maxdebayser commented on code in PR #7831:
URL: https://github.com/apache/iceberg/pull/7831#discussion_r1288975288
##########
python/pyiceberg/io/pyarrow.py:
##########
@@ -1025,3 +1039,335 @@ def map_key_partner(self, partner_map:
Optional[pa.Array]) -> Optional[pa.Array]
def map_value_partner(self, partner_map: Optional[pa.Array]) ->
Optional[pa.Array]:
return partner_map.items if isinstance(partner_map, pa.MapArray) else
None
+
+
+_PRIMITIVE_TO_PHYSICAL = {
+ BooleanType(): "BOOLEAN",
+ IntegerType(): "INT32",
+ LongType(): "INT64",
+ FloatType(): "FLOAT",
+ DoubleType(): "DOUBLE",
+ DateType(): "INT32",
+ TimeType(): "INT64",
+ TimestampType(): "INT64",
+ TimestamptzType(): "INT64",
+ StringType(): "BYTE_ARRAY",
+ UUIDType(): "FIXED_LEN_BYTE_ARRAY",
+ BinaryType(): "BYTE_ARRAY",
+}
+_PHYSICAL_TYPES = set(_PRIMITIVE_TO_PHYSICAL.values()).union({"INT96"})
+
+
+class StatsAggregator:
+ current_min: Any
+ current_max: Any
+ trunc_length: Optional[int]
+
+ def __init__(self, iceberg_type: PrimitiveType, physical_type_string: str,
trunc_length: Optional[int] = None) -> None:
+ self.current_min = None
+ self.current_max = None
+ self.trunc_length = trunc_length
+
+ if physical_type_string not in _PHYSICAL_TYPES:
+ raise ValueError(f"Unknown physical type {physical_type_string}")
+
+ if physical_type_string == "INT96":
+ raise NotImplementedError("Statistics not implemented for INT96
physical type")
+
+ expected_physical_type = _PRIMITIVE_TO_PHYSICAL[iceberg_type]
+ if expected_physical_type != physical_type_string:
+ raise ValueError(
+ f"Unexpected physical type {physical_type_string} for
{iceberg_type}, expected {expected_physical_type}"
+ )
+
+ self.primitive_type = iceberg_type
+
+ def serialize(self, value: Any) -> bytes:
+ if self.primitive_type == UUIDType():
+ value = uuid.UUID(bytes=value)
+
+ return to_bytes(self.primitive_type, value)
+
+ def add_min(self, val: Any) -> None:
+ self.current_min = val if self.current_min is None else min(val,
self.current_min)
+
+ def add_max(self, val: Any) -> None:
+ self.current_max = val if self.current_max is None else max(val,
self.current_max)
+
+ def get_min(self) -> bytes:
+ return self.serialize(
+ self.current_min
+ if self.trunc_length is None
+ else
TruncateTransform(width=self.trunc_length).transform(self.primitive_type)(self.current_min)
+ )
+
+ def get_max(self) -> Optional[bytes]:
+ if self.current_max is None:
+ return None
+
+ if self.primitive_type == StringType():
+ if type(self.current_max) != str:
+ raise ValueError("Expected the current_max to be a string")
+
+ s_result = self.current_max[: self.trunc_length]
+ if s_result != self.current_max:
+ chars = [*s_result]
+
+ for i in range(-1, -len(s_result) - 1, -1):
+ try:
+ to_inc = ord(chars[i])
+ # will raise exception if the highest unicode code is
reached
+ _next = chr(to_inc + 1)
+ chars[i] = _next
+ return self.serialize("".join(chars))
+ except ValueError:
+ pass
+ return None # didn't find a valid upper bound
+ return self.serialize(s_result)
+ elif self.primitive_type == BinaryType():
+ if type(self.current_max) != bytes:
+ raise ValueError("Expected the current_max to be bytes")
+ b_result = self.current_max[: self.trunc_length]
+ if b_result != self.current_max:
+ _bytes = [*b_result]
+ for i in range(-1, -len(b_result) - 1, -1):
+ if _bytes[i] < 255:
+ _bytes[i] += 1
+ return b"".join([i.to_bytes(1, byteorder="little") for
i in _bytes])
+ return None
+
+ return self.serialize(b_result)
+ else:
+ return self.serialize(self.current_max)[: self.trunc_length]
+
+
+DEFAULT_TRUNCATION_LENGTH = 16
+TRUNCATION_EXPR = r"^truncate\((\d+)\)$"
+
+
+class MetricModeTypes(Enum):
+ TRUNCATE = "truncate"
