Init

streaming/base/converters/dataframe_to_mds.py

@@ -6,6 +6,7 @@
 import logging
 import os
 import shutil
+import time
 from collections.abc import Iterable
 from typing import Any, Callable, Dict, Iterable, Optional, Tuple
 
@@ -224,8 +225,8 @@ def write_mds(iterator: Iterable):
         ],
                         axis=1)
 
-    if dataframe is None or dataframe.isEmpty():
-        raise ValueError(f'Input dataframe is None or Empty!')
+    if dataframe is None:
+        raise ValueError(f'Input dataframe must be provided')
 
     if not mds_kwargs:
         mds_kwargs = {}
@@ -261,6 +262,9 @@ def write_mds(iterator: Iterable):
     if cu.remote is None:
         mds_path = (cu.local, '')
     else:
+        if dataframe.isStreaming:
+            raise ValueError(
+                'dataframe_to_mds currently only supports outputting to a local directory')
         mds_path = (cu.local, cu.remote)
 
     # Prepare partition schema
@@ -269,21 +273,59 @@ def write_mds(iterator: Iterable):
         StructField('mds_path_remote', StringType(), False),
         StructField('fail_count', IntegerType(), False)
     ])
-    partitions = dataframe.mapInPandas(func=write_mds, schema=result_schema).collect()
+    mapped_df = dataframe.mapInPandas(func=write_mds, schema=result_schema)
+
+    if mapped_df.isStreaming:
+
+        def merge_and_log(df: DataFrame, batch_id: int):
+            partitions = df.collect()
+            if len(partitions) == 0:
+                return
+
+            if merge_index:
+                index_files = [
+                    (row['mds_path_local'], row['mds_path_remote']) for row in partitions
+                ]
+                lock_file_path = os.path.join(out, '.merge.lock')
+                # Acquire the lock.
+                while True:
+                    try:
+                        fd = os.open(lock_file_path, os.O_CREAT | os.O_EXCL | os.O_WRONLY)
+                    except OSError:
+                        time.sleep(1)  # File already exists, wait and try again
+                    else:
+                        break
+                do_merge_index(index_files, out, keep_local=keep_local, download_timeout=60)
+                # Release the lock.
+                os.close(fd)
+
+            sum_fail_count = 0
+            for row in partitions:
+                sum_fail_count += row['fail_count']
+
+            if sum_fail_count > 0:
+                logger.warning(
+                    f'[Batch #{batch_id}] Total failed records = {sum_fail_count}\nOverall records {dataframe.count()}'
+                )
+
+        mapped_df.writeStream.foreachBatch(merge_and_log).start()
+        return None, 0
+    else:
+        partitions = mapped_df.collect()
 
-    if merge_index:
-        index_files = [(row['mds_path_local'], row['mds_path_remote']) for row in partitions]
-        do_merge_index(index_files, out, keep_local=keep_local, download_timeout=60)
+        if merge_index:
+            index_files = [(row['mds_path_local'], row['mds_path_remote']) for row in partitions]
+            do_merge_index(index_files, out, keep_local=keep_local, download_timeout=60)
 
-    if cu.remote is not None:
-        if 'keep_local' in mds_kwargs and mds_kwargs['keep_local'] == False:
-            shutil.rmtree(cu.local, ignore_errors=True)
+        if cu.remote is not None:
+            if 'keep_local' in mds_kwargs and mds_kwargs['keep_local'] == False:
+                shutil.rmtree(cu.local, ignore_errors=True)
 
-    sum_fail_count = 0
-    for row in partitions:
-        sum_fail_count += row['fail_count']
+        sum_fail_count = 0
+        for row in partitions:
+            sum_fail_count += row['fail_count']
 
-    if sum_fail_count > 0:
-        logger.warning(
-            f'Total failed records = {sum_fail_count}\nOverall records {dataframe.count()}')
-    return mds_path, sum_fail_count
+        if sum_fail_count > 0:
+            logger.warning(
+                f'Total failed records = {sum_fail_count}\nOverall records {dataframe.count()}')
+        return mds_path, sum_fail_count