Re: Join a datastream with tables stored in Hive

[Leonard Xu]

Hi, Krzysztof   

> * I have a high pace stream of events coming in Kafka. 
> * I have some dimension tables stored in Hive. These tables are changed 
> daily. I can keep a snapshot for each day. 

For this use case, Flink supports temporal join the latest hive partition as 
temporal table now, you can refer the example in doc [1], and this feature will 
come soon with nearly Flink 1.12 release.

Best,
Leonard
[1] 
https://ci.apache.org/projects/flink/flink-docs-master/dev/table/connectors/hive/hive_read_write.html#temporal-join-the-latest-partition

Re: Join a datastream with tables stored in Hive

[Leonard Xu]

Hi, Maciej

> 
> I didn't find a SQL solution to this problem.
> 

Now Flink provides the SQL solution, you can see the doc[1], the Flink-1.12 
document link that posted by Chesnay should have updated but not..., I’ll check 
the document of 1.12.

Best,
Leonard
[1] 
https://ci.apache.org/projects/flink/flink-docs-master/dev/table/streaming/joins.html#event-time-temporal-joins
 

Re: Join a datastream with tables stored in Hive

[Maciej Bryński]

Hi,
There is an implementation only for temporal tables which needs some
Java/Scala coding (no SQL-only implementation).
On the same page there is annotation:
Attention Flink does not support event time temporal table joins currently.

So this is the reason, I'm asking this question.
My use case:
I want to join the Kafka stream with a table from JDBC source.
Every record in Kafka has event time. Also records in JDBC are versioned.
I didn't find a SQL solution to this problem.

Regards,
Maciek

wt., 1 gru 2020 o 20:31 Chesnay Schepler  napisał(a):

> According to the documentation
> 
> this is already implemented.
>
> On 12/1/2020 3:53 PM, maverick wrote:
>
> Hi Kurt,
> Is there any Jira task for tracking progress of adding event time support to
> temporal joins ?
>
> Regards,
> Maciek
>
>
>
> --
> Sent from: 
> http://apache-flink-user-mailing-list-archive.2336050.n4.nabble.com/
>
>
>

-- 
Maciek Bryński

Re: Join a datastream with tables stored in Hive

[Chesnay Schepler]

According to the documentation 
 
this is already implemented.


On 12/1/2020 3:53 PM, maverick wrote:

Hi Kurt,
Is there any Jira task for tracking progress of adding event time support to
temporal joins ?

Regards,
Maciek



--
Sent from: http://apache-flink-user-mailing-list-archive.2336050.n4.nabble.com/

Re: Join a datastream with tables stored in Hive

[maverick]

Hi Kurt,
Is there any Jira task for tracking progress of adding event time support to
temporal joins ?

Regards,
Maciek



--
Sent from: http://apache-flink-user-mailing-list-archive.2336050.n4.nabble.com/

Re: Join a datastream with tables stored in Hive

[Kurt Young]

Great, looking forward to hearing from you again.

Best,
Kurt


On Mon, Dec 16, 2019 at 10:22 PM Krzysztof Zarzycki 
wrote:

