Example stateful applications
This article contains code examples for custom stateful applications. Databricks recommends using built-in stateful methods for common operations such as aggregations and joins.
The patterns in this article use the transformWithState operator and associated classes available in Public Preview in Databricks Runtime 16.2 and above. See Build a custom stateful application.
Note
Python uses the transformWithStateInPandas operator to provide the same functionality. The examples below provide code in Python and Scala.
Requirements
The transformWithState operator and the related APIs and classes have the following requirements:
- Available in Databricks Runtime 16.2 and above.
- Compute must use dedicated or no-isolation access mode.
- You must use the RocksDB state store provider. Databricks recommends enabling RocksDB as part of the compute configuration.
Note
To enable the RocksDB state store provider for the current session, run the following:
spark.conf.set("spark.sql.streaming.stateStore.providerClass", "org.apache.spark.sql.execution.streaming.state.RocksDBStateStoreProvider")
Slowly changing dimension (SCD) type 1
The following code is an example of implementing SCD type 1 using transformWithState. SCD type 1 only tracks the most recent value for a given field.
Note
You can use Streaming tables and APPLY CHANGES INTO to implement SCD type 1 or type 2 using Delta Lake-backed tables. This example implements SCD type 1 in the state store, which provides lower latency for near real-time applications.
Python
# Import 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:
# 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, timer_values) -> 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 if 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
# Apply the stateful transformation to the input DataFrame
(df.groupBy("user")
.transformWithStateInPandas(
statefulProcessor=SCDType1StatefulProcessor(),
outputStructType=output_schema,
outputMode="Update",
timeMode="None",
)
.writeStream... # Continue with stream writing configuration
)
Scala
// 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] = _
// 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",
Encoders.product[UserLocation], 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
}
}
}
}
Slowly changing dimension (SCD) type 2
The following notebooks contain an example of implementing SCD type 2 using transformWithState in Python or Scala.
SCD Type 2 Python
SCD Type 2 Scala
Downtime detector
transformWithState implements timers to allow you to take action based on elapsed time, even if no records for a given key are processed in a microbatch.
The following example implements a pattern for a downtime detector. Each time a new value is seen for a given key, it updates the lastSeen state value, clears any existing timers, and resets a timer for the future.
When a timer expires, the application emits the elapsed time since the last observed event for the key. It then sets a new timer to emit an update 10 seconds later.
Python
import datetime
import time
class DownTimeDetectorStatefulProcessor(StatefulProcessor):
def init(self, handle: StatefulProcessorHandle) -> None:
# Define 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 duration
downtime_duration = timerValues.getCurrentProcessingTimeInMs() - int(time.time() * 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 existing state or use epoch start if not exists
if self.last_seen.exists():
latest_from_existing = self.last_seen.get()
else:
latest_from_existing = datetime.fromtimestamp(0)
# If new data is more recent than 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
Scala
import java.sql.Timestamp
import org.apache.spark.sql.Encoders
// 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.
class DowntimeDetector(duration: Duration) extends
StatefulProcessor[String, (String, Timestamp), (String, Duration)] {
@transient private var _lastSeen: ValueState[Timestamp] = _
override def init(outputMode: OutputMode, timeMode: TimeMode): Unit = {
_lastSeen = getHandle.getValueState[Timestamp]("lastSeen", Encoders.TIMESTAMP, 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, Duration)] = {
val latestRecordFromNewRows = inputRows.maxBy(_._2.getTime)
// Use getOrElse to initiate state variable if it doesn't exist
val latestTimestampFromExistingRows = _lastSeen.getOption().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()" just because we don't know ahead of time what
// the timestamp of the existing timer is.
getHandle.listTimers().foreach(timer => getHandle.deleteTimer(timer))
_lastSeen.update(latestTimestampFromNewRows)
// Use timerValues to schedule a timer using processing time.
getHandle.registerTimer(timerValues.getCurrentProcessingTimeInMs() + duration.toMillis)
} else {
// No new latest timestamp, so no need to update state or set a timer.
