Cast 's', 'ms' and 'ns' PyArrow timestamp to 'us' precision on write

mkdocs/docs/configuration.md

@@ -305,4 +305,8 @@ PyIceberg uses multiple threads to parallelize operations. The number of workers
 
 # Backward Compatibility
 
-Previous versions of Java (`<1.4.0`) implementations incorrectly assume the optional attribute `current-snapshot-id` to be a required attribute in TableMetadata. This means that if `current-snapshot-id` is missing in the metadata file (e.g. on table creation), the application will throw an exception without being able to load the table. This assumption has been corrected in more recent Iceberg versions. However, it is possible to force PyIceberg to create a table with a metadata file that will be compatible with previous versions. This can be configured by setting the `legacy-current-snapshot-id` entry as "True" in the configuration file, or by setting the `PYICEBERG_LEGACY_CURRENT_SNAPSHOT_ID` environment variable. Refer to the [PR discussion](https://github.com/apache/iceberg-python/pull/473) for more details on the issue
+Previous versions of Java (`<1.4.0`) implementations incorrectly assume the optional attribute `current-snapshot-id` to be a required attribute in TableMetadata. This means that if `current-snapshot-id` is missing in the metadata file (e.g. on table creation), the application will throw an exception without being able to load the table. This assumption has been corrected in more recent Iceberg versions. However, it is possible to force PyIceberg to create a table with a metadata file that will be compatible with previous versions. This can be configured by setting the `legacy-current-snapshot-id` property as "True" in the configuration file, or by setting the `PYICEBERG_LEGACY_CURRENT_SNAPSHOT_ID` environment variable. Refer to the [PR discussion](https://github.com/apache/iceberg-python/pull/473) for more details on the issue
+
+# Nanoseconds Support
+
+PyIceberg currently only supports upto microsecond precision in its TimestampType. PyArrow timestamp types in 's' and 'ms' will be upcast automatically to 'us' precision timestamps on write. Timestamps in 'ns' precision can also be downcast automatically on write if desired. This can be configured by setting the `downcast-ns-timestamp-to-us-on-write` property as "True" in the configuration file, or by setting the `PYICEBERG_DOWNCAST_NS_TIMESTAMP_TO_US_ON_WRITE` environment variable. Refer to the [nanoseconds timestamp proposal document](https://docs.google.com/document/d/1bE1DcEGNzZAMiVJSZ0X1wElKLNkT9kRkk0hDlfkXzvU/edit#heading=h.ibflcctc9i1d) for more details on the long term roadmap for nanoseconds support

pyiceberg/catalog/__init__.py

@@ -49,6 +49,7 @@
 from pyiceberg.schema import Schema
 from pyiceberg.serializers import ToOutputFile
 from pyiceberg.table import (
+    DOWNCAST_NS_TIMESTAMP_TO_US_ON_WRITE,
     CommitTableRequest,
     CommitTableResponse,
     CreateTableTransaction,
@@ -675,8 +676,11 @@ def _convert_schema_if_needed(schema: Union[Schema, "pa.Schema"]) -> Schema:
 
             from pyiceberg.io.pyarrow import _ConvertToIcebergWithoutIDs, visit_pyarrow
 
+            downcast_ns_timestamp_to_us = Config().get_bool(DOWNCAST_NS_TIMESTAMP_TO_US_ON_WRITE) or False
             if isinstance(schema, pa.Schema):
-                schema: Schema = visit_pyarrow(schema, _ConvertToIcebergWithoutIDs())  # type: ignore
+                schema: Schema = visit_pyarrow(  # type: ignore
+                    schema, _ConvertToIcebergWithoutIDs(downcast_ns_timestamp_to_us=downcast_ns_timestamp_to_us)
+                )
                 return schema
         except ModuleNotFoundError:
             pass

pyiceberg/io/pyarrow.py

@@ -154,6 +154,7 @@
     UUIDType,
 )
 from pyiceberg.utils.concurrent import ExecutorFactory
+from pyiceberg.utils.config import Config
 from pyiceberg.utils.datetime import millis_to_datetime
 from pyiceberg.utils.singleton import Singleton
 from pyiceberg.utils.truncate import truncate_upper_bound_binary_string, truncate_upper_bound_text_string
@@ -470,7 +471,9 @@ def __setstate__(self, state: Dict[str, Any]) -> None:
 
 
 def schema_to_pyarrow(
-    schema: Union[Schema, IcebergType], metadata: Dict[bytes, bytes] = EMPTY_DICT, include_field_ids: bool = True
+    schema: Union[Schema, IcebergType],
+    metadata: Dict[bytes, bytes] = EMPTY_DICT,
+    include_field_ids: bool = True,
 ) -> pa.schema:
     return visit(schema, _ConvertToArrowSchema(metadata, include_field_ids))
 
