Virtual CISO

Databases are a fundamental component of all information frameworks and are utilised alike by all businesses for saving critical business data. Hence, the overall health of a database system is of immediate importance for any database administrator. Finding database issues in time can help the application remain healthy and accessible. Without solid monitoring in place, database outages can go unnoticed until it is too late, and the business is losing money and customers. System administrators generally deploy database management or monitoring tools to help facilitate in keeping all performance metrics in check.

Top five best practices for proactively monitoring database performance are:

  • Monitor Availability and Resource Consumption
  • Measure and Compare Throughput
  • Monitor Expensive Queries
  • Track Database Changes
  • Monitor Logs

The Need to Monitor a Database

A properly configured Database Monitoring regimen has a number of benefits. For example:

  • Proactive monitoring is always better than a reactive approach. It saves a lot of time and resources if the warning signs themselves are acted upon instead of being informed of a major incident after it has taken place.
  • Databases are often blamed for an application slowness or outage. A database management system (DBMS) can help pinpoint issues and streamline troubleshooting in such cases.
  • Apart from performance metrics, it is also equally important to keep a check on automated backup, security-related events, logs, scheduled tasks, throughput, availability among many others.

Kinds of Databases

Almost all organisations use a combination of various databases instead of just one. Naturally, databases belonging to different vendors will provide different metrics against their performance and can be measured. Moreover, the level of granularity of these metrics will also be of varying degrees. This highlights the need for a flexible DBMS. Below are the overarching categories under which most databases fall based on their usage and structuring of data:

  • Online transaction processing (OLTP) systems generally use Relational Database
  • Data warehouse systems host large volumes of low-velocity data called Columnar Databases
  • Mobile or web apps use NoSQL databases to host metadata or status information
  • In-memory databases are used for fast performance

Also, there are three types of databases based on architecture:

  • One-tier architecture — The client and server reside on the same machine as the database.
  • Two-tier architecture — Data is stored on a server and clients (like PCs) run the presentation layer.
  • Three-tier architecture — The application layer exists between the database and the client, so a server controls the user requests and DBMS responses based on functional logic and rules.

Metrics from each of these categories will be further affected by:

  • Vendor software (e.g. SQL Server vs. Oracle, MongoDB vs. Cassandra, Redshift vs. Greenplum)
  • On-premise vs. Cloud-hosting
On-premise
  • Physical or virtualized hardware
Cloud-hosted
  • Managed or non-managed

What Database Metrics should be Monitored?

The following are a list of categories and some important metrics that form the crux of Database Monitoring and provide significant insight into the database environment:

Categories:
  • Infrastructure – Most poor query performances can be traced down to issues related to the hardware or network like a disk running low on storage or a network which is saturated.
  • Availability – The metric to be checked first and foremost, this ensures that the database is accessible and available.
  • Throughput – Throughput metrics like connection wait time for database endpoints, number of active database connections, should be collected during times of varying workloads in order to set appropriate baselines to check against.
  • Performance — These metrics can indicate potential bottlenecks.
Metrics:
  • Disk I/O – One of the most relied-on metrics when it comes to databases, this refers to the read/write transactions and can often signal bottlenecks.
  • Memory – As is the case with any system, memory is an inherently essential metric and an insufficient amount of it can cause a multitude of issues, for example, page faults.
  • User connections – A large number of users querying the database simultaneously could bring down performance.
  • CPU workload – A high CPU utilisation can point to multiple issues like a blocked database query or a large number of simultaneous user connections, overburdened hardware, etc.
  • Transaction log configuration— Maintaining a backup copy, tracking log size, and otherwise ensure the log isn’t corrupted allows for a safe recovery in the case of a system failure.
  • App performance metrics – Provide insight into the web app speed and include requests per second, data in/data out, peak response time, and average response time.
  • User experience metrics – Used for measuring the satisfaction of the end-user.

Common Database Issues/Use Cases

  • Scalability. It must be ensured that the present DB design will be able to adapt to a rise in data volumes. A database management solution should be able to highlight high latency as a consequence of configuration and help support high transactional throughput.
  • Query slowness. Database Monitoring software should be able to show appropriate data, including customizable, dynamic performance metrics.
  • Workload optimization. A DBMS capable of looking at the production usage patterns can help shed light on an inefficient workload.
  • Automation. In order to reduce the manual labor, tasks like management and issue mitigation can be automated. A DBMS that uses machine learning in this scenario can come in handy to pick up on large variations from baselines.
  • Alerting. Monitoring of any kind is most useful in conjunction with alerting. Automated anomaly detection and the capability to alert DBAs via notifications can save time spent on searching for the root cause of an issue.

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