Aplicaciones con estado de ejemplo

Esta página contiene ejemplos de código para aplicaciones de streaming con estado personalizadas usando el transformWithState operador. Databricks recomienda usar métodos con estado integrados para operaciones comunes, como agregaciones y combinaciones.

Consulte Crear una aplicación personalizada con estado con transformWithState.

Requisitos

El operador transformWithState y las API y clases relacionadas tienen los siguientes requisitos:

• Disponible en Databricks Runtime 16.2 y versiones posteriores.
• El modo de acceso estándar es compatible con Python (transformWithStateInPandas y basado en filas transformWithState) en Databricks Runtime 16.3 y versiones posteriores, y con Scala (transformWithState) en Databricks Runtime 17.3 y versiones posteriores.
• RocksDB es el proveedor de almacén de estado predeterminado en Databricks Runtime 17.3 y versiones posteriores. Para las versiones de Databricks Runtime inferiores a la 17.3, debe configurar el proveedor de almacén de estado de RocksDB. Databricks recomienda habilitar RocksDB como parte de la configuración de proceso.

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

Dimensión de variación lenta (SCD) de tipo 1

El código siguiente es un ejemplo de implementación del tipo SCD 1 mediante transformWithState. El tipo SCD 1 solo realiza un seguimiento del valor más reciente de un campo determinado.

Pitón

# 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()

Dimensión de variación lenta tipo 2 (SCD)

Los cuadernos siguientes contienen un ejemplo de implementación del tipo 2 de SCD mediante transformWithState en Python o Scala.

SCD Tipo 2 Python

Obtener el cuaderno

SCD tipo 2 Scala

Obtener el cuaderno

Detector de tiempo de inactividad

transformWithState implementa temporizadores para permitirle tomar medidas en función del tiempo transcurrido, incluso si no se procesan registros para una clave determinada en un microproceso.

En el ejemplo siguiente se implementa un patrón para un detector de tiempo de inactividad. Cada vez que se ve un nuevo valor para una clave determinada, actualiza el valor de estado lastSeen, borra los temporizadores existentes y restablece un temporizador para el futuro.

Cuando expira un temporizador, la aplicación emite el tiempo transcurrido desde el último evento observado para la clave. A continuación, establece un nuevo temporizador para emitir una actualización de 10 segundos más tarde.

Para ejecutar el ejemplo de principio a fin, establece como fuente de streaming una única lectura de sensor. Como los temporizadores usan tiempo de procesamiento, el controlador utiliza un processingTime disparador y espera antes de detener la consulta para que los temporizadores se activen.

Pitón

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)

Migración de la información de estado existente

En el ejemplo siguiente se muestra cómo implementar una aplicación con estado que acepta un estado inicial. Puede agregar el control de estado inicial a cualquier aplicación con estado, pero el estado inicial solo se puede establecer al inicializar la aplicación por primera vez.

En este ejemplo se utiliza el lector statestore para cargar la información de estado existente desde una ruta de punto de control. Un caso de uso de ejemplo para este patrón es migrar desde aplicaciones con estado heredadas a transformWithState.

Pitón

# 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)
  }
}

Migración de la tabla Delta al almacén de estado para la inicialización

Los cuadernos siguientes contienen un ejemplo de inicialización de valores de almacén de estado desde una tabla Delta mediante transformWithState en Python o Scala.

Inicialización del estado de Delta Python

Obtener el cuaderno

Inicialización del estado de Delta Scala

Obtener el cuaderno

Seguimiento de sesiones

Los cuadernos siguientes contienen un ejemplo de seguimiento de sesión mediante transformWithState en Python o Scala.

Seguimiento de sesiones de Python

Obtener el cuaderno

Seguimiento de sesiones de Scala

Obtener el cuaderno

Combinación personalizada de flujo a flujo mediante transformWithState

En el código siguiente se muestra una combinación de flujo a flujo personalizada en varias secuencias mediante transformWithState. Puede usar este enfoque en lugar de un operador de combinación integrado por los siguientes motivos:

• Debe usar el modo de salida de actualización que no admite combinaciones de flujo a flujo. Esto es especialmente útil para aplicaciones de menor latencia.
• Debe seguir realizando combinaciones para las filas de llegada tardía (después de la expiración de la marca de agua).
• Debe realizar combinaciones de flujo a flujo de varios a varios.

Este ejemplo te ofrece un control total sobre la lógica de caducidad de los estados, lo que permite ampliar dinámicamente el periodo de retención para administrar eventos fuera de orden incluso después del punto de referencia.

En el siguiente ejemplo, los eventos de perfil, preferencias y actividad llegan en un único flujo, cada uno de ellos etiquetado con un record_type. El procesador almacena en búfer cada tipo de registro en el estado, y un temporizador de tiempo de procesamiento emite la unión enriquecida poco después de que llegue un evento de actividad. El estado de perfil y preferencias caduca tras una hora de inactividad mediante un TTL, y cada actividad se elimina del estado una vez que se ha unido.

Pitón

# 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)

Cálculo de top-K

En el ejemplo siguiente se usa una clase ListState con una cola de prioridad para mantener y actualizar los elementos K principales de una secuencia para cada clave de grupo casi en tiempo real.

Top-K Python

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Top-K Scala

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