Hi Jorn,
Thanks I uploaded the Scala code to my GitHub --> md_streaming.scala
https://github.com/michTalebzadeh/Flink
Regards,
Dr Mich Talebzadeh
LinkedIn *
https://www.linkedin.com/profile/view?id=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
(At the end of your code)
> On 8. Aug 2018, at 00:29, Jörn Franke wrote:
>
> Hi Mich,
>
> Would it be possible to share the full source code ?
> I am missing a call to streamExecEnvironment.execute
>
> Best regards
>
>> On 8. Aug 2018, at 00:02, Mich Talebzadeh wrote:
>>
>> Hi Fabian,
>>
Hi Mich,
Would it be possible to share the full source code ?
I am missing a call to streamExecEnvironment.execute
Best regards
> On 8. Aug 2018, at 00:02, Mich Talebzadeh wrote:
>
> Hi Fabian,
>
> Reading your notes above I have converted the table back to DataStream.
>
> val tableEnv
Hi Fabian,
Reading your notes above I have converted the table back to DataStream.
val tableEnv = TableEnvironment.getTableEnvironment(streamExecEnv)
tableEnv.registerDataStream("priceTable", splitStream, 'key, 'ticker,
'timeissued, 'price)
val key =
Thanks again.
The Hbase connector works fine in Flink
// Start Hbase table stuff
val tableName = "MARKETDATAHBASESPEEDFLINK"
val hbaseConf = HBaseConfiguration.create()
// Connecting to remote Hbase
hbaseConf.set("hbase.master", hbaseHost)
A *Table*Source [1], is a special input connector for Flink's relational
APIs (Table API and SQL) [2].
You can transform and filter with these APIs as well (it's probably even
easier). In SQL this would be the SELECT and WHERE clauses of a query.
However, there is no *Table*Sink for HBase and you
Thanks Fabian. That was very useful.
How about an operation like below?
// create builder
val KafkaTableSource = Kafka011JsonTableSource.builder()
// set Kafka topic
.forTopic(topicsValue)
// set Kafka consumer properties
Hi, Mich:
You can add write a sink function for that.
On Mon, Jul 30, 2018 at 2:58 PM Mich Talebzadeh
wrote:
>
> Hi,
>
> I have a Kafka topic that transmits 100 security prices ever 2 seconds.
>
> In Spark streaming I go through the RDD and walk through rows one by one
> and check prices
> In
