timechart

Description

Creates a time series chart with corresponding table of statistics.

A timechart is a statistical aggregation applied to a field to produce a chart, with time used as the X-axis. You can specify a split-by field, where each distinct value of the split-by field becomes a series in the chart. If you use an eval expression, the split-by clause is required. With the limit and agg options, you can specify series filtering. These options are ignored if you specify an explicit where-clause. If you set limit=0, no series filtering occurs.

Syntax

The required syntax is in bold.

timechart

[sep=<string>]

[format=<string>]

[partial=<bool>]

[cont=<bool>]

[limit=<chart-limit-opt>]

[agg=<stats-agg-term>]

[<bin-options>... ]

( (<single-agg> [BY <split-by-clause>] ) | (<eval-expression>) BY <split-by-clause> )

[<dedup_splitvals>]

Required arguments

When specifying timechart command arguments, either <single-agg> or <eval-expression> BY <split-by-clause> is required.

eval-expression: Syntax: <math-exp> | <concat-exp> | <compare-exp> | <bool-exp> | <function-call>; Description: A combination of literals, fields, operators, and functions that represent the value of your destination field. For these evaluations to work, your values need to be valid for the type of operation. For example, with the exception of addition, arithmetic operations might not produce valid results if the values are not numerical. Additionally, the search can concatenate the two operands if they are both strings. When concatenating values with a period '.' the search treats both values as strings, regardless of their actual data type.

single-agg: Syntax: count | <stats-func>(<field>); Description: A single aggregation applied to a single field, including an evaluated field. For <stats-func>, see Stats function options. No wildcards are allowed. The field must be specified, except when using the count function, which applies to events as a whole.

split-by-clause: Syntax: <field> (<tc-options>)... [<where-clause>]; Description: Specifies a field to split the results by. If field is numerical, default discretization is applied. Discretization is defined with the tc-options. Use the <where-clause> to specify the number of columns to include. See the tc options and the where clause sections in this topic.

Optional arguments

agg=<stats-agg-term>: Syntax:agg=( <stats-func> ( <evaled-field> | <wc-field> ) [AS <wc-field>] ); Description: A statistical aggregation function. See Stats function options. The function can be applied to an eval expression, or to a field or set of fields. Use the AS clause to place the result into a new field with a name that you specify. You can use wild card characters in field names.

bin-options: Syntax: bins | minspan | span | <start-end> | aligntime; Description: Options that you can use to specify discrete bins, or groups, to organize the information. The bin-options set the maximum number of bins, not the target number of bins. See the Bin options section in this topic.; Default: bins=100

cont: Syntax: cont=<bool>; Description: Specifies whether the chart is continuous or not. If set to true, the Search application fills in the time gaps.; Default: true

dedup_splitvals: Syntax: dedup_splitvals=<boolean>; Description: Specifies whether to remove duplicate values in multivalued <split-by-clause> fields.; Default: false

fixedrange: Syntax: fixedrange=<bool>; Description: Specifies whether or not to enforce the earliest and latest times of the search. Setting fixedrange=false allows the timechart command to constrict or expand to the time range covered by all events in the dataset.; Default: true

format: Syntax: format=<string>; Description: Used to construct output field names when multiple data series are used in conjunction with a split-by-field. format takes precedence over sep and allows you to specify a parameterized expression with the stats aggregator and function ($AGG$) and the value of the split-by-field ($VAL$).

limit

Syntax: limit=<int>

Description: Specifies a limit for the number of distinct values of the split-by field to return. If set to limit=0, all distinct values are used. Setting limit=N keeps the N highest scoring distinct values of the split-by field. All other values are grouped into 'OTHER', as long as useother is not set to false. The scoring is determined as follows:

If a single aggregation is specified, the score is based on the sum of the values in the aggregation for that split-by value. For example, for timechart avg(foo) BY <field> the avg(foo) values are added up for each value of <field> to determine the scores.
If multiple aggregations are specified, the score is based on the frequency of each value of <field>. For example, for timechart avg(foo) max(bar) BY <field>, the top scoring values for <field> are the most common values of <field>.

