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Aggregate functions

Aggregate functions summarize the values from each event to create a single, meaningful value. Common aggregate functions include Average, Count, Minimum, Maximum, Standard Deviation, Sum, and Variance.

Most aggregate functions are used with numeric fields. However, there are some functions that you can use with either alphabetic string fields or numeric fields. The function descriptions indicate which functions you can use with alphabetic strings.

For an overview, see statistical and charting functions.

avg(X)

Description

Returns the average of the values of field X.

Usage

You can use this function with the chart, stats, and timechart commands, and also with sparkline() charts.

For a list of the related statistical and charting commands that you can use with this function, see Statistical and charting functions.

Basic examples

The following example returns the average (mean) "size" for each distinct "host".

... | stats avg(size) BY host


The following example returns the average "thruput" of each "host" for each 5 minute time span.

... | bin _time span=5m | stats avg(thruput) BY _time host


The following example charts the ratio of the average (mean) "size" to the maximum "delay" for each distinct "host" and "user" pair.

... | chart eval(avg(size)/max(delay)) AS ratio BY host user


The following example displays a timechart of the average of cpu_seconds by processor, rounded to 2 decimal points.

... | timechart eval(round(avg(cpu_seconds),2)) BY processor


Extended example

Chart the average number of events in a transaction, based on transaction duration.

This example uses the sample data from the Search Tutorial. To try this example on your own Splunk instance, you must download the sample data and follow the instructions to get the tutorial data into Splunk. Use the time range All time when you run the search.
  1. Run the following search to create a chart to show the average number of events in a transaction based on the duration of the transaction.

    sourcetype=access_* status=200 action=purchase | transaction clientip maxspan=30m | chart avg(eventcount) by duration span=log2

    The transaction command adds two fields to the results duration and eventcount. The eventcount field tracks the number of events in a single transaction.

    In this search, the transactions are piped into the chart command. The avg() function is used to calculate the average number of events for each duration. Because the duration is in seconds and you expect there to be many values, the search uses the span argument to bucket the duration into bins using logarithm with a base of 2.

  2. Use the field format option to enable number formatting. This images shows the results on the Statistics tab. There are 2 columns in the results: duration and average of event count.

  3. Click the Visualization tab and change the display to a pie chart. This images shows the results on the Visualization tab.  The chart type has been changed to a pie chart.

    Each wedge of the pie chart represents a duration for the event transactions. You can hover over a wedge to see the average values.

count(X) or c(X)

Description

Returns the number of occurrences of the field X. To indicate a specific field value to match, format X as eval(field="value"). Processes field values as strings. To use this function, you can specify count(X), or the abbreviation c(X).

Usage

You can use the count(X) function with the chart, stats, and timechart commands, and also with sparkline() charts.

Basic examples

The following example returns the count of events where the status field has the value "404".

...| stats count(eval(status="404")) AS count_status BY sourcetype

This example uses an eval expression with the count function. See Using eval expressions in stats functions.


The following example separates search results into 10 bins and returns the count of raw events for each bin.

... | bin size bins=10 | stats count(_raw) BY size


The following example generates a sparkline chart to count the events that use the _raw field.

... sparkline(count)


The following example generates a sparkline chart to count the events that have the user field.

... sparkline(count(user))


The following example uses the timechart command to count the events where the action field contains the value purchase.

sourcetype=access_* | timechart count(eval(action="purchase")) BY productName usenull=f useother=f

Extended examples

Count the number of earthquakes that occurred for each magnitude range

This search uses recent earthquake data downloaded from the USGS Earthquakes website. The data is a comma separated ASCII text file that contains magnitude (mag), coordinates (latitude, longitude), region (place), etc., for each earthquake recorded.

You can download a current CSV file from the USGS Earthquake Feeds and upload the file to your Splunk instance. This example uses the All Earthquakes data from the past 30 days.

  1. Run the following search to calculate the number of earthquakes that occurred in each magnitude range. This data set is comprised of events over a 30-day period.

    source=all_month.csv | chart count AS "Number of Earthquakes" BY mag span=1 | rename mag AS "Magnitude Range"

    • This search uses span=1 to define each of the ranges for the magnitude field, mag.
    • The rename command is then used to rename the field to "Magnitude Range".