}
Iterator.empty
}
override def handleExpiredTimer(
key: String,
timerValues: TimerValues,
expiredTimerInfo: ExpiredTimerInfo): Iterator[(String, Duration)] = {
val latestTimestamp = _lastSeen.get()
val downtimeDuration = new Duration(
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, downtimeDuration))
}
}
Migrate existing state information
The following example demonstrates how to implement a stateful application that accepts an initial state. You can add initial state handling to any stateful application, but the initial state can only be set when first initializing the application.
This example uses the statestore reader to load existing state information from a checkpoint path. An example use case for this pattern is migrating from legacy stateful applications to transformWithState.
Python
# Import 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 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 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 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] = _
// 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",
Encoders.scalaInt, 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)
}
}
Migrate Delta table to state store for intialization
The following notebooks contain an example of initializing state store values from a Delta table using transformWithState in Python or Scala.
Initialize state from Delta Python
Initialize state from Delta Scala
Session tracking
The following notebooks contain an example of session tracking using transformWithState in Python or Scala.
Session tracking Python
Session tracking Scala
Custom stream-stream join using transformWithState
The following code demonstrates a custom stream-stream join across multiple streams using transformWithState. You might use this approach instead of a built-in join operator for the following reasons:
- You need to use the update output mode which does not support stream-stream joins. This is especially useful for lower latency applications.
- You need to continue to perform joins for late-arriving rows (after watermark expiry).
- You need to perform many-to-many stream-stream joins.
This example gives the user full control over state expiration logic, allowing for dynamic retention period extension to handle out-of-order events even after the watermark.
Python
# Import necessary libraries
import pandas as pd
from pyspark.sql.streaming import StatefulProcessor, StatefulProcessorHandle
from pyspark.sql.types import StructType, StructField, StringType, TimestampType
from typing import Iterator
# 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):
# Initialize stateful storage for user profiles, preferences, and event tracking.
def init(self, handle: StatefulProcessorHandle) -> None:
# Define schemas for different types of state data
profile_schema = StructType([
StructField("name", StringType(), True),
StructField("email", StringType(), True),
StructField("updated_at", TimestampType(), True)
])
preferences_schema = StructType([
StructField("preferred_category", StringType(), True),
StructField("updated_at", TimestampType(), True)
])
activity_schema = StructType([
StructField("event_type", StringType(), True),
StructField("timestamp", TimestampType(), True)
])
# Initialize state storage for user profiles, preferences, and activity
self.profile_state = handle.getMapState("user_profiles", "string", profile_schema)
self.preferences_state = handle.getMapState("user_preferences", "string", preferences_schema)
self.activity_state = handle.getMapState("user_activity", "string", activity_schema)
# Process incoming events and update state
def handleInputRows(self, key, rows: Iterator[pd.DataFrame], timer_values) -> Iterator[pd.DataFrame]:
df = pd.concat(rows, ignore_index=True)
output_rows = []
for _, row in df.iterrows():
user_id = row["user_id"]
if "event_type" in row: # User activity event
self.activity_state.update_value(user_id, row.to_dict())
# Set a timer to process this event after a 10-second delay
self.getHandle().registerTimer(timer_values.get_current_processing_time_in_ms() + (10 * 1000))
elif "name" in row: # Profile update
self.profile_state.update_value(user_id, row.to_dict())
elif "preferred_category" in row: # Preference update
self.preferences_state.update_value(user_id, row.to_dict())
# No immediate output; processing will happen when timer expires
return iter([])
