Azure Functions
An Azure service that provides an event-driven serverless compute platform.
5,911 questions
Azure functions runs faster on Cloud and slow on local device.
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poojan.naik
0
Reputation points
I have been Azure functions and developing apps using Python for more than a month. Earlier I did not face any issues while developing. The following is my system specs:
RAM-> 16 GB
Processor -> i5 8th Gen
Storage-> 256 GB SSD
OS-> Linux
After one fine weekend, when I started my Azure functions on the local dev server, the API took 4 mins for request processing while the actual code only ran for 3 seconds. The same API was working under the runtime of 2ms on the cloud. I updated the func tools version and rebooted my device. I also shut down 3 windows of Chrome and started the Azure function. The issue still persists. I have checked the RAM usage and there is still more than 5 GB of free RAM. I have been unable to test my code locally.
Azure Functions
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Lohith Goudagere Nagaraj 0 Reputation points Microsoft Employee2023-05-24T13:48:24.7566667+00:00 Are you connecting to any azure resource in your functions code?
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poojan.naik 0 Reputation points
2023-05-24T13:52:08.67+00:00 2,
one being the Postgresql server on Azure cloud and the second, in the later stages of the request processing, the Azure blob storage. However, Before reaching out to both of these resources, as per debugging, the Request reaches the route pretty quickly, but after that, it takes nearly 3 mins to print the first logging statement before accessing any resource or performing any CPU-based task. -
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VasimTamboli 5,215 Reputation points2023-05-24T14:26:46.31+00:00 If you're experiencing slower performance of Azure Functions on your local device compared to running them on the cloud, there are a few factors that could contribute to this issue. Here are some possible reasons and suggestions to troubleshoot the problem:
Resource limitations: While your system specs seem adequate for running Azure Functions locally, there might be other processes or services consuming system resources and affecting the performance. Ensure that there are no other resource-intensive applications running in the background and try closing unnecessary processes.
Local network issues: Azure Functions running locally might rely on network connectivity, and any network issues can impact the performance. Ensure that you have a stable and reliable network connection and check if there are any network-related errors or delays in the logs.
Function dependencies: Azure Functions may have dependencies on external services, databases, or APIs. If these dependencies are not accessible or are slower locally compared to the cloud environment, it can affect the overall performance. Check if the dependencies are properly configured and accessible from your local environment.
Local debugging configuration: Verify that your local debugging configuration is correct and matches the cloud environment. Ensure that you're running the latest version of Azure Functions Core Tools and that your local runtime is compatible with the version used in the cloud.
Code optimizations: Review your code to ensure there are no performance bottlenecks or inefficient algorithms that could slow down the processing. Consider profiling your code locally to identify any areas that might be causing performance issues.
Azure Functions host configuration: Check the host configuration files (e.g.,
host.json) to ensure they are properly configured for your local environment. Pay attention to settings such as scaling limits, concurrency, and other performance-related configurations.Consider using the Azure Functions Premium plan: Azure Functions running on the Consumption plan may have certain limitations and cold start delays. Consider using the Azure Functions Premium plan, which provides enhanced performance and features such as dedicated instances and faster scaling.
By investigating these potential causes and making appropriate adjustments, you should be able to troubleshoot and improve the performance of your Azure Functions locally.
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