How can we compare two dataframes in spark scala to find difference between these 2 files, which column ??...
0
I have two files and I created two dataframes prod1 and prod2 out of it.I need to find the records with column names and values that are not matching in both the dfs.
id_sk is the primary key .all the cols are string datatype
dataframe 1 (prod1)
id_sk | uuid|name
1 |10 |a
2 |20 |b
3 |30 |c
dataframe 2 (prod2)
id_sk | uuid|name
2 |20 |b-upd
3 |30-up|c
4 |40 |d
so I need the result dataframe in the below format.
id|col_name|values
2 |name |b,b-upd
3 |uuid |30,30-up
I did a inner join and compared the unmatched records.
I am getting the result as follows :
id_sk | uuid_prod1|uid_prod2|name_prod1|name_prod2
2 |20 |20 |b |b-upd
3 |30 |30-up |c |c
val commmon_rec = prod1.join(prod2,prod1("id_sk")===prod2("id_sk"),"inner").select(prod1("id_sk").alias("id_sk_prod1"),prod1("uuid").alias("uuid_prod1"),prod1("name").alias("name_prod1"),prod1("name").alias("name_prod2")
val compare = spark.sql("select ...from common_rec where col_prod1<>col_prod2")
scala apache-spark dataframe rdd
edited Jan 20 at 12:09
Brian Tompsett - 汤莱恩
4,2231338101
asked Jan 20 at 7:11
SHAMPA PRAMANIKSHAMPA PRAMANIK
1
add a comment |
0
I have two files and I created two dataframes prod1 and prod2 out of it.I need to find the records with column names and values that are not matching in both the dfs.
id_sk is the primary key .all the cols are string datatype
dataframe 1 (prod1)
id_sk | uuid|name
1 |10 |a
2 |20 |b
3 |30 |c
dataframe 2 (prod2)
id_sk | uuid|name
2 |20 |b-upd
3 |30-up|c
4 |40 |d
so I need the result dataframe in the below format.
id|col_name|values
2 |name |b,b-upd
3 |uuid |30,30-up
I did a inner join and compared the unmatched records.
I am getting the result as follows :
id_sk | uuid_prod1|uid_prod2|name_prod1|name_prod2
2 |20 |20 |b |b-upd
3 |30 |30-up |c |c
val commmon_rec = prod1.join(prod2,prod1("id_sk")===prod2("id_sk"),"inner").select(prod1("id_sk").alias("id_sk_prod1"),prod1("uuid").alias("uuid_prod1"),prod1("name").alias("name_prod1"),prod1("name").alias("name_prod2")
val compare = spark.sql("select ...from common_rec where col_prod1<>col_prod2")
scala apache-spark dataframe rdd
edited Jan 20 at 12:09
Brian Tompsett - 汤莱恩
4,2231338101
asked Jan 20 at 7:11
SHAMPA PRAMANIKSHAMPA PRAMANIK
1
Possible duplicate of Compare two Spark dataframes
– Andronicus
Jan 20 at 7:18
add a comment |
0
0
0
I have two files and I created two dataframes prod1 and prod2 out of it.I need to find the records with column names and values that are not matching in both the dfs.
id_sk is the primary key .all the cols are string datatype
dataframe 1 (prod1)
id_sk | uuid|name
1 |10 |a
2 |20 |b
3 |30 |c
dataframe 2 (prod2)
id_sk | uuid|name
2 |20 |b-upd
3 |30-up|c
4 |40 |d
so I need the result dataframe in the below format.
id|col_name|values
2 |name |b,b-upd
3 |uuid |30,30-up
I did a inner join and compared the unmatched records.
I am getting the result as follows :
id_sk | uuid_prod1|uid_prod2|name_prod1|name_prod2
2 |20 |20 |b |b-upd
3 |30 |30-up |c |c
val commmon_rec = prod1.join(prod2,prod1("id_sk")===prod2("id_sk"),"inner").select(prod1("id_sk").alias("id_sk_prod1"),prod1("uuid").alias("uuid_prod1"),prod1("name").alias("name_prod1"),prod1("name").alias("name_prod2")
val compare = spark.sql("select ...from common_rec where col_prod1<>col_prod2")
scala apache-spark dataframe rdd
edited Jan 20 at 12:09
Brian Tompsett - 汤莱恩
4,2231338101
asked Jan 20 at 7:11
SHAMPA PRAMANIKSHAMPA PRAMANIK
1
I have two files and I created two dataframes prod1 and prod2 out of it.I need to find the records with column names and values that are not matching in both the dfs.
id_sk is the primary key .all the cols are string datatype
dataframe 1 (prod1)
id_sk | uuid|name
1 |10 |a
2 |20 |b
3 |30 |c
dataframe 2 (prod2)
id_sk | uuid|name
2 |20 |b-upd
3 |30-up|c
4 |40 |d
so I need the result dataframe in the below format.
id|col_name|values
2 |name |b,b-upd
3 |uuid |30,30-up
I did a inner join and compared the unmatched records.
