diff --git a/packages/gooddata-fdw/src/gooddata_fdw/executor.py b/packages/gooddata-fdw/src/gooddata_fdw/executor.py index f884a377c..41dcb6168 100644 --- a/packages/gooddata-fdw/src/gooddata_fdw/executor.py +++ b/packages/gooddata-fdw/src/gooddata_fdw/executor.py @@ -2,7 +2,7 @@ from __future__ import annotations from collections.abc import Generator -from typing import Any, NamedTuple +from typing import Any, ClassVar, NamedTuple from gooddata_sdk import GoodDataSdk @@ -43,7 +43,10 @@ def execute( class InsightExecutor(Executor): - _COLUMN_VALIDATORS = [col_val.LocalIdOptionValidator(), col_val.IdOptionValidator(mandatory=False)] + _COLUMN_VALIDATORS: ClassVar[list[col_val.ColumnValidator]] = [ + col_val.LocalIdOptionValidator(), + col_val.IdOptionValidator(mandatory=False), + ] def __init__(self, inputs: InitData) -> None: super().__init__(inputs, self._COLUMN_VALIDATORS) diff --git a/packages/gooddata-pandas/pyproject.toml b/packages/gooddata-pandas/pyproject.toml index 3627ab9d8..80dd5ebe2 100644 --- a/packages/gooddata-pandas/pyproject.toml +++ b/packages/gooddata-pandas/pyproject.toml @@ -32,7 +32,6 @@ dependencies = [ "gooddata-sdk~=1.62.0", "pandas>=2.0.0,<3.0.0", ] - classifiers = [ "Development Status :: 5 - Production/Stable", "Environment :: Console", @@ -47,6 +46,9 @@ classifiers = [ "Typing :: Typed", ] +[project.optional-dependencies] +arrow = ["pyarrow>=23.0.1"] + [project.urls] Documentation = "https://gooddata-pandas.readthedocs.io/en/v1.62.0" Source = "https://github.com/gooddata/gooddata-python-sdk" @@ -63,6 +65,7 @@ test = [ "python-dotenv~=1.0.0", "pyyaml", "tests_support", + "pyarrow>=16.1.0", ] [tool.ty.analysis] diff --git a/packages/gooddata-pandas/src/gooddata_pandas/__init__.py b/packages/gooddata-pandas/src/gooddata_pandas/__init__.py index 261259e0b..e0e542a96 100644 --- a/packages/gooddata-pandas/src/gooddata_pandas/__init__.py +++ b/packages/gooddata-pandas/src/gooddata_pandas/__init__.py @@ -1,6 +1,16 @@ # (C) 2021 GoodData Corporation from gooddata_pandas._version import __version__ +from gooddata_pandas.arrow_types import TypesMapper + +try: + from gooddata_pandas.arrow_convertor import convert_arrow_table_to_dataframe +except ImportError: + + def convert_arrow_table_to_dataframe(*args, **kwargs): + raise ImportError("pyarrow is required for Arrow support. Install it with: pip install gooddata-pandas[arrow]") + + from gooddata_pandas.dataframe import DataFrameFactory from gooddata_pandas.good_pandas import GoodPandas from gooddata_pandas.result_convertor import LabelOverrides diff --git a/packages/gooddata-pandas/src/gooddata_pandas/arrow_convertor.py b/packages/gooddata-pandas/src/gooddata_pandas/arrow_convertor.py new file mode 100644 index 000000000..9c4b6dabc --- /dev/null +++ b/packages/gooddata-pandas/src/gooddata_pandas/arrow_convertor.py @@ -0,0 +1,561 @@ +# (C) 2026 GoodData Corporation +from __future__ import annotations + +import json +from typing import Callable + +import pandas + +from gooddata_pandas.arrow_types import TypesMapper + +try: + import pyarrow as pa +except ImportError as _exc: + raise ImportError( + "pyarrow is required for Arrow support. Install it with: pip install gooddata-pandas[arrow]" + ) from _exc + +# Strings-only mapper: Arrow-backed StringDtype for both string variants. +# Memory and speed win on string columns; all other types stay as default +# (float64, object) — fully backward compatible with the JSON execution path. +_ARROW_STRINGS_MAPPER: dict = { + pa.string(): pandas.StringDtype("pyarrow"), + pa.large_string(): pandas.StringDtype("pyarrow"), +} + +# Schema metadata keys written by the GoodData /binary endpoint. +_META_XTAB = "x-gdc-xtab-v1" +_META_MODEL = "x-gdc-model-v1" +_META_VIEW = "x-gdc-view-v1" + +# Control columns present in every GoodData Arrow table. +_COL_ROW_TYPE = "__row_type" +_COL_TOTAL_REF_PREFIX = "__total_ref" + +# Data-field name prefixes (metric_group_N / grand_total_N). +_FIELD_METRIC_GROUP = "metric_group_" +_FIELD_GRAND_TOTAL = "grand_total_" + +# gdc field-metadata type discriminator values. +_GDC_TYPE_METRIC = "metric" +_GDC_TYPE_TOTAL = "total" + +_REQUIRED_SCHEMA_KEYS = (_META_XTAB, _META_MODEL, _META_VIEW) + + +def _parse_schema_metadata(table: pa.Table) -> dict: + """ + Decode and return all GoodData schema metadata keys from an Arrow table. + + Raises ValueError when metadata is absent or either required key is missing. + Both x-gdc-xtab-v1 and x-gdc-model-v1 must be present to build a DataFrame; + without them the index, column names, and metric titles cannot be reconstructed. + """ + if not table.schema.metadata: + raise ValueError( + "Arrow table has no schema metadata. Expected GoodData metadata keys: " + ", ".join(_REQUIRED_SCHEMA_KEYS) + ) + schema_meta = {k.decode(): json.loads(v) for k, v in table.schema.metadata.items()} + missing = [k for k in _REQUIRED_SCHEMA_KEYS if k not in schema_meta] + if missing: + raise ValueError( + f"Arrow table schema metadata is missing required key(s): {', '.join(missing)}. " + "The table must originate from the GoodData /binary execution endpoint." + ) + return schema_meta + + +def _get_row_types(table: pa.Table) -> list: + """ + Return the __row_type column as a Python list. + + Raises ValueError when the column is absent — it is a required control column + in every GoodData Arrow table (0=data, 1/2=total). + """ + if _COL_ROW_TYPE not in table.schema.names: + raise ValueError( + f"Arrow table is missing required control column '{_COL_ROW_TYPE}'. " + "The table must originate from the GoodData /binary execution endpoint." + ) + return table.column(_COL_ROW_TYPE).to_pylist() + + +def _label_ref_to_id_map(xtab_meta: dict) -> dict[str, str]: + """Map 'l0', 'l1', ... to actual label local IDs.""" + return {ref: info["labelId"] for ref, info in xtab_meta["labelMetadata"].items()} + + +def _label_title(label_id: str, model_meta: dict) -> str | None: + """Get the display title for a label from model metadata.""" + info = model_meta["labels"].get(label_id, {}) + return info.get("labelTitle") or info.get("attributeTitle") or label_id + + +def _metric_title(metric_idx: int, model_meta: dict, xtab_meta: dict) -> str: + """ + Get the display title for a metric by its Arrow index (0 = m0, 1 = m1, ...). + + Matches the JSON-path behaviour: use the model metadata title when present, + otherwise fall back to the local identifier. + """ + local_ids = model_meta["requestedShape"]["metrics"] + local_id = local_ids[metric_idx] + info = model_meta["metrics"].get(local_id, {}) + return info.get("title") or local_id + + +def _build_inline_index( + table: pa.Table, + row_label_refs: list[str], + label_ref_to_id: dict[str, str], + model_meta: dict, + xtab_meta: dict, + resolved_mapper: Callable | None = None, +) -> pandas.Index | None: + """ + Build the pandas index from Arrow row attribute columns. + + Arrow rows encode either normal data (row_type=0) or totals (row_type=2). + For total rows the primary label column contains the lowercase aggregation + function name (e.g. 'sum'); secondary label columns that cover deeper levels + may be empty string — both are filled with the uppercased aggregation name. + + Returns None when there are no row attribute labels (e.g. metrics-only results). + """ + if not row_label_refs: + return None + + col_ids = [label_ref_to_id[ref] for ref in row_label_refs] + row_types = _get_row_types(table) + + # Build a per-row aggregation-name lookup for total rows. + # totalsMetadata maps "t0", "t1", ... → {aggregation: "sum", ...} + # __total_ref column (may carry a Unicode suffix) maps each row to the + # indices of the totals it belongs to. + totals_meta = xtab_meta.get("totalsMetadata", {}) + total_ref_vals: list = [None] * table.num_rows + if totals_meta: + for field in table.schema: + if field.name.startswith(_COL_TOTAL_REF_PREFIX): + total_ref_vals = table.column(field.name).to_pylist() + break + + # Precompute per-row aggregation name and kept-label set for total rows. + agg_for_row: list[str | None] = [None] * table.num_rows + kept_labels_for_row: list[frozenset] = [frozenset()] * table.num_rows + for i, rt in enumerate(row_types): + if rt == 0: + continue + + refs = total_ref_vals[i] + if not refs: + continue + key = f"t{refs[0]}" + entry = totals_meta.get(key, {}) + agg = entry.get("aggregation") + agg_for_row[i] = agg.upper() if agg else None + kept_labels_for_row[i] = frozenset(entry.get("rowLabels", [])) + + arrays: list[list] = [] + for ref, lid in zip(row_label_refs, col_ids): + values = table.column(lid).to_pylist() + processed = [] + for i, v in enumerate(values): + if row_types[i] != 0 and isinstance(v, str): + if ref in kept_labels_for_row[i]: + # Outer label kept as real attribute value in a subtotal row. + processed.append(v) + elif v == "": + # Aggregated level left empty by the server — fill with agg name. + processed.append(agg_for_row[i] if agg_for_row[i] else v) + else: + # Aggregation function marker (e.g. 'sum') — uppercase it. + processed.append(v.upper()) + else: + processed.append(v) + arrays.append(processed) + + names = [_label_title(lid, model_meta) for lid in col_ids] + + # Apply resolved_mapper to the string arrays if provided. + string_dtype = resolved_mapper(pa.string()) if resolved_mapper else None + typed_arrays = [pandas.array(arr, dtype=string_dtype) for arr in arrays] if string_dtype else arrays + + # A single-level MultiIndex is indistinguishable from a flat Index for the + # JSON-path output, so return a plain Index in that case. + if len(col_ids) == 1: + return pandas.Index(typed_arrays[0], name=names[0]) + return pandas.MultiIndex.from_arrays(typed_arrays, names=names) + + +def _build_field_index( + data_fields: list, + col_label_refs: list[str], + label_ref_to_id: dict[str, str], + model_meta: dict, + xtab_meta: dict, +) -> pandas.Index: + """ + Build the pandas index from Arrow field (column) metadata. + + Each data field (metric_group_N or grand_total_N) encodes one combination of + column-attribute label values plus a metric, stored in the 'gdc' field metadata key. + + For metric_group_N: + gdc = {"type": "metric", "index": N, "label_values": [...], ...