_corrupt_record error when reading a JSON file into Spark
PythonJsonDataframePysparkPython Problem Overview
I've got this JSON file
{
"a": 1,
"b": 2
}
which has been obtained with Python json.dump method. Now, I want to read this file into a DataFrame in Spark, using pyspark. Following documentation, I'm doing this
> sc = SparkContext() > > sqlc = SQLContext(sc) > > df = sqlc.read.json('my_file.json') > > print df.show()
The print statement spits out this though:
+---------------+
|_corrupt_record|
+---------------+
| {|
| "a": 1, |
| "b": 2|
| }|
+---------------+
Anyone knows what's going on and why it is not interpreting the file correctly?
Python Solutions
Solution 1 - Python
If you want to leave your JSON file as it is (without stripping new lines characters \n), include multiLine=True keyword argument
sc = SparkContext()
sqlc = SQLContext(sc)
df = sqlc.read.json('my_file.json', multiLine=True)
print df.show()
Solution 2 - Python
You need to have one json object per row in your input file, see http://spark.apache.org/docs/latest/api/python/pyspark.sql.html#pyspark.sql.DataFrameReader.json
If your json file looks like this it will give you the expected dataframe:
{ "a": 1, "b": 2 }
{ "a": 3, "b": 4 }
....
df.show()
+---+---+
| a| b|
+---+---+
| 1| 2|
| 3| 4|
+---+---+
Solution 3 - Python
In Spark 2.2+ you can read json file of multiline using following command.
val dataframe = spark.read.option("multiline",true).json( " filePath ")
if there is json object per line then,
val dataframe = spark.read.json(filepath)
Solution 4 - Python
Adding to @Bernhard's great answer
# original file was written with pretty-print inside a list
with open("pretty-printed.json") as jsonfile:
js = json.load(jsonfile)
# write a new file with one object per line
with open("flattened.json", 'a') as outfile:
for d in js:
json.dump(d, outfile)
outfile.write('\n')
Solution 5 - Python
I want to share my experience in which I have a JSON column String but with Python notation, which means I have None instead of null, False instead of false and True instead of true.
When parsing this column, spark returns me a column named _corrupt_record. So what I had to do before parsing the JSON String is replacing the Python notation with the standard JSON notation:
df.withColumn("json_notation",
F.regexp_replace(F.regexp_replace(F.regexp_replace("_corrupt_record", "None", "null"), "False", "false") ,"True", "true")
After this transformation I was then able to use for example the function F.from_json() on the json_notation column and here Pyspark was able to correctly parse the JSON object.