I am using Pyspark and I would like to read files of type Apache Arrow, which have ".arrow" as extension. I unfortunately couldn't find any way to do this, would be grateful for any help.
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I am writing spark output to an external system that does not like file extensions (I know, I know, don't start).
Something like:
df.write.partitionBy("date").parquet(some_path)
creates files like: some/path/date=2021-01-01/part-00000-77dd02e8-1a67-4f0d-9c07-b55b4f2e5efc-c000.snappy.parquet
And that makes that external system unhappy.
I am looking for a way to tell spark to write that file without extension.
I know I can just rename it afterwards, but that seems ... stupid (and there's a lot of files) :/
Is there some option I could use to tell spark to just write it the way I want?
df.write.partitionBy("date").parquet(some_path_ends_with_file_name)
I am using pycharm as IDE for pyspark development. I have added pyspark library as content root in my project in pycharm. But still it is not showing different methods that can be applied on pressing (ctrl + space)
e.g. in below code, read will return DataFrameReader object. Pycharm is providing methods applicable on sparkSession. However after read, I am not getting any suggestion of methods applicable on DataFrameReader like option, format, parquet etc.
sparkSession.read.option("header", "true").csv("sample.csv")
Do I have to do some extra setting in pycharm or is there any other better editor for pyspark development?
How to convert EDI format file to CSV file using spark or scala?
You can use a tool like this to create a mapping from EDI format to CSV and then generate a code in that tool. This code then can be used to convert EDI to CSV in Spark.
For open source solutions, I think your best bet is EDI Reader from BerryWorks. Haven't tried it myself, but apparently this is what Hortonworks recommends, and I'd trust their judgement in the Big Data area. I'm not involved with either, for the matters of disclosure.
From there, it's still a matter of converting EDI XML representation to CSV. Given that XML processing is not part of vanilla Spark, again, your options are rather limited here. Try Databricks spark-xml maybe?
I am wondering if it is possible to use Notepad ++ to create a macro that I can use to find the following values of "SUPP1, SUPP2, SUPP3, SUPP4, WAVSL" and bookmark the lines they are present on.
I have roughly 90 files to sift through and locate records within these files, copying them to another file. I know I can do this manually, but any time that can be saved here is appreciated. Let me know if you have any suggestions.
Thanks,
Brandon
Found out the answer here. Part of the issue was the version on the server for Notepad++ was around 5.6.9 before they had the "Mark All" feature and it was supported for scripting in macros. I found a newer version on another server that is around 6.8 or 6.9 and it worked recording the macro there for usage.
Thanks,
Brandon
I have a set of large xml files, zipped together in a singe file and many such zip files. I was using Mapreduce earlier to parse the xml using custom inputformat and recordreader setting the splittable=false and reading the zip and xml file.
I am new to Spark. Can someone help me how can I prevent spark from splitting the zip file and process multiple zips in parallel as I am able to do in MR.
AFAIk ! The answer to your question is provided here by #holden :
Please take a look ! Thanks :)