ericm-db commented on code in PR #47133:
URL: https://github.com/apache/spark/pull/47133#discussion_r1679749407
##########
python/pyspark/sql/pandas/serializers.py:
##########
@@ -1116,3 +1121,88 @@ def init_stream_yield_batches(batches):
batches_to_write = init_stream_yield_batches(serialize_batches())
return ArrowStreamSerializer.dump_stream(self, batches_to_write,
stream)
+
+
+class TransformWithStateInPandasSerializer(ArrowStreamPandasUDFSerializer):
+ """
+ Serializer used by Python worker to evaluate UDF for
transformWithStateInPandasSerializer.
+
+ Parameters
+ ----------
+ timezone : str
+ A timezone to respect when handling timestamp values
+ safecheck : bool
+ If True, conversion from Arrow to Pandas checks for overflow/truncation
+ assign_cols_by_name : bool
+ If True, then Pandas DataFrames will get columns by name
+ arrow_max_records_per_batch : int
+ Limit of the number of records that can be written to a single
ArrowRecordBatch in memory.
+ """
+
+ def __init__(
+ self,
+ timezone,
+ safecheck,
+ assign_cols_by_name,
+ arrow_max_records_per_batch):
+ super(
+ TransformWithStateInPandasSerializer,
+ self
+ ).__init__(timezone, safecheck, assign_cols_by_name)
+
+ # self.state_server_port = state_server_port
+
+ # # open client connection to state server socket
+ self.arrow_max_records_per_batch = arrow_max_records_per_batch
+ self.key_offsets = None
+
+ # Nothing special here, we need to create the handle and read
+ # data in groups.
+ def load_stream(self, stream):
+ """
+ Read ArrowRecordBatches from stream, deserialize them to populate a
list of pair
+ (data chunk, state), and convert the data into a list of pandas.Series.
+
+ Please refer the doc of inner function `gen_data_and_state` for more
details how
+ this function works in overall.
+
+ In addition, this function further groups the return of
`gen_data_and_state` by the state
+ instance (same semantic as grouping by grouping key) and produces an
iterator of data
+ chunks for each group, so that the caller can lazily materialize the
data chunk.
+ """
+ import pyarrow as pa
+ from itertools import tee
+
+ def generate_data_batches(batches):
+ for batch in batches:
+ data_pandas = [self.arrow_to_pandas(c) for c in
pa.Table.from_batches([batch]).itercolumns()]
Review Comment:
Not sure if this is a common pattern in Python, but this line is a little
hard to read
##########
python/pyspark/sql/tests/pandas/test_pandas_transform_with_state.py:
##########
@@ -0,0 +1,152 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import random
+import shutil
+import string
+import sys
+import tempfile
+import pandas as pd
+from pyspark.sql.streaming import StatefulProcessor, StatefulProcessorHandle
+from typing import Iterator
+
+import unittest
+from typing import cast
+
+from pyspark import SparkConf
+from pyspark.sql.streaming.state import GroupStateTimeout, GroupState
+from pyspark.sql.types import (
+ LongType,
+ StringType,
+ StructType,
+ StructField,
+ Row,
+)
+from pyspark.testing.sqlutils import (
+ ReusedSQLTestCase,
+ have_pandas,
+ have_pyarrow,
+ pandas_requirement_message,
+ pyarrow_requirement_message,
+)
+from pyspark.testing.utils import eventually
+
+
+@unittest.skipIf(
+ not have_pandas or not have_pyarrow,
+ cast(str, pandas_requirement_message or pyarrow_requirement_message),
+)
+class TransformWithStateInPandasTestsMixin:
+ @classmethod
+ def conf(cls):
+ cfg = SparkConf()
+ cfg.set("spark.sql.shuffle.partitions", "5")
+ cfg.set("spark.sql.streaming.stateStore.providerClass",
+
"org.apache.spark.sql.execution.streaming.state.RocksDBStateStoreProvider")
+ return cfg
+
+ def _test_apply_in_pandas_with_state_basic(self, func, check_results):
+ input_path = tempfile.mkdtemp()
+
+ def prepare_test_resource():
+ with open(input_path + "/text-test.txt", "w") as fw:
+ fw.write("hello\n")
+ fw.write("this\n")
+
+ prepare_test_resource()
+
+ df = self.spark.readStream.format("text").load(input_path)
+
+ for q in self.spark.streams.active:
+ q.stop()
+ self.assertTrue(df.isStreaming)
+
+ output_type = StructType(
+ [StructField("key", StringType()), StructField("countAsString",
StringType())]
+ )
+ state_type = StructType([StructField("c", LongType())])
+
+ q = (
+ df.groupBy(df["value"])
+ .transformWithStateInPandas(stateful_processor =
SimpleStatefulProcessor(),
+ outputStructType=output_type,
+ outputMode="Update",
+ timeMode="None")
+ .writeStream.queryName("this_query")
+ .foreachBatch(check_results)
+ .outputMode("update")
+ .start()
+ )
+
+ self.assertEqual(q.name, "this_query")
+ self.assertTrue(q.isActive)
+ q.processAllAvailable()
Review Comment:
Should we include `q.awaitTermination()`?
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