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Storing Date Values As Characters (What’s Really Happening) May 26, 2016

Posted by Richard Foote in 12c, Function Based Indexes, Oracle Indexes, Storing Dates as Characters.
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For something that’s generally considered an extremely bad idea, I’ve lost count of the number of times I’ve come across applications that insist on storing date values as characters within the database. We’ve all seen them …

I recently got called in to assist a customer who was having issues with a POC in relation to the database not appearing to want to use the In-Memory Database option as expected. In various key scenarios, the CBO kept coming up with execution plans that used index plans (they were hesitant to drop these particular indexes), when if it only just used the Database In-Memory store, the queries ran so much faster. So I was called in to find out what’s really happening and it turned out that the main culprit was indeed queries against columns where dates were stored as characters within the database. In the process, we found another issue with some “invalid” date values. Go figure.

Interestingly, both issues could be addressed by creating a new index on the date column …

I’ve kinda replicated the scenario here with the following little test case. I’ll begin by creating a table with a varchar2 field (bowie_date) that actually stores a whole bunch of “dates”:

SQL> create table bowie (id number, code number, bowie_date varchar2(42));
Table created

SQL> insert into bowie select rownum, mod(rownum,1000), to_char(sysdate-mod(rownum,10000), 'YYYY MM DD') from dual connect by level <=1000000;

1000000 rows created.

SQL> commit;

Commit complete.

So the bowie_date column basically has 10000 different dates, with each date evenly distributed with 100 occurrences for each date.

I’ll now collect statistics on the table:

SQL> exec dbms_stats.gather_table_stats(ownname=>null, tabname=>'BOWIE');
PL/SQL procedure successfully completed.

SQL> select column_name, num_distinct, density, histogram, hidden_column, virtual_column from dba_tab_cols where table_name='BOWIE';

COLUMN_NAME  NUM_DISTINCT DENSITY          HISTOGRAM HID VIR
------------ ------------ ---------- --------------- --- ---
BOWIE_DATE          10000      .0001 NONE            NO  NO
CODE                 1000       .001 NONE            NO  NO
ID                1000000    .000001 NONE            NO  NO

 

So the bowie_date column indeed has 10000 distinct dates.

I’ll now create a standard b-tree index on this column:

SQL> create index bowie_date_i on bowie(bowie_date);
Index created.

So the data looks as follows with the bowie_date a varchar2 column that actually contains date data, with the following min/max ranges:

SQL> select * from bowie where rownum <11;

        ID       CODE BOWIE_DATE
---------- ---------- ----------
       916        916 2013 11 22
       917        917 2013 11 21
       918        918 2013 11 20
       919        919 2013 11 19
       920        920 2013 11 18
       921        921 2013 11 17
       922        922 2013 11 16
       923        923 2013 11 15
       924        924 2013 11 14
       925        925 2013 11 13

10 rows selected.

SQL> select min(bowie_date) min, max(bowie_date) max from bowie;

MIN        MAX
---------- ----------
1989 01 09 2016 05 26

If we run a query that uses an equality predicate as follows:

SQL> select * from bowie where bowie_date = '2015 06 13';
100 rows selected.

Execution Plan
----------------------------------------------------------
Plan hash value: 1525056162

----------------------------------------------------------------------------------------------------
| Id | Operation                             | Name         | Rows | Bytes | Cost (%CPU)|     Time |
----------------------------------------------------------------------------------------------------
| 0  | SELECT STATEMENT                      |              |  100 |  2000 |     103 (0)| 00:00:01 |
| 1  | TABLE ACCESS BY INDEX ROWID BATCHED   | BOWIE        |  100 | 2000  |     103 (0)| 00:00:01 |
|* 2 | INDEX RANGE SCAN                      | BOWIE_DATE_I |  100 |       |       3 (0)| 00:00:01 |
----------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

2 - access("BOWIE_DATE"='2015 06 13')

Statistics
----------------------------------------------------------
1 recursive calls
0 db block gets
110 consistent gets
16 physical reads
0 redo size
4383 bytes sent via SQL*Net to client
618 bytes received via SQL*Net from client
8 SQL*Net roundtrips to/from client
0 sorts (memory)
0 sorts (disk)
100 rows processed 

The CBO gets things just about right. 100 rows are estimated and indeed 100 rows are retrieved. So we have confidence the CBO has made the right decision in using the index here as so few rows are actually retrieved.

However, if we run a range scan predicate such as the following:

SQL> select * from bowie where bowie_date between '2015 10 10' and '2015 12 31';

8300 rows selected.