Ties in scoring are broken lexicographically, based on the value of the split-by field. For example, 'BAR' takes precedence over 'bar', which takes precedence over 'foo'. See Usage.

partial: Syntax: partial=<bool>; Description: Controls if partial time bins should be retained or not. Only the first and last bin can be partial.; Default: True. Partial time bins are retained.

sep: Syntax: sep=<string>; Description: Used to construct output field names when multiple data series are used in conjunctions with a split-by field. This is equivalent to setting format to $AGG$<sep>$VAL$.

Stats function options

stats-func: Syntax: The syntax depends on the function that you use. Refer to the table below.; Description: Statistical functions that you can use with the timechart command. Each time you invoke the timechart command, you can use one or more functions. However, you can only use one BY clause. See Usage.

The following table lists the supported functions by type of function. Use the links in the table to see descriptions and examples for each function. For an overview about using functions with commands, see Statistical and charting functions.

Type of function	Supported functions and syntax
Aggregate functions	`avg()` `count()` `distinct_count()` `estdc()` `estdc_error()`	`exactperc<int>()` `max()` `median()` `min()` `mode()`	`perc<int>()` `range()` `stdev()` `stdevp()`	`sum()` `sumsq()` `upperperc<int>()` `var()` `varp()`
Event order functions	`earliest()`	`first()`	`last()`	`latest()`
Multivalue stats and chart functions	`list(X)`	`values(X)`
Time functions	`per_day()`	`per_hour()`	`per_minute()`	`per_second()`

Bin options

bins: Syntax: bins=<int>; Description: Sets the maximum number of bins to discretize into. This does not set the target number of bins. It finds the smallest bin size that results in no more than N distinct bins. Even though you specify a number such as 300, the resulting number of bins might be much lower.; Default: 100

minspan: Syntax: minspan=<span-length>; Description: Specifies the smallest span granularity to use automatically inferring span from the data time range. See Usage.

span: Syntax: span=<log-span> | span=<span-length> | span=<snap-to-time>; Description: Sets the size of each bin, using either a log-based span, a span length based on time, or a span that snaps to a specific time. For descriptions of each of these options, see Span options.

The starting time of a bin might not match your local timezone. see Usage.

<start-end>: Syntax: end=<num> | start=<num>; Description: Sets the minimum and maximum extents for numerical bins. Data outside of the [start, end] range is discarded.

aligntime: Syntax: aligntime=(earliest | latest | <time-specifier>); Description: Align the bin times to something other than base UNIX time (epoch 0). The aligntime option is valid only when doing a time-based discretization. Ignored if span is in days, months, or years.

Span options

<log-span>: Syntax: [<num>]log[<num>]; Description: Sets to log-based span. The first number is a coefficient. The second number is the base. If the first number is supplied, it must be a real number >= 1.0 and < base. Base, if supplied, must be real number > 1.0 (strictly greater than 1).

<span-length>: Syntax: <time_unit>[<timescale>]; Description: A span of each bin, based on time. If the timescale is provided, this is used as a time range. If not, this is an absolute bin length.

<time_unit>: Syntax: <int>; Description: An integer that represents the time span units. For example 5 or 30. If not specified, 1 is assumed.; Default: 1

<timescale>: Syntax: <sec> | <min> | <hr> | <day> | <week> | <month> | <subseconds>; Description: The timescale interval.; Default: <sec>

Timescale	Valid syntax	Description
<sec>	s \| sec \| secs \| second \| seconds	Timescale in seconds.
<min>	m \| min \| mins \| minute \| minutes	Timescale in minutes.
<hr>	h \| hr \| hrs \| hour \| hours	Timescale in hours.
<day>	d \| day \| days	Timescale in days.
<week>	w \| week \| weeks	Timescale in weeks.
<month>	mon \| month \| months	Timescale in months.
<subseconds>	us \| ms \| cs \| ds	Timescale in microseconds (us), milliseconds (ms), centiseconds (cs), or deciseconds (ds)

<snap-to-time>: Syntax: [+|-] [<time_integer>] <relative_time_unit>@<snap_to_time_unit>; Description: A span of each bin, based on a relative time unit and a snap to time unit. The <snap-to-time> must include a relative_time_unit, the @ symbol, and a snap_to_time_unit. The offset, represented by the plus (+) or minus (-) is optional. If the <time_integer> is not specified, 1 is the default. For example if you specify w as the relative_time_unit, 1 week is assumed.

