I have a file class.fmt
How can I read this file using pyspark?
I don't have experience with this file format.
Related
I have a .dat file exported from a mainframe system. It is EBCIDIC encoded(cp037). I would like to load the contents into a pandas or spark dataframe.
I tried using "iconv" to convert the file to ascii, it does not support conversion from cp037. "iconv -l" does not list cp037.
What is the best way to achieve this?
I am trying to read some avro files into a Spark dataframe and have the below sitution:
The avro file schema is defined as
Schema(
org.apache.avro.Schema
.create(org.apache.avro.Schema.Type.BYTES),
"ByteBlob", "1.0");
The file has a nested json structure stored as a simple bytes schema in the avro file.
I can't seem to find a way to read this into a dataframe in spark. Any pointers on how I can read files like these?
Output from avro-tools:
hadoop jar avro-tools/avro-tools-1.10.2.jar getmeta /projects/syslog_paranoids/encrypted/dhr/complete/visibility/zeeklog/202207251345/1.0/202207251351/stg-prd-dhrb-edg-003.data.ne1.yahoo.com_1658690707314_zeeklog_1.0_202207251349_202207251349_6c64f2210c568092c1892d60b19aef36.6.avro
avro.schema "bytes"
avro.codec deflate
The tojson function within avro-tools is able to read the file properly and return a json output contained in the file.
How can I convert a .numbers file to an .xlsx file using the xlsx library in nodejs? The documentation states that Numbers is not included in the library by default, and you must use the xlsx.zuhl.js, xlsx.zuhl.mjs scripts. In the documentation for xlsx library there is example how to use script for exporting Numbers files, could you write an example of how you can use this script to read Numbers file format and convert it to .xlsx?
Is there any other way to convert .numbers file to .xlsx?
I am new learner for Pyspark. I got a requirement in my project to read JSON file with a schema and need to convert it to CSV file.
Can some one help me how to proceed this request using PYspark.
You can load JSON and write CSV with SparkSession.
spark = SparkSession.builder.master("local").appName("ETL").getOrCreate()
spark.read.json(path-to-txt)
spark.write.csv(path-to-csv)
I am new to Spark.
I can load the .json file in Spark. What if there are thousands of .json files in a folder. picture of .json files in the folder
And I have a csv file, which classifies the .json files with labels.picture of csv file
What should I do with Spark if I want to load and save the data.(for example.I want to load the first information in csv, but it is text information. But it gives the path of .json, and I want to load the .json, then save the output. So I will know the first Trusted label graph's json information.)
For the JSON:
jsonRDD = sql_context.read.json("path/to/json_folder/");
For CSV install spark-csv from here Databricks' spark-csv
csvRDD = sql_context.read.load("path/to/csv_folder/",format='com.databricks.spark.csv',header='true',inferSchema='true')