    The results appear on the Statistics tab and look something like this:

    Magnitude Range Number of Earthquakes
    -1-0 18
    0-1 2088
    1-2 3005
    2-3 1026
    3-4 194
    4-5 452
    5-4 109
    6-7 11
    7-8 3

Count the number of different page requests for each Web server

This example uses the sample data from the Search Tutorial but should work with any format of Apache web access log. To try this example on your own Splunk instance, you must download the sample data and follow the instructions to get the tutorial data into Splunk. Use the time range All time when you run the search.
  1. Run the following search to use the chart command to determine the number of different page requests, GET and POST, that occurred for each Web server.

    sourcetype=access_* | chart count(eval(method="GET")) AS GET, count(eval(method="POST")) AS POST BY host

    This example uses eval expressions to specify the different field values for the stats command to count. The first clause uses the count() function to count the Web access events that contain the method field value GET. Then, using the AS keyword, the field that represents these results is renamed GET.

    The second clause does the same for POST events. The counts of both types of events are then separated by the web server, using the BY clause with the host field.

    The results appear on the Statistics tab and look something like this:

    host GET POST
    www1 8431 5197
    www2 8097 4815
    www3 8338 4654
  2. Click the Visualization tab. If necessary, format the results as a column chart. This chart displays the total count of events for each event type, GET or POST, based on the host value. This image shows a column chart. There are 2 series in the chart, GET and POST. The host values are on the X axis.

distinct_count(X) or dc(X)

Description

Returns the count of distinct values of the field X. This function processes field values as strings. To use this function, you can specify distinct_count(X), or the abbreviation dc(X).

Usage

You can use this function with the chart, stats, and timechart commands, and also with sparkline() charts.

Basic examples

The following example removes duplicate results with the same "host" value and return the total count of the remaining results.

... | stats dc(host)


The following example generates sparklines for the distinct count of devices and renames the field, "numdevices".

...sparkline(dc(device)) AS numdevices


The following example counts the distinct sources for each sourcetype, and buckets the count for each five minute spans.

...sparkline(dc(source),5m) BY sourcetype


Extended example

This example uses the sample data from the Search Tutorial. To try this example on your own Splunk instance, you must download the sample data and follow the instructions to get the tutorial data into Splunk. Use the time range Yesterday when you run the search.
  1. Run the following search to count the number of different customers who purchased something from the Buttercup Games online store yesterday. The search organizes the count by the type of product (accessories, t-shirts, and type of games) that customers purchased.

    sourcetype=access_* action=purchase | stats dc(clientip) BY categoryId

    • This example first searches for purchase events, action=purchase.
    • These results are piped into the stats command and the dc() function counts the number of different users who make purchases.
    • The BY clause is used to break up this number based on the different category of products, the categoryId.


    The results appear on the Statistics tab and look something like this:

    categoryId dc(clientip)
    ACCESSORIES 37
    ARCADE 58
    NULL 8
    SHOOTER 31
    SIMULATION 34
    SPORTS 13
    STRATEGY 74
    TEE 38

estdc(X)

Description

Returns the estimated count of the distinct values of the field X. This function processes field values as strings. The string values 1.0 and 1 are considered distinct values and counted separately.

Usage

You can use this function with the chart, stats, and timechart commands.

Basic examples

The following example removes duplicate results with the same "host" value and returns the estimated total count of the remaining results.

... | stats estdc(host)

The results look something like this:

estdc(host)
6


The following example generates sparklines for the estimated distinct count of devices and renames the field, "numdevices".

...sparkline(estdc(device)) AS numdevices


The following example estimates the distinct count for the sources for each sourcetype. The results are displayed for each five minute span in sparkline charts.

...sparkline(estdc(source),5m) BY sourcetype

estdc_error(X)

Description

Returns the theoretical error of the estimated count of the distinct values of the field X. The error represents a ratio of the absolute_value(estimate_distinct_count - real_distinct_count)/real_distinct_count. This function processes field values as strings.

Usage

You can use this function with the chart, stats, and timechart commands.

Basic examples

The following example determines the error ratio for the estimated distinct count of the "host" values.

... | stats estdc_error(host)

exactperc<X>(Y)

Description

Returns a percentile value of the numeric field Y.

Usage

You can use this function with the chart, stats, and timechart commands, and also with sparkline() charts.

The exactperc function provides the exact value, but is very resource expensive for high cardinality fields. The exactperc function can consume a large amount of memory in the search head, which might impact how long it takes for a search to complete.