@@ -663,21 +666,23 @@ def _combine_positional_deletes(positional_deletes: List[pa.ChunkedArray], start
     return np.subtract(np.setdiff1d(np.arange(start_index, end_index), all_chunks, assume_unique=False), start_index)
 
 
-def pyarrow_to_schema(schema: pa.Schema, name_mapping: Optional[NameMapping] = None) -> Schema:
+def pyarrow_to_schema(
+    schema: pa.Schema, name_mapping: Optional[NameMapping] = None, downcast_ns_timestamp_to_us: bool = False
+) -> Schema:
     has_ids = visit_pyarrow(schema, _HasIds())
     if has_ids:
-        visitor = _ConvertToIceberg()
+        visitor = _ConvertToIceberg(downcast_ns_timestamp_to_us=downcast_ns_timestamp_to_us)
     elif name_mapping is not None:
-        visitor = _ConvertToIceberg(name_mapping=name_mapping)
+        visitor = _ConvertToIceberg(name_mapping=name_mapping, downcast_ns_timestamp_to_us=downcast_ns_timestamp_to_us)
     else:
         raise ValueError(
             "Parquet file does not have field-ids and the Iceberg table does not have 'schema.name-mapping.default' defined"
         )
     return visit_pyarrow(schema, visitor)
 
 
-def _pyarrow_to_schema_without_ids(schema: pa.Schema) -> Schema:
-    return visit_pyarrow(schema, _ConvertToIcebergWithoutIDs())
+def _pyarrow_to_schema_without_ids(schema: pa.Schema, downcast_ns_timestamp_to_us: bool = False) -> Schema:
+    return visit_pyarrow(schema, _ConvertToIcebergWithoutIDs(downcast_ns_timestamp_to_us=downcast_ns_timestamp_to_us))
 
 
 def _pyarrow_schema_ensure_large_types(schema: pa.Schema) -> pa.Schema:
@@ -849,9 +854,10 @@ class _ConvertToIceberg(PyArrowSchemaVisitor[Union[IcebergType, Schema]]):
     _field_names: List[str]
     _name_mapping: Optional[NameMapping]
 
-    def __init__(self, name_mapping: Optional[NameMapping] = None) -> None:
+    def __init__(self, name_mapping: Optional[NameMapping] = None, downcast_ns_timestamp_to_us: bool = False) -> None:
         self._field_names = []
         self._name_mapping = name_mapping
+        self._downcast_ns_timestamp_to_us = downcast_ns_timestamp_to_us
 
     def _field_id(self, field: pa.Field) -> int:
         if self._name_mapping:
@@ -918,11 +924,24 @@ def primitive(self, primitive: pa.DataType) -> PrimitiveType:
             return TimeType()
         elif pa.types.is_timestamp(primitive):
             primitive = cast(pa.TimestampType, primitive)
-            if primitive.unit == "us":
-                if primitive.tz == "UTC" or primitive.tz == "+00:00":
-                    return TimestamptzType()
-                elif primitive.tz is None:
-                    return TimestampType()
+            if primitive.unit in ("s", "ms", "us"):
+                # Supported types, will be upcast automatically to 'us'
+                pass
+            elif primitive.unit == "ns":
+                if self._downcast_ns_timestamp_to_us:
+                    logger.warning("Iceberg does not yet support 'ns' timestamp precision. Downcasting to 'us'.")
+                else:
+                    raise TypeError(
+                        "Iceberg does not yet support 'ns' timestamp precision. Use 'downcast-ns-timestamp-to-us-on-write' configuration property to automatically downcast 'ns' to 'us' on write."
+                    )
+            else:
+                raise TypeError(f"Unsupported precision for timestamp type: {primitive.unit}")
+
+            if primitive.tz == "UTC" or primitive.tz == "+00:00":
+                return TimestamptzType()
+            elif primitive.tz is None:
+                return TimestampType()
+
         elif pa.types.is_binary(primitive) or pa.types.is_large_binary(primitive):
             return BinaryType()
         elif pa.types.is_fixed_size_binary(primitive):
@@ -1010,8 +1029,11 @@ def _task_to_record_batches(
     with fs.open_input_file(path) as fin:
         fragment = arrow_format.make_fragment(fin)
         physical_schema = fragment.physical_schema
-        file_schema = pyarrow_to_schema(physical_schema, name_mapping)
-
+        # In V1 and V2 table formats, we only support Timestamp 'us' in Iceberg Schema
+        # Hence it is reasonable to always cast 'ns' timestamp to 'us' on read.
+        # When V3 support is introduced, we will update `downcast_ns_timestamp_to_us` flag based on
+        # the table format version.
+        file_schema = pyarrow_to_schema(physical_schema, name_mapping, downcast_ns_timestamp_to_us=True)
         pyarrow_filter = None
         if bound_row_filter is not AlwaysTrue():
             translated_row_filter = translate_column_names(bound_row_filter, file_schema, case_sensitive=case_sensitive)
@@ -1049,7 +1071,7 @@ def _task_to_record_batches(
                     arrow_table = pa.Table.from_batches([batch])
                     arrow_table = arrow_table.filter(pyarrow_filter)
                     batch = arrow_table.to_batches()[0]
-            yield to_requested_schema(projected_schema, file_project_schema, batch)
+            yield to_requested_schema(projected_schema, file_project_schema, batch, downcast_ns_timestamp_to_us=True)
             current_index += len(batch)
 