Execution Plan
----------------------------------------------------------
Plan hash value: 1525056162

----------------------------------------------------------------------------------------------------
| Id | Operation                          | Name         | Rows | Bytes | Cost (%CPU)|     Time |
----------------------------------------------------------------------------------------------------
| 0  | SELECT STATEMENT                   |              |  100 | 2000  |     104 (0)| 00:00:01 |
| 1  | TABLE ACCESS BY INDEX ROWID BATCHED| BOWIE        |  100 | 2000  |     104 (0)| 00:00:01 |
|*2  | INDEX RANGE SCAN                   | BOWIE_DATE_I |  100 |       |       3 (0)| 00:00:01 |
----------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

2 - access("BOWIE_DATE">='2015 10 10' AND "BOWIE_DATE"<='2015 12 31')

Statistics
----------------------------------------------------------
1 recursive calls
0 db block gets
8881 consistent gets
38 physical reads
0 redo size
315219 bytes sent via SQL*Net to client
6635 bytes received via SQL*Net from client
555 SQL*Net roundtrips to/from client
0 sorts (memory)
0 sorts (disk)
8300 rows processed

The CBO has got the costings wrong here. It still estimates  only 100 rows are to be returned when in actual fact 8300 rows come back.

If we select an even larger “date” range:

SQL> select * from bowie where bowie_date between '2000 10 10' and '2016 12 31';

570800 rows selected.

Execution Plan
----------------------------------------------------------
Plan hash value: 1525056162

----------------------------------------------------------------------------------------------------
| Id | Operation                          | Name         | Rows | Bytes | Cost (%CPU)| Time     |
----------------------------------------------------------------------------------------------------
|  0 | SELECT STATEMENT                   |              |  116 |  2320 |     120 (0)| 00:00:01 |
|  1 | TABLE ACCESS BY INDEX ROWID BATCHED| BOWIE        |  116 |  2320 |     120 (0)| 00:00:01 |
|* 2 | INDEX RANGE SCAN                   | BOWIE_DATE_I |  116 |       |       3 (0)| 00:00:01 |
----------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

2 - access("BOWIE_DATE">='2000 10 10' AND "BOWIE_DATE"<='2016 12 31')

Statistics
----------------------------------------------------------
1 recursive calls
0 db block gets
610491 consistent gets
1704 physical reads
0 redo size
21575496 bytes sent via SQL*Net to client
419135 bytes received via SQL*Net from client
38055 SQL*Net roundtrips to/from client
0 sorts (memory)
0 sorts (disk)
570800 rows processed

The CBO has got things seriously wrong here. We’re actually returning over 1/2 the table, some 570,800 rows but the CBO thinks only 116 rows will be returned. Why ?

The problem comes back to storing date values as characters. The CBO has absolutely no idea that these “characters” are actually meant to be dates and has no idea that the only valid ranges of values are date values.

With a possible range between “1989 01 09” and “2016 05 26“, any character range/combination of values (up to the 42 column size) could potentially exist in this column ( value ‘1zxgs$.jKN6tglhasgdlhlhd23bkbk?k’ for example).

So the required range between ‘2000 10 10’ and ‘2016 12 31’ actually represents a relatively narrow range of possible values within the range of all possible values (especially as the leading column differs between the min/max).

Hence why the CBO is estimating such a low number of rows to be returned and hence why the CBO is deciding to incorrectly use the index. “Hiding” the meaning and distribution of values from the CBO in this manner can be problematic to say the least.

Worse of course is also the possibility of “invalid” dates being entered as the database has no implicit way to police the integrity of the data:

SQL> insert into bowie values (1000001, 42, '2016 6 31');

1 row created.

SQL> rollback;

Rollback complete.

We know there’s no such date as 31st June but Oracle has no idea that this is logically invalid data. Or the value ‘lh;ghsgdsd7gdGLH96bb’ for that matter …

Did I mention that storing dates in a character column is a really really bad idea …

Now there are a couple of ways to help address these issues if changing the column and its datatype is not possible due to the application. One way is to create a function-based index as follows on a date version of the column:

SQL> create index bowie_date_fn_i on bowie(to_date(bowie_date,'YYYY MM DD'));

Index created.

If we run the equality query but this time referencing the to_date function:

SQL> select * from bowie where to_date(bowie_date, 'YYYY MM DD') = '13-JUN-2015';

100 rows selected.

