[spark] branch master updated: [SPARK-40509][SS][PYTHON] Add example for applyInPandasWithState

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The following commit(s) were added to refs/heads/master by this push:
     new 38599e9a368 [SPARK-40509][SS][PYTHON] Add example for applyInPandasWithState
38599e9a368 is described below

commit 38599e9a3684d2bdb7561a9c1d6b20ac89bdcf9b
Author: Chaoqin Li <ch...@databricks.com>
AuthorDate: Wed Sep 28 16:37:29 2022 +0900

    [SPARK-40509][SS][PYTHON] Add example for applyInPandasWithState
    
    ### What changes were proposed in this pull request?
    An example for applyInPandasWithState usage. This example split lines into words, group by words as key and use the state per key to track session of each key.
    
    ### Why are the changes needed?
    To demonstrate the usage of applyInPandasWithState
    
    ### Does this PR introduce _any_ user-facing change?
    No.
    
    ### How was this patch tested?
    This is an example that can be run manually.
    
     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`
    
    Closes #38013 from chaoqin-li1123/session_example.
    
    Authored-by: Chaoqin Li <ch...@databricks.com>
    Signed-off-by: Jungtaek Lim <ka...@gmail.com>
---
 .../structured_network_wordcount_session_window.py | 139 +++++++++++++++++++++
 1 file changed, 139 insertions(+)

diff --git a/examples/src/main/python/sql/streaming/structured_network_wordcount_session_window.py b/examples/src/main/python/sql/streaming/structured_network_wordcount_session_window.py
new file mode 100644
index 00000000000..77ddd0f415f
--- /dev/null
+++ b/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"]))
+                end = max(end, max(pdf["timestamp"]))
+                count = count + len(pdf)
+            if state.exists:
+                old_session = state.get
+                count = count + old_session[0]
+                start = old_session[1]
+                end = max(end, old_session[2])
+            state.update((count, start, end))
+            state.setTimeoutDuration(10000)
+            yield pd.DataFrame()
+
+    # Group the data by window and word and compute the count of each group
+    sessions = events.groupBy(events["sessionId"]).applyInPandasWithState(
+        func,
+        session_schema,
+        session_state_schema,
+        "Update",
+        GroupStateTimeout.ProcessingTimeTimeout,
+    )
+
+    # Start running the query that prints the windowed word counts to the console
+    query = sessions.writeStream.outputMode("update").format("console").start()
+
+    query.awaitTermination()


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