[Dist] Allow reading and writing single-column vector Parquet files. (#5098)

* Allow reading and writing single-column vector Parquet files.

These files are commonly produced by Spark ML's feature processing code.

* [Dist] Only write single-column vector files for Parquet in tests.

tests/tools/test_dist_part.py

@@ -82,6 +82,7 @@ def _test_chunk_graph(
     num_chunks,
     data_fmt = 'numpy',
     edges_fmt = 'csv',
+    vector_rows = False,
     num_chunks_nodes = None,
     num_chunks_edges = None,
     num_chunks_node_data = None,
@@ -91,6 +92,7 @@ def _test_chunk_graph(
 
         g = create_chunked_dataset(root_dir, num_chunks,
                 data_fmt=data_fmt, edges_fmt=edges_fmt,
+                vector_rows=vector_rows,
                 num_chunks_nodes=num_chunks_nodes,
                 num_chunks_edges=num_chunks_edges,
                 num_chunks_node_data=num_chunks_node_data,
@@ -191,6 +193,11 @@ def _test_chunk_graph(
 def test_chunk_graph_basics(num_chunks, data_fmt, edges_fmt):
     _test_chunk_graph(num_chunks, data_fmt=data_fmt, edges_fmt=edges_fmt)
 
+@pytest.mark.parametrize("num_chunks", [1, 8])
+@pytest.mark.parametrize("vector_rows", [True, False])
+def test_chunk_graph_vector_rows(num_chunks, vector_rows):
+    _test_chunk_graph(num_chunks, data_fmt='parquet', edges_fmt='parquet', vector_rows=vector_rows)
+
 
 @pytest.mark.parametrize(
     "num_chunks, "

tests/tools/utils.py

@@ -6,18 +6,25 @@ import torch
 
 import dgl
 from distpartitioning import array_readwriter
+from distpartitioning.array_readwriter.parquet import ParquetArrayParser
 from files import setdir
 
 
-def _chunk_numpy_array(arr, fmt_meta, chunk_sizes, path_fmt):
+def _chunk_numpy_array(arr, fmt_meta, chunk_sizes, path_fmt, vector_rows=False):
     paths = []
     offset = 0
 
     for j, n in enumerate(chunk_sizes):
         path = os.path.abspath(path_fmt % j)
         arr_chunk = arr[offset: offset + n]
+        shape = arr_chunk.shape
         logging.info("Chunking %d-%d" % (offset, offset + n))
-        array_readwriter.get_array_parser(**fmt_meta).write(path, arr_chunk)
+        # If requested we write multi-column arrays as single-column vector Parquet files
+        array_parser = array_readwriter.get_array_parser(**fmt_meta)
+        if isinstance(array_parser, ParquetArrayParser) and len(shape) > 1 and shape[1] > 1:
+            array_parser.write(path, arr_chunk, vector_rows=vector_rows)
+        else:
+            array_parser.write(path, arr_chunk)
         offset += n
         paths.append(path)
 
@@ -76,7 +83,8 @@ def _initialize_num_chunks(g, num_chunks, kwargs=None):
 
 
 def _chunk_graph(
-    g, name, ndata_paths, edata_paths, num_chunks, data_fmt, edges_format, **kwargs
+    g, name, ndata_paths, edata_paths, num_chunks, data_fmt, edges_format,
+    vector_rows=False, **kwargs
 ):
     # First deal with ndata and edata that are homogeneous
     # (i.e. not a dict-of-dict)
@@ -190,6 +198,7 @@ def _chunk_graph(
                         writer_fmt_meta,
                         chunk_sizes,
                         key + "-%d." + file_suffix,
+                        vector_rows=vector_rows,
                     )
                     ndata_meta[key] = ndata_key_meta
 
@@ -230,6 +239,7 @@ def _chunk_graph(
                         writer_fmt_meta,
                         chunk_sizes,
                         key + "-%d." + file_suffix,
+                        vector_rows=vector_rows,
                     )
                     edata_meta[key] = edata_key_meta
 