 
@@ -1248,8 +1270,12 @@ def project_batches(
             total_row_count += len(batch)
 
 
-def to_requested_schema(requested_schema: Schema, file_schema: Schema, batch: pa.RecordBatch) -> pa.RecordBatch:
-    struct_array = visit_with_partner(requested_schema, batch, ArrowProjectionVisitor(file_schema), ArrowAccessor(file_schema))
+def to_requested_schema(
+    requested_schema: Schema, file_schema: Schema, batch: pa.RecordBatch, downcast_ns_timestamp_to_us: bool = False
+) -> pa.RecordBatch:
+    struct_array = visit_with_partner(
+        requested_schema, batch, ArrowProjectionVisitor(file_schema, downcast_ns_timestamp_to_us), ArrowAccessor(file_schema)
+    )
 
     arrays = []
     fields = []
@@ -1263,8 +1289,9 @@ def to_requested_schema(requested_schema: Schema, file_schema: Schema, batch: pa
 class ArrowProjectionVisitor(SchemaWithPartnerVisitor[pa.Array, Optional[pa.Array]]):
     file_schema: Schema
 
-    def __init__(self, file_schema: Schema):
+    def __init__(self, file_schema: Schema, downcast_ns_timestamp_to_us: bool = False):
         self.file_schema = file_schema
+        self.downcast_ns_timestamp_to_us = downcast_ns_timestamp_to_us
 
     def _cast_if_needed(self, field: NestedField, values: pa.Array) -> pa.Array:
         file_field = self.file_schema.find_field(field.field_id)
@@ -1275,7 +1302,15 @@ def _cast_if_needed(self, field: NestedField, values: pa.Array) -> pa.Array:
                 # if file_field and field_type  (e.g. String) are the same
                 # but the pyarrow type of the array is different from the expected type
                 # (e.g. string vs larger_string), we want to cast the array to the larger type
-                return values.cast(target_type)
+                safe = True
+                if (
+                    pa.types.is_timestamp(target_type)
+                    and target_type.unit == "us"
+                    and pa.types.is_timestamp(values.type)
+                    and values.type.unit == "ns"
+                ):
+                    safe = False
+                return values.cast(target_type, safe=safe)
         return values
 
     def _construct_field(self, field: NestedField, arrow_type: pa.DataType) -> pa.Field:
@@ -1899,7 +1934,7 @@ def data_file_statistics_from_parquet_metadata(
 
 
 def write_file(io: FileIO, table_metadata: TableMetadata, tasks: Iterator[WriteTask]) -> Iterator[DataFile]:
-    from pyiceberg.table import PropertyUtil, TableProperties
+    from pyiceberg.table import DOWNCAST_NS_TIMESTAMP_TO_US_ON_WRITE, PropertyUtil, TableProperties
 
     parquet_writer_kwargs = _get_parquet_writer_kwargs(table_metadata.properties)
     row_group_size = PropertyUtil.property_as_int(
@@ -1918,8 +1953,14 @@ def write_parquet(task: WriteTask) -> DataFile:
         else:
             file_schema = table_schema
 
+        downcast_ns_timestamp_to_us = Config().get_bool(DOWNCAST_NS_TIMESTAMP_TO_US_ON_WRITE) or False
         batches = [
-            to_requested_schema(requested_schema=file_schema, file_schema=table_schema, batch=batch)
+            to_requested_schema(
+                requested_schema=file_schema,
+                file_schema=table_schema,
+                batch=batch,
+                downcast_ns_timestamp_to_us=downcast_ns_timestamp_to_us,
+            )
             for batch in task.record_batches
         ]
         arrow_table = pa.Table.from_batches(batches)

pyiceberg/table/__init__.py

@@ -146,6 +146,7 @@
     transform_dict_value_to_str,
 )
 from pyiceberg.utils.concurrent import ExecutorFactory
+from pyiceberg.utils.config import Config
 from pyiceberg.utils.datetime import datetime_to_millis
 from pyiceberg.utils.deprecated import deprecated
 from pyiceberg.utils.singleton import _convert_to_hashable_type
@@ -161,7 +162,7 @@
 
 ALWAYS_TRUE = AlwaysTrue()
 TABLE_ROOT_ID = -1
-
+DOWNCAST_NS_TIMESTAMP_TO_US_ON_WRITE = "downcast-ns-timestamp-to-us-on-write"
 _JAVA_LONG_MAX = 9223372036854775807
 
 
@@ -176,11 +177,14 @@ def _check_schema_compatible(table_schema: Schema, other_schema: "pa.Schema") ->
     """
     from pyiceberg.io.pyarrow import _pyarrow_to_schema_without_ids, pyarrow_to_schema
 
+    downcast_ns_timestamp_to_us = Config().get_bool(DOWNCAST_NS_TIMESTAMP_TO_US_ON_WRITE) or False
     name_mapping = table_schema.name_mapping
     try:
-        task_schema = pyarrow_to_schema(other_schema, name_mapping=name_mapping)
+        task_schema = pyarrow_to_schema(
+            other_schema, name_mapping=name_mapping, downcast_ns_timestamp_to_us=downcast_ns_timestamp_to_us
+        )
     except ValueError as e:
-        other_schema = _pyarrow_to_schema_without_ids(other_schema)
+        other_schema = _pyarrow_to_schema_without_ids(other_schema, downcast_ns_timestamp_to_us=downcast_ns_timestamp_to_us)
         additional_names = set(other_schema.column_names) - set(table_schema.column_names)
         raise ValueError(
             f"PyArrow table contains more columns: {', '.join(sorted(additional_names))}. Update the schema first (hint, use union_by_name)."

tests/integration/test_add_files.py

@@ -16,13 +16,15 @@
 # under the License.
 # pylint:disable=redefined-outer-name
 
+import os
 from datetime import date
 from typing import Iterator, Optional
 
 import pyarrow as pa
 import pyarrow.parquet as pq
 import pytest
 from pyspark.sql import SparkSession
+from pytest_mock.plugin import MockerFixture
 
 from pyiceberg.catalog import Catalog
 from pyiceberg.exceptions import NoSuchTableError
@@ -36,6 +38,7 @@
     IntegerType,
     NestedField,
     StringType,
+    TimestamptzType,
 )
 
 TABLE_SCHEMA = Schema(
@@ -448,3 +451,59 @@ def test_add_files_snapshot_properties(spark: SparkSession, session_catalog: Cat
 
     assert "snapshot_prop_a" in summary
     assert summary["snapshot_prop_a"] == "test_prop_a"
+
+
+@pytest.mark.integration
+def test_timestamp_tz_ns_downcast_on_read(session_catalog: Catalog, format_version: int, mocker: MockerFixture) -> None:
+    nanoseconds_schema_iceberg = Schema(NestedField(1, "quux", TimestamptzType()))
+
+    nanoseconds_schema = pa.schema([
+        ("quux", pa.timestamp("ns", tz="UTC")),
+    ])
+
+    arrow_table = pa.Table.from_pylist(
+        [
+            {
+                "quux": 1615967687249846175,  # 2021-03-17 07:54:47.249846159
+            }
+        ],
+        schema=nanoseconds_schema,
+    )
+    mocker.patch.dict(os.environ, values={"PYICEBERG_DOWNCAST_NS_TIMESTAMP_TO_US_ON_WRITE": "True"})
+
+    identifier = f"default.timestamptz_ns_added{format_version}"
+
+    try:
+        session_catalog.drop_table(identifier=identifier)
+    except NoSuchTableError:
+        pass
+
+    tbl = session_catalog.create_table(
+        identifier=identifier,
+        schema=nanoseconds_schema_iceberg,
+        properties={"format-version": str(format_version)},
+        partition_spec=PartitionSpec(),
+    )
+
+    file_paths = [f"s3://warehouse/default/test_timestamp_tz/v{format_version}/test-{i}.parquet" for i in range(5)]
+    # write parquet files
+    for file_path in file_paths:
+        fo = tbl.io.new_output(file_path)
+        with fo.create(overwrite=True) as fos:
+            with pq.ParquetWriter(fos, schema=nanoseconds_schema) as writer:
+                writer.write_table(arrow_table)
+
+    # add the parquet files as data files
+    tbl.add_files(file_paths=file_paths)
+
+    assert tbl.scan().to_arrow() == pa.concat_tables(
+        [
+            arrow_table.cast(
+                pa.schema([
+                    ("quux", pa.timestamp("us", tz="UTC")),
+                ]),
+                safe=False,
+            )
+        ]
+        * 5
+    )