Converting Column headings into Row data - ms-access-2013

I have a table in an Access Database that has columns that I would like to convert to be Row data.
I found a code in here
converting database columns into associated row data
I am new to VBA and I just don't know how to use this code.
I have attached some sample data
How the table currently is set up it is 14 columns long.
+-------+--------+-------------+-------------+-------------+----------------+
| ID | Name | 2019-10-31 | 2019-11-30 | 2019-12-31 | ... etc ... |
+-------+--------+-------------+-------------+-------------+----------------+
| 555 | Fred | 1 | 4 | 12 | |
| 556 | Barney| 5 | 33 | 24 | |
| 557 | Betty | 4 | 11 | 76 | |
+-------+--------+-------------+-------------+-------------+----------------+
I would like the output to be
+-------+------------+-------------+
| ID | Date | HOLB |
+-------+------------+-------------+
| 555 | 2019-10-31| 1 |
| 555 | 2019-11-30| 4 |
| 555 | 2019-12-31| 12 |
| 556 | 2019-10-31| 5 |
| 556 | 2019-11-30| 33 |
| 556 | 2019-12-31| 24 |
+-------+--------+-------------+---+
How can I modify this code into a Module and call the module in a query?
Or any other idea you may have.

Related

How to optimize an Oracle UNION ALL of two 3-sec queries that takes over 200 secs ... even with rownum

