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 u...@spark.apache.org redundant. That is all you guys and girl had to do , point me to his book. https://leanpub.com/beautiful-spark https://mungingdata.com/writing-beautiful-apache-spark2-code-with-scala/ On Sat, 28 Mar 2020, 16:49 Zahid Rahman, <zahidr1...@gmail.com> 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 : > > 127.0.0.1 hostname local > > To > > 127.0.0.1hostnamelocal > > 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 log4j.properties > 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 > https://link.medium.com/PUz88PIql3 > > What impression would you have ? > > > > On Sat, 28 Mar 2020, 15:50 Jeff Evans, <jeffrey.wayne.ev...@gmail.com> > 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 <zahidr1...@gmail.com> 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, <russell.spit...@gmail.com> >>> 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, >>>> 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> >>>>> >>>>
