CODEWITHSUNDEEP
apache-spark-sql
JKB
JKB

Reputation: 73

pyspark pass multiple options in dataframe

I am new to python and pyspark. I would like to know how can I write the below spark dataframe function in pyspark:

val df = spark.read.format("jdbc").options(
       Map(
        "url" -> "jdbc:someDB", 
        "user" -> "root", 
        "password" -> "password", 
        "dbtable" -> "tableName", 
        "driver" -> "someDriver")).load()

I tried to write as below in pyspark. But, getting syntax error:

df = spark.read.format("jdbc").options(
      map(lambda : ("url","jdbc:someDB"), ("user","root"), ("password","password"), ("dbtable","tableName"), ("driver","someDriver"))).load()

Thanks in Advance

Upvotes: 3

Views: 9558

Answers (3)

Czaporka
Czaporka

Reputation: 2407

In PySpark, pass the options as keyword arguments:

df = spark.read\
    .format("jdbc")\
    .options(
        url="jdbc:someDB",
        user="root",
        password="password",
        dbtable="tableName",
        driver="someDriver",
    )\
    .load()

Sometimes it's handy to keep them in a dict and unpack them later using the splat operator:

options = {
    "url": "jdbc:someDB",
    "user": "root",
    "password": "password",
    "dbtable": "tableName",
    "driver": "someDriver",
}

df = spark.read\
    .format("jdbc")\
    .options(**options)\
    .load()

Regarding the code snippets from your question: you happened to mix up two different concepts of "map":

  • Map in Scala is a data structure also known as "associative array" or "dictionary", equivalent to Python's dict
  • map in Python is a higher-order function you can use for applying a function to an iterable, e.g.:
In [1]: def square(x: int) -> int:
   ...:     return x**2
   ...:

In [2]: list(map(square, [1, 2, 3, 4, 5]))
Out[2]: [1, 4, 9, 16, 25]

In [3]: # or just use a lambda

In [4]: list(map(lambda x: x**2, [1, 2, 3, 4, 5]))
Out[4]: [1, 4, 9, 16, 25]

Upvotes: 20

foadgr
foadgr

Reputation: 11

To load a CSV file with multiple parameters, pass the arguments to load():

df = spark.read.load("examples/src/main/resources/people.csv",
                     format="csv", sep=":", inferSchema="true", header="true")

Here's the documentation for that.

Upvotes: 0

congyh
congyh

Reputation: 76

Try to use option() instead:

df = spark.read \
    .format("jdbc") \
    .option("url","jdbc:someDB") \
    .option("user","root") \
    .option("password","password") \
    .option("dbtable","tableName") \ 
    .option("driver","someDriver") \
    .load()

Upvotes: 4

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