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Getting response as "InQueue" while getting output of any language #460

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QL-Ritik-Chauhan opened this issue Apr 3, 2024 · 1 comment

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@QL-Ritik-Chauhan
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QL-Ritik-Chauhan commented Apr 3, 2024

Explaination : I have setup own judge api environment on docker and it's working fine
Api's like https://judge0.qkkalabs.com/languages are woking fine for me and giving desired output
While creating submission by hitting this api
https://judge0.qkkalabs.com/submissions?base64_encoded=true&fields=*
I am getting token but while getting output of code by hitting api

Query : https://judge0-ce.p.rapidapi.com/submissions/1b4b9444-bead-4fd2-901c-f0f307cacf2e?base64_encoded=true&fields=*
I am always getting this output
{
"source_code": "Y29uc29sZS5sb2coIjExMiIp\n",
"language_id": 63,
"stdin": "SnVkZ2Uw\n",
"expected_output": null,
"stdout": null,
"status_id": 1,
"created_at": "2024-04-03T06:50:24.655Z",
"finished_at": null,
"time": null,
"memory": null,
"stderr": null,
"token": "248dd9ad-d9f7-4cce-a14e-ff9d42306d35",
"number_of_runs": 1,
"cpu_time_limit": "5.0",
"cpu_extra_time": "1.0",
"wall_time_limit": "10.0",
"memory_limit": 128000,
"stack_limit": 64000,
"max_processes_and_or_threads": 60,
"enable_per_process_and_thread_time_limit": false,
"enable_per_process_and_thread_memory_limit": false,
"max_file_size": 1024,
"compile_output": null,
"exit_code": null,
"exit_signal": null,
"message": null,
"wall_time": null,
"compiler_options": null,
"command_line_arguments": null,
"redirect_stderr_to_stdout": false,
"callback_url": null,
"additional_files": null,
"enable_network": false,
"status": {
"id": 1,
"description": "In Queue"
},
"language": {
"id": 63,
"name": "JavaScript (Node.js 12.14.0)"
}
}

I used to call it in settimeout and setTimeInterval also but the response is same
Please provide me answer regarding this issue

@thisis-vishal
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This generally happens when the docker machine don't have enough threads to run multiple program simultaneously, check the judge.conf file for system_info, config info and available threads increase the resources accordingly by changing the config file and restart the docker container.

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