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From Mich Talebzadeh <mich.talebza...@gmail.com>
Subject Re: Stream which needs to be “joined” with another Stream of “Reference” data.
Date Mon, 03 May 2021 19:06:51 GMT
Well that versioned table is CDC trail files that are landed on an external
storage as immutable data.

What happens is that you read the table itself at time T0 and then keep
reading committed transaction changes as trail files.

Kafka can do that as well. Read the files (CDC changes from say Oracle redo
logs for committed transactions) and land them somewhere as trail files. Then
you can use a SQL query to determine the latest version before you apply
further transformations.

WITH ranked AS (
  SELECT
    *,
    ROW_NUMBER() OVER (
      PARTITION BY device_id
      ORDER BY <event_timestamp> DESC
    ) AS rank
  FROM device_table
)
SELECT *
FROM ranked
WHERE rank = 1


If your data on reference table does not change in an hour's time, then you
will be OK reading as snapshot at time T0


HTH


   view my Linkedin profile
<https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/>



*Disclaimer:* Use it at your own risk. Any and all responsibility for any
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On Mon, 3 May 2021 at 19:48, Eric Beabes <mailinglists19@gmail.com> wrote:

> I was looking for something like "Processing Time Temporal Join" in Flink
> as described here:
>
>
> https://ci.apache.org/projects/flink/flink-docs-stable/dev/table/streaming/joins.html#processing-time-temporal-join
>
>
> On Mon, May 3, 2021 at 11:07 AM Mich Talebzadeh <mich.talebzadeh@gmail.com>
> wrote:
>
>> You are welcome Yuri. However, I stand corrected :)
>>
>>
>>    view my Linkedin profile
>> <https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/>
>>
>>
>>
>> *Disclaimer:* Use it at your own risk. Any and all responsibility for
>> any loss, damage or destruction of data or any other property which may
>> arise from relying on this email's technical content is explicitly
>> disclaimed. The author will in no case be liable for any monetary damages
>> arising from such loss, damage or destruction.
>>
>>
>>
>>
>> ‪On Mon, 3 May 2021 at 19:02, ‫"Yuri Oleynikov (‫יורי אולייניקוב‬‎)"‬‎
<
>> yurkao@gmail.com> wrote:‬
>>
>>> Always nice to learn something new about jdbc.
>>> Thanks, Mich **thumbsup**
>>>
>>>
>>> On 3 May 2021, at 20:54, Mich Talebzadeh <mich.talebzadeh@gmail.com>
>>> wrote:
>>>
>>> 
>>> i would have assumed that reference data like device_id are pretty
>>> static so a snapshot will do.
>>>
>>> JDBC connection is lazy so it will not materialise until the join uses
>>> it. Then data will be collected from the underlying RDBMS table for
>>> COMMITED transactions
>>>
>>> However, this is something that I discussed in another thread
>>>
>>> *Spark Streaming with Files*
>>>
>>> There is an option that one can trigger once
>>>
>>>               result = streamingDataFrame.select( \
>>>                      col("parsed_value.rowkey").alias("rowkey") \
>>>                    , col("parsed_value.ticker").alias("ticker") \
>>>                    , col("parsed_value.timeissued").alias("timeissued") \
>>>                    , col("parsed_value.price").alias("price")). \
>>>                      writeStream. \
>>>                      outputMode('append'). \
>>>                      option("truncate", "false"). \
>>>                      foreachBatch(sendToSink). \
>>>                      queryName('trailFiles'). \
>>>                     * trigger(once = True). \*
>>>  *                    option('checkpointLocation', checkpoint_path). \*
>>>                      start(data_path)
>>>
>>> This means that the streaming job will run for all data connected and
>>> terminate. In that case JDBC connection will be refreshed according to your
>>> batch interval that restarts the streaming process for unprocessed data and
>>> critically your JDBC snapshot will be updated as read
>>>
