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.
Related
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
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.
So, I'm reading data from a JSON file and creating a DataFrame. Usually, I would use
sqlContext.read.json("//line//to//some-file.json")
Problem is that my JSON file isn't consistent. So, for each line in the file, there are 2 JSONs. Each line looks like this
{...data I don't need....}, {...data I need....}
I only need my DataFrame to be formed from the data I need, i.e. the second JSON of each line. So I read each line as a string and substring the part that I need, like so
val lines = sc.textFile(link, 2)
val part = lines.map( x => x.substring(x.lastIndexOf('{')).trim)
I want to get all the elements in 'part' as an Array[String] then turn the Array[String] into one string and make the DataFrame. Like so
val strings = part .collect() //doesn't work
val strings = part.take(1000) //works
val jsonStr = "[".concat(strings.mkString(", ")).concat("]")
The problem is, if I call part.collect(), it doesn't work but if I call part.take(N) it works. However, I'd like to get all my data and not just the first N.
Also, if I try part.take(part.count().toInt) it still doesn't work.
Any Ideas??
EDIT
I realized my problem after a good sleep. It was a silly mistake on my part. The very last line of my input file had a different format from the rest of the file.
So part.take(N) would work for all N less than part.count(). That's why part.collect() wasn't working.
Thanks for the help though!
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 !
This question is related to this.
I am processing an S3 folder containing csv.gz files in Spark. Each csv.gz file has a header that contains column names. This has been solved by the above SO link and the solution looks like this:
val rdd = sc.textFile("s3://.../my-s3-path").mapPartitions(_.drop(1))
The problem now is that it looks like some of the files have newline ('\n') at the end (we assume we are not sure which file). So when converting the RDD to DataFrame, I'm getting some error. The question now is:
How do I get rid of the last line of each file if it is '\n'?
Why not a simple filter:
val rdd = sc.textFile("s3...").filter(line => !line.equalsIgnoreCase("\n")).mapPartition...
Or filter any empty line:
val rdd = sc.textFile("s3...").filter(line => !line.trim().isEmpty)...