Pyspark streaming

PySpark Structured Streaming

When to Use

Load this skill when the user needs to:

• Build streaming pipelines with PySpark (Kafka → Iceberg, Kafka → Delta, file watching, etc.)
• Configure watermarks and event-time windowing
• Handle late data, deduplication, and stateful aggregations
• Write to Kafka, Delta Lake, Iceberg, or other sinks from a stream
• Debug streaming queries, monitor lag, and tune checkpointing

---

Core Model

Streaming = unbounded table that grows row by row
           ┌─────────────────────────────┐
Source ──► │  Infinite Input Table       │
           │  (new rows = new events)    │
           └────────────┬────────────────┘
                        │ trigger
                        ▼
                 Incremental Query
                        │
                        ▼
           ┌─────────────────────────────┐
           │  Result Table               │  Output mode determines
           │  (updated every trigger)    │  what gets written to sink
           └─────────────────────────────┘

Every StreamingQuery maintains a checkpoint directory — offsets read, state snapshots, committed sink batches. Restart resumes from exactly this point → exactly-once delivery when sink is idempotent.

---

SparkSession Setup

from pyspark.sql import SparkSession

spark = (
    SparkSession.builder
    .appName("orders-streaming")
    # Kafka connector (Maven coordinate)
    .config("spark.jars.packages", "org.apache.spark:spark-sql-kafka-0-10_2.13:3.5.0")
    # RocksDB state store for large stateful workloads
    .config("spark.sql.streaming.stateStore.providerClass",
            "org.apache.spark.sql.execution.streaming.state.RocksDBStateStoreProvider")
    # Adaptive query execution
    .config("spark.sql.adaptive.enabled", "true")
    .getOrCreate()
)
spark.sparkContext.setLogLevel("WARN")

---

Sources

Kafka Source

from pyspark.sql.functions import col, from_json, schema_of_json
from pyspark.sql.types import StructType, StructField, StringType, LongType, TimestampType

# Schema for JSON value
order_schema = StructType([
    StructField("order_id",   LongType(),      False),
    StructField("user_id",    LongType(),      False),
    StructField("amount",     StringType(),    True),
    StructField("status",     StringType(),    True),
    StructField("event_time", TimestampType(), True),
])

raw = (
    spark.readStream
    .format("kafka")
    .option("kafka.bootstrap.servers", "broker:9092")
    .option("subscribe", "orders")                    # or subscribePattern for regex
    .option("startingOffsets", "latest")              # latest | earliest | JSON offset map
    .option("maxOffsetsPerTrigger", 50_000)           # rate-limit per micro-batch
    .option("failOnDataLoss", "false")                # don't fail if topic is compacted
    .option("kafka.session.timeout.ms", "30000")
    .option("kafka.max.poll.records", "500")
    .option("includeHeaders", "true")                 # expose Kafka headers column
    .load()
)

# Kafka DataFrame schema: key(binary), value(binary), topic, partition, offset, timestamp, headers
orders = (
    raw
    .selectExpr("CAST(key AS STRING) AS order_key", "CAST(value AS STRING) AS json_value",
                "timestamp AS kafka_ts", "partition", "offset")
    .withColumn("data", from_json(col("json_value"), order_schema))
    .select("order_key", "kafka_ts", "offset", "partition", "data.*")
)

Kafka source options:

Option	Default	Notes
subscribe	—	Comma-separated topic list
subscribePattern	—	Java regex over topic names
assign	—	JSON {"topic":[0,1,2]} for specific partitions
startingOffsets	latest (stream) / earliest (batch)
maxOffsetsPerTrigger	unlimited	Cap throughput per trigger
minOffsetsPerTrigger	0	Minimum before trigger fires
maxTriggerDelay	15m	Max wait even if minOffsets not met
failOnDataLoss	true	Set false for compacted topics
kafka.group.id	auto	Override Spark's managed consumer group

File Source (Auto-Ingest)

# Watch a directory for new Parquet files — triggers on new files only
file_stream = (
    spark.readStream
    .schema(order_schema)
    .option("maxFilesPerTrigger", 10)    # process up to 10 files per batch
    .option("latestFirst", "false")
    .parquet("s3a://datalake/landing/orders/")
)

Rate Source (Testing / Benchmarking)

rate_stream = (
    spark.readStream
    .format("rate")
    .option("rowsPerSecond", 1000)
    .option("numPartitions", 4)
    .load()
)
# Columns: timestamp (event-time), value (monotonically increasing long)