> Thanks Kurt for your answers.
>
> Summing up, I feel like the option 1 (i.e. join with temporal table
> function) requires some coding around a source, that needs to pull data
> once a day. But otherwise, bring the following benefits:
> * I don't have to put dicts in another store like Hbase. All stays in
> Hive + Flink.
> * I'll be able to make a true temporal join - event-time based.
> * I believe I will be able to build a history reprocessing program based
> on the same logic (i.e. same SQL). At least for a particular day -
> processing multiple days would be tricky, because I will need to pull
> multiple versions of the dictionary.
> Plus, looking up dict values will be much faster and resource optimal when
> dict is stored in a state instead of uncached Hbase. It's especially
> important in a case when we want to reprocess historical, archived stream
> with a speed of millions of events/sec.
>
> I understand that option 2 is easier to implement. I may do a PoC of it as
> well.
> OK, I believe I know enough to get my hands dirty with the code. I can
> share later on what I was able to accomplish. And probably more questions
> will show up when I finally start the implementation.
>
> Thanks
> Krzysztof
>
> pon., 16 gru 2019 o 03:14 Kurt Young  napisał(a):
>
>> Hi Krzysztof, thanks for the discussion, you raised lots of good
>> questions, I will try to reply them
>> one by one.
>>
>> Re option 1:
>>
>> > Question 1: do I need to write that Hive source or can I use something
>> ready, like Hive catalog integration? Or maybe reuse e.g. HiveTableSource
>> class?
>>
>> I'm not sure if you can reuse the logic of `HiveTableSource`. Currently
>> `HiveTableSource` works
>> as batch mode, it will read all data at once and stop. But what you need
>> is wait until next day after
>> finish. What you can try is reuse the logic of `HiveTableInputFormat`,
>> and wrap the "monitoring"
>> logic outside.
>>
>> > Question/worry 2:  the state would grow inifinitely if I had infinite
>> number of keys, but not only infinite number of versions of all keys.
>>
>> The temporal table function doesn't support watermark based state clean
>> up yet, but what you can
>> try is idle state retention [1]. So even if you have infinite number of
>> keys, for example say you have
>> different join keys every day, the old keys will not be touched in next
>> days and become idle and will
>> be deleted by framework.
>>
>> > Question 3: Do you imagine that I could use the same logic for both
>> stream processing and reprocessing just by replacing sources and sinks?
>>
>> Generally speaking, yes I think so. With event time based join, we should
>> be able to reuse the logic
>> of normal stream processing and reprocessing historical data. Although
>> there will definitely exists some
>> details should be addressed, like event time and watermarks.
>>
>> Re option 2:
>>
>> > maybe implement Hive/JDBC-based LookupableTableSource that  pulls the
>> whole dictionary to memory
>>
>> You can do this manually but I would recommend you go with the first
>> choice which loads hive table
>> to HBase periodically. It's much more easier and efficient. And this
>> approach you mentioned also
>> seems a little bit duplicate with the temporal table function solution.
>>
>> > this option is available only with Blink engine and also only with use
>> of Flink SQL, no Table API?
>>
>> I'm afraid yes, you can only use it with SQL for now.
>>
>> > do you think it would be possible to use the same logic / SQL for
>> reprocessing?
>>
>> Given the fact this solution is based on processing time, I don't think
>> it can cover the use case of
>> reprocessing, except if you can accept always joining with latest day
>> even during backfilling. But we
>> are also aiming to resolve this shortcoming maybe in 1 or 2 releases.
>>
>> Best,
>> Kurt
>>
>> [1]
>> https://ci.apache.org/projects/flink/flink-docs-master/dev/table/streaming/query_configuration.html#idle-state-retention-time
>>
>>
>> On Sat, Dec 14, 2019 at 3:41 AM Krzysztof Zarzycki 
>> wrote:
>>
>>> Very interesting, Kurt! Yes, I also imagined it's rather a very common
>>> case. In my company we currently have 3 clients wanting this functionality.
>>> I also just realized this slight difference between Temporal Join and
>>> Temporal Table Function Join, that there are actually two methods:)
>>>
>>> Regarding option 1:
>>> So I would need to:
>>> * write a Datastream API source, that pulls Hive dictionary table every
>>> let's say day, assigns event time column to rows and creates a stream of
>>> it. It does that and only that.
>>> * create a table (from Table API) out of it, assigning one of the
>>> columns as an event time column.
>>> * then use table.createTemporalTableFunction(>> time column>)
>>> * finally join my main data stream with the temporal table function (let
>>> me use short name 

Re: Join a datastream with tables stored in Hive

[Krzysztof Zarzycki]

Thanks Kurt for your answers.