# Perform lookup after delay, handling out-of-order and late-arriving events.
def handleExpiredTimer(self, key, timer_values, expired_timer_info) -> Iterator[pd.DataFrame]:
# Retrieve stored state for the user
user_activity = self.activity_state.get_value(key)
user_profile = self.profile_state.get_value(key)
user_preferences = self.preferences_state.get_value(key)
if user_activity:
# Combine data from different states into a single output row
output_row = {
"user_id": key,
"event_type": user_activity["event_type"],
"timestamp": user_activity["timestamp"],
"profile_name": user_profile.get("name") if user_profile else None,
"email": user_profile.get("email") if user_profile else None,
"preferred_category": user_preferences.get("preferred_category") if user_preferences else None
}
return iter([pd.DataFrame([output_row])])
return iter([])
def close(self) -> None:
# No cleanup needed
pass
# Apply transformWithState to the input DataFrame
(df.groupBy("user_id")
.transformWithStateInPandas(
statefulProcessor=CustomStreamJoinProcessor(),
outputStructType=output_schema,
outputMode="Append",
timeMode="ProcessingTime"
)
.writeStream... # Continue with stream writing configuration
)
Scala
// Import necessary libraries
import org.apache.spark.sql.Encoders
import org.apache.spark.sql.streaming._
import org.apache.spark.sql.types.TimestampType
import java.sql.Timestamp
// Define a case class for enriched user events, combining user 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 stream-stream join
class CustomStreamJoinProcessor extends StatefulProcessor[String, UserEvent, EnrichedUserEvent] {
// Transient state variables to store user profiles, preferences, and activities
@transient private var _profileState: MapState[String, UserProfile] = _
@transient private var _preferencesState: MapState[String, UserPreferences] = _
@transient private var _activityState: MapState[String, UserEvent] = _
// Initialize state stores
override def init(outputMode: OutputMode, timeMode: TimeMode): Unit = {
_profileState = getHandle.getMapState[String, UserProfile]("profileState", Encoders.product[UserProfile], TTLConfig.NONE)
_preferencesState = getHandle.getMapState[String, UserPreferences]("preferencesState", Encoders.product[UserPreferences], TTLConfig.NONE)
_activityState = getHandle.getMapState[String, UserEvent]("activityState", Encoders.product[UserEvent], TTLConfig.NONE)
}
// Handle incoming user events
override def handleInputRows(
key: String,
inputRows: Iterator[UserEvent],
timerValues: TimerValues): Iterator[EnrichedUserEvent] = {
inputRows.foreach { event =>
if (event.event_type.nonEmpty) {
// Update activity state and set a timer for 10 seconds in the future
_activityState.update(key, event)
getHandle.registerTimer(timerValues.getCurrentProcessingTimeInMs() + 10000)
}
}
Iterator.empty
}
// Handle expired timers to produce enriched events
override def handleExpiredTimer(
key: String,
timerValues: TimerValues,
expiredTimerInfo: ExpiredTimerInfo): Iterator[EnrichedUserEvent] = {
// Retrieve user data from state stores
val userEvent = _activityState.getOption(key)
val userProfile = _profileState.getOption(key)
val userPreferences = _preferencesState.getOption(key)
if (userEvent.isDefined) {
// Create and return an enriched event if user activity exists
val enrichedEvent = EnrichedUserEvent(
user_id = key,
event_type = userEvent.get.event_type,
timestamp = userEvent.get.timestamp,
profile_name = userProfile.map(_.name),
email = userProfile.map(_.email),
preferred_category = userPreferences.map(_.preferred_category)
)
Iterator.single(enrichedEvent)
} else {
Iterator.empty
}
}
}
// Apply the custom stateful processor to the input DataFrame
val enrichedStream = df
.groupByKey(_.user_id)
.transformWithState(
new CustomStreamJoinProcessor(),
TimeMode.ProcessingTime(),
OutputMode.Append()
)
// Write the enriched stream to Delta Lake
enrichedStream.writeStream
.format("delta")
.outputMode("append")
.option("checkpointLocation", "/mnt/delta/checkpoints")
.start("/mnt/delta/enriched_events")
Top-K computation
The following example uses a ListState with a priority queue to maintain and update the top K elements in a stream for each group key in near real-time.