sungwy commented on code in PR #931:
URL: https://github.com/apache/iceberg-python/pull/931#discussion_r1766801854
##########
mkdocs/docs/api.md:
##########
@@ -353,6 +353,127 @@ lat: [[52.371807,37.773972,53.11254],[53.21917]]
long: [[4.896029,-122.431297,6.0989],[6.56667]]
```
+### Partial overwrites
+
+You can use overwrite with an overwrite filter
`tbl.overwrite(df,overwrite_filter)` to delete partial table data which matches
the filter before appending new data.
Review Comment:
```suggestion
When using the `overwrite` API, you can use an `overwrite_filter` to delete
data that that matches the filter before appending new data into the table.
```
##########
mkdocs/docs/api.md:
##########
@@ -353,6 +353,127 @@ lat: [[52.371807,37.773972,53.11254],[53.21917]]
long: [[4.896029,-122.431297,6.0989],[6.56667]]
```
+### Partial overwrites
+
+You can use overwrite with an overwrite filter
`tbl.overwrite(df,overwrite_filter)` to delete partial table data which matches
the filter before appending new data.
+
+For example, with an iceberg table created as:
+
+```python
+from pyiceberg.catalog import load_catalog
+
+catalog = load_catalog("default")
+
+from pyiceberg.schema import Schema
+from pyiceberg.types import NestedField, StringType, DoubleType
+
+schema = Schema(
+ NestedField(1, "city", StringType(), required=False),
+ NestedField(2, "lat", DoubleType(), required=False),
+ NestedField(3, "long", DoubleType(), required=False),
+)
+
+tbl = catalog.create_table("default.cities", schema=schema)
+```
+
+And with initial data populating the table:
+
+```python
+import pyarrow as pa
+df = pa.Table.from_pylist(
+ [
+ {"city": "Amsterdam", "lat": 52.371807, "long": 4.896029},
+ {"city": "San Francisco", "lat": 37.773972, "long": -122.431297},
+ {"city": "Drachten", "lat": 53.11254, "long": 6.0989},
+ {"city": "Paris", "lat": 48.864716, "long": 2.349014},
+ ],
+)
+tbl.append(df)
+```
+
+You can overwrite the record of `Paris` with a record of `New York`:
+
+```python
+from pyiceberg.expressions import EqualTo
+df = pa.Table.from_pylist(
+ [
+ {"city": "New York", "lat": 40.7128, "long": 74.0060},
+ ]
+)
+tbl.overwrite(df, overwrite_filter=EqualTo('city', "Paris"))
+```
+
+This results in such data if data is printed by `tbl.scan().to_arrow()`:
+
+```python
+pyarrow.Table
+city: large_string
+lat: double
+long: double
+----
+city: [["New York"],["Amsterdam","Drachten","Paris"]]
+lat: [[40.7128],[52.371807,53.11254,48.864716]]
+long: [[74.006],[4.896029,6.0989,2.349014]]
+```
+
+If the PyIceberg table is partitioned, you can use
`tbl.dynamic_partition_overwrite(df)` to replace the partitions with new ones
provided in the dataframe. The partitions to be replaced are detected
automatically.
Review Comment:
```suggestion
If the PyIceberg table is partitioned, you can use
`tbl.dynamic_partition_overwrite(df)` to replace the existing partitions with
new ones provided in the dataframe. The partitions to be replaced are detected
automatically from the provided arrow table.
```
##########
mkdocs/docs/api.md:
##########
@@ -353,6 +353,127 @@ lat: [[52.371807,37.773972,53.11254],[53.21917]]
long: [[4.896029,-122.431297,6.0989],[6.56667]]
```
+### Partial overwrites
+
+You can use overwrite with an overwrite filter
`tbl.overwrite(df,overwrite_filter)` to delete partial table data which matches
the filter before appending new data.