I am working to further optimize the following query. I have already had a lot of success optimizing the query up to this point, but now I am looking for help from others for ideas. I appreciate your help.
The following query takes over 200 seconds to complete yet the two individual WITH clause queries (receiving_1 and receiving_2) that make up the "UNION ALL" take less than 4 seconds each.
receiving_1 -- 58 recs in 3.4 secs; cost 295 (no UNION)
receiving_2 -- 0 recs in 3.8 secs; cost 295 (no UNION)
UNION ALL of 1+2 -- 58 recs in 203.1 secs; cost 300 (UNION ALL)
I was hoping "rownum" would help optimize the "UNION ALL", but it did not. This attempt was based on a note from AskTom --> Files --> SQLTechniques.zip (between 61 and 72)
When you have two queries that run light speed separately
But not so together
Generally a mixed “CBO/RBO” problem
Use of RBO with a feature that kicks in the CBO
Rownum can be a temporary fix till all things are CBO
NOTE: One optimization that avoids this issue is embedded replacement parameters or cursor parameters that greatly reduce the rows at key points in the query. However, because I need to run this query through a database link against a read-only database, the following options are not available for this query.
dbms_sql over database link: query is too long; dbs_sql does not accept a lob over database link; also DBAs don't allow the running of any PL/SQL in the read-only database.
pipelined function: pipelined function has known bug over database link between 19c and 11g so fails
sys_refcursor via PL/SQL: cursors are not supported over database links
set parameter values in PL/SQL package and use them in view: not allowed to execute any code in read-only database (I am still bugging the DBAs to allow this. This might be my best hope if I am not able to optimize the below query to run as a view.)
NOTE: In an attempt at providing a good test case with as little code as possible, the below SQL (4,584 chars, 88 line breaks) is a greatly simplified version of the actual SQL (93,683 chars, 2,014 line breaks) I am developing. The original SQL has already gone through several rounds of optimizations to bring the runtime down from over an hour to under 10 seconds. This latest challenge is that the SQL has to be rewritten as a view as opposed to a SQL file with embedded substitution parameters. The substitution parameters were key to the optimizations up to now. I am now refactoring the SQL to work as a view in Oracle 11gR2 to be called via a database link from Oracle 19c. A view has no parameters, obviously. The driving parameters (ledger, OU, period) will be WHERE clause conditions when querying the view.
Big picture: The actual SQL under development is an amalgamation of 6 report queries that share a lot of duplicated code. My work is to convert all six source report queries into an efficient extract for importing into a new database schema for reconciliation purposes.
with
gccs as (
select
gcc.code_combination_id
from apps.gl_code_combinations gcc
where 1=1
AND regexp_like(gcc.segment1,'^[0-9]{4}$') -- only include 4-digit numbers
AND gcc.segment1 BETWEEN to_char('1500') and to_char('1504')
AND regexp_like(gcc.segment3,'^[0-9]{4}$') -- only include 4-digit numbers
AND ( gcc.segment3 BETWEEN to_char('5000') and to_char('5999')
OR gcc.segment3 in ('6000','6002','6003','6004','6005','6006','6007','6008','6009','6010','6011','6012','6013','6014','6017','6018','6019','6020','6021','6022','6023','6024','6025','6026','6027','6028','6029','6030','6031','6032',
'6033','6034','6035','6036','6037','6038','6039','6040','6041','6042','6043','6044','6045','6046','6047','6048','6049','6050','6051','6052','6053','6054','6055','6056','6058','6060','6061','6062','6063','6064',
'6065','6066','6067','6068','6069','6070','6071','6072','6073','6074','6075','6076','6077','6084','6085','6086','6087','6088','6089','6090','6091','6092','6093','6094','6095','6096','6097','6098','6099','6100',
'6101','6102','6103','6104','6105','6106','6120','6121','6130','6131','6140','6150','6151','6155','6158','6162','6990','6991','6992','6993','6994','6995','6996','6997','6998'))
AND regexp_like(gcc.segment6,'^[0-9]{4}$') -- only include 4-digit numbers
AND gcc.segment6 BETWEEN to_char('3000') and to_char('4999')
) -- select count(*) code_combination_id from gccs;
,
base_query as (
SELECT
gl.LEDGER_ID,
hou.organization_id,
gper.PERIOD_NAME,
XAL.AE_HEADER_ID,
XAL.AE_LINE_NUM,
GJB.STATUS BATCH_STATUS,
GJH.JE_CATEGORY,
XAL.ACCOUNTING_CLASS_CODE,
GJH.JE_SOURCE,
GJH.JE_HEADER_ID,
GJL.JE_LINE_NUM
FROM apps.gl_ledgers gl
join apps.hr_operating_units hou
on hou.set_of_books_id = gl.ledger_id
join APPS.GL_JE_LINES GJL
on exists (select null from gccs where gccs.CODE_COMBINATION_ID = GJL.CODE_COMBINATION_ID)
AND (nvl(gjl.stat_amount,0) <> 0 OR (NVL (gjl.accounted_dr, 0) - NVL (gjl.accounted_cr, 0)) <> 0)
join APPS.GL_PERIODS gper
on GJL.PERIOD_NAME = gper.PERIOD_NAME
join APPS.GL_JE_HEADERS GJH
on GJH.JE_HEADER_ID = GJL.JE_HEADER_ID
AND GJH.PERIOD_NAME = gper.PERIOD_NAME
AND GJH.LEDGER_ID = gl.ledger_id
AND GJH.JE_SOURCE = 'Cost Management'
join APPS.GL_JE_BATCHES GJB
on GJB.JE_BATCH_ID = GJH.JE_BATCH_ID
join APPS.GL_IMPORT_REFERENCES GIR
on GIR.JE_HEADER_ID = GJL.JE_HEADER_ID
AND GIR.JE_LINE_NUM = GJL.JE_LINE_NUM
join APPS.XLA_AE_LINES XAL
on XAL.GL_SL_LINK_TABLE = GIR.GL_SL_LINK_TABLE
AND XAL.GL_SL_LINK_ID = GIR.GL_SL_LINK_ID
AND XAL.LEDGER_ID = gl.LEDGER_ID
),
receiving_1 as (
select distinct
bq.LEDGER_ID,
