY, SATURDAY. For example, SINCE YESTERDAY UNTIL NOW.
• We also support YEAR, QUARTER, MONTH, WEEK, DAY, HOUR, MINUTE, SECOND. For these cases, you can combine SINCE with THIS or LAST. For instance, SINCE LAST MONTH UNTIL THIS WEEK. You can also include AGO, as in SINCE 3 WEEKS AGO UNTIL 10 MINUTES AGO.

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
  ...

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

Use the UNTIL clause to define the end of a time range across which to return data. Once a time range has been specified, the data will be preserved and can be reviewed after the time range has ended.

See Use the time picker to adjust time settings for detailed information and examples.

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.
• If you want to simulate a SQL join, use custom attributes in a WHERE or FACET clause.

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.

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

By default, query results are displayed in the time zone of the browser you're using.

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.

For example, the query clause SINCE Monday UNTIL Tuesday WITH TIMEZONE 'America/New_York' will return data recorded from Monday at midnight, Eastern Standard Time, until midnight Tuesday, Eastern Standard Time.

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 timeseries 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 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

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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 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.

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.

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.

  SELECT cardinality(metric_name, include:{attribute_list}, exclude:{attribute_list})

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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 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()

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

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

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 minuteOf() function to extract only the minute portion (that is, seconds 0 to 59) of an attribute holding a valid timestamp value.

Use the mod() function to return the floor modulus after dividing the value of the provided numeric attribute (the first argument, or dividend) by a numeric value (the second argument, or divisor). This modulo operation can be used within a WHERE clause condition to filter to an arbitrary subset of results or within a FACET clause as a way to subdivide the result set.

FROM Transaction SELECT * WHERE mod(port, 2) = 1

FROM NrDailyUsage SELECT uniques(hostId, 10000) SINCE 1 day AGO FACET mod(hostId, 10)

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

The first argument requires an aggregator function against the desired attribute. Use exactly two arguments (arguments after the first two will be ignored). If the attribute is not numeric, this function returns a value of 100%.

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

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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 timeseries 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

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

Use the round() function to return the rounded value of an attribute.

Optionally round() can take a second argument, to_nearest, to round the first argument to the closest multiple of the second one. to_nearest can be fractional.

SELECT round(n [, to_nearest])

Use the stddev() function to return one standard deviation 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 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. Arguments after the first will be ignored. 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 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. Currently, only one capture group is supported.

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

  Tip

  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

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

  SELECT capture(errorMessage, r'(?P<firstWord>\S+)\s.+') FROM Transaction SINCE 1 hour ago where errorMessage is not null

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  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]+) .*')

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  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

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  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

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

It takes a single argument. Arguments after the first will be ignored.

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

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

Use the histogram() function to generate histograms. It takes three arguments:

• Attribute name

• Maximum value of the sample range

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

  Histogram of response times from PageView events

  This query results in a histogram of response times ranging up to 10 seconds over 20 buckets.

  SELECT histogram(duration, 10, 20) FROM PageView SINCE 1 week ago

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  Prometheus histogram buckets

  histogram() accepts Prometheus histogram buckets:

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

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  New Relic distribution metric

  histogram() accepts Distribution metric as an input:

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

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  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

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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. Arguments after the first will be ignored.

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 string() function to convert a numeric, boolean, tuple, or array value to a string value.

It takes two arguments, one optional:

• Attribute name
• Precision: if present, enforces a limit on the number of decimal places included when converting floating-point numeric values.

FROM PageView SELECT string(duration, precision: 2)

FROM PageView SELECT string(average(duration), precision: 2)

FROM PageView SELECT count(*) FACET string(tuple(asnLatitude, asnLongitude), precision: 2)