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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 18:07:39 GMT
You are welcome Yuri. However, I stand corrected :)


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‪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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