chore: prepare v0.4.2 (#1041)

Author: linhr

Cargo.lock

@@ -5,7 +5,7 @@ members = [
 resolver = "2"
 
 [workspace.package]
-version = "0.4.1"
+version = "0.4.2"
 authors = ["LakeSail <[email protected]>"]
 edition = "2021"
 homepage = "https://lakesail.com"

Cargo.toml

@@ -98,14 +98,26 @@ df.write.format("delta").mode("overwrite").option("overwriteSchema", "true").sav
 
 ### Time Travel
 
-You can use the time travel feature of Delta Lake to query historical versions of a Delta table.
+You can use the time travel feature to query historical versions of a Delta table.
 
 ```python
 df = spark.read.format("delta").option("versionAsOf", "0").load(path)
+df = spark.read.format("delta").option("timestampAsOf", "2025-01-02T03:04:05.678").load(path)
 ```
 
 Time travel is not available for Spark SQL in Sail yet, but we plan to support it soon.
 
+### Column Mapping
+
+You can write Delta tables with column mapping enabled. The supported column mapping modes are `name` and `id`. You must write to a new Delta table to enable column mapping.
+
+```python
+df.write.format("delta").option("columnMappingMode", "name").save(path)
+df.write.format("delta").option("columnMappingMode", "id").save(path)
+```
+
+Existing Delta tables with column mapping can be read as usual.
+
 ### More Features
 
 We will continue adding more examples for advanced Delta Lake features as they become available in Sail.

docs/guide/formats/delta.md

@@ -78,6 +78,18 @@ SELECT * FROM metrics WHERE year > 2024;
 
 :::
 
+### Time Travel
+
+You can use the time travel feature to query tags, branches, or historical versions of an Iceberg table.
+
+```python
+df = spark.read.format("iceberg").option("snapshotId", "123").load(path)
+df = spark.read.format("iceberg").option("timestampAsOf", "2025-01-02T03:04:05.678").load(path)
+df = spark.read.format("iceberg").option("branch", "main").load(path)
+```
+
+Time travel is not available for Spark SQL in Sail yet, but we plan to support it soon.
+
 ### More Features
 
 We will continue adding more examples for advanced Iceberg features as they become available in Sail.

docs/guide/formats/iceberg.md

@@ -5,6 +5,34 @@ next: false
 
 # Changelog
 
+## 0.4.2
+
+_November 13, 2025_
+
+- Supported column mapping for Delta Lake ([#985](https://github.com/lakehq/sail/pull/985)).
+- Supported time travel for Iceberg ([#1039](https://github.com/lakehq/sail/pull/1039)).
+- Supported Unity Catalog ([#1005](https://github.com/lakehq/sail/pull/1005)).
+- Improved Iceberg integration ([#1006](https://github.com/lakehq/sail/pull/1006), [#1009](https://github.com/lakehq/sail/pull/1009), and [#1042](https://github.com/lakehq/sail/pull/1042)).
+- Added the `luhn_check` SQL function ([#909](https://github.com/lakehq/sail/pull/909)).
+- Improved the following SQL functions ([#909](https://github.com/lakehq/sail/pull/909) and [#1024](https://github.com/lakehq/sail/pull/1024)):
+  - `bit_count`
+  - `bit_get`
+  - `getbit`
+  - `crc32`
+  - `sha`
+  - `sha1`
+  - `expm1`
+  - `pmod`
+  - `width_bucket`
+  - `bitmap_count`
+  - `to_date`
+- Added the `try_avg` SQL aggregate function ([#1012](https://github.com/lakehq/sail/pull/1012)).
+- Supported the `try_sum` and `try_avg` SQL aggregate functions in window expressions ([#1040](https://github.com/lakehq/sail/pull/1040)).
+
+### Contributors
+
+Huge thanks to [@davidlghellin](https://github.com/davidlghellin) for the contribution!
+
 ## 0.4.1
 
 _November 2, 2025_

docs/reference/changelog/index.md

@@ -1,6 +1,6 @@
 [project]
 name = "pysail"
-version = "0.4.1"
+version = "0.4.2"
 description = "Sail Python library"
 authors = [
     { name = "LakeSail", email = "[email protected]" },

pyproject.toml

@@ -2,6 +2,6 @@
 # We have a CI step that verifies this.
 # We cannot use the Hatch dynamic version feature since the project
 # may be built with Maturin outside of Hatch.
-__version__: str = "0.4.1"
+__version__: str = "0.4.2"
 
 __all__ = ["__version__"]

python/pysail/__init__.py

@@ -1,3 +1,4 @@
+import platform
 import time
 from datetime import datetime, timezone
 
@@ -44,6 +45,7 @@ def test_iceberg_time_travel_by_snapshot_id(spark, tmp_path):
         catalog.drop_table(identifier)
 
 
+@pytest.mark.skipif(platform.system() == "Windows", reason="not working on Windows")
 def test_iceberg_time_travel_by_timestamp(spark, tmp_path):
     table_path = tmp_path / "tt_by_timestamp"
     table_path.mkdir(parents=True, exist_ok=True)