---

Output Modes

Mode	When to Use	Constraints
append	Row written once, never updated	Requires watermark for windowed aggregations; no aggregation on unbounded data
update	Only changed rows written each trigger	Aggregations allowed; no sort
complete	Full result table written each trigger	Aggregations only; do not use for large result tables

# append — for raw event logging, windowed aggs with watermark
query = stream.writeStream.outputMode("append").format("delta").start(path)

# update — for aggregations that need live updates (dashboards)
query = stream.writeStream.outputMode("update").format("console").start()

# complete — for global aggregations (total counts, global topN)
query = stream.writeStream.outputMode("complete").format("memory").queryName("totals").start()

---

Triggers

from pyspark.sql.streaming import Trigger

# ProcessingTime — fixed micro-batch interval
.trigger(processingTime="30 seconds")
.trigger(processingTime="1 minute")

# AvailableNow — process all available data, then stop (replaces One-time trigger)
# Use for scheduled batch-style streaming jobs
.trigger(availableNow=True)

# Once — deprecated in favor of AvailableNow (same semantics, single batch)
.trigger(once=True)

# Continuous — experimental low-latency mode (~1ms); limited operations
.trigger(continuous="1 second")

Rule: use availableNow=True for Airflow-orchestrated streaming jobs (bounded run). Use processingTime for always-on streaming.

---

Watermarks & Event-Time Windows

Watermark Mechanics

Watermark = max(event_time seen) - watermark_delay

Window closes (and output emitted in append mode) when:
  watermark > window_end_time

Late records with event_time < watermark are DROPPED.

from pyspark.sql.functions import col, window, avg, count, sum as spark_sum

# Define watermark on event-time column
windowed_agg = (
    orders
    .withWatermark("event_time", "10 minutes")   # tolerate up to 10 min late arrivals
    .groupBy(
        window(col("event_time"), "5 minutes"),  # tumbling window
        col("status"),
    )
    .agg(
        count("*").alias("cnt"),
        spark_sum("amount").alias("total_amount"),
    )
)

Tumbling Window

# Non-overlapping, fixed-size windows
from pyspark.sql.functions import window

df.groupBy(window("event_time", "10 minutes")).agg(count("*"))
# Window column: window.start, window.end

Sliding Window

# Overlapping windows — duration=10min, slide=5min
df.groupBy(window("event_time", "10 minutes", "5 minutes")).agg(count("*"))

Session Window

# Variable-size windows — gap closes after inactivity
from pyspark.sql.functions import session_window

df.groupBy(
    session_window("event_time", "5 minutes"),  # new session after 5 min silence
    col("user_id"),
).agg(count("*").alias("events_in_session"))

Output Mode with Watermarks

Combination	Supported	Notes
Windowed agg + watermark + append	✅	Results emitted once window is closed past watermark
Windowed agg + watermark + update	✅	Partial results emitted each trigger, state trimmed by watermark
Windowed agg + no watermark + append	❌	Error: append requires watermark for bounded state
Any agg + complete	✅	Full table every trigger; watermark not required but state never trimmed

---

Streaming Deduplication

# Deduplicate within watermark window
# Records with same (order_id, event_time) within 1 hour are deduplicated
deduplicated = (
    orders
    .withWatermark("event_time", "1 hour")
    .dropDuplicates(["order_id", "event_time"])  # dedup keys
)

# Without watermark: dedup across entire stream (state grows unbounded — only for small key spaces)
deduplicated_global = orders.dropDuplicates(["order_id"])

---

foreachBatch — Custom Sink Logic

The most powerful pattern: process each micro-batch as a static DataFrame. Enables multi-sink writes, upserts, and custom transformations.

from delta.tables import DeltaTable

def upsert_to_delta(batch_df, batch_id):
    """Merge streaming micro-batch into Delta Lake table."""
    # batch_df is a standard static DataFrame
    batch_df.persist()   # cache if reusing multiple times

