Metrics are the basic values you need to correlate different factors, understand historic trends, and measure changes in consumption, performance, and error rates. Without metrics, there is no visibility, and without visibility, performance optimization is impossible.

When combined with alerting, metrics can be very powerful: You can configure rules and take action when metrics fall outside a given range, trigger a notification, or automatically add more resources autoscale.

But knowing what metrics to collect can be challenging. There is no one-size-fits-all metric for all application types and workloads, but metrics can be grouped into the following three main categories: work, resource, and event. Work metrics indicate the health of a system based on its output.

Applications rarely function with a single component. Instead, multiple components work together to make a system work. In a production system, these include low-level CPU, memory, disk and high-level components database, third-party services.

Resource metrics target resources a system needs to do its job. A live system generates events—actions or activities happening within the system. For instance, you could fire an event whenever your system fails to process a customer payment. Common top-level performance metrics include uptime, memory, CPU utilization, response time, throughput, load averages, lead time, and error rates.

Uptime measures the shortest possible time it takes a system to restore from any downtime, giving you the availability of a system. Some certain tasks and applications require heavy CPU usage, while others have any CPU resource requirement. For instance, API gateways require higher CPU usage.

Note: This is probably one of the most misunderstood metrics and can be misleading. Throughput represents the maximum amount of work a system handles per unit of time and is best tracked as requests per minute RPM. A drop in throughput indicates a bottleneck, preventing consistent delivery results.

Errors can then also be easily traced to their root cause and resolved. Large systems generate tons of metrics every day, so you need to know which metrics are relevant to performance. Due to the uniqueness of each application and workload, its impractical and ineffective to collect the same metrics for every system, as performance metrics for each workload and application type differ.

There are two famous frameworks used for monitoring: the four golden signals of monitoring explained in the highly influential Google Site Reliability Engineering book and the USE Method. While we will not delve into the golden signal approach in this post, we will discuss the USE Method briefly and show how it can be applied to database workloads.

The USE framework was originally developed by Brendan Gregg to track saturation, utilization, and errors for every resource, including all the functional components of a physical server busses, disks, CPUs, etc. This provides an understanding of:.

Instead of following some common anti-patterns, like changing things randomly until the problem goes away, you can leverage the USE Method to collect the right metrics, which will in turn help you gain visibility into the server. The performance of a database server deteriorates when it has more work than it can process at a given time.

Incoming queries are then queued until the database has capacity to process them. So to address this, you start collecting metrics on its throughput. In addition, a database server also requires some low-level resources like disks that can get used up or corrupted.

For this, you can measure resource utilization. Of course, no system is immune to errors. Database operations generate error events when they fail, and tracking the number of errors generated is a good way to know when a database server operation is failing.

One of the most commonly seen performance metrics is CPU utilization—considered one of the most-essential measurement tools when it comes to evaluating system performance. This is why scaling strategies are often based on CPU usage, where the decision to scale up or down depends on whether CPU utilization exceeds certain thresholds.

Understanding how much of your CPU is stalled not making progress can help you make better performance-tuning decisions. PMCs are bits of code that count, measure, and monitor events that occur at the CPU level, like the number of accesses to off-chip memory, instructions or cycles that a program executed, and the associated cache misses.

PMCs track these events, giving you insight into the behavior of your infrastructure. This is one reason PCMs are considered a valuable tool for debugging and optimizing application performance.

On the other hand, Instructions Per Cycle IPC , commonly known as commands per cycle, gives you the average number of instructions executed per clock cycle—a measurement that helps you understand the number of tasks a CPU can conduct in a single cycle. You would also benefit from tuning your hardware, such as using faster memory, busses, and interconnect.

On the other hand, an IPC of greater than 1 likely means your task is heavy on CPU, so optimizing and reducing your code execution time will be a better performance-tuning decision. For different workloads and applications, it is important to track the right performance metrics, which we introduced in this article.

Some key use cases include:. Performance optimization is closely related to other technologies and terms in the data lakehouse ecosystem. Some of these include:. Dremio is a powerful data lakehouse platform that enables organizations to leverage their data for analytics and insights.

