I have a CSV file and I want to read that file and store it in case class. As I know A CSV is a comma separated values file. But in case of my csv file there are some data which have already comma itself. and it creates new column for every comma. So the problem how to split data from that.
1st data
04/20/2021 16:20(1st column) Here a bunch of basic techniques that suit most businesses, and easy-to-follow steps that can help you create a strategy for your social media marketing goals.(2nd column)
2nd data
11-07-2021 12:15(1st column) Focus on attracting real followers who are genuinely interested in your content, and make the most of your social media marketing efforts.(2nd column)
var i=0
var length=0
val data=Source.fromFile(file)
for (line <- data.getLines) {
val cols = line.split(",").map(_.trim)
length = cols.length
while(i<length){
//println(cols(i))
i=i+1
}
i=0
}
If you are reading a complex CSV file then the ideal solution is to use an existing library. Here is a link to the ScalaDex search results for CSV.
ScalaDex CSV Search
However, based on the comments, it appears that you might actually be wanting to read data stored in a Google Sheet. If that is the case, you can utilize the fact that you have some flexibility to save the data in a text file yourself. When I want to read data from a Google Sheet in Scala, the approach I use first is to save the file in a format that isn't hard to read. If the fields have embedded commas but no tabs, which is common, then I will save the file as a TSV and parse that with split("\t").
A simple bit of code that only uses the standard library might look like the following:
val source = scala.io.Source.fromFile("data.tsv")
val data = source.getLines.map(_.split("\t")).toArray
source.close
After this, data will be an Array[Array[String]] with your data in it that you can process as you desire.
Of course, if your data includes both tabs and commas then you'll really want to use one of those more robust external libraries.
You could use univocity CSV parser for faster stuffs.
You can also use it for creation as well.
Univocity parsers
Related
I have the task of reading a one line json file into spark. I´ve thought about either modifying the input file so that it fits spark.read.json(path) or read the whole file and modify it inmemory to make it fit the previous line as shown bellow:
import spark.implicit._
val file = sc.textFile(path).collect()(0)
val data = file.split("},").map(json => s"$json}")
val ds = data.toSeq.toDF()
Is there a way of directly reading the json or read the one line file into multiple rows?
Edit:
Sorry I didn´t crealy explain the json format, all the json in the same line:
{"key":"value"},{"key":"value2"},{"key":"value2"}
If imported with spark.read.json(path) it would only take the first value.
Welcome to SO HugoDife! I believe single line load is what spark.read.json() does and you are perhaps looking for this answer. If not maybe you want to adjust your question with a data example.
How do I make clean test data for pyspark? I have figured something out that seems pretty good, but parts seem a little awkward, so I'm posting.
Let's say I have a dataframe df with a complicated schema and a small number of rows. I want test data checked into my repo. I don't want a binary file. At this point, I'm not sure the best way to proceed -but I'm thinking i have a file like
test_fn.py
and it has this in it
schema_str='struct<eventTimestamp:timestamp,list_data:array<struct<valueA:string,valueB:string,valueC:boolean>>>'
to get the schema in txt format, using the df.schema.simpleString() function. Then to get the rows - I do
lns = [row.json_txt for row in df.select((F.to_json(F.struct('*'))).alias('json_txt')).collect()]
now I put those lines in my test_fn.py file, or I could have a .json file in the repo.
Now to run the test, I have to make a dataframe with the correct schema and data from this text. It seems the only way spark will parse the simple string is if I create a dataframe with it, that is I can't pass that simple string to the from_json function? So this is a little awkward which is why I thought I'd post -
schema2 = spark.createDataFrame(data=[], schema=schema_str).schema
lns = # say I read the lns back from above
df_txt = spark.createDataFrame(data=lns, schema=T.StringType())
I see df_txt just has one column called 'value'
df_json = df_txt.select(F.from_json('value', schema=schema2).alias('xx'))
sel = ['xx.%s' % nm for nm in df_json.select('xx').schema.fields[0].dataType.fieldNames()]
df2 = df_json.select(*sel)
Now df2 should be the same as df1 - which I see is the case from the deepdiff module.
I have a data source which is stored as a large number of gzipped, csv files. The header info for this source is a separate file.