Examples

See the perc<X>(Y) function.

max(X)

Description

Returns the maximum value of the field X. If the values of X are non-numeric, the maximum value is found using lexicographical ordering.

Processes field values as numbers if possible, otherwise processes field values as strings.

Usage

You can use this function with the chart, stats, and timechart commands, and also with sparkline() charts.

Lexicographical order sorts items based on the values used to encode the items in computer memory. In Splunk software, this is almost always UTF-8 encoding, which is a superset of ASCII.

  • Numbers are sorted before letters. Numbers are sorted based on the first digit. For example, the numbers 10, 9, 70, 100 are sorted lexicographically as 10, 100, 70, 9.
  • Uppercase letters are sorted before lowercase letters.
  • Symbols are not standard. Some symbols are sorted before numeric values. Other symbols are sorted before or after letters.

Basic examples

This example returns the maximum value of "size".

... | max(size)

Extended example

Calculate aggregate statistics for the magnitudes of earthquakes in an area

This search uses recent earthquake data downloaded from the USGS Earthquakes website. The data is a comma separated ASCII text file that contains magnitude (mag), coordinates (latitude, longitude), region (place), etc., for each earthquake recorded.

You can download a current CSV file from the USGS Earthquake Feeds and upload the file to your Splunk instance. This example uses the All Earthquakes data from the past 30 days.

  1. Search for earthquakes in and around California. Calculate the number of earthquakes that were recorded. Use statistical functions to calculate the minimum, maximum, range (the difference between the min and max), and average magnitudes of the recent earthquakes. List the values by magnitude type.

    source=all_month.csv place=*California* | stats count, max(mag), min(mag), range(mag), avg(mag) BY magType

    The results appear on the Statistics tab and look something like this:

    magType count max(mag) min(mag) range(mag) avg(mag)
    H 123 2.8 0.0 2.8 0.549593
    MbLg 1 0 0 0 0.0000000
    Md 1565 3.2 0.1 3.1 1.056486
    Me 2 2.0 1.6 .04 1.800000
    Ml 1202 4.3 -0.4 4.7 1.226622
    Mw 6 4.9 3.0 1.9 3.650000
    ml 10 1.56 0.19 1.37 0.934000

mean(X)

Description

Returns the arithmetic mean of the field X.

The mean values should be exactly the same as the values calculated using the avg() function.

Usage

You can use this function with the chart, stats, and timechart commands, and also with sparkline() charts.

Basic examples

The following example returns the mean of "kbps" values:

... | stats mean(kbps)

Extended example

This search uses recent earthquake data downloaded from the USGS Earthquakes website. The data is a comma separated ASCII text file that contains magnitude (mag), coordinates (latitude, longitude), region (place), etc., for each earthquake recorded.

You can download a current CSV file from the USGS Earthquake Feeds and upload the file to your Splunk instance. This example uses the All Earthquakes data from the past 30 days.

  1. Run the following search to find the mean, standard deviation, and variance of the magnitudes of recent quakes by magnitude type.

    source=usgs place=*California* | stats count mean(mag), stdev(mag), var(mag) BY magType

    The results appear on the Statistics tab and look something like this:

    magType count mean(mag) std(mag) var(mag)
    H 123 0.549593 0.356985 0.127438
    MbLg 1 0.000000 0.000000 0.000000
    Md 1565 1.056486 0.580042 0.336449
    Me 2 1.800000 0.346410 0.120000
    Ml 1202 1.226622 0.629664 0.396476
    Mw 6 3.650000 0.716240 0.513000
    ml 10 0.934000 0.560401 0.314049

median(X)

Description

Returns the middle-most value of the field X.

Usage

You can use this function with the chart, stats, and timechart commands.

If you have an even number of events, by default the median calculation is approximated to the higher of the two values. To receive a more accurate median value with an even number of events, change the perc_method in the limits.conf file.

  1. See How to edit a configuration file in the Admin manual.

    Only users with file system access, such as system administrators, can edit the configuration files. Never change or copy the configuration files in the default directory. The files in the default directory must remain intact and in their original location. Make the changes in the local directory.

  2. In the [stats | sistats] stanza, change the perc_method setting to interpolated.

If you are using Splunk Cloud and want to edit the configuration file, file a Support ticket.