} + → tuple: (*label_values, metric_title) + + For grand_total_N (subtotals): + gdc = {"type": "total", "agg_function": "sum", "metric_index": N, + "label_values": [...], ...} + where label_values may be partial (covers only the outer label levels). + → tuple: (*label_values, AGG, ..., AGG, metric_title) + (remaining levels padded with the uppercased aggregation name) + + When there are no column labels (metrics-only dimension) the index is a flat + Index of metric titles rather than a MultiIndex. + """ + n_col_labels = len(col_label_refs) + col_label_ids = [label_ref_to_id[ref] for ref in col_label_refs] + + tuples: list = [] + for field in data_fields: + gdc = json.loads(field.metadata[b"gdc"]) + label_values: list = list(gdc.get("label_values", [])) + + if gdc["type"] == _GDC_TYPE_METRIC: + m_title = _metric_title(gdc["index"], model_meta, xtab_meta) + else: # "total" + m_title = _metric_title(gdc["metric_index"], model_meta, xtab_meta) + agg = gdc["agg_function"].upper() + # Pad remaining column-label levels with the aggregation name + label_values = label_values + [agg] * (n_col_labels - len(label_values)) + + if n_col_labels == 0: + tuples.append(m_title) + else: + tuples.append(tuple(label_values) + (m_title,)) + + if n_col_labels == 0: + # No column attribute labels — just a flat Index of metric names + return pandas.Index(tuples, name=None) + + names = [_label_title(lid, model_meta) for lid in col_label_ids] + [None] + return pandas.MultiIndex.from_tuples(tuples, names=names) + + +def _wrap_for_columns(idx: pandas.Index | None) -> pandas.Index | None: + """ + Wrap a plain flat Index as 1-tuples when it is used as the DataFrame column + index. The JSON-path output always uses at least 1-tuples for column labels + so that the shape is consistent regardless of how many attribute levels are + present. MultiIndex (2+ levels) and None (→ RangeIndex) are returned as-is. + """ + if idx is None or isinstance(idx, pandas.MultiIndex): + return idx + return pandas.Index([(v,) for v in idx]) + + +def compute_row_totals_indexes(table: pa.Table, execution_dims: list) -> list[list[int]]: + """ + Compute row_totals_indexes compatible with DataFrameMetadata from an Arrow table. + + Returns a list with one inner list per header column in the output-row dimension, + matching the JSON-path DataFrameMetadata.row_totals_indexes format (one slot per + dimension header, including the measureGroup placeholder which always → []). + + Non-transposed case (Arrow rows = output rows): + Every Arrow row with __row_type != 0 is a total row; it appears in the + total-indexes list for every attribute-level header in the row dimension. + + Transposed / metrics-only case (Arrow fields = output rows): + Total rows are grand_total_N fields. A field is total at level j only when + j >= len(gdc["label_values"]), i.e. that label level is being aggregated. + """ + schema_meta = _parse_schema_metadata(table) + xtab_meta = schema_meta[_META_XTAB] + is_transposed = schema_meta[_META_VIEW]["isTransposed"] + + row_label_refs: list[str] = xtab_meta["computedShape"]["rows"] + col_label_refs: list[str] = xtab_meta["computedShape"]["cols"] + label_ref_to_id = _label_ref_to_id_map(xtab_meta) + id_to_ref = {v: k for k, v in label_ref_to_id.items()} + + use_field_rows = is_transposed or not row_label_refs + output_row_refs = col_label_refs if use_field_rows else row_label_refs + output_row_ref_set = set(output_row_refs) + + # Find which execution dimension contains the output-row attribute refs. + # Use attributeHeader["label"]["id"] (GoodData label object ID) which matches + # the labelId values in the Arrow labelMetadata — not localIdentifier, which is + # the user-given alias and may differ. + def _label_ids_in_dim(dim: dict) -> set: + return {h["attributeHeader"]["label"]["id"] for h in dim.get("headers", []) if "attributeHeader" in h} + + if output_row_refs: + ref_label_ids = {label_ref_to_id[r] for r in output_row_refs} + row_dim = next( + (dim for dim in execution_dims if ref_label_ids <= _label_ids_in_dim(dim)), + execution_dims[0] if execution_dims else {}, + ) + else: + # Metrics-only: the dimension containing measureGroupHeaders is the output-row dim. + row_dim = next( + (dim for dim in execution_dims if any("measureGroupHeaders" in h for h in dim.get("headers", []))), + execution_dims[0] if execution_dims else {}, + ) + + if use_field_rows: + # Output rows are the data fields in schema order. + all_data_fields = [ + f for f in table.schema if f.name.startswith(_FIELD_METRIC_GROUP) or f.name.startswith(_FIELD_GRAND_TOTAL) + ] + + result: list[list[int]] = [] + attr_level = 0 # position within output_row_refs + for header in row_dim.get("headers", []): + if "measureGroupHeaders" in header: + result.append([]) + else: + label_id = header["attributeHeader"]["label"]["id"] + ref = id_to_ref.get(label_id) + if ref is None or ref not in output_row_ref_set: + result.append([]) + else: + j = attr_level + total_idxs = [ + k + for k, f in enumerate(all_data_fields) + if (gdc := json.loads(f.metadata[b"gdc"]))["type"] == _GDC_TYPE_TOTAL + and j >= len(gdc.get("label_values", [])) + ] + result.append(total_idxs) + attr_level += 1 + return result + + else: + # Output rows are Arrow rows; every total row (row_type != 0) is listed + # in the total-indexes for every attribute level. + row_types = _get_row_types(table) + total_row_idxs = [i for i, rt in enumerate(row_types) if rt != 0] + + result = [] + for header in row_dim.get("headers", []): + if "measureGroupHeaders" in header: + result.append([]) + else: + label_id = header["attributeHeader"]["label"]["id"] + ref = id_to_ref.get(label_id) + if ref is None or ref not in output_row_ref_set: + result.append([]) + else: + result.append(total_row_idxs) + return result + + +def _compute_primary_labels_from_inline( + table: pa.Table, + label_refs: list[str], + label_ref_to_id: dict[str, str], + xtab_meta: dict, +) -> dict[int, dict[str, str]]: + """Build primary_labels mapping from Arrow row-attribute columns. + + For each attribute level j the result maps primary_label_value → display_label_value + for every data row (row_type==0). Total rows are excluded because their column + values are aggregation-function markers, not real attribute values. + + When primaryLabelId == labelId the mapping is identity ({v: v}). + When they differ the function looks for a separate Arrow column named + primaryLabelId; failing that it falls back to identity. + """ + result: dict[int, dict[str, str]] = {} + label_meta = xtab_meta.get("labelMetadata", {}) + row_types = _get_row_types(table) + data_row_mask = [rt == 0 for rt in row_types] + + for j, ref in enumerate(label_refs): + info = label_meta.get(ref, {}) + label_id = label_ref_to_id.get(ref, info.get("labelId", "")) + primary_label_id = info.get("primaryLabelId", label_id) + + display_vals = table.column(label_id).to_pylist() + + if label_id == primary_label_id: + mapping: dict[str, str] = { + v: v for v, is_data in zip(display_vals, data_row_mask) if is_data and isinstance(v, str) + } + elif primary_label_id in table.schema.names: + primary_vals = table.column(primary_label_id).to_pylist() + mapping = { + p: d + for p, d, is_data in zip(primary_vals, display_vals, data_row_mask) + if is_data and isinstance(p, str) and isinstance(d, str) + } + else: + # Fallback: identity (primary label data not present in table) + mapping = {v: v for v, is_data in zip(display_vals, data_row_mask) if is_data and isinstance(v, str)} + + result[j] = mapping + return result + + +def _compute_primary_labels_from_fields( + all_data_fields: list, + n_col_labels: int, +) -> dict[int, dict[str, str]]: + """Build primary_labels mapping from Arrow field (column) metadata. + + Each metric_group field carries gdc.label_values (display) and + gdc.primary_label_values (primary) for every column-label level. Total + (grand_total) fields are skipped, matching the JSON-path behaviour where + totalHeader rows do not contribute to primary_attribute_labels_mapping. + """ + result: dict[int, dict[str, str]] = {} + if n_col_labels == 0: + return result + + for field in all_data_fields: + gdc = json.loads(field.metadata[b"gdc"]) + if gdc["type"] != _GDC_TYPE_METRIC: + continue + label_values: list = gdc.get("label_values", []) + primary_label_values: list = gdc.get("primary_label_values", []) + for j in range(min(n_col_labels, len(label_values), len(primary_label_values))): + display = label_values[j] + primary = primary_label_values[j] + if not isinstance(display, str) or not isinstance(primary, str): + continue + if j not in result: + result[j] = {} + result[j][primary] = display + + return result + + +def compute_primary_labels( + table: pa.Table, +) -> tuple[dict[int, dict[str, str]], dict[int, dict[str, str]]]: + """ + Compute primary_labels_from_index and primary_labels_from_columns from an Arrow table. + + Mirrors the primary_attribute_labels_mapping built by the JSON execution path so that + DataFrameMetadata is fully populated by the Arrow path too. + + Returns: + (primary_labels_from_index, primary_labels_from_columns) + """ + schema_meta = _parse_schema_metadata(table) + xtab_meta = schema_meta[_META_XTAB] + is_transposed = schema_meta[_META_VIEW]["isTransposed"] + + label_ref_to_id = _label_ref_to_id_map(xtab_meta) + row_label_refs: list[str] = xtab_meta["computedShape"]["rows"] + col_label_refs: list[str] = xtab_meta["computedShape"]["cols"] + + all_data_fields = [ + f for f in table.schema if f.name.startswith(_FIELD_METRIC_GROUP) or f.name.startswith(_FIELD_GRAND_TOTAL) + ] + + inline_primary = ( + _compute_primary_labels_from_inline(table, row_label_refs, label_ref_to_id, xtab_meta) if row_label_refs else {} + ) + field_primary = _compute_primary_labels_from_fields(all_data_fields, len(col_label_refs)) + + # Mirror convert_arrow_table_to_dataframe's orientation logic: + # isTransposed or no inline → field cols become output rows (index) + if is_transposed or not row_label_refs: + return field_primary, inline_primary + return inline_primary, field_primary + + +def convert_arrow_table_to_dataframe( + table: pa.Table, + self_destruct: bool = False, + types_mapper: TypesMapper = TypesMapper.DEFAULT, + custom_mapping: dict | None = None, +) -> pandas.DataFrame: + """ + Convert a pyarrow Table returned by the GoodData /binary execution endpoint + into a pandas DataFrame matching the output of the JSON-based execution path. + + Arrow table structure + --------------------- + Control columns: + __row_type int8 0=data row, 2=total/grand-total row + __total_ref list (present only when totals exist) indices being summed + + Row attribute columns: + One column per label in computedShape.rows, named by the label local ID. + For total rows (row_type=2) the value is the lowercase aggregation function + name (e.g. 