Some aggregate functions can accept not only argument columns (used for compression), but a set of parameters – constants for initialization. The syntax is two pairs of brackets instead of one. The first is for parameters, and the second is for arguments.
histogramCalculates an adaptive histogram. It does not guarantee precise results.
The functions uses A Streaming Parallel Decision Tree Algorithm. The borders of histogram bins are adjusted as new data enters a function. In common case, the widths of bins are not equal.
Arguments
values — Expression resulting in input values.
Parameters
number_of_bins — Upper limit for the number of bins in the histogram. The function automatically calculates the number of bins. It tries to reach the specified number of bins, but if it fails, it uses fewer bins.
Returned values
Array of Tuples of the following format:
lower — Lower bound of the bin.upper — Upper bound of the bin.height — Calculated height of the bin.Example
You can visualize a histogram with the bar function, for example:
In this case, you should remember that you do not know the histogram bin borders.
sequenceMatchChecks whether the sequence contains an event chain that matches the pattern.
Syntax
Note
Events that occur at the same second may lay in the sequence in an undefined order affecting the result.
Arguments
timestamp — Column considered to contain time data. Typical data types are Date and DateTime. You can also use any of the supported UInt data types.
cond1, cond2 — Conditions that describe the chain of events. Data type: UInt8. You can pass up to 32 condition arguments. The function takes only the events described in these conditions into account. If the sequence contains data that isn't described in a condition, the function skips them.
Parameters
pattern — Pattern string. See Pattern syntax.Returned values
Type: UInt8.
(?N) — Matches the condition argument at position N. Conditions are numbered in the [1, 32] range. For example, (?1) matches the argument passed to the cond1 parameter.
.* — Matches any number of events. You do not need conditional arguments to match this element of the pattern.
(?t operator value) — Sets the time in seconds that should separate two events. For example, pattern (?1)(?t>1800)(?2) matches events that occur more than 1800 seconds from each other. An arbitrary number of any events can lay between these events. You can use the >=, >, <, <=, == operators.
Examples
Consider data in the t table:
Perform the query:
The function found the event chain where number 2 follows number 1. It skipped number 3 between them, because the number is not described as an event. If we want to take this number into account when searching for the event chain given in the example, we should make a condition for it.
In this case, the function couldn't find the event chain matching the pattern, because the event for number 3 occurred between 1 and 2. If in the same case we checked the condition for number 4, the sequence would match the pattern.
See Also
sequenceCountCounts the number of event chains that matched the pattern. The function searches event chains that do not overlap. It starts to search for the next chain after the current chain is matched.
Note
Events that occur at the same second may lay in the sequence in an undefined order affecting the result.
Syntax
Arguments
timestamp — Column considered to contain time data. Typical data types are Date and DateTime. You can also use any of the supported UInt data types.
cond1, cond2 — Conditions that describe the chain of events. Data type: UInt8. You can pass up to 32 condition arguments. The function takes only the events described in these conditions into account. If the sequence contains data that isn't described in a condition, the function skips them.
Parameters
pattern — Pattern string. See Pattern syntax.Returned values
Type: UInt64.
Example
Consider data in the t table:
Count how many times the number 2 occurs after the number 1 with any amount of other numbers between them:
sequenceMatchEventsReturn event timestamps of longest event chains that matched the pattern.
Note
Events that occur at the same second may lay in the sequence in an undefined order affecting the result.
Syntax
Arguments
timestamp — Column considered to contain time data. Typical data types are Date and DateTime. You can also use any of the supported UInt data types.
cond1, cond2 — Conditions that describe the chain of events. Data type: UInt8. You can pass up to 32 condition arguments. The function takes only the events described in these conditions into account. If the sequence contains data that isn't described in a condition, the function skips them.
Parameters
pattern — Pattern string. See Pattern syntax.Returned values
Type: Array.
Example
Consider data in the t table:
Return timestamps of events for longest chain
See Also
windowFunnelSearches for event chains in a sliding time window and calculates the maximum number of events that occurred from the chain.
The function works according to the algorithm:
The function searches for data that triggers the first condition in the chain and sets the event counter to 1. This is the moment when the sliding window starts.
If events from the chain occur sequentially within the window, the counter is incremented. If the sequence of events is disrupted, the counter isn't incremented.
If the data has multiple event chains at varying points of completion, the function will only output the size of the longest chain.
Syntax
Arguments
timestamp — Name of the column containing the timestamp. Data types supported: Date, DateTime and other unsigned integer types (note that even though timestamp supports the UInt64 type, it's value can't exceed the Int64 maximum, which is 2^63 - 1).cond — Conditions or data describing the chain of events. UInt8.Parameters
window — Length of the sliding window, it is the time interval between the first and the last condition. The unit of window depends on the timestamp itself and varies. Determined using the expression timestamp of cond1 <= timestamp of cond2 <= ... <= timestamp of condN <= timestamp of cond1 + window.mode — It is an optional argument. One or more modes can be set.