The option is used only with the week timescale unit. It cannot be used with other timescale units such as minutes or quarters.

tc options

The <tc-option> is part of the <split-by-clause>.

tc-option: Syntax: <bin-options> | usenull=<bool> | useother=<bool> | nullstr=<string> | otherstr=<string>; Description: Timechart options for controlling the behavior of splitting by a field.

bin-options

See the Bin options section in this topic.

nullstr: Syntax: nullstr=<string>; Description: If usenull=true, specifies the label for the series that is created for events that do not contain the split-by field.; Default: NULL

otherstr: Syntax: otherstr=<string>; Description: If useother=true, specifies the label for the series that is created in the table and the graph.; Default: OTHER

usenull: Syntax: usenull=<bool>; Description: Controls whether or not a series is created for events that do not contain the split-by field. The label for the series is controlled by the nullstr option.; Default: true

useother: Syntax: useother=<bool>; Description: You specify which series to include in the results table by using the <agg>, <limit>, and <where-clause> options. The useother option specifies whether to merge all of the series not included in the results table into a single new series. If useother=true, the label for the series is controlled by the otherstr option.; Default: true

where clause

The <where-clause> is part of the <split-by-clause>. The <where-clause> is comprised of two parts, a single aggregation and some options. See Use the where clause to control the number of series returned.

where clause: Syntax: <single-agg> <where-comp>; Description: Specifies the criteria for including particular data series when a field is given in the <tc-by-clause>. The most common use of this option is to look for spikes in your data rather than overall mass of distribution in series selection. The default value finds the top ten series by area under the curve. Alternately one could replace sum with max to find the series with the ten highest spikes. Essentially the default is the same as specifying where sum in top10. The <where-clause> has no relation to the where command.

<where-comp>: Syntax: <wherein-comp> | <wherethresh-comp>; Description: Specify either a grouping for the series or the threshold for the series.

<wherein-comp>: Syntax: (in | notin) (top | bottom)<int>; Description: A grouping criteria that requires the aggregated series value be in or not in some top or bottom group.

<wherethresh-comp>: Syntax: (< | >) [" "] <num>; Description: A threshold criteria that requires the aggregated series value be greater than or less than some numeric threshold. You can specify the threshold with or without a space between the sign and the number.

Usage

The timechart command is a transforming command. See Command types.

bins and span arguments

The timechart command accepts either the bins argument OR the span argument. If you specify both bins and span, span is used. The bins argument is ignored.

If you do not specify either bins or span, the timechart command uses the default bins=100.

Default time spans

If you use the predefined time ranges in the time range picker, and do not specify the span argument, the following table shows the default span that is used.

Time range	Default time span
Last 15 minutes	10 seconds
Last 60 minutes	1 minute
Last 4 hours	5 minutes
Last 24 hours	30 minutes
Last 7 days	1 day
Last 30 days	1 day
Previous year	1 month

(Thanks to Splunk users MuS and Martin Mueller for their help in compiling this default time span information.)

Spans used when minspan is specified

When you specify a minspan value, the span that is used for the search must be equal to or greater than one of the span threshold values in the following table. For example, if you specify minspan=15m that is equivalent to 900 seconds. The minimum span that can be used is 1800 seconds, or 30 minutes.

Span threshold	Time equivalents
1 second
5 seconds
10 seconds
30 seconds
60 seconds	1 minute
300 seconds	5 minutes
600 seconds	10 minutes
1800 seconds	30 minutes
3600 seconds	1 hour
86400 seconds	1 day
2592000 seconds	30 days

Bin time spans and local time

The span argument always rounds down the starting date for the first bin. There is no guarantee that the bin start time used by the timechart command corresponds to your local timezone. In part this is due to differences in daylight savings time for different locales. To use day boundaries, use span=1d. Do not use not span=86400s, or span=1440m, or span=24h.

