Add gh action to lint python (#59)

.github/workflows/python-lint.yml

@@ -0,0 +1,39 @@
+name: Python Linting
+
+on:
+  push:
+    branches: [ "main" ]
+    paths:
+      - 'py-denormalized/**'
+  pull_request:
+    branches: [ "main" ]
+    paths:
+      - 'py-denormalized/**'
+  workflow_dispatch:
+
+jobs:
+  lint:
+    runs-on: ubuntu-latest
+    defaults:
+      run:
+        working-directory: py-denormalized  # Set the working directory for all run steps
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.x'
+          cache: 'pip'
+
+      - name: Install Ruff
+        run: pip install ruff
+
+      - name: Run Ruff
+        # Ruff will look for pyproject.toml in the py-denormalized directory
+        run: |
+          # Run Ruff linter
+          ruff check .
+          # Run Ruff formatter
+          ruff format . --check

.github/workflows/python.yml

@@ -9,10 +9,12 @@ on:
   push:
     branches:
       - main
-      - master
     tags:
       - "*"
   pull_request:
+    branches: [ "main" ]
+    paths:
+      - 'py-denormalized/**'  # Only trigger on changes in this directory
   workflow_dispatch:
 
 permissions:

py-denormalized/pyproject.toml

@@ -33,8 +33,8 @@ dev-dependencies = [
 
 # Enable docstring linting using the google style guide
 [tool.ruff.lint]
-select = ["E4", "E7", "E9", "F", "D", "W"]
-ignore = ["D103"]
+# select = ["E4", "E7", "E9", "F", "D", "W"]
+ignore = ["F", "D100", "D101", "D102", "D103", "D107"]
 
 [tool.ruff.lint.pydocstyle]
 convention = "google"

py-denormalized/python/denormalized/feast_data_stream.py

@@ -3,7 +3,6 @@
 
 import pyarrow as pa
 from denormalized._d_internal import PyDataStream
-from denormalized.datafusion import Expr
 from feast import FeatureStore, Field
 from feast.data_source import PushMode
 from feast.type_map import pa_to_feast_value_type

py-denormalized/python/denormalized/utils.py

@@ -6,8 +6,7 @@ def to_internal_expr(expr: Expr | str) -> internal_exprs:
     """Convert a single Expr or string to internal exprs."""
     return Expr.column(expr).expr if isinstance(expr, str) else expr.expr
 
+
 def to_internal_exprs(exprs: list[Expr] | list[str]) -> list[internal_exprs]:
     """Convert a list of Expr or string to a list of internal exprs."""
-    return [
-         to_internal_expr(arg) for arg in exprs
-    ]
+    return [to_internal_expr(arg) for arg in exprs]

py-denormalized/python/examples/udaf_example.py

@@ -9,7 +9,6 @@
 
 from denormalized import Context
 from denormalized.datafusion import Accumulator, col
-from denormalized.datafusion import functions as f
 from denormalized.datafusion import udaf
 
 
@@ -27,6 +26,7 @@ def signal_handler(sig, frame):
     "reading": 0.0,
 }
 
+
 class TotalValuesRead(Accumulator):
     # Define the state type as a struct containing a map
     acc_state_type = pa.struct([("counts", pa.map_(pa.string(), pa.int64()))])
@@ -45,14 +45,18 @@ def merge(self, states: pa.Array) -> None:
             return
         for state in states:
             if state is not None:
-                counts_map = state.to_pylist()[0] # will always be one element struct
+                counts_map = state.to_pylist()[0]  # will always be one element struct
                 for k, v in counts_map["counts"]:
                     self.counts[k] += v
 
     def state(self) -> List[pa.Scalar]:
         # Convert current state to Arrow array format
         result = {"counts": dict(self.counts.items())}
-        return [pa.scalar(result, type=pa.struct([("counts", pa.map_(pa.string(), pa.int64()))]))]
+        return [
+            pa.scalar(
+                result, type=pa.struct([("counts", pa.map_(pa.string(), pa.int64()))])
+            )
+        ]
 
     def evaluate(self) -> pa.Scalar:
         return self.state()[0]
@@ -69,8 +73,11 @@ def print_batch(rb: pa.RecordBatch):
         return
     print(rb)
 
+
 ctx = Context()
-ds = ctx.from_topic("temperature", json.dumps(sample_event), bootstrap_server, "occurred_at_ms")
+ds = ctx.from_topic(
+    "temperature", json.dumps(sample_event), bootstrap_server, "occurred_at_ms"
+)
 
 ds = ds.window(
     [],

py-denormalized/python/examples/udf_example.py

@@ -64,6 +64,4 @@ def print_batch(rb: pa.RecordBatch):
         col("count"),
         lit(1400.0),
     ),
-).sink(
-    print_batch
-)
+).sink(print_batch)