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>> 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?