+ NONE = "none"
+ COUNTS = "counts"
+ FULL = "full"
+
+
+DEFAULT_METRICS_MODE_KEY = "write.metadata.metrics.default"
+COLUMN_METRICS_MODE_KEY_PREFIX = "write.metadata.metrics.column"
+
+
+@dataclass(frozen=True)
+class MetricsMode(Singleton):
+ type: MetricModeTypes
+ length: Optional[int] = None
+
+
+def match_metrics_mode(mode: str) -> MetricsMode:
+ sanitized_mode = mode.lower()
+ if sanitized_mode.startswith("truncate"):
+ m = re.match(TRUNCATION_EXPR, mode, re.IGNORECASE)
+ if m:
+ length = int(m[1])
+ if length < 1:
+ raise ValueError("Truncation length must be larger than 0")
+ return MetricsMode(MetricModeTypes.TRUNCATE, int(m[1]))
+ else:
+ raise ValueError(f"Malformed truncate: {mode}")
+ elif sanitized_mode.startswith("none"):
+ return MetricsMode(MetricModeTypes.NONE)
+ elif sanitized_mode.startswith("counts"):
+ return MetricsMode(MetricModeTypes.COUNTS)
+ elif sanitized_mode.startswith("full"):
+ return MetricsMode(MetricModeTypes.FULL)
+ else:
+ raise ValueError(f"Unsupported metrics mode: {mode}")
+
+
+@dataclass(frozen=True)
+class StatisticsCollector:
+ field_id: int
+ iceberg_type: PrimitiveType
+ mode: MetricsMode
+ column_name: str
+
+
+class
PyArrowStatisticsCollector(PreOrderSchemaVisitor[List[StatisticsCollector]]):
+ _field_id: int = 0
+ _schema: Schema
+ _properties: Dict[str, str]
+
+ def __init__(self, schema: Schema, properties: Dict[str, str]):
+ self._schema = schema
+ self._properties = properties
+
+ def schema(self, schema: Schema, struct_result: Callable[[],
List[StatisticsCollector]]) -> List[StatisticsCollector]:
+ return struct_result()
+
+ def struct(
+ self, struct: StructType, field_results: List[Callable[[],
List[StatisticsCollector]]]
+ ) -> List[StatisticsCollector]:
+ return list(chain(*[result() for result in field_results]))
+
+ def field(self, field: NestedField, field_result: Callable[[],
List[StatisticsCollector]]) -> List[StatisticsCollector]:
+ self._field_id = field.field_id
+ result = field_result()
+ return result
+
+ def list(self, list_type: ListType, element_result: Callable[[],
List[StatisticsCollector]]) -> List[StatisticsCollector]:
+ self._field_id = list_type.element_id
+ return element_result()
+
+ def map(
+ self,
+ map_type: MapType,
+ key_result: Callable[[], List[StatisticsCollector]],
+ value_result: Callable[[], List[StatisticsCollector]],
+ ) -> List[StatisticsCollector]:
+ self._field_id = map_type.key_id
+ k = key_result()
+ self._field_id = map_type.value_id
+ v = value_result()
+ return k + v
+
+ def primitive(self, primitive: PrimitiveType) -> List[StatisticsCollector]:
+ column_name = self._schema.find_column_name(self._field_id)
+ if column_name is None:
+ raise ValueError(f"Column for field {self._field_id} not found")
+
+ metrics_mode = MetricsMode(MetricModeTypes.TRUNCATE,
DEFAULT_TRUNCATION_LENGTH)
+
+ default_mode = self._properties.get(DEFAULT_METRICS_MODE_KEY)
+ if default_mode:
+ metrics_mode = match_metrics_mode(default_mode)
+
+ col_mode =
self._properties.get(f"{COLUMN_METRICS_MODE_KEY_PREFIX}.{column_name}")
+ if col_mode:
+ metrics_mode = match_metrics_mode(col_mode)
+
+ if (
+ not (isinstance(primitive, StringType) or isinstance(primitive,
BinaryType))
+ and metrics_mode.type == MetricModeTypes.TRUNCATE
+ ):
+ metrics_mode = MetricsMode(MetricModeTypes.FULL)
+
+ return [StatisticsCollector(field_id=self._field_id,
iceberg_type=primitive, mode=metrics_mode, column_name=column_name)]
+
+
+def fill_parquet_file_metadata(
+ df: DataFile,
+ parquet_metadata: pq.FileMetaData,
+ file_size: int,
+ table_metadata: TableMetadata,
+) -> None:
+ """
+ Computes and fills the following fields of the DataFile object.