Bin time spans versus per_* functions

The functions, per_day(), per_hour(), per_minute(), and per_second() are aggregator functions and are not responsible for setting a time span for the resultant chart. These functions are used to get a consistent scale for the data when an explicit span is not provided. The resulting span can depend on the search time range.

For example, per_hour() converts the field value so that it is a rate per hour, or sum()/<hours in the span>. If your chart span ends up being 30m, it is sum()*2.

If you want the span to be 1h, you still have to specify the argument span=1h in your search.

You can do per_hour() on one field and per_minute() (or any combination of the functions) on a different field in the same search.

Subsecond bin time spans

Subsecond span timescales—time spans that are made up of deciseconds (ds), centiseconds (cs), milliseconds (ms), or microseconds (us)—should be numbers that divide evenly into a second. For example, 1s = 1000ms. This means that valid millisecond span values are 1, 2, 4, 5, 8, 10, 20, 25, 40, 50, 100, 125, 200, 250, or 500ms. In addition, span = 1000ms is not allowed. Use span = 1s instead.

Split-by fields

If you specify a split-by field, ensure that you specify the bins and span arguments before the split-by field. If you specify these arguments after the split-by field, Splunk software assumes that you want to control the bins on the split-by field, not on the time axis.

If you use chart or timechart, you cannot use a field that you specify in a function as your split-by field as well. For example, you will not be able to run:

... | chart sum(A) by A span=log2

However, you can work around this with an eval expression, for example:

... | eval A1=A | chart sum(A) by A1 span=log2

Functions and memory usage

Some functions are inherently more expensive, from a memory standpoint, than other functions. For example, the distinct_count function requires far more memory than the count function. The values and list functions also can consume a lot of memory.

If you are using the distinct_count function without a split-by field or with a low-cardinality split-by by field, consider replacing the distinct_count function with the the estdc function (estimated distinct count). The estdc function might result in significantly lower memory usage and run times.

Lexicographical order

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.

You can specify a custom sort order that overrides the lexicographical order. See the blog Order Up! Custom Sort Orders.

Basic examples

1. Chart the product of the average "CPU" and average "MEM" for each "host"

For each minute, compute the product of the average "CPU" and average "MEM" for each "host".

... | timechart span=1m eval(avg(CPU) * avg(MEM)) BY host

2. Chart the average of cpu_seconds by processor

This example uses an eval expression that includes a statistical function, avg to calculate the average of cpu_seconds field, rounded to 2 decimal places. The results are organized by the values in the processor field. When you use a eval expression with the timechart command, you must also use BY clause.

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

3. Chart the average of "CPU" for each "host"

For each minute, calculate the average value of "CPU" for each "host".

... | timechart span=1m avg(CPU) BY host

4. Chart the average "cpu_seconds" by "host" and remove outlier values

Calculate the average "cpu_seconds" by "host". Remove outlying values that might distort the timechart axis.

... | timechart avg(cpu_seconds) BY host | outlier action=tf

5. Chart the average "thruput" of hosts over time

... | timechart span=5m avg(thruput) BY host

6. Chart the eventypes by source_ip

For each minute, count the eventypes by source_ip, where the count is greater than 10.

sshd failed OR failure | timechart span=1m count(eventtype) BY source_ip usenull=f WHERE count>10

7. Align the chart time bins to local time

Align the time bins to 5am (local time). Set the span to 12h. The bins will represent 5am - 5pm, then 5pm - 5am (the next day), and so on.

...| timechart _time span=12h aligntime=@d+5h

8. In a multivalue BY field, remove duplicate values

For each unique value of mvfield, return the average value of field. Deduplicates the values in the mvfield.

