Mixing
How to join datasets with values between other values?
0
I have a use case where I need to join 2 data-frames.
ID view
ID BookTime
1 2
1 5
2 8
2 3
3 4
FareRule view
Start End Fare
1 3 10
3 6 20
6 10 25
Output is a result of join by checking the BookTime from the ID table. The Fare is computed based on the window that is between Start and End from FareRule.
ID FareDue
1 10
1 20
2 25
2 20
3 20
I am creating a view out of these data-frames and using CROSS JOIN to join them. But as we know, CROSS join is expensive so is there a better way to join them?
SELECT
ID,
Fare AS FareDue
FROM
ID
CROSS JOIN
FareRule
WHERE
BookTime >=Start
AND
BookTime< End
apache-spark apache-spark-sql
edited Nov 21 '18 at 11:01
Jacek Laskowski
44.3k18131265
asked Nov 20 '18 at 19:49
Amaan KhanAmaan Khan
467
add a comment |
0
I have a use case where I need to join 2 data-frames.
ID view
ID BookTime
1 2
1 5
2 8
2 3
3 4
FareRule view
Start End Fare
1 3 10
3 6 20
6 10 25
Output is a result of join by checking the BookTime from the ID table. The Fare is computed based on the window that is between Start and End from FareRule.
ID FareDue
1 10
1 20
2 25
2 20
3 20
I am creating a view out of these data-frames and using CROSS JOIN to join them. But as we know, CROSS join is expensive so is there a better way to join them?
SELECT
ID,
Fare AS FareDue
FROM
ID
CROSS JOIN
FareRule
WHERE
BookTime >=Start
AND
BookTime< End
apache-spark apache-spark-sql
edited Nov 21 '18 at 11:01
Jacek Laskowski
44.3k18131265
asked Nov 20 '18 at 19:49
Amaan KhanAmaan Khan
467
Possible duplicate of How to improve broadcast Join speed in Spark
– user10465355
Nov 20 '18 at 19:55
Why are you using cross join? From the looks of it you sgould be getting the same result with a regular join.
– Vitaliy
Nov 21 '18 at 6:34
Like other SQL query engines Spark has a query optimizer & there is no difference between CROSS JOIN (or comma) with WHERE vs INNER JOIN ON. Read about the general idea of relational query optimization/implementation anywhere. (Including dozens of published academic textbooks free online in pdf.) Read about the optimizer Catalyst & writing optimal Spark SQL queries anywhere. (Many implementers who authored the ACM paper "Spark SQL: Relational Data Processing in Spark" work at Databricks.) PS What did you learn from googling re spark sql query optimization before you considered asking?
– philipxy
Nov 28 '18 at 21:13
add a comment |
0
0
0
1
I have a use case where I need to join 2 data-frames.
ID view
ID BookTime
1 2
1 5
2 8
2 3
3 4
FareRule view
Start End Fare
1 3 10
3 6 20
6 10 25
Output is a result of join by checking the BookTime from the ID table. The Fare is computed based on the window that is between Start and End from FareRule.
ID FareDue
1 10
1 20
2 25
2 20
3 20
I am creating a view out of these data-frames and using CROSS JOIN to join them. But as we know, CROSS join is expensive so is there a better way to join them?
SELECT
ID,
Fare AS FareDue
FROM
ID
CROSS JOIN
FareRule
WHERE
BookTime >=Start
AND
BookTime< End
apache-spark apache-spark-sql
edited Nov 21 '18 at 11:01
Jacek Laskowski
44.3k18131265
asked Nov 20 '18 at 19:49
Amaan KhanAmaan Khan
467
I have a use case where I need to join 2 data-frames.
ID view
ID BookTime
1 2
1 5
2 8
2 3
3 4
FareRule view
Start End Fare
1 3 10
3 6 20
6 10 25
Output is a result of join by checking the BookTime from the ID table. The Fare is computed based on the window that is between Start and End from FareRule.
ID FareDue
1 10
1 20
2 25
2 20
3 20
I am creating a view out of these data-frames and using CROSS JOIN to join them. But as we know, CROSS join is expensive so is there a better way to join them?
SELECT
ID,
Fare AS FareDue
FROM
ID
CROSS JOIN
FareRule
WHERE
BookTime >=Start
AND
BookTime< End
apache-spark apache-spark-sql
apache-spark apache-spark-sql
edited Nov 21 '18 at 11:01
Jacek Laskowski
44.3k18131265
asked Nov 20 '18 at 19:49
Amaan KhanAmaan Khan
467
edited Nov 21 '18 at 11:01
Jacek Laskowski
44.3k18131265
asked Nov 20 '18 at 19:49
Amaan KhanAmaan Khan
467
edited Nov 21 '18 at 11:01
Jacek Laskowski
44.3k18131265
edited Nov 21 '18 at 11:01
Jacek Laskowski
44.3k18131265
edited Nov 21 '18 at 11:01
Jacek Laskowski
44.3k18131265
44.3k18131265
asked Nov 20 '18 at 19:49
Amaan KhanAmaan Khan
467
asked Nov 20 '18 at 19:49
Amaan KhanAmaan Khan
467
asked Nov 20 '18 at 19:49
Amaan KhanAmaan Khan
467
467
Possible duplicate of How to improve broadcast Join speed in Spark
– user10465355
Nov 20 '18 at 19:55
Why are you using cross join? From the looks of it you sgould be getting the same result with a regular join.
– Vitaliy
Nov 21 '18 at 6:34
Like other SQL query engines Spark has a query optimizer & there is no difference between CROSS JOIN (or comma) with WHERE vs INNER JOIN ON. Read about the general idea of relational query optimization/implementation anywhere. (Including dozens of published academic textbooks free online in pdf.) Read about the optimizer Catalyst & writing optimal Spark SQL queries anywhere. (Many implementers who authored the ACM paper "Spark SQL: Relational Data Processing in Spark" work at Databricks.) PS What did you learn from googling re spark sql query optimization before you considered asking?
– philipxy
Nov 28 '18 at 21:13
add a comment |
Possible duplicate of How to improve broadcast Join speed in Spark
– user10465355
Nov 20 '18 at 19:55
Why are you using cross join? From the looks of it you sgould be getting the same result with a regular join.
– Vitaliy
Nov 21 '18 at 6:34
Like other SQL query engines Spark has a query optimizer & there is no difference between CROSS JOIN (or comma) with WHERE vs INNER JOIN ON. Read about the general idea of relational query optimization/implementation anywhere. (Including dozens of published academic textbooks free online in pdf.) Read about the optimizer Catalyst & writing optimal Spark SQL queries anywhere. (Many implementers who authored the ACM paper "Spark SQL: Relational Data Processing in Spark" work at Databricks.) PS What did you learn from googling re spark sql query optimization before you considered asking?
– philipxy
Nov 28 '18 at 21:13
Possible duplicate of How to improve broadcast Join speed in Spark
– user10465355
Nov 20 '18 at 19:55
Possible duplicate of How to improve broadcast Join speed in Spark
– user10465355
Nov 20 '18 at 19:55
Why are you using cross join? From the looks of it you sgould be getting the same result with a regular join.
– Vitaliy
Nov 21 '18 at 6:34
Why are you using cross join? From the looks of it you sgould be getting the same result with a regular join.
– Vitaliy
Nov 21 '18 at 6:34
Like other SQL query engines Spark has a query optimizer & there is no difference between CROSS JOIN (or comma) with WHERE vs INNER JOIN ON. Read about the general idea of relational query optimization/implementation anywhere. (Including dozens of published academic textbooks free online in pdf.) Read about the optimizer Catalyst & writing optimal Spark SQL queries anywhere. (Many implementers who authored the ACM paper "Spark SQL: Relational Data Processing in Spark" work at Databricks.) PS What did you learn from googling re spark sql query optimization before you considered asking?
