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From Xingcan Cui <xingc...@gmail.com>
Subject Re: A "per operator instance" window all ?
Date Mon, 19 Feb 2018 01:08:46 GMT
Hi Julien,

sorry for my misunderstanding before. For now, the window can only be defined on a KeyedStream
or an ordinary DataStream but with parallelism = 1. I’d like to provide three options for
your scenario.

1. If your external data is static and can be fit into the memory, you can use ManagedStates
<https://ci.apache.org/projects/flink/flink-docs-master/dev/stream/state/state.html#using-managed-keyed-state>
to cache them without considering the querying problem.
2. Or you can use a CustomPartitioner <https://ci.apache.org/projects/flink/flink-docs-master/dev/stream/operators/#physical-partitioning>
to manually distribute your alert data and simulate an window operation by yourself in a ProcessFuncton.
3. You may also choose to use some external systems such as in-memory store, which can work
as a cache for your queries.

Best,
Xingcan

> On 19 Feb 2018, at 5:55 AM, Julien <jmassiot77@gmail.com> wrote:
> 
> Hi Xingcan,
> 
> Thanks for your answer.
> Yes, I understand that point:
> if I have 100 resource IDs with parallelism of 4, then each operator instance will handle
about 25 keys
> 
> The issue I have is that I want, on a given operator instance, to group those 25 keys
together in order to do only 1 query to an external system per operator instance:
> 
> on a given operator instance, I will do 1 query for my 25 keys
> so with the 4 operator instances, I will do 4 query in parallel (with about 25 keys per
query)
> 
> I do not know how I can do that.
> 
> If I define a window on my keyed stream (with for example stream.key(_.resourceId).window(TumblingProcessingTimeWindows.of(Time.milliseconds(500))),
then my understanding is that the window is "associated" to the key. So in this case, on a
given operator instance, I will have 25 of those windows (one per key), and I will do 25 queries
(instead of 1).
> 
> Do you understand my point ?
> Or maybe am I missing something ?
> 
> I'd like to find a way on operator instance 1 to group all the alerts received on those
25 resource ids and do 1 query for those 25 resource ids.
> Same thing for operator instance 2, 3 and 4.
> 
> 
> Thank you,
> Regards.
> 
> 
> On 18/02/2018 14:43, Xingcan Cui wrote:
>> Hi Julien,
>> 
>> the cardinality of your keys (e.g., resource ID) will not be restricted to the parallelism.
For instance, if you have 100 resource IDs processed by KeyedStream with parallelism 4, each
operator instance will handle about 25 keys. 
>> 
>> Hope that helps.
>> 
>> Best,
>> Xingcan
>> 
>>> On 18 Feb 2018, at 8:49 PM, Julien <jmassiot77@gmail.com <mailto:jmassiot77@gmail.com>>
wrote:
>>> 
>>> Hi,
>>> 
>>> I am pretty new to flink and I don't know what will be the best way to deal with
the following use case:
>>> 
>>> as an input, I recieve some alerts from a kafka topic
>>> an alert is linked to a network resource (like router-1, router-2, switch-1,
switch-2, ...)
>>> so an alert has two main information (the alert id and the resource id of the
resource on which this alert has been raised)
>>> then I need to do a query to an external system in order to enrich the alert
with additional information on the resource
>>> 
>>> (A "natural" candidate for the key on this stream will be the resource id)
>>> 
>>> The issue I have is that regarding the query to the external system:
>>> I do not want to do 1 query per resource id
>>> I want to do a small number of queries in parallel (for example 4 queries in
parallel every 500ms), each query requesting the external system for several alerts linked
to several resource id
>>> Currently, I don't know what will be the best way to deal with that:
>>> I can key my stream on the resource id and then define a processing time window
of 500ms and when the trigger is ok, then I do my query
>>> by doing so, I will "group" several alerts in a single query, but they will all
be linked to the same resource.
>>> so I will do 1 query per resource id (which will be too much in my use case)
>>> I can also do a windowAll on a non keyed stream
>>> by doing so, I will "group" together alerts from different resource ids, but
from what I've read in such a case the parallelism will always be one.
>>> so in this case, I will only do 1 query whereas I'd like to have some parallelism
>>> I am thinking that a way to deal with that will be:
>>> 
>>> define the resource id as the key of stream and put a parallelism of 4
>>> and then having a way to do a windowAll on this keyed stream
>>> which is that, on a given operator instance, I will "group" on the same window
all the keys (ie all the resource ids) managed by this operator instance
>>> with a parallelism of 4, I will do 4 queries in parallel (1 per operator instance,
and each query will be for several alerts linked to several resource ids)
>>> But after looking at the documentation, I cannot see this ability (having a windowAll
on a keyed stream).
>>> 
>>> Am I missing something?
>>> 
>>> What will be the best way to deal with such a use case?
>>> 
>>> 
>>> I've tried for example to review my key and to do something like "resourceId.hahsCode%<max
nb of queries in parallel>" and then to use a time window.
>>> 
>>> In my example above, the <max nb of queries in parallel> will be 4. And
all my keys will be 0, 1, 2 or 3.
>>> 
>>> The issue with this approach is that due to the way the operatorIdx is computed
based on the key, it does not distribute well my processing:
>>> 
>>> when this partitioning logic from the "KeyGroupRangeAssignment" class is applied
>>>     /**
>>>      * Assigns the given key to a parallel operator index.
>>>      *
>>>      * @param key the key to assign
>>>      * @param maxParallelism the maximum supported parallelism, aka the number
of key-groups.
>>>      * @param parallelism the current parallelism of the operator
>>>      * @return the index of the parallel operator to which the given key should
be routed.
>>>      */
>>>     public static int assignKeyToParallelOperator(Object key, int maxParallelism,
int parallelism) {
>>>         return computeOperatorIndexForKeyGroup(maxParallelism, parallelism, assignToKeyGroup(key,
maxParallelism));
>>>     }
>>> 
>>>     /**
>>>      * Assigns the given key to a key-group index.
>>>      *
>>>      * @param key the key to assign
>>>      * @param maxParallelism the maximum supported parallelism, aka the number
of key-groups.
>>>      * @return the key-group to which the given key is assigned
>>>      */
>>>     public static int assignToKeyGroup(Object key, int maxParallelism) {
>>>         return computeKeyGroupForKeyHash(key.hashCode(), maxParallelism);
>>>     }
>>> key 0, 1, 2 and 3 are only assigned to operator 2 and 3 (so 2 over my 4 operators
will not have anything to do)
>>> 
>>> So, what will be the best way to deal with that?
>>> 
>>> 
>>> 
>>> Thank you in advance for your support.
>>> 
>>> Regards.
>>> 
>>> 
>>> 
>>> Julien.
>>> 
>>> 
>> 
> 


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