'sum') — we uppercase it. + + Data columns (in schema order, may be interleaved): + metric_group_N double regular data — gdc metadata has type="metric" + grand_total_N double subtotals — gdc metadata has type="total" + + Transposition + ------------- + The server may place either the output-row dimension or the output-column + dimension as Arrow rows. x-gdc-view-v1.isTransposed records which choice was + made: + + isTransposed=False: + Arrow rows → output rows (inline_index) + Arrow cols → output columns (field_index) + data matrix stays as-is + + isTransposed=True: + Arrow rows → output columns (inline_index) + Arrow cols → output rows (field_index) + data matrix is transposed + + When there are no row attribute labels (inline_index is None) the field + dimension always becomes the output rows regardless of isTransposed. + """ + if types_mapper is TypesMapper.DEFAULT: + resolved_mapper: Callable | None = None + elif types_mapper is TypesMapper.ARROW_STRINGS: + resolved_mapper = _ARROW_STRINGS_MAPPER.get + elif types_mapper is TypesMapper.CUSTOM: + if custom_mapping is None: + raise ValueError("custom_mapping must be provided when types_mapper=TypesMapper.CUSTOM") + resolved_mapper = custom_mapping.get + else: + raise ValueError("Unknown types_mapper value") + + schema_meta = _parse_schema_metadata(table) + xtab_meta = schema_meta[_META_XTAB] + model_meta = schema_meta[_META_MODEL] + is_transposed = schema_meta[_META_VIEW]["isTransposed"] + + label_ref_to_id = _label_ref_to_id_map(xtab_meta) + + # Collect all data columns in their original schema order. + # metric_group_N and grand_total_N may be interleaved (e.g. for subtotals + # grouped by the outer dimension label), so preserve schema ordering. + all_data_fields = [ + f for f in table.schema if f.name.startswith(_FIELD_METRIC_GROUP) or f.name.startswith(_FIELD_GRAND_TOTAL) + ] + + data_field_names = [f.name for f in all_data_fields] + data_matrix = ( + table.select(data_field_names) + .to_pandas(self_destruct=self_destruct, types_mapper=resolved_mapper) + .to_numpy(dtype=float, na_value=float("nan")) + ) # shape: (n_arrow_rows, n_data_cols) + + # computedShape.rows → label refs for Arrow row attribute columns + # computedShape.cols → label refs encoded in field metadata + row_label_refs: list[str] = xtab_meta["computedShape"]["rows"] + col_label_refs: list[str] = xtab_meta["computedShape"]["cols"] + + inline_index = _build_inline_index(table, row_label_refs, label_ref_to_id, model_meta, xtab_meta, resolved_mapper) + field_index = _build_field_index(all_data_fields, col_label_refs, label_ref_to_id, model_meta, xtab_meta) + + # When there are no row attribute labels (inline_index is None) the server + # packs everything into the field dimension; always use field_index as rows. + if is_transposed or inline_index is None: + return pandas.DataFrame(data_matrix.T, index=field_index, columns=_wrap_for_columns(inline_index)) + + return pandas.DataFrame(data_matrix, index=inline_index, columns=_wrap_for_columns(field_index)) diff --git a/packages/gooddata-pandas/src/gooddata_pandas/arrow_types.py b/packages/gooddata-pandas/src/gooddata_pandas/arrow_types.py new file mode 100644 index 000000000..8d7167f12 --- /dev/null +++ b/packages/gooddata-pandas/src/gooddata_pandas/arrow_types.py @@ -0,0 +1,21 @@ +# (C) 2026 GoodData Corporation +from __future__ import annotations + +from enum import Enum + + +class TypesMapper(Enum): + """ + Controls how Arrow column types are mapped to pandas dtypes during conversion. + + DEFAULT — no remapping; produces float64 and object strings, identical to + the JSON execution path. Safe default, fully backward compatible. + ARROW_STRINGS — strings use Arrow-backed StringDtype (lower memory, faster); + all numeric types unchanged. Recommended next step for services. + CUSTOM — use the custom_mapping dict passed alongside. Raises ValueError + if custom_mapping is not provided. + """ + + DEFAULT = "default" + ARROW_STRINGS = "arrow_strings" + CUSTOM = "custom" diff --git a/packages/gooddata-pandas/src/gooddata_pandas/dataframe.py b/packages/gooddata-pandas/src/gooddata_pandas/dataframe.py index d431a95e5..75b531ab2 100644 --- a/packages/gooddata-pandas/src/gooddata_pandas/dataframe.py +++ b/packages/gooddata-pandas/src/gooddata_pandas/dataframe.py @@ -1,7 +1,10 @@ # (C) 2021 GoodData Corporation from __future__ import annotations -from typing import Callable, Literal, Union +from typing import TYPE_CHECKING, Callable, Literal, Union + +if TYPE_CHECKING: + import pyarrow as pa import pandas from gooddata_api_client import models @@ -16,7 +19,20 @@ ResultSizeDimensions, ) +from gooddata_pandas.arrow_types import TypesMapper from gooddata_pandas.data_access import compute_and_extract + +try: + from gooddata_pandas.arrow_convertor import ( + compute_primary_labels, + compute_row_totals_indexes, + convert_arrow_table_to_dataframe, + ) + + _ARROW_AVAILABLE = True +except ImportError: + _ARROW_AVAILABLE = False + from gooddata_pandas.result_convertor import ( _DEFAULT_PAGE_SIZE, DataFrameMetadata, @@ -51,6 +67,12 @@ class DataFrameFactory: - for_exec_def(self, exec_def: ExecutionDefinition, label_overrides: Optional[LabelOverrides] = None, result_size_dimensions_limits: ResultSizeDimensions = (), result_size_bytes_limit: Optional[int] = None, page_size: int = _DEFAULT_PAGE_SIZE,) -> Tuple[pandas.DataFrame, DataFrameMetadata]: + - for_exec_def_arrow(self, exec_def: ExecutionDefinition, + on_execution_submitted: Optional[Callable[[Execution], None]] = None) + -> Tuple[pandas.DataFrame, DataFrameMetadata]: + - for_arrow_table(self, table: pa.Table, + execution_response: Optional[BareExecutionResponse] = None) + -> Tuple[pandas.DataFrame, DataFrameMetadata]: - for_exec_result_id(self, result_id: str, label_overrides: Optional[LabelOverrides] = None, result_cache_metadata: Optional[ResultCacheMetadata] = None, result_size_dimensions_limits: ResultSizeDimensions = (), @@ -282,7 +304,8 @@ def for_created_visualization( pandas.DataFrame: A DataFrame instance. """ execution_definition = self._sdk.compute.build_exec_def_from_chat_result( - created_visualizations_response, is_cancellable=is_cancellable + created_visualizations_response, + is_cancellable=is_cancellable, ) return self.for_exec_def( exec_def=execution_definition, @@ -376,6 +399,124 @@ def for_exec_def( grand_totals_position=grand_totals_position, ) + def for_exec_def_arrow( + self, + exec_def: ExecutionDefinition, + on_execution_submitted: Callable[[Execution], None] | None = None, + self_destruct: bool = False, + types_mapper: TypesMapper = TypesMapper.DEFAULT, + custom_mapping: dict | None = None, + ) -> tuple[pandas.DataFrame, DataFrameMetadata]: + """ + Creates a DataFrame from an execution definition using the Arrow IPC binary format. + + Compared to for_exec_def(), this skips the page-by-page JSON deserialization and + converts the result in one shot via pyarrow, which is significantly faster for large results. + + Returns the same ``(DataFrame, DataFrameMetadata)`` tuple as :meth:`for_exec_def` so that + callers can switch between the two paths without changing their code. + + Requires pyarrow to be installed (pip install gooddata-pandas[arrow]). + + Args: + exec_def (ExecutionDefinition): Execution definition. + on_execution_submitted (Optional[Callable[[Execution], None]]): Callback fired after + the execution is submitted to the backend. + self_destruct (bool): If True, Arrow buffers are freed during conversion, reducing + peak native memory at the cost of not being able to reuse the table. + types_mapper (TypesMapper): Controls how Arrow types are mapped to pandas dtypes. + ``TypesMapper.DEFAULT`` (default) — no mapping; produces float64 and object + strings, identical to the JSON execution path. + ``TypesMapper.ARROW_STRINGS`` — strings use Arrow-backed StringDtype (lower + memory, faster); all numeric types unchanged. + ``TypesMapper.CUSTOM`` — uses ``custom_mapping`` dict; raises ValueError if + ``custom_mapping`` is not provided. + custom_mapping (Optional[dict]): Arrow type → pandas dtype mapping dict. + Only used when ``types_mapper=TypesMapper.CUSTOM``, ignored otherwise. + + Returns: + Tuple[pandas.DataFrame, DataFrameMetadata] + + """ + if not _ARROW_AVAILABLE: + raise ImportError( + "pyarrow is required to use for_exec_def_arrow(). Install it with: pip install gooddata-pandas[arrow]" + ) + + execution = self._sdk.compute.for_exec_def(workspace_id=self._workspace_id, exec_def=exec_def) + + if on_execution_submitted is not None: + on_execution_submitted(execution) + + exec_response = execution.bare_exec_response + table = exec_response.read_result_arrow() + df = convert_arrow_table_to_dataframe( + table, self_destruct=self_destruct, types_mapper=types_mapper, custom_mapping=custom_mapping + ) + row_totals_indexes = compute_row_totals_indexes(table, exec_response.dimensions) + primary_labels_from_index, primary_labels_from_columns = compute_primary_labels(table) + metadata = DataFrameMetadata( + row_totals_indexes=row_totals_indexes, + column_totals_indexes=[], + execution_response=exec_response, + primary_labels_from_index=primary_labels_from_index, + primary_labels_from_columns=primary_labels_from_columns, + ) + return df, metadata + + def for_arrow_table( + self, + table: pa.Table, + execution_response: BareExecutionResponse | None = None, + self_destruct: bool = False, + types_mapper: TypesMapper = TypesMapper.DEFAULT, + custom_mapping: dict | None = None, + ) -> tuple[pandas.DataFrame, DataFrameMetadata]: + """ + Creates a DataFrame from an already-obtained PyArrow Table. + + Use this when you have fetched Arrow IPC bytes yourself (e.g. from the raw export + REST API or a future Flight RPC endpoint) and converted them to a ``pa.Table``. + For the common case of submitting an execution and reading the result in one call, + use :meth:`for_exec_def_arrow` instead. + + Args: + table: PyArrow Table as returned by the GoodData binary execution endpoint. + execution_response: Optional ``BareExecutionResponse`` from the execution that + produced this table. When provided, ``DataFrameMetadata.row_totals_indexes`` + is computed accurately and ``DataFrameMetadata.execution_response`` is + populated. When omitted, ``row_totals_indexes`` is an empty list and + ``execution_response`` is ``None`` in the returned metadata. + self_destruct: If True, Arrow buffers are freed during conversion, reducing + peak native memory at the cost of not being able to reuse the table. + types_mapper: Controls how Arrow types are mapped to pandas dtypes. + custom_mapping: Arrow type → pandas dtype mapping dict. + Only used when ``types_mapper=TypesMapper.CUSTOM``. + + Returns: + Tuple[pandas.DataFrame, DataFrameMetadata] + """ + if not _ARROW_AVAILABLE: + raise ImportError( + "pyarrow is required to use for_arrow_table(). Install it with: pip install gooddata-pandas[arrow]" + ) + + df = convert_arrow_table_to_dataframe( + table, self_destruct=self_destruct, types_mapper=types_mapper, custom_mapping=custom_mapping + ) + row_totals_indexes = ( + compute_row_totals_indexes(table, execution_response.dimensions) if execution_response is not None else [] + ) + primary_labels_from_index, primary_labels_from_columns = compute_primary_labels(table) + metadata = DataFrameMetadata( + row_totals_indexes=row_totals_indexes, + column_totals_indexes=[], + execution_response=execution_response, + primary_labels_from_index=primary_labels_from_index, + primary_labels_from_columns=primary_labels_from_columns, + ) + return df, metadata + def for_exec_result_id( self, result_id: str, diff --git a/packages/gooddata-pandas/src/gooddata_pandas/result_convertor.py b/packages/gooddata-pandas/src/gooddata_pandas/result_convertor.py index fdc953979..5c7f08588 100644 --- a/packages/gooddata-pandas/src/gooddata_pandas/result_convertor.py +++ b/packages/gooddata-pandas/src/gooddata_pandas/result_convertor.py @@ -442,7 +442,7 @@ class DataFrameMetadata: row_totals_indexes: list[list[int]] column_totals_indexes: list[list[int]] - execution_response: BareExecutionResponse + execution_response: BareExecutionResponse | None primary_labels_from_index: dict[int, dict[str, str]] primary_labels_from_columns: dict[int, dict[str, str]] diff --git a/packages/gooddata-pandas/tests/dataframe/fixtures/arrow/dim_m_c/dataframe.parquet b/packages/gooddata-pandas/tests/dataframe/fixtures/arrow/dim_m_c/dataframe.parquet new file mode 100644 index 000000000..52b9f49c3 Binary files /dev/null and b/packages/gooddata-pandas/tests/dataframe/fixtures/arrow/dim_m_c/dataframe.parquet differ diff --git a/packages/gooddata-pandas/tests/dataframe/fixtures/arrow/dim_m_c/meta.json b/packages/gooddata-pandas/tests/dataframe/fixtures/arrow/dim_m_c/meta.json new file mode 100644 index 000000000..72fab9424 --- /dev/null +++ b/packages/gooddata-pandas/tests/dataframe/fixtures/arrow/dim_m_c/meta.json @@ -0,0 +1,58 @@ +{ + "result_id": "d112ed32d89e491e5282d5e9d60a59fd176a2dd9:ba3f6632101fb9d1afb000c9bdac588c5a8d6d0c28a312516f69b743e0552aae", + "row_totals_indexes": [ + [] + ], + "primary_labels_from_index": {}, + "primary_labels_from_columns": { + "0": { + "Clothing": "Clothing", + "Electronics": "Electronics", + "Home": "Home", + "Outdoor": "Outdoor" + } + }, + "dimensions": [ + { + "headers": [ + { + "measureGroupHeaders": [ + { + "localIdentifier": "price" + }, + { + "localIdentifier": "order_amount", + "format": "$#,##0", + "name": "Order Amount" + } + ] + } + ] + }, + { + "headers": [ + { + "attributeHeader": { + "localIdentifier": "category", + "label": { + "id": "products.category", + "type": "label" + }, + "labelName": "Category", + "attribute": { + "id": "products.category", + "type": "attribute" + }, + "attributeName": "Category", + "granularity": null, + "primaryLabel": { + "id": "products.category", + "type": "label" + }, + "valueType": "TEXT" + } + } + ] + } + ] +} diff --git a/packages/gooddata-pandas/tests/dataframe/fixtures/arrow/dim_m_c/result.arrow b/packages/gooddata-pandas/tests/dataframe/fixtures/arrow/dim_m_c/result.arrow new file mode 100644 index 000000000..71d185be3 Binary files /dev/null and b/packages/gooddata-pandas/tests/dataframe/fixtures/arrow/dim_m_c/result.arrow differ diff --git a/packages/gooddata-pandas/tests/dataframe/fixtures/arrow/dim_m_cs/dataframe.parquet b/packages/gooddata-pandas/tests/dataframe/fixtures/arrow/dim_m_cs/dataframe.parquet new file mode 100644 index 000000000..cfe8b750c Binary files /dev/null and b/packages/gooddata-pandas/tests/dataframe/fixtures/arrow/dim_m_cs/dataframe.parquet differ diff --git a/packages/gooddata-pandas/tests/dataframe/fixtures/arrow/dim_m_cs/meta.json b/packages/gooddata-pandas/tests/dataframe/fixtures/arrow/dim_m_cs/meta.json new file mode 100644 index 000000000..bb3571a41 --- /dev/null +++ b/packages/gooddata-pandas/tests/dataframe/fixtures/arrow/dim_m_cs/meta.json @@ -0,0 +1,84 @@ +{ + "result_id": "8168440bbfd910f40c33e06a804cb116bcf0f201:734518631e68f6b8d7415109b3dc2397635b3b39501cd9688d29005f76cdd125", + "row_totals_indexes": [ + [] + ], + "primary_labels_from_index": {}, + "primary_labels_from_columns": { + "0": { + "Clothing": "Clothing", + "Electronics": "Electronics", + "Home": "Home", + "Outdoor": "Outdoor" + }, + "1": { + "Canceled": "Canceled", + "Delivered": "Delivered", + "Returned": "Returned" + } + }, + "dimensions": [ + { + "headers": [ + { + "measureGroupHeaders": [ + { + "localIdentifier": "price" + }, + { + "localIdentifier": "order_amount", + "format": "$#,##0", + "name": "Order Amount" + } + ] + } + ] + }, + { + "headers": [ + { + "attributeHeader": { + "localIdentifier": "category", + "label": { + "id": "products.category", + "type": "label" + }, + "labelName": "Category", + "attribute": { + "id": "products.category", + "type": "attribute" + }, + "attributeName": "Category", + "granularity": null, + "primaryLabel": { + "id": "products.category", + "type": "label" + }, + "valueType": "TEXT" + } + }, + { + "attributeHeader": { + "localIdentifier": "status", + "label": { + "id": "order_status", + "type": "label" + }, + "labelName": "Order status", + "attribute": { + "id": "order_status", + "type": "attribute" + }, + "attributeName": "Order status", + "granularity": null, + "primaryLabel": { + "id": "order_status", + "type": "label" + }, + "valueType": "TEXT" + } + } + ] + } + ] +} diff --git a/packages/gooddata-pandas/tests/dataframe/fixtures/arrow/dim_m_cs/result.arrow b/packages/gooddata-pandas/tests/dataframe/fixtures/arrow/dim_m_cs/result.arrow new file mode 100644 index 000000000..780898463 Binary files /dev/null and b/packages/gooddata-pandas/tests/dataframe/fixtures/arrow/dim_m_cs/result.arrow differ diff --git a/packages/gooddata-pandas/tests/dataframe/fixtures/arrow/dim_m_csy/dataframe.parquet b/packages/gooddata-pandas/tests/dataframe/fixtures/arrow/dim_m_csy/dataframe.parquet new file mode 100644 index 000000000..3d5c9c2a6 Binary files /dev/null and b/packages/gooddata-pandas/tests/dataframe/fixtures/arrow/dim_m_csy/dataframe.parquet differ diff --git a/packages/gooddata-pandas/tests/dataframe/fixtures/arrow/dim_m_csy/meta.json b/packages/gooddata-pandas/tests/dataframe/fixtures/arrow/dim_m_csy/meta.json new file mode 100644 index 000000000..176c53090 --- /dev/null +++ b/packages/gooddata-pandas/tests/dataframe/fixtures/arrow/dim_m_csy/meta.json @@ -0,0 +1,117 @@ +{ + "result_id": "e0720d36b043b0ca22a0b9f28815270339eda948:a92a2acabe0c2451de87de371b267307d1916667f20a9344eee20e20a874ad46", + "row_totals_indexes": [ + [] + ], + "primary_labels_from_index": {}, + "primary_labels_from_columns": { + "0": { + "Clothing": "Clothing", + "Electronics": "Electronics", + "Home": "Home", + "Outdoor": "Outdoor" + }, + "1": { + "Canceled": "Canceled", + "Delivered": "Delivered", + "Returned": "Returned" + }, + "2": { + "2022": "2022", + "2023": "2023", + "2024": "2024", + "2025": "2025", + "2026": "2026" + } + }, + "dimensions": [ + { + "headers": [ + { + "measureGroupHeaders": [ + { + "localIdentifier": "price" + }, + { + "localIdentifier": "order_amount", + "format": "$#,##0", + "name": "Order Amount" + } + ] + } + ] + }, + { + "headers": [ + { + "attributeHeader": { + "localIdentifier": "category", + "label": { + "id": "products.category", + "type": "label" + }, + "labelName": "Category", + "attribute": { + "id": "products.category", + "type": "attribute" + }, + "attributeName": "Category", + "granularity": null, + "primaryLabel": { + "id": "products.category", + "type": "label" + }, + "valueType": "TEXT" + } + }, + { + "attributeHeader": { + "localIdentifier": "status", + "label": { + "id": "order_status", + "type": "label" + }, + "labelName": "Order status", + "attribute": { + "id": "order_status", + "type": "attribute" + }, + "attributeName": "Order