How to efficiently unpivot MULTIPLE columns in Hive?
My data is structured like in the below table:
| Name | Foo_A | Foo_B | Foo_C | Bar_A | Bar_B | Bar_C |
--------------------------------------------------------
| abcd | 16 | 32 | 14 | 52 | 41 | 17 |
| ... | ... | ... | ... | ... | ... | ... |
I am looking to query the data in Hive in a way such that it looks like this:
| Name | Class | FooVal | BarVal |
----------------------------------
| abcd | A | 16 | 52 |
| abcd | B | 32 | 41 |
| abcd | C | 14 | 17 |
| ... | ... | ... | ... |
I am already aware of and am using a UNION ALL, but what would be a more efficient way of doing this using "LATERAL VIEW explode" a map data type?
hive query-optimization hiveql unpivot
edited Nov 20 '18 at 17:50
leftjoin
8,92522151
asked Nov 20 '18 at 16:27
lizliz
112
add a comment |
My data is structured like in the below table:
| Name | Foo_A | Foo_B | Foo_C | Bar_A | Bar_B | Bar_C |
--------------------------------------------------------
| abcd | 16 | 32 | 14 | 52 | 41 | 17 |
| ... | ... | ... | ... | ... | ... | ... |
I am looking to query the data in Hive in a way such that it looks like this:
| Name | Class | FooVal | BarVal |
----------------------------------
| abcd | A | 16 | 52 |
| abcd | B | 32 | 41 |
| abcd | C | 14 | 17 |
| ... | ... | ... | ... |
I am already aware of and am using a UNION ALL, but what would be a more efficient way of doing this using "LATERAL VIEW explode" a map data type?
hive query-optimization hiveql unpivot
edited Nov 20 '18 at 17:50
leftjoin
8,92522151
asked Nov 20 '18 at 16:27
lizliz
112
add a comment |
My data is structured like in the below table:
| Name | Foo_A | Foo_B | Foo_C | Bar_A | Bar_B | Bar_C |
--------------------------------------------------------
| abcd | 16 | 32 | 14 | 52 | 41 | 17 |
| ... | ... | ... | ... | ... | ... | ... |
I am looking to query the data in Hive in a way such that it looks like this:
| Name | Class | FooVal | BarVal |
----------------------------------
| abcd | A | 16 | 52 |
| abcd | B | 32 | 41 |
| abcd | C | 14 | 17 |
| ... | ... | ... | ... |
I am already aware of and am using a UNION ALL, but what would be a more efficient way of doing this using "LATERAL VIEW explode" a map data type?
hive query-optimization hiveql unpivot
edited Nov 20 '18 at 17:50
leftjoin
8,92522151
asked Nov 20 '18 at 16:27
lizliz
112
My data is structured like in the below table:
| Name | Foo_A | Foo_B | Foo_C | Bar_A | Bar_B | Bar_C |
--------------------------------------------------------
| abcd | 16 | 32 | 14 | 52 | 41 | 17 |
| ... | ... | ... | ... | ... | ... | ... |
I am looking to query the data in Hive in a way such that it looks like this:
| Name | Class | FooVal | BarVal |
----------------------------------
| abcd | A | 16 | 52 |
| abcd | B | 32 | 41 |
| abcd | C | 14 | 17 |
| ... | ... | ... | ... |
I am already aware of and am using a UNION ALL, but what would be a more efficient way of doing this using "LATERAL VIEW explode" a map data type?
hive query-optimization hiveql unpivot
hive query-optimization hiveql unpivot
edited Nov 20 '18 at 17:50
leftjoin
8,92522151
asked Nov 20 '18 at 16:27
lizliz
112
edited Nov 20 '18 at 17:50
leftjoin
8,92522151
asked Nov 20 '18 at 16:27
lizliz
112
edited Nov 20 '18 at 17:50
leftjoin
8,92522151
edited Nov 20 '18 at 17:50
leftjoin
8,92522151
edited Nov 20 '18 at 17:50
leftjoin
8,92522151
8,92522151
asked Nov 20 '18 at 16:27
lizliz
112
asked Nov 20 '18 at 16:27
lizliz
112
asked Nov 20 '18 at 16:27
lizliz
112
112
add a comment |
add a comment |
2 Answers
2
active
oldest
votes
CROSS JOIN with class stack (see code example) will multiply main table rows x3, one row per class, then use case statements to derive your columns depending on class value. CROSS JOIN with small dataset (3 rows) should be transformed to map join and will execute very fast on mappers.
