by Markus Winand.

Performance Impacts of Data Volume

The amount of data stored in a database has a great impact on its performance. It is usually accepted that a query becomes slower with additional data in the database. But how great is the performance impact if the data volume doubles? And how can we improve this ratio? These are the key questions when discussing database scalability.

As an example we analyze the response time of the following query when using two different indexes. The index definitions will remain unknown for the time being—they will be revealed during the course of the discussion.

SELECT count(*)
  FROM scale_data
 WHERE section = ?
   AND id2 = ?

The column SECTION has a special purpose in this query: it controls the data volume. The bigger the SECTION number becomes, the more rows the query selects. Figure 3.1 shows the response time for a small SECTION.

Figure 3.1 Performance Comparison

There is a considerable performance difference between the two indexing variants. Both response times are still well below a tenth of a second so even the slower query is probably fast enough in most cases. However the performance chart shows only one test point. Discussing scalability means to look at the performance impact when changing environmental parameters—such as the data volume.


Scalability shows the dependency of performance on factors like the data volume.

A performance value is just a single data point on a scalability chart.

Figure 3.2 shows the response time over the SECTION number—that means for a growing data volume.

Figure 3.2 Scalability by Data Volume

The chart shows a growing response time for both indexes. On the right hand side of the chart, when the data volume is a hundred times as high, the faster query needs more than twice as long as it originally did while the response time of the slower query increased by a factor of 20 to more than one second.


Appendix C, “Example Schema has the scripts to repeat this test in an Oracle, PostgreSQL or SQL Server database.

The response time of an SQL query depends on many factors. The data volume is one of them. If a query is fast enough under certain testing conditions, it does not mean it will be fast enough in production. That is especially the case in development environments that have only a fraction of the data of the production system.

It is, however, no surprise that the queries get slower when the data volume grows. But the striking gap between the two indexes is somewhat unexpected. What is the reason for the different growth rates?

It should be easy to find the reason by comparing both execution plans.

The execution plans are almost identical—they just use a different index. Even though the cost values reflect the speed difference, the reason is not visible in the execution plan.

It seems like we are facing a “slow index experience”; the query is slow although it uses an index. Nevertheless we do not believe in the myth of the “broken index” anymore. Instead, we remember the two ingredients that make an index lookup slow: (1) the table access, and (2) scanning a wide index range.

Neither execution plan shows a TABLE ACCESS BY INDEX ROWID operation so one execution plan must scan a wider index range than the other. So where does an execution plan show the scanned index range? In the predicate information of course!


Pay attention to the predicate information.

The predicate information is by no means an unnecessary detail you can omit as was done above. An execution plan without predicate information is incomplete. That means you cannot see the reason for the performance difference in the plans shown above. If we look at the complete execution plans, we can see the difference.


The execution plan was simplified for clarity. The appendix explains the details of the “Predicate Information” section in an Oracle execution plan.

The difference is obvious now: only the condition on SECTION is an access predicate when using the SCALE_SLOW index. The database reads all rows from the section and discards those not matching the filter predicate on ID2. The response time grows with the number of rows in the section. With the SCALE_FAST index, the database uses all conditions as access predicates. The response time grows with the number of selected rows.


Filter predicates are like unexploded ordnance devices. They can explode at any time.

The last missing pieces in our puzzle are the index definitions. Can we reconstruct the index definitions from the execution plans?

The definition of the SCALE_SLOW index must start with the column SECTION—otherwise it could not be used as access predicate. The condition on ID2 is not an access predicate—so it cannot follow SECTION in the index definition. That means the SCALE_SLOW index must have minimally three columns where SECTION is the first and ID2 not the second. That is exactly how it is in the index definition used for this test:

CREATE INDEX scale_slow ON scale_data (section, id1, id2)

The database cannot use ID2 as access predicate due to column ID1 in the second position.

The definition of the SCALE_FAST index must have columns SECTION and ID2 in the first two positions because both are used for access predicates. We can nonetheless not say anything about their order. The index that was used for the test starts with the SECTION column and has the extra column ID1 in the third position:

CREATE INDEX scale_fast ON scale_data (section, id2, id1)

The column ID1 was just added so this index has the same size as SCALE_SLOW—otherwise you might get the impression the size causes the difference.

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Markus Winand teaches efficient SQL—inhouse and online. He minimizes the development time using modern SQL and optimizes the runtime with smart indexing—for that he also published the book SQL Performance Explained.

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