status", + "granularity": null, + "primaryLabel": { + "id": "order_status", + "type": "label" + }, + "valueType": "TEXT" + } + }, + { + "attributeHeader": { + "localIdentifier": "year", + "label": { + "id": "date.year", + "type": "label" + }, + "labelName": "Date - Year", + "attribute": { + "id": "date.year", + "type": "attribute" + }, + "attributeName": "Date - Year", + "granularity": "YEAR", + "primaryLabel": { + "id": "date.year", + "type": "label" + }, + "format": { + "locale": "en-US", + "pattern": "y", + "timezone": "Etc/UTC" + }, + "valueType": "TEXT" + } + } + ] + } + ] +} diff --git a/packages/gooddata-pandas/tests/dataframe/fixtures/arrow/dim_m_csy/result.arrow b/packages/gooddata-pandas/tests/dataframe/fixtures/arrow/dim_m_csy/result.arrow new file mode 100644 index 000000000..a7c6e5a5c Binary files /dev/null and b/packages/gooddata-pandas/tests/dataframe/fixtures/arrow/dim_m_csy/result.arrow differ diff --git a/packages/gooddata-pandas/tests/dataframe/fixtures/arrow/dim_r_cm/dataframe.parquet b/packages/gooddata-pandas/tests/dataframe/fixtures/arrow/dim_r_cm/dataframe.parquet new file mode 100644 index 000000000..dedeabf1f Binary files /dev/null and b/packages/gooddata-pandas/tests/dataframe/fixtures/arrow/dim_r_cm/dataframe.parquet differ diff --git a/packages/gooddata-pandas/tests/dataframe/fixtures/arrow/dim_r_cm/meta.json b/packages/gooddata-pandas/tests/dataframe/fixtures/arrow/dim_r_cm/meta.json new file mode 100644 index 000000000..af67d53bc --- /dev/null +++ b/packages/gooddata-pandas/tests/dataframe/fixtures/arrow/dim_r_cm/meta.json @@ -0,0 +1,87 @@ +{ + "result_id": "7d21dde2d76befdec0a32017aede0a68f9811a6d:01ba1a7231afda52d099d9e03b3ca595b72000e7eeeeb9ecdb32470bb7a1f236", + "row_totals_indexes": [ + [] + ], + "primary_labels_from_index": { + "0": { + "Midwest": "Midwest", + "Northeast": "Northeast", + "South": "South", + "Unknown": "Unknown", + "West": "West" + } + }, + "primary_labels_from_columns": { + "0": { + "Clothing": "Clothing", + "Electronics": "Electronics", + "Home": "Home", + "Outdoor": "Outdoor" + } + }, + "dimensions": [ + { + "headers": [ + { + "attributeHeader": { + "localIdentifier": "region", + "label": { + "id": "region", + "type": "label" + }, + "labelName": "Region", + "attribute": { + "id": "region", + "type": "attribute" + }, + "attributeName": "Region", + "granularity": null, + "primaryLabel": { + "id": "region", + "type": "label" + }, + "valueType": "TEXT" + } + } + ] + }, + { + "headers": [ + { + "attributeHeader": { + "localIdentifier": "category", + "label": { + "id": "products.category", + "type": "label" + }, + "labelName": "Category", + "attribute": { + "id": "products.category", + "type": "attribute" + }, + "attributeName": "Category", + "granularity": null, + "primaryLabel": { + "id": "products.category", + "type": "label" + }, + "valueType": "TEXT" + } + }, + { + "measureGroupHeaders": [ + { + "localIdentifier": "price" + }, + { + "localIdentifier": "order_amount", + "format": "$#,##0", + "name": "Order Amount" + } + ] + } + ] + } + ] +} diff --git a/packages/gooddata-pandas/tests/dataframe/fixtures/arrow/dim_r_cm/result.arrow b/packages/gooddata-pandas/tests/dataframe/fixtures/arrow/dim_r_cm/result.arrow new file mode 100644 index 000000000..643711226 Binary files /dev/null and b/packages/gooddata-pandas/tests/dataframe/fixtures/arrow/dim_r_cm/result.arrow differ diff --git a/packages/gooddata-pandas/tests/dataframe/fixtures/arrow/dim_r_csm/dataframe.parquet b/packages/gooddata-pandas/tests/dataframe/fixtures/arrow/dim_r_csm/dataframe.parquet new file mode 100644 index 000000000..415bdef02 Binary files /dev/null and b/packages/gooddata-pandas/tests/dataframe/fixtures/arrow/dim_r_csm/dataframe.parquet differ diff --git a/packages/gooddata-pandas/tests/dataframe/fixtures/arrow/dim_r_csm/meta.json b/packages/gooddata-pandas/tests/dataframe/fixtures/arrow/dim_r_csm/meta.json new file mode 100644 index 000000000..1bd798489 --- /dev/null +++ b/packages/gooddata-pandas/tests/dataframe/fixtures/arrow/dim_r_csm/meta.json @@ -0,0 +1,113 @@ +{ + "result_id": "e840c04777add8cdc63b7c3bc0cab8447c0fcd79:b6d9e8541e0dfb53b34a8c91d05503326598e6a4b4cf807aca4d7beabc194195", + "row_totals_indexes": [ + [] + ], + "primary_labels_from_index": { + "0": { + "Midwest": "Midwest", + "Northeast": "Northeast", + "South": "South", + "Unknown": "Unknown", + "West": "West" + } + }, + "primary_labels_from_columns": { + "0": { + "Clothing": "Clothing", + "Electronics": "Electronics", + "Home": "Home", + "Outdoor": "Outdoor" + }, + "1": { + "Canceled": "Canceled", + "Delivered": "Delivered", + "Returned": "Returned" + } + }, + "dimensions": [ + { + "headers": [ + { + "attributeHeader": { + "localIdentifier": "region", + "label": { + "id": "region", + "type": "label" + }, + "labelName": "Region", + "attribute": { + "id": "region", + "type": "attribute" + }, + "attributeName": "Region", + "granularity": null, + "primaryLabel": { + "id": "region", + "type": "label" + }, + "valueType": "TEXT" + } + } + ] + }, + { + "headers": [ + { + "attributeHeader": { + "localIdentifier": "category", + "label": { + "id": "products.category", + "type": "label" + }, + "labelName": "Category", + "attribute": { + "id": "products.category", + "type": "attribute" + }, + "attributeName": "Category", + "granularity": null, + "primaryLabel": { + "id": "products.category", + "type": "label" + }, + "valueType": "TEXT" + } + }, + { + "attributeHeader": { + "localIdentifier": "status", + "label": { + "id": "order_status", + "type": "label" + }, + "labelName": "Order status", + "attribute": { + "id": "order_status", + "type": "attribute" + }, + "attributeName": "Order status", + "granularity": null, + "primaryLabel": { + "id": "order_status", + "type": "label" + }, + "valueType": "TEXT" + } + }, + { + "measureGroupHeaders": [ + { + "localIdentifier": "price" + }, + { + "localIdentifier": "order_amount", + "format": "$#,##0", + "name": "Order Amount" + } + ] + } + ] + } + ] +} diff --git a/packages/gooddata-pandas/tests/dataframe/fixtures/arrow/dim_r_csm/result.arrow b/packages/gooddata-pandas/tests/dataframe/fixtures/arrow/dim_r_csm/result.arrow new file mode 100644 index 000000000..f3ac6a481 Binary files /dev/null and b/packages/gooddata-pandas/tests/dataframe/fixtures/arrow/dim_r_csm/result.arrow differ diff --git a/packages/gooddata-pandas/tests/dataframe/fixtures/arrow/dim_r_csym/dataframe.parquet b/packages/gooddata-pandas/tests/dataframe/fixtures/arrow/dim_r_csym/dataframe.parquet new file mode 100644 index 000000000..41ee96dea Binary files /dev/null and b/packages/gooddata-pandas/tests/dataframe/fixtures/arrow/dim_r_csym/dataframe.parquet differ diff --git a/packages/gooddata-pandas/tests/dataframe/fixtures/arrow/dim_r_csym/meta.json b/packages/gooddata-pandas/tests/dataframe/fixtures/arrow/dim_r_csym/meta.json new file mode 100644 index 000000000..73b34d61e --- /dev/null +++ b/packages/gooddata-pandas/tests/dataframe/fixtures/arrow/dim_r_csym/meta.json @@ -0,0 +1,146 @@ +{ + "result_id": "252184a2fc8d93830942a935322a1187ea8fd6a2:c9853f896b8ed29bad9fa440d00c49558e0a06731742b202ecddd6babdfa4932", + "row_totals_indexes": [ + [] + ], + "primary_labels_from_index": { + "0": { + "Midwest": "Midwest", + "Northeast": "Northeast", + "South": "South", + "Unknown": "Unknown", + "West": "West" + } + }, + "primary_labels_from_columns": { + "0": { + "Clothing": "Clothing", + "Electronics": "Electronics", + "Home": "Home", + "Outdoor": "Outdoor" + }, + "1": { + "Canceled": "Canceled", + "Delivered": "Delivered", + "Returned": "Returned" + }, + "2": { + "2022": "2022", + "2023": "2023", + "2024": "2024", + "2025": "2025", + "2026": "2026" + } + }, + "dimensions": [ + { + "headers": [ + { + "attributeHeader": { + "localIdentifier": "region", + "label": { + "id": "region", + "type": "label" + }, + "labelName": "Region", + "attribute": { + "id": "region", + "type": 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"Massachusetts", + "New Hampshire": "New Hampshire", + "New Jersey": "New Jersey", + "New York": "New York", + "Pennsylvania": "Pennsylvania", + "Alabama": "Alabama", + "Arkansas": "Arkansas", + "Delaware": "Delaware", + "District of Columbia": "District of Columbia", + "Florida": "Florida", + "Georgia": "Georgia", + "Kentucky": "Kentucky", + "Louisiana": "Louisiana", + "Maryland": "Maryland", + "Mississippi": "Mississippi", + "North Carolina": "North Carolina", + "Oklahoma": "Oklahoma", + "South Carolina": "South Carolina", + "Tennessee": "Tennessee", + "Texas": "Texas", + "Virginia": "Virginia", + "West Virginia": "West Virginia", + "Rhode Island": "Rhode Island", + "Alaska": "Alaska", + "Arizona": "Arizona", + "California": "California", + "Colorado": "Colorado", + "Hawaii": "Hawaii", + "Idaho": "Idaho", + "Montana": "Montana", + "Nevada": "Nevada", + "New Mexico": "New Mexico", + "Oregon": "Oregon", + "Utah": "Utah", + "Washington": "Washington" + } + }, + "dimensions": [ + { + "headers": [ + { + "attributeHeader": { + "localIdentifier": "product_category", + "label": { + "id": "products.category", + "type": "label" + }, + "labelName": "Category", + "attribute": { + "id": "products.category", + "type": "attribute" + }, + "attributeName": "Category", + "granularity": null, + "primaryLabel": { + "id": "products.category", + "type": "label" + }, + "valueType": "TEXT" + } + } + ] + }, + { + "headers": [ + { + "attributeHeader": { + "localIdentifier": "region", + "label": { + "id": "region", + "type": "label" + }, + "labelName": "Region", + "attribute": { + "id": "region", + "type": "attribute" + }, + "attributeName": "Region", + "granularity": null, + "primaryLabel": { + "id": "region", + "type": "label" + }, + "valueType": "TEXT" + } + }, + { + "attributeHeader": { + "localIdentifier": "state", + "label": { + "id": "state", + "type": "label" + }, + "labelName": "State", + "attribute": { + "id": "state", + "type": "attribute" + }, + "attributeName": "State", + "granularity": null, + "primaryLabel": { + "id": "state", + "type": "label" + }, + "valueType": "TEXT" + } + }, + { + "measureGroupHeaders": [ + { + "localIdentifier": "price" + }, + { + "localIdentifier": "order_amount", + "format": "$#,##0", + "name": "Order Amount" + } + ] + } + ] + } + ] +} diff --git a/packages/gooddata-pandas/tests/dataframe/fixtures/arrow/wide_few_rows_many_cols/result.arrow b/packages/gooddata-pandas/tests/dataframe/fixtures/arrow/wide_few_rows_many_cols/result.arrow new file mode 100644 index 000000000..50ecd8333 Binary files /dev/null and b/packages/gooddata-pandas/tests/dataframe/fixtures/arrow/wide_few_rows_many_cols/result.arrow differ diff --git a/packages/gooddata-pandas/tests/dataframe/test_dataframe_for_exec_def_arrow.py b/packages/gooddata-pandas/tests/dataframe/test_dataframe_for_exec_def_arrow.py new file mode 100644 index 000000000..d361c79f8 --- /dev/null +++ b/packages/gooddata-pandas/tests/dataframe/test_dataframe_for_exec_def_arrow.py @@ -0,0 +1,504 @@ +# (C) 2026 GoodData Corporation +from __future__ import annotations + +import ast +import json +import re +from pathlib import Path +from unittest.mock import MagicMock + +import numpy +import pandas +import pyarrow as pa +import pytest +from gooddata_pandas.arrow_convertor import ( + _build_field_index, + _build_inline_index, + _compute_primary_labels_from_fields, + _compute_primary_labels_from_inline, + compute_primary_labels, + compute_row_totals_indexes, + convert_arrow_table_to_dataframe, +) +from gooddata_pandas.arrow_types import TypesMapper +from gooddata_pandas.dataframe import DataFrameFactory +from pyarrow import ipc + +_ARROW_FIXTURES = Path(__file__).parent / "fixtures" / "arrow" + + +def _cases() -> list[str]: + manifest_path = _ARROW_FIXTURES / "manifest.json" + if not manifest_path.exists(): + return [] + return [ + c["name"] + for c in json.loads(manifest_path.read_text()) + if (_ARROW_FIXTURES / c["name"] / "result.arrow").exists() + ] + + +def _load_case(name: str) -> tuple[pa.Table, pandas.DataFrame, dict]: + """Return (arrow_table, expected_df, meta) for a ground-truth case.""" + case_dir = _ARROW_FIXTURES / name + with open(case_dir / "result.arrow", "rb") as f: + table = ipc.open_file(f).read_all() + expected = pandas.read_parquet(case_dir / "dataframe.parquet") + # Parquet serialises MultiIndex column tuples as strings — restore them. + cols = expected.columns + if not isinstance(cols, pandas.MultiIndex) and len(cols) > 0: + first = str(cols[0]) + if first.startswith("(") and first.endswith(")"): + expected.columns = pandas.MultiIndex.from_tuples([ast.literal_eval(c) for c in cols]) + meta = json.loads((case_dir / "meta.json").read_text()) + return table, expected, meta + + +def _assert_df_matches(actual_df: pandas.DataFrame, expected_df: pandas.DataFrame) -> None: + assert actual_df.shape == expected_df.shape + # Compare values only — storage dtype may differ across types_mapper presets. + assert actual_df.index.tolist() == expected_df.index.tolist(), ( + f"row index mismatch\n expected: {expected_df.index.tolist()[:5]}\n actual: {actual_df.index.tolist()[:5]}" + ) + assert actual_df.columns.tolist() == expected_df.columns.tolist(), ( + f"column index mismatch\n expected: {expected_df.columns.tolist()[:5]}\n actual: {actual_df.columns.tolist()[:5]}" + ) + exp_vals = expected_df.to_numpy(dtype=float, na_value=float("nan")) + act_vals = actual_df.to_numpy(dtype=float, na_value=float("nan")) + both_nan = numpy.isnan(exp_vals) & numpy.isnan(act_vals) + assert (numpy.isclose(exp_vals, act_vals, equal_nan=False) | both_nan).all() + + +@pytest.mark.parametrize("case_name", _cases()) +def test_arrow_converter(case_name: str) -> None: + """Classic (default) conversion matches the JSON-path ground truth.""" + table, expected_df, meta = _load_case(case_name) + actual_df = convert_arrow_table_to_dataframe(table) # types_mapper="classic" + + _assert_df_matches(actual_df, expected_df) + assert compute_row_totals_indexes(table, meta["dimensions"]) == meta["row_totals_indexes"] + + +@pytest.mark.parametrize("case_name", _cases()) +def test_arrow_converter_arrow_strings(case_name: str) -> None: + """arrow_strings preset: numeric values identical to classic; string columns use Arrow-backed dtype.""" + table, expected_df, meta = _load_case(case_name) + actual_df = convert_arrow_table_to_dataframe(table, types_mapper=TypesMapper.ARROW_STRINGS) + + _assert_df_matches(actual_df, expected_df) + + # String index levels must use the Arrow-backed StringDtype. + idx = actual_df.index + if isinstance(idx, pandas.MultiIndex): + for level in idx.levels: + if level.dtype == object: + # object dtype means a numeric or non-string level — skip + continue + assert isinstance(level.dtype, pandas.StringDtype) and level.dtype.na_value is pandas.NA, ( + f"Expected Arrow-backed StringDtype on index level, got {level.dtype}" + ) + elif idx.dtype != object: + assert isinstance(idx.dtype, pandas.StringDtype), ( + f"Expected Arrow-backed StringDtype on flat index, got {idx.dtype}" + ) + + +def test_arrow_converter_custom_mapping() -> None: + """CUSTOM preset with an explicit mapping works end-to-end.""" + cases = _cases() + assert cases, "no fixtures available" + table, expected_df, _ = _load_case(cases[0]) + mapping = {pa.string(): pandas.StringDtype("pyarrow"), pa.large_string(): pandas.StringDtype("pyarrow")} + actual_df = convert_arrow_table_to_dataframe(table, types_mapper=TypesMapper.CUSTOM, custom_mapping=mapping) + _assert_df_matches(actual_df, expected_df) + + +def test_arrow_converter_custom_mapping_missing() -> None: + """CUSTOM preset without custom_mapping raises ValueError.""" + cases = _cases() + assert cases, "no fixtures available" + table, _, _ = _load_case(cases[0]) + with pytest.raises(ValueError, match="custom_mapping"): + convert_arrow_table_to_dataframe(table, types_mapper=TypesMapper.CUSTOM) + + +def test_arrow_converter_unknown_types_mapper() -> None: + """An unrecognised types_mapper value raises ValueError.""" + cases = _cases() + assert cases, "no fixtures available" + table, _, _ = _load_case(cases[0]) + with pytest.raises(ValueError, match="Unknown types_mapper"): + convert_arrow_table_to_dataframe(table, types_mapper=object()) # type: ignore[arg-type] + + +# --------------------------------------------------------------------------- +# compute_primary_labels — ground-truth fixture tests +# --------------------------------------------------------------------------- + + +@pytest.mark.parametrize("case_name", _cases()) +def test_compute_primary_labels(case_name: str) -> None: + """compute_primary_labels matches primary_labels stored in meta.json ground truth.""" + table, _, meta = _load_case(case_name) + primary_from_index, primary_from_cols = compute_primary_labels(table) + # meta.json stores dict keys as strings (JSON limitation) — convert back to int. + expected_idx = {int(k): v for k, v in meta["primary_labels_from_index"].items()} + expected_cols = {int(k): v for k, v in meta["primary_labels_from_columns"].items()} + assert primary_from_index == expected_idx + assert primary_from_cols == expected_cols + + +# --------------------------------------------------------------------------- +# _compute_primary_labels_from_inline — unit tests for non-identity branches +# --------------------------------------------------------------------------- + + +def test_primary_labels_from_inline_separate_column() -> None: + """When primaryLabelId != labelId and a separate column exists, it is used.""" + table = pa.table( + { + "__row_type": pa.array([0, 0], type=pa.int8()), + "display_label": pa.array(["New York", "Los Angeles"], type=pa.string()), + "primary_label": pa.array(["ny", "la"], type=pa.string()), + } + ) + xtab_meta = {"labelMetadata": {"l0": {"labelId": "display_label", "primaryLabelId": "primary_label"}}} + result = _compute_primary_labels_from_inline( + table, + label_refs=["l0"], + label_ref_to_id={"l0": "display_label"}, + xtab_meta=xtab_meta, + ) + assert result == {0: {"ny": "New York", "la": "Los Angeles"}} + + +def test_primary_labels_from_inline_fallback_identity() -> None: + """When the primaryLabelId column is absent, falls back to identity mapping.""" + table = pa.table( + { + "__row_type": pa.array([0, 0], type=pa.int8()), + "display_label": pa.array(["New York", "Los Angeles"], type=pa.string()), + # no "primary_label" column + } + ) + xtab_meta = {"labelMetadata": {"l0": {"labelId": "display_label", "primaryLabelId": "primary_label"}}} + result = _compute_primary_labels_from_inline( + table, + label_refs=["l0"], + label_ref_to_id={"l0": "display_label"}, + xtab_meta=xtab_meta, + ) + assert result == {0: {"New York": "New York", "Los Angeles": "Los Angeles"}} + + +# --------------------------------------------------------------------------- +# _compute_primary_labels_from_fields — non-string skip branch +# --------------------------------------------------------------------------- + + +def test_primary_labels_from_fields_skips_non_string() -> None: + """Non-string entries in label_values / primary_label_values are skipped.""" + gdc_data = { + "type": "metric", + "index": 0, + "label_values": [123, "cat"], + "primary_label_values": [None, "primary_cat"], + } + field = pa.field( + "metric_group_0", + pa.float64(), + metadata={b"gdc": json.dumps(gdc_data).encode()}, + ) + result = _compute_primary_labels_from_fields([field], n_col_labels=2) + # j=0 skipped (123 is not str); j=1 included + assert result == {1: {"primary_cat": "cat"}} + + +# --------------------------------------------------------------------------- +# DataFrameFactory.for_arrow_table — unit test (no live server needed) +# --------------------------------------------------------------------------- + + +_FOR_ARROW_TABLE_CASES = [ + pytest.param("dim_r_m", id="no-totals-flat"), + pytest.param("dim_m_c", id="transposed"), + pytest.param("tot_d0_sub_grand", id="row-subtotals"), + pytest.param("totals_both_dims", id="both-dims-totals"), +] + + +@pytest.mark.parametrize("case_name", _FOR_ARROW_TABLE_CASES) +def