Basic examples

Consider the following list of values, which counts the number of different customers who purchased something from the Buttercup Games online store yesterday. The values are organized by the type of product (accessories, t-shirts, and type of games) that customers purchased.

categoryId count
ACCESSORIES 37
ARCADE 58
NULL 8
SIMULATION 34
SPORTS 13
STRATEGY 74
TEE 38

When the list is sorted the median, or middle-most value, is 37.

categoryId count
NULL 8
SPORTS 13
SIMULATION 34
ACCESSORIES 37
TEE 38
ARCADE 58
STRATEGY 74

min(X)

Description

Returns the minimum value of the field X. If the values of X are non-numeric, the minimum value is found using lexicographical ordering.

This function processes field values as numbers if possible, otherwise processes field values as strings.

Usage

You can use this function with the chart, stats, and timechart commands.

Lexicographical order sorts items based on the values used to encode the items in computer memory. In Splunk software, this is almost always UTF-8 encoding, which is a superset of ASCII.

  • Numbers are sorted before letters. Numbers are sorted based on the first digit. For example, the numbers 10, 9, 70, 100 are sorted lexicographically as 10, 100, 70, 9.
  • Uppercase letters are sorted before lowercase letters.
  • Symbols are not standard. Some symbols are sorted before numeric values. Other symbols are sorted before or after letters.

Basic examples

The following example returns the minimum size and maximum size of the HotBucketRoller component in the _internal index.

index=_internal component=HotBucketRoller | stats min(size), max(size)


The following example returns a list of processors and calculates the minimum cpu_seconds and the maximum cpu_seconds.

index=_internal | chart min(cpu_seconds), max(cpu_seconds) BY processor

Extended example

See the Extended example for the max() function. That example includes the min() function.

mode(X)

Description

Returns the most frequent value of the field X.

Processes field values as strings.

Usage

You can use this function with the chart, stats, and timechart commands.

Basic examples

The mode returns the most frequent value. Consider the following data:

firstname surname age
Claudia Garcia 32
David Mayer 45
Alex Garcia 29
Wei Zhang 45
Javier Garcia 37

When you search for the mode in the age field, the value 45 is returned.

...| stats mode(age)

You can also use mode with fields that contain string values. When you search for the mode in the surname field, the value Garcia is returned.

...| stats mode(surname)

Here's another set of sample data:

_time host sourcetype
04-06-2020 17:06:23.000 PM www1 access_combined
04-06-2020 10:34:19.000 AM www1 access_combined
04-03-2020 13:52:18.000 PM www2 access_combined
04-02-2020 07:39:59.000 AM www3 access_combined
04-01-2020 19:35:58.000 PM www1 access_combined

If you run a search that looks for the mode in the host field, the value www1 is returned because it is the most common value in the host field. For example:

... |stats mode(host)

The results will look something like this:

mode(host)
www1

perc<X>(Y)

Description

The percentile functions return the X-th percentile value of the numeric field Y. You can think of this as an estimate of where the top X% starts. For example, a 95th percentile says that 95% of the values in field Y are below the estimate and 5% of the values in field Y are above the estimate.

Valid values of X are floating point numbers between 0 and 100, such as 99.95.

There are three different percentile functions that you can use:

Function Description
perc<X>(Y) or the abbreviation p<X>(Y)
Use the perc function to calculate an approximate threshold, such that of the values in field Y, X percent fall below the threshold. The perc function returns a single number that represents the lower end of the approximate values for the percentile requested.
upperperc<X>(Y) When there are more than 1000 values, the upperperc function gives the approximate upper bound for the percentile requested. Otherwise the upperperc function returns the same percentile as the perc function.
exactperc<X>(Y) The exactperc function provides the exact value, but is very resource expensive for high cardinality fields. The exactperc function can consume a large amount of memory, which might impact how long it takes for a search to complete.

The percentile functions process field values as strings.

The perc and upperperc functions give approximate values for the integer percentile requested. The approximation algorithm that is used, which is based on dynamic compression of a radix tree, provides a strict bound of the actual value for any percentile.

Usage

You can use this function with the chart, stats, and timechart commands.

Differences between Splunk and Excel percentile algorithms

If there are less than 1000 distinct values, the Splunk percentile functions use the nearest rank algorithm. See http://en.wikipedia.org/wiki/Percentile#Nearest_rank. Excel uses the NIST interpolated algorithm, which basically means you can get a value for a percentile that does not exist in the actual data, which is not possible for the nearest rank approach.