+
+ - file_format
+ - record_count
+ - file_size_in_bytes
+ - column_sizes
+ - value_counts
+ - null_value_counts
+ - nan_value_counts
+ - lower_bounds
+ - upper_bounds
+ - split_offsets
+
+ Args:
+ df (DataFile): A DataFile object representing the Parquet file for
which metadata is to be filled.
+ parquet_metadata (pyarrow.parquet.FileMetaData): A pyarrow metadata
object.
+ file_size (int): The total compressed file size cannot be retrieved
from the metadata and hence has to
+ be passed here. Depending on the kind of file system and pyarrow
library call used, different
+ ways to obtain this value might be appropriate.
+ table_metadata (pyiceberg.table.metadata.TableMetadata): The Iceberg
table metadata. It is required to
+ compute the mapping if column position to iceberg schema type id.
It's also used to set the mode
+ for column metrics collection
+ """
+ schema = next(filter(lambda s: s.schema_id ==
table_metadata.current_schema_id, table_metadata.schemas))
+
+ stats_columns = pre_order_visit(schema, PyArrowStatisticsCollector(schema,
table_metadata.properties))
+
+ if parquet_metadata.num_columns != len(stats_columns):
+ raise ValueError(
+ f"Number of columns in metadata ({len(stats_columns)}) is
different from the number of columns in pyarrow table
({parquet_metadata.num_columns})"
+ )
+
+ column_sizes: Dict[int, int] = {}
+ value_counts: Dict[int, int] = {}
+ split_offsets: List[int] = []
+
+ null_value_counts: Dict[int, int] = {}
+ nan_value_counts: Dict[int, int] = {}
+
+ col_aggs = {}
+
+ for r in range(parquet_metadata.num_row_groups):
+ # References:
+ #
https://github.com/apache/iceberg/blob/fc381a81a1fdb8f51a0637ca27cd30673bd7aad3/parquet/src/main/java/org/apache/iceberg/parquet/ParquetUtil.java#L232
+ #
https://github.com/apache/parquet-mr/blob/ac29db4611f86a07cc6877b416aa4b183e09b353/parquet-hadoop/src/main/java/org/apache/parquet/hadoop/metadata/ColumnChunkMetaData.java#L184
+
+ row_group = parquet_metadata.row_group(r)
+
+ data_offset = row_group.column(0).data_page_offset
+ dictionary_offset = row_group.column(0).dictionary_page_offset
+
+ if row_group.column(0).has_dictionary_page and dictionary_offset <
data_offset:
+ split_offsets.append(dictionary_offset)
+ else:
+ split_offsets.append(data_offset)
+
+ for pos, stats_col in enumerate(stats_columns):
+ field_id = stats_col.field_id
+
+ column = row_group.column(pos)
+
+ column_sizes[field_id] = column_sizes.get(field_id, 0) +
column.total_compressed_size
+
+ if stats_col.mode == MetricsMode(MetricModeTypes.NONE):
+ continue
+
+ value_counts[field_id] = value_counts.get(field_id, 0) +
column.num_values
+
+ if column.is_stats_set:
+ try:
+ statistics = column.statistics
+
+ null_value_counts[field_id] =
null_value_counts.get(field_id, 0) + statistics.null_count
+
+ if stats_col.mode == MetricsMode(MetricModeTypes.COUNTS):
+ continue
+
+ if field_id not in col_aggs:
+ col_aggs[field_id] = StatsAggregator(
+ stats_col.iceberg_type, statistics.physical_type,
stats_col.mode.length
+ )
+
+ col_aggs[field_id].add_min(statistics.min)
+ col_aggs[field_id].add_max(statistics.max)
+
+ except pyarrow.lib.ArrowNotImplementedError as e:
+ logger.warning(e)
+ else:
+ logger.warning("PyArrow statistics missing for column %d when
writing file", pos)
Review Comment:
Thanks. It's always better to protect ourselves against bugs in other
libraries.
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