In Spark 2.2, you can read from Kafka in batch mode, and then use the json
reader to infer schema:
val df = spark.read.format("kafka")...
.select($"value.cast("string"))
val json = spark.read.json(df)
val schema = json.schema
While the above should be slow (since you're reading almost all data
Hi Burak,
Thank you , for the inputs, would definitely try the options.
The reason we don't have an unified schema is because we are trying to
consume data from different topics that contains data from different tables
from a DB, and so each table has different columns.
Regards,
Satyajit.
On
Hi,
What about a custom streaming Sink that would stop the query after addBatch
has been called?
Pozdrawiam,
Jacek Laskowski
https://about.me/JacekLaskowski
Spark Structured Streaming https://bit.ly/spark-structured-streaming
Mastering Apache Spark 2 https://bit.ly/mastering-apache-spark
Hi Jacek,
For now , i am using Thread.sleep() on driver, to make sure my streaming
query receives some data and and stop it, before the control reaches
querying memory table.
Let me know if there is any better way of handling it.
Regards,
Satyajit.
On Sun, Dec 10, 2017 at 10:43 PM, satyajit
Hi Jacek,
Thank you for responding back,
i have tried memory sink, and below is what i did
val fetchValue =
debeziumRecords.selectExpr("value").withColumn("tableName",
functions.get_json_object($"value".cast(StringType), "$.schema.name"))
.withColumn("operation",
Hi,
What about memory sink? That could work.
Pozdrawiam,
Jacek Laskowski
https://about.me/JacekLaskowski
Spark Structured Streaming https://bit.ly/spark-structured-streaming
Mastering Apache Spark 2 https://bit.ly/mastering-apache-spark
Follow me at https://twitter.com/jaceklaskowski
On
Hi All,
I would like to infer JSON schema from a sample of data that i receive
from, Kafka Streams(specific topic), and i have to infer the schema as i am
going to receive random JSON string with different schema for each topic,
so i chose to go ahead with below steps,
a. readStream from