    # Deduplicate within batch first (micro-batch may have duplicates)
    deduped = (
        batch_df
        .dropDuplicates(["order_id"])
        .filter(col("order_id").isNotNull())
    )

    target = DeltaTable.forPath(spark, "s3a://datalake/silver/orders")
    (
        target.alias("t")
        .merge(deduped.alias("s"), "t.order_id = s.order_id")
        .whenMatchedUpdate(set={
            "status":     "s.status",
            "amount":     "s.amount",
            "updated_at": "s.event_time",
        })
        .whenNotMatchedInsertAll()
        .execute()
    )
    batch_df.unpersist()

query = (
    orders
    .writeStream
    .outputMode("update")           # update mode with foreachBatch
    .foreachBatch(upsert_to_delta)
    .option("checkpointLocation", "s3a://checkpoints/orders-silver/")
    .trigger(processingTime="1 minute")
    .start()
)

foreachBatch: Multi-Sink Write

def write_to_multiple_sinks(batch_df, batch_id):
    batch_df.persist()

    # Sink 1: Delta Lake silver
    batch_df.write.format("delta").mode("append").save("s3a://silver/orders/")

    # Sink 2: Kafka output topic (transform + forward)
    (
        batch_df
        .selectExpr("CAST(order_id AS STRING) AS key",
                    "to_json(struct(*)) AS value")
        .write
        .format("kafka")
        .option("kafka.bootstrap.servers", "broker:9092")
        .option("topic", "orders-enriched")
        .save()
    )

    # Sink 3: Metrics table (aggregation)
    (
        batch_df
        .groupBy("status")
        .agg(count("*").alias("cnt"))
        .write.format("delta").mode("append").save("s3a://gold/order_metrics/")
    )

    batch_df.unpersist()

query = (
    orders.writeStream
    .foreachBatch(write_to_multiple_sinks)
    .option("checkpointLocation", "s3a://checkpoints/orders-multi/")
    .start()
)

---

Kafka Sink

# Write stream to Kafka topic
(
    orders
    .selectExpr(
        "CAST(order_id AS STRING) AS key",
        "to_json(struct(order_id, user_id, amount, status, event_time)) AS value",
    )
    .writeStream
    .format("kafka")
    .option("kafka.bootstrap.servers", "broker:9092")
    .option("topic", "orders-processed")
    .option("kafka.acks", "all")
    .option("kafka.enable.idempotence", "true")
    .option("checkpointLocation", "s3a://checkpoints/orders-kafka-sink/")
    .trigger(processingTime="30 seconds")
    .start()
)

---

Delta Lake / Iceberg Sink

# Delta Lake — append
(
    orders.writeStream
    .format("delta")
    .outputMode("append")
    .option("checkpointLocation", "s3a://checkpoints/orders/")
    .option("mergeSchema", "true")           # auto schema evolution
    .partitionBy("dt")                       # partition by date column
    .trigger(processingTime="5 minutes")
    .start("s3a://datalake/silver/orders/")
)

# Iceberg — append via foreachBatch (recommended for Iceberg + Spark)
def write_to_iceberg(batch_df, batch_id):
    batch_df.writeTo("silver.orders").append()

(
    orders.writeStream
    .foreachBatch(write_to_iceberg)
    .option("checkpointLocation", "s3a://checkpoints/orders-iceberg/")
    .trigger(processingTime="5 minutes")
    .start()
)

---

Stream–Stream Joins

from pyspark.sql.functions import expr

orders  = spark.readStream.format("kafka").option(...).load()
payments = spark.readStream.format("kafka").option(...).load()

# Both streams need watermarks for state cleanup
orders_wm  = orders.withWatermark("order_time", "1 hour")
payments_wm = payments.withWatermark("payment_time", "30 minutes")

joined = orders_wm.join(
    payments_wm,
    expr("""
        orders.order_id = payments.order_id AND
        payments.payment_time BETWEEN
            orders.order_time AND
            orders.order_time + INTERVAL 30 MINUTES
    """),
    "inner",   # inner | leftOuter | full (limited support)
)

Global watermark rule: the combined watermark = min(stream_A_watermark, stream_B_watermark). A slow stream delays the other's state cleanup. Set spark.sql.streaming.multipleWatermarkPolicy=max (drops data from slow stream) only when latency > data completeness.

---

Checkpointing & Fault Tolerance

# Checkpoint = commit log of: source offsets + state snapshots + sink commits
# Location MUST be persistent storage (HDFS, S3, GCS — not local fs in cluster mode)

(
    stream.writeStream
    .option("checkpointLocation", "s3a://checkpoints/my-job/v1/")
    # Change checkpoint path = restart from scratch with new state
    # Keep same path = resume from last committed batch
    .start()
)

Checkpoint directory layout:

checkpoints/my-job/v1/
  commits/          # committed batch IDs
  offsets/          # source offsets per batch
  sources/          # source metadata
  state/            # stateful operