#46 Improve Readability of TableRead Impletation

pypaimon/api/table_read.py

@@ -31,14 +31,14 @@
 class TableRead(ABC):
     """To read data from data splits."""
 
-    @abstractmethod
-    def to_arrow(self, splits: List[Split]) -> pa.Table:
-        """Read data from splits and converted to pyarrow.Table format."""
-
     @abstractmethod
     def to_arrow_batch_reader(self, splits: List[Split]) -> pa.RecordBatchReader:
         """Read data from splits and converted to pyarrow.RecordBatchReader format."""
 
+    @abstractmethod
+    def to_arrow(self, splits: List[Split]) -> pa.Table:
+        """Read data from splits and converted to pyarrow.Table format."""
+
     @abstractmethod
     def to_pandas(self, splits: List[Split]) -> pd.DataFrame:
         """Read data from splits and converted to pandas.DataFrame format."""

pypaimon/py4j/java_implementation.py

@@ -177,23 +177,20 @@ def file_paths(self) -> List[str]:
 class TableRead(table_read.TableRead):
 
     def __init__(self, j_table_read, j_read_type, catalog_options):
-        self._j_table_read = j_table_read
-        self._j_read_type = j_read_type
-        self._catalog_options = catalog_options
-        self._j_bytes_reader = None
         self._arrow_schema = java_utils.to_arrow_schema(j_read_type)
+        self._j_bytes_reader = get_gateway().jvm.InvocationUtil.createParallelBytesReader(
+            j_table_read, j_read_type, TableRead._get_max_workers(catalog_options))
 
-    def to_arrow(self, splits):
-        record_batch_reader = self.to_arrow_batch_reader(splits)
-        return pa.Table.from_batches(record_batch_reader, schema=self._arrow_schema)
-
-    def to_arrow_batch_reader(self, splits):
-        self._init()
+    def to_arrow_batch_reader(self, splits) -> pa.RecordBatchReader:
         j_splits = list(map(lambda s: s.to_j_split(), splits))
         self._j_bytes_reader.setSplits(j_splits)
         batch_iterator = self._batch_generator()
         return pa.RecordBatchReader.from_batches(self._arrow_schema, batch_iterator)
 
+    def to_arrow(self, splits) -> pa.Table:
+        record_batch_reader = self.to_arrow_batch_reader(splits)
+        return pa.Table.from_batches(record_batch_reader, schema=self._arrow_schema)
+
     def to_pandas(self, splits: List[Split]) -> pd.DataFrame:
         return self.to_arrow(splits).to_pandas()
 
@@ -213,19 +210,13 @@ def to_ray(self, splits: List[Split]) -> "ray.data.dataset.Dataset":
 
         return ray.data.from_arrow(self.to_arrow(splits))
 
-    def _init(self):
-        if self._j_bytes_reader is None:
-            # get thread num
-            max_workers = self._catalog_options.get(constants.MAX_WORKERS)
-            if max_workers is None:
-                # default is sequential
-                max_workers = 1
-            else:
-                max_workers = int(max_workers)
-            if max_workers <= 0:
-                raise ValueError("max_workers must be greater than 0")
-            self._j_bytes_reader = get_gateway().jvm.InvocationUtil.createParallelBytesReader(
-                self._j_table_read, self._j_read_type, max_workers)
+    @staticmethod
+    def _get_max_workers(catalog_options):
+        # default is sequential
+        max_workers = int(catalog_options.get(constants.MAX_WORKERS, 1))
+        if max_workers <= 0:
+            raise ValueError("max_workers must be greater than 0")
+        return max_workers
 
     def _batch_generator(self) -> Iterator[pa.RecordBatch]:
         while True: