Date: Fri, 29 Mar 2024 16:18:14 +0100 (CET)
Message-ID: <1269718526.177.1711725494018@vmisdata19.uni-oldenburg.de>
Subject: Exported From Confluence
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In this tutorial you will learn how to write aggregation queries=
and learn about the window concept in Odysseus.
We will use the same setting as in Simple Query Processing. So you should follow steps 1-=
4.
Aggregation
Odysseus provides a wide range of aggregation operators. In this tutoria=
l we will focus on the typical aggreations average (AVG), count (COUNT), su=
m (SUM), minimum (MIN) and maximum (MAX).
Crate a new Odysseus Script file with the PQL template an name it aggreg=
ation1. Change #RUNQUERY to #ADDQUERY. By this, the query will only be inst=
alled an not started, so its easier to see what happens.
Write the following query:
#PARSER=
PQL
#TRANSCFG Standard
#ADDQUERY
out =3D AGGREGATE({
aggregations=3D[['COUNT', 'price', 'COUNT_price', 'integer']]
},
nexmark:bid
)
Here you can see the aggreation operator that connects to source nexmark=
:bid. The aggregate functions are defined by the aggregations parameter. In=
this example you can see four entries. The first entry defines the aggreag=
te function, COUNT in this case. The second entry, the attribute can should=
be used for the aggregation. For COUNT is does not matter, which attribute=
of the input element should be treated, but one attribute must be given, s=
o be choose price. The third parameter defines the name of the output attri=
bute, i.e. what is the new name for the output and finally, the last parame=
ter determines the output data type. This parameter is optional.
Now execute the script, and show the result stream (e.g. as a table). No=
w start the query (e.g. with the selection of the Start Query Button).
The result should be something like this:
Here you can see 15 bids that have entered the system.
Other aggregations can be added easily, by adding another value to the a=
ggregations parameter.
Remove the old query.
In the Project Exploer
- right click on the aggregation1.qry file and choose Copy
- right clock on the Project Name (FirstSteps) and choose Paste
- Enter a new name for the query: aggregation2.qry
- Open the new file by double clicking in the project explorer
Replace the input with the following:
#PARSER=
PQL
#TRANSCFG Standard
#ADDQUERY
out =3D AGGREGATE({
aggregations=3D[
['COUNT', 'price', 'COUNT_price', 'integer'],
['AVG', 'price', 'AVG_price'],
['SUM', 'price', 'SUM_price'],
['MIN', 'price', 'MIN_price'],
['MAX', 'price', 'MAX_price']
] =20
},
nexmark:bid
)
The results of the query should look somehow like the following:
Now its time to highlight the Metadata part a little more.
In the last column you can see two values, separated by "|". The two val=
ues show the validity of the event. 7500|8000 e.g. means, that this event w=
as valid in real time starting at 7500 (in Nexmark this means after 7.5 sec=
onds) and ends before 8000, mathematical this is a right o=
pen interval: [7500,8000).
If you look at the values, you can see that they are not overlapping. Th=
is should be rather clear, because the count e.g. can not be 1 and 2 a=
t the same time. The metadata show, that every 500 (in this case millisecon=
ds) a new bid is send to the system and a new aggregation is build.
Another important think to note is, that a new aggregation will be calcu=
lated, when a new event arrives a the system. For this example it take 8 se=
conds before the first value is created. For streams with a low data rate t=
he AssureHeartbeat operator can be used to produce values on a regular base.
There is also the possibility to define groups (as in SQL group by). The=
below in Element Bas=
ed Windows for an example.
Window
In the example above you can see that the aggregated values always conta=
in the information of all previous events.Typically, the older the event is=
, the less interesting is it. An auction system provider may not only be in=
terested in the alltime highest bit, but e.g. the highest bit for a specifi=
c time (e.g. the last 24 hours) or related to a number of bit that have bee=
n posed.
For this, Odysseus allows to define windows. A window defines a subset o=
f elements that should be treated together, e.g. should be aggregated.These=
subset can overlap (so called sliding windows) or be disjunct (tumbling wi=
ndows).
The following picture shows such a window. In this case 6 elements are i=
n the window.
Odysseus provides three types of windows:
- Time based
- Element based
- Predicate based
Time Based Window
The time based window allows to define windows based on the time.
Create a new PQL script and name it timewindow1:
#PARSER=
PQL
#TRANSCFG Standard
#ADDQUERY
out =3D TIMEWINDOW({SIZE=3D10000},nexmark:bid)
In this example we define a time based window over the bid source with a=
size of 10000, here 10 seconds.
If you run the query and look at the results you will see something like=
this:
If you look at the metadata you can see that each element has a time int=
erval ranging from it start timestamp to a future time that lies 10000 ahea=
d, e.g. 7500 to 17500, 8000 to 18000 and so on. The meaning of the time is,=
that elements that are valid at the same time should be treated together. =
If you look e.g. at time 10000 there are 6 events which time interval conta=
ins this value. The seventh element (with start time stamp 10500) is part o=
f another window.
We can now combine the window with the aggregation.
Remove the running query.
