-
-
Save nguyenvulebinh/794c296b1133feb80e46e812ef50f7fc to your computer and use it in GitHub Desktop.
| import typing as T | |
| import cytoolz.curried as tz | |
| import pyspark | |
| from pyspark.sql.functions import explode | |
| def schema_to_columns(schema: pyspark.sql.types.StructType) -> T.List[T.List[str]]: | |
| columns = list() | |
| def helper(schm: pyspark.sql.types.StructType, prefix: list = None): | |
| if prefix is None: | |
| prefix = list() | |
| for item in schm.fields: | |
| if isinstance(item.dataType, pyspark.sql.types.StructType): | |
| helper(item.dataType, prefix + [item.name]) | |
| else: | |
| columns.append(prefix + [item.name]) | |
| helper(schema) | |
| return columns | |
| def flatten_array(frame: pyspark.sql.DataFrame) -> (pyspark.sql.DataFrame, BooleanType): | |
| have_array = False | |
| aliased_columns = list() | |
| for column, t_column in frame.dtypes: | |
| if t_column.startswith('array<'): | |
| have_array = True | |
| c = explode(frame[column]).alias(column) | |
| else: | |
| c = tz.get_in([column], frame) | |
| aliased_columns.append(c) | |
| return (frame.select(aliased_columns), have_array) | |
| def flatten_frame(frame: pyspark.sql.DataFrame) -> pyspark.sql.DataFrame: | |
| aliased_columns = list() | |
| for col_spec in schema_to_columns(frame.schema): | |
| c = tz.get_in(col_spec, frame) | |
| if len(col_spec) == 1: | |
| aliased_columns.append(c) | |
| else: | |
| aliased_columns.append(c.alias(':'.join(col_spec))) | |
| return frame.select(aliased_columns) | |
| def flatten_all(frame: pyspark.sql.DataFrame) -> pyspark.sql.DataFrame: | |
| frame = flatten_frame(frame) | |
| (frame, have_array) = flatten_array(frame) | |
| if have_array: | |
| return flatten_all(frame) | |
| else: | |
| return frame |
@reddysk18 have you tried https://gist.github.com/AxREki/70988ae40d1d82db15832575c175c41c ?
I solve a couple issues there.
@reddysk18 Can you share the schema before and after flatenning the dataframe ?
Hello @AxREki, i am seeing te same issue as @reddysk18. the dataframe has been successfully flatten but no data is being displayed.
DF before:
|-- Portfolio: struct (nullable = true)
|-- HoldingBreakdown: struct (nullable = true)
| | |-- Holding: struct (nullable = true)
| | | |-- HoldingDetail: array (nullable = true)
| | | | |-- element: struct (containsNull = true)
| | | | | |-- HoldingDetailId: string (nullable = true)
| | | | | |-- DetailHoldingTypeId: string (nullable = true)
| | | | | |-- Country: struct (nullable = true)
| | | | | | |-- _VALUE: string (nullable = true)
| | | | | | |-- __Id: string (nullable = false)
DF after:
|-- Portfolio_HoldingBreakdown_Holding_HoldingDetail_HoldingDetailId: string (nullable = true)
|-- Portfolio_HoldingBreakdown_Holding_HoldingDetail_DetailHoldingTypeId: string (nullable = true)
|-- Portfolio_HoldingBreakdown_Holding_HoldingDetail_Country__VALUE: string (nullable = true)
|-- Portfolio_HoldingBreakdown_Holding_HoldingDetail_Country___Id: string (nullable = true)
flattened_df.count()
0
df.count()
1025
@pingilis
Have you tried this : https://gist.github.com/AxREki/70988ae40d1d82db15832575c175c41c
I solve some issues there about empty dataframes
Hi @AxREki, yes i have tried the updated gist. still i see no data.
if t_column.startswith('array<') and i == 0:
I have tried an other way around to flatten which worked but still do not see any data with the data frame after flattening.
used the below code. i am new to python so could not understand the breakdown.
def flatten(schema, prefix=None):
fields = []
for field in schema.fields:
name = prefix + '.' + field.name if prefix else field.name
dtype = field.dataType
if isinstance(dtype, StructType):
fields += flatten(dtype, prefix=name)
else:
fields.append(name)
return fields
def explodeDF(df):
for (name, dtype) in df.dtypes:
if "array" in dtype:
df = df.withColumn(name, explode(name))
return df
def df_is_flat(df):
for (_, dtype) in df.dtypes:
if ("array" in dtype) or ("struct" in dtype):
return False
return True
def flatJson(jdf):
keepGoing = True
while(keepGoing):
fields = flatten(jdf.schema)
new_fields = [item.replace(".", "_") for item in fields]
jdf = jdf.select(fields).toDF(*new_fields)
jdf = explodeDF(jdf)
if df_is_flat(jdf):
keepGoing = False
return jdf
this too flattens the df but returns no data. could you know why is this ?
Even your code seems to work
Do you have a sample of data you can share ?
Hello @AxREki, yes the code works when all the datasets have data in them. if in case there is no data the results is 0 rows all together.
For example if there is no data as mentioned below then it would not work.
data = {
"HoldinBreakdown": {
"Holding": {
"HoldingDetail": [{
"HoldingDetailId":
"DetailHoldingTypeId":
"Country": {
"_VALUE": "France",
"ID":"un"
}
}
]
}
}
}
Any way i have used explode_outer instead of just explode, which works well but while exploding arrays its creating duplicate records.
@ghost Thanks for your feedback. Glad to see you found a way.
@ghost
I faced with a similar issue as you - I got the right structure and flatten schema but there is no data. The root cause of it is - explode function, it will drop records if there are nulls in the columns. I fixed that using outer_explode (that takes care of the null and behave as a left outer join in SQL). Hope this will help
@reddysk18 You should install cytoolz on the cluster first