What are Throughput metrics? Finding the right Throughput metrics can be daunting, especially when you're busy working on your day-to-day tasks. This is why we've curated a list of examples for your inspiration.
Copy these examples into your preferred tool, or adopt Tability to ensure you remain accountable.
Find Throughput metrics with AI While we have some examples available, it's likely that you'll have specific scenarios that aren't covered here. You can use our free AI metrics generator below to generate your own strategies.
Examples of Throughput metrics and KPIs 1. Data Processing Throughput Measures the amount of data processed successfully within a given time frame, typically in gigabytes per second (GB/s)
What good looks like for this metric: Varies by system but often >1 GB/s for high-performing systems
Ideas to improve this metric Increase hardware capabilities Optimise software algorithms Implement data compression techniques Use parallel processing Upgrade network infrastructure 2. Latency Time taken from input to desired data processing action, measured in milliseconds (ms)
What good looks like for this metric: <100 ms for high-performing systems
Ideas to improve this metric Enhance server response time Minimise data travel distance Optimise application code Utilise content delivery networks Implement load balancers 3. Error Rate Percentage of errors during data processing compared to total operations, measured as a %
What good looks like for this metric: <5% for acceptable performance
Ideas to improve this metric Implement error-handling codes Train systems with more robust datasets Regularly update software Conduct thorough system testing Improve data input validity checks 4. Disk I/O Rate Measures read and write operations per second on storage devices, expressed in IOPS (input/output operations per second)
What good looks like for this metric: >10,000 IOPS for SSDs, lower for HDDs
Ideas to improve this metric Upgrade to faster storage solutions Redistribute data loads Increase cache sizes Use faster file systems Optimise database queries 5. Resource Utilisation Percentage of CPU, memory, and network bandwidth being used, expressed as a %
What good looks like for this metric: 75-85% for efficient resource use
Ideas to improve this metric Perform regular system monitoring Distribute workloads more evenly Implement scalable cloud solutions Prioritise critical processes Utilise virtualisation
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1. Throughput Measures the number of log files processed per minute to ensure the service meets the 40k requirement
What good looks like for this metric: 40,000 log files per minute
Ideas to improve this metric Optimize log processing algorithms Upgrade server hardware Use a load balancer to distribute requests Implement batch processing for logs Minimize unnecessary logging 2. Latency Measures the time it takes to process each log file from receipt to completion
What good looks like for this metric: Less than 100 milliseconds
Ideas to improve this metric Streamline data pathways Prioritise real-time log processing Identify and remove processing bottlenecks Utilise caching mechanisms Optimize database queries 3. Error Rate Tracks the percentage of log files that are not processed correctly
What good looks like for this metric: Less than 1%
Ideas to improve this metric Implement robust error handling mechanisms Conduct regular integration tests Utilise validation before processing logs Enhance logging system for transparency Review and improve exception handling 4. Resource Utilisation Measures the use of CPU, memory, and network to ensure efficient handling of logs
What good looks like for this metric: Below 80% for CPU and memory utilisation
Ideas to improve this metric Optimize code for better performance Implement vertical or horizontal scaling Regularly monitor and adjust resource allocation Use lightweight libraries or frameworks Run performance diagnostics regularly 5. System Uptime Tracks the percentage of time the system is operational and able to handle log files
What good looks like for this metric: 99.9% uptime
Ideas to improve this metric Implement redundancies in infrastructure Schedule regular maintenance Monitor system health continuously Use reliable cloud services Establish quick recovery protocols
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Tracking your Throughput metrics Having a plan is one thing, sticking to it is another.
Setting good strategies is only the first challenge. The hard part is to avoid distractions and make sure that you commit to the plan. A simple weekly ritual will greatly increase the chances of success.
A tool like Tability can also help you by combining AI and goal-setting to keep you on track.
More metrics recently published We have more examples to help you below.
Planning resources OKRs are a great way to translate strategies into measurable goals. Here are a list of resources to help you adopt the OKR framework:
Tability's check-ins will save you hours and increase transparency
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