I am getting the result as follows :
id_sk | uuid_prod1|uid_prod2|name_prod1|name_prod2
2 |20 |20 |b |b-upd
3 |30 |30-up |c |c
val commmon_rec = prod1.join(prod2,prod1("id_sk")===prod2("id_sk"),"inner").select(prod1("id_sk").alias("id_sk_prod1"),prod1("uuid").alias("uuid_prod1"),prod1("name").alias("name_prod1"),prod1("name").alias("name_prod2")
val compare = spark.sql("select ...from common_rec where col_prod1<>col_prod2")
scala apache-spark dataframe rdd
scala apache-spark dataframe rdd
edited Jan 20 at 12:09
Brian Tompsett - 汤莱恩
4,2231338101
asked Jan 20 at 7:11
SHAMPA PRAMANIKSHAMPA PRAMANIK
1
edited Jan 20 at 12:09
Brian Tompsett - 汤莱恩
4,2231338101
asked Jan 20 at 7:11
SHAMPA PRAMANIKSHAMPA PRAMANIK
1
edited Jan 20 at 12:09
Brian Tompsett - 汤莱恩
4,2231338101
edited Jan 20 at 12:09
Brian Tompsett - 汤莱恩
4,2231338101
edited Jan 20 at 12:09
Brian Tompsett - 汤莱恩
4,2231338101
4,2231338101
asked Jan 20 at 7:11
SHAMPA PRAMANIKSHAMPA PRAMANIK
1
asked Jan 20 at 7:11
SHAMPA PRAMANIKSHAMPA PRAMANIK
1
asked Jan 20 at 7:11
SHAMPA PRAMANIKSHAMPA PRAMANIK
1
1
Possible duplicate of Compare two Spark dataframes
– Andronicus
Jan 20 at 7:18
add a comment |
Possible duplicate of Compare two Spark dataframes
– Andronicus
Jan 20 at 7:18
Possible duplicate of Compare two Spark dataframes
– Andronicus
Jan 20 at 7:18
Possible duplicate of Compare two Spark dataframes
– Andronicus
Jan 20 at 7:18
add a comment |
1 Answer
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This is a possible solution:
//to create a joined DF with column "col_name"
//if columns "name" and "uuid" contains different values:
var output = df1.join(df2, df1.col("id_sk")===df2.col("id_sk"))
.where(df1.col("name")=!=df2.col("name") || df1.col("uuid")=!=df2.col("uuid"))
.withColumn("col_name", when(df1.col("name")=!=df2.col("name"), "name")
.otherwise(when(df1.col("uuid")=!=df2.col("uuid"), "uuid")))
//to create the new "col_values" column
//containing concatenated values:
output = output.withColumn("col_values", when(output.col("col_name")==="name", when(df1.col("name")=!=df2.col("name"), concat_ws(",", df1.col("name"), df2.col("name")) ))
.when(output.col("col_name")==="uuid", when(df1.col("uuid")=!=df2.col("uuid"), concat_ws(",", df1.col("uuid"), df2.col("uuid")) )))
output = output.select(df1.col("id_sk"), output.col("col_name"), output.col("col_values"))
+-----+--------+----------+
|id_sk|col_name|col_values|
+-----+--------+----------+
| 2| name| b,b-up|
| 3| uuid| 30,30-up|
+-----+--------+----------+
Note that I don't think this is the best possible solution, but just a starting point (for example what if one row have more than one different column values?).
answered Jan 21 at 11:52
pheeleeppoopheeleeppoo
85811420
add a comment |
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1 Answer
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votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
0
This is a possible solution:
//to create a joined DF with column "col_name"
//if columns "name" and "uuid" contains different values:
var output = df1.join(df2, df1.col("id_sk")===df2.col("id_sk"))
.where(df1.col("name")=!=df2.col("name") || df1.col("uuid")=!=df2.col("uuid"))
.withColumn("col_name", when(df1.col("name")=!=df2.col("name"), "name")
.otherwise(when(df1.col("uuid")=!=df2.col("uuid"), "uuid")))
//to create the new "col_values" column
//containing concatenated values:
output = output.withColumn("col_values", when(output.col("col_name")==="name", when(df1.col("name")=!=df2.col("name"), concat_ws(",", df1.col("name"), df2.col("name")) ))
.when(output.col("col_name")==="uuid", when(df1.col("uuid")=!=df2.col("uuid"), concat_ws(",", df1.col("uuid"), df2.col("uuid")) )))
output = output.select(df1.col("id_sk"), output.col("col_name"), output.col("col_values"))
+-----+--------+----------+
|id_sk|col_name|col_values|
+-----+--------+----------+
| 2| name| b,b-up|
| 3| uuid| 30,30-up|
+-----+--------+----------+
Note that I don't think this is the best possible solution, but just a starting point (for example what if one row have more than one different column values?).
