File size: 9,615 Bytes
6bff5d9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 | """PandasCompiler — IR → callable that runs against a DataFrame.
For tabular sources. The callable encapsulates the chain of operations
(filter → select/agg → sort → limit) so the executor can apply them
to a DataFrame loaded from a Parquet blob.
Returns a `CompiledPandas` dataclass (mirrors `CompiledSql`) whose `.apply`
is a pure function `(pd.DataFrame) -> pd.DataFrame`. No LLM, no I/O.
"""
from __future__ import annotations
import re
from collections.abc import Callable
from dataclasses import dataclass
from typing import Any
import pandas as pd
from ...catalog.models import Catalog, Column, Source, Table
from ..ir.models import AggSelect, ColumnSelect, FilterClause, OrderByClause, QueryIR, SelectItem
from .base import BaseCompiler
@dataclass
class CompiledPandas:
"""Compiled IR as a pandas operation chain.
`apply(df)` executes the full filter → select/agg → sort → limit
pipeline and returns the result as a new DataFrame.
`output_columns` lists the expected column names so callers can label
an empty result without inspecting rows.
"""
apply: Callable[[pd.DataFrame], pd.DataFrame]
output_columns: list[str]
class PandasCompilerError(Exception):
pass
class PandasCompiler(BaseCompiler):
"""Deterministic IR → pandas op chain. No LLM."""
def __init__(self, catalog: Catalog) -> None:
self._catalog = catalog
def compile(self, ir: QueryIR) -> CompiledPandas:
_, table, cols_by_id = self._lookup(ir)
output_columns = _output_column_names(ir.select, cols_by_id)
# Capture IR fields explicitly so the closure is self-contained
_filters = ir.filters
_select = ir.select
_group_by = ir.group_by
_order_by = ir.order_by
_limit = ir.limit
_cols = cols_by_id
def apply(df: pd.DataFrame) -> pd.DataFrame:
df = _apply_filters(df, _filters, _cols)
has_agg = any(isinstance(s, AggSelect) for s in _select)
if has_agg:
df = _apply_agg(df, _select, _group_by, _cols)
else:
df = _apply_select(df, _select, _cols)
if _order_by:
df = _apply_orderby(df, _order_by, _select, _cols)
if _limit is not None:
df = df.head(_limit)
return df.reset_index(drop=True)
return CompiledPandas(apply=apply, output_columns=output_columns)
# ------------------------------------------------------------------
# Catalog lookup (mirrors SqlCompiler._lookup)
# ------------------------------------------------------------------
def _lookup(self, ir: QueryIR) -> tuple[Source, Table, dict[str, Column]]:
source = next((s for s in self._catalog.sources if s.source_id == ir.source_id), None)
if source is None:
raise PandasCompilerError(f"source_id {ir.source_id!r} not in catalog")
table = next((t for t in source.tables if t.table_id == ir.table_id), None)
if table is None:
raise PandasCompilerError(
f"table_id {ir.table_id!r} not in source {ir.source_id!r}"
)
return source, table, {c.column_id: c for c in table.columns}
# ---------------------------------------------------------------------------
# Module-level helpers (pure functions — easier to test in isolation)
# ---------------------------------------------------------------------------
def _output_column_names(select: list[SelectItem], cols_by_id: dict[str, Column]) -> list[str]:
names = []
for s in select:
if isinstance(s, ColumnSelect):
names.append(s.alias or cols_by_id[s.column_id].name)
else:
names.append(_agg_output_name(s, cols_by_id))
return names
def _agg_output_name(s: AggSelect, cols_by_id: dict[str, Column]) -> str:
if s.alias:
return s.alias
if s.fn == "count" and s.column_id is None:
return "count"
return f"{s.fn}_{cols_by_id[s.column_id].name}"
def _like_to_regex(pattern: str) -> str:
"""Convert SQL LIKE pattern to Python regex string (no anchors — use fullmatch)."""
parts: list[str] = []
for ch in pattern:
if ch == "%":
parts.append(".*")
elif ch == "_":
parts.append(".")