+
+For example, with an iceberg table created as:
+
+```python
+from pyiceberg.catalog import load_catalog
+
+catalog = load_catalog("default")
+
+from pyiceberg.schema import Schema
+from pyiceberg.types import NestedField, StringType, DoubleType
+
+schema = Schema(
+ NestedField(1, "city", StringType(), required=False),
+ NestedField(2, "lat", DoubleType(), required=False),
+ NestedField(3, "long", DoubleType(), required=False),
+)
+
+tbl = catalog.create_table("default.cities", schema=schema)
+```
+
+And with initial data populating the table:
+
+```python
+import pyarrow as pa
+df = pa.Table.from_pylist(
+ [
+ {"city": "Amsterdam", "lat": 52.371807, "long": 4.896029},
+ {"city": "San Francisco", "lat": 37.773972, "long": -122.431297},
+ {"city": "Drachten", "lat": 53.11254, "long": 6.0989},
+ {"city": "Paris", "lat": 48.864716, "long": 2.349014},
+ ],
+)
+tbl.append(df)
+```
+
+You can overwrite the record of `Paris` with a record of `New York`:
+
+```python
+from pyiceberg.expressions import EqualTo
+df = pa.Table.from_pylist(
+ [
+ {"city": "New York", "lat": 40.7128, "long": 74.0060},
+ ]
+)
+tbl.overwrite(df, overwrite_filter=EqualTo('city', "Paris"))
+```
+
+This results in such data if data is printed by `tbl.scan().to_arrow()`:
+
+```python
+pyarrow.Table
+city: large_string
+lat: double
+long: double
+----
+city: [["New York"],["Amsterdam","Drachten","Paris"]]
+lat: [[40.7128],[52.371807,53.11254,48.864716]]
+long: [[74.006],[4.896029,6.0989,2.349014]]
+```
+
+If the PyIceberg table is partitioned, you can use
`tbl.dynamic_partition_overwrite(df)` to replace the partitions with new ones
provided in the dataframe. The partitions to be replaced are detected
automatically.
+To try out it, you could firstly create a same PyIceberg table with partition
specified on `"city"` field:
+
+```python
+from pyiceberg.schema import Schema
+from pyiceberg.types import DoubleType, NestedField, StringType
+
+schema = Schema(
+ NestedField(1, "city", StringType(), required=False),
+ NestedField(2, "lat", DoubleType(), required=False),
+ NestedField(3, "long", DoubleType(), required=False),
+)
+
+tbl = catalog.create_table(
+ "default.cities",
+ schema=schema,
+ partition_spec=PartitionSpec(PartitionField(source_id=1, field_id=1001,
transform=IdentityTransform(), name="city_identity"))
+)
+```
+
+And then suppose the data for the partition of `"paris"` is wrong:
Review Comment:
```suggestion
And we want to overwrite the data for the partition of `"Paris"`:
```
##########
mkdocs/docs/api.md:
##########
@@ -353,6 +353,127 @@ lat: [[52.371807,37.773972,53.11254],[53.21917]]
long: [[4.896029,-122.431297,6.0989],[6.56667]]
```
+### Partial overwrites
+
+You can use overwrite with an overwrite filter
`tbl.overwrite(df,overwrite_filter)` to delete partial table data which matches
the filter before appending new data.
+
+For example, with an iceberg table created as:
+
+```python
+from pyiceberg.catalog import load_catalog
+
+catalog = load_catalog("default")
+
+from pyiceberg.schema import Schema
+from pyiceberg.types import NestedField, StringType, DoubleType
+
+schema = Schema(
+ NestedField(1, "city", StringType(), required=False),
+ NestedField(2, "lat", DoubleType(), required=False),
+ NestedField(3, "long", DoubleType(), required=False),
+)
+
+tbl = catalog.create_table("default.cities", schema=schema)
+```
+
+And with initial data populating the table:
+
+```python
+import pyarrow as pa
+df = pa.Table.from_pylist(
+ [
+ {"city": "Amsterdam", "lat": 52.371807, "long": 4.896029},
+ {"city": "San Francisco", "lat": 37.773972, "long": -122.431297},
+ {"city": "Drachten", "lat": 53.11254, "long": 6.0989},
+ {"city": "Paris", "lat": 48.864716, "long": 2.349014},
+ ],
+)
+tbl.append(df)
+```
+
+You can overwrite the record of `Paris` with a record of `New York`:
+
+```python
+from pyiceberg.expressions import EqualTo
+df = pa.Table.from_pylist(
+ [
+ {"city": "New York", "lat": 40.7128, "long": 74.0060},
+ ]
+)
+tbl.overwrite(df, overwrite_filter=EqualTo('city', "Paris"))
+```
+
+This results in such data if data is printed by `tbl.scan().to_arrow()`:
+
+```python
+pyarrow.Table
+city: large_string
+lat: double
+long: double
+----
+city: [["New York"],["Amsterdam","Drachten","Paris"]]
+lat: [[40.7128],[52.371807,53.11254,48.864716]]
+long: [[74.006],[4.896029,6.0989,2.349014]]
+```
+
+If the PyIceberg table is partitioned, you can use
`tbl.dynamic_partition_overwrite(df)` to replace the partitions with new ones
provided in the dataframe. The partitions to be replaced are detected
automatically.
