This is probably more of a question for the user support list, but I
believe I understand the issue.
Schema inside of spark refers to the structure of the output rows, for
example the schema for a particular dataframe could be
(User: Int, Password: String) - Two Columns the first is User of type int
and the second is Password of Type String.
When you pass the schema from one reader to another, you are only
copyting this structure, not all of the other options associated with the
dataframe.
This is usually useful when you are reading from sources with different
options but data that needs to be read into the same structure.
The other properties such as "format" and "options" exist independently of
Schema. This is helpful if I was reading from both MySQL and
a comma separated file for example. While the Schema is the same, the
options like ("inferSchema") do not apply to both MySql and CSV and
format actually picks whether to us "JDBC" or "CSV" so copying that
wouldn't be helpful either.
I hope this clears things up,
Russ
On Sat, Mar 28, 2020, 12:33 AM Zahid Rahman <zahidr1...@gmail.com> wrote:
> Hi,
> version: spark-3.0.0-preview2-bin-hadoop2.7
>
> As you can see from the code :
>
> STEP 1: I create a object of type static frame which holds all the
> information to the datasource (csv files).
>
> STEP 2: Then I create a variable called staticSchema assigning the
> information of the schema from the original static data frame.
>
> STEP 3: then I create another variable called val streamingDataFrame of
> type spark.readStream.
> and Into the .schema function parameters I pass the object staticSchema
> which is meant to hold the information to the csv files including the
> .load(path) function etc.
>
> So then when I am creating val StreamingDataFrame and passing it
> .schema(staticSchema)
> the variable StreamingDataFrame should have all the information.
> I should only have to call .option("maxFilePerTrigger",1) and not .format
> ("csv") .option("header","true").load("/data/retail-data/by-day/*.csv")
> Otherwise what is the point of passing .schema(staticSchema) to
> StreamingDataFrame.
>
> You can replicate it using the complete code below.
>
> import org.apache.spark.sql.SparkSession
> import org.apache.spark.sql.functions.{window,column,desc,col}
>
> object RetailData {
>
> def main(args: Array[String]): Unit = {
>
> // create spark session
> val spark =
> SparkSession.builder().master("spark://192.168.0.38:7077").appName("Retail
> Data").getOrCreate();
> // set spark runtime configuration
> spark.conf.set("spark.sql.shuffle.partitions","5")
>
> spark.conf.set("spark.sql.streaming.forceDeleteTempCheckpointLocation","True")
>
> // create a static frame
> val staticDataFrame = spark.read.format("csv")
> .option ("header","true")
> .option("inferschema","true")
> .load("/data/retail-data/by-day/*.csv")
>
>
> staticDataFrame.createOrReplaceTempView("retail_data")
> val staticSchema = staticDataFrame.schema
>
> staticDataFrame
> .selectExpr(
> "CustomerId","UnitPrice * Quantity as total_cost", "InvoiceDate")
> .groupBy(col("CustomerId"),
> window(col("InvoiceDate"),
> "1 day"))
> .sum("total_cost")
> .sort(desc("sum(total_cost)"))
> .show(2)
>
> val streamingDataFrame = spark.readStream
> .schema(staticSchema)
> .format("csv")
> .option("maxFilesPerTrigger", 1)
> .option("header","true")
> .load("/data/retail-data/by-day/*.csv")
>
> println(streamingDataFrame.isStreaming)
>
> // lazy operation so we will need to call a streaming action to start the
> action
> val purchaseByCustomerPerHour = streamingDataFrame
> .selectExpr(
> "CustomerId",
> "(UnitPrice * Quantity) as total_cost",
> "InvoiceDate")
> .groupBy(
> col("CustomerId"), window(col("InvoiceDate"), "1 day"))
> .sum("total_cost")
>
> // stream action to write to console
> purchaseByCustomerPerHour.writeStream
> .format("console")
> .queryName("customer_purchases")
> .outputMode("complete")
> .start()
>
> } // main
>
> } // object
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
> val staticSchema = staticDataFrame.schema
>
>
>
>
>
>
>
>
>
>
>
>
>
> Backbutton.co.uk
> ¯\_(ツ)_/¯
> ♡۶Java♡۶RMI ♡۶
> Make Use Method {MUM}
> makeuse.org
> <http://www.backbutton.co.uk>
>