Summing up, I feel like the option 1 (i.e. join with temporal table
function) requires some coding around a source, that needs to pull data
once a day. But otherwise, bring the following benefits:
* I don't have to put dicts in another store like Hbase. All stays in
Hive + Flink.
* I'll be able to make a true temporal join - event-time based.
* I believe I will be able to build a history reprocessing program based on
the same logic (i.e. same SQL). At least for a particular day - processing
multiple days would be tricky, because I will need to pull multiple
versions of the dictionary.
Plus, looking up dict values will be much faster and resource optimal when
dict is stored in a state instead of uncached Hbase. It's especially
important in a case when we want to reprocess historical, archived stream
with a speed of millions of events/sec.

I understand that option 2 is easier to implement. I may do a PoC of it as
well.
OK, I believe I know enough to get my hands dirty with the code. I can
share later on what I was able to accomplish. And probably more questions
will show up when I finally start the implementation.

Thanks
Krzysztof

pon., 16 gru 2019 o 03:14 Kurt Young  napisał(a):

> Hi Krzysztof, thanks for the discussion, you raised lots of good
> questions, I will try to reply them
> one by one.
>
> Re option 1:
>
> > Question 1: do I need to write that Hive source or can I use something
> ready, like Hive catalog integration? Or maybe reuse e.g. HiveTableSource
> class?
>
> I'm not sure if you can reuse the logic of `HiveTableSource`. Currently
> `HiveTableSource` works
> as batch mode, it will read all data at once and stop. But what you need
> is wait until next day after
> finish. What you can try is reuse the logic of `HiveTableInputFormat`, and
> wrap the "monitoring"
> logic outside.
>
> > Question/worry 2:  the state would grow inifinitely if I had infinite
> number of keys, but not only infinite number of versions of all keys.
>
> The temporal table function doesn't support watermark based state clean up
> yet, but what you can
> try is idle state retention [1]. So even if you have infinite number of
> keys, for example say you have
> different join keys every day, the old keys will not be touched in next
> days and become idle and will
> be deleted by framework.
>
> > Question 3: Do you imagine that I could use the same logic for both
> stream processing and reprocessing just by replacing sources and sinks?
>
> Generally speaking, yes I think so. With event time based join, we should
> be able to reuse the logic
> of normal stream processing and reprocessing historical data. Although
> there will definitely exists some
> details should be addressed, like event time and watermarks.
>
> Re option 2:
>
> > maybe implement Hive/JDBC-based LookupableTableSource that  pulls the
> whole dictionary to memory
>
> You can do this manually but I would recommend you go with the first
> choice which loads hive table
> to HBase periodically. It's much more easier and efficient. And this
> approach you mentioned also
> seems a little bit duplicate with the temporal table function solution.
>
> > this option is available only with Blink engine and also only with use
> of Flink SQL, no Table API?
>
> I'm afraid yes, you can only use it with SQL for now.
>
> > do you think it would be possible to use the same logic / SQL for
> reprocessing?
>
> Given the fact this solution is based on processing time, I don't think it
> can cover the use case of
> reprocessing, except if you can accept always joining with latest day even
> during backfilling. But we
> are also aiming to resolve this shortcoming maybe in 1 or 2 releases.
>
> Best,
> Kurt
>
> [1]
> https://ci.apache.org/projects/flink/flink-docs-master/dev/table/streaming/query_configuration.html#idle-state-retention-time
>
>
> On Sat, Dec 14, 2019 at 3:41 AM Krzysztof Zarzycki 
> wrote:
>
>> Very interesting, Kurt! Yes, I also imagined it's rather a very common
>> case. In my company we currently have 3 clients wanting this functionality.
>> I also just realized this slight difference between Temporal Join and
>> Temporal Table Function Join, that there are actually two methods:)
>>
>> Regarding option 1:
>> So I would need to:
>> * write a Datastream API source, that pulls Hive dictionary table every
>> let's say day, assigns event time column to rows and creates a stream of
>> it. It does that and only that.
>> * create a table (from Table API) out of it, assigning one of the columns
>> as an event time column.
>> * then use table.createTemporalTableFunction(> column>)
>> * finally join my main data stream with the temporal table function (let
>> me use short name TTF from now) from my dictionary, using Flink SQL and 
>> LATERAL
>> TABLE (Rates(o.rowtime)) AS r construct.
>> And so I should achieve my temporal event-time based join with versioned
>> dictionaries!
>> Question 1: do I need to write that Hive source or can I use 

Re: Join a datastream with tables stored in Hive

[Kurt Young]

Hi Krzysztof, thanks for the discussion, you raised lots of good questions,
I will try to reply them
one by one.