bq.organization_id,
bq.period_name,
bq.AE_HEADER_ID,
bq.AE_LINE_NUM
FROM base_query bq
WHERE bq.BATCH_STATUS = 'P'
AND UPPER(bq.JE_CATEGORY) = UPPER('Receiving')
),
receiving_2 as (
select distinct
bq.LEDGER_ID,
bq.organization_id,
bq.period_name,
bq.AE_HEADER_ID,
bq.AE_LINE_NUM
FROM base_query bq
WHERE bq.ACCOUNTING_CLASS_CODE = 'INTRA'
AND bq.BATCH_STATUS = 'P'
AND UPPER(bq.JE_CATEGORY) = UPPER('Receiving')
),
receiving_all as (
-- the use of "rownum" below sometimes helps to optimize "union all", but does not help in this query
select rownum, x.* from receiving_1 x -- 58 recs in 3.4 secs cost 295 (no UNION)
union all -- 58 recs in 203.1 secs cost 300 (UNION ALL)
select rownum, x.* from receiving_2 x -- 0 recs in 3.8 secs cost 295 (no UNION)
)
select count(*)
from receiving_all
where ledger_id = 2021
and organization_id = 82
and period_name = 'SEP-2020';
AUTOTRACE for receiving_1 part of query"
COUNT(*)
----------
58
Execution Plan
----------------------------------------------------------
-------------------------------------------------------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Pstart| Pstop |
-------------------------------------------------------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 1 | 35 | 295 (1)| | |
| 1 | SORT AGGREGATE | | 1 | 35 | | | |
| 2 | VIEW | | 1 | 35 | 295 (1)| | |
| 3 | COUNT | | | | | | |
| 4 | VIEW | | 1 | 35 | 295 (1)| | |
| 5 | HASH UNIQUE | | 1 | 266 | 295 (1)| | |
| 6 | NESTED LOOPS | | 1 | 266 | 294 (1)| | |
| 7 | NESTED LOOPS | | 1 | 266 | 294 (1)| | |
| 8 | NESTED LOOPS | | 1 | 237 | 236 (1)| | |
| 9 | NESTED LOOPS | | 1 | 214 | 178 (1)| | |
| 10 | NESTED LOOPS | | 1 | 207 | 176 (1)| | |
| 11 | NESTED LOOPS | | 1 | 183 | 176 (1)| | |
| 12 | NESTED LOOPS | | 1 | 152 | 173 (1)| | |
| 13 | NESTED LOOPS | | 1 | 148 | 173 (1)| | |
| 14 | NESTED LOOPS | | 1 | 123 | 172 (1)| | |
| 15 | NESTED LOOPS | | 1 | 101 | 169 (1)| | |
| 16 | NESTED LOOPS | | 1 | 97 | 169 (1)| | |
| 17 | NESTED LOOPS | | 1 | 53 | 167 (1)| | |
| 18 | SORT UNIQUE | | 1 | 21 | 22 (0)| | |
| 19 | TABLE ACCESS BY INDEX ROWID| GL_CODE_COMBINATIONS | 1 | 21 | 22 (0)| | |
| 20 | INDEX RANGE SCAN | GL_CODE_COMBINATIONS_N1 | 117 | | 8 (0)| | |
| 21 | TABLE ACCESS BY INDEX ROWID | GL_JE_LINES | 16 | 512 | 144 (0)| | |
| 22 | INDEX RANGE SCAN | GL_JE_LINES_N1 | 319 | | 4 (0)| | |
| 23 | TABLE ACCESS BY INDEX ROWID | GL_JE_HEADERS | 1 | 44 | 2 (0)| | |
| 24 | INDEX UNIQUE SCAN | GL_JE_HEADERS_U1 | 1 | | 1 (0)| | |
| 25 | INDEX UNIQUE SCAN | GL_LEDGERS_U2 | 1 | 4 | 0 (0)| | |
| 26 | TABLE ACCESS BY INDEX ROWID | HR_ORGANIZATION_INFORMATION | 1 | 22 | 3 (0)| | |
| 27 | INDEX RANGE SCAN | HR_ORGANIZATION_INFORMATIO_FK1 | 30 | | 1 (0)| | |
| 28 | INDEX RANGE SCAN | GL_PERIODS_U1 | 1 | 25 | 1 (0)| | |
| 29 | INDEX UNIQUE SCAN | HR_ORGANIZATION_UNITS_PK | 1 | 4 | 0 (0)| | |
| 30 | TABLE ACCESS BY INDEX ROWID | HR_ORGANIZATION_INFORMATION | 1 | 31 | 3 (0)| | |
| 31 | INDEX RANGE SCAN | HR_ORGANIZATION_INFORMATIO_FK2 | 2 | | 1 (0)| | |
| 32 | INDEX UNIQUE SCAN | HR_ALL_ORGANIZATION_UNTS_TL_PK | 1 | 24 | 0 (0)| | |
| 33 | TABLE ACCESS BY INDEX ROWID | GL_JE_BATCHES | 1 | 7 | 2 (0)| | |
| 34 | INDEX UNIQUE SCAN | GL_JE_BATCHES_U1 | 1 | | 1 (0)| | |
| 35 | TABLE ACCESS BY INDEX ROWID | GL_IMPORT_REFERENCES | 105 | 2415 | 58 (0)| | |
| 36 | INDEX RANGE SCAN | GL_IMPORT_REFERENCES_N1 | 105 | | 3 (0)| | |
| 37 | PARTITION LIST ALL | | 1 | | 57 (0)| 1 | 19 |
| 38 | INDEX RANGE SCAN | XLA_AE_LINES_N4 | 1 | | 57 (0)| 1 | 19 |
| 39 | TABLE ACCESS BY LOCAL INDEX ROWID | XLA_AE_LINES | 1 | 29 | 58 (0)| 1 | 1 |
-------------------------------------------------------------------------------------------------------------------------------------
Note
-----
- 'PLAN_TABLE' is old version
Statistics
----------------------------------------------------------
99 recursive calls
0 db block gets
3500320 consistent gets
0 physical reads
116 redo size
346 bytes sent via SQL*Net to client
4388 bytes received via SQL*Net from client
2 SQL*Net roundtrips to/from client
1 sorts (memory)
0 sorts (disk)
1 rows processed
AUTOTRACE for receiving_2 part of query:
COUNT(*)
----------
0
Execution Plan
----------------------------------------------------------
-------------------------------------------------------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Pstart| Pstop |
-------------------------------------------------------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 1 | 35 | 295 (1)| | |
| 1 | SORT AGGREGATE | | 1 | 35 | | | |
| 2 | VIEW | | 1 | 35 | 295 (1)| | |
| 3 | COUNT | | | | | | |
| 4 | VIEW | | 1 | 35 | 295 (1)| | |
| 5 | HASH UNIQUE | | 1 | 279 | 295 (1)| | |
| 6 | NESTED LOOPS | | 1 | 279 | 294 (1)| | |
| 7 | NESTED LOOPS | | 1 | 279 | 294 (1)| | |
| 8 | NESTED LOOPS | | 1 | 272 | 292 (1)| | |
| 9 | NESTED LOOPS | | 1 | 230 | 234 (1)| | |
| 10 | NESTED LOOPS | | 1 | 207 | 176 (1)| | |
| 11 | NESTED LOOPS | | 1 | 183 | 176 (1)| | |
| 12 | NESTED LOOPS | | 1 | 152 | 173 (1)| | |
| 13 | NESTED LOOPS | | 1 | 148 | 173 (1)| | |
| 14 | NESTED LOOPS | | 1 | 126 | 170 (1)| | |
| 15 | NESTED LOOPS | | 1 | 122 | 170 (1)| | |
| 16 | NESTED LOOPS | | 1 | 97 | 169 (1)| | |
| 17 | NESTED LOOPS | | 1 | 53 | 167 (1)| | |
| 18 | SORT UNIQUE | | 1 | 21 | 22 (0)| | |
| 19 | TABLE ACCESS BY INDEX ROWID| GL_CODE_COMBINATIONS | 1 | 21 | 22 (0)| | |
| 20 | INDEX RANGE SCAN | GL_CODE_COMBINATIONS_N1 | 117 | | 8 (0)| | |
| 21 | TABLE ACCESS BY INDEX ROWID | GL_JE_LINES | 16 | 512 | 144 (0)| | |
