Pyspark - converting json string to DataFrame

Question

I have a test2.json file that contains simple json:

{  "Name": "something",  "Url": "https://stackoverflow.com",  "Author": "jangcy",  "BlogEntries": 100,  "Caller": "jangcy"}

I have uploaded my file to blob storage and I create a DataFrame from it:

df = spark.read.json("/example/data/test2.json")

then I can see it without any problems:

df.show()
+------+-----------+------+---------+--------------------+
|Author|BlogEntries|Caller|     Name|                 Url|
+------+-----------+------+---------+--------------------+
|jangcy|        100|jangcy|something|https://stackover...|
+------+-----------+------+---------+--------------------+

Second scenario: I have really the same json string declared within my notebook:

newJson = '{  "Name": "something",  "Url": "https://stackoverflow.com",  "Author": "jangcy",  "BlogEntries": 100,  "Caller": "jangcy"}'

I can print it etc. But now if I'd like to create a DataFrame from it:

df = spark.read.json(newJson)

I get the 'Relative path in absolute URI' error:

'java.net.URISyntaxException: Relative path in absolute URI: {  "Name":%20%22something%22,%20%20%22Url%22:%20%22https:/stackoverflow.com%22,%20%20%22Author%22:%20%22jangcy%22,%20%20%22BlogEntries%22:%20100,%20%20%22Caller%22:%20%22jangcy%22%7D'
Traceback (most recent call last):
  File "/usr/hdp/current/spark2-client/python/pyspark/sql/readwriter.py", line 249, in json
    return self._df(self._jreader.json(self._spark._sc._jvm.PythonUtils.toSeq(path)))
  File "/usr/hdp/current/spark2-client/python/lib/py4j-0.10.4-src.zip/py4j/java_gateway.py", line 1133, in __call__
    answer, self.gateway_client, self.target_id, self.name)
  File "/usr/hdp/current/spark2-client/python/pyspark/sql/utils.py", line 79, in deco
    raise IllegalArgumentException(s.split(': ', 1)[1], stackTrace)
pyspark.sql.utils.IllegalArgumentException: 'java.net.URISyntaxException: Relative path in absolute URI: {  "Name":%20%22something%22,%20%20%22Url%22:%20%22https:/stackoverflow.com%22,%20%20%22Author%22:%20%22jangcy%22,%20%20%22BlogEntries%22:%20100,%20%20%22Caller%22:%20%22jangcy%22%7D'

Should I apply additional transformations to the newJson string? If yes, what should them be? Please forgive me, if this is too trivial, as I am very new to Python and Spark.

I am using Jupyter notebook with PySpark3 Kernel.

Thanks in advance.

Solution

You can do the following

newJson = '{"Name":"something","Url":"https://stackoverflow.com","Author":"jangcy","BlogEntries":100,"Caller":"jangcy"}'
df = spark.read.json(sc.parallelize([newJson]))
df.show(truncate=False)

which should give

+------+-----------+------+---------+-------------------------+
|Author|BlogEntries|Caller|Name     |Url                      |
+------+-----------+------+---------+-------------------------+
|jangcy|100        |jangcy|something|https://stackoverflow.com|
+------+-----------+------+---------+-------------------------+