Date: Thu, 28 Mar 2024 22:25:12 +0100 (CET)
Message-ID: <628555331.73.1711661112873@vmisdata19.uni-oldenburg.de>
Subject: Exported From Confluence
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Joins tuple from two input streams iff their timestamps are over=
lapping and if the optional predicate validates to true.
Parameters
Parameters for the elem=
ent join
- elementsizeport0, elementsizeport1 (see below)
- group_by_port_0, group_by_port_1 (see below)
- keepEndTimestamp (see below)<=
/li>
The card parameter describes how input elements can be joined. This opti=
onal information can be used to optimize processing (i.e. using less memory=
, because elements can earlier be discarded).
-
- ONE_ONE: In each input stream there is exactly one corresponding object=
. With this setting, windows can potentially be avoided.
- ONE_MANY: Each element in the right input stream has exactly one corres=
ponding element in the left input stream
- MANY_ONE: Each element in the left input stream has exactly one corresp=
onding element in the right input stream
- MANY_MANY: For each element in both input streams there may be multiple=
corresponding elements.
Example
PQL
output =
=3D join({predicate =3D 'auction_id =3D auction'}, left, right)
CQL
SELECT =
* FROM left, right WHERE auction_id =3D auction
Element Join
Sometimes it is necessary to have an element window before a join, for e=
xample, to only use the latest element for each or from one of the two inpu=
t ports. Unfortunately, an element window has a blocking behavior. Using th=
e element join avoids this blocking behavior by integrating the element win=
dow into the join operator itself. Therefore, there is no need to manually =
add an element window operator before this join.
With the parameters elementsizeport0 and elementsizeport1, the size of t=
he element window can be defined for each input port. Optionally, the count=
er can be grouped, for example by a certain id, as can be seen in the examp=
le below.
JOIN({
elementsizeport1 =3D 1,
elementsizeport0 =3D 1,
group_by_port_1 =3D ['id_right'],
group_by_port_0 =3D ['id_left'] =20
}, left, right)
When using an element window inside of the join, the blocking behavior i=
s omitted, but the end-timestamp of the results cannot be known. Therefore,=
the end-timestamp is removed (set to infinity) in this case. If you want t=
o keep the (semantically incorrect) end timestamp, you can use the keepEndT=
imestamp parameter and set it to true.
Behavior of the Element Jo=
in
The element join does not limit the number of elements in the respective=
SweepArea to the size of the window, but only joins the n newest =
possible matches. Therefore, if a new element enters the join operator, the=
older elements cannot simply be removed. This is depicted in the image bel=
ow, where the behavior is wrong:
The second element on the right removes the previous element. Then, on t=
he left side, an element enters the operator that would need to be joined w=
ith the now removed element. Hence, we don't get the results we need. There=
fore, the Element Join in Odysseus behaves differently:
Here, the later element on the left is joined with "not-the-newest" elem=
ent on the right, even though the element size on the right is set to one. =
Here we can see, that the elements on the left are joined with the newest p=
ossible element of the other side.
Why is there no end times=
tamp?
The following example shows the creation of end timestamps while using a=
n element window.
As can be seen, the end timestamp is set here. In the result elements, t=
he time intervals of the first two elements are overlapping, indicating tha=
t at the point in time 8 and 9 there are two results, even though the eleme=
nt window should only create one result for each time instance. That's why =
the end timestamp is set to an "unknown" infinity by default.
Using Heartbeats
You can use heartbeats to clean up the SweepAreas of the Element Join ea=
rlier to reduce memory consumption. The heartbeats do not change the query =
results. The cleanup in a Element Join with element size set to one can be =
seen in the Figure below:
Grouping / Partitions
You can use groups to have the count of n elements for each gro=
up. Here's an example with element size set to one (for bow sides), groupin=
g by the id and with heartbeats:
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