| 22 | INDEX RANGE SCAN | GL_JE_LINES_N1 | 319 | | 4 (0)| | |
| 23 | TABLE ACCESS BY INDEX ROWID | GL_JE_HEADERS | 1 | 44 | 2 (0)| | |
| 24 | INDEX UNIQUE SCAN | GL_JE_HEADERS_U1 | 1 | | 1 (0)| | |
| 25 | INDEX RANGE SCAN | GL_PERIODS_U1 | 1 | 25 | 1 (0)| | |
| 26 | INDEX UNIQUE SCAN | GL_LEDGERS_U2 | 1 | 4 | 0 (0)| | |
| 27 | TABLE ACCESS BY INDEX ROWID | HR_ORGANIZATION_INFORMATION | 1 | 22 | 3 (0)| | |
| 28 | INDEX RANGE SCAN | HR_ORGANIZATION_INFORMATIO_FK1 | 30 | | 1 (0)| | |
| 29 | INDEX UNIQUE SCAN | HR_ORGANIZATION_UNITS_PK | 1 | 4 | 0 (0)| | |
| 30 | TABLE ACCESS BY INDEX ROWID | HR_ORGANIZATION_INFORMATION | 1 | 31 | 3 (0)| | |
| 31 | INDEX RANGE SCAN | HR_ORGANIZATION_INFORMATIO_FK2 | 2 | | 1 (0)| | |
| 32 | INDEX UNIQUE SCAN | HR_ALL_ORGANIZATION_UNTS_TL_PK | 1 | 24 | 0 (0)| | |
| 33 | TABLE ACCESS BY INDEX ROWID | GL_IMPORT_REFERENCES | 105 | 2415 | 58 (0)| | |
| 34 | INDEX RANGE SCAN | GL_IMPORT_REFERENCES_N1 | 105 | | 3 (0)| | |
| 35 | PARTITION LIST ALL | | 1 | 42 | 58 (0)| 1 | 19 |
| 36 | TABLE ACCESS BY LOCAL INDEX ROWID | XLA_AE_LINES | 1 | 42 | 58 (0)| 1 | 19 |
| 37 | INDEX RANGE SCAN | XLA_AE_LINES_N4 | 1 | | 57 (0)| 1 | 19 |
| 38 | INDEX UNIQUE SCAN | GL_JE_BATCHES_U1 | 1 | | 1 (0)| | |
| 39 | TABLE ACCESS BY INDEX ROWID | GL_JE_BATCHES | 1 | 7 | 2 (0)| | |
-------------------------------------------------------------------------------------------------------------------------------------
Note
-----
- 'PLAN_TABLE' is old version
Statistics
----------------------------------------------------------
99 recursive calls
0 db block gets
3451912 consistent gets
0 physical reads
0 redo size
345 bytes sent via SQL*Net to client
4385 bytes received via SQL*Net from client
2 SQL*Net roundtrips to/from client
1 sorts (memory)
0 sorts (disk)
1 rows processed
AUTOTRACE for UNION ALL of full query that includes both request_1 and request_2
COUNT(*)
----------
58
Execution Plan
----------------------------------------------------------
----------------------------------------------------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Pstart| Pstop |
----------------------------------------------------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 1 | 35 | 300 (1)| | |
| 1 | TEMP TABLE TRANSFORMATION | | | | | | |
| 2 | LOAD AS SELECT | SYS_TEMP_0FD9D6767_FC3F7B00 | | | | | |
| 3 | NESTED LOOPS | | 1 | 279 | 294 (1)| | |
| 4 | NESTED LOOPS | | 1 | 279 | 294 (1)| | |
| 5 | NESTED LOOPS | | 1 | 237 | 236 (1)| | |
| 6 | NESTED LOOPS | | 1 | 214 | 178 (1)| | |
| 7 | NESTED LOOPS | | 1 | 207 | 176 (1)| | |
| 8 | NESTED LOOPS | | 1 | 183 | 176 (1)| | |
| 9 | NESTED LOOPS | | 1 | 152 | 173 (1)| | |
| 10 | NESTED LOOPS | | 1 | 148 | 173 (1)| | |
| 11 | NESTED LOOPS | | 1 | 126 | 170 (1)| | |
| 12 | NESTED LOOPS | | 1 | 122 | 170 (1)| | |
| 13 | NESTED LOOPS | | 1 | 78 | 168 (1)| | |
| 14 | NESTED LOOPS | | 1 | 53 | 167 (1)| | |
| 15 | SORT UNIQUE | | 1 | 21 | 22 (0)| | |
| 16 | TABLE ACCESS BY INDEX ROWID| GL_CODE_COMBINATIONS | 1 | 21 | 22 (0)| | |
| 17 | INDEX RANGE SCAN | GL_CODE_COMBINATIONS_N1 | 117 | | 8 (0)| | |
| 18 | TABLE ACCESS BY INDEX ROWID | GL_JE_LINES | 16 | 512 | 144 (0)| | |
| 19 | INDEX RANGE SCAN | GL_JE_LINES_N1 | 319 | | 4 (0)| | |
| 20 | INDEX RANGE SCAN | GL_PERIODS_U1 | 1 | 25 | 1 (0)| | |
| 21 | TABLE ACCESS BY INDEX ROWID | GL_JE_HEADERS | 1 | 44 | 2 (0)| | |
| 22 | INDEX UNIQUE SCAN | GL_JE_HEADERS_U1 | 1 | | 1 (0)| | |
| 23 | INDEX UNIQUE SCAN | GL_LEDGERS_U2 | 1 | 4 | 0 (0)| | |
| 24 | TABLE ACCESS BY INDEX ROWID | HR_ORGANIZATION_INFORMATION | 1 | 22 | 3 (0)| | |
| 25 | INDEX RANGE SCAN | HR_ORGANIZATION_INFORMATIO_FK1 | 30 | | 1 (0)| | |
| 26 | INDEX UNIQUE SCAN | HR_ORGANIZATION_UNITS_PK | 1 | 4 | 0 (0)| | |
| 27 | TABLE ACCESS BY INDEX ROWID | HR_ORGANIZATION_INFORMATION | 1 | 31 | 3 (0)| | |
| 28 | INDEX RANGE SCAN | HR_ORGANIZATION_INFORMATIO_FK2 | 2 | | 1 (0)| | |
| 29 | INDEX UNIQUE SCAN | HR_ALL_ORGANIZATION_UNTS_TL_PK | 1 | 24 | 0 (0)| | |
| 30 | TABLE ACCESS BY INDEX ROWID | GL_JE_BATCHES | 1 | 7 | 2 (0)| | |
| 31 | INDEX UNIQUE SCAN | GL_JE_BATCHES_U1 | 1 | | 1 (0)| | |
| 32 | TABLE ACCESS BY INDEX ROWID | GL_IMPORT_REFERENCES | 105 | 2415 | 58 (0)| | |
| 33 | INDEX RANGE SCAN | GL_IMPORT_REFERENCES_N1 | 105 | | 3 (0)| | |
| 34 | PARTITION LIST ALL | | 1 | | 57 (0)| 1 | 19 |
| 35 | INDEX RANGE SCAN | XLA_AE_LINES_N4 | 1 | | 57 (0)| 1 | 19 |
| 36 | TABLE ACCESS BY LOCAL INDEX ROWID | XLA_AE_LINES | 1 | 42 | 58 (0)| 1 | 1 |
| 37 | SORT AGGREGATE | | 1 | 35 | | | |
| 38 | VIEW | | 2 | 70 | 6 (34)| | |
| 39 | UNION-ALL | | | | | | |
| 40 | COUNT | | | | | | |
| 41 | VIEW | | 1 | 35 | 3 (34)| | |
| 42 | HASH UNIQUE | | 1 | 44 | 3 (34)| | |
| 43 | VIEW | | 1 | 44 | 2 (0)| | |
| 44 | TABLE ACCESS FULL | SYS_TEMP_0FD9D6767_FC3F7B00 | 1 | 74 | 2 (0)| | |
| 45 | COUNT | | | | | | |
| 46 | VIEW | | 1 | 35 | 3 (34)| | |
| 47 | HASH UNIQUE | | 1 | 61 | 3 (34)| | |
| 48 | VIEW | | 1 | 61 | 2 (0)| | |
| 49 | TABLE ACCESS FULL | SYS_TEMP_0FD9D6767_FC3F7B00 | 1 | 74 | 2 (0)| | |
----------------------------------------------------------------------------------------------------------------------------------
Note
-----
- 'PLAN_TABLE' is old version
Statistics
----------------------------------------------------------
766 recursive calls
83076 db block gets
270487689 consistent gets
81660 physical reads
676 redo size
346 bytes sent via SQL*Net to client
4582 bytes received via SQL*Net from client
2 SQL*Net roundtrips to/from client
1 sorts (memory)
0 sorts (disk)
1 rows processed

