Beautiful Spark Code

classic Classic list List threaded Threaded
1 message Options
Reply | Threaded
Open this post in threaded view

Beautiful Spark Code

Zahid Rahman
You will be please to learn that Mr. Mathew Powers have seen to my needs and answers all my questions. 

He has seen to all my needs.

Mr Powers  has shut me up !!!

Mr Powers has made Google search stackoverflow and [hidden email] redundant.

That is all you guys and girl had to do , point me to his book.

On Sat, 28 Mar 2020, 16:49 Zahid Rahman, <[hidden email]> wrote:
Thanks for the tip! 

But if the first thing you come across 
Is somebody  using the trim function to strip away spaces in /etc/hostnames like so from : hostname local


Then there is a log error message showing the outcome of unnecessarily using the trim function.

Especially when one of the spark core functionality is to read lines from files separated by a space, comma.

Also have you seen the
Setting to ERROR and in one case FATAL
for suppressing discrepancies.

Please May I draw your attention and attention of all in the community to this page Which shows turning on compiler WARNINGS  before releasing software and other software best practices.

“The Power of 10 — NASA’s Rules for Coding” by Riccardo Giorato 

What impression  would you have  ?

On Sat, 28 Mar 2020, 15:50 Jeff Evans, <[hidden email]> wrote:
Dude, you really need to chill. Have you ever worked with a large open source project before? It seems not. Even so, insinuating there are tons of bugs that were left uncovered until you came along (despite the fact that the project is used by millions across many different organizations) is ludicrous. Learn a little bit of humility

If you're new to something, assume you have made a mistake rather than that there is a bug. Lurk a bit more, or even do a simple Google search, and you will realize Sean is a very senior committer (i.e. expert) in Spark, and has been for many years. He, and everyone else participating in these lists, is doing it voluntarily on their own time. They're not being paid to handhold you and quickly answer to your every whim.

On Sat, Mar 28, 2020, 10:46 AM Zahid Rahman <[hidden email]> wrote:
So the schema is limited to holding only the DEFINITION of schema. For example as you say  the columns, I.e. first column User:Int 2nd column String:password. 

Not location of source I.e. csv file with or without header.  SQL DB tables.

I am pleased for once I am wrong about being another bug, and it was a design decision adding flexibility.

On Sat, 28 Mar 2020, 15:24 Russell Spitzer, <[hidden email]> wrote:
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,

On Sat, Mar 28, 2020, 12:33 AM Zahid Rahman <[hidden email]> wrote:
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://").appName("Retail Data").getOrCreate();
// set spark runtime configuration

// create a static frame
val staticDataFrame ="csv")
.option ("header","true")

val staticSchema = staticDataFrame.schema

"CustomerId","UnitPrice * Quantity as total_cost", "InvoiceDate")
"1 day"))

val streamingDataFrame = spark.readStream
.option("maxFilesPerTrigger", 1)


// lazy operation so we will need to call a streaming action to start the action
val purchaseByCustomerPerHour = streamingDataFrame
"(UnitPrice * Quantity) as total_cost",
col("CustomerId"), window(col("InvoiceDate"), "1 day"))

// stream action to write to console

} // main

} // object

val staticSchema = staticDataFrame.schema

♡۶Java♡۶RMI ♡۶
Make Use Method {MUM}