Question 121704 in Redis Question posted in
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Redis – How to read DBF file in PySpark

I have a requirement to read and process .DBF File in PySpark but I didn’t get any library that how can I read that like we read the CSV, JSON, Parquet or other file.

Please help to read this file.
I’m block at starting level only. after creating spark session how to read the .DBF file.
dbfread is the library available in python to read dbf files. But I need to read in PySpark and not only using Python.

Code :

from pyspark.sql import SparkSession
spark = (SparkSession.builder
  .master("local[*]")
  .appName("dbf-file-read")
  .getOrCreate())

Now How to Start with .DBF File Read?

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2

Answers


  1. 0

    It seems that it is not possible to load .dbf using pyspark.
    Try to use this python "dbfread" package to read and convert your data to the dict format.
    Then utilize spark.createdataframe() function to switch from dict to DF. After that, you can apply pyspark transformations on your data (make use of workers).

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  2. 0

    Install this python library for read DBF file into python.

    pip install dbfread

    from dbfread import DBF

for record in DBF('path/to/dbf/file.dbf'):
     print(record)

    This code will read DBF file file and return as python dictionary.

    OrderedDict([(‘NAME’, ‘Alice’), (‘BIRTHDATE’, datetime.date(1987, 3,
    1))])

    OrderedDict([(‘NAME’, ‘Bob’), (‘BIRTHDATE’, datetime.date(1980, 11,
    12))])

    Now convert the dict to spark dataframe
    sample code:

    from pyspark.sql import SparkSession
from pyspark.sql.types import StructType, StructField, StringType, IntegerType
from dbfread import DBF

# create a SparkSession
spark = SparkSession.builder.appName("DBF Reader").getOrCreate()

# define the schema of the DataFrame
schema = StructType([
    StructField("col1", StringType(), True),
    StructField("col2", IntegerType(), True),
    # add more fields as needed
])

# read the DBF file using dbfread
records = DBF("path/to/dbf/file.dbf", encoding="latin1")

# create a list of dictionaries containing the data
data = [dict(record) for record in records]

# create a PySpark DataFrame from the data
df = spark.createDataFrame(data, schema)

# show the contents of the DataFrame
df.show()
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