Execution Plan
----------------------------------------------------------
Plan hash value: 960797537

-------------------------------------------------------------------------------------------------------
| Id | Operation                           | Name            | Rows  | Bytes  | Cost (%CPU)|     Time |
-------------------------------------------------------------------------------------------------------
| 0  | SELECT STATEMENT                    |                 | 10000 |   273K |     893 (2)| 00:00:01 |
| 1  | TABLE ACCESS BY INDEX ROWID BATCHED | BOWIE           | 10000 |   273K |     893 (2)| 00:00:01 |
|* 2 | INDEX RANGE SCAN                    | BOWIE_DATE_FN_I |  4000 |        |       3 (0)| 00:00:01 |
-------------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

2 - access(TO_DATE("BOWIE_DATE",'YYYY MM DD')=TO_DATE(' 2015-06-13 00:00:00', 'syyyy-mm-dd hh24:mi:ss'))

Statistics
----------------------------------------------------------
12 recursive calls
0 db block gets
113 consistent gets
16 physical reads
0 redo size
3268 bytes sent via SQL*Net to client
618 bytes received via SQL*Net from client
8 SQL*Net roundtrips to/from client
0 sorts (memory)
0 sorts (disk)
100 rows processed

We notice the CBO has got the row estimate way wrong here, thinking that 10000 rows, not 100 rows are to be returned. Why ?

Remember, when we create a function-based index, Oracle creates a hidden virtual column on the table as Oracle needs a way to store the statistics associated with the result set from the function. But these statistics aren’t populated until we next collect statistics on the table (or explicitly for just the hidden columns) and without the column statistics, the CBO can make poor assumptions:

SQL> select column_name, data_type, num_distinct, density, histogram, hidden_col
umn, virtual_column from dba_tab_cols where table_name='BOWIE';

COLUMN_NAME  DATA_TYPE NUM_DISTINCT    DENSITY HISTOGRAM       HID VIR
------------ --------- ------------ ---------- --------------- --- ---
SYS_NC00004$ DATE                              NONE            YES YES
BOWIE_DATE   VARCHAR2         10000      .0001 NONE            NO  NO
CODE         NUMBER            1000       .001 NONE            NO  NO
ID           NUMBER         1000000    .000001 NONE            NO  NO

There are no column statistics for the virtual column but we note the data type of the virtual column is DATE. So let’s collect new statistics on the table:

SQL> exec dbms_stats.gather_table_stats(ownname=>null, tabname=>'BOWIE');

PL/SQL procedure successfully completed.
         .
SQL> select column_name, data_type, num_distinct, density, histogram, hidden_col
umn, virtual_column from dba_tab_cols where table_name='BOWIE';

COLUMN_NAME  DATA_TYPE NUM_DISTINCT    DENSITY HISTOGRAM       HID VIR
------------ --------- ------------ ---------- --------------- --- ---
SYS_NC00004$ DATE             10000      .0001 NONE            YES YES
BOWIE_DATE   VARCHAR2         10000      .0001 HYBRID          NO  NO
CODE         NUMBER            1000       .001 NONE            NO  NO
ID           NUMBER         1000000    .000001 NONE            NO  NO

There are actually two key differences in the above statistics, but in this post I’ll just focus on the fact that the hidden virtual column now has associated statistics (I’ll discuss the other key difference in my next blog post).

When we re-run the query:

SQL> select * from bowie where to_date(bowie_date, 'YYYY MM DD') = '13-JUN-2015';

100 rows selected.
Execution Plan
----------------------------------------------------------
Plan hash value: 960797537

-------------------------------------------------------------------------------------------------------
| Id | Operation                           | Name            | Rows | Bytes | Cost (%CPU) | Time     |
-------------------------------------------------------------------------------------------------------
| 0  | SELECT STATEMENT                    |                 |  100 |  2800 |     102 (0) | 00:00:01 |
| 1  | TABLE ACCESS BY INDEX ROWID BATCHED | BOWIE           |  100 |  2800 |     102 (0) | 00:00:01 |
|* 2 | INDEX RANGE SCAN                    | BOWIE_DATE_FN_I |  100 |       |       3 (0) | 00:00:01 |
-------------------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------

2 - access(TO_DATE("BOWIE_DATE",'YYYY MM DD')=TO_DATE(' 2015-06-13 00:00:00', 'syyyy-mm-dd hh24:mi:ss'))
Statistics
----------------------------------------------------------
0 recursive calls
0 db block gets
108 consistent gets
0 physical reads
0 redo size
3268 bytes sent via SQL*Net to client
618 bytes received via SQL*Net from client
8 SQL*Net roundtrips to/from client
0 sorts (memory)
0 sorts (disk)
100 rows processed

The CBO has now got the estimate spot on.

If we now run the previous range scan query that accessed 1/2 the table, referencing the to_date function:

SQL> select * from bowie where to_date(bowie_date, 'YYYY MM DD') between '10-OCT-2000' and '31-DEC-2015';

556100 rows selected.

Execution Plan
----------------------------------------------------------
Plan hash value: 1845943507

---------------------------------------------------------------------------
| Id | Operation         | Name  | Rows  | Bytes  | Cost (%CPU)| Time     |
---------------------------------------------------------------------------
| 0  | SELECT STATEMENT  |       |  556K |    14M |   1328 (34)| 00:00:01 |
|* 1 | TABLE ACCESS FULL | BOWIE |  556K |    14M |   1328 (34)| 00:00:01 |
---------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

1 - filter(TO_DATE("BOWIE_DATE",'YYYY MM DD')>=TO_DATE(' 2000-10-10
00:00:00', 'syyyy-mm-dd hh24:mi:ss') AND TO_DATE("BOWIE_DATE",'YYYY MM
DD')<=TO_DATE(' 2015-12-31 00:00:00', 'syyyy-mm-dd hh24:mi:ss'))

Statistics
----------------------------------------------------------
8 recursive calls
0 db block gets
40444 consistent gets
0 physical reads
0 redo size
18804277 bytes sent via SQL*Net to client
408355 bytes received via SQL*Net from client
37075 SQL*Net roundtrips to/from client
0 sorts (memory)
0 sorts (disk)
556100 rows processed

The CBO has got the estimates pretty well spot on and is now performing the far more efficient Full Table Scan. The CBO knows that the virtual column is of type DATE and therefore can much more accurately determine the actual cardinality estimates for the range scan on the “date” column.

If we now run the other corresponding range scan that returned a moderate number of rows:

SQL> select * from bowie where to_date(bowie_date, 'YYYY MM DD') between '10-OCT-2015' and '31-DEC-2015';

8300 rows selected.

Execution Plan
----------------------------------------------------------
Plan hash value: 1845943507

---------------------------------------------------------------------------
| Id | Operation         | Name  | Rows | Bytes  | Cost (%CPU) | Time     |
---------------------------------------------------------------------------
| 0  | SELECT STATEMENT  |       | 8401 |   229K |   1205 (27) | 00:00:01 |
|* 1 | TABLE ACCESS FULL | BOWIE | 8401 |   229K |   1205 (27) | 00:00:01 |
---------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

1 - filter(TO_DATE("BOWIE_DATE",'YYYY MM DD')>=TO_DATE(' 2015-10-10
00:00:00', 'syyyy-mm-dd hh24:mi:ss') AND TO_DATE("BOWIE_DATE",'YYYY MM
DD')<=TO_DATE(' 2015-12-31 00:00:00', 'syyyy-mm-dd hh24:mi:ss'))

Statistics
----------------------------------------------------------
14 recursive calls
0 db block gets
4058 consistent gets
0 physical reads
0 redo size
282075 bytes sent via SQL*Net to client
6635 bytes received via SQL*Net from client
555 SQL*Net roundtrips to/from client
1 sorts (memory)
0 sorts (disk)
8300 rows processed

We notice that again the CBO has got the row estimate just about right and because the index has a poor clustering factor, the CBO still decided to go down the Full Table Scan path.

Even if we don’t use the index all that much (perhaps the Database In-Memory store is a better option for most queries) , it can still play an important role in policing the integrity of the data. An attempt to insert an invalid date will now automatically be captured by the database and fail:

SQL> insert into bowie values (1000001, 42, '2016 6 31');
insert into bowie values (1000001, 42, '2016 6 31')
*
ERROR at line 1:
ORA-01839: date not valid for month specified

As the function-based index can only be populated or updated if a valid date is inserted into the table, any attempt to insert an invalid date will fail thus protecting the integrity of the data.

The best “fix” here is to store these dates in a date field within the database, where the above issues are automatically addressed. If this is not possible, then the introduction and usage of an associated function-based index can certainly assist the CBO in making the correct decision.

However, if data integrity is not a concern (the application does a fantastic job of it and no one ever has to perform manual data fixes directly in the database), then there’s another option to help make the CBO do the right thing.

The clue is back in how I collected the table statistics, which I’ll discuss in my next post.

Comments»

1. Datatypes for DATES | Learning is not a spectator sport - May 29, 2016

[…] Foote has written a post about not using the DATE datatype for storing […]


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