Database performance monitoring tools
The previous article presented an introduction SQL and NoSQL. This article builds on these topics by reviewing six of the top performance metrics to capture to assess the health of your database in your enterprise application.
Specifically this article reviews the following:
- Business Transactions
- Query Performance
- User and Query Conflicts
- NoSQL Databases
Business Transactions provide insight into real user behavior: they capture real-time performance that real users are experiencing as they interact with your application. As mentioned in the previous article, measuring the performance of a business transaction involves capturing the response time of a business transaction holistically as well as measuring the response times of its constituent tiers. These response times can then be compared with the baseline that best meets your business needs to determine normalcy.
If you were to measure only a single aspect of your application, I would encourage you to measure the behavior of your business transactions. While container metrics can provide a wealth of information and can help you determine when to auto-scale your environment, your business transactions determine the performance of your application. Instead of asking for the CPU usage of your application server you should be asking whether or not your users can complete their business transactions and if those business transactions are behaving optimally.
As a little background, business transactions are identified by their entry-point, which is the interaction with your application that starts the business transaction.
Once a business transaction is defined, its performance is measured across your entire application ecosystem. The performance of each business transaction is evaluated against its baseline to assess normalcy. For example, we might determine that if the response time of the business transaction is slower than two standard deviations from the average response time for this baseline that it is behaving abnormally, as shown in figure 1.
Figure 1 Evaluating BT Response Time Against its Baseline
The baseline used to assess the business transaction is consistent for the hour in which the business transaction is running, but the business transaction is being refined by each business transaction execution. For example, if you have chosen a baseline that compares business transactions against the average response time for the hour of the day and the day of the week after the current hour is over, all business transactions executed in that hour will be incorporated into the baseline for next week. Through this mechanism an application can evolve over time without requiring the original baseline to be thrown away and rebuilt; you can consider it as a window moving over time.
In summary, business transactions are the most reflective measurement of the user experience, so they are the most important metric to capture.
The most obvious place to look for poor query performance is in the query itself. Problems can result from queries that take too long to identify the required data or bring the data back. Look for these issues in queries:
Selecting more data than needed
It is not enough to write queries that return the appropriate rows; queries that return too many columns can cause slowness both in selecting the rows and retrieving the data. It is better to list the required columns rather than writing SELECT*. When the query is based on selecting specific fields, the plan may identify a covering index, which can speed up the results. A covering index includes all the fields used in the query. This means that the database can generate the results just from the index. It does not need to go to the underlying table to build the result. Additionally, listing the columns required in the result reduces the data that’s transmitted, which also benefits performance.
Inefficient joins between tables
Joins cause the database to bring multiple sets of data into memory and compare values, which can generate many database reads and significant CPU. Depending on how the tables are indexed, the join may require scanning all the rows of both tables. A poorly written join on two large tables that requires a complete scan of each one is very computationally expensive. Other factors that slow down joins include joining on columns that are different data types, requiring conversions, or a join condition that includes LIKE, which prevents the use of indexes. Avoid defaulting to using a full outer join; use inner joins when appropriate to bring back only the desired data.
Too few or too many indexes
When there aren’t any indexes that the query optimizer can use, the database needs to resort to table scans to produce query results, which generates a large amount of disk input/output (I/O). Proper indexes also reduce the need for sorting results. Indexes on non-unique values do not provide as much help as unique indexes in generating results. If the keys are large, the indexes become large as well, and using them creates more disk I/O. 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