Dremio users would be interested in performance optimization because it allows them to:. Bring your users closer to the data with organization-wide self-service analytics and lakehouse flexibility , scalability , and performance at a fraction of the cost.

Run Dremio anywhere with self-managed software or Dremio Cloud. POWERED BY. Explore Dremio. Get Started. Apache Iceberg: The Definitive Guide Everything you need to know about Apache Iceberg table architecture, and how to structure and optimize Iceberg tables for maximum performance.

Home Wikis Performance Optimization. What is Performance Optimization? How Performance Optimization Works Performance optimization utilizes various techniques and methodologies to enhance the efficiency of data processing and analytics. Why Performance Optimization is Important Performance optimization plays a vital role in maximizing the value and potential of data lakehouse environments.

Here are some reasons why it is important: Improved Data Processing Speed: Performance optimization techniques help organizations process and analyze data faster, enabling real-time or near-real-time decision-making. Enhanced User Experience: Faster query response times and reduced latency improve the overall user experience, allowing for more interactive and responsive data exploration.

Resource Efficiency: Performance optimization ensures optimal utilization of computing resources, minimizing costs and maximizing ROI. Scalability: By optimizing data workflows, performance optimization allows organizations to scale their data lakehouse environments to handle ever-increasing data volumes.

The Most Important Performance Optimization Use Cases Performance optimization is applicable to various use cases within a data lakehouse environment. Some key use cases include: Ad Hoc Analytics: Enabling users to run ad hoc queries on large datasets with minimal latency. Real-time Data Exploration: Supporting interactive data exploration and visualization for immediate insights.

Machine Learning: Optimizing data processing for training and inference in machine learning models. Streaming Analytics: Achieving real-time analytics on streaming data for time-sensitive decision-making.

Other Technologies or Terms Related to Performance Optimization Performance optimization is closely related to other technologies and terms in the data lakehouse ecosystem.

Some of these include: Data Warehousing : Traditional data warehousing involves optimizing data storage and retrieval for structured data.

Common top-level performance metrics include uptime, memory, CPU utilization, response time, throughput, load averages, lead time, and error rates. Uptime measures the shortest possible time it takes a system to restore from any downtime, giving you the availability of a system.

Some certain tasks and applications require heavy CPU usage, while others have any CPU resource requirement. For instance, API gateways require higher CPU usage. Note: This is probably one of the most misunderstood metrics and can be misleading.

Throughput represents the maximum amount of work a system handles per unit of time and is best tracked as requests per minute RPM. A drop in throughput indicates a bottleneck, preventing consistent delivery results. Errors can then also be easily traced to their root cause and resolved. Large systems generate tons of metrics every day, so you need to know which metrics are relevant to performance.

Due to the uniqueness of each application and workload, its impractical and ineffective to collect the same metrics for every system, as performance metrics for each workload and application type differ.

There are two famous frameworks used for monitoring: the four golden signals of monitoring explained in the highly influential Google Site Reliability Engineering book and the USE Method. While we will not delve into the golden signal approach in this post, we will discuss the USE Method briefly and show how it can be applied to database workloads.

The USE framework was originally developed by Brendan Gregg to track saturation, utilization, and errors for every resource, including all the functional components of a physical server busses, disks, CPUs, etc.

This provides an understanding of:. Instead of following some common anti-patterns, like changing things randomly until the problem goes away, you can leverage the USE Method to collect the right metrics, which will in turn help you gain visibility into the server.

The performance of a database server deteriorates when it has more work than it can process at a given time. Incoming queries are then queued until the database has capacity to process them. So to address this, you start collecting metrics on its throughput. In addition, a database server also requires some low-level resources like disks that can get used up or corrupted.

For this, you can measure resource utilization. Of course, no system is immune to errors. Database operations generate error events when they fail, and tracking the number of errors generated is a good way to know when a database server operation is failing. One of the most commonly seen performance metrics is CPU utilization—considered one of the most-essential measurement tools when it comes to evaluating system performance.

This is why scaling strategies are often based on CPU usage, where the decision to scale up or down depends on whether CPU utilization exceeds certain thresholds. Understanding how much of your CPU is stalled not making progress can help you make better performance-tuning decisions.