Re option 1:

> Question 1: do I need to write that Hive source or can I use something
ready, like Hive catalog integration? Or maybe reuse e.g. HiveTableSource
class?

I'm not sure if you can reuse the logic of `HiveTableSource`. Currently
`HiveTableSource` works
as batch mode, it will read all data at once and stop. But what you need is
wait until next day after
finish. What you can try is reuse the logic of `HiveTableInputFormat`, and
wrap the "monitoring"
logic outside.

> Question/worry 2:  the state would grow inifinitely if I had infinite
number of keys, but not only infinite number of versions of all keys.

The temporal table function doesn't support watermark based state clean up
yet, but what you can
try is idle state retention [1]. So even if you have infinite number of
keys, for example say you have
different join keys every day, the old keys will not be touched in next
days and become idle and will
be deleted by framework.

> Question 3: Do you imagine that I could use the same logic for both
stream processing and reprocessing just by replacing sources and sinks?

Generally speaking, yes I think so. With event time based join, we should
be able to reuse the logic
of normal stream processing and reprocessing historical data. Although
there will definitely exists some
details should be addressed, like event time and watermarks.

Re option 2:

> maybe implement Hive/JDBC-based LookupableTableSource that  pulls the
whole dictionary to memory

You can do this manually but I would recommend you go with the first choice
which loads hive table
to HBase periodically. It's much more easier and efficient. And this
approach you mentioned also
seems a little bit duplicate with the temporal table function solution.

> this option is available only with Blink engine and also only with use of
Flink SQL, no Table API?

I'm afraid yes, you can only use it with SQL for now.

> do you think it would be possible to use the same logic / SQL for
reprocessing?

Given the fact this solution is based on processing time, I don't think it
can cover the use case of
reprocessing, except if you can accept always joining with latest day even
during backfilling. But we
are also aiming to resolve this shortcoming maybe in 1 or 2 releases.

Best,
Kurt

[1]
https://ci.apache.org/projects/flink/flink-docs-master/dev/table/streaming/query_configuration.html#idle-state-retention-time


On Sat, Dec 14, 2019 at 3:41 AM Krzysztof Zarzycki 
wrote:

> Very interesting, Kurt! Yes, I also imagined it's rather a very common
> case. In my company we currently have 3 clients wanting this functionality.
> I also just realized this slight difference between Temporal Join and
> Temporal Table Function Join, that there are actually two methods:)
>
> Regarding option 1:
> So I would need to:
> * write a Datastream API source, that pulls Hive dictionary table every
> let's say day, assigns event time column to rows and creates a stream of
> it. It does that and only that.
> * create a table (from Table API) out of it, assigning one of the columns
> as an event time column.
> * then use table.createTemporalTableFunction( column>)
> * finally join my main data stream with the temporal table function (let
> me use short name TTF from now) from my dictionary, using Flink SQL and 
> LATERAL
> TABLE (Rates(o.rowtime)) AS r construct.
> And so I should achieve my temporal event-time based join with versioned
> dictionaries!
> Question 1: do I need to write that Hive source or can I use something
> ready, like Hive catalog integration? Or maybe reuse e.g. HiveTableSource
> class?
>
> Question/worry 2: One thing that worried me is this comment in the docs:
>
> *Note: State retention defined in a query configuration
> 
>  is
> not yet implemented for temporal joins. This means that the required state
> to compute the query result might grow infinitely depending on the number
> of distinct primary keys for the history table.  *
>
> On the other side, I find this comment: *By definition of event
> time, watermarks
>  
> allow
> the join operation to move forward in time and discard versions of the
> build table that are no longer necessary because no incoming row with lower
> or equal timestamp is expected.*
> So I believe that the state would grow inifinitely if I had infinite
> number of keys, but not only infinite number of versions of all keys. Which
> is fine. Do you confirm?
>
> Question 3: I need to be able to cover also reprocessing or backfilling of
> historical data. Let's say I would need to join data stream and
> (versioned/snapshotted) dictionaries stored on HDFS. Do you imagine that I
> could use the same logic for both stream processing and reprocessing just

