How to Optimize SQL Queries in Visual Basic 3.0 and 4.0

MORE INFORMATION

• Compact your database. To do this, use the CompactDatabase statement as in this code:

        DBEngine.CompactDatabase "C:\VB\BIBLIO.MDB","C:\VB\BIBLIO2.MDB"
        ' Do other things here ...
        Kill "C:\VB\BIBLIO.BAK"
        Name "C:\VB\BIBLIO.MDB" As "C:\VB\BIBLIO.BAK"
        Name "C:\VB\BIBLIO2.MDB" As "C:\VB\BIBLIO.MDB"
   
  						

  This speeds up queries because it writes all the data in a table into contiguous pages on the hard disk. Scanning sequential pages is much faster than scanning fragmented pages.
• Avoid expressions in query output. Exressions in query output can cause query optimization problems if that query is later used as the input to another query. In the following example, the Query1 query is used as input for a second query:

        Dim DB As Database
        Dim RS As RecordSet
        Set DB = DBEngine.Workspaces(0).Opendatabase("Biblio.MDB")
        DB.CreateQueryDef("Query1", "SELECT IIF([Au_ID]=1,"Hello","Goodbye")_
           AS X FROM Authors")
        Set RS = DB.OpenRecordSet("SELECT * FROM Query1 WHERE X='Hello'")
   
  						

  Because the IIF() expression in Query1 cannot be optimized, the query in the OpenRecordSet also cannot be optimized. If an expression gets buried deeply enough in a query tree, you can forget that it is there. As a result, your entire string of queries cannot be optimized.
  
  Here's a better way to write the second query:

        Set RS = DB.OpenRecordSet("SELECT * FROM Authors WHERE [Au_ID]=1")
  
  						

• Place GROUP BY clauses in the same table as aggregates. This is an issue when you are joining two tables. For example, if you join two tables on the Customer Name field, and then run a query that performs a GROUP BY operation on the Customer Name field, make sure that both the GROUP BY field (Customer Name) and the field that is in the aggregate (Sum, Count, and so on) come from the same table.
• When you create a "totals" query, use the GROUP BY clause on as few fields as possible. The more fields in the GROUP BY clause, the longer the query takes to execute.
• If possible, place a GROUP BY clause on a table before joining it to another table, rather than joining the two tables and doing the GROUP BY in the same query as the join. For example, instead of this query:

        SELECT Orders.[Company ID], Count(Orders.[Order ID]) AS
           [CountOfOrder ID] FROM Customers INNER JOIN Orders ON
           Customers.[Customer ID] = Orders.[Customer ID] GROUP BY
           Orders.[Company Name];
  
  						

  Break the query into two separate queries, such as these:

        SELECT Customers.[Company ID] FROM Customers GROUP BY
           Customers.[Company ID];
  
        SELECT Orders.[Customer ID], Count(Orders.[Order ID]) AS
           [CountOfOrder ID] FROM Q1 INNER JOIN Orders ON Q1.[Customer ID] =
           Orders.[Customer ID] GROUP BY Orders.[Customer ID];
  
  						

• When joining tables, try to index the fields on both sides of a join. This can speed query execution by allowing the query optimizer to use more sophisticated internal join strategy.
• Index fields as much as possible. If a database is not updated frequently, place an index on all fields that are used in a join or in a restriction. With the use of Rushmore query optimization technology in Microsoft Access version 2.0, the Microsoft Jet database engine is able to take advantage of multiple indexes on a single table, which makes indexing multiple fields advantageous.
• Try to construct your queries so that Rushmore technology can be used to help optimize them. Rushmore is a data-access technology that permits sets of records to be queried very efficiently. With Rushmore, when you use certain types of expressions in query criteria, your query will run much faster.
  
  Rushmore does not automatically speed up all your queries. You must construct your queries in a certain way for Rushmore to be able to improve them.
• Use COUNT(*) rather than COUNT([Column Name]) to determine the number of records in a table. This is faster because there are special optimizations in the Microsoft Jet database engine that allow COUNT(*) to be executed much faster than COUNT([Column Name]).