Exempel på tillståndskänsliga program

Den här sidan har kodexempel för anpassade tillståndsfulla streamingapplikationer som använder operatören transformWithState . Databricks rekommenderar att du använder inbyggda tillståndskänsliga metoder för vanliga åtgärder som sammansättningar och kopplingar.

Se hur du bygger ett anpassat program med tillstånd med transformWithState.

Krav

transformWithState-operatorn och de relaterade API:erna och klasserna har följande krav:

• Tillgänglig i Databricks Runtime 16.2 och senare.
• Standardåtkomstläge stöds för Python (transformWithStateInPandas och radbaserat transformWithState) i Databricks Runtime 16.3 och senare och för Scala (transformWithState) i Databricks Runtime 17.3 och senare.
• RocksDB är standardtillståndslagringsprovidern i Databricks Runtime 17.3 och senare. För Databricks Runtime-versioner under 17.3 måste du konfigurera RocksDB-tillståndslagringsprovidern. Databricks rekommenderar att du aktiverar RocksDB som en del av beräkningskonfigurationen.

spark.conf.set("spark.sql.streaming.stateStore.providerClass", "org.apache.spark.sql.execution.streaming.state.RocksDBStateStoreProvider")

Långsamt föränderlig dimensionstyp (SCD) 1

Följande kod är ett exempel på hur du implementerar SCD-typ 1 med hjälp av transformWithState. SCD-typ 1 spårar bara det senaste värdet för ett visst fält.

Pytonorm

# Import the necessary libraries
import pandas as pd
from pyspark.sql.streaming import StatefulProcessor, StatefulProcessorHandle
from pyspark.sql.types import StructType, StructField, LongType, StringType
from typing import Iterator

# Set the state store provider to RocksDB
spark.conf.set("spark.sql.streaming.stateStore.providerClass", "org.apache.spark.sql.execution.streaming.state.RocksDBStateStoreProvider")

# Define the output schema for the streaming query
output_schema = StructType([
    StructField("user", StringType(), True),
    StructField("time", LongType(), True),
    StructField("location", StringType(), True)
])

# Define a custom StatefulProcessor for slowly changing dimension type 1 (SCD1) operations
class SCDType1StatefulProcessor(StatefulProcessor):
    def init(self, handle: StatefulProcessorHandle) -> None:
        self.handle = handle
        # Define the schema for the state value
        value_state_schema = StructType([
            StructField("user", StringType(), True),
            StructField("time", LongType(), True),
            StructField("location", StringType(), True)
        ])
        # Initialize the state to store the latest location for each user
        self.latest_location = handle.getValueState("latestLocation", value_state_schema)

    def handleInputRows(self, key, rows, timerValues) -> Iterator[pd.DataFrame]:
        # Find the row with the maximum time value
        max_row = None
        max_time = float('-inf')
        for pdf in rows:
            for _, pd_row in pdf.iterrows():
                time_value = pd_row["time"]
                if time_value > max_time:
                    max_time = time_value
                    max_row = tuple(pd_row)

        # Check whether state exists and update if necessary
        exists = self.latest_location.exists()
        if not exists or max_row[1] > self.latest_location.get()[1]:
            # Update the state with the new max row
            self.latest_location.update(max_row)
            # Yield the updated row
            yield pd.DataFrame(
                {"user": (max_row[0],), "time": (max_row[1],), "location": (max_row[2],)}
            )
        # Yield an empty DataFrame if no update is needed
        yield pd.DataFrame()

    def close(self) -> None:
        # No cleanup needed
        pass

import uuid

# Create a dedicated schema for the example tables
spark.sql("CREATE SCHEMA IF NOT EXISTS main.stateful_examples")

# Seed a small Delta table to use as the streaming source
spark.sql("DROP TABLE IF EXISTS main.stateful_examples.scd1_source")
spark.createDataFrame(
    [("u1", 1, "NYC"), ("u1", 3, "SF"), ("u1", 2, "LA"), ("u2", 5, "London")],
    "user string, time long, location string",
).write.saveAsTable("main.stateful_examples.scd1_source")

df = spark.readStream.table("main.stateful_examples.scd1_source")

