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from <stream-name> {<conditions>}#window.<window-name>(<parameters>)
select ( {<attribute-name>} | ‘*’ |)
insert [<output-type>] into <stream-name> 

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There are several types of windows.

1. lengthWindow Length windowswindow - a sliding window that keeps last N events.
2. Time window - a sliding window that keeps events arrived within the last T time period.
3. Time batch window - a time window that processes events in batches. A loop collects the incoming events arrived within last T time period, and outputs them as a batch.
4. Length batch window - a length window that outputs events as a batch only at the nth event arrival.
5. Time length window (not supported in the current version) - a sliding window that keeps the last N events that arrived within the last T time period.
6. Unique window - keeps only the latest events that are unique according to the given unique attribute.
7. First unique window  - keeps the first events that are unique according to the given unique attribute.
8. External Time Window - a sliding window that processes according to timestamps defined externally (Defined as an attribute in the incoming stream) 

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1. sum
2. avg
3. max
4. min
5. count
6. median (not supported in current version)
7. stddev (not supported in current version) 
8. avedev (not supported in current version) 

Info
titleNote

For most of the aggregate functions such as sum(), max() etc, the default output data type remains to be the same as the inputs. But for functions that involve division, such as avg(), the output type is double. For others that do not require floating points, such as count(), the output type is int.These Aggregate function gives different data types as an output based on the input data type. Please see the below table to get more information on this.

 Input Data Type Output Data Type
sumdouble double
float double
int long
long long
 
avglong double
double double
int double
 long long
 

max
& min

 

long long
double double
int int
float float
 
count  long

Aggregate function must be named using ‘as’ keyword. Thus name can be used for referring that attribute, and will be used as the attribute name in the output stream. Following examples shows some queries.

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A sliding window that keeps last N events.

From the events having price >= 20 of the StockExchangeStream stream, output the expiring events of the length window to the StockQuote stream. Here the output events will have symbol and the per symbol average price as their attributes, only if the  per symbol average price > 50.

from StockExchangeStream[price >= 20]#window.length(50) 
select symbol, avg(price) as avgPrice

group by symbol having avgPrice>50 
insert into StockQuote for expired-events 


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A sliding window that keeps events arrived within the last T time period.

From the events having symbol == 20 of the StockExchangeStream stream, output the both the newly arriving and expiring events of the time window to the IBMStockQuote stream. Here the output events will have maximum, average and minimum prices that has arrived within last minute as their attributes.

from StockExchangeStream[symbol == 'IBM']#window.time( 1 min ) 
select max(price) as maxPrice, avg(price) as avgPrice, min(price) as minPrice
insert into IBMStockQuote for all-events  

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A time window that processes events in batches. This in a loop collects the incoming events arrived within last T time period and outputs them as a batch.

From the events of the StockExchangeStream stream, output the events per every 2 minutes from the timeBatch window to the StockQuote stream. Here the output events will have symbol and the per symbol sum of volume for last 2 minutes as their attributes.

from StockExchangeStream#window.timeBatch( 2 min
select symbol, sum(volume) as totalVolume
group by symbol 
insert into StockQuote 

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A length window that outputs events as a batch only at the nth event arrival.

From the events having price >= 20 of the StockExchangeStream stream, output the expiring events of the lengthBatch window to the StockQuote stream. Here the output events will have symbol and the per symbol average price> 50 as their attributes.

from StockExchangeStream[price >= 20]#window.lengthBatch(50) 
select symbol, avg(price) as avgPrice
group by symbol having avgPrice>50

insert into StockQuote  for expired-events

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A  window that keeps only the latest events that are unique according to the given attribute.

From the events of the StockExchangeStream stream, output the expiring events of the unique window to the StockQuote stream. The output events have symbol, price and volume as their attributes.

from StockExchangeStream#window.unique("symbol"
select symbol, price, volume

insert into StockQuote for expired-events

Info
titleInfo

Here, the output event is the immediate previous event having the same symbol of the current event.

Unique window is mostly used in Join Queries, E.g If you want to get the current stock price of any symbol, you can join the SymbolStream (has an attribute symbol) with a Unique window  as follows;

from SymbolStream#window.lenght(1) unidirectional join StockExchangeStream#window.unique("symbol"
select StockExchangeStream.symbol as symbol,StockExchangeStream.price as lastTradedPrice

insert  into StockQuote You can find a sample at http://ushanib.blogspot.com/2013/02/sample-demonstrate-unique-window-and.html

First unique window
Anchor
FirstUniqueWindow
FirstUniqueWindow

A window that keeps the first events that are unique according to the given unique attribute

From the events of the StockExchangeStream stream, output the events of the firstUnique window to the StockQuote stream. The output events have symbol, price and volume as their attributes.

from StockExchangeStream#window.firstUnique("symbol"
select symbol, price, volume

insert into StockQuote 

Info
titleinfo

Here, the output event is the first event arriving for each symbol.

FirstUnique window is mostly used in Join Queries, E.g If you want to know if a symbol has ever been traded in this StockExchange, you can join the SymbolStream (has an attribute symbol) with a First unique window as follows;

from SymbolStream#window.lenght(1) unidirectional join StockExchangeStream#window.firstUnique("symbol"
select StockExchangeStream.symbol as symbol

insert  into AvailableSymbolStream You can find a sample at http://ushanib.blogspot.com/2013/02/sample-demonstrate-unique-window-and.html

 

External Time Window
Anchor
ExternalTimeWindow
ExternalTimeWindow

A window that would process time not according to the host system time, but according to timestamps provided externally by the input stream.

From the events of the LoginEvents stream, output the events of the login events of last 5 seconds based on the attribute 'timeStamp' of the stream. The output events have timeStamp, ip and count of login events during last 5 seconds as their attributes.

from LoginEvents#window.externalTime(timeStamp,5 sec)
select timeStamp, ip, count(timeStamp) as lastFiveSecLoginCount
insert into slidingFiveSecLoginInfo for all-events ;


Supported units for time windows

The following units are supported when specifying the time for a time window.

UnitSyntax
Yearyear | years
Monthmonth | months
Weekweek | weeks
Dayday | days
Hourhour | hours
Minutesminute | minutes | min
Secondssecond | seconds | sec
Millisecondsmillisecond | milliseconds

Note that each unit supports both the singular and plural format (e.g. second, seconds) and some units have a shortened form (i.e. sec, min).

Following examples use different formats available for 'minutes'.

from StockExchangeStream[symbol == 'WSO2']#window.time( 1 minute ) 
select max(price) as maxPrice, avg(price) as avgPrice, min(price) as minPrice
insert into WSO2StockQuote for all-events  

from StockExchangeStream[symbol == 'FBX']#window.timeBatch( 5 minutes ) 
select max(price) as maxPrice, avg(price) as avgPrice, volume
insert into FBStockQuote

from StockExchangeStream[symbol == 'FBX']#window.timeBatch( 5 min ) 
select max(price) as maxPrice, avg(price) as avgPrice, volume
insert into FBStockQuote

Excerpt
hiddentrue

Siddhi windows in wiki format

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