strange issue with glom() method with Pyspark DataFrame
I am using spark version 2.3 and facing an strange issue with dates while using the glom method to see the partitions size.
Below is my dataframe.
df1_data = spark.sql("""
SELECT *
from udb.partitioned_table_df1 where VEH_ENGINE in
(
'ABCDP3F27HL239911'
'ABCDP3F27HL230011'
)
""");
+-----------------+-------------------------+------------------------+----------
-----------+
| VEH_ENGINE |VEH_COUNTRY |VEH_RETAIL_SALE_DATE | VEH_MODEL_YEAR|
+-----------------+-------------------------+------------------------+---------------------+
|ABCDP3F27HL239911| CAN| 0001-01-01| 2017|
|ABCDP3F27HL230011| USA| 0001-01-01| 2018|
+-----------------+-------------------------+------------------------+---------------------+
At the source we have default start date as '0001-01-01' and same date has been loaded to pyspark dataframe as date column. no issues.
I can perform rest of the operations;join,filters etc as usual.
but I am facing an issue when I was looking at the spark partitions which I normally do.
partitionSizedf = df1_data.rdd.glom().map(len).collect()
I am getting below error:
ValueError: ('ordinal must be >= 1', <function <lambda> at 0x7fcc8d1c5848>, (u'ABCDP3F27HL239911', u'CAN', -719164, 2017))
pyspark
asked Nov 16 '18 at 8:52
vikrant ranavikrant rana
6521317
add a comment |
I am using spark version 2.3 and facing an strange issue with dates while using the glom method to see the partitions size.
Below is my dataframe.
df1_data = spark.sql("""
SELECT *
from udb.partitioned_table_df1 where VEH_ENGINE in
(
'ABCDP3F27HL239911'
'ABCDP3F27HL230011'
)
""");
+-----------------+-------------------------+------------------------+----------
-----------+
| VEH_ENGINE |VEH_COUNTRY |VEH_RETAIL_SALE_DATE | VEH_MODEL_YEAR|
+-----------------+-------------------------+------------------------+---------------------+
|ABCDP3F27HL239911| CAN| 0001-01-01| 2017|
|ABCDP3F27HL230011| USA| 0001-01-01| 2018|
+-----------------+-------------------------+------------------------+---------------------+
At the source we have default start date as '0001-01-01' and same date has been loaded to pyspark dataframe as date column. no issues.
I can perform rest of the operations;join,filters etc as usual.
but I am facing an issue when I was looking at the spark partitions which I normally do.
partitionSizedf = df1_data.rdd.glom().map(len).collect()
I am getting below error:
ValueError: ('ordinal must be >= 1', <function <lambda> at 0x7fcc8d1c5848>, (u'ABCDP3F27HL239911', u'CAN', -719164, 2017))
pyspark
asked Nov 16 '18 at 8:52
vikrant ranavikrant rana
6521317
add a comment |
I am using spark version 2.3 and facing an strange issue with dates while using the glom method to see the partitions size.
Below is my dataframe.
df1_data = spark.sql("""
SELECT *
from udb.partitioned_table_df1 where VEH_ENGINE in
(
'ABCDP3F27HL239911'
'ABCDP3F27HL230011'
)
""");
+-----------------+-------------------------+------------------------+----------
-----------+
| VEH_ENGINE |VEH_COUNTRY |VEH_RETAIL_SALE_DATE | VEH_MODEL_YEAR|
+-----------------+-------------------------+------------------------+---------------------+
|ABCDP3F27HL239911| CAN| 0001-01-01| 2017|
|ABCDP3F27HL230011| USA| 0001-01-01| 2018|
+-----------------+-------------------------+------------------------+---------------------+
At the source we have default start date as '0001-01-01' and same date has been loaded to pyspark dataframe as date column. no issues.
I can perform rest of the operations;join,filters etc as usual.
but I am facing an issue when I was looking at the spark partitions which I normally do.
partitionSizedf = df1_data.rdd.glom().map(len).collect()
I am getting below error:
ValueError: ('ordinal must be >= 1', <function <lambda> at 0x7fcc8d1c5848>, (u'ABCDP3F27HL239911', u'CAN', -719164, 2017))
pyspark
asked Nov 16 '18 at 8:52
vikrant ranavikrant rana
6521317
I am using spark version 2.3 and facing an strange issue with dates while using the glom method to see the partitions size.
Below is my dataframe.
df1_data = spark.sql("""
SELECT *
from udb.partitioned_table_df1 where VEH_ENGINE in
(
'ABCDP3F27HL239911'
'ABCDP3F27HL230011'
)
""");
+-----------------+-------------------------+------------------------+----------
-----------+
| VEH_ENGINE |VEH_COUNTRY |VEH_RETAIL_SALE_DATE | VEH_MODEL_YEAR|
+-----------------+-------------------------+------------------------+---------------------+
|ABCDP3F27HL239911| CAN| 0001-01-01| 2017|
|ABCDP3F27HL230011| USA| 0001-01-01| 2018|
+-----------------+-------------------------+------------------------+---------------------+
At the source we have default start date as '0001-01-01' and same date has been loaded to pyspark dataframe as date column. no issues.
I can perform rest of the operations;join,filters etc as usual.
but I am facing an issue when I was looking at the spark partitions which I normally do.
partitionSizedf = df1_data.rdd.glom().map(len).collect()
I am getting below error:
ValueError: ('ordinal must be >= 1', <function <lambda> at 0x7fcc8d1c5848>, (u'ABCDP3F27HL239911', u'CAN', -719164, 2017))
pyspark
pyspark
asked Nov 16 '18 at 8:52
vikrant ranavikrant rana
6521317
asked Nov 16 '18 at 8:52
vikrant ranavikrant rana
6521317
asked Nov 16 '18 at 8:52
vikrant ranavikrant rana
6521317
asked Nov 16 '18 at 8:52
vikrant ranavikrant rana
6521317
asked Nov 16 '18 at 8:52
vikrant ranavikrant rana
6521317
6521317
add a comment |
add a comment |
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