Eli Miller created SPARK-17891:
----------------------------------
Summary: SQL-based three column join loses first column
Key: SPARK-17891
URL: https://issues.apache.org/jira/browse/SPARK-17891
Project: Spark
Issue Type: Question
Affects Versions: 2.0.1
Reporter: Eli Miller
Hi all,
I hope that this is not a known issue (I haven't had any luck finding anything
similar in Jira or the mailing lists but I could be searching with the wrong
terms). I just started to experiment with Spark SQL and am seeing what appears
to be a bug. When using Spark SQL to join two tables with a three column inner
join, the first column join is ignored. The example code that I have starts
with two tables *T1*:
{noformat}
+---+---+---+---+
| a| b| c| d|
+---+---+---+---+
| 1| 2| 3| 4|
+---+---+---+---+
{noformat}
and *T2*:
{noformat}
+---+---+---+---+
| b| c| d| e|
+---+---+---+---+
| 2| 3| 4| 5|
| -2| 3| 4| 6|
| 2| -3| 4| 7|
+---+---+---+---+
{noformat}
Joining *T1* to *T2* on *b*, *c* and *d* (in that order):
{code:sql}
SELECT t1.a, t1.b, t2.b, t1.c,t2.c, t1.d, t2.d, t2.e
FROM t1, t2
WHERE t1.b = t2.b AND t1.c = t2.c AND t1.d = t2.d
{code}
results in the following (note that *T1.b* != *T2.b* in the first row):
{noformat}
+---+---+---+---+---+---+---+---+
| a| b| b| c| c| d| d| e|
+---+---+---+---+---+---+---+---+
| 1| 2| -2| 3| 3| 4| 4| 6|
| 1| 2| 2| 3| 3| 4| 4| 5|
+---+---+---+---+---+---+---+---+
{noformat}
Switching the predicate order to *c*, *b* and *d*:
{code:sql}
SELECT t1.a, t1.b, t2.b, t1.c,t2.c, t1.d, t2.d, t2.e
FROM t1, t2
WHERE t1.c = t2.c AND t1.b = t2.b AND t1.d = t2.d
{code}
yields different results (now *T1.c* != *T2.c* in the first row):
{noformat}
+---+---+---+---+---+---+---+---+
| a| b| b| c| c| d| d| e|
+---+---+---+---+---+---+---+---+
| 1| 2| 2| 3| -3| 4| 4| 7|
| 1| 2| 2| 3| 3| 4| 4| 5|
+---+---+---+---+---+---+---+---+
{noformat}
Is this expected?
I started to research this a bit and one thing that jumped out at me was the
ordering of the HashedRelationBroadcastMode concatenation in the plan (this is
from the *b*, *c*, *d* ordering):
{noformat}
...
*Project [a#0, b#1, b#9, c#2, c#10, d#3, d#11, e#12]
+- *BroadcastHashJoin [b#1, c#2, d#3], [b#9, c#10, d#11], Inner, BuildRight
:- *Project [a#0, b#1, c#2, d#3]
: +- *Filter ((isnotnull(b#1) && isnotnull(c#2)) && isnotnull(d#3))
: +- *Scan csv [a#0,b#1,c#2,d#3] Format: CSV, InputPaths:
file:/home/eli/git/IENG/what/target/classes/t1.csv, PartitionFilters: [],
PushedFilters: [IsNotNull(b), IsNotNull(c), IsNotNull(d)], ReadSchema:
struct<a:int,b:int,c:int,d:int>
+- BroadcastExchange
HashedRelationBroadcastMode(List((shiftleft((shiftleft(cast(input[0, int, true]
as bigint), 32) | (cast(input[1, int, true] as bigint) & 4294967295)), 32) |
(cast(input[2, int, true] as bigint) & 4294967295))))
+- *Project [b#9, c#10, d#11, e#12]
+- *Filter ((isnotnull(c#10) && isnotnull(b#9)) && isnotnull(d#11))
+- *Scan csv [b#9,c#10,d#11,e#12] Format: CSV, InputPaths:
file:/home/eli/git/IENG/what/target/classes/t2.csv, PartitionFilters: [],
PushedFilters: [IsNotNull(c), IsNotNull(b), IsNotNull(d)], ReadSchema:
struct<b:int,c:int,d:int,e:int>]
{noformat}
If this concatenated byte array is ever truncated to 64 bits in a comparison,
the leading column will be lost and could result in this behavior.
I will attach my example code and data. Please let me know if I can provide any
other details.
Best regards,
Eli
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