As I don't know the underlying tables, their structure and their data, I can only guess. Having said that, I'd try the following things (not knowing if any of the steps are helpful or not!):
remove the rownum from receiving_all
add the where clause from the main query to each of the subqueries in receiving_all. The optimizer might be able to infer this, but I am not sure.
change union all to union in receiving_all and remove the distinct from receiving_1 and receiving_2
receiving_2 seems to be included in receiving_2?
If 4. is not the case, I would combine both into a single query with WHERE UPPER(bq.JE_CATEGORY) = UPPER('Receiving') AND ( (<conditions for 1>) OR (<conditions for 2>) )
I would try if UPPER(bq.JE_CATEGORY) can be avoided. Functions prevent index access.
In case you can create tables, I'd unroll the values of gccs into two tables, one for segment_1, and another table for segment_2.
If 7. is not an option, I'd add the hint select /*+ materialize */ gcc.code_combination_id to gcc.
I'm very curious to see which suggestion has any positive impact...

Pivot Table in Hive and Create Multiple Columns for Unique Combinations

I want to pivot the following table
| ID | Code | date | qty |
| 1 | A | 1/1/19 | 11 |
| 1 | A | 2/1/19 | 12 |
| 2 | B | 1/1/19 | 13 |
| 2 | B | 2/1/19 | 14 |
| 3 | C | 1/1/19 | 15 |
| 3 | C | 3/1/19 | 16 |
into
| ID | Code | mth_1(1/1/19) | mth_2(2/1/19) | mth_3(3/1/19) |
| 1 | A | 11 | 12 | 0 |
| 2 | B | 13 | 14 | 0 |
| 3 | C | 15 | 0 | 16 |
I am new to hive, i am not sure how to implement it.
NOTE: I don't want to do mapping because my month values change over time.