Re: Join a datastream with tables stored in Hive

[Krzysztof Zarzycki]

Very interesting, Kurt! Yes, I also imagined it's rather a very common
case. In my company we currently have 3 clients wanting this functionality.
I also just realized this slight difference between Temporal Join and
Temporal Table Function Join, that there are actually two methods:)

Regarding option 1:
So I would need to:
* write a Datastream API source, that pulls Hive dictionary table every
let's say day, assigns event time column to rows and creates a stream of
it. It does that and only that.
* create a table (from Table API) out of it, assigning one of the columns
as an event time column.
* then use table.createTemporalTableFunction()
* finally join my main data stream with the temporal table function (let me
use short name TTF from now) from my dictionary, using Flink SQL and LATERAL
TABLE (Rates(o.rowtime)) AS r construct.
And so I should achieve my temporal event-time based join with versioned
dictionaries!
Question 1: do I need to write that Hive source or can I use something
ready, like Hive catalog integration? Or maybe reuse e.g. HiveTableSource
class?

Question/worry 2: One thing that worried me is this comment in the docs:

*Note: State retention defined in a query configuration

is
not yet implemented for temporal joins. This means that the required state
to compute the query result might grow infinitely depending on the number
of distinct primary keys for the history table.  *

On the other side, I find this comment: *By definition of event
time, watermarks

allow
the join operation to move forward in time and discard versions of the
build table that are no longer necessary because no incoming row with lower
or equal timestamp is expected.*
So I believe that the state would grow inifinitely if I had infinite number
of keys, but not only infinite number of versions of all keys. Which is
fine. Do you confirm?

Question 3: I need to be able to cover also reprocessing or backfilling of
historical data. Let's say I would need to join data stream and
(versioned/snapshotted) dictionaries stored on HDFS. Do you imagine that I
could use the same logic for both stream processing and reprocessing just
by replacing sources and sinks? Maybe after some slight modifications?


Regarding option 2:
Here I understand the current limitation (which will stay for some time )
is that the join can happen only on processing time, which means join only
with the latest version of dictionaries.
Accepting that, I understand I would need to do:
a) load Hive table to e.g. HBase and then use HBaseTableSource on it., OR
b) maybe implement Hive/JDBC-based LookupableTableSource that  pulls the
whole dictionary to memory (or even to Flink state, if it is possible to
use it from TableFunction).
Then use this table and my Kafka stream table in temporal join expressed
with Flink SQL.
What do you think, is that feasible?
Do I understand correctly, that this option is available only with Blink
engine and also only with use of Flink SQL, no Table API?

Same question comes up regarding reprocessing: do you think it would be
possible to use the same logic / SQL for reprocessing?