– philipxy
Nov 28 '18 at 21:13
Like other SQL query engines Spark has a query optimizer & there is no difference between CROSS JOIN (or comma) with WHERE vs INNER JOIN ON. Read about the general idea of relational query optimization/implementation anywhere. (Including dozens of published academic textbooks free online in pdf.) Read about the optimizer Catalyst & writing optimal Spark SQL queries anywhere. (Many implementers who authored the ACM paper "Spark SQL: Relational Data Processing in Spark" work at Databricks.) PS What did you learn from googling re spark sql query optimization before you considered asking?
– philipxy
Nov 28 '18 at 21:13
add a comment |
1 Answer
1
active
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-1
Given the following datasets:
val id = Seq((1, 2), (1, 5), (2, 8), (2, 3), (3, 4)).toDF("ID", "BookTime")
scala> id.show
+---+--------+
| ID|BookTime|
+---+--------+
| 1| 2|
| 1| 5|
| 2| 8|
| 2| 3|
| 3| 4|
+---+--------+
val fareRule = Seq((1,3,10), (3,6,20), (6,10,25)).toDF("start", "end", "fare")
scala> fareRule.show
+-----+---+----+
|start|end|fare|
+-----+---+----+
| 1| 3| 10|
| 3| 6| 20|
| 6| 10| 25|
+-----+---+----+
You simply join them together using between expression.
val q = id.join(fareRule).where('BookTime between('start, 'end)).select('id, 'fare)
scala> q.show
+---+----+
| id|fare|
+---+----+
| 1| 10|
| 1| 20|
| 2| 25|
| 2| 10|
| 2| 20|
| 3| 20|
+---+----+
You may want to adjust between so the boundaries are exclusive on one side. between by default uses the lower bound and upper bound, inclusive.
answered Nov 21 '18 at 11:00
Jacek LaskowskiJacek Laskowski
44.3k18131265
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
-1
Given the following datasets:
val id = Seq((1, 2), (1, 5), (2, 8), (2, 3), (3, 4)).toDF("ID", "BookTime")
scala> id.show
+---+--------+
| ID|BookTime|
+---+--------+
| 1| 2|
| 1| 5|
| 2| 8|
| 2| 3|
| 3| 4|
+---+--------+
val fareRule = Seq((1,3,10), (3,6,20), (6,10,25)).toDF("start", "end", "fare")
scala> fareRule.show
+-----+---+----+
|start|end|fare|
+-----+---+----+
| 1| 3| 10|
| 3| 6| 20|
| 6| 10| 25|
+-----+---+----+
You simply join them together using between expression.
val q = id.join(fareRule).where('BookTime between('start, 'end)).select('id, 'fare)
scala> q.show
+---+----+
| id|fare|
+---+----+
| 1| 10|
| 1| 20|
| 2| 25|
| 2| 10|
| 2| 20|
| 3| 20|
+---+----+
You may want to adjust between so the boundaries are exclusive on one side. between by default uses the lower bound and upper bound, inclusive.
answered Nov 21 '18 at 11:00
Jacek LaskowskiJacek Laskowski
44.3k18131265
add a comment |
-1
Given the following datasets:
val id = Seq((1, 2), (1, 5), (2, 8), (2, 3), (3, 4)).toDF("ID", "BookTime")
scala> id.show
+---+--------+
| ID|BookTime|
+---+--------+
| 1| 2|
| 1| 5|
| 2| 8|
| 2| 3|
| 3| 4|
+---+--------+
val fareRule = Seq((1,3,10), (3,6,20), (6,10,25)).toDF("start", "end", "fare")
scala> fareRule.show
+-----+---+----+
|start|end|fare|
+-----+---+----+
| 1| 3| 10|
| 3| 6| 20|
| 6| 10| 25|
+-----+---+----+
You simply join them together using between expression.