Oracle "Total" plan cost is really less than some of it's elements

I cannot figure out why sometimes, the total cost of a plan can be a very small number whereas looking inside the plan we can find huge costs. (indeed the query is very slow).
Can somebody explain me that?
Here is an example.
Apparently the costful part comes from a field in the main select that does a listagg on a subview and the join condition with this subview contains a complex condition (we can join on one field or another).
| Id | Operation | Name | Rows | Bytes | Cost |
----------------------------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 1 | 875 | 20 |
| 1 | SORT GROUP BY | | 1 | 544 | |
| 2 | VIEW | | 1 | 544 | 3 |
| 3 | SORT UNIQUE | | 1 | 481 | 3 |
| 4 | NESTED LOOPS | | | | |
| 5 | NESTED LOOPS | | 3 | 1443 | 2 |
| 6 | TABLE ACCESS BY INDEX ROWID | | 7 | 140 | 1 |
| 7 | INDEX RANGE SCAN | | 7 | | 1 |
| 8 | INDEX UNIQUE SCAN | | 1 | | 1 |
| 9 | TABLE ACCESS BY INDEX ROWID | | 1 | 461 | 1 |
| 10 | SORT GROUP BY | | 1 | 182 | |
| 11 | NESTED LOOPS | | | | |
| 12 | NESTED LOOPS | | 8 | 1456 | 3 |
| 13 | NESTED LOOPS | | 8 | 304 | 2 |
| 14 | TABLE ACCESS BY INDEX ROWID | | 7 | 154 | 1 |
| 15 | INDEX RANGE SCAN | | 7 | | 1 |
| 16 | INDEX RANGE SCAN | | 1 | 16 | 1 |
| 17 | INDEX RANGE SCAN | | 1 | | 1 |
| 18 | TABLE ACCESS BY INDEX ROWID | | 1 | 144 | 1 |
| 19 | SORT GROUP BY | | 1 | 268 | |
| 20 | VIEW | | 1 | 268 | 9 |
| 21 | SORT UNIQUE | | 1 | 108 | 9 |
| 22 | CONCATENATION | | | | |
| 23 | NESTED LOOPS | | | | |
| 24 | NESTED LOOPS | | 1 | 108 | 4 |
| 25 | NESTED LOOPS | | 1 | 79 | 3 |
| 26 | NESTED LOOPS | | 1 | 59 | 2 |
| 27 | TABLE ACCESS BY INDEX ROWID | | 1 | 16 | 1 |
| 28 | INDEX UNIQUE SCAN | | 1 | | 1 |
| 29 | TABLE ACCESS BY INDEX ROWID | | 1 | 43 | 1 |
| 30 | INDEX RANGE SCAN | | 1 | | 1 |
| 31 | TABLE ACCESS BY INDEX ROWID | | 1 | 20 | 1 |
| 32 | INDEX UNIQUE SCAN | | 1 | | 1 |
| 33 | INDEX UNIQUE SCAN | | 1 | | 1 |
| 34 | TABLE ACCESS BY INDEX ROWID | | 1 | 29 | 1 |
| 35 | NESTED LOOPS | | | | |
| 36 | NESTED LOOPS | | 1 | 108 | 4 |
| 37 | NESTED LOOPS | | 1 | 79 | 3 |
| 38 | NESTED LOOPS | | 1 | 59 | 2 |
| 39 | TABLE ACCESS BY INDEX ROWID | | 4 | 64 | 1 |
| 40 | INDEX RANGE SCAN | | 2 | | 1 |
| 41 | TABLE ACCESS BY INDEX ROWID | | 1 | 43 | 1 |
| 42 | INDEX RANGE SCAN | | 1 | | 1 |
| 43 | TABLE ACCESS BY INDEX ROWID | | 1 | 20 | 1 |
| 44 | INDEX UNIQUE SCAN | | 1 | | 1 |
| 45 | INDEX UNIQUE SCAN | | 1 | | 1 |
| 46 | TABLE ACCESS BY INDEX ROWID | | 1 | 29 | 1 |
| 47 | SORT GROUP BY | | 1 | 330 | |
| 48 | VIEW | | 1 | 330 | 26695 |
| 49 | SORT UNIQUE | | 1 | 130 | 26695 |
| 50 | CONCATENATION | | | | |
| 51 | HASH JOIN ANTI | | 1 | 130 | 13347 |
| 52 | NESTED LOOPS | | | | |
| 53 | NESTED LOOPS | | 1 | 110 | 4 |
| 54 | NESTED LOOPS | | 1 | 81 | 3 |
| 55 | NESTED LOOPS | | 1 | 61 | 2 |
| 56 | TABLE ACCESS BY INDEX ROWID | | 1 | 16 | 1 |
| 57 | INDEX UNIQUE SCAN | | 1 | | 1 |
| 58 | TABLE ACCESS BY INDEX ROWID | | 1 | 45 | 1 |
| 59 | INDEX RANGE SCAN | | 1 | | 1 |
| 60 | TABLE ACCESS BY INDEX ROWID | | 1 | 20 | 1 |
| 61 | INDEX UNIQUE SCAN | | 1 | | 1 |
| 62 | INDEX UNIQUE SCAN | | 1 | | 1 |
| 63 | TABLE ACCESS BY INDEX ROWID | | 1 | 29 | 1 |
| 64 | VIEW | | 164K| 3220K| 13341 |
| 65 | NESTED LOOPS | | | | |
| 66 | NESTED LOOPS | | 164K| 11M| 13341 |
| 67 | NESTED LOOPS | | 164K| 8535K| 10041 |
| 68 | TABLE ACCESS BY INDEX ROWID | | 164K| 6924K| 8391 |
| 69 | INDEX SKIP SCAN | | 2131K| | 163 |
| 70 | INDEX UNIQUE SCAN | | 1 | 10 | 1 |
| 71 | INDEX UNIQUE SCAN | | 1 | | 1 |
| 72 | TABLE ACCESS BY INDEX ROWID | | 1 | 20 | 1 |
| 73 | HASH JOIN ANTI | | 2 | 260 | 13347 |
| 74 | NESTED LOOPS | | | | |
| 75 | NESTED LOOPS | | 2 | 220 | 4 |
| 76 | NESTED LOOPS | | 2 | 162 | 3 |
| 77 | NESTED LOOPS | | 2 | 122 | 2 |
| 78 | TABLE ACCESS BY INDEX ROWID | | 4 | 64 | 1 |
| 79 | INDEX RANGE SCAN | | 2 | | 1 |
| 80 | TABLE ACCESS BY INDEX ROWID | | 1 | 45 | 1 |
| 81 | INDEX RANGE SCAN | | 1 | | 1 |
| 82 | TABLE ACCESS BY INDEX ROWID | | 1 | 20 | 1 |
| 83 | INDEX UNIQUE SCAN | | 1 | | 1 |
| 84 | INDEX UNIQUE SCAN | | 1 | | 1 |
| 85 | TABLE ACCESS BY INDEX ROWID | | 1 | 29 | 1 |
| 86 | VIEW | | 164K| 3220K| 13341 |
| 87 | NESTED LOOPS | | | | |
| 88 | NESTED LOOPS | | 164K| 11M| 13341 |
| 89 | NESTED LOOPS | | 164K| 8535K| 10041 |
| 90 | TABLE ACCESS BY INDEX ROWID | | 164K| 6924K| 8391 |
| 91 | INDEX SKIP SCAN | | 2131K| | 163 |
| 92 | INDEX UNIQUE SCAN | | 1 | 10 | 1 |
| 93 | INDEX UNIQUE SCAN | | 1 | | 1 |
| 94 | TABLE ACCESS BY INDEX ROWID | | 1 | 20 | 1 |
| 95 | NESTED LOOPS OUTER | | 1 | 875 | 20 |
| 96 | NESTED LOOPS OUTER | | 1 | 846 | 19 |
| 97 | NESTED LOOPS OUTER | | 1 | 800 | 18 |
| 98 | NESTED LOOPS OUTER | | 1 | 776 | 17 |
| 99 | NESTED LOOPS OUTER | | 1 | 752 | 16 |
| 100 | NESTED LOOPS OUTER | | 1 | 641 | 15 |
| 101 | NESTED LOOPS OUTER | | 1 | 576 | 14 |
| 102 | NESTED LOOPS OUTER | | 1 | 554 | 13 |
| 103 | NESTED LOOPS OUTER | | 1 | 487 | 12 |
| 104 | NESTED LOOPS OUTER | | 1 | 434 | 11 |
| 105 | NESTED LOOPS | | 1 | 368 | 10 |
| 106 | NESTED LOOPS | | 1 | 102 | 9 |
| 107 | NESTED LOOPS OUTER | | 1 | 85 | 8 |
| 108 | NESTED LOOPS | | 1 | 68 | 7 |
| 109 | NESTED LOOPS | | 50 | 2700 | 6 |
| 110 | HASH JOIN | | 53 | 1696 | 5 |
| 111 | INLIST ITERATOR | | | | |
| 112 | TABLE ACCESS BY INDEX ROWID| | 520 | 10400 | 3 |
| 113 | INDEX RANGE SCAN | | 520 | | 1 |
| 114 | INLIST ITERATOR | | | | |
| 115 | TABLE ACCESS BY INDEX ROWID| | 91457 | 1071K| 1 |
| 116 | INDEX UNIQUE SCAN | | 2 | | 1 |
| 117 | TABLE ACCESS BY INDEX ROWID | | 1 | 22 | 1 |
| 118 | INDEX UNIQUE SCAN | | 1 | | 1 |
| 119 | TABLE ACCESS BY INDEX ROWID | | 1 | 14 | 1 |
| 120 | INDEX UNIQUE SCAN | | 1 | | 1 |
| 121 | TABLE ACCESS BY INDEX ROWID | | 1 | 17 | 1 |
| 122 | INDEX UNIQUE SCAN | | 1 | | 1 |
| 123 | TABLE ACCESS BY INDEX ROWID | | 1 | 17 | 1 |
| 124 | INDEX UNIQUE SCAN | | 1 | | 1 |
| 125 | TABLE ACCESS BY INDEX ROWID | | 1 | 266 | 1 |
| 126 | INDEX UNIQUE SCAN | | 1 | | 1 |
| 127 | TABLE ACCESS BY INDEX ROWID | | 1 | 66 | 1 |
| 128 | INDEX UNIQUE SCAN | | 1 | | 1 |
| 129 | TABLE ACCESS BY INDEX ROWID | | 1 | 53 | 1 |
| 130 | INDEX UNIQUE SCAN | | 1 | | 1 |
| 131 | TABLE ACCESS BY INDEX ROWID | | 1 | 67 | 1 |
| 132 | INDEX UNIQUE SCAN | | 1 | | 1 |
| 133 | INDEX RANGE SCAN | | 1 | 22 | 1 |
| 134 | TABLE ACCESS BY INDEX ROWID | | 1 | 65 | 1 |
| 135 | INDEX UNIQUE SCAN | | 1 | | 1 |
| 136 | TABLE ACCESS BY INDEX ROWID | | 1 | 111 | 1 |
| 137 | INDEX UNIQUE SCAN | | 1 | | 1 |
| 138 | TABLE ACCESS BY INDEX ROWID | | 1 | 24 | 1 |
| 139 | INDEX UNIQUE SCAN | | 1 | | 1 |
| 140 | TABLE ACCESS BY INDEX ROWID | | 1 | 24 | 1 |
| 141 | INDEX UNIQUE SCAN | | 1 | | 1 |
| 142 | TABLE ACCESS BY INDEX ROWID | | 1 | 46 | 1 |
| 143 | INDEX UNIQUE SCAN | | 1 | | 1 |
| 144 | TABLE ACCESS BY INDEX ROWID | | 1 | 29 | 1 |
| 145 | INDEX UNIQUE SCAN | | 1 | | 1 |
----------------------------------------------------------------------------------------------------------