Thank you for continuing discussion with me. I believe we're here on a
subject of a really important design for the community.
Krzysztof

pt., 13 gru 2019 o 09:39 Kurt Young  napisał(a):

> Sorry I forgot to paste the reference url.
>
> Best,
> Kurt
>
> [1]
> https://ci.apache.org/projects/flink/flink-docs-master/dev/table/streaming/joins.html#join-with-a-temporal-table-function
> [2]
> https://ci.apache.org/projects/flink/flink-docs-master/dev/table/streaming/joins.html#join-with-a-temporal-table
>
> On Fri, Dec 13, 2019 at 4:37 PM Kurt Young  wrote:
>
>> Hi Krzysztof,
>>
>> What you raised also interested us a lot to achieve in Flink.
>> Unfortunately, there
>> is no in place solution in Table/SQL API yet, but you have 2 options
>> which are both
>> close to this thus need some modifications.
>>
>> 1. The first one is use temporal table function [1]. It needs you to
>> write the logic of
>> reading hive tables and do the daily update inside the table function.
>> 2. The second choice is to use temporal table join [2], which only works
>> with processing
>> time now (just like the simple solution you mentioned), and need the
>> table source has
>> look up capability (like hbase). Currently, hive connector doesn't
>> support look up, so to
>> make this work, you need to sync the content to other storages which
>> support look up,
>> like HBase.
>>
>> Both solutions are not ideal now, and we also aims to improve this maybe
>> in the following
>> release.
>>
>> Best,
>> Kurt
>>
>>
>> On Fri, Dec 13, 2019 at 1:44 AM Krzysztof Zarzycki 
>> wrote:
>>
>>> Hello dear Flinkers,
>>> If this kind of question was asked on the groups, I'm sorry for a
>>> duplicate. Feel free to just point me 

Re: Join a datastream with tables stored in Hive

[Kurt Young]

Sorry I forgot to paste the reference url.

Best,
Kurt

[1]
https://ci.apache.org/projects/flink/flink-docs-master/dev/table/streaming/joins.html#join-with-a-temporal-table-function
[2]
https://ci.apache.org/projects/flink/flink-docs-master/dev/table/streaming/joins.html#join-with-a-temporal-table

On Fri, Dec 13, 2019 at 4:37 PM Kurt Young  wrote:

> Hi Krzysztof,
>
> What you raised also interested us a lot to achieve in Flink.
> Unfortunately, there
> is no in place solution in Table/SQL API yet, but you have 2 options which
> are both
> close to this thus need some modifications.
>
> 1. The first one is use temporal table function [1]. It needs you to write
> the logic of
> reading hive tables and do the daily update inside the table function.
> 2. The second choice is to use temporal table join [2], which only works
> with processing
> time now (just like the simple solution you mentioned), and need the table
> source has
> look up capability (like hbase). Currently, hive connector doesn't support
> look up, so to
> make this work, you need to sync the content to other storages which
> support look up,
> like HBase.
>
> Both solutions are not ideal now, and we also aims to improve this maybe
> in the following
> release.
>
> Best,
> Kurt
>
>
> On Fri, Dec 13, 2019 at 1:44 AM Krzysztof Zarzycki 
> wrote:
>
>> Hello dear Flinkers,
>> If this kind of question was asked on the groups, I'm sorry for a
>> duplicate. Feel free to just point me to the thread.
>> I have to solve a probably pretty common case of joining a datastream to
>> a dataset.
>> Let's say I have the following setup:
>> * I have a high pace stream of events coming in Kafka.
>> * I have some dimension tables stored in Hive. These tables are changed
>> daily. I can keep a snapshot for each day.
>>
>> Now conceptually, I would like to join the stream of incoming events to
>> the dimension tables (simple hash join). we can consider two cases:
>> 1) simpler, where I join the stream with the most recent version of the
>> dictionaries. (So the result is accepted to be nondeterministic if the job
>> is retried).
>> 2) more advanced, where I would like to do temporal join of the stream
>> with dictionaries snapshots that were valid at the time of the event. (This
>> result should be deterministic).
>>
>> The end goal is to do aggregation of that joined stream, store results in
>> Hive or more real-time analytical store (Druid).
>>
>> Now, could you please help me understand is any of these cases
>> implementable with declarative Table/SQL API? With use of temporal joins,
>> catalogs, Hive integration, JDBC connectors, or whatever beta features
>> there are now. (I've read quite a lot of Flink docs about each of those,
>> but I have a problem to compile this information in the final design.)
>> Could you please help me understand how these components should
>> cooperate?
>> If that is impossible with Table API, can we come up with the easiest
>> implementation using Datastream API ?
>>
>> Thanks a lot for any help!
>> Krzysztof
>>
>

Re: Join a datastream with tables stored in Hive

[Kurt Young]

Hi Krzysztof,

What you raised also interested us a lot to achieve in Flink.
Unfortunately, there
is no in place solution in Table/SQL API yet, but you have 2 options which
are both
close to this thus need some modifications.

