I have the following code:
print(df.show(3))
print(df.columns)
df.select('port', 'key', 'return_b', 'return_a', 'return_c', 'return_d', 'return_g').write.format("parquet").save("qwe.parquet")
For some reason this doesn't write the Dataframe into the parquet file with the headers. The print statement above shows me those columns exist but the parquet file doesn't have those headers.
I have also tried:
df.write.option("header", "true").mode("overwrite").parquet(write_folder)
You may find df.to_parquet(...) more convenient.
If you wish to project down to selected columns,
do that first,
and then write to parquet.
Related
I am trying to upload a tab delimited text file in databricks notebooks, but all the column values are getting pushed into one column value
here is the sql code I am using
Create table if not exists database.table
using text
options (path 's3bucketpath.txt', header "true")
I also tried using csv
The same things happens if i'm reading into a spark dataframe
I am expecting to see the columns separated out with their header. Has anyone come across this issue and figured out a solution?
Have you tried to add a sep option to specify that you're using tab-separated values?
Create table if not exists database.table
using csv
options (path 's3bucketpath.txt', header 'true', sep '\t')
I am trying to create a temp view from a number of parquet files, but it does not work so far. As a first step, I am trying to create a dataframe by reading parquets from a path. I want to load all parquet files into the df, but so far I dont even manage to load a single one, as you can see on the screenshot below. Can anyone help me out here? Thanks
Info: batch_source_path is the string in column "path", row 1
Your data is in Delta format and this is how you must read:
data = spark.read.load('your_path_here', format='delta')
I have a csv file that contains large number of columns. I want to load just one column from that file using spark.
I know that we can use select statement to filter a column. But what i want, while doing the read operation itself, it should load just one column.
In this way, i should be able to avoid extra memory getting used by other columns. Is there any way to do this?
Spark will load complete file and parse for columns. As you mentioned, you can use select to restrict columns in dataframe, so dataframe will have only one column.
Spark will load the complete file in memory and will filter down the column you want with the help of select statements which you have mentioned.
Because all the read operation in spark, reads and scans the whole file as a distributed stream reader gets created (the reader gets instantiated at every node where the data has been stored).
And if your problem is to read the data column-wise then you can store the file in parquet format and read that file. Indeed, parquet is columnar storage and it is exactly meant for this type of use case(you can verify it using the explain).
I'm loading a CSV file to a spark DataFrame
At this point I'm doing some parsing and validation, if the validation is failed - I want to write the original CSV line to a different file
Is it possible getting the original string from the DataFrame object?
I thought about getting the ln number from the DataFrame, and extracting it from the original file
I guess it will be better using the DF object, but if not possible - extract from file
I have 1000+ parquet files in a folder, which is a partitioned folder.
Now we got a requirement to use those files to perform some transformations on it.
i need to create data frame using those parquet file. Any suggestions?
Try below code:
DF = sqlContext.read.parquet(r"<folderpath>/*")
* indicates all files present under the specified folder.
DF will be a dataframe that will contain the data from all the parquet files inside the <folderpath>. Then you can perform your transformation on DF.