PMCs are bits of code that count, measure, and monitor events that occur at the CPU level, like the number of accesses to off-chip memory, instructions or cycles that a program executed, and the associated cache misses. PMCs track these events, giving you insight into the behavior of your infrastructure.

This is one reason PCMs are considered a valuable tool for debugging and optimizing application performance. On the other hand, Instructions Per Cycle IPC , commonly known as commands per cycle, gives you the average number of instructions executed per clock cycle—a measurement that helps you understand the number of tasks a CPU can conduct in a single cycle.

You would also benefit from tuning your hardware, such as using faster memory, busses, and interconnect. On the other hand, an IPC of greater than 1 likely means your task is heavy on CPU, so optimizing and reducing your code execution time will be a better performance-tuning decision.

For different workloads and applications, it is important to track the right performance metrics, which we introduced in this article. While CPU utilization is one of the top metrics in most performance monitoring tools, it does not measure how busy a processor is.

Better alternatives like PMCs and IPC give you a clearer picture of CPU utilization and guide you in making better performance-tuning decisions. Organizations need to constantly manage and optimize the performance of their systems, and the right metrics are necessary to gain the proper insight to make these decisions.

Application performance management APM refers to the practice of monitoring and managing the performance and availability of software applications. APM tools typically use monitoring, analysis, and reporting techniques to provide insights into how applications are performing and identify potential issues that could impact their performance or availability.

The primary goal of APM is to help […]. In our modern, tech-centric world, applications are key drivers of business productivity and service delivery. In this competitive landscape, every millisecond counts.

Yet performance bottlenecks lurk, ready to trip up even the most robust applications, causing a cascade of inefficiencies that can cost time, customer satisfaction, and ultimately, money. Application performance monitoring APM is a process of monitoring and managing the performance and availability of software applications.

APM tools are used to identify and troubleshoot issues that may be impacting the performance of an application, and to optimize the performance of an application in order to enhance user experience.

APM involves monitoring various metrics […]. Some of these include:. Dremio is a powerful data lakehouse platform that enables organizations to leverage their data for analytics and insights. Dremio users would be interested in performance optimization because it allows them to:.

Bring your users closer to the data with organization-wide self-service analytics and lakehouse flexibility , scalability , and performance at a fraction of the cost. Run Dremio anywhere with self-managed software or Dremio Cloud.

POWERED BY. Explore Dremio. Get Started. Apache Iceberg: The Definitive Guide Everything you need to know about Apache Iceberg table architecture, and how to structure and optimize Iceberg tables for maximum performance.

Home Wikis Performance Optimization. What is Performance Optimization? How Performance Optimization Works Performance optimization utilizes various techniques and methodologies to enhance the efficiency of data processing and analytics. Why Performance Optimization is Important Performance optimization plays a vital role in maximizing the value and potential of data lakehouse environments.

Here are some reasons why it is important: Improved Data Processing Speed: Performance optimization techniques help organizations process and analyze data faster, enabling real-time or near-real-time decision-making. Enhanced User Experience: Faster query response times and reduced latency improve the overall user experience, allowing for more interactive and responsive data exploration.

Resource Efficiency: Performance optimization ensures optimal utilization of computing resources, minimizing costs and maximizing ROI. Scalability: By optimizing data workflows, performance optimization allows organizations to scale their data lakehouse environments to handle ever-increasing data volumes.

The Most Important Performance Optimization Use Cases Performance optimization is applicable to various use cases within a data lakehouse environment. Some key use cases include: Ad Hoc Analytics: Enabling users to run ad hoc queries on large datasets with minimal latency.

Real-time Data Exploration: Supporting interactive data exploration and visualization for immediate insights. Machine Learning: Optimizing data processing for training and inference in machine learning models.

Streaming Analytics: Achieving real-time analytics on streaming data for time-sensitive decision-making. Other Technologies or Terms Related to Performance Optimization Performance optimization is closely related to other technologies and terms in the data lakehouse ecosystem.

Some of these include: Data Warehousing : Traditional data warehousing involves optimizing data storage and retrieval for structured data. Data Pipelines : Efficiently processing and transforming data from various sources to the data lakehouse.

Data Governance: Ensuring data quality , security, and compliance while optimizing performance.