Performance monitoring and alerting are very crucial to measure the performance metrics of an application running in a production environment. In this blog post, we are going to talk about two popular open-source tools Prometheus and Grafana. This blog post is aimed at anyone who is trying to learn Prometheus and Grafana or has some experience with these tools.

What is Prometheus – It is an open-source tool for scrapping the performance metrics of any given hardware resource (CPU, VM, Cloud Virtual Machine, Router, etc.). Prometheus scraps the data with the timestamp which can be stored on a server and accessed using the PromQL. Prometheus has superb support API which makes Prometheus integration with any resource present in the Datacenter. Prometheus architecture is really scalable and 3rd party libraries and makes it more powerful.

What is Grafana – In Lehman’s term Grafana is a user interface for viewing the metrics scraped by Prometheus from various resources. Grafana is an open-source analytics and visualization tool. Grafana does not store any data, but instead, it relies on Prometheus to send the data so that dashboard can be prepared. Also, Grafana is used for sending notifications and mail alerts based on various thresholds. One of the cool features of the Grafana is Grafana Labs where you can go and download the dashboard prepared by other developers so that you do not have to re-invent the wheel.

1. Installation of Prometheus
Prometheus has a various ways of installation for example – pre-compiled binaries, from source, Docker. But to keep the installation simple we are going to use the pre-compiled binaries for installing Prometheus onto my Ubuntu Linux machine.

Go to Download Page of Prometheus and select the prometheus-x.xx.x.linux-amd64.tar.gz file for download. (Note- Here x.xx.x is version number)
Extract the download binary file. (Note- Replace x.xx.x with the downloaded version of Prometheus)

tar xf prometheus-x.xx.x.linux-amd64.tar.gz

3. Go into the extracted directory cd prometheus-x.xx.x.linux-amd64.tar.gz

4.Start the Prometheus server with the command. /Prometheus

5. The Prometheus server should start on port 9090
6. You can access the Prometheus graph UI by visit http://localhost:9090/graph

7. You can access the Prometheus metrics UI by Visit http://localhost:9090/metrics.
(I have used my Prometheus instance public IP)

2. How to create and Install Node exporter
Node exporter is responsible for fetching the statistics from various hardware and virtual resources in the format which Prometheus can understand and with the help of the Prometheus server those statistics can be exposed on port 9100.

There are many third-party Node exporters which can be used by SREs as well as DevOps based on their application needs. But primarily we look for the following metrics –

CPU usage
Memory usage
Disk usage
Network usage
2.1 How to Install Node exporter.
After installing the Prometheus in the previous step, the next package, we are going to install is **Node Exporter **. Node exported is used for collecting various hardware and kernel-level metrics of your machine.

Here are the download and installation steps –

Download the binary of Node exporter based on the operating system. (In my case I am using Ubuntu Linux machine)

3. Extract the download node exporter binary file. (Note- Replace x.xx.x with the downloaded version of node exporter)

tar xf node_exporter-*.*-amd64.tar.gz

4.Go into the extracted directory sudo mv node_exporter-*.*-amd64.tar.gz nodeexporter

The post Prometheus, Node Exporter and Grafana Setup in AWS EC2 Environment first appeared on CalibreQA|Functional|Performance|Automation|CHAOS|Reliablity Testing|Mobile Application Testing.

Database Design: A well-designed database schema with appropriate indexing, relationships, and normalization can significantly improve query performance. Ensure that the database structure is optimized for the application’s data access patterns.
Indexing: Use indexes judiciously to speed up data retrieval operations. Indexes on columns frequently used in WHERE clauses can reduce query execution times.
Query Optimization: Write efficient SQL queries that minimize the amount of data processed and fetched from the database. Avoid complex subqueries and use database query optimization tools where available.
Caching: Implement caching mechanisms to store frequently accessed data in memory. Caching can reduce the load on the database and improve response times for read-heavy workloads.
Connection Pooling: Use connection pooling to manage database connections efficiently. Creating and closing connections for each user request can be resource-intensive and slow.
Resource Monitoring: Continuously monitor database performance metrics, such as CPU utilization, memory usage, and I/O activity. Detect and address resource bottlenecks promptly.
Normalization and Denormalization:Strike a balance between normalization (for data integrity) and denormalization (for query performance). Denormalization can reduce the need for complex joins but may require additional storage.
Partitioning: Partition large tables into smaller, more manageable chunks. This can improve query performance and maintenance tasks like backups.
Optimized Data Types: Use appropriate data types for columns to minimize storage requirements and improve query performance. Avoid using unnecessarily large data types.
Database Maintenance: Regularly perform database maintenance tasks such as index rebuilding, table optimization, and data archiving to ensure optimal performance over time.
Query Execution Plans: Analyze query execution plans to identify slow queries and potential indexing or optimization opportunities.
Concurrency Control: Implement appropriate concurrency control mechanisms to handle simultaneous database transactions efficiently.
Load Balancing: Use database load balancing to distribute queries across multiple database instances or replicas. This can improve both read and write performance.
Scaling Strategies: Plan for database scaling as the application grows. Options include vertical scaling (adding more resources to a single server) and horizontal scaling (distributing the database across multiple servers).
Backup and Recovery: Implement reliable backup and recovery strategies to minimize downtime in case of data loss or system failures.
Security and Permissions: Ensure that database security is robust and that user permissions are appropriately configured. Security-related queries should not introduce unnecessary overhead.
Logging and Monitoring: Implement comprehensive logging and monitoring to detect and diagnose performance issues in real-time. Log slow queries and errors for analysis.
Testing and Benchmarking: Conduct performance testing and benchmarking to simulate real-world scenarios and identify performance bottlenecks before they impact users.
Documentation: Maintain documentation that includes database schema diagrams, indexing strategies, and performance optimization plans for reference and knowledge sharing.

By addressing these key points and continuously monitoring and optimizing your database, you can ensure that it operates efficiently, delivers fast response times, and supports the performance requirements of your application.

The post Unlocking Seamless Speed: Elevating Database Performance Testing in the Software Industry first appeared on CalibreQA|Functional|Performance|Automation|CHAOS|Reliablity Testing|Mobile Application Testing.