1. The first one is use temporal table function [1]. It needs you to write
the logic of
reading hive tables and do the daily update inside the table function.
2. The second choice is to use temporal table join [2], which only works
with processing
time now (just like the simple solution you mentioned), and need the table
source has
look up capability (like hbase). Currently, hive connector doesn't support
look up, so to
make this work, you need to sync the content to other storages which
support look up,
like HBase.

Both solutions are not ideal now, and we also aims to improve this maybe in
the following
release.

Best,
Kurt


On Fri, Dec 13, 2019 at 1:44 AM Krzysztof Zarzycki 
wrote:

> Hello dear Flinkers,
> If this kind of question was asked on the groups, I'm sorry for a
> duplicate. Feel free to just point me to the thread.
> I have to solve a probably pretty common case of joining a datastream to a
> dataset.
> Let's say I have the following setup:
> * I have a high pace stream of events coming in Kafka.
> * I have some dimension tables stored in Hive. These tables are changed
> daily. I can keep a snapshot for each day.
>
> Now conceptually, I would like to join the stream of incoming events to
> the dimension tables (simple hash join). we can consider two cases:
> 1) simpler, where I join the stream with the most recent version of the
> dictionaries. (So the result is accepted to be nondeterministic if the job
> is retried).
> 2) more advanced, where I would like to do temporal join of the stream
> with dictionaries snapshots that were valid at the time of the event. (This
> result should be deterministic).
>
> The end goal is to do aggregation of that joined stream, store results in
> Hive or more real-time analytical store (Druid).
>
> Now, could you please help me understand is any of these cases
> implementable with declarative Table/SQL API? With use of temporal joins,
> catalogs, Hive integration, JDBC connectors, or whatever beta features
> there are now. (I've read quite a lot of Flink docs about each of those,
> but I have a problem to compile this information in the final design.)
> Could you please help me understand how these components should cooperate?
> If that is impossible with Table API, can we come up with the easiest
> implementation using Datastream API ?
>
> Thanks a lot for any help!
> Krzysztof
>

Join a datastream with tables stored in Hive

[Krzysztof Zarzycki]

Hello dear Flinkers,
If this kind of question was asked on the groups, I'm sorry for a
duplicate. Feel free to just point me to the thread.
I have to solve a probably pretty common case of joining a datastream to a
dataset.
Let's say I have the following setup:
* I have a high pace stream of events coming in Kafka.
* I have some dimension tables stored in Hive. These tables are changed
daily. I can keep a snapshot for each day.

Now conceptually, I would like to join the stream of incoming events to the
dimension tables (simple hash join). we can consider two cases:
1) simpler, where I join the stream with the most recent version of the
dictionaries. (So the result is accepted to be nondeterministic if the job
is retried).
2) more advanced, where I would like to do temporal join of the stream with
dictionaries snapshots that were valid at the time of the event. (This
result should be deterministic).

The end goal is to do aggregation of that joined stream, store results in
Hive or more real-time analytical store (Druid).

Now, could you please help me understand is any of these cases
implementable with declarative Table/SQL API? With use of temporal joins,
catalogs, Hive integration, JDBC connectors, or whatever beta features
there are now. (I've read quite a lot of Flink docs about each of those,
but I have a problem to compile this information in the final design.)
Could you please help me understand how these components should cooperate?
If that is impossible with Table API, can we come up with the easiest
implementation using Datastream API ?

Thanks a lot for any help!
Krzysztof