[GitHub] [spark] HyukjinKwon commented on a diff in pull request #38013: [SPARK-40509][SS][PYTHON] Add example for applyInPandasWithState

[GitBox <gi...@apache.org>]

HyukjinKwon commented on code in PR #38013:
URL: https://github.com/apache/spark/pull/38013#discussion_r1029932042


##########
examples/src/main/python/sql/streaming/structured_network_wordcount_session_window.py:
##########
@@ -0,0 +1,139 @@
+#
+# 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.
+#
+
+r"""
+ Split lines into words, group by words and use the state per key to track session of each key.
+
+ Usage: structured_network_wordcount_windowed.py <hostname> <port>
+ <hostname> and <port> describe the TCP server that Structured Streaming
+ would connect to receive data.
+
+ To run this on your local machine, you need to first run a Netcat server
+    `$ nc -lk 9999`
+ and then run the example
+    `$ bin/spark-submit
+    examples/src/main/python/sql/streaming/structured_network_wordcount_session_window.py
+    localhost 9999`
+"""
+import sys
+import math
+from typing import Iterable, Any
+
+import pandas as pd
+
+from pyspark.sql import SparkSession
+from pyspark.sql.functions import explode
+from pyspark.sql.functions import split
+from pyspark.sql.types import (
+    LongType,
+    StringType,
+    StructType,
+    StructField,
+)
+from pyspark.sql.streaming.state import GroupStateTimeout, GroupState
+
+if __name__ == "__main__":
+    if len(sys.argv) != 3:
+        msg = "Usage: structured_network_wordcount_session_window.py <hostname> <port>"
+        print(msg, file=sys.stderr)
+        sys.exit(-1)
+
+    host = sys.argv[1]
+    port = int(sys.argv[2])
+
+    spark = SparkSession.builder.appName(
+        "StructuredNetworkWordCountSessionWindow"
+    ).getOrCreate()
+
+    # Create DataFrame representing the stream of input lines from connection to host:port
+    lines = (
+        spark.readStream.format("socket")
+        .option("host", host)
+        .option("port", port)
+        .option("includeTimestamp", "true")
+        .load()
+    )
+
+    # Split the lines into words, retaining timestamps, each word become a sessionId
+    events = lines.select(
+        explode(split(lines.value, " ")).alias("sessionId"),
+        lines.timestamp.cast("long"),
+    )
+
+    # Type of output records.
+    session_schema = StructType(
+        [
+            StructField("sessionId", StringType()),
+            StructField("count", LongType()),
+            StructField("start", LongType()),
+            StructField("end", LongType()),
+        ]
+    )
+    # Type of group state.
+    # Omit the session id in the state since it is available as group key
+    session_state_schema = StructType(
+        [
+            StructField("count", LongType()),
+            StructField("start", LongType()),
+            StructField("end", LongType()),
+        ]
+    )
+
+    def func(
+        key: Any, pdf_iter: Iterable[pd.DataFrame], state: GroupState
+    ) -> Iterable[pd.DataFrame]:
+        if state.hasTimedOut:
+            count, start, end = state.get
+            state.remove()
+            yield pd.DataFrame(
+                {
+                    "sessionId": [key[0]],
+                    "count": [count],
+                    "start": [start],
+                    "end": [end],
+                }
+            )
+        else:
+            start = math.inf
+            end = 0
+            count = 0
+            for pdf in pdf_iter:
+                start = min(start, min(pdf["timestamp"]))

Review Comment:
   I suspect the return type is not matched to the SQL type provided. Do you mind show the reproducer?



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