You can specify that the Excel method should be used by changing the settings in the [stats] stanza in the limits.conf file. Change the perc_method setting to interpolated instead of nearest-rank.


Splunk algorithm with more than 1000 distinct values

If there are more than 1000 distinct values for the field, the percentiles are approximated using a custom radix-tree digest-based algorithm. This algorithm is much faster and uses much less memory, a constant amount, than an exact computation, which uses memory in linear relation to the number of distinct values. By default this approach limits the approximation error to < 1% of rank error. That means if you ask for 95th percentile, the number you get back is between the 94th and 96th percentile.

You always get the exact percentiles even for more than 1000 distinct values by using the exactperc function compared to the perc.

Basic examples

Consider this list of values Y = {10,9,8,7,6,5,4,3,2,1}.

The following example returns 5.5.

...| stats perc50(Y)


The following example returns 9.55.

...| stats perc95(Y)

Extended example

Consider the following set of data, which shows the number of visitors for each hour a store is open:

hour visitors
0800 0
0900 212
1000 367
1100 489
1200 624
1300 609
1400 492
1500 513
1600 376
1700 337

This data resides in the visitor_count index. You can use the streamstats command to create a cumulative total for the visitors.

index=visitor_count | streamstats sum(visitors) as 'visitors total'

The results from this search look like this:

hour visitors visitors total
0800 0 0
0900 212 212
1000 367 579
1100 489 1068
1200 624 1692
1300 609 2301
1400 492 2793
1500 513 3306
1600 376 3673
1700 337 4010

Let's add the stats command with the perc function to determine the 50th and 95th percentiles.

index=visitor_count | streamstats sum(visitors) as 'visitors total' | stats perc50('visitors total') perc95('visitors total')

The results from this search look like this:

perc50(visitors total) perc95(visitors total)
1996.5 3858.35

The perc50 estimates the 50th percentile, when 50% of the visitors had arrived. You can see from the data that the 50th percentile was reached between visitor number 1996 and 1997, which was sometime between 1200 and 1300 hours. The perc95 estimates the 95th percentile, when 95% of the visitors had arrived. The 95th percentile was reached with visitor 3858, which occurred between 1600 and 1700 hours.

range(X)

Description

Returns the difference between the max and min values of the field X. The values of field X must be numeric.

Usage

You can use this function with the chart, stats, and timechart commands, and also with sparkline() charts.

Basic example

This example uses events that list the numeric sales for each product and quarter, for example:

products quarter sales quota
ProductA QTR1 1200 1000
ProductB QTR1 1400 1550
ProductC QTR1 1650 1275
ProductA QTR2 1425 1300
ProductB QTR2 1175 1425
ProductC QTR2 1550 1450
ProductA QTR3 1300 1400
ProductB QTR3 1250 1125
ProductC QTR3 1375 1475
ProductA QTR4 1550 1300
ProductB QTR4 1700 1225
ProductC QTR4 1625 1350

It is easiest to understand the range if you also determine the min and max values. To determine the range of sales by product, run this search:

source="addtotalsData.csv" | chart sum(sales) min(sales) max(sales) range(sales) BY products

The results appear on the Statistics tab and look something like this:

quarter sum(sales) min(sales) max(sales) range(sales)
QTR1 4250 1200 1650 450
QTR2 4150 1175 1550 375
QTR3 3925 1250 1375 125
QTR4 4875 1550 1700 150

The range(sales) is the max(sales) minus the min(sales).

Extended example

See the Extended example for the max() function. That example includes the range() function.

stdev(X)

Description

Returns the sample standard deviation of the field X.

Usage

You can use this function with the chart, stats, and timechart commands, and also with sparkline() charts.

Basic examples

This example returns the standard deviation of wildcarded fields "*delay" which can apply to both, "delay" and "xdelay".

... | stats stdev(*delay)

Extended example

This search uses recent earthquake data downloaded from the USGS Earthquakes website. The data is a comma separated ASCII text file that contains magnitude (mag), coordinates (latitude, longitude), region (place), etc., for each earthquake recorded.

You can download a current CSV file from the USGS Earthquake Feeds and upload the file to your Splunk instance. This example uses the All Earthquakes data from the past 30 days.

