Exemplo de aplicativos com monitoração de estado

Esta página contém exemplos de código para aplicações de streaming personalizadas com estado que usam o transformWithState operador. Databricks recomenda o uso de métodos stateful integrados para operações comuns, como agregações e uniões.

Veja como criar uma aplicação personalizada com estado com transformWithState.

Requerimentos

O operador transformWithState e as APIs e classes relacionadas têm os seguintes requisitos:

• Disponível no Databricks Runtime 16.2 e superior.
• O modo de acesso padrão é suportado para Python (transformWithStateInPandas e baseado transformWithStateem linhas) no Databricks Runtime 16.3 e superiores, e para Scala (transformWithState) no Databricks Runtime 17.3 e superiores.
• O RocksDB é o fornecedor padrão de armazenamento de estado no Databricks Runtime 17.3 e superiores. Para as versões do Databricks Runtime abaixo da 17.3, deve configurar o fornecedor do repositório de estado do RocksDB. O Databricks recomenda habilitar o RocksDB como parte da configuração de computação.

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

Dimensão de mudança lenta (SCD) tipo 1

O código a seguir é um exemplo de implementação do SCD tipo 1 usando transformWithState. SCD tipo 1 rastreia apenas o valor mais recente para um determinado campo.

Python

# 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"))

linguagem de programação 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()

Dimensão de mudança lenta (SCD - tipo 2)

Os blocos de anotações a seguir contêm um exemplo de implementação do SCD tipo 2 usando transformWithState em Python ou Scala.

SCD Tipo 2 Python

Obter bloco de notas

SCD Tipo 2 Scala

Obter bloco de notas

Detetor de tempo de inatividade

transformWithState implementa temporizadores para permitir que você execute ações com base no tempo decorrido, mesmo que nenhum registro para uma determinada chave seja processado em um microlote.

O exemplo a seguir implementa um padrão para um detetor de tempo de inatividade. Cada vez que um novo valor é visto para uma determinada chave, ele atualiza o valor do estado lastSeen, limpa todos os temporizadores existentes e redefine um temporizador para o futuro.

Quando um temporizador expira, o aplicativo emite o tempo decorrido desde o último evento observado para a chave. Em seguida, define um novo temporizador para emitir uma atualização 10 segundos depois.

Para executar o exemplo do início ao fim, introduza uma única leitura de um sensor como origem do fluxo de dados. Como os temporizadores usam tempo de processamento, o controlador usa um acionador processingTime e espera antes de parar a consulta, para que os temporizadores sejam acionados.

Python

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"))

linguagem de programação 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)

Migrar informações de estado existentes

O exemplo a seguir demonstra como implementar um aplicativo com estado que aceita um estado inicial. Você pode adicionar manipulação de estado inicial a qualquer aplicativo com monitoração de estado, mas o estado inicial só pode ser definido ao inicializar o aplicativo pela primeira vez.

Este exemplo usa o leitor statestore para carregar informações de estado existentes de um caminho de ponto de verificação. Um exemplo de caso de uso para este padrão é a migração de aplicações com estado legadas para transformWithState.

Python

# 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

linguagem de programação 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)
  }
}

Migrar tabela Delta para armazenamento de estado para inicialização

Os blocos de anotações a seguir contêm um exemplo de inicialização de valores de armazenamento de estado de uma tabela Delta usando transformWithState em Python ou Scala.

Inicializar o estado a partir do Delta Python

Obter bloco de notas

Inicializar estado a partir de Delta Scala

Obter bloco de notas

Acompanhamento de sessões

Os blocos de anotações a seguir contêm um exemplo de controle de sessão usando transformWithState em Python ou Scala.

Acompanhamento de sessões em Python

Obter bloco de notas

Acompanhamento de sessão Scala

Obter bloco de notas

Junção personalizada de fluxos usando transformWithState

O código a seguir demonstra uma união personalizada entre fluxos múltiplos usando transformWithState. Você pode usar essa abordagem em vez de um operador de junção interno pelos seguintes motivos:

• Você precisa usar o modo de atualização de saída que não suporta junções entre fluxos. Isso é especialmente útil para aplicativos de baixa latência.
• Você precisa continuar a executar junções para linhas que chegam tarde (após o vencimento da marca d'água).
• Você precisa executar junções muitos-para-muitos entre fluxos.

Este exemplo dá-lhe controlo total da lógica de expiração do estado, permitindo prolongar dinamicamente o período de retenção para processar eventos fora de ordem, mesmo após a watermark.

No exemplo seguinte, os eventos de perfil, preferência e atividade chegam num único fluxo, cada um etiquetado com um record_type. O processador armazena cada tipo de registo no estado, e um temporizador de tempo de processamento emite a junção enriquecida pouco tempo após a chegada de um evento de atividade. O perfil e o estado de preferência expiram após uma hora de inatividade usando um TTL, e cada atividade é apagada do estado assim que foi juntada.

Python

# 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"))

linguagem de programação 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 cálculo

O exemplo a seguir usa um ListState com uma fila de prioridade para manter e atualizar os principais elementos K em um fluxo para cada chave de grupo quase em tempo real.

Top-K Python

Obter bloco de notas

Top-K Scala

Obter bloco de notas