+To try out it, you could firstly create a same PyIceberg table with partition
specified on `"city"` field:
+
+```python
+from pyiceberg.schema import Schema
+from pyiceberg.types import DoubleType, NestedField, StringType
+
+schema = Schema(
+ NestedField(1, "city", StringType(), required=False),
+ NestedField(2, "lat", DoubleType(), required=False),
+ NestedField(3, "long", DoubleType(), required=False),
+)
+
+tbl = catalog.create_table(
+ "default.cities",
+ schema=schema,
+ partition_spec=PartitionSpec(PartitionField(source_id=1, field_id=1001,
transform=IdentityTransform(), name="city_identity"))
+)
+```
+
+And then suppose the data for the partition of `"paris"` is wrong:
+
+```python
+import pyarrow as pa
+
+df = pa.Table.from_pylist(
+ [
+ {"city": "Amsterdam", "lat": 52.371807, "long": 4.896029},
+ {"city": "San Francisco", "lat": 37.773972, "long": -122.431297},
+ {"city": "Drachten", "lat": 53.11254, "long": 6.0989},
+ {"city": "Paris", "lat": -48.864716, "long": -2.349014},
+ ],
+)
+tbl.append(df)
+```
+
+Then you could use dynamic overwrite on this partition:
+
+```python
+df_corrected = pa.Table.from_pylist([
+ {"city": "Paris", "lat": 48.864716, "long": 2.349014}
+])
+tbl.dynamic_partition_overwrite(df_corrected)
+```
+
+This results in such data if data is printed by `tbl.scan().to_arrow()`:
Review Comment:
```suggestion
This produces the following result with `tbl.scan().to_arrow()`:
```
##########
mkdocs/docs/api.md:
##########
@@ -353,6 +353,127 @@ lat: [[52.371807,37.773972,53.11254],[53.21917]]
long: [[4.896029,-122.431297,6.0989],[6.56667]]
```
+### Partial overwrites
+
+You can use overwrite with an overwrite filter
`tbl.overwrite(df,overwrite_filter)` to delete partial table data which matches
the filter before appending new data.
+
+For example, with an iceberg table created as:
+
+```python
+from pyiceberg.catalog import load_catalog
+
+catalog = load_catalog("default")
+
+from pyiceberg.schema import Schema
+from pyiceberg.types import NestedField, StringType, DoubleType
+
+schema = Schema(
+ NestedField(1, "city", StringType(), required=False),
+ NestedField(2, "lat", DoubleType(), required=False),
+ NestedField(3, "long", DoubleType(), required=False),
+)
+
+tbl = catalog.create_table("default.cities", schema=schema)
+```
+
+And with initial data populating the table:
+
+```python
+import pyarrow as pa
+df = pa.Table.from_pylist(
+ [
+ {"city": "Amsterdam", "lat": 52.371807, "long": 4.896029},
+ {"city": "San Francisco", "lat": 37.773972, "long": -122.431297},
+ {"city": "Drachten", "lat": 53.11254, "long": 6.0989},
+ {"city": "Paris", "lat": 48.864716, "long": 2.349014},
+ ],
+)
+tbl.append(df)
+```
+
+You can overwrite the record of `Paris` with a record of `New York`:
+
+```python
+from pyiceberg.expressions import EqualTo
+df = pa.Table.from_pylist(
+ [
+ {"city": "New York", "lat": 40.7128, "long": 74.0060},
+ ]
+)
+tbl.overwrite(df, overwrite_filter=EqualTo('city', "Paris"))
+```
+
+This results in such data if data is printed by `tbl.scan().to_arrow()`:
+
+```python
+pyarrow.Table
+city: large_string
+lat: double
+long: double
+----
+city: [["New York"],["Amsterdam","Drachten","Paris"]]
+lat: [[40.7128],[52.371807,53.11254,48.864716]]
+long: [[74.006],[4.896029,6.0989,2.349014]]
+```
+
+If the PyIceberg table is partitioned, you can use
`tbl.dynamic_partition_overwrite(df)` to replace the partitions with new ones
provided in the dataframe. The partitions to be replaced are detected
automatically.
