Analysing operational characteristics for different JSON-based static data load and reload approaches #35
brabsterrun_analysis.yml
on: workflow_dispatch
generate_dataset
2m 25s
Matrix: measure_performance
generate_summary
5s
Artifacts
Produced during runtime
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data_sample
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1.04 GB |
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perf-output-load_dbm.py
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716 Bytes |
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perf-output-load_interned_pickle.py
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933 Bytes |
sha256:32dbbaaef314d98bb2d2fe86499f623a984220f8622f3cfa58db26530aed9cd9
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perf-output-load_interning_json.py
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2.47 KB |
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perf-output-load_interning_jsonl.py
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3.54 KB |
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perf-output-load_json.py
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1.14 KB |
sha256:69de73cddb11709ba20840ca492494ef286e8adf4ad8cded085c156ef2943ccc
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perf-output-load_jsonl.py
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1.52 KB |
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perf-output-load_pandas_json.py
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4.5 KB |
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perf-output-load_pandas_jsonl.py
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5.63 KB |
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perf-output-load_pickle.py
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876 Bytes |
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perf-output-load_polars_json.py
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1.66 KB |
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perf-output-load_polars_jsonl.py
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1.39 KB |
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perf-output-load_shelve.py
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752 Bytes |
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summary
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21.9 KB |
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