Monitoring Database Performance: Strategies For Success

Database performance monitoring is a critical discipline that involves tracking specific metrics to understand the health and behaviour of database systems. It helps identify issues that impact the performance and efficiency of applications and systems, such as slow-performing applications or system outages. By monitoring key metrics like CPU and memory usage, slow queries, and database request volume, organisations can optimise database performance, improve user experiences, and reduce costs. Database monitoring tools enable teams to gain valuable insights, troubleshoot issues, and make informed decisions to ensure high-performing and reliable applications.

Monitor availability and resource consumption

Monitoring availability and resource consumption is a basic but critical aspect of database performance monitoring. This involves regularly checking that databases are online and operational during business and non-business hours. While this can be done manually, a good monitoring tool will automatically alert teams to any outages.

Resource consumption monitoring focuses on infrastructure-related resources such as CPU, memory, disk, and network usage. High CPU usage, low memory, insufficient disk space, or abnormal network traffic can all impact database performance. Monitoring these resources allows administrators to be alerted to potential issues before they compromise performance.

For example, high CPU usage may indicate that a query is not optimally formulated or that there is a lack of indexes. Monitoring memory usage can help identify inefficient queries or the need for additional CPUs. Tracking disk space usage is important to ensure that there is enough space for database operations, while monitoring network traffic can help identify abnormal behaviour or security breaches.

By monitoring availability and resource consumption, administrators can ensure that databases are accessible and performing optimally, maintaining the performance of systems and applications that rely on them.

Measure and compare throughput

Measuring and comparing throughput is a critical aspect of database performance monitoring. Throughput refers to the volume of work done by a database system over a specific period, typically quantified as the number of queries executed per unit of time (e.g., per second, minute, or hour). Monitoring throughput provides insights into the efficiency of the database in processing incoming queries.

To effectively measure and compare throughput, several steps should be followed:

Define Metrics and Collection Methods:

Before initiating measurements, it is crucial to establish the specific metrics that will be used. Common metrics include transactions per second (TPS), queries per second (QPS), read/write operations per second (IOPS), or data transfer rate (MB/s). Each metric has its advantages and disadvantages, depending on the nature and complexity of the database workload. Additionally, the method of data collection should be determined, whether through built-in tools, third-party software, or custom scripts.

Select Appropriate Tools:

A variety of tools are available to measure database throughput, including Database Management Systems (DBMS) tools such as Oracle Enterprise Manager or MySQL Workbench, operating system (OS) tools like Task Manager, and benchmarking tools such as HammerDB or Sysbench. Application Performance Monitoring (APM) tools like New Relic or Datadog offer specialised software to track and visualise database performance.

Establish a Baseline:

It is important to establish a baseline for database throughput under normal or expected conditions. This involves simulating realistic or anticipated loads, such as user requests, transactions, queries, or data volume. The tests should be conducted over a sufficient duration to capture fluctuations and trends in throughput over time and should be repeated periodically to monitor changes.

Measure and Compare:

By comparing the measured throughput with the established baseline, you can identify deviations or anomalies. Any significant variation from the baseline warrants further investigation to optimise performance. Throughput measurements can also be compared across different database systems or configurations to identify areas for improvement.

Optimise Performance:

Based on the throughput analysis, performance optimisation can be achieved by addressing bottlenecks or enhancing factors that improve throughput. This may include upgrading hardware or software, configuring settings, or refactoring database design to increase efficiency.

By following these steps, database administrators can effectively measure and compare throughput, enabling them to fine-tune the performance of their database systems and ensure optimal efficiency in processing queries.

Monitor expensive queries

Monitoring expensive queries is an important part of database performance monitoring. Expensive queries are those that use a lot of resources, such as memory, disk, and network. These queries can slow down the performance of a database and impact the user experience.

To monitor expensive queries, database administrators can use tools like SQL Server Activity Monitor, which has a "Recent Expensive Queries" pane. This pane shows queries executed in the last 30 seconds, ordered by CPU usage. Administrators can also sort by executions per minute and logical reads per second to identify queries using more resources than normal.

Another way to monitor expensive queries is by using SQL Profiler with specific parameters to find queries with a longer duration.

Additionally, database logs can be used to identify slow queries. By configuring the database to capture slow queries, administrators can then analyse the log files to find queries with longer query times, high wait times, or missing indexes.

By monitoring expensive queries, administrators can identify and optimise queries that are impacting database performance and user experience.

Track database changes

Tracking database changes is an important aspect of database management, as it helps identify performance issues, resource shortages, and user access anomalies. Here are some methods and tools to achieve this:

Database Triggers

Database triggers are a common way to track changes and can be used to write records to a "history" table whenever data is modified, deleted, or new rows are added. This method is available in most relational database management systems (RDBMS) and can be implemented on each table to track changes and the time of modification.

Temporal Tables

Temporal tables are a feature in some RDBMS, such as SQL Server 2016+ and Oracle, which allow for easy tracking of data changes over time. They provide the ability to query data within a specific time range, making it easier to compare data between two points in time.

Change Data Capture (CDC)

CDC is supported in all currently supported versions of SQL Server and Oracle, and possibly other RDBMS. It helps track changes and deletions in your database, although it can result in additional storage overhead.

Soft Deletion

Instead of permanently deleting records, you can use soft deletion by marking records as deleted (along with a timestamp) using an "instead of" trigger. This approach, combined with CDC or temporal tables, provides a comprehensive solution for tracking deleted data.

Auditing

Auditing is a feature available in some RDBMS, such as Oracle, that allows you to track changes made to the database. It is important to move the audit table to another tablespace before starting the audit to avoid performance issues due to increased storage requirements.

Third-Party Tools

There are also third-party tools available, such as Bemi, SQL Historian, and ApexSQL Source Control, that can help track database changes. These tools offer various features, such as integration with source control systems, simultaneous development support, and the ability to restore previous versions.

Monitor slow queries

Slow queries can have a significant impact on the performance of a database and the applications that rely on it. They can cause the database to become overloaded, leading to decreased performance and increased response times for users. This can result in a poor user experience, with users having to wait a long time for their requests to be fulfilled.

To monitor slow queries, you can use various tools and methods. Here are some approaches:

Using a Monitoring Tool

Database monitoring tools such as Splunk, SolarWinds Database Performance Analyzer (DPA), and Query Monitor can help identify slow queries. These tools provide insights into database performance and allow you to track the time taken for a query to complete. They also measure resource usage, helping you understand the impact of queries on the database system.

Examining Query Logs

Database logs, such as slow query logs, can provide valuable information about slow queries. By enabling slow query logging in your database system, you can record queries that take longer than a specified amount of time to execute. You can then analyse these logs to identify queries with long execution times or those that are frequently executed, impacting the overall performance.

Using Specific Database Features

Different database systems offer features to help identify slow queries. For example, in PostgreSQL, you can use the pg_stat_statements extension, which collects statistics about all SQL statements executed by the server, including execution times and the number of executions. Similarly, in SQL Server, you can use the sys.dm_exec_query_stats view to find long-running queries, and the sys.dm_exec_query_plan view to examine the execution plan of a cached query.

Optimising Query Performance

Once you have identified slow queries, you can take steps to improve their performance. This may include optimising the query structure, using appropriate data types, and utilising set-based queries instead of cursors. Additionally, ensuring that statistics are up to date provides the query optimizer with accurate information on data distribution, leading to more efficient query execution.

Using Query Hints

In some cases, you can use query hints to improve slow queries. For example, the MAXDOP hint can be used to control the degree of parallelism for a query, reducing the number of resource waits. However, it is important to note that query hints should be used carefully and only when necessary, as they can impact the query optimizer's ability to choose the best execution plan.

Frequently asked questions