The total cost of a statement is usually equal to or greater than the cost of any of its child operations. There are at least 4 exceptions to this rule.
Your plan looks like #3 but we can't be sure without looking at code.
1. FILTER
Execution plans may depend on conditions at run-time. These conditions cause FILTER operations that will dynamically decide which query block to execute. The example below uses a static condition but still demonstrates the concept. Part of the subquery is very expensive but the condition negates the whole thing.
explain plan for select * from dba_objects cross join dba_objects where 1 = 2;
select * from table(dbms_xplan.display(format => 'basic +cost'));
Plan hash value: 3258663795
--------------------------------------------------------------------
| Id | Operation | Name | Cost (%CPU)|
--------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 0 (0)|
| 1 | FILTER | | |
| 2 | MERGE JOIN CARTESIAN | | 11M (3)|
...
2. COUNT STOPKEY
Execution plans sum child operations up until the final cost. But child operations will not always finish. In the example below it may be correct to say that part of the plan costs 214. But because of the condition where rownum <= 1 only part of that child operation may run.
explain plan for
select /*+ no_query_transformation */ *
from (select * from dba_objects join dba_objects using (owner))
where rownum <= 1;
select * from table(dbms_xplan.display(format => 'basic +cost'));
Plan hash value: 2132093199
-------------------------------------------------------------------------------
| Id | Operation | Name | Cost (%CPU)|
-------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 4 (0)|
| 1 | COUNT STOPKEY | | |
| 2 | VIEW | | 4 (0)|
| 3 | VIEW | | 4 (0)|
| 4 | NESTED LOOPS | | 4 (0)|
| 5 | VIEW | DBA_OBJECTS | 2 (0)|
| 6 | UNION-ALL | | |
| 7 | HASH JOIN | | 3 (34)|
| 8 | INDEX FULL SCAN | I_USER2 | 1 (0)|
| 9 | VIEW | _CURRENT_EDITION_OBJ | 1 (0)|
| 10 | FILTER | | |
| 11 | HASH JOIN | | 214 (3)|
...
3. Subqueries in the SELECT column list
Cost aggregation does not include subqueries in the SELECT column list. A query like select ([expensive query]) from dual; will have a very small total cost. I don't understand the reason for this; Oracle estimates the subquery and he number of rows in the FROM, surely it could multiply them together for a total cost.
explain plan for
select dummy,(select count(*) from dba_objects cross join dba_objects) from dual;
select * from table(dbms_xplan.display(format => 'basic +cost'));
Plan hash value: 3705842531
---------------------------------------------------------------
| Id | Operation | Name | Cost (%CPU)|
---------------------------------------------------------------
| 0 | SELECT STATEMENT | | 2 (0)|
| 1 | SORT AGGREGATE | | |
| 2 | MERGE JOIN CARTESIAN | | 11M (3)|
...
4. Other - rounding? bugs?
About 0.01% of plans still have unexplainable cost issues. I can't find any pattern among them. Perhaps it's just a rounding issue or some rare optimizer bugs. There will always be some weird cases with a any model as complicated as the optimizer.
Check for more exceptions
This query can find other exceptions, it returns all plans where the first cost is less than the maximum cost.
select *
from
(
--First and Max cost per plan.
select
sql_id, plan_hash_value, id, cost
,max(cost) keep (dense_rank first order by id)
over (partition by sql_id, plan_hash_value) first_cost
,max(cost)
over (partition by sql_id, plan_hash_value) max_cost
,max(case when operation = 'COUNT' and options = 'STOPKEY' then 1 else 0 end)
over (partition by sql_id, plan_hash_value) has_count_stopkey
,max(case when operation = 'FILTER' and options is null then 1 else 0 end)
over (partition by sql_id, plan_hash_value) has_filter
,count(distinct(plan_hash_value))
over () total_plans
from v$sql_plan
--where sql_id = '61a161nm1ttjj'
order by 1,2,3
)
where first_cost < max_cost
--It's easy to exclude FILTER and COUNT STOPKEY.
and has_filter = 0
and has_count_stopkey = 0
order by 1,2,3;

