DELETE query performance

Question

Original query

delete B from 
TABLE_BASE B , 
TABLE_INC  I 
where B.ID = I.IDID and B.NUM = I.NUM;

Performanace stats for above query

+-------------------+---------+-----------+
|    Response Time  | SumCPU  | ImpactCPU |
+-------------------+---------+-----------+
|   00:05:29.190000 |   2852  |  319672   |
+-------------------+---------+-----------+

---

Optimized Query 1

DEL FROM TABLE_BASE WHERE (ID, NUM) IN 
(SELECT ID, NUM FROM TABLE_INC);

Stats for above query

+-----------------+--------+-----------+
|   QryRespTime   | SumCPU | ImpactCPU |
+-----------------+--------+-----------+
| 00:00:00.570000 |  15.42 |     49.92 |
+-----------------+--------+-----------+

---

Optimized Query 2

DELETE FROM TABLE_BASE B WHERE EXISTS
(SELECT * FROM TABLE_INC I WHERE B.ID = I.ID AND B.NUM = I.NUM);

Stats for above query

+-----------------+--------+-----------+
|   QryRespTime   | SumCPU | ImpactCPU |
+-----------------+--------+-----------+
| 00:00:00.400000 |  11.96 |     44.93 |
+-----------------+--------+-----------+

My question -

• How/Why does the Optimized Query 1 and 2 significantly affect the performance so much ?
• What is the best practice for such DELETE queries ?
• Should I choose Query 1 or Query 2 ? Which one is ideal/better/reliable? I feel Query 1 would be ideal because instead of SELECT * I am using SELECT ID,NUM reducing to only two columns but Query 2 is showing better results.

---

QUERY 1

 This query is optimized using type 2 profile T2_Linux64, profileid 21.
  1) First, we lock TEMP_DB.TABLE_BASE for write on a
     reserved RowHash to prevent global deadlock.
  2) Next, we lock TEMP_DB_T.TABLE_INC for access, and we
     lock TEMP_DB.TABLE_BASE for write.
  3) We execute the following steps in parallel.
       1) We do an all-AMPs RETRIEVE step from
          TEMP_DB.TABLE_BASE by way of an all-rows scan
          with no residual conditions into Spool 2 (all_amps), which is
          redistributed by the hash code of (
          TEMP_DB.TABLE_BASE.NUM,
          TEMP_DB.TABLE_BASE.ID) to all AMPs.  Then
          we do a SORT to order Spool 2 by row hash.  The size of Spool
          2 is estimated with low confidence to be 168,480 rows (
          5,054,400 bytes).  The estimated time for this step is 0.03
          seconds.
       2) We do an all-AMPs RETRIEVE step from
          TEMP_DB_T.TABLE_INC by way of an all-rows scan
          with no residual conditions into Spool 3 (all_amps), which is
          redistributed by the hash code of (
          TEMP_DB_T.TABLE_INC.NUM,
          TEMP_DB_T.TABLE_INC.ID) to all AMPs.  Then
          we do a SORT to order Spool 3 by row hash and the sort key in
          spool field1 eliminating duplicate rows.  The size of Spool 3
          is estimated with high confidence to be 5,640 rows (310,200
          bytes).  The estimated time for this step is 0.03 seconds.
  4) We do an all-AMPs JOIN step from Spool 2 (Last Use) by way of an
     all-rows scan, which is joined to Spool 3 (Last Use) by way of an
     all-rows scan.  Spool 2 and Spool 3 are joined using an inclusion
     merge join, with a join condition of ("(ID = ID) AND
     (NUM = NUM)").  The result goes into Spool 1 (all_amps),
     which is redistributed by the hash code of (
     TEMP_DB.TABLE_BASE.ROWID) to all AMPs.  Then we do
     a SORT to order Spool 1 by row hash and the sort key in spool
     field1 eliminating duplicate rows.  The size of Spool 1 is
     estimated with no confidence to be 168,480 rows (3,032,640 bytes).
     The estimated time for this step is 1.32 seconds.
  5) We do an all-AMPs MERGE DELETE to
     TEMP_DB.TABLE_BASE from Spool 1 (Last Use) via the
     row id.  The size is estimated with no confidence to be 168,480
     rows.  The estimated time for this step is 42.95 seconds.
  6) We spoil the parser's dictionary cache for the table.
  7) Finally, we send out an END TRANSACTION step to all AMPs involved
     in processing the request.
  -> No rows are returned to the user as the result of statement 1.

