I don't know how the resulting plan for you query looks like. You can
print it via `env.sqlQuery().explain()`. But I could imagine that by
simplifying the query you would also simplify the number of retraction
messages/operators in the pipeline.
Regards,
Timo
On 05.03.21 13:28, Yik San Chan wrote:
Hi Timo,
If I understand correctly, the UDF only simplifies the query, but not
doing anything functionally different. Please correct me if I am wrong,
thank you!
Best,
Yik San
On Thu, Mar 4, 2021 at 8:34 PM Timo Walther <twal...@apache.org
<mailto:twal...@apache.org>> wrote:
Yes, implementing a UDF might be the most convenient option for some
use
cases. The accumulator of such a UDF could take the two timestamps and
perform the two aggregations at once.
The upsert-kafka connector can apply the updates to the Kafka log. If
you enable log compaction in Kafka, Kafka will clean up the log and
make
sure to only keep the most recent one.
Regards,
Timo
On 04.03.21 11:59, Yik San Chan wrote:
> Hi Timo,
>
> Thanks for the reply!
>
> > You could filter the deletions manually in DataStream API
before writing
> them to Kafka.
>
> Yah I agree this helps the issue, though I will need to mix up
SQL and
> DataStream API.
>
> > To simplify the query you could also investigate to implement
your own
> aggregate function and combine the Top 2 and ListAgg into one
operation.
>
> Do you mean implement an UDF to do so?
>
> Besides, is 'upsert-kafka' connector designed for this use case?
>
> Thank you.
>
> On Thu, Mar 4, 2021 at 4:41 PM Timo Walther <twal...@apache.org
<mailto:twal...@apache.org>
> <mailto:twal...@apache.org <mailto:twal...@apache.org>>> wrote:
>
> Hi Yik,
>
> if I understand you correctly you would like to avoid the
deletions in
> your stream?
>
> You could filter the deletions manually in DataStream API before
> writing
> them to Kafka. Semantically the deletions are required to
produce a
> correct result because the runtime is not aware of a key for
idempotent
> updates.
>
> To simplify the query you could also investigate to implement
your own
> aggregate function and combine the Top 2 and ListAgg into one
operation.
>
> Regards,
> Timo
>
> On 28.02.21 09:55, Yik San Chan wrote:
> > I define a `Transaction` class:
> >
> > ```scala
> > case class Transaction(accountId: Long, amount: Long,
timestamp:
> Long)
> > ```
> >
> > The `TransactionSource` simply emits `Transaction` with
some time
> > interval. Now I want to compute the last 2 transaction
timestamp
> of each
> > account id, see code below:
> >
> > ```scala
> > import org.apache.flink.streaming.api.scala.{DataStream,
> > StreamExecutionEnvironment, _}
> > import org.apache.flink.table.api.EnvironmentSettings
> > import
org.apache.flink.table.api.bridge.scala.StreamTableEnvironment
> > import org.apache.flink.walkthrough.common.entity.Transaction
> > import
org.apache.flink.walkthrough.common.source.TransactionSource
> >
> > object LastNJob {
> >
> > final val QUERY =
> > """
> > |WITH last_n AS (
> > | SELECT accountId, `timestamp`
> > | FROM (
> > | SELECT *,
> > | ROW_NUMBER() OVER (PARTITION BY accountId
> ORDER BY
> > `timestamp` DESC) AS row_num
> > | FROM transactions
> > | )
> > | WHERE row_num <= 2
> > |)
> > |SELECT accountId, LISTAGG(CAST(`timestamp` AS STRING))
> > last2_timestamp
> > |FROM last_n
> > |GROUP BY accountId
> > |""".stripMargin
> >
> > def main(args: Array[String]): Unit = {
> > val settings: EnvironmentSettings =
> >
>
EnvironmentSettings.newInstance().useBlinkPlanner().inStreamingMode().build()
> > val streamEnv: StreamExecutionEnvironment =
> > StreamExecutionEnvironment.getExecutionEnvironment
> > val tableEnv: StreamTableEnvironment =
> > StreamTableEnvironment.create(streamEnv, settings)
> >
> > val txnStream: DataStream[Transaction] = streamEnv
> > .addSource(new TransactionSource)
> > .name("transactions")
> >
> > tableEnv.createTemporaryView("transactions", txnStream)
> >
> > tableEnv.executeSql(QUERY).print()
> > }
> > }
> > ```
> >
> > When I run the program, I get:
> >
> > ```
> > +----+----------------------+--------------------------------+
> > | op | accountId | last2_timestamp |
> > +----+----------------------+--------------------------------+
> > | +I | 1 | 1546272000000 |
> > | +I | 2 | 1546272360000 |
> > | +I | 3 | 1546272720000 |
> > | +I | 4 | 1546273080000 |
> > | +I | 5 | 1546273440000 |
> > | -U | 1 | 1546272000000 |
> > | +U | 1 | 1546272000000,1546273800000 |
> > | -U | 2 | 1546272360000 |
> > | +U | 2 | 1546272360000,1546274160000 |
> > | -U | 3 | 1546272720000 |
> > | +U | 3 | 1546272720000,1546274520000 |
> > | -U | 4 | 1546273080000 |
> > | +U | 4 | 1546273080000,1546274880000 |
> > | -U | 5 | 1546273440000 |
> > | +U | 5 | 1546273440000,1546275240000 |
> > | -U | 1 | 1546272000000,1546273800000 |
> > | +U | 1 | 1546273800000 |
> > | -U | 1 | 1546273800000 |
> > | +U | 1 | 1546273800000,1546275600000 |
> > (to continue)
> > ```
> >
> > Let's focus on the last transaction (from above) of
accountId=1.
> When
> > there is a new transaction from account 1 that happens at
> > timestamp=1546275600000, there are 4 operations in total.
> >
> > ```
> > +----+----------------------+--------------------------------+
> > | op | accountId | last2_timestamp |
> > +----+----------------------+--------------------------------+
> > | -U | 1 | 1546272000000,1546273800000 |
> > | +U | 1 | 1546273800000 |
> > | -U | 1 | 1546273800000 |
> > | +U | 1 | 1546273800000,1546275600000 |
> > ```
> >
> > While I only want to emit the below "new status" to my
downstream
> (let's
> > say another Kafka topic) via some sort of merging:
> >
> > ```
> > +----------------------+--------------------------------+
> > | accountId | last2_timestamp |
> > +----------------------+--------------------------------+
> > | 1 | 1546273800000,1546275600000 |
> > ```
> >
> > So that my downstream is able to consume literally "the last 2
> > transaction timestamps of each account":
> > ```
> > +----------------------+--------------------------------+
> > | accountId | last2_timestamp |
> > +----------------------+--------------------------------+
> > | 1 | 1546272000000 |
> > | 1 | 1546272000000,1546273800000 |
> > | 1 | 1546273800000,1546275600000 |
> > (to continue)
> > ```
> >
> > What is the right way to do this?
>