test_for_arrow_table_without_execution_response(case_name: str) -> None: + """for_arrow_table with no execution_response: DataFrame matches, metadata is empty.""" + if case_name not in _cases(): + pytest.skip(f"fixture {case_name!r} not available") + table, expected_df, _ = _load_case(case_name) + gdf = DataFrameFactory(MagicMock(), "test_workspace") + df, metadata = gdf.for_arrow_table(table) + _assert_df_matches(df, expected_df) + assert metadata.execution_response is None + assert metadata.row_totals_indexes == [] + assert metadata.column_totals_indexes == [] + + +@pytest.mark.parametrize("case_name", _FOR_ARROW_TABLE_CASES) +def test_for_arrow_table_with_execution_response(case_name: str) -> None: + """for_arrow_table with execution_response populates row_totals_indexes from meta.json.""" + if case_name not in _cases(): + pytest.skip(f"fixture {case_name!r} not available") + table, expected_df, meta = _load_case(case_name) + mock_exec_response = MagicMock() + mock_exec_response.dimensions = meta["dimensions"] + gdf = DataFrameFactory(MagicMock(), "test_workspace") + df, metadata = gdf.for_arrow_table(table, execution_response=mock_exec_response) + _assert_df_matches(df, expected_df) + assert metadata.execution_response is mock_exec_response + assert metadata.row_totals_indexes == meta["row_totals_indexes"] + assert metadata.column_totals_indexes == [] + + +def test_build_inline_index_total_row_no_totals_meta() -> None: + """Total rows with absent totalsMetadata hit the 'if not refs: continue' branch.""" + table = pa.table( + { + "__row_type": pa.array([0, 2], type=pa.int8()), + "region": pa.array(["West", "sum"], type=pa.string()), + } + ) + xtab_meta = { + "labelMetadata": {"l0": {"labelId": "region", "primaryLabelId": "region"}}, + # intentionally no "totalsMetadata" + } + model_meta = { + "labels": {"region": {"labelTitle": "Region"}}, + "requestedShape": {"metrics": []}, + } + idx = _build_inline_index( + table, + row_label_refs=["l0"], + label_ref_to_id={"l0": "region"}, + model_meta=model_meta, + xtab_meta=xtab_meta, + ) + assert idx is not None + # Total row: no agg info available → value is uppercased as-is + assert idx.tolist() == ["West", "SUM"] + assert idx.name == "Region" + + +def test_compute_row_totals_measure_in_row_dim() -> None: + """measureGroupHeaders entry inside the non-transposed row_dim appends [].""" + table, _, _ = _load_case("dim_r_m") + # Inject a dimension that also contains a measureGroupHeaders entry + custom_dims = [ + { + "headers": [ + {"attributeHeader": {"label": {"id": "region"}, "localIdentifier": "region"}}, + {"measureGroupHeaders": [{"localIdentifier": "price"}]}, + ] + } + ] + result = compute_row_totals_indexes(table, custom_dims) + # dim_r_m has no total rows → total_row_idxs=[] for the attr level; + assert result == [[], []] + + +def test_compute_row_totals_unknown_attr_in_row_dim() -> None: + """Unknown attributeHeader in the non-transposed row_dim appends [].""" + table, _, _ = _load_case("dim_r_m") + custom_dims = [ + { + "headers": [ + {"attributeHeader": {"label": {"id": "region"}, "localIdentifier": "region"}}, + {"attributeHeader": {"label": {"id": "unknown_attr"}, "localIdentifier": "unknown"}}, + ] + } + ] + result = compute_row_totals_indexes(table, custom_dims) + assert result == [[], []] + + +def test_compute_row_totals_field_rows_unknown_attr() -> None: + """Unknown attributeHeader in the field-rows row_dim appends [].""" + table, _, _ = _load_case("dim_m_c") + # dim_m_c: transposed, col_label_refs=[] → metrics-only, row_dim found by measureGroupHeaders + custom_dims = [ + { + "headers": [ + {"measureGroupHeaders": [{"localIdentifier": "m0"}]}, + {"attributeHeader": {"label": {"id": "unknown_attr"}, "localIdentifier": "unknown"}}, + ] + }, + { + "headers": [ + {"attributeHeader": {"label": {"id": "products.category"}, "localIdentifier": "category"}}, + ] + }, + ] + result = compute_row_totals_indexes(table, custom_dims) + assert result == [[], []] + + +def test_convert_arrow_no_schema_metadata_raises() -> None: + """Arrow table without any schema metadata raises ValueError with a clear message.""" + table = pa.table({"col": [1, 2, 3]}) + assert table.schema.metadata is None + with pytest.raises(ValueError, match="no schema metadata"): + convert_arrow_table_to_dataframe(table) + + +@pytest.mark.parametrize("missing_key", ["x-gdc-xtab-v1", "x-gdc-model-v1", "x-gdc-view-v1"]) +def test_convert_arrow_missing_required_key_raises(missing_key: str) -> None: + """Arrow table missing any of the three required schema keys raises ValueError naming it.""" + table, _, _ = _load_case("dim_r_m") + stripped_meta = {k: v for k, v in table.schema.metadata.items() if k != missing_key.encode()} + table = table.replace_schema_metadata(stripped_meta) + with pytest.raises(ValueError, match=re.escape(missing_key)): + convert_arrow_table_to_dataframe(table) + + +def test_convert_arrow_missing_row_type_column_raises() -> None: + """Arrow table missing __row_type control column raises ValueError.""" + table, _, _ = _load_case("dim_r_m") + # Drop __row_type column. + idx = table.schema.get_field_index("__row_type") + table = table.remove_column(idx) + with pytest.raises(ValueError, match="__row_type"): + convert_arrow_table_to_dataframe(table) + + +# --------------------------------------------------------------------------- +# self_destruct parameter +# --------------------------------------------------------------------------- + + +@pytest.mark.parametrize( + "case_name", + [ + pytest.param("dim_r_m", id="no-totals"), + pytest.param("totals_grand_row_sum", id="row-totals"), + pytest.param("dim_m_c", id="transposed"), + ], +) +def test_arrow_converter_self_destruct(case_name: str) -> None: + """self_destruct=True produces the same DataFrame as the default (False).""" + if case_name not in _cases(): + pytest.skip(f"fixture {case_name!r} not available") + # Load twice: self_destruct modifies table buffers in-place. + table_normal, _, _ = _load_case(case_name) + table_destruct, _, _ = _load_case(case_name) + expected = convert_arrow_table_to_dataframe(table_normal, self_destruct=False) + actual = convert_arrow_table_to_dataframe(table_destruct, self_destruct=True) + _assert_df_matches(actual, expected) + + +# --------------------------------------------------------------------------- +# _build_field_index — direct unit tests for total-label padding +# --------------------------------------------------------------------------- + + +def _make_field(name: str, gdc: dict) -> pa.Field: + return pa.field(name, pa.float64(), metadata={b"gdc": json.dumps(gdc).encode()}) + + +def test_build_field_index_subtotal_padding() -> None: + """grand_total fields with partial label_values are right-padded with the agg name.""" + col_label_refs = ["l0", "l1"] # two col-label levels: status, year + label_ref_to_id = {"l0": "status", "l1": "year"} + model_meta = { + "labels": { + "status": {"labelTitle": "Status"}, + "year": {"labelTitle": "Year"}, + }, + "requestedShape": {"metrics": ["price", "order_amount"]}, + "metrics": { + "price": {"title": "Price"}, + "order_amount": {"title": "Order Amount"}, + }, + } + data_fields = [ + # regular metric: full label_values → no padding + _make_field("metric_group_0", {"type": "metric", "index": 0, "label_values": ["Active", "2023"]}), + # subtotal: 1 label kept (status), year padded with "SUM" + _make_field( + "grand_total_0", {"type": "total", "metric_index": 0, "agg_function": "sum", "label_values": ["Active"]} + ), + # grand total: no labels → both levels padded with "SUM" + _make_field("grand_total_1", {"type": "total", "metric_index": 1, "agg_function": "sum", "label_values": []}), + ] + + idx = _build_field_index(data_fields, col_label_refs, label_ref_to_id, model_meta, xtab_meta={}) + + assert isinstance(idx, pandas.MultiIndex) + assert idx.tolist() == [ + ("Active", "2023", "Price"), # regular + ("Active", "SUM", "Price"), # subtotal: status kept, year padded + ("SUM", "SUM", "Order Amount"), # grand total: both levels padded + ] + assert idx.names == ["Status", "Year", None] + + +def test_build_field_index_no_col_labels() -> None: + """When there are no column label refs the index is a flat Index of metric titles.""" + model_meta = { + "labels": {}, + "requestedShape": {"metrics": ["price", "order_amount"]}, + "metrics": {"price": {"title": "Price"}, "order_amount": {"title": "Order Amount"}}, + } + data_fields = [ + _make_field("metric_group_0", {"type": "metric", "index": 0, "label_values": []}), + _make_field("metric_group_1", {"type": "metric", "index": 1, "label_values": []}), + ] + + idx = _build_field_index(data_fields, col_label_refs=[], label_ref_to_id={}, model_meta=model_meta, xtab_meta={}) + + assert isinstance(idx, pandas.Index) + assert not isinstance(idx, pandas.MultiIndex) + assert idx.tolist() == ["Price", "Order Amount"] + + +def test_build_field_index_asymmetric_depth() -> None: + """Two total fields with different label_values lengths are padded independently.""" + col_label_refs = ["l0", "l1"] + label_ref_to_id = {"l0": "status", "l1": "year"} + model_meta = { + "labels": {"status": {"labelTitle": "Status"}, "year": {"labelTitle": "Year"}}, + "requestedShape": {"metrics": ["price", "order_amount"]}, + "metrics": {"price": {"title": "Price"}, "order_amount": {"title": "Order Amount"}}, + } + data_fields = [ + # price: 1 label kept → pads year only + _make_field( + "grand_total_0", {"type": "total", "metric_index": 0, "agg_function": "sum", "label_values": ["Active"]} + ), + # order_amount: 0 labels kept → pads both + _make_field("grand_total_1", {"type": "total", "metric_index": 1, "agg_function": "max", "label_values": []}), + ] + + idx = _build_field_index(data_fields, col_label_refs, label_ref_to_id, model_meta, xtab_meta={}) + + assert idx.tolist() == [ + ("Active", "SUM", "Price"), + ("MAX", "MAX", "Order