@@ -250,6 +260,7 @@ def chunk_graph(
     output_path,
     data_fmt="numpy",
     edges_fmt='csv',
+    vector_rows=False,
     **kwargs,
 ):
     """
@@ -276,6 +287,10 @@ def chunk_graph(
         The output directory saving the chunked graph.
     data_fmt : str
         Format of node/edge data: 'numpy' or 'parquet'.
+    edges_fmt : str
+        Format of edges files: 'csv' or 'parquet'.
+    vector_rows : str
+        When true will write parquet files as single-column vector row files.
     kwargs : dict
         Key word arguments to control chunk details.
     """
@@ -287,12 +302,14 @@ def chunk_graph(
             edata[key] = os.path.abspath(edata[key])
     with setdir(output_path):
         _chunk_graph(
-            g, name, ndata_paths, edata_paths, num_chunks, data_fmt, edges_fmt, **kwargs
+            g, name, ndata_paths, edata_paths, num_chunks, data_fmt, edges_fmt,
+            vector_rows, **kwargs
         )
 
 
 def create_chunked_dataset(
-    root_dir, num_chunks, data_fmt="numpy", edges_fmt='csv', **kwargs):
+    root_dir, num_chunks, data_fmt="numpy", edges_fmt='csv',
+    vector_rows=False, **kwargs):
     """
     This function creates a sample dataset, based on MAG240 dataset.
 
@@ -531,6 +548,7 @@ def create_chunked_dataset(
         output_path=output_dir,
         data_fmt=data_fmt,
         edges_fmt=edges_fmt,
+        vector_rows=vector_rows,
         **kwargs,
     )
     print("Done with creating chunked graph")

tools/distpartitioning/array_readwriter/parquet.py

 import logging
 
+import numpy as np
 import pandas as pd
 import pyarrow
 import pyarrow.parquet
@@ -16,26 +17,44 @@ class ParquetArrayParser(object):
         logging.info("Reading from %s using parquet format" % path)
         metadata = pyarrow.parquet.read_metadata(path)
         metadata = metadata.schema.to_arrow_schema().metadata
+
         # As parquet data are tabularized, we assume the dim of ndarray is 2.
         # If not, it should be explictly specified in the file as metadata.
+        if metadata:
             shape = metadata.get(b"shape", None)
+        else:
+            shape = None
         table = pyarrow.parquet.read_table(path, memory_map=True)
-        logging.info("Done reading from %s" % path)
+
+        data_types = table.schema.types
+        # Spark ML feature processing produces single-column parquet files where each row is a vector object
+        if len(data_types) == 1 and isinstance(data_types[0], pyarrow.ListType):
+            arr = np.array(table.to_pandas().iloc[:, 0].to_list())
+            logging.debug(f"Parquet data under {path} converted from single vector per row to ndarray")
+        else:
             arr = table.to_pandas().to_numpy()
         if not shape:
             logging.warning(
                 "Shape information not found in the metadata, read the data as "
                 "a 2 dim array."
             )
+        logging.info("Done reading from %s" % path)
         shape = tuple(eval(shape.decode())) if shape else arr.shape
         return arr.reshape(shape)
 
-    def write(self, path, array):
+    def write(self, path, array, vector_rows=False):
         logging.info("Writing to %s using parquet format" % path)
         shape = array.shape
         if len(shape) > 2:
             array = array.reshape(shape[0], -1)
+        if vector_rows:
+            table = pyarrow.table(
+                [pyarrow.array(array.tolist())],
+                names=["vector"])
+            logging.info("Writing to %s using single-vector rows..." % path)
+        else:
             table = pyarrow.Table.from_pandas(pd.DataFrame(array))
             table = table.replace_schema_metadata({"shape": str(shape)})
+
         pyarrow.parquet.write_table(table, path)
         logging.info("Done writing to %s" % path)