'strict_deduplication' — If the same condition holds for the sequence of events, then such repeating event interrupts further processing. Note: it may work unexpectedly if several conditions hold for the same event.'strict_order' — Don't allow interventions of other events. E.g. in the case of A->B->D->C, it stops finding A->B->C at the D and the max event level is 2.'strict_increase' — Apply conditions only to events with strictly increasing timestamps.'strict_once' — Count each event only once in the chain even if it meets the condition several timesReturned value
The maximum number of consecutive triggered conditions from the chain within the sliding time window. All the chains in the selection are analyzed.
Type: Integer.
Example
Determine if a set period of time is enough for the user to select a phone and purchase it twice in the online store.
Set the following chain of events:
eventID = 1003).eventID = 1007, product = 'phone').eventID = 1009).eventID = 1010).Input table:
Find out how far the user user_id could get through the chain in a period in January-February of 2019.
Query:
Result:
retentionThe function takes as arguments a set of conditions from 1 to 32 arguments of type UInt8 that indicate whether a certain condition was met for the event. Any condition can be specified as an argument (as in WHERE).
The conditions, except the first, apply in pairs: the result of the second will be true if the first and second are true, of the third if the first and third are true, etc.
Syntax
Arguments
cond — An expression that returns a UInt8 result (1 or 0).Returned value
The array of 1 or 0.
Type: UInt8.
Example
Let's consider an example of calculating the retention function to determine site traffic.
1. Create a table to illustrate an example.
Input table:
Query:
Result:
2. Group users by unique ID uid using the retention function.
Query:
Result:
3. Calculate the total number of site visits per day.
Query:
Result:
Where:
r1- the number of unique visitors who visited the site during 2020-01-01 (the cond1 condition).r2- the number of unique visitors who visited the site during a specific time period between 2020-01-01 and 2020-01-02 (cond1 and cond2 conditions).r3- the number of unique visitors who visited the site during a specific time period on 2020-01-01 and 2020-01-03 (cond1 and cond3 conditions).Calculates the number of different values of the argument up to a specified limit, N. If the number of different argument values is greater than N, this function returns N + 1, otherwise it calculates the exact value.
Recommended for use with small Ns, up to 10. The maximum value of N is 100.
For the state of an aggregate function, this function uses the amount of memory equal to 1 + N * the size of one value of bytes. When dealing with strings, this function stores a non-cryptographic hash of 8 bytes; the calculation is approximated for strings.
For example, if you had a table that logs every search query made by users on your website. Each row in the table represents a single search query, with columns for the user ID, the search query, and the timestamp of the query. You can use uniqUpTo to generate a report that shows only the keywords that produced at least 5 unique users.
uniqUpTo(4)(UserID) calculates the number of unique UserID values for each SearchPhrase, but it only counts up to 4 unique values. If there are more than 4 unique UserID values for a SearchPhrase, the function returns 5 (4 + 1). The HAVING clause then filters out the SearchPhrase values for which the number of unique UserID values is less than 5. This will give you a list of search keywords that were used by at least 5 unique users.
This function behaves the same as sumMap except that it also accepts an array of keys to filter with as a parameter. This can be especially useful when working with a high cardinality of keys.
Syntax
sumMapFiltered(keys_to_keep)(keys, values)
Parameters
Returned Value
Example
Query:
Result:
sumMapFilteredWithOverflowThis function behaves the same as sumMap except that it also accepts an array of keys to filter with as a parameter. This can be especially useful when working with a high cardinality of keys. It differs from the sumMapFiltered function in that it does summation with overflow - i.e. returns the same data type for the summation as the argument data type.
Syntax
sumMapFilteredWithOverflow(keys_to_keep)(keys, values)
Parameters
Returned Value
Example
In this example we create a table sum_map, insert some data into it and then use both sumMapFilteredWithOverflow and sumMapFiltered and the toTypeName function for comparison of the result. Where requests was of type UInt8 in the created table, sumMapFiltered has promoted the type of the summed values to UInt64 to avoid overflow whereas sumMapFilteredWithOverflow has kept the type as UInt8 which is not large enough to store the result - i.e. overflow has occurred.
Query:
Result:
sequenceNextNodeReturns a value of the next event that matched an event chain.
Experimental function, SET allow_experimental_funnel_functions = 1 to enable it.
Syntax
Parameters
direction — Used to navigate to directions.
base — Used to set the base point.
event1.event1.Arguments
timestamp — Name of the column containing the timestamp. Data types supported: Date, DateTime and other unsigned integer types.event_column — Name of the column containing the value of the next event to be returned. Data types supported: String and Nullable(String).base_condition — Condition that the base point must fulfill.event1, event2, ... — Conditions describing the chain of events. UInt8.Returned values
event_column[next_index] — If the pattern is matched and next value exists.NULL - If the pattern isn't matched or next value doesn't exist.Type: Nullable(String).
Example
It can be used when events are A->B->C->D->E and you want to know the event following B->C, which is D.
The query statement searching the event following A->B:
Result:
Behavior for forward and head
Behavior for backward and tail
Behavior for forward and first_match
Behavior for backward and last_match
Behavior for base_condition
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