Create a new PQL query (window_aggregation1) with the following input:=
p>
#PARSER=
PQL
#TRANSCFG Standard
#ADDQUERY
windowed =3D TIMEWINDOW({SIZE=3D10000},nexmark:bid)
out =3D AGGREGATE({
aggregations=3D[
['COUNT', 'price', 'COUNT_price', 'integer'],
['AVG', 'price', 'AVG_price'],
['SUM', 'price', 'SUM_price'],
['MIN', 'price', 'MIN_price'],
['MAX', 'price', 'MAX_price']
] =20
},
windowed
)
If you run the query the result should look like this:
You can see, that the count attribute is counted up to 20. This mean tha=
t in this case 10000 (here 10 seconds) contains 20 events. If you look at t=
he result of the above window query, you can see that the first and the twe=
ntieth element have an overlapping time stamp, and the 21th element not. So=
the new window only treats element 2 to 21.
Note, in this szenario the elements are generated in a constant fashion,=
so the size of the window is always 20. In "real" szenarios this is typica=
lly not the case and the size of the windows in terms of containg elements =
differ (e.g. count the number of cars for an hour).
The window we defined is called a sliding window. The query delivers the=
aggregations about the bids that were posed in the last 10 seconds.
Sometimes, the result should not be in a sliding way. E.g. if a provider=
wants to know how many bids are posed in 10 second intervals. If the size =
is 10 seconds, this is called a tumbling window.
A tumbling window can be defined with one of the two parameters: ADVANCE=
or SLIDE. The difference is how the start time stamp of the elements in th=
e window are treated. Let us start with ADVANCE.
Remove all running queries.
Create a new PQL script timewindow2.qry as in the following:
#PARSER=
PQL
#TRANSCFG Standard
#ADDQUERY
out =3D TIMEWINDOW({SIZE=3D10000, ADVANCE=3D10000},nexmark:bid)
Running should show you something like this:
You can see, that the timestamps are different. Each events belonging to=
the same window has the same end time stamp (e.g. 10.000, 20.000, 30.000).=
If you run the following aggregation query (window_aggregation2)
#PARSER=
PQL
#TRANSCFG Standard
#ADDQUERY
windowed =3D TIMEWINDOW({SIZE=3D10000, ADVANCE=3D10000},nexmark:bid)
out =3D AGGREGATE({
aggregations=3D[
['COUNT', 'price', 'COUNT_price', 'integer'],
['AVG', 'price', 'AVG_price'],
['SUM', 'price', 'SUM_price'],
['MIN', 'price', 'MIN_price'],
['MAX', 'price', 'MAX_price']
] =20
},
windowed
)
you will see the following output (when no other query is running!):
As you can see, the aggregations count up to 20 and then start again at =
1. This is not exactly, what the provider wanted. The problem here is, that=
the elments have different start time stamps and only those parts of the e=
lements are aggregated where the time intervals overlap.
A way to cope with this is to use another parameter called SLIDE. With s=
lide all start timestamp are set to the same value. This can be interpreted=
as reducing the granularity of the time domain.
So remove all queries. A start a new PQL query (timewindow3)
#PARSER=
PQL
#TRANSCFG Standard
#ADDQUERY
out =3D TIMEWINDOW({SIZE=3D10000, SLIDE=3D10000},nexmark:bid)
After starting this query, you should see something like:
Now you can see, that for one window all time intervals are the same.
If we now run the following query (window_aggregation3):
#PARSER=
PQL
#TRANSCFG Standard
#ADDQUERY
windowed =3D TIMEWINDOW({SIZE=3D10000, SLIDE=3D10000},nexmark:bid)
out =3D AGGREGATE({
aggregations=3D[
['COUNT', 'price', 'COUNT_price', 'integer'],
['AVG', 'price', 'AVG_price'],
['SUM', 'price', 'SUM_price'],
['MIN', 'price', 'MIN_price'],
['MAX', 'price', 'MAX_price']
] =20
},
windowed
)
You will get the following output:
Note: You could ignore the first line, it was created because when the q=
uery was stopped.
Here you can see the aggregation over 10 second intervals.
Element Based Window
Another way to define a window, is based on elements, e.g. the last 10 e=
lements received. In Odysseus, the element based window is implemented with=
timestamps, too.
Create a new PQL script:
#PARSER=
PQL
#TRANSCFG Standard
#ADDQUERY
out =3D ELEMENTWINDOW({SIZE=3D10}, nexmark:bid)
Here you can sse the operator ELEMENTWINDOW with the parameter SIZE, tha=
t defines a window containing 10 elements.
If you start the query, you will see:
If you look at the time stamps, you will see that the end timestamp of t=
he first element is 12500. This is exactly the start time stamp of the elev=
enth element, that should not be in the same window as the first element (s=
ize =3D 10). To be able to determine the end time stamp the output of the f=
irst element, the system needs to be wait until the eleventh element has ar=
rived at the system. So, using element based windows may leed to a higher l=
atency.
In the same way, ADVANCE and SLIDE are used in the time based windows, t=
hese parameters can be used here.
A special case in element based windows is the partition parameter. Typi=
cally, the size is defined over all elements in the stream. With the partit=
ion parameter you can define an attribute that partitions the stream. In th=
is case, the window will defined over elements where the value of this attr=
ibute are the same.
Note: If the stream has a low data rate, it may take very long before th=
e first elements are created!
TODO: Example for Partition Window
Group By
TODO and Partition Window
Predicate Based Window=
The most generic way to define windows
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