83 lines
2.7 KiB
Python
83 lines
2.7 KiB
Python
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"""集中序列化工具（用于将执行结果中的复杂对象转换为 JSON 友好的预览格式）。
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设计原则：
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- 将 DataFrame/大对象的序列化集中到一个位置，便于维护与优化。
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- 在同一次进程内尽量避免对相同对象重复序列化（基于 id 缓存），以减少 CPU/内存开销。
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"""
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from typing import Any, Dict
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# 简单的按对象 id 缓存，避免在同一次执行中重复序列化同一对象。
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# 这是一个进程级缓存，生命周期与进程相同；适用于短期请求场景。
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_OBJ_SERIALIZE_CACHE: Dict[int, Any] = {}
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def smart_serialize(value: Any, preview_limit: int = 20) -> Any:
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"""Serialize outputs into JSON-friendly, preview-first representation.
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- Polars DataFrame -> {"__type":"DataFrame", "columns":..., "preview": [...], "rows": n}
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- list/tuple/dict -> recursively serialize
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- primitives -> pass through
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Caches serialization results per-object-id to avoid duplicate work within
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the same process/request.
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"""
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cache_key = id(value)
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# Fast path: return cached serialization when available and object identity matches
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cached = _OBJ_SERIALIZE_CACHE.get(cache_key)
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if cached is not None and cached[0] is value:
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return cached[1]
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# Local import to avoid hard dependency at module import time
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try:
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import polars as pl
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except Exception:
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pl = None
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def _ser(v: Any) -> Any:
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# DataFrame
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if pl is not None and isinstance(v, pl.DataFrame):
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try:
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cols = list(v.columns)
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preview = v.head(preview_limit).to_dicts()
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return {
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"__type": "DataFrame",
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"columns": cols,
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"preview": preview,
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"rows": v.height,
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}
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except Exception:
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return {"__type": "DataFrame", "columns": [], "preview": [], "rows": 0}
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# list/tuple
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if isinstance(v, (list, tuple)):
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return [_ser(x) for x in v]
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# dict
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if isinstance(v, dict):
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return {k: _ser(val) for k, val in v.items()}
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# primitives
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try:
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if isinstance(v, (str, int, float, bool)) or v is None:
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return v
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import numpy as _np
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if isinstance(v, (_np.generic,)):
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return v.item()
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except Exception:
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pass
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# fallback to string
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try:
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return str(v)
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except Exception:
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return None
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result = _ser(value)
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try:
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_OBJ_SERIALIZE_CACHE[cache_key] = (value, result)
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except Exception:
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# ignore cache failures
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pass
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return result
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