+To try out it, you could firstly create a same PyIceberg table with partition
specified on `"city"` field:
+
+```python
+from pyiceberg.schema import Schema
+from pyiceberg.types import DoubleType, NestedField, StringType
+
+schema = Schema(
+ NestedField(1, "city", StringType(), required=False),
+ NestedField(2, "lat", DoubleType(), required=False),
+ NestedField(3, "long", DoubleType(), required=False),
+)
+
+tbl = catalog.create_table(
+ "default.cities",
+ schema=schema,
+ partition_spec=PartitionSpec(PartitionField(source_id=1, field_id=1001,
transform=IdentityTransform(), name="city_identity"))
+)
+```
+
+And then suppose the data for the partition of `"paris"` is wrong:
+
+```python
+import pyarrow as pa
+
+df = pa.Table.from_pylist(
+ [
+ {"city": "Amsterdam", "lat": 52.371807, "long": 4.896029},
+ {"city": "San Francisco", "lat": 37.773972, "long": -122.431297},
+ {"city": "Drachten", "lat": 53.11254, "long": 6.0989},
+ {"city": "Paris", "lat": -48.864716, "long": -2.349014},
+ ],
+)
+tbl.append(df)
+```
+
+Then you could use dynamic overwrite on this partition:
Review Comment:
```suggestion
Then we can call `dynamic_partition_overwrite` with this arrow table:
```
##########
mkdocs/docs/api.md:
##########
@@ -353,6 +353,127 @@ lat: [[52.371807,37.773972,53.11254],[53.21917]]
long: [[4.896029,-122.431297,6.0989],[6.56667]]
```
+### Partial overwrites
+
+You can use overwrite with an overwrite filter
`tbl.overwrite(df,overwrite_filter)` to delete partial table data which matches
the filter before appending new data.
+
+For example, with an iceberg table created as:
+
+```python
+from pyiceberg.catalog import load_catalog
+
+catalog = load_catalog("default")
+
+from pyiceberg.schema import Schema
+from pyiceberg.types import NestedField, StringType, DoubleType
+
+schema = Schema(
+ NestedField(1, "city", StringType(), required=False),
+ NestedField(2, "lat", DoubleType(), required=False),
+ NestedField(3, "long", DoubleType(), required=False),
+)
+
+tbl = catalog.create_table("default.cities", schema=schema)
+```
+
+And with initial data populating the table:
+
+```python
+import pyarrow as pa
+df = pa.Table.from_pylist(
+ [
+ {"city": "Amsterdam", "lat": 52.371807, "long": 4.896029},
+ {"city": "San Francisco", "lat": 37.773972, "long": -122.431297},
+ {"city": "Drachten", "lat": 53.11254, "long": 6.0989},
+ {"city": "Paris", "lat": 48.864716, "long": 2.349014},
+ ],
+)
+tbl.append(df)
+```
+
+You can overwrite the record of `Paris` with a record of `New York`:
+
+```python
+from pyiceberg.expressions import EqualTo
+df = pa.Table.from_pylist(
+ [
+ {"city": "New York", "lat": 40.7128, "long": 74.0060},
+ ]
+)
+tbl.overwrite(df, overwrite_filter=EqualTo('city', "Paris"))
+```
+
+This results in such data if data is printed by `tbl.scan().to_arrow()`:
Review Comment:
```suggestion
This produces the following result with `tbl.scan().to_arrow()`:
```
##########
pyiceberg/table/__init__.py:
##########
@@ -456,6 +461,89 @@ def append(self, df: pa.Table, snapshot_properties:
Dict[str, str] = EMPTY_DICT)
for data_file in data_files:
append_files.append_data_file(data_file)
+ def _build_partition_predicate(self, partition_records: Set[Record]) ->
BooleanExpression:
+ partition_spec = self.table_metadata.spec()
+ schema = self.table_metadata.schema()
+ partition_fields = [schema.find_field(field.source_id).name for field
in partition_spec.fields]
+
+ expr: BooleanExpression = AlwaysFalse()
+ for partition_record in partition_records:
+ match_partition_expression: BooleanExpression = AlwaysTrue()
+
+ for pos, partition_field in enumerate(partition_fields):
+ predicate = (
+ EqualTo(Reference(partition_field), partition_record[pos])
+ if partition_record[pos] is not None
+ else IsNull(Reference(partition_field))
+ )
+ match_partition_expression = And(match_partition_expression,
predicate)
+ expr = Or(expr, match_partition_expression)
+ return expr
+
+ def dynamic_partition_overwrite(self, df: pa.Table, snapshot_properties:
Dict[str, str] = EMPTY_DICT) -> None:
+ """
+ Shorthand for overwriting existing partitions with a PyArrow table.