...| timechart avg(field) BY mvfield dedup_splitval=true

Extended examples

1. Chart revenue for the different products

This example uses the sample dataset from the Search Tutorial and a field lookup to add more information to the event data. To try this example for yourself:

Download the tutorialdata.zip file from this topic in the Search Tutorial and follow the instructions to upload the file to your Splunk deployment.
Download the Prices.csv.zip file from this topic in the Search Tutorial and follow the instructions to set up your field lookup.
Use the time range Yesterday when you run the search.

The tutorialdata.zip file includes a productId field that is the catalog number for the items sold at the Buttercup Games online store. The field lookup uses the prices.csv file to add two new fields to your events: productName, which is a descriptive name for the item, and price, which is the cost of the item.

Chart the revenue for the different products that were purchased yesterday.

sourcetype=access_* action=purchase | timechart per_hour(price) by productName usenull=f useother=f

This example searches for all purchase events (defined by the action=purchase).
The results are piped into timechart command.
The per_hour() function sums up the values of the price field for each productName and organizes the total by time.

This search produces the following table of results in the Statistics tab. To format the numbers to the proper digits for currency, click the format icon in the column heading. On the Number Formatting tab, select the Precision.

Click the Visualization tab. If necessary, change the chart to a column chart. On the Format menu, the General tab contains the Stack Mode option where you can change the chart to a stacked chart.

After you create this chart, you can position your mouse pointer over each section to view more metrics for the product purchased at that hour of the day.

Notice that the chart does not display the data in hourly spans. Because a span is not provided (such as span=1hr), the per_hour() function converts the value so that it is a sum per hours in the time range (which in this example is 24 hours).

2. Chart daily purchases by product type

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.

Chart the number of purchases made daily for each type of product.

sourcetype=access_* action=purchase | timechart span=1d count by categoryId usenull=f

This example searches for all purchases events, defined by the action=purchase, and pipes those results into the timechart command.
The span=1day argument buckets the count of purchases over the week into daily chunks.
The usenull=f argument ignore any events that contain a NULL value for categoryId.

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

_time	ACCESSORIES	ARCADE	SHOOTER	SIMULATION	SPORTS	STRATEGY	TEE
2018-03-29	5	17	6	3	5	32	9
2018-03-30	62	63	39	30	22	127	56
2018-03-31	65	94	38	42	34	128	60
2018-04-01	54	82	42	39	13	115	66
2018-04-02	52	63	45	42	22	124	52
2018-04-03	46	76	34	42	19	123	59
2018-04-04	57	70	36	38	20	130	56
2018-04-05	46	72	35	37	13	106	46

Click the Visualization tab. If necessary, change the chart to a column chart.

Compare the number of different items purchased each day and over the course of the week.

3. Display results in 1 week intervals

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.

This search counts the number of earthquakes in Alaska where the magnitude is greater than or equal to 3.5. The results are organized in spans of 1 week, where the week begins on Monday.

source=all_month.csv place=*alaska* mag>=3.5 | timechart span=w@w1 count BY mag

The <by-clause> is used to group the earthquakes by magnitude.
You can only use week spans with the snap-to span argument in the timechart command. For more information, see Specify a snap to time unit.

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

_time	3.5	3.6	3.7	3.8	4	4.1	4.1	4.3	4.4	4.5	OTHER
2018-03-26	3	3	2	2	3	1	0	2	1	1	1
2018-04-02	5	7	2	0	3	2	1	0	0	1	1
2018-04-09	2	3	1	2	0	2	1	1	0	1	2
2018-04-16	6	5	0	1	2	2	2	0	0	2	1
2018-04-23	2	0	0	0	0	2	1	2	2	0	1

4. Count the revenue for each item over time

This example uses the sample dataset from the Search Tutorial and a field lookup to add more information to the event data. Before you run this example:

Download the data set from this topic in the Search Tutorial and follow the instructions to upload it to your Splunk deployment.
Download the Prices.csv.zip file from this topic in the Search Tutorial and follow the instructions to set up your field lookup.

The original data set includes a productId field that is the catalog number for the items sold at the Buttercup Games online store. The field lookup adds two new fields to your events: productName, which is a descriptive name for the item, and price, which is the cost of the item.