val q = id.join(fareRule).where('BookTime between('start, 'end)).select('id, 'fare)
scala> q.show
+---+----+
| id|fare|
+---+----+
| 1| 10|
| 1| 20|
| 2| 25|
| 2| 10|
| 2| 20|
| 3| 20|
+---+----+
You may want to adjust between so the boundaries are exclusive on one side. between by default uses the lower bound and upper bound, inclusive.
answered Nov 21 '18 at 11:00
Jacek LaskowskiJacek Laskowski
44.3k18131265
add a comment |
-1
-1
-1
Given the following datasets:
val id = Seq((1, 2), (1, 5), (2, 8), (2, 3), (3, 4)).toDF("ID", "BookTime")
scala> id.show
+---+--------+
| ID|BookTime|
+---+--------+
| 1| 2|
| 1| 5|
| 2| 8|
| 2| 3|
| 3| 4|
+---+--------+
val fareRule = Seq((1,3,10), (3,6,20), (6,10,25)).toDF("start", "end", "fare")
scala> fareRule.show
+-----+---+----+
|start|end|fare|
+-----+---+----+
| 1| 3| 10|
| 3| 6| 20|
| 6| 10| 25|
+-----+---+----+
You simply join them together using between expression.
val q = id.join(fareRule).where('BookTime between('start, 'end)).select('id, 'fare)
scala> q.show
+---+----+
| id|fare|
+---+----+
| 1| 10|
| 1| 20|
| 2| 25|
| 2| 10|
| 2| 20|
| 3| 20|
+---+----+
You may want to adjust between so the boundaries are exclusive on one side. between by default uses the lower bound and upper bound, inclusive.
answered Nov 21 '18 at 11:00
Jacek LaskowskiJacek Laskowski
44.3k18131265
Given the following datasets:
val id = Seq((1, 2), (1, 5), (2, 8), (2, 3), (3, 4)).toDF("ID", "BookTime")
scala> id.show
+---+--------+
| ID|BookTime|
+---+--------+
| 1| 2|
| 1| 5|
| 2| 8|
| 2| 3|
| 3| 4|
+---+--------+
val fareRule = Seq((1,3,10), (3,6,20), (6,10,25)).toDF("start", "end", "fare")
scala> fareRule.show
+-----+---+----+
|start|end|fare|
+-----+---+----+
| 1| 3| 10|
| 3| 6| 20|
| 6| 10| 25|
+-----+---+----+
You simply join them together using between expression.
val q = id.join(fareRule).where('BookTime between('start, 'end)).select('id, 'fare)
scala> q.show
+---+----+
| id|fare|
+---+----+
| 1| 10|
| 1| 20|
| 2| 25|
| 2| 10|
| 2| 20|
| 3| 20|
+---+----+
You may want to adjust between so the boundaries are exclusive on one side. between by default uses the lower bound and upper bound, inclusive.
answered Nov 21 '18 at 11:00
Jacek LaskowskiJacek Laskowski
44.3k18131265
answered Nov 21 '18 at 11:00
Jacek LaskowskiJacek Laskowski
44.3k18131265
answered Nov 21 '18 at 11:00
Jacek LaskowskiJacek Laskowski
44.3k18131265
answered Nov 21 '18 at 11:00
Jacek LaskowskiJacek Laskowski
44.3k18131265
44.3k18131265
add a comment |
add a comment |
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Possible duplicate of How to improve broadcast Join speed in Spark
– user10465355
Nov 20 '18 at 19:55
Why are you using cross join? From the looks of it you sgould be getting the same result with a regular join.
– Vitaliy
Nov 21 '18 at 6:34
Like other SQL query engines Spark has a query optimizer & there is no difference between CROSS JOIN (or comma) with WHERE vs INNER JOIN ON. Read about the general idea of relational query optimization/implementation anywhere. (Including dozens of published academic textbooks free online in pdf.) Read about the optimizer Catalyst & writing optimal Spark SQL queries anywhere. (Many implementers who authored the ACM paper "Spark SQL: Relational Data Processing in Spark" work at Databricks.) PS What did you learn from googling re spark sql query optimization before you considered asking?
– philipxy
Nov 28 '18 at 21:13