I'm trying to save a DataFrame to a path as Parquet files. The issue is: the display() function shows a bunch of results in "Prop_0" but whenever I try to save them, only the first one gets converted and goes to the path.
The code I'm using is:
dbutils.fs.rm(Path_1, True)
avroFile = spark.read.format('com.databricks.spark.avro').load(Path_1)
avroFile.write.mode("overwrite").save(Path_2, format="parquet")
This is expected behaviour, Hadoop File Format is used by Spark and this file format requires data to be partitioned - that's why you have part- files.
I'm able to run the above code without any issue.
You may use the below method to save spark DataFrame as parquet files.
Related
We are trying to load a large'ish excel file from a mounted Azure Data Lake location using pyspark on Databricks.
We have used pyspark.pandas to load and we have used spark-excel to load, not with a lot of success
PySpark.Pandas
import pyspark.pandas as ps
df = ps.read_excel("dbfs:/mnt/aadata/ds/data/test.xlsx",engine="openpyxl")
We are experiencing some conversion error as below
ArrowTypeError: Expected bytes, got a 'int' object
spark-excel
df=spark.read.format("com.crealytics.spark.excel") \
.option("header", "true") \
.option("inferSchema","false") \
.load('dbfs:/mnt/aadata/ds/data/test.xlsx')
We are able to load a smaller file, but a larger file gives the following error
org.apache.poi.util.RecordFormatException: Tried to allocate an array of length 185,568,653, but the maximum length for this record type is 100,000,000.
Is there any other way to load excel files in databricks with pyspark?
In your Excel file, there's probably some kind of weird format, or some kind of special character, that is preventing it from working. Save the Excel file as a CSV file, and re-try. You should easily be able to load a CSV file, because it has no weird things of any kind, whereas Excel has all kinds of weird things embedded in it.
I am quite new to PySpark, I am trying to read and then save a CSV file using Azure Databricks.
After saving the file I see many other files like "_Committed","_Started","_Success" and finally the CSV file with a totally different name.
I have already checked using DataFrame repartition(1) and coalesce(1) but this only deals when the CSV file itself was partitioned by Spark. Is there anything that can be done using PySpark?
You can do the following:
df.toPandas().to_csv(path/to/file.csv)
It will create a single file csv as you expect.
Those are default Log files created when saving from PySpark . We can't eliminate this.
Using coalesce(1) you can save in a single file without partition.
I have Qubole connected to Azure data lake, and I can start a spark cluster, and run PySpark on it. However, I can't save any native Python output, like text files or CSVs. I can't save anything other than Spark SQL DataFrames.
What should I do to resolve this?
Thank you in advance!
If I understand your question correctly,I believe you are unable to download the result of pyspark command output into text or CSVs while you are able to do so for the spark sql command command output in a nice tabular format.
Unfortunately, there is no direct field separator for the output text for a Python or Shell command outputs. You will need to get your output comma separated so you can download the raw output and save it as a csv.
If this is not what you meant, Please share more details as to what exactly are you trying to do along with screenshots details. As that will help us answer your question better.
I resolved it. I needed to add the file to the PySpark session using textFile() details and sample code here
For any file I want, I need to add it to the spark session. For example, if I needed to add a .py file from Azure data lake, I need to add it using addPyFile() with the path to the file.
Assuming I have an excel sheet already open, make some changes in the file and use pd.read_excel to create a dataframe based on that sheet, I understand that the dataframe will only reflect the data in the last saved version of the excel file. I would have to save the sheet first in order for pandas dataframe to take into account the change.
Is there anyway for pandas or other python packages to read an opened excel file and be able to refresh its data real time (without saving or closing the file)?
Have you tried using mitosheet package? It doesn't answer your question directly, but it allows you working on pandas dataframes as you would do in excel sheets. In this way, you may edit the data on the fly as in excel and still get a pandas dataframe as a result (meanwhile generating the code to perform the same operations with python). Does this help?
There is no way to do this. The table is not saved to disk, so pandas can not read it from disk.
Be careful not to over-engineer, that being said:
Depending on your use case, if this is really needed, I could theoretically imagine a Robotic Process Automation like e.g. BluePrism, UiPath or PowerAutomate loading live data from Excel into a Python environment with a pandas DataFrame continuously and then changing it.
This use case would have to be a really important process though, otherwise licensing RPA is not worth it here.
df = pd.read_excel("path")
In variable explorer you can see the data if you run the program in SPYDER ide
I want to save dataframe to s3 but when I save the file to s3 , it creates empty file with ${folder_name}, in which I want to save the file.
Syntax to save the dataframe :-
f.write.parquet("s3n://bucket-name/shri/test")
It saves the file in test folder but it creates $test under shri .
Is there a way I can save it without creating that extra folder?
I was able to do it by using below code.
df.write.parquet("s3a://bucket-name/shri/test.parquet",mode="overwrite")
As far as I know, there is no way to control the naming of the actual parquet files. When you write a dataframe to parquet, you specify what the directory name should be, and spark creates the appropriate parquet files under that directory.