>>> This can be done through airflow etc. You won't lose data as the
>>> checkpoint will mark processed records.
>>>
>>> That might be an option.
>>>
>>> HTH
>>>
>>>
>>>    view my Linkedin profile
>>> <https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/>
>>>
>>>
>>>
>>> *Disclaimer:* Use it at your own risk. Any and all responsibility for
>>> any loss, damage or destruction of data or any other property which may
>>> arise from relying on this email's technical content is explicitly
>>> disclaimed. The author will in no case be liable for any monetary damages
>>> arising from such loss, damage or destruction.
>>>
>>>
>>>
>>>
>>> ‪On Mon, 3 May 2021 at 18:27, ‫"Yuri Oleynikov (‫יורי אולייניקוב‬‎)"‬‎
<
>>> yurkao@gmail.com> wrote:‬
>>>
>>>> You can do the enrichment with stream(events)-static(device table) join
>>>> when the device table is slow changing dimension (let’s say once a day
>>>> change) and it’s in delta format, then for every micro batch with
>>>> stream-static John the device table will be rescanned and up to date device
>>>> data will be loaded.
>>>>
>>>> If device table is not slow dimension(once an hour change), then you’d
>>>> probably need stream-stream join but I’m not sure if RDBMS (aka jdbc) in
>>>> Spark supports streaming mode.
>>>> So I’d better sync jdbc with parquet/delta periodically in order to
>>>> emulate streaming source
>>>>
>>>>
>>>> On 3 May 2021, at 20:02, Eric Beabes <mailinglists19@gmail.com> wrote:
>>>>
>>>> 
>>>> 1) Device_id might be different for messages in a batch.
>>>> 2) It's a Streaming application. The IOT messages are getting read in a
>>>> Structured Streaming job in a "Stream". The Dataframe would need to be
>>>> updated every hour. Have you done something similar in the past? Do you
>>>> have an example to share?
>>>>
>>>> On Mon, May 3, 2021 at 9:52 AM Mich Talebzadeh <
>>>> mich.talebzadeh@gmail.com> wrote:
>>>>
>>>>> Can you please clarify:
>>>>>
>>>>>
>>>>>    1. The IOT messages in one batch have the same device_id or every
>>>>>    row has different device_id?
>>>>>    2. The RDBMS table can be read through JDBC in Spark and a
>>>>>    dataframe can be created on. Does that work for you? You do not really
need
>>>>>    to stream the reference table.
>>>>>
>>>>>
>>>>> HTH
>>>>>
>>>>>
>>>>>
>>>>>    view my Linkedin profile
>>>>> <https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/>
>>>>>
>>>>>
>>>>>
>>>>> *Disclaimer:* Use it at your own risk. Any and all responsibility for
>>>>> any loss, damage or destruction of data or any other property which may
>>>>> arise from relying on this email's technical content is explicitly
>>>>> disclaimed. The author will in no case be liable for any monetary damages
>>>>> arising from such loss, damage or destruction.
>>>>>
>>>>>
>>>>>
>>>>>
>>>>> On Mon, 3 May 2021 at 17:37, Eric Beabes <mailinglists19@gmail.com>
>>>>> wrote:
>>>>>
>>>>>> I would like to develop a Spark Structured Streaming job that reads
>>>>>> messages in a Stream which needs to be “joined” with another
Stream of
>>>>>> “Reference” data.
>>>>>>
>>>>>> For example, let’s say I’m reading messages from Kafka coming
in from
>>>>>> (lots of) IOT devices. This message has a ‘device_id’. We have
a DEVICE
>>>>>> table on a relational database. What I need to do is “join” the
‘device_id’
>>>>>> in the message with the ‘device_id’ on the table to enrich the
incoming
>>>>>> message. Somewhere I read that, this can be done by joining two streams.
I
>>>>>> guess, we can create a “Stream” that reads the DEVICE table once
every hour
>>>>>> or so.
>>>>>>
>>>>>> Questions:
>>>>>> 1) Is this the right way to solve this use case?
>>>>>> 2) Should we use a Stateful Stream for reading DEVICE table with
>>>>>> State timeout set to an hour?
>>>>>> 3) What would happen while the DEVICE state is getting updated from
>>>>>> the table on the relational database?
>>>>>>
>>>>>> Guidance would be greatly appreciated. Thanks.
>>>>>>
>>>>>

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