set hive.auto.convert.join=true; --this enables map-join
select t.Name,
s.class,
case s.class when 'A' then t.Foo_A
when 'B' then t.foo_B
when 'C' then t.foo_C
end as FooVal,
case s.class when 'A' then t.Bar_A
when 'B' then t.Bar_B
when 'C' then t.Bar_C
end as BarVal
from table t
cross join (select stack(3,'A','B','C') as class) s
;
It will scan the table only once and perform much better than UNION ALL approach.
answered Nov 20 '18 at 17:36
leftjoinleftjoin
8,92522151
add a comment |
Thanks for reply! Please find below another way of doing it which is faster than CROSS JOIN.
select t1.ID, t2.key_1 as class, t2.FooVal, t3.BarVal
from table t1
LATERAL VIEW explode (map(
'A', Foo_A,
'B', Foo_B,
'C', Foo_C
)) t2 as key_1, FooVal
LATERAL VIEW explode (map(
'A', Bar_A,
'B', Bar_B,
'C', Bar_C
)) t3 as key_2, BarVal
where t2.key_1 = t3.key_2;
answered Dec 20 '18 at 16:15
lizliz
112
I'm sorry, but this solution is not faster. Because cross join normally will execute as map-join. And your query does the same twice, multiplying rows x9, after that you filtering rows.
– leftjoin
Feb 5 at 13:34
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
CROSS JOIN with class stack (see code example) will multiply main table rows x3, one row per class, then use case statements to derive your columns depending on class value. CROSS JOIN with small dataset (3 rows) should be transformed to map join and will execute very fast on mappers.
set hive.auto.convert.join=true; --this enables map-join
select t.Name,
s.class,
case s.class when 'A' then t.Foo_A
when 'B' then t.foo_B
when 'C' then t.foo_C
end as FooVal,
case s.class when 'A' then t.Bar_A
when 'B' then t.Bar_B
when 'C' then t.Bar_C
end as BarVal
from table t
cross join (select stack(3,'A','B','C') as class) s
;
It will scan the table only once and perform much better than UNION ALL approach.
answered Nov 20 '18 at 17:36
leftjoinleftjoin
8,92522151
add a comment |
CROSS JOIN with class stack (see code example) will multiply main table rows x3, one row per class, then use case statements to derive your columns depending on class value. CROSS JOIN with small dataset (3 rows) should be transformed to map join and will execute very fast on mappers.
set hive.auto.convert.join=true; --this enables map-join
select t.Name,
s.class,
case s.class when 'A' then t.Foo_A
when 'B' then t.foo_B
when 'C' then t.foo_C
end as FooVal,
case s.class when 'A' then t.Bar_A
when 'B' then t.Bar_B
when 'C' then t.Bar_C
end as BarVal
from table t
cross join (select stack(3,'A','B','C') as class) s
;
It will scan the table only once and perform much better than UNION ALL approach.
answered Nov 20 '18 at 17:36
leftjoinleftjoin
8,92522151
add a comment |
CROSS JOIN with class stack (see code example) will multiply main table rows x3, one row per class, then use case statements to derive your columns depending on class value. CROSS JOIN with small dataset (3 rows) should be transformed to map join and will execute very fast on mappers.
set hive.auto.convert.join=true; --this enables map-join
select t.Name,
s.class,
case s.class when 'A' then t.Foo_A
when 'B' then t.foo_B
when 'C' then t.foo_C
end as FooVal,
case s.class when 'A' then t.Bar_A
when 'B' then t.Bar_B
when 'C' then t.Bar_C
end as BarVal
from table t
cross join (select stack(3,'A','B','C') as class) s
;
It will scan the table only once and perform much better than UNION ALL approach.
answered Nov 20 '18 at 17:36
leftjoinleftjoin
8,92522151
CROSS JOIN with class stack (see code example) will multiply main table rows x3, one row per class, then use case statements to derive your columns depending on class value. CROSS JOIN with small dataset (3 rows) should be transformed to map join and will execute very fast on mappers.
set hive.auto.convert.join=true; --this enables map-join
select t.Name,
s.class,
case s.class when 'A' then t.Foo_A
when 'B' then t.foo_B
when 'C' then t.foo_C
end as FooVal,
case s.class when 'A' then t.Bar_A
when 'B' then t.Bar_B
when 'C' then t.Bar_C
end as BarVal
from table t
cross join (select stack(3,'A','B','C') as class) s
;
It will scan the table only once and perform much better than UNION ALL approach.
