How to maintain the order of values while doing rollup in a spark Dataframe
How can I do a rollup of the below dataframe ,i.e have only a single record for the common key and its values as a tuple and maintain the order of the values .
I am able to do the roll up but not able to maintain the order of values.
+-------------
| key| val|
+-------------
| A|4816|
| A|5732|
| A|5542|
| B|5814|
| B|5812|
| B|5499|
| C|5992|
| C|7299|
| C|5193|
Expected O/P
key | val
A | (4816, 5732, 5542)
B | (5814, 5812, 5499)
C | (5992, 7299, 5193)
How can I maintain the order of values while doing the rollup?
scala apache-spark
asked Jun 1 '18 at 10:54
ArjunArjun
969
add a comment |
How can I do a rollup of the below dataframe ,i.e have only a single record for the common key and its values as a tuple and maintain the order of the values .
I am able to do the roll up but not able to maintain the order of values.
+-------------
| key| val|
+-------------
| A|4816|
| A|5732|
| A|5542|
| B|5814|
| B|5812|
| B|5499|
| C|5992|
| C|7299|
| C|5193|
Expected O/P
key | val
A | (4816, 5732, 5542)
B | (5814, 5812, 5499)
C | (5992, 7299, 5193)
How can I maintain the order of values while doing the rollup?
scala apache-spark
asked Jun 1 '18 at 10:54
ArjunArjun
969
You will have to generate a new column for ordering before you create a dataframe because dataframes are distributed and without ordering information, there is no way to preserve order.
– Ramesh Maharjan
Jun 1 '18 at 11:26
add a comment |
How can I do a rollup of the below dataframe ,i.e have only a single record for the common key and its values as a tuple and maintain the order of the values .
I am able to do the roll up but not able to maintain the order of values.
+-------------
| key| val|
+-------------
| A|4816|
| A|5732|
| A|5542|
| B|5814|
| B|5812|
| B|5499|
| C|5992|
| C|7299|
| C|5193|
Expected O/P
key | val
A | (4816, 5732, 5542)
B | (5814, 5812, 5499)
C | (5992, 7299, 5193)
How can I maintain the order of values while doing the rollup?
scala apache-spark
asked Jun 1 '18 at 10:54
ArjunArjun
969
How can I do a rollup of the below dataframe ,i.e have only a single record for the common key and its values as a tuple and maintain the order of the values .
I am able to do the roll up but not able to maintain the order of values.
+-------------
| key| val|
+-------------
| A|4816|
| A|5732|
| A|5542|
| B|5814|
| B|5812|
| B|5499|
| C|5992|
| C|7299|
| C|5193|
Expected O/P
key | val
A | (4816, 5732, 5542)
B | (5814, 5812, 5499)
C | (5992, 7299, 5193)
How can I maintain the order of values while doing the rollup?
scala apache-spark
scala apache-spark
asked Jun 1 '18 at 10:54
ArjunArjun
969
asked Jun 1 '18 at 10:54
ArjunArjun
969
asked Jun 1 '18 at 10:54
ArjunArjun
969
asked Jun 1 '18 at 10:54
ArjunArjun
969
asked Jun 1 '18 at 10:54
ArjunArjun
969
969
You will have to generate a new column for ordering before you create a dataframe because dataframes are distributed and without ordering information, there is no way to preserve order.
– Ramesh Maharjan
Jun 1 '18 at 11:26
add a comment |
You will have to generate a new column for ordering before you create a dataframe because dataframes are distributed and without ordering information, there is no way to preserve order.
– Ramesh Maharjan
Jun 1 '18 at 11:26
You will have to generate a new column for ordering before you create a dataframe because dataframes are distributed and without ordering information, there is no way to preserve order.
– Ramesh Maharjan
Jun 1 '18 at 11:26
You will have to generate a new column for ordering before you create a dataframe because dataframes are distributed and without ordering information, there is no way to preserve order.
– Ramesh Maharjan
Jun 1 '18 at 11:26
add a comment |
1 Answer
1
active
oldest
votes
The short answer is you don't. In general case DataFrames are not ordered, therefore there is nothing to preserve. Furthermore aggregations require shuffle, and as such, don't guarantee any processing order of operations.
In specific cases you can try something similar to:
import org.apache.spark.sql.functions._
df
.withColumn("id", monotonically_increasing_id)
.groupBy("key")
.agg(collect_list(struct($"id", $"val")).alias("val"))
.select($"key", sort_array($"val").getItem("val").alias("val"))
but use it at your own risk, and only if you fully understand guarantees of the upstream execution plan.
edited Jun 1 '18 at 11:59
hi-zir
20.4k62864
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
The short answer is you don't. In general case DataFrames are not ordered, therefore there is nothing to preserve. Furthermore aggregations require shuffle, and as such, don't guarantee any processing order of operations.
In specific cases you can try something similar to:
import org.apache.spark.sql.functions._
df
.withColumn("id", monotonically_increasing_id)
.groupBy("key")
.agg(collect_list(struct($"id", $"val")).alias("val"))
.select($"key", sort_array($"val").getItem("val").alias("val"))
but use it at your own risk, and only if you fully understand guarantees of the upstream execution plan.
edited Jun 1 '18 at 11:59
hi-zir
20.4k62864
add a comment |
The short answer is you don't. In general case DataFrames are not ordered, therefore there is nothing to preserve. Furthermore aggregations require shuffle, and as such, don't guarantee any processing order of operations.
In specific cases you can try something similar to:
import org.apache.spark.sql.functions._
df
.withColumn("id", monotonically_increasing_id)
.groupBy("key")
.agg(collect_list(struct($"id", $"val")).alias("val"))
.select($"key", sort_array($"val").getItem("val").alias("val"))
but use it at your own risk, and only if you fully understand guarantees of the upstream execution plan.
edited Jun 1 '18 at 11:59
hi-zir
20.4k62864
add a comment |
The short answer is you don't. In general case DataFrames are not ordered, therefore there is nothing to preserve. Furthermore aggregations require shuffle, and as such, don't guarantee any processing order of operations.
In specific cases you can try something similar to:
import org.apache.spark.sql.functions._
df
.withColumn("id", monotonically_increasing_id)
.groupBy("key")
.agg(collect_list(struct($"id", $"val")).alias("val"))
.select($"key", sort_array($"val").getItem("val").alias("val"))
but use it at your own risk, and only if you fully understand guarantees of the upstream execution plan.
edited Jun 1 '18 at 11:59
hi-zir
20.4k62864
The short answer is you don't. In general case DataFrames are not ordered, therefore there is nothing to preserve. Furthermore aggregations require shuffle, and as such, don't guarantee any processing order of operations.
In specific cases you can try something similar to:
import org.apache.spark.sql.functions._
df
.withColumn("id", monotonically_increasing_id)
.groupBy("key")
.agg(collect_list(struct($"id", $"val")).alias("val"))
.select($"key", sort_array($"val").getItem("val").alias("val"))
but use it at your own risk, and only if you fully understand guarantees of the upstream execution plan.
edited Jun 1 '18 at 11:59
hi-zir
20.4k62864
edited Jun 1 '18 at 11:59
hi-zir
20.4k62864
edited Jun 1 '18 at 11:59
hi-zir
20.4k62864
edited Jun 1 '18 at 11:59
hi-zir
20.4k62864
20.4k62864
answered Jun 1 '18 at 11:23
user9880935
add a comment |
add a comment |
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You will have to generate a new column for ordering before you create a dataframe because dataframes are distributed and without ordering information, there is no way to preserve order.
– Ramesh Maharjan
Jun 1 '18 at 11:26