Count the total revenue made for each item sold at the shop over the last 7 days. This example shows two different searches to generate the calculations.

Search 1: The first search uses the span argument to bucket the times of the search results into 1 day increments. The search then uses the sum() function to add the price for each product_name.; sourcetype=access_* action=purchase | timechart span=1d sum(price) by productName usenull=f

Search 2: This second search uses the per_day() function to calculate the total of the price values for each day.; sourcetype=access_* action=purchase | timechart per_day(price) by productName usenull=f

Both searches produce similar results. Search 1 produces values with two decimal places. Search 2 produces values with six decimal places. The following image shows the results from Search 1.

Click the Visualization tab. If necessary, change the chart to a column chart.

Now you can compare the total revenue made for items purchased each day and over the course of the week.

5. Chart product views and purchases for a single day

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 Yesterday when you run the search.

Chart a single day's views and purchases at the Buttercup Games online store.

sourcetype=access_* | timechart per_hour(eval(method="GET")) AS Views, per_hour(eval(action="purchase")) AS Purchases

This search uses the per_hour() function and eval expressions to search for page views (method=GET) and purchases (action=purchase).
The results of the eval expressions are renamed as Views and Purchases, respectively.

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

_time	Views	Purchases
2018-04-05 00:00:00	150.000000	44.000000
2018-04-05 00:30:00	166.000000	54.000000
2018-04-05 01:00:00	214.000000	72.000000
2018-04-05 01:30:00	242.000000	80.000000
2018-04-05 02:00:00	158.000000	26.000000
2018-04-05 02:30:00	166.000000	20.000000
2018-04-05 03:00:00	220.000000	56.000000

Click the Visualization tab. Format the results as an area chart.

The difference between the two areas indicates that many of the views did not become to purchases. If all of the views became purchases, you would expect the areas to overlay on top each other completely. There would be no difference between the two areas.

Use the where clause to control the number of series returned

The following examples use the where clause to control the number of series values returned in the time-series chart.

Example 1: Show the 5 most rare series based on the minimum count values. All other series values will be labeled as "other".

index=_internal | timechart span=1h count by source WHERE min in bottom5

Example 2: Show the 5 most frequent series based on the maximum values. All other series values will be labeled as "other".

index=_internal | timechart span=1h count by source WHERE max in top5

This search and the previous one return six data series, the five top or bottom series specified and the series labeled other. To hide the other series, specify the argument useother=f.

Example 3: Show the source series count of INFO events, but only where the total number of events is larger than 100. All other series values are labeled as "other".

index=_internal | timechart span=1h sum(eval(if(log_level=="INFO",1,0))) by source WHERE sum > 100

Example 4: Use the where clause with the count function to measure the total number of events over the period. This yields results similar to using the sum function.

The following two searches return the sources series with a total count of events greater than 100. All other series values are labeled as "other".

index=_internal | timechart span=1h count by source WHERE count > 100

index=_internal | timechart span=1h count by source WHERE sum > 100

Related answers from Splunk Community

timechart

Description

Syntax

Required arguments

Optional arguments

Stats function options

Bin options

Span options

tc options

where clause

Usage

bins and span arguments

Default time spans

Spans used when minspan is specified

Bin time spans and local time

Bin time spans versus per_* functions

Subsecond bin time spans

Split-by fields

Functions and memory usage

Lexicographical order

Basic examples

1. Chart the product of the average "CPU" and average "MEM" for each "host"

2. Chart the average of cpu_seconds by processor

3. Chart the average of "CPU" for each "host"

4. Chart the average "cpu_seconds" by "host" and remove outlier values

5. Chart the average "thruput" of hosts over time

6. Chart the eventypes by source_ip

7. Align the chart time bins to local time

8. In a multivalue BY field, remove duplicate values

Extended examples

1. Chart revenue for the different products

2. Chart daily purchases by product type

3. Display results in 1 week intervals

4. Count the revenue for each item over time

5. Chart product views and purchases for a single day

Use the where clause to control the number of series returned

See also

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