else:
parts.append(re.escape(ch))
return "".join(parts)
def _apply_filters(
df: pd.DataFrame,
filters: list[FilterClause],
cols_by_id: dict[str, Column],
) -> pd.DataFrame:
if not filters:
return df
mask = pd.Series(True, index=df.index)
for f in filters:
col_name = cols_by_id[f.column_id].name
series = df[col_name]
op, val = f.op, f.value
if op == "=":
mask &= series == val
elif op == "!=":
mask &= series != val
elif op == "<":
mask &= series < val
elif op == "<=":
mask &= series <= val
elif op == ">":
mask &= series > val
elif op == ">=":
mask &= series >= val
elif op == "in":
mask &= series.isin(val)
elif op == "not_in":
mask &= ~series.isin(val)
elif op == "is_null":
mask &= series.isna()
elif op == "is_not_null":
mask &= series.notna()
elif op == "like":
mask &= series.astype(str).str.fullmatch(_like_to_regex(val), case=True, na=False)
elif op == "between":
mask &= (series >= val[0]) & (series <= val[1])
return df[mask].copy()
def _apply_select(
df: pd.DataFrame,
select: list[SelectItem],
cols_by_id: dict[str, Column],
) -> pd.DataFrame:
col_names = [cols_by_id[s.column_id].name for s in select if isinstance(s, ColumnSelect)]
result = df[col_names].copy()
rename_map = {
cols_by_id[s.column_id].name: s.alias
for s in select
if isinstance(s, ColumnSelect) and s.alias
}
if rename_map:
result = result.rename(columns=rename_map)
return result
def _scalar_agg(df: pd.DataFrame, s: AggSelect, cols_by_id: dict[str, Column]) -> Any:
if s.fn == "count" and s.column_id is None:
return int(len(df))
col_name = cols_by_id[s.column_id].name
series = df[col_name]
match s.fn:
case "count":
return int(series.count())
case "count_distinct":
return int(series.nunique())
case "sum":
return series.sum()
case "avg":
return series.mean()
case "min":
return series.min()
case "max":
return series.max()
raise PandasCompilerError(f"unhandled agg fn {s.fn!r}")
def _group_agg_series(
grouped: Any,
s: AggSelect,
cols_by_id: dict[str, Column],
) -> pd.Series:
if s.fn == "count" and s.column_id is None:
return grouped.size()
col_name = cols_by_id[s.column_id].name
match s.fn:
case "count":
return grouped[col_name].count()
case "count_distinct":
return grouped[col_name].nunique()
case "sum":
return grouped[col_name].sum()
case "avg":
return grouped[col_name].mean()
case "min":
return grouped[col_name].min()
case "max":
return grouped[col_name].max()
raise PandasCompilerError(f"unhandled agg fn {s.fn!r}")
def _apply_agg(
df: pd.DataFrame,
select: list[SelectItem],
group_by: list[str],
cols_by_id: dict[str, Column],
) -> pd.DataFrame:
agg_items = [s for s in select if isinstance(s, AggSelect)]
col_items = [s for s in select if isinstance(s, ColumnSelect)]
group_col_names = [cols_by_id[col_id].name for col_id in group_by]
if group_col_names:
grouped = df.groupby(group_col_names, sort=False)
series_list = [
_group_agg_series(grouped, s, cols_by_id).rename(_agg_output_name(s, cols_by_id))
for s in agg_items
]
result = pd.concat(series_list, axis=1).reset_index()
rename_map = {
cols_by_id[s.column_id].name: s.alias
for s in col_items
if s.alias
}
if rename_map:
result = result.rename(columns=rename_map)
else:
row = {
_agg_output_name(s, cols_by_id): _scalar_agg(df, s, cols_by_id)
for s in agg_items
}
result = pd.DataFrame([row])
return result
def _resolve_order_col(
col_id_or_alias: str,
select: list[SelectItem],
cols_by_id: dict[str, Column],
) -> str:
"""Map an order_by column_id (or alias) to the actual output column name."""
for s in select:
if isinstance(s, ColumnSelect) and s.column_id == col_id_or_alias:
return s.alias or cols_by_id[s.column_id].name
if isinstance(s, AggSelect) and s.column_id == col_id_or_alias:
return _agg_output_name(s, cols_by_id)
return col_id_or_alias # treat as alias / output name directly
def _apply_orderby(
df: pd.DataFrame,
order_by: list[OrderByClause],
select: list[SelectItem],
cols_by_id: dict[str, Column],
) -> pd.DataFrame:
sort_cols: list[str] = []
ascending: list[bool] = []
for ob in order_by:
out_name = _resolve_order_col(ob.column_id, select, cols_by_id)
if out_name in df.columns:
sort_cols.append(out_name)
ascending.append(ob.dir == "asc")
if sort_cols:
return df.sort_values(by=sort_cols, ascending=ascending)
return df
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