Amount"), + ] + + +# --------------------------------------------------------------------------- +# compute_row_totals_indexes — empty execution_dims fallback +# --------------------------------------------------------------------------- + + +@pytest.mark.parametrize( + "case_name", + [ + pytest.param("dim_r_m", id="non-transposed"), + pytest.param("dim_m_c", id="transposed"), + pytest.param("tot_d0_sub", id="with-totals"), + ], +) +def test_compute_row_totals_empty_dims(case_name: str) -> None: + """execution_dims=[] falls back to an empty row_dim and returns [].""" + if case_name not in _cases(): + pytest.skip(f"fixture {case_name!r} not available") + table, _, _ = _load_case(case_name) + assert compute_row_totals_indexes(table, []) == [] diff --git a/packages/gooddata-sdk/pyproject.toml b/packages/gooddata-sdk/pyproject.toml index a30da72b7..6baf2bb5f 100644 --- a/packages/gooddata-sdk/pyproject.toml +++ b/packages/gooddata-sdk/pyproject.toml @@ -49,6 +49,9 @@ classifiers = [ "Typing :: Typed", ] +[project.optional-dependencies] +arrow = ["pyarrow>=23.0.1"] + [project.urls] Documentation = "https://www.gooddata.com/docs/python-sdk/1.62.0" Source = "https://github.com/gooddata/gooddata-python-sdk" @@ -68,6 +71,7 @@ test = [ "python-dotenv~=1.0.0", "deepdiff~=8.5.0", "tests_support", + "pyarrow>=16.1.0", ] [tool.ty.analysis] diff --git a/packages/gooddata-sdk/src/gooddata_sdk/catalog/data_source/entity_model/data_source.py b/packages/gooddata-sdk/src/gooddata_sdk/catalog/data_source/entity_model/data_source.py index d3a92a038..43b0163fb 100644 --- a/packages/gooddata-sdk/src/gooddata_sdk/catalog/data_source/entity_model/data_source.py +++ b/packages/gooddata-sdk/src/gooddata_sdk/catalog/data_source/entity_model/data_source.py @@ -62,7 +62,7 @@ class CatalogDataSourceBase(Base): credentials: Credentials = field(repr=False) alternative_data_source_id: str | None = None - @type.validator # type: ignore[attr-defined] + @type.validator # type: ignore def _check_allowed_values(self, attribute: Attribute, value: str) -> None: value_in_allowed(self.__class__, attribute, value, JsonApiDataSourceInAttributes) diff --git a/packages/gooddata-sdk/src/gooddata_sdk/catalog/entity.py b/packages/gooddata-sdk/src/gooddata_sdk/catalog/entity.py index 88efa82d5..111c61c5a 100644 --- a/packages/gooddata-sdk/src/gooddata_sdk/catalog/entity.py +++ b/packages/gooddata-sdk/src/gooddata_sdk/catalog/entity.py @@ -256,7 +256,7 @@ def is_part_of_api(cls, entity: dict[str, Any]) -> bool: return cls.USER_KEY in entity @classmethod - def from_api(cls, attributes: dict[str, Any]) -> BasicCredentials: # type: ignore[override] + def from_api(cls, attributes: dict[str, Any]) -> BasicCredentials: # type: ignore # Credentials are not returned from security reasons return cls( username=attributes[cls.USER_KEY], @@ -277,7 +277,7 @@ def is_part_of_api(cls, entity: dict[str, Any]) -> bool: return cls.USER_KEY in entity and cls.PRIVATE_KEY in entity @classmethod - def from_api(cls, attributes: dict[str, Any]) -> KeyPairCredentials: # type: ignore[override] + def from_api(cls, attributes: dict[str, Any]) -> KeyPairCredentials: # type: ignore # Credentials are not returned for security reasons return cls( username=attributes[cls.USER_KEY], @@ -297,7 +297,7 @@ def is_part_of_api(cls, entity: dict[str, Any]) -> bool: return cls.CLIENT_ID in entity and cls.CLIENT_SECRET in entity @classmethod - def from_api(cls, attributes: dict[str, Any]) -> ClientSecretCredentials: # type: ignore[override] + def from_api(cls, attributes: dict[str, Any]) -> ClientSecretCredentials: # type: ignore # Credentials are not returned for security reasons return cls( client_id=attributes[cls.CLIENT_ID], diff --git a/packages/gooddata-sdk/src/gooddata_sdk/catalog/export/service.py b/packages/gooddata-sdk/src/gooddata_sdk/catalog/export/service.py index 8bf69a212..43609f8b0 100644 --- a/packages/gooddata-sdk/src/gooddata_sdk/catalog/export/service.py +++ b/packages/gooddata-sdk/src/gooddata_sdk/catalog/export/service.py @@ -329,6 +329,39 @@ def export_tabular_by_visualization_id( max_retry=max_retry, ) + def get_raw_export_bytes( + self, + workspace_id: str, + export_id: str, + timeout: float = 60.0, + retry: float = 0.2, + max_retry: float = 5.0, + ) -> bytes: + """ + Poll the raw export endpoint for an already-submitted export and return the Arrow IPC bytes. + + The caller is responsible for submitting the AFM execution and the export request + (via ``actions_api.create_raw_export``), then passing the resulting ``export_id`` here. + + Args: + workspace_id (str): The ID of the target workspace. + export_id (str): Export ID returned by ``create_raw_export``. + timeout (float): Total seconds to wait for the export to complete. Defaults to 60.0. + retry (float): Initial wait between retries in seconds. Defaults to 0.2. + max_retry (float): Maximum wait between retries in seconds. Defaults to 5.0. + + Returns: + bytes: Arrow IPC bytes that can be read with ``pyarrow.ipc.open_file``. + """ + return self._get_exported_content( + workspace_id=workspace_id, + export_id=export_id, + get_func=self._actions_api.get_raw_export, + timeout=timeout, + retry=retry, + max_retry=max_retry, + ) + def export_slides( self, workspace_id: str, diff --git a/packages/gooddata-sdk/src/gooddata_sdk/compute/model/execution.py b/packages/gooddata-sdk/src/gooddata_sdk/compute/model/execution.py index a81b807ac..4063a6c56 100644 --- a/packages/gooddata-sdk/src/gooddata_sdk/compute/model/execution.py +++ b/packages/gooddata-sdk/src/gooddata_sdk/compute/model/execution.py @@ -2,7 +2,10 @@ from __future__ import annotations import logging -from typing import Any, Union +from typing import TYPE_CHECKING, Any, Union + +if TYPE_CHECKING: + import pyarrow from attrs import define, field from attrs.setters import frozen as frozen_attr @@ -11,6 +14,13 @@ from gooddata_api_client.model.afm_cancel_tokens import AfmCancelTokens from gooddata_api_client.model.result_spec import ResultSpec +try: + import pyarrow as _pyarrow + from pyarrow import ipc as _ipc +except ImportError: + _pyarrow = None # type: ignore + _ipc = None # type: ignore + from gooddata_sdk.client import GoodDataApiClient from gooddata_sdk.compute.model.attribute import Attribute from gooddata_sdk.compute.model.filter import Filter @@ -372,6 +382,31 @@ def read_result( ) return ExecutionResult(execution_result) + def read_result_arrow(self) -> pyarrow.Table: + """ + Reads the full execution result as a pyarrow Table. + + The binary endpoint returns the complete result in one shot (no paging). + Requires pyarrow to be installed (pip install gooddata-sdk[arrow]). + """ + if _ipc is None: + raise ImportError( + "pyarrow is required to use read_result_arrow(). Install it with: pip install gooddata-sdk[arrow]" + ) + import io + + response = self._actions_api.retrieve_result_binary( + workspace_id=self._workspace_id, + result_id=self.result_id, + _preload_content=False, + **({"x_gdc_cancel_token": self.cancel_token} if self.cancel_token else {}), + ) + try: + buf = io.BytesIO(response.read()) + finally: + response.release_conn() + return _ipc.open_file(buf).read_all() + def cancel(self) -> None: """ Cancels the execution backing this execution result. diff --git a/packages/gooddata-sdk/src/gooddata_sdk/compute/model/filter.py b/packages/gooddata-sdk/src/gooddata_sdk/compute/model/filter.py index 12e35e811..94171f156 100644 --- a/packages/gooddata-sdk/src/gooddata_sdk/compute/model/filter.py +++ b/packages/gooddata-sdk/src/gooddata_sdk/compute/model/filter.py @@ -9,7 +9,7 @@ from gooddata_api_client.model.inline_filter_definition_inline import InlineFilterDefinitionInline if find_spec("icu") is not None: - from icu import Locale, SimpleDateFormat # type: ignore[import-not-found] + from icu import Locale, SimpleDateFormat # type: ignore import gooddata_api_client.models as afm_models from gooddata_api_client.model_utils import OpenApiModel diff --git a/packages/gooddata-sdk/src/gooddata_sdk/type_converter.py b/packages/gooddata-sdk/src/gooddata_sdk/type_converter.py index 4d3e3ee08..0e0e70444 100644 --- a/packages/gooddata-sdk/src/gooddata_sdk/type_converter.py +++ b/packages/gooddata-sdk/src/gooddata_sdk/type_converter.py @@ -27,7 +27,7 @@ def to_type(self, value: str) -> Any: def to_external_type(self, value: str) -> Any: typed_value = self.to_type(value) if self._EXTERNAL_CONVERSION_FNC: - return self._EXTERNAL_CONVERSION_FNC(typed_value) + return self._EXTERNAL_CONVERSION_FNC(typed_value) # type: ignore else: return typed_value diff --git a/packages/gooddata-sdk/tests/export/test_export_service.py b/packages/gooddata-sdk/tests/export/test_export_service.py index 705ae305b..36f34f9ba 100644 --- a/packages/gooddata-sdk/tests/export/test_export_service.py +++ b/packages/gooddata-sdk/tests/export/test_export_service.py @@ -5,6 +5,7 @@ # (./packages/gooddata-sdk/tests/export -> /tmp/exports). Fix with: chmod -R 777 tests/export/exports/ import os from pathlib import Path +from unittest.mock import MagicMock, patch import pytest from gooddata_sdk import ( @@ -20,6 +21,7 @@ SimpleMetric, TableDimension, ) +from gooddata_sdk.catalog.export.service import ExportService from tests_support.vcrpy_utils import get_vcr gd_vcr = get_vcr() @@ -107,6 +109,27 @@ def test_export_settings_grand_totals_position_defaults_to_none(): assert settings.grand_totals_position is None +def test_get_raw_export_bytes_delegates_to_get_exported_content() -> None: + """get_raw_export_bytes delegates to _get_exported_content with the raw export func.""" + + mock_api_client = MagicMock() + svc = ExportService(mock_api_client) + fake_bytes = b"arrow_ipc" + + with patch.object(ExportService, "_get_exported_content", return_value=fake_bytes) as mock_get: + result = svc.get_raw_export_bytes("ws-id", "export-id-123") + + mock_get.assert_called_once_with( + workspace_id="ws-id", + export_id="export-id-123", + 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