mysql table unaligned in console output when using UTF8

I like to use mysql client. But when using UTF-8, the tables on the console are unaligned:
> set names utf8;
> [some query]
+--------+---------+---------------------------------+-----------------------------+----------+---------+-----------+-------+---------+-----------+
| RuleId | TaxonId | Note | NoteSci | MinCount | DayFrom | MonthFrom | DayTo | MonthTo | ExtraNote |
+--------+---------+---------------------------------+-----------------------------+----------+---------+-----------+-------+---------+-----------+
| 722 | 10090 | sedmihlásek malý | Hippolais caligata | 1 | 1 | 1 | 31 | 12 | NULL |
| 727 | 10059 | Anseranas semipalmata | husovec strakatý | 1 | 1 | 1 | 31 | 12 | NULL |
| 728 | 10062 | Cygnus atratus | labuť černá | 1 | 1 | 1 | 31 | 12 | NULL |
| 729 | 10094 | Anser cygnoides | husa labutí | 1 | 1 | 1 | 31 | 12 | NULL |
| 730 | 10063 | Tadorna cana | husice šedohlavá | 1 | 1 | 1 | 31 | 12 | NULL |
| 731 | 10031 | Cairina moschata f. domestica | pižmovka domácí | 20 | 1 | 1 | 31 | 12 | NULL |
| 732 | 10088 | Cairina scutulata | pižmovka bělokřídlá | 1 | 1 | 1 | 31 | 12 | NULL |
| 733 | 10087 | Anas sibilatrix | hvízdák chilský | 1 | 1 | 1 | 31 | 12 | NULL |
| 734 | 10077 | Anas platyrhynchos f. domestica | kachna domácí | 1000 | 1 | 1 | 31 | 12 | NULL |
| 735 | 10086 | Anas hottentota | čírka hottentotská | 1 | 1 | 1 | 31 | 12 | NULL |
|
This is apparently because mysql client will compute the widths of the columns using string length which doesn't take UTF-8 characters into account - so then there is exactly one space missing for each accented character (because these actually take two bytes).
Do you know possible workaround for this problem?

Run your mysql client with charset option:
mysql -uUSER -p DATABASE --default-character-set=utf8
(USER and DATABASE should be replaced with actual credentials data)

SphinxSE distinct empty result

I run this query in sphinx se console:
SELECT #distinct FROM all_ips GROUP BY ip1;
I get this result:
+------+--------+
| id | weight |
+------+--------+
| 1 | 1 |
| 2 | 1 |
| 3 | 1 |
| 9 | 1 |
| 15 | 1 |
| 16 | 1 |
| 17 | 1 |
| 20 | 1 |
| 21 | 1 |
| 25 | 1 |
| 26 | 1 |
| 27 | 1 |
| 31 | 1 |
| 32 | 1 |
| 38 | 1 |
| 39 | 1 |
| 40 | 1 |
| 46 | 1 |
| 50 | 1 |
| 51 | 1 |
+------+--------+
20 rows in set (0.57 sec)
How can i get number of unique values? Why #distinct column doesn't show up in results?

1) I dont think that is sphinxSE - do you really mean sphinxQL? That looks more like sphinxQL.
2) Distinct of what column? You need to sell sphinx what attribute you want to count the distinct values in. In sphinxQL use COUNT(DISTINCT column_name)

You will require simple SQL statement for getting count. Something like this
SELECT count(ip1),ip1
FROM all_ips
GROUP BY ip1;

Resources