  1. Run the following search to find the mean, standard deviation, and variance of the magnitudes of recent quakes by magnitude type.

    source=usgs place=*California* | stats count mean(mag), stdev(mag), var(mag) BY magType

    The results appear on the Statistics tab and look something like this:

    magType count mean(mag) std(mag) var(mag)
    H 123 0.549593 0.356985 0.127438
    MbLg 1 0.000000 0.000000 0.000000
    Md 1565 1.056486 0.580042 0.336449
    Me 2 1.800000 0.346410 0.120000
    Ml 1202 1.226622 0.629664 0.396476
    Mw 6 3.650000 0.716240 0.513000
    ml 10 0.934000 0.560401 0.314049

stdevp(X)

Description

Returns the population standard deviation of the field X.

Usage

You can use this function with the chart, stats, and timechart commands, and also with sparkline() charts.

Basic examples

Extended example

sum(X)

Description

Returns the sum of the values of the field X.

Usage

You can use this function with the chart, stats, and timechart commands, and also with sparkline() charts.

Basic examples

You can create totals for any numeric field. For example:

...| stats sum(bytes)

The results look something like this:

sum(bytes)
21502


You can rename the column using the AS keyword:

...| stats sum(bytes) AS "total bytes"

The results look something like this:

total bytes
21502


You can organize the results using a BY clause:

...| stats sum(bytes) AS "total bytes" by date_hour

The results look something like this:

date_hour total bytes
07 6509
11 3726
15 6569
23 4698

sumsq(X)

Description

Returns the sum of the squares of the values of the field X.

The sum of the squares is used to evaluate the variance of a dataset from the dataset mean. A large sum of the squares indicates a large variance, which tells you that individual values fluctuate widely from the mean.

Usage

You can use this function with the chart, stats, and timechart commands, and also with sparkline() charts.

Basic examples

The following table contains the temperatures taken every day at 8 AM for a week.

You calculate the mean of the these temperatures and get 48.9 degrees. To calculate the deviation from the mean for each day, take the temperature and subtract the mean. If you square each number, you get results like this:

day temp mean deviation square of temperatures
sunday 65 48.9 16.1 260.6
monday 42 48.9 -6.9 47.0
tuesday 40 48.9 -8.9 78.4
wednesday 31 48.9 -17.9 318.9
thursday 47 48.9 -1.9 3.4
friday 53 48.9 4.1 17.2
saturday 64 48.9 15.1 229.3

Take the total of the squares, 954.9, and divide by 6 which is the number of days minus 1. This gets you the sum of squares for this series of temperatures. The standard deviation is the square root of the sum of the squares. The larger the standard deviation the larger the fluctuation in temperatures during the week.

You can calculate the mean, sum of the squares, and standard deviation with a few statistical functions:

...|stats mean(temp), sumsq(temp), stdev(temp)

This search returns these results:

mean(temp) sumsq(temp) stdev(temp)
48.857142857142854 17664 12.615183595289349

upperperc<X>(Y)

Description

Returns an approximate percentile value, based on the requested percentile X of the numeric field Y.

When there are more than 1000 values, the upperperc function gives the approximate upper bound for the percentile requested. Otherwise the upperperc function returns the same percentile as the perc function.

See the perc<X>(Y) function.

Usage

You can use this function with the chart, stats, and timechart commands, and also with sparkline() charts.

Examples

See the perc function.

var(X)

Description

Returns the sample variance of the field X.

Usage

You can use this function with the chart, stats, and timechart commands, and also with sparkline() charts.

Basic examples

Extended example

See the Extended example for the mean() function. That example includes the var() function.

varp(X)

Description

Returns the population variance of the field X.

Usage

You can use this function with the chart, stats, and timechart commands, and also with sparkline() charts.

Basic examples

Last modified on 20 May, 2020
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This documentation applies to the following versions of Splunk® Enterprise: 7.1.0, 7.1.1, 7.1.2, 7.1.3, 7.1.4, 7.1.5, 7.1.6, 7.1.7, 7.1.8, 7.1.9, 7.1.10, 7.2.0, 7.2.1, 7.2.2, 7.2.3, 7.2.4, 7.2.5, 7.2.6, 7.2.7, 7.2.8, 7.2.9, 7.2.10, 7.3.0, 7.3.1, 7.3.2, 7.3.4, 7.3.5, 7.3.3, 8.0.0, 8.0.1, 8.0.2, 8.0.3, 8.0.4


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