pyspark dataframe joining of two dataframe
I have two dataframes say df1 and df2:
df1 has fields as CI_NAME,CLOSE_TIME,CH_ID
and df2 has fields as NAME,TIMESTAMP,MEM_CONSUMED.
Basically df1 has records of software updates done to the system and df2 has monitoring records of the system.
I need to add a field in df1 named cpu_util_avg_before_update by comparing CI_NAME equal to NAME field of df2 and CLOSE_TIME between TIMESTAMP - 7 days and TIMESTAMP and then take the average of MEM_CONSUMED.
How can I do that, any help would be appreciated as I have tried udf but that is not taking dataframe as input.
Thanks
here is the code that I tried:
from pyspark.sql.functions import col,udf,struct
from dateutil import parser
import datetime
@udf
def memavgbeforeupdate(structx,df2):
df=df2.where(col("name")==structx[1] & (col("timestamp")>parser.parse(structx[0])-datetime.timedelta(days=10) & col("timestamp")<parser.parse(structx[0])+datetime.timedelta(days=10)))
df=df.where(col("mem_consumed_average")!="NaN").where(col("mem_consumed_average").isNotNull())
if df.rdd.isEmpty():
return -1
else:
df1=df.select("mem_consumed_average")
return float(str(df1.select(mean(col("mem_consumed_average"))).collect()[0]).split("=")[1].split(")")[0])
df3=df1.withColumn("mem_avg_before_update",memavgbeforeupdate(struct(col("CLOSE_TIME"),col("CI_NAME")),df2))
But that's not working and throwing error as:
'DataFrame' object has no attribute '_get_object_id'
apache-spark dataframe pyspark data-science
asked Nov 22 at 12:52
Neeraj Kumar
62
add a comment |
I have two dataframes say df1 and df2:
df1 has fields as CI_NAME,CLOSE_TIME,CH_ID
and df2 has fields as NAME,TIMESTAMP,MEM_CONSUMED.
Basically df1 has records of software updates done to the system and df2 has monitoring records of the system.
I need to add a field in df1 named cpu_util_avg_before_update by comparing CI_NAME equal to NAME field of df2 and CLOSE_TIME between TIMESTAMP - 7 days and TIMESTAMP and then take the average of MEM_CONSUMED.
How can I do that, any help would be appreciated as I have tried udf but that is not taking dataframe as input.
Thanks
here is the code that I tried:
from pyspark.sql.functions import col,udf,struct
from dateutil import parser
import datetime
@udf
def memavgbeforeupdate(structx,df2):
df=df2.where(col("name")==structx[1] & (col("timestamp")>parser.parse(structx[0])-datetime.timedelta(days=10) & col("timestamp")<parser.parse(structx[0])+datetime.timedelta(days=10)))
df=df.where(col("mem_consumed_average")!="NaN").where(col("mem_consumed_average").isNotNull())
if df.rdd.isEmpty():
return -1
else:
df1=df.select("mem_consumed_average")
return float(str(df1.select(mean(col("mem_consumed_average"))).collect()[0]).split("=")[1].split(")")[0])
df3=df1.withColumn("mem_avg_before_update",memavgbeforeupdate(struct(col("CLOSE_TIME"),col("CI_NAME")),df2))
But that's not working and throwing error as:
'DataFrame' object has no attribute '_get_object_id'
apache-spark dataframe pyspark data-science
asked Nov 22 at 12:52
Neeraj Kumar
62
You cannot useDataFrameinudf. You'll have to rewrite this as a combination of joins and aggregations. Could you please provide edit your question and provide reproducible example?
– user10465355
Nov 22 at 20:17
add a comment |
I have two dataframes say df1 and df2:
df1 has fields as CI_NAME,CLOSE_TIME,CH_ID
and df2 has fields as NAME,TIMESTAMP,MEM_CONSUMED.
Basically df1 has records of software updates done to the system and df2 has monitoring records of the system.
I need to add a field in df1 named cpu_util_avg_before_update by comparing CI_NAME equal to NAME field of df2 and CLOSE_TIME between TIMESTAMP - 7 days and TIMESTAMP and then take the average of MEM_CONSUMED.
How can I do that, any help would be appreciated as I have tried udf but that is not taking dataframe as input.
Thanks
here is the code that I tried:
from pyspark.sql.functions import col,udf,struct
from dateutil import parser
import datetime
@udf
def memavgbeforeupdate(structx,df2):
df=df2.where(col("name")==structx[1] & (col("timestamp")>parser.parse(structx[0])-datetime.timedelta(days=10) & col("timestamp")<parser.parse(structx[0])+datetime.timedelta(days=10)))
df=df.where(col("mem_consumed_average")!="NaN").where(col("mem_consumed_average").isNotNull())
if df.rdd.isEmpty():
return -1
else:
df1=df.select("mem_consumed_average")
return float(str(df1.select(mean(col("mem_consumed_average"))).collect()[0]).split("=")[1].split(")")[0])
df3=df1.withColumn("mem_avg_before_update",memavgbeforeupdate(struct(col("CLOSE_TIME"),col("CI_NAME")),df2))
But that's not working and throwing error as:
'DataFrame' object has no attribute '_get_object_id'
apache-spark dataframe pyspark data-science
asked Nov 22 at 12:52
Neeraj Kumar
62
I have two dataframes say df1 and df2:
df1 has fields as CI_NAME,CLOSE_TIME,CH_ID
and df2 has fields as NAME,TIMESTAMP,MEM_CONSUMED.
Basically df1 has records of software updates done to the system and df2 has monitoring records of the system.
I need to add a field in df1 named cpu_util_avg_before_update by comparing CI_NAME equal to NAME field of df2 and CLOSE_TIME between TIMESTAMP - 7 days and TIMESTAMP and then take the average of MEM_CONSUMED.
How can I do that, any help would be appreciated as I have tried udf but that is not taking dataframe as input.
Thanks
here is the code that I tried:
from pyspark.sql.functions import col,udf,struct
from dateutil import parser
import datetime
@udf
def memavgbeforeupdate(structx,df2):
df=df2.where(col("name")==structx[1] & (col("timestamp")>parser.parse(structx[0])-datetime.timedelta(days=10) & col("timestamp")<parser.parse(structx[0])+datetime.timedelta(days=10)))
df=df.where(col("mem_consumed_average")!="NaN").where(col("mem_consumed_average").isNotNull())
if df.rdd.isEmpty():
return -1
else:
df1=df.select("mem_consumed_average")
return float(str(df1.select(mean(col("mem_consumed_average"))).collect()[0]).split("=")[1].split(")")[0])
df3=df1.withColumn("mem_avg_before_update",memavgbeforeupdate(struct(col("CLOSE_TIME"),col("CI_NAME")),df2))
But that's not working and throwing error as:
'DataFrame' object has no attribute '_get_object_id'
apache-spark dataframe pyspark data-science
apache-spark dataframe pyspark data-science
asked Nov 22 at 12:52
Neeraj Kumar
62
asked Nov 22 at 12:52
Neeraj Kumar
62
edited Nov 25 at 18:33
asked Nov 22 at 12:52
Neeraj Kumar
62
asked Nov 22 at 12:52
Neeraj Kumar
62
asked Nov 22 at 12:52
Neeraj Kumar
62
62
You cannot useDataFrameinudf. You'll have to rewrite this as a combination of joins and aggregations. Could you please provide edit your question and provide reproducible example?
– user10465355
Nov 22 at 20:17
add a comment |
You cannot useDataFrameinudf. You'll have to rewrite this as a combination of joins and aggregations. Could you please provide edit your question and provide reproducible example?
– user10465355
Nov 22 at 20:17
You cannot use
DataFrame in udf. You'll have to rewrite this as a combination of joins and aggregations. Could you please provide edit your question and provide reproducible example?– user10465355
Nov 22 at 20:17
You cannot use
DataFrame in udf. You'll have to rewrite this as a combination of joins and aggregations. Could you please provide edit your question and provide reproducible example?– user10465355
Nov 22 at 20:17
add a comment |
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You cannot use
DataFrameinudf. You'll have to rewrite this as a combination of joins and aggregations. Could you please provide edit your question and provide reproducible example?– user10465355
Nov 22 at 20:17