answered Jan 21 at 11:52
pheeleeppoopheeleeppoo
85811420
add a comment |
0
This is a possible solution:
//to create a joined DF with column "col_name"
//if columns "name" and "uuid" contains different values:
var output = df1.join(df2, df1.col("id_sk")===df2.col("id_sk"))
.where(df1.col("name")=!=df2.col("name") || df1.col("uuid")=!=df2.col("uuid"))
.withColumn("col_name", when(df1.col("name")=!=df2.col("name"), "name")
.otherwise(when(df1.col("uuid")=!=df2.col("uuid"), "uuid")))
//to create the new "col_values" column
//containing concatenated values:
output = output.withColumn("col_values", when(output.col("col_name")==="name", when(df1.col("name")=!=df2.col("name"), concat_ws(",", df1.col("name"), df2.col("name")) ))
.when(output.col("col_name")==="uuid", when(df1.col("uuid")=!=df2.col("uuid"), concat_ws(",", df1.col("uuid"), df2.col("uuid")) )))
output = output.select(df1.col("id_sk"), output.col("col_name"), output.col("col_values"))
+-----+--------+----------+
|id_sk|col_name|col_values|
+-----+--------+----------+
| 2| name| b,b-up|
| 3| uuid| 30,30-up|
+-----+--------+----------+
Note that I don't think this is the best possible solution, but just a starting point (for example what if one row have more than one different column values?).
answered Jan 21 at 11:52
pheeleeppoopheeleeppoo
85811420
add a comment |
0
0
0
This is a possible solution:
//to create a joined DF with column "col_name"
//if columns "name" and "uuid" contains different values:
var output = df1.join(df2, df1.col("id_sk")===df2.col("id_sk"))
.where(df1.col("name")=!=df2.col("name") || df1.col("uuid")=!=df2.col("uuid"))
.withColumn("col_name", when(df1.col("name")=!=df2.col("name"), "name")
.otherwise(when(df1.col("uuid")=!=df2.col("uuid"), "uuid")))
//to create the new "col_values" column
//containing concatenated values:
output = output.withColumn("col_values", when(output.col("col_name")==="name", when(df1.col("name")=!=df2.col("name"), concat_ws(",", df1.col("name"), df2.col("name")) ))
.when(output.col("col_name")==="uuid", when(df1.col("uuid")=!=df2.col("uuid"), concat_ws(",", df1.col("uuid"), df2.col("uuid")) )))
output = output.select(df1.col("id_sk"), output.col("col_name"), output.col("col_values"))
+-----+--------+----------+
|id_sk|col_name|col_values|
+-----+--------+----------+
| 2| name| b,b-up|
| 3| uuid| 30,30-up|
+-----+--------+----------+
Note that I don't think this is the best possible solution, but just a starting point (for example what if one row have more than one different column values?).
answered Jan 21 at 11:52
pheeleeppoopheeleeppoo
85811420
This is a possible solution:
//to create a joined DF with column "col_name"
//if columns "name" and "uuid" contains different values:
var output = df1.join(df2, df1.col("id_sk")===df2.col("id_sk"))
.where(df1.col("name")=!=df2.col("name") || df1.col("uuid")=!=df2.col("uuid"))
.withColumn("col_name", when(df1.col("name")=!=df2.col("name"), "name")
.otherwise(when(df1.col("uuid")=!=df2.col("uuid"), "uuid")))
//to create the new "col_values" column
//containing concatenated values:
output = output.withColumn("col_values", when(output.col("col_name")==="name", when(df1.col("name")=!=df2.col("name"), concat_ws(",", df1.col("name"), df2.col("name")) ))
.when(output.col("col_name")==="uuid", when(df1.col("uuid")=!=df2.col("uuid"), concat_ws(",", df1.col("uuid"), df2.col("uuid")) )))
output = output.select(df1.col("id_sk"), output.col("col_name"), output.col("col_values"))
+-----+--------+----------+
|id_sk|col_name|col_values|
+-----+--------+----------+
| 2| name| b,b-up|
| 3| uuid| 30,30-up|
+-----+--------+----------+
Note that I don't think this is the best possible solution, but just a starting point (for example what if one row have more than one different column values?).
answered Jan 21 at 11:52
pheeleeppoopheeleeppoo
85811420
answered Jan 21 at 11:52
pheeleeppoopheeleeppoo
85811420
answered Jan 21 at 11:52
pheeleeppoopheeleeppoo
85811420
answered Jan 21 at 11:52
pheeleeppoopheeleeppoo
85811420
85811420
add a comment |
add a comment |
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Possible duplicate of Compare two Spark dataframes
– Andronicus
Jan 20 at 7:18