feat: Implement max_batches safety limit for Power BI data fetching and refine DAX batch query type handling for numeric and string values.

src/dataprocessor/domain/powerbi_reader.py

@@ -17,6 +17,7 @@ class PowerBIReader:
     group_by_columns: list[str] = None
     batch_size: int = 10000
     order_by_column: str | None = None
+    max_batches: int = 100
 
     @classmethod
     async def create(
@@ -29,6 +30,7 @@ class PowerBIReader:
         group_by_columns: list[str] = None,
         batch_size: int = 10000,
         order_by_column: str | None = None,
+        max_batches: int = 100,
         **kwargs,
     ) -> "PowerBIReader":
         return cls(
@@ -39,6 +41,7 @@ class PowerBIReader:
             group_by_columns=group_by_columns or [],
             batch_size=batch_size,
             order_by_column=order_by_column,
+            max_batches=max_batches,
             **kwargs,
         )
 
@@ -83,7 +86,29 @@ class PowerBIReader:
         )
         return f"EVALUATE SUMMARIZECOLUMNS({group_cols}, {measure_clauses})"
 
-    def _dax_query_batch(self, last_value: str | int | None = None) -> str:
+    @staticmethod
+    def _is_numeric_value(value: str | int | float | None) -> bool:
+        """Check if a value is numeric (including numeric strings).
+
+        Args:
+            value: The value to check.
+
+        Returns:
+            True if the value is numeric or a string that represents a number.
+        """
+        if value is None:
+            return False
+        if isinstance(value, (int, float)):
+            return True
+        if isinstance(value, str):
+            try:
+                float(value)
+                return True
+            except ValueError:
+                return False
+        return False
+
+    def _dax_query_batch(self, last_value: str | int | float | None = None) -> str:
         """Generate a batched DAX query using TOPN and keyset pagination.
 
         Uses ORDER BY with the order_by_column for deterministic ordering,
@@ -107,11 +132,14 @@ class PowerBIReader:
             )
 
         # Subsequent batches: filter rows where order_col > last_value
-        # Handle string vs numeric values
-        if isinstance(last_value, str):
-            filter_value = f'"{last_value}"'
+        # IMPORTANT: Detect if value is numeric (even if stored as string)
+        # to ensure proper numeric comparison in DAX, not string comparison
+        if self._is_numeric_value(last_value):
+            # Use numeric literal (no quotes) for proper numeric comparison
+            filter_value = str(float(last_value) if '.' in str(last_value) else int(last_value))
         else:
-            filter_value = str(last_value)
+            # Genuine string value - quote it for DAX
+            filter_value = f'"{last_value}"'
 
         return (
             f"EVALUATE TOPN({self.batch_size}, "
@@ -188,6 +216,17 @@ class PowerBIReader:
 
         while True:
             batch_num += 1
+
+            # Safety limit to prevent runaway requests
+            if batch_num > self.max_batches:
+                import logging
+                logging.warning(
+                    f"Reached max_batches limit ({self.max_batches}) for table "
+                    f"'{self.table_name}'. Stopping batch fetch. "
+                    f"Total rows fetched so far: {sum(len(df) for df in all_dfs)}"
+                )
+                break
+
             dax_query = self._dax_query_batch(last_value)
             df = await self._execute_query(dax_query)
 

src/dataprocessor/domain/preprocessor.py

@@ -151,6 +151,7 @@ class TableConfig:
     group_by_columns: List[str] = field(default_factory=list)
     batch_size: int = 10000
     order_by_column: str | None = None
+    max_batches: int = 100
     steps: List[Dict[str, Any]] = field(default_factory=list)
 
 
@@ -263,6 +264,7 @@ class Preprocessor:
                 group_by_columns=table_data.get("group_by_columns", []),
                 batch_size=table_data.get("batch_size", 10000),
                 order_by_column=table_data.get("order_by_column"),
+                max_batches=table_data.get("max_batches", 100),
                 steps=table_data.get("steps", []),
             )
             table_configs.append(table_config)

src/dataprocessor/schemas.py

@@ -51,6 +51,11 @@ class TableConfigSchema(BaseModel):
         description="Column to order by for batch fetching. Required for batching to work.",
         example="I_ID",
     )
+    max_batches: int = Field(
+        default=100,
+        description="Maximum number of batches to fetch. Safety limit to prevent runaway requests.",
+        example=100,
+    )
     steps: List[Dict[str, Any]] = Field(
         default_factory=list,
         description="List of preprocessing steps to apply",

src/dataprocessor/service.py

@@ -155,6 +155,7 @@ class DataProcessorService:
                 group_by_columns=table_config.group_by_columns,
                 batch_size=table_config.batch_size,
                 order_by_column=table_config.order_by_column,
+                max_batches=table_config.max_batches,
             )
 
             # Step 2: Read data from Power BI