answered Nov 20 '18 at 17:36
leftjoinleftjoin
8,92522151
edited Nov 20 '18 at 18:14
answered Nov 20 '18 at 17:36
leftjoinleftjoin
8,92522151
answered Nov 20 '18 at 17:36
leftjoinleftjoin
8,92522151
answered Nov 20 '18 at 17:36
leftjoinleftjoin
8,92522151
8,92522151
add a comment |
add a comment |
Thanks for reply! Please find below another way of doing it which is faster than CROSS JOIN.
select t1.ID, t2.key_1 as class, t2.FooVal, t3.BarVal
from table t1
LATERAL VIEW explode (map(
'A', Foo_A,
'B', Foo_B,
'C', Foo_C
)) t2 as key_1, FooVal
LATERAL VIEW explode (map(
'A', Bar_A,
'B', Bar_B,
'C', Bar_C
)) t3 as key_2, BarVal
where t2.key_1 = t3.key_2;
answered Dec 20 '18 at 16:15
lizliz
112
I'm sorry, but this solution is not faster. Because cross join normally will execute as map-join. And your query does the same twice, multiplying rows x9, after that you filtering rows.
– leftjoin
Feb 5 at 13:34
add a comment |
Thanks for reply! Please find below another way of doing it which is faster than CROSS JOIN.
select t1.ID, t2.key_1 as class, t2.FooVal, t3.BarVal
from table t1
LATERAL VIEW explode (map(
'A', Foo_A,
'B', Foo_B,
'C', Foo_C
)) t2 as key_1, FooVal
LATERAL VIEW explode (map(
'A', Bar_A,
'B', Bar_B,
'C', Bar_C
)) t3 as key_2, BarVal
where t2.key_1 = t3.key_2;
answered Dec 20 '18 at 16:15
lizliz
112
I'm sorry, but this solution is not faster. Because cross join normally will execute as map-join. And your query does the same twice, multiplying rows x9, after that you filtering rows.
– leftjoin
Feb 5 at 13:34
add a comment |
Thanks for reply! Please find below another way of doing it which is faster than CROSS JOIN.
select t1.ID, t2.key_1 as class, t2.FooVal, t3.BarVal
from table t1
LATERAL VIEW explode (map(
'A', Foo_A,
'B', Foo_B,
'C', Foo_C
)) t2 as key_1, FooVal
LATERAL VIEW explode (map(
'A', Bar_A,
'B', Bar_B,
'C', Bar_C
)) t3 as key_2, BarVal
where t2.key_1 = t3.key_2;
answered Dec 20 '18 at 16:15
lizliz
112
Thanks for reply! Please find below another way of doing it which is faster than CROSS JOIN.
select t1.ID, t2.key_1 as class, t2.FooVal, t3.BarVal
from table t1
LATERAL VIEW explode (map(
'A', Foo_A,
'B', Foo_B,
'C', Foo_C
)) t2 as key_1, FooVal
LATERAL VIEW explode (map(
'A', Bar_A,
'B', Bar_B,
'C', Bar_C
)) t3 as key_2, BarVal
where t2.key_1 = t3.key_2;
answered Dec 20 '18 at 16:15
lizliz
112
answered Dec 20 '18 at 16:15
lizliz
112
answered Dec 20 '18 at 16:15
lizliz
112
answered Dec 20 '18 at 16:15
lizliz
112
112
I'm sorry, but this solution is not faster. Because cross join normally will execute as map-join. And your query does the same twice, multiplying rows x9, after that you filtering rows.
– leftjoin
Feb 5 at 13:34
add a comment |
I'm sorry, but this solution is not faster. Because cross join normally will execute as map-join. And your query does the same twice, multiplying rows x9, after that you filtering rows.
– leftjoin
Feb 5 at 13:34
I'm sorry, but this solution is not faster. Because cross join normally will execute as map-join. And your query does the same twice, multiplying rows x9, after that you filtering rows.
– leftjoin
Feb 5 at 13:34
I'm sorry, but this solution is not faster. Because cross join normally will execute as map-join. And your query does the same twice, multiplying rows x9, after that you filtering rows.
– leftjoin
Feb 5 at 13:34
add a comment |
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