I'd like to load this data into spark for manipulation - is there an easy way to get spark to figure out the schema/load the headers? There are literally hundreds of columns, and they might change between runs, would strongly prefer not to do this by hand
This can easily be done in spark :
if your header file is : headers.csv and it only contains header then simply first load this file with header set as true :
val headerCSV = spark.read.format("CSV").option("header","true").load("/home/shivansh/Desktop/header.csv")
then get the Columns out in the form of Array:
val columns = headerCSV.columns
Then read the other file without the header information and pass this file as the header:
spark.read.format("CSV").load("/home/shivansh/Desktop/fileWithoutHeader.csv").toDF(columns:_*)
This will result in the DF with the combined value !
In a pig script I saved a table using PigStorage('|').
I have in the corresponding hadoop folder files like
part-r-00000
etc.
What is the best way to load it in Spark/Scala ? In this table I have 3 fields: Int, String, Float
I tried:
text = sc.hadoopFile("file", classOf[TextInputFormat], classOf[LongWritable], classOf[Text], sc.defaultMinPartitions)
But then I would need somehow to split each line. Is there a better way to do it?
If I were coding in python I would create a Dataframe indexed by the first field and whose columns are the values found in the string field and coefficients the float values. But I need to use scala to use the pca module. And the dataframes don't seem that close to python's ones
Thanks for the insight
PigStorage creates a text file without schema information so you need to do that work yourself something like
sc.textFile("file") // or directory where the part files are
val data = csv.map(line => {
vals=line.split("|")
(vals(0).toInt,vals(1),vals(2).toDouble)}
)
I am working on an ingestion feature that will take a strongly formatted .xlsx file and import the records to a temp storage table and then process the rows to create db records.
One of the columns is strictly formatted as "Text" but it seems like the Open XML API handles the columns cells differently on a row-by-row basis. Some of the values while appearing to be numeric values are truly not (which is why we format the column as Text) -
some examples are "211377", "211727.01", "209395.388", "209395.435"
what these values represent is not important but what happens is that some values (using the Open XML API v2.5 library) will be read in properly as text whether retrieved from the Shared Strings collection or simply from InnerXML property while others get sucked in as numbers with what appears to be appended rounding or precision.
For example the "211377", "211727.01" and "209395.435" all come in exactly as they are in the spreadsheet but the "209395.388" value is being pulled in as "209395.38800000001" (there are others that this happens to as well).
There seems to be no rhyme or reason to which values get messed up and which ones which import fine. What is really frustrating is that if I use the native Import feature in SQL Server Management Studio and ingest the same spreadsheet to a temp table this does not happen - so how is that the SSMS import can handle these values as purely text for all rows but the Open XML API cannot.
To begin the answer you main problem seems to be values,
"209395.388" value is being pulled in as "209395.38800000001"
Yes in .xlsx file value is stored as 209395.38800000001 instead of 209395.388. And it's the correct format to store floating point numbers; nothing wrong in it. You van simply confirm it by following code snippet
string val = "209395.38800000001"; // <= What we extract from Open Xml
Console.WriteLine(double.Parse(val)); // < = Simply pass it to double and print
The output is :
209395.388 // <= yes the expected value
So there's nothing wrong in the value you extract from .xlsx using Open Xml SDK.
Now to cells, yes cell can have verity of formats. Numbers, text, boleans or shared string text. And you can styles to a cell which would format your string to a desired output in Excel. (Ex - Date Time format, Forced strings etc.). And this the way Excel handle the vast verity of data. It need this kind of formatting and .xlsx file format had to be little complex to support all.
My advice is to use a proper parse method set at extracted values to identify what format it represent (For example to determine whether its a number or a text) and apply what type of parse.
ex : -
string val = "209395.38800000001";
Console.WriteLine(float.Parse(val)); // <= Float parse will be deduce a different value ; 209395.4
Update :
Here's how value is saved in internal XML
Try for yourself ;
Make an .xlsx file with value 209395.388 -> Change extention to .zip -> Unzip it -> goto worksheet folder -> open Sheet1
You will notice that value is stored as 209395.38800000001 as scene in attached image.. So nothing wrong on API for extracting stored number. It's your duty to decide what format to apply.
But if you make the whole column Text before adding data, you will see that .xlsx hold data as it is; simply said as string.