MySQL Reporting requirements sometimes require both unknown column and row values, necessitating a more powerful means of generating crosstabs. Today's article presents Prepared Statements, which dynamically generate the SQL and assign it to a variable so that we can tailor the output based on the number of data values.
During the past several weeks, we've been learning how to create crosstabs in MySQL. We've now covered the basics of fetching the row and column data, as well as how to overcome some of the challenges inherent to outputting data as columns. As we saw, organizing data into columns can be an arduous task due to the SQL language's natural tendency of appending data to rows. We can transpose row data to columns easily enough, but the number of possible data values in the horizontal axis need to be known before hand.Unfortunately, there will be times that your reporting requirements will require both unknown column and row values, or have a tendency to change often enough to invalidate previous code. In such instances, you need a more powerful means of generating crosstabs. Today's article presents just such a mechanism: Prepared Statements. By dynamically generating the SQL and assigning it to a variable, we can tailor the output based on the number of data values, thus unburdening us from having to anticipate changes.
How Static Is the Data Really?
Way back in the Tips for Simplifying Crosstab Query Statements article, we were introduced to a crosstab report that displayed the number of cases by Region broken down by month, and later, by year as well. No sooner was the article published than someone asked the question "What happens when a new Region is introduced?" The answer is simple: an extra column must be added to the SELECT field list. This is straightforward enough to do and can be expected to occur very rarely, as Regions are what you would call a static data set. Other than Regions, other geographic entities, including continents, countries, counties, provinces, states, and cities can also be considered to be static. Having said that, even fixed data sets such as time frames can vary enormously. Now I'm not referring to the elasticity of time-space, as discovered by Einstein, but rather, how the start and end points of a SELECT query depend on reporting needs. With regards to our own crosstab query, going from a single year to multiple ones necessitated many changes to the SQL.At the other end of the spectrum is variable data, which can change drastically from one report to the other. Imagine reporting on Starbucks coffee houses in the nineties boom period! Since you could expect the number of shops to increase on an almost daily basis, you’d definitely need a more flexible approach!
Steps in Converting the Query into a Prepared Statement
Going from an SQL statement to a Prepared Statement will be done in two steps:- First, we'll rewrite the query to generate the Prepared Statement whose output will vary according to the number of columns.
- Second, we'll insert the SQL generating Prepared Statement into a stored proc, so that we can create the Prepared Statement and execute it in one fell swoop.
Dynamically Generating the SQL Statement
In order to dynamically generate an SQL string, we’ll be using the CONCAT() and GROUP_CONCAT() string functions.The CONCAT() function accepts a variable number of string parameters, and returns another string, which is comprised of all the input parameters joined together in the order that they were passed in. The following code would concatenate a name in last name (comma) first name format:
SELECT CONCAT(last_name, ", ", first_name) AS NAME
FROM CLIENTS;
Produces:
NAME
_____________
Jones, Paul
McDonald, Jim
Miller, Bruce
Portman, BessThe GROUP_CONCAT() function returns a string result with the concatenated non-NULL values from a group. Here, it's used to aggregate all the rows from the TA_CASES table and return the collection of SELECT list expressions that makes up the horizontal axis of the crosstab. The following query returns a string value that replaces the SQL statement of our previous crosstab query:SELECT concat(
"SELECT CASE WHEN Month_Num IS NULL", "\n",
" THEN 'TOTAL'", "\n",
" ELSE Month", "\n",
" END AS 'Month',", "\n",
group_concat( DISTINCT concat(" REGION_", REGION_CODE,
" AS 'REGION ", REGION_CODE, "',", "\n"
)
order by REGION_CODE
separator ''
),
" TOTAL", "\n",
"FROM ( SELECT MONTH(CREATION_DATE)\t\t\t\t\t\t\t\tAS Month_Num,", "\n",
"\t\tMONTHNAME(CREATION_DATE)\t\t\t\t\t\t\t\t\tAS 'Month',", "\n",
group_concat(
DISTINCT concat("\t\t\t\tCOUNT(CASE WHEN REGION_CODE ='", REGION_CODE,
"' THEN FEE_NUMBER ELSE NULL END) AS 'REGION_",
REGION_CODE, "',", "\n"
)
order by REGION_CODE
separator ''
),
" COUNT(*)\t\t\t\t\t\t\t\t\t\t\t\t\tAS 'TOTAL'", "\n",
" FROM TA_CASES", "\n",
" WHERE YEAR(CREATION_DATE)=", YEAR(CREATION_DATE), "\n",
" GROUP BY Month_Num WITH ROLLUP) AS CA;"
) statement
FROM TA_CASES
WHERE YEAR(CREATION_DATE)=1998;Here is the resulting SQL code as created by our dynamic SQL generator:SELECT CASE WHEN Month_Num IS NULL
THEN 'TOTAL'
ELSE Month
END AS 'Month',
REGION_01 AS 'REGION 01',
REGION_02 AS 'REGION 02',
REGION_03 AS 'REGION 03',
REGION_04 AS 'REGION 04',
REGION_05 AS 'REGION 05',
TOTAL
FROM (SELECT MONTH(CREATION_DATE)
MONTHNAME(CREATION_DATE)
COUNT(CASE WHEN REGION_CODE ='01' THEN FEE_NUMBER ELSE NULL END) AS 'REGION_01',
COUNT(CASE WHEN REGION_CODE ='02' THEN FEE_NUMBER ELSE NULL END) AS 'REGION_02',
COUNT(CASE WHEN REGION_CODE ='03' THEN FEE_NUMBER ELSE NULL END) AS 'REGION_03',
COUNT(CASE WHEN REGION_CODE ='04' THEN FEE_NUMBER ELSE NULL END) AS 'REGION_04',
COUNT(CASE WHEN REGION_CODE ='05' THEN FEE_NUMBER ELSE NULL END) AS 'REGION_05',
COUNT(*)
FROM TA_CASES
WHERE YEAR(CREATION_DATE)=1998
GROUP BY Month_Num WITH ROLLUP) AS CA;Moving the Prepared Statement into a Stored Procedure
Placing our code in a stored proc will make running the query a lot easier as it can generate the statement and then execute it to retrieve the results. Here is the code for the stored proc:CREATE PROCEDURE `p_case_counts_per_region_by_month`()
LANGUAGE SQL
NOT DETERMINISTIC
CONTAINS SQL
SQL SECURITY DEFINER
BEGIN
SELECT concat(
"SELECT CASE WHEN Month_Num IS NULL", "\n",
" THEN 'TOTAL'", "\n",
" ELSE Month", "\n",
" END AS 'Month',", "\n",
group_concat( DISTINCT concat(" REGION_", REGION_CODE,
" AS 'REGION ", REGION_CODE, "',", "\n"
)
order by REGION_CODE
separator ''
),
" TOTAL", "\n",
"FROM ( SELECT MONTH(CREATION_DATE)\t\t\t\t\t\t\t\tAS Month_Num,", "\n",
"\t\tMONTHNAME(CREATION_DATE)\t\t\t\t\t\t\t\t\tAS 'Month',", "\n",
group_concat(
DISTINCT concat("\t\t\t\tCOUNT(CASE WHEN REGION_CODE ='", REGION_CODE,
"' THEN FEE_NUMBER ELSE NULL END) AS 'REGION_",
REGION_CODE, "',", "\n"
)
order by REGION_CODE
separator ''
),
" COUNT(*)\t\t\t\t\t\t\t\t\t\t\t\t\tAS 'TOTAL'", "\n",
" FROM TA_CASES", "\n",
" WHERE YEAR(CREATION_DATE)=", YEAR(CREATION_DATE), "\n",
" GROUP BY Month_Num WITH ROLLUP) AS CA;"
) statement
into @case_counts_per_region_by_month
FROM TA_CASES
WHERE YEAR(CREATION_DATE)=1998;
prepare case_counts_per_region_by_month
from @case_counts_per_region_by_month;
execute case_counts_per_region_by_month;
deallocate prepare case_counts_per_region_by_month;
ENDInside the procedure, we generate the SQL for the query as we did above, but within a proc we can save it to a variable using the SELECT INTO syntax. A Prepared Statement is then utilized to execute the generated code. A SELECT INTO can only be used where the SQL returns exactly one row; yet another reason that generating the SQL statement as a string works so well!
A Prepared Statement is a combination of three separate SQL statements:
- PREPARE prepares a statement for execution.
- EXECUTE executes a prepared statement.
- DEALLOCATE PREPARE releases a prepared statement.
mysql> call p_case_counts_per_region_by_month;Here is the record set that is returned by our proc:| Month | REGION 01 | REGION 02 | REGION 03 | REGION 04 | REGION 05 | TOTAL |
| April | 13 | 33 | 76 | 2 | 47 | 171 |
| May | 17 | 55 | 209 | 1 | 143 | 425 |
| June | 8 | 63 | 221 | 1 | 127 | 420 |
| July | 13 | 104 | 240 | 6 | 123 | 486 |
| August | 18 | 121 | 274 | 9 | 111 | 533 |
| September | 25 | 160 | 239 | 2 | 88 | 514 |
| October | 9 | 88 | 295 | 2 | 127 | 521 |
| November | 2 | 86 | 292 | 2 | 120 | 502 |
| December | 1 | 128 | 232 | 6 | 155 | 522 |
| TOTAL | 106 | 838 | 2078 | 31 | 1041 | 4094 |
Source: http://www.databasejournal.com