+
+ The function detects partition values in the provided arrow table that
using the current table
+ partition spec, and deletes existing partitions matching these values.
Finally, the
+ data in the table is appended to the table.
Review Comment:
```suggestion
The function detects partition values in the provided arrow table
using the current
partition spec, and deletes existing partitions matching these
values. Finally, the
data in the table is appended to the table.
```
##########
mkdocs/docs/api.md:
##########
@@ -353,6 +353,127 @@ lat: [[52.371807,37.773972,53.11254],[53.21917]]
long: [[4.896029,-122.431297,6.0989],[6.56667]]
```
+### Partial overwrites
+
+You can use overwrite with an overwrite filter
`tbl.overwrite(df,overwrite_filter)` to delete partial table data which matches
the filter before appending new data.
+
+For example, with an iceberg table created as:
+
+```python
+from pyiceberg.catalog import load_catalog
+
+catalog = load_catalog("default")
+
+from pyiceberg.schema import Schema
+from pyiceberg.types import NestedField, StringType, DoubleType
+
+schema = Schema(
+ NestedField(1, "city", StringType(), required=False),
+ NestedField(2, "lat", DoubleType(), required=False),
+ NestedField(3, "long", DoubleType(), required=False),
+)
+
+tbl = catalog.create_table("default.cities", schema=schema)
+```
+
+And with initial data populating the table:
+
+```python
+import pyarrow as pa
+df = pa.Table.from_pylist(
+ [
+ {"city": "Amsterdam", "lat": 52.371807, "long": 4.896029},
+ {"city": "San Francisco", "lat": 37.773972, "long": -122.431297},
+ {"city": "Drachten", "lat": 53.11254, "long": 6.0989},
+ {"city": "Paris", "lat": 48.864716, "long": 2.349014},
+ ],
+)
+tbl.append(df)
+```
+
+You can overwrite the record of `Paris` with a record of `New York`:
+
+```python
+from pyiceberg.expressions import EqualTo
+df = pa.Table.from_pylist(
+ [
+ {"city": "New York", "lat": 40.7128, "long": 74.0060},
+ ]
+)
+tbl.overwrite(df, overwrite_filter=EqualTo('city', "Paris"))
+```
+
+This results in such data if data is printed by `tbl.scan().to_arrow()`:
+
+```python
+pyarrow.Table
+city: large_string
+lat: double
+long: double
+----
+city: [["New York"],["Amsterdam","Drachten","Paris"]]
+lat: [[40.7128],[52.371807,53.11254,48.864716]]
+long: [[74.006],[4.896029,6.0989,2.349014]]
+```
+
+If the PyIceberg table is partitioned, you can use
`tbl.dynamic_partition_overwrite(df)` to replace the partitions with new ones
provided in the dataframe. The partitions to be replaced are detected
automatically.
+To try out it, you could firstly create a same PyIceberg table with partition
specified on `"city"` field:
Review Comment:
```suggestion
For example, with an iceberg table with a partition specified on `"city"`
field:
```
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