---

QUERY 2 EXPLAIN PLAN

 This query is optimized using type 2 profile T2_Linux64, profileid 21.
  1) First, we lock TEMP_DB.TABLE_BASE for write on a reserved RowHash to
     prevent global deadlock.
  2) Next, we lock TEMP_DB_T.TABLE_INC for access, and we
     lock TEMP_DB.TABLE_BASE for write.
  3) We execute the following steps in parallel.
       1) We do an all-AMPs RETRIEVE step from TEMP_DB.TABLE_BASE by way of
          an all-rows scan with no residual conditions into Spool 2
          (all_amps), which is redistributed by the hash code of (
          TEMP_DB.TABLE_BASE.NUM, TEMP_DB.TABLE_BASE.ID) to all AMPs.
          Then we do a SORT to order Spool 2 by row hash.  The size of
          Spool 2 is estimated with low confidence to be 168,480 rows (
          5,054,400 bytes).  The estimated time for this step is 0.03
          seconds.
       2) We do an all-AMPs RETRIEVE step from
          TEMP_DB_T.TABLE_INC by way of an all-rows scan
          with no residual conditions into Spool 3 (all_amps), which is
          redistributed by the hash code of (
          TEMP_DB_T.TABLE_INC.NUM,
          TEMP_DB_T.TABLE_INC.ID) to all AMPs.  Then
          we do a SORT to order Spool 3 by row hash and the sort key in
          spool field1 eliminating duplicate rows.  The size of Spool 3
          is estimated with high confidence to be 5,640 rows (310,200
          bytes).  The estimated time for this step is 0.03 seconds.
  4) We do an all-AMPs JOIN step from Spool 2 (Last Use) by way of an
     all-rows scan, which is joined to Spool 3 (Last Use) by way of an
     all-rows scan.  Spool 2 and Spool 3 are joined using an inclusion
     merge join, with a join condition of ("(NUM = NUM) AND
     (ID = ID)").  The result goes into Spool 1 (all_amps), which
     is redistributed by the hash code of (TEMP_DB.TABLE_BASE.ROWID) to all
     AMPs.  Then we do a SORT to order Spool 1 by row hash and the sort
     key in spool field1 eliminating duplicate rows.  The size of Spool
     1 is estimated with no confidence to be 168,480 rows (3,032,640
     bytes).  The estimated time for this step is 1.32 seconds.
  5) We do an all-AMPs MERGE DELETE to TEMP_DB.TABLE_BASE from Spool 1 (Last
     Use) via the row id.  The size is estimated with no confidence to
     be 168,480 rows.  The estimated time for this step is 42.95
     seconds.
  6) We spoil the parser's dictionary cache for the table.
  7) Finally, we send out an END TRANSACTION step to all AMPs involved
     in processing the request.
  -> No rows are returned to the user as the result of statement 1.

For TABLE_BASE

+----------------+----------+
|  table_bytes   | skewness |
+----------------+----------+
| 16842085888.00 |    22.78 |
+----------------+----------+

For TABLE_INC

+-------------+----------+
| table_bytes | skewness |
+-------------+----------+
|  5317120.00 |    44.52 |
+-------------+----------+

Answer 1

What's the relation between TABLE_BASE and TABLE_INC?

If it's one-to-many Q1 probably creates a huge spool first while Q2&3 might apply DISTINCT before the join.

Regarding IN vs. EXISTS there should be hardly any difference, did you check dbc.QryLogStepsV?

Edit:

If (ID,Num) is the PI of the target table rewriting to a MERGE DELETE should provide best performance:

MERGE INTO TABLE_BASE AS tgt
USING TABLE_INC AS src
ON src.ID = tgt.ID,
AND src.Num = tgt.Num
WHEN MATCHED 
THE DELETE