# Apply the stateful transformation to the input DataFrame
q = (
    df.groupBy("user")
    .transformWithStateInPandas(
        statefulProcessor=SCDType1StatefulProcessor(),
        outputStructType=output_schema,
        outputMode="Update",
        timeMode="None",
    )
    .writeStream.format("memory")
    .queryName("scd1_output")
    .option("checkpointLocation", f"/tmp/checkpoint_{uuid.uuid4()}")
    .trigger(availableNow=True)
    .start()
)

q.awaitTermination()

# Each user keeps only its latest location by time: u1 -> SF (time 3), u2 -> London (time 5)
display(spark.sql("SELECT user, time, location FROM scd1_output ORDER BY user"))

Scala

import org.apache.spark.sql.streaming._

// Define a case class to represent user location data
case class UserLocation(
    user: String,
    time: Long,
    location: String)

// Define a stateful processor for slowly changing dimension type 1 (SCD1) operations
class SCDType1StatefulProcessor extends StatefulProcessor[String, UserLocation, UserLocation] {
  import org.apache.spark.sql.{Encoders}

  // Transient value state to store the latest location for each user
  @transient private var _latestLocation: ValueState[UserLocation] = _

  private val userLocationEncoder = Encoders.product[UserLocation]

  // Initialize the state store
  override def init(
      outputMode: OutputMode,
      timeMode: TimeMode): Unit = {
    // Create a value state named "locationState" using UserLocation encoder
    // TTLConfig.NONE means the state has no expiration
    _latestLocation = getHandle.getValueState[UserLocation]("locationState",
      userLocationEncoder, TTLConfig.NONE)
  }

  // Process input rows and update state
  override def handleInputRows(
      key: String,
      inputRows: Iterator[UserLocation],
      timerValues: TimerValues): Iterator[UserLocation] = {
    // Find the location with the maximum timestamp from input rows
    val maxNewLocation = inputRows.maxBy(_.time)

    // Update state and emit output if:
    // 1. No previous state exists, or
    // 2. New location has a more recent timestamp than the stored one
    if (_latestLocation.getOption().isEmpty || maxNewLocation.time > _latestLocation.get().time) {
      _latestLocation.update(maxNewLocation)
      Iterator.single(maxNewLocation)  // Emit the updated location
    } else {
      Iterator.empty  // No update needed, emit nothing
    }
  }
}

import spark.implicits._
import java.util.UUID

// Create a dedicated schema for the example tables
spark.sql("CREATE SCHEMA IF NOT EXISTS main.stateful_examples")

// Seed a small Delta table to use as the streaming source
spark.sql("DROP TABLE IF EXISTS main.stateful_examples.scd1_source_scala")
Seq(
  UserLocation("u1", 1L, "NYC"),
  UserLocation("u1", 3L, "SF"),
  UserLocation("u1", 2L, "LA"),
  UserLocation("u2", 5L, "London")
).toDF().write.saveAsTable("main.stateful_examples.scd1_source_scala")

val q = spark.readStream
  .table("main.stateful_examples.scd1_source_scala")
  .as[UserLocation]
  .groupByKey(_.user)
  .transformWithState(
    new SCDType1StatefulProcessor(),
    TimeMode.None(),
    OutputMode.Update()
  )
  .writeStream
  .format("memory")
  .queryName("scd1_output_scala")
  .option("checkpointLocation", s"/tmp/checkpoint_${UUID.randomUUID()}")
  .trigger(Trigger.AvailableNow())
  .start()

q.awaitTermination()

// Each user keeps only its latest location by time: u1 -> SF (time 3), u2 -> London (time 5)
spark.sql("SELECT user, time, location FROM scd1_output_scala ORDER BY user").show()

Långsamt föränderlig dimension (SCD) typ 2

Följande notebook-filer innehåller ett exempel på hur du implementerar SCD-typ 2 med hjälp av transformWithState i Python eller Scala.

SCD-typ 2 Python

Hämta anteckningsbok

SCD typ 2 Scala

Hämta anteckningsbok

Stilleståndstidsdetektor

transformWithState implementerar timers så att du kan vidta åtgärder baserat på förfluten tid, även om inga poster för en viss nyckel bearbetas i en mikrobatch.

I följande exempel implementeras ett mönster för en stilleståndstidsdetektor. Varje gång ett nytt värde visas för en viss nyckel uppdateras lastSeen tillståndsvärdet, rensar alla befintliga timers och återställer en timer för framtiden.

När en timer upphör att gälla genererar programmet den tid som förflutit sedan den senaste observerade händelsen för nyckeln. Den anger sedan en ny timer för att generera en uppdatering 10 sekunder senare.

För att köra exemplet hela vägen, mata in ett enda sensorvärde som strömningskälla. Eftersom timerfunktionerna använder bearbetningstid använder drivrutinen en processingTime utlösare och väntar en stund innan den stoppar frågan så att timerfunktionerna utlöses.

Pytonorm

import datetime
import time
import uuid
import pandas as pd
from pyspark.sql.streaming import StatefulProcessor, StatefulProcessorHandle
from pyspark.sql.types import StructType, StructField, StringType, TimestampType
from typing import Iterator

spark.conf.set("spark.sql.streaming.stateStore.providerClass", "org.apache.spark.sql.execution.streaming.state.RocksDBStateStoreProvider")

class DownTimeDetectorStatefulProcessor(StatefulProcessor):
    def init(self, handle: StatefulProcessorHandle) -> None:
        # Define the schema for the state value (timestamp)
        state_schema = StructType([StructField("value", TimestampType(), True)])
        self.handle = handle
        # Initialize state to store the last seen timestamp for each key
        self.last_seen = handle.getValueState("last_seen", state_schema)

    def handleExpiredTimer(self, key, timerValues, expiredTimerInfo) -> Iterator[pd.DataFrame]:
        latest_from_existing = self.last_seen.get()
        # Calculate downtime as the elapsed time between the last observed event and now
        downtime_duration = timerValues.getCurrentProcessingTimeInMs() - int(latest_from_existing[0].timestamp() * 1000)
        # Register a new timer for 10 seconds in the future
        self.handle.registerTimer(timerValues.getCurrentProcessingTimeInMs() + 10000)
        # Yield a DataFrame with the key and downtime duration
        yield pd.DataFrame(
            {
                "id": key,
                "timeValues": str(downtime_duration),
            }
        )

    def handleInputRows(self, key, rows, timerValues) -> Iterator[pd.DataFrame]:
        # Find the row with the maximum timestamp
        max_row = max((tuple(pdf.iloc[0]) for pdf in rows), key=lambda row: row[1])

        # Get the latest timestamp from the existing state or use epoch start if a timestamp doesn't exist
        if self.last_seen.exists():
            latest_from_existing = self.last_seen.get()[0]
        else:
            latest_from_existing = datetime.datetime.fromtimestamp(0)

        # If the new data is more recent than the existing state
        if latest_from_existing < max_row[1]:
            # Delete all existing timers
            for timer in self.handle.listTimers():
                self.handle.deleteTimer(timer)
            # Update the last seen timestamp
            self.last_seen.update((max_row[1],))

        # Register a new timer for 5 seconds in the future
        self.handle.registerTimer(timerValues.getCurrentProcessingTimeInMs() + 5000)

        # Get current processing time in milliseconds
        timestamp_in_millis = str(timerValues.getCurrentProcessingTimeInMs())

        # Yield a DataFrame with the key and current timestamp
        yield pd.DataFrame({"id": key, "timeValues": timestamp_in_millis})

    def close(self) -> None:
        # No cleanup needed
        pass

# Create a dedicated schema for the example tables
spark.sql("CREATE SCHEMA IF NOT EXISTS main.stateful_examples")

# Seed a small Delta table with a sensor reading to use as the streaming source
spark.sql("DROP TABLE IF EXISTS main.stateful_examples.sensor_events")
spark.createDataFrame(
    [("sensor1", datetime.datetime(2024, 1, 1, 12, 0, 0))],
    "id string, timestamp timestamp",
).write.saveAsTable("main.stateful_examples.sensor_events")

df = spark.readStream.table("main.stateful_examples.sensor_events")

# Output schema: the key and a time value (processing time or elapsed downtime)
output_schema = StructType([
    StructField("id", StringType(), True),
    StructField("timeValues", StringType(), True),
])

# ProcessingTime mode enables the timers that detect downtime
q = (
    df.groupBy("id")
    .transformWithStateInPandas(
        statefulProcessor=DownTimeDetectorStatefulProcessor(),
        outputStructType=output_schema,
        outputMode="Update",
        timeMode="ProcessingTime",
    )
    .writeStream.format("memory")
    .queryName("downtime_output")
    .option("checkpointLocation", f"/tmp/checkpoint_{uuid.uuid4()}")
    .trigger(processingTime="5 seconds")
    .start()
)

# Wait past the timers so they fire, then stop the query
time.sleep(30)
q.stop()

# When a timer fires, it emits the elapsed time in milliseconds since the last observed event
display(spark.sql("SELECT * FROM downtime_output"))

Scala

import java.sql.Timestamp
import org.apache.spark.sql.Encoders
import org.apache.spark.sql.streaming._
import spark.implicits._
import java.util.UUID

// The (String, Timestamp) schema represents an (id, time). We want to do downtime
// detection on every single unique sensor, where each sensor has a sensor ID.
// downtimeThresholdMs is the timer duration in milliseconds.
class DowntimeDetector(downtimeThresholdMs: Long) extends
  StatefulProcessor[String, (String, Timestamp), (String, Long)] {

  @transient private var _lastSeen: ValueState[Timestamp] = _

  private val timestampEncoder = Encoders.TIMESTAMP

  override def init(outputMode: OutputMode, timeMode: TimeMode): Unit = {
    _lastSeen = getHandle.getValueState[Timestamp]("lastSeen", timestampEncoder, TTLConfig.NONE)
  }

  // The logic here is as follows: find the largest timestamp seen so far. Set a timer for
  // the duration later.
  override def handleInputRows(
      key: String,
      inputRows: Iterator[(String, Timestamp)],
      timerValues: TimerValues): Iterator[(String, Long)] = {
    val latestRecordFromNewRows = inputRows.maxBy(_._2.getTime)

    // Use getOrElse to initiate state variable if it doesn't exist
    val latestTimestampFromExistingRows = Option(_lastSeen.get()).getOrElse(new Timestamp(0))
    val latestTimestampFromNewRows = latestRecordFromNewRows._2

    if (latestTimestampFromNewRows.after(latestTimestampFromExistingRows)) {
      // Cancel the one existing timer, since we have a new latest timestamp.
      // We call "listTimers()" because we don't know ahead of time what
      // the timestamp of the existing timer will be.
      getHandle.listTimers().foreach(timer => getHandle.deleteTimer(timer))

      _lastSeen.update(latestTimestampFromNewRows)
      // Use timerValues to schedule a timer using processing time.
      getHandle.registerTimer(timerValues.getCurrentProcessingTimeInMs() + downtimeThresholdMs)
    } else {
      // No new latest timestamp, so there is no need to update the state or set a timer.
    }

    Iterator.empty
  }

  override def handleExpiredTimer(
    key: String,
    timerValues: TimerValues,
    expiredTimerInfo: ExpiredTimerInfo): Iterator[(String, Long)] = {
      val latestTimestamp = _lastSeen.get()
      // Downtime is the elapsed time in milliseconds between the last observed event and now
      val downtimeDurationMs =
        timerValues.getCurrentProcessingTimeInMs() - latestTimestamp.getTime

      // Register another timer that will fire in 10 seconds.
      // Timers can be registered anywhere but init()
      getHandle.registerTimer(timerValues.getCurrentProcessingTimeInMs() + 10000)

      Iterator((key, downtimeDurationMs))
  }
}

// Create a dedicated schema for the example tables
spark.sql("CREATE SCHEMA IF NOT EXISTS main.stateful_examples")

// Seed a small Delta table with a sensor reading to use as the streaming source
spark.sql("DROP TABLE IF EXISTS main.stateful_examples.sensor_events_scala")
Seq(
  ("sensor1", Timestamp.valueOf("2024-01-01 12:00:00"))
).toDF("id", "timestamp").write.saveAsTable("main.stateful_examples.sensor_events_scala")

// ProcessingTime mode enables the timers that detect downtime
val q = spark.readStream
  .table("main.stateful_examples.sensor_events_scala")
  .as[(String, Timestamp)]
  .groupByKey(_._1)
  .transformWithState(
    new DowntimeDetector(5000L),
    TimeMode.ProcessingTime(),
    OutputMode.Update()
  )
  .writeStream
  .format("memory")
  .queryName("downtime_output_scala")
  .option("checkpointLocation", s"/tmp/checkpoint_${UUID.randomUUID()}")
  .trigger(Trigger.ProcessingTime("5 seconds"))
  .start()

// Wait past the timers so they fire, then stop the query
Thread.sleep(30000)
q.stop()

// When a timer fires, it emits the elapsed time in milliseconds since the last observed event
spark.sql("SELECT * FROM downtime_output_scala").show(false)

Migrera befintlig tillståndsinformation

I följande exempel visas hur du implementerar ett tillståndskänsligt program som accepterar ett initialt tillstånd. Du kan lägga till inledande tillståndshantering i alla tillståndskänsliga program, men det inledande tillståndet kan bara anges när programmet initieras först.

I det här exemplet används statestore läsare för att läsa in befintlig tillståndsinformation från en kontrollpunktssökväg. Ett exempel på användningsfall för det här mönstret är att migrera från äldre tillståndskänsliga program till transformWithState.

Pytonorm

# Import the necessary libraries
import pandas as pd
from pyspark.sql.streaming import StatefulProcessor, StatefulProcessorHandle
from pyspark.sql.types import StructType, StructField, LongType, StringType, IntegerType
from typing import Iterator

# Set RocksDB as the state store provider for better performance
spark.conf.set("spark.sql.streaming.stateStore.providerClass", "org.apache.spark.sql.execution.streaming.state.RocksDBStateStoreProvider")

"""
Input schema is as below

input_schema = StructType(
    [StructField("id", StringType(), True)],
    [StructField("value", StringType(), True)]
)
"""

# Define the output schema for the streaming query
output_schema = StructType([
    StructField("id", StringType(), True),
    StructField("accumulated", StringType(), True)
])

class AccumulatedCounterStatefulProcessorWithInitialState(StatefulProcessor):

    def init(self, handle: StatefulProcessorHandle) -> None:
        # Define the schema for the state value (integer)
        state_schema = StructType([StructField("value", IntegerType(), True)])
        # Initialize state to store the accumulated counter for each id
        self.counter_state = handle.getValueState("counter_state", state_schema)
        self.handle = handle

    def handleInputRows(self, key, rows, timerValues) -> Iterator[pd.DataFrame]:
        # Check if state exists for the current key
        exists = self.counter_state.exists()
        if exists:
            value_row = self.counter_state.get()
            existing_value = value_row[0]
        else:
            existing_value = 0

        accumulated_value = existing_value

        # Process input rows and accumulate values
        for pdf in rows:
            value = pdf["value"].astype(int).sum()
            accumulated_value += value

        # Update the state with the new accumulated value
        self.counter_state.update((accumulated_value,))

        # Yield a DataFrame with the key and accumulated value
        yield pd.DataFrame({"id": key, "accumulated": str(accumulated_value)})

    def handleInitialState(self, key, initialState, timerValues) -> None:
        # Initialize the state with the provided initial value
        init_val = initialState.at[0, "initVal"]
        self.counter_state.update((init_val,))

    def close(self) -> None:
        # No cleanup needed
        pass

# Load initial state from a checkpoint directory
initial_state = spark.read.format("statestore")
  .option("path", "$checkpointsDir")
  .load()

# Apply the stateful transformation to the input DataFrame
df.groupBy("id")
  .transformWithStateInPandas(
      statefulProcessor=AccumulatedCounterStatefulProcessorWithInitialState(),
      outputStructType=output_schema,
      outputMode="Update",
      timeMode="None",
      initialState=initial_state,
  )
  .writeStream...  # Continue with stream writing configuration

Scala

// Import the necessary libraries
import org.apache.spark.sql.streaming._
import org.apache.spark.sql.{Dataset, Encoder, Encoders, DataFrame}
import org.apache.spark.sql.types._

// Define a stateful processor that can handle the initial state
class InitialStateStatefulProcessor extends StatefulProcessorWithInitialState[String, (String, String, String), (String, String), (String, Int)] {
  // Transient value state to store the accumulated value
  @transient protected var valueState: ValueState[Int] = _

  private val intEncoder = Encoders.scalaInt

  // Initialize the state store
  override def init(
      outputMode: OutputMode,
      timeMode: TimeMode): Unit = {
    // Create a value state named "valueState" using Int encoder
    // TTLConfig.NONE means the state has no automatic expiration
    valueState = getHandle.getValueState[Int]("valueState",
      intEncoder, TTLConfig.NONE)
  }

  // Process input rows and update state
  override def handleInputRows(
      key: String,
      inputRows: Iterator[(String, String, String)],
      timerValues: TimerValues): Iterator[(String, String)] = {
    var existingValue = 0
    // Retrieve existing value from state if it exists
    if (valueState.exists()) {
      existingValue += valueState.get()
    }
    var accumulatedValue = existingValue
    // Accumulate values from input rows
    for (row <- inputRows) {
      accumulatedValue += row._2.toInt
    }
    // Update the state with the new accumulated value
    valueState.update(accumulatedValue)
    // Return the key and accumulated value as a string
    Iterator((key, accumulatedValue.toString))
  }

  // Handle initial state when provided
  override def handleInitialState(
      key: String, initialState: (String, Int), timerValues: TimerValues): Unit = {
    // Update the state with the initial value
    valueState.update(initialState._2)
  }
}

Migrera Delta-tabellen till tillståndslagring för initiering

Följande notebook-filer innehåller ett exempel på initiering av tillståndslagringsvärden från en Delta-tabell med hjälp av transformWithState i Python eller Scala.

Initiera tillstånd med Delta Python

Hämta anteckningsbok

Initialisera tillstånd från Delta Scala

Hämta anteckningsbok

Sessionsspårning

Följande notebook-filer innehåller ett exempel på sessionsspårning med transformWithState i Python eller Scala.

Sessionsspårning i Python

Hämta anteckningsbok

Sessionsspårning i Scala

Hämta anteckningsbok

Anpassad stream-stream-koppling med hjälp av transformWithState

Följande kod visar en anpassad stream-stream-koppling över flera strömmar med hjälp av transformWithState. Du kan använda den här metoden i stället för en inbyggd kopplingsoperator av följande skäl:

• Du måste använda uppdateringsutdataläget som inte stöder stream-stream-kopplingar. Detta är särskilt användbart för program med lägre svarstid.
• Du behöver fortsätta att utföra kopplingar för sena rader (efter att vattenstämpeln har upphört att gälla).
• Du måste utföra många-till-många-stream-kopplingar.

Detta exempel ger dig full kontroll över tillståndets utgångslogik, vilket möjliggör dynamisk förlängning av lagringsperioden för att hantera händelser utanför ordning även efter vattenstämpeln.

I följande exempel anländer profil-, preferens- och aktivitetshändelser i en enda ström, var och en märkt med en record_type. Processorn buffrar varje posttyp i tillstånd, och en processtidstimer sänder den berikade joinen kort efter att en aktivitetshändelse har anlänt. Profil- och preferenstillstånd upphör efter en timmes inaktivitet med TTL, och varje aktivitet rensas från tillstånd när den har anslutits.

Pytonorm

# Import the necessary libraries
import pandas as pd
import time
import uuid
from datetime import datetime
from pyspark.sql.streaming import StatefulProcessor, StatefulProcessorHandle
from pyspark.sql.types import StructType, StructField, StringType, TimestampType
from typing import Iterator

spark.conf.set("spark.sql.streaming.stateStore.providerClass", "org.apache.spark.sql.execution.streaming.state.RocksDBStateStoreProvider")

# Define output schema for the joined data
output_schema = StructType([
    StructField("user_id", StringType(), True),
    StructField("event_type", StringType(), True),
    StructField("timestamp", TimestampType(), True),
    StructField("profile_name", StringType(), True),
    StructField("email", StringType(), True),
    StructField("preferred_category", StringType(), True)
])

class CustomStreamJoinProcessor(StatefulProcessor):
    # Buffer each user's profile, preference, and activity records in state.
    def init(self, handle: StatefulProcessorHandle) -> None:
        self.handle = handle

        profile_schema = StructType([
            StructField("name", StringType(), True),
            StructField("email", StringType(), True)
        ])
        preferences_schema = StructType([
            StructField("preferred_category", StringType(), True)
        ])
        activity_schema = StructType([
            StructField("event_type", StringType(), True),
            StructField("timestamp", TimestampType(), True)
        ])

        # One value state per record type. The grouping key is user_id, so each
        # state holds the latest record of that type for the user.
        # Profile and preference state expire after an hour of inactivity via TTL
        self.profile_state = handle.getValueState("userProfile", profile_schema, ttlDurationMs=3600000)
        self.preferences_state = handle.getValueState("userPreferences", preferences_schema, ttlDurationMs=3600000)
        self.activity_state = handle.getValueState("userActivity", activity_schema)

    # Route each incoming record by its type and buffer it in state. When an
    # activity event arrives, set a timer to emit the enriched join after a delay.
    def handleInputRows(self, key, rows: Iterator[pd.DataFrame], timerValues) -> Iterator[pd.DataFrame]:
        for pdf in rows:
            for _, row in pdf.iterrows():
                record_type = row["record_type"]
                if record_type == "activity":
                    self.activity_state.update((row["event_type"], row["timestamp"]))
                    # Set a timer to process this event after a 10-second delay
                    self.handle.registerTimer(timerValues.getCurrentProcessingTimeInMs() + 10000)
                elif record_type == "profile":
                    self.profile_state.update((row["name"], row["email"]))
                elif record_type == "preference":
                    self.preferences_state.update((row["preferred_category"],))

        # No immediate output; the enriched row is emitted when the timer expires
        return iter([])

    # Perform the lookup after the delay, handling out-of-order and late-arriving records.
    def handleExpiredTimer(self, key, timerValues, expiredTimerInfo) -> Iterator[pd.DataFrame]:
        if not self.activity_state.exists():
            return iter([])

        activity = self.activity_state.get()
        profile = self.profile_state.get() if self.profile_state.exists() else None
        preferences = self.preferences_state.get() if self.preferences_state.exists() else None

        # Combine data from the different states into a single output row
        output_row = {
            "user_id": key[0],
            "event_type": activity[0],
            "timestamp": activity[1],
            "profile_name": profile[0] if profile else None,
            "email": profile[1] if profile else None,
            "preferred_category": preferences[0] if preferences else None
        }
        # The activity has been consumed by this join, so clear it from state
        self.activity_state.clear()
        return iter([pd.DataFrame([output_row])])

    def close(self) -> None:
        pass

# Create a dedicated schema for the example tables
spark.sql("CREATE SCHEMA IF NOT EXISTS main.stateful_examples")

# Seed a small Delta table with profile, preference, and activity records for one user
spark.sql("DROP TABLE IF EXISTS main.stateful_examples.user_events")
input_schema = StructType([
    StructField("user_id", StringType()),
    StructField("record_type", StringType()),
    StructField("event_type", StringType()),
    StructField("timestamp", TimestampType()),
    StructField("name", StringType()),
    StructField("email", StringType()),
    StructField("preferred_category", StringType())
])
spark.createDataFrame(
    [
        ("u1", "profile", None, None, "Alice", "alice@example.com", None),
        ("u1", "preference", None, None, None, None, "electronics"),
        ("u1", "activity", "purchase", datetime(2024, 1, 1, 12, 0, 0), None, None, None),
    ],
    input_schema,
).write.saveAsTable("main.stateful_examples.user_events")

df = spark.readStream.table("main.stateful_examples.user_events")

# Apply transformWithState. ProcessingTime mode enables the timer that fires the join.
q = (
    df.groupBy("user_id")
    .transformWithStateInPandas(
        statefulProcessor=CustomStreamJoinProcessor(),
        outputStructType=output_schema,
        outputMode="Append",
        timeMode="ProcessingTime",
    )
    .writeStream.format("memory")
    .queryName("enriched_events")
    .option("checkpointLocation", f"/tmp/checkpoint_{uuid.uuid4()}")
    .trigger(processingTime="5 seconds")
    .start()
)

# Wait past the 10-second timer so it fires, then stop the query
time.sleep(30)
q.stop()

# The enriched row joins the activity with the buffered profile and preference
display(spark.sql("SELECT * FROM enriched_events"))

Scala

// Import the necessary libraries
import org.apache.spark.sql.streaming._
import org.apache.spark.sql.Encoders
import spark.implicits._
import java.sql.Timestamp
import java.util.UUID
import java.time.Duration

// Unified input record: every event arrives on one stream, tagged by record_type
case class UserRecord(
    user_id: String,
    record_type: String,
    event_type: Option[String],
    timestamp: Option[Timestamp],
    name: Option[String],
    email: Option[String],
    preferred_category: Option[String]
)

case class UserActivity(event_type: String, timestamp: Timestamp)
case class UserProfile(name: String, email: String)
case class UserPreferences(preferred_category: String)

// Enriched user event combining activity with profile and preference data
case class EnrichedUserEvent(
    user_id: String,
    event_type: String,
    timestamp: Timestamp,
    profile_name: Option[String],
    email: Option[String],
    preferred_category: Option[String]
)

// Custom stateful processor for the stream-stream join
class CustomStreamJoinProcessor extends StatefulProcessor[String, UserRecord, EnrichedUserEvent] {
  // One value state per record type. The grouping key is user_id, so each state
  // holds the latest record of that type for the user.
  @transient private var _profileState: ValueState[UserProfile] = _
  @transient private var _preferencesState: ValueState[UserPreferences] = _
  @transient private var _activityState: ValueState[UserActivity] = _

  override def init(outputMode: OutputMode, timeMode: TimeMode): Unit = {
    // Profile and preference state expire after an hour of inactivity via TTL
    _profileState = getHandle.getValueState[UserProfile]("profileState", Encoders.product[UserProfile], TTLConfig(Duration.ofHours(1)))
    _preferencesState = getHandle.getValueState[UserPreferences]("preferencesState", Encoders.product[UserPreferences], TTLConfig(Duration.ofHours(1)))
    _activityState = getHandle.getValueState[UserActivity]("activityState", Encoders.product[UserActivity], TTLConfig.NONE)
  }

  // Route each incoming record by its type and buffer it in state. When an
  // activity event arrives, set a timer to emit the enriched join after a delay.
  override def handleInputRows(
      key: String,
      inputRows: Iterator[UserRecord],
      timerValues: TimerValues): Iterator[EnrichedUserEvent] = {
    inputRows.foreach { rec =>
      rec.record_type match {
        case "activity" =>
          _activityState.update(UserActivity(rec.event_type.getOrElse(""), rec.timestamp.orNull))
          getHandle.registerTimer(timerValues.getCurrentProcessingTimeInMs() + 10000)
        case "profile" =>
          _profileState.update(UserProfile(rec.name.getOrElse(""), rec.email.getOrElse("")))
        case "preference" =>
          _preferencesState.update(UserPreferences(rec.preferred_category.getOrElse("")))
        case _ =>
      }
    }
    Iterator.empty
  }

  // When the timer expires, join the buffered activity with the latest profile and preference
  override def handleExpiredTimer(
      key: String,
      timerValues: TimerValues,
      expiredTimerInfo: ExpiredTimerInfo): Iterator[EnrichedUserEvent] = {
    if (!_activityState.exists()) {
      Iterator.empty
    } else {
      val activity = _activityState.get()
      val profile = if (_profileState.exists()) Some(_profileState.get()) else None
      val preferences = if (_preferencesState.exists()) Some(_preferencesState.get()) else None
      // The activity has been consumed by this join, so clear it from state
      _activityState.clear()
      Iterator.single(EnrichedUserEvent(
        user_id = key,
        event_type = activity.event_type,
        timestamp = activity.timestamp,
        profile_name = profile.map(_.name),
        email = profile.map(_.email),
        preferred_category = preferences.map(_.preferred_category)
      ))
    }
  }
}

// Create a dedicated schema for the example tables
spark.sql("CREATE SCHEMA IF NOT EXISTS main.stateful_examples")

// Seed a small Delta table with profile, preference, and activity records for one user
spark.sql("DROP TABLE IF EXISTS main.stateful_examples.user_events_scala")
Seq(
  UserRecord("u1", "profile", None, None, Some("Alice"), Some("alice@example.com"), None),
  UserRecord("u1", "preference", None, None, None, None, Some("electronics")),
  UserRecord("u1", "activity", Some("purchase"), Some(Timestamp.valueOf("2024-01-01 12:00:00")), None, None, None)
).toDF().write.saveAsTable("main.stateful_examples.user_events_scala")

// Apply the custom stateful processor. ProcessingTime mode enables the join timer.
val enrichedStream = spark.readStream
  .table("main.stateful_examples.user_events_scala")
  .as[UserRecord]
  .groupByKey(_.user_id)
  .transformWithState(
    new CustomStreamJoinProcessor(),
    TimeMode.ProcessingTime(),
    OutputMode.Append()
  )

val q = enrichedStream.writeStream
  .format("memory")
  .queryName("enriched_events_scala")
  .option("checkpointLocation", s"/tmp/checkpoint_${UUID.randomUUID()}")
  .trigger(Trigger.ProcessingTime("5 seconds"))
  .start()

// Wait past the 10-second timer so it fires, then stop the query
Thread.sleep(30000)
q.stop()

// The enriched row joins the activity with the buffered profile and preference
spark.sql("SELECT * FROM enriched_events_scala").show(false)

Top-K-beräkning

I följande exempel används en ListState med en prioritetskö för att underhålla och uppdatera de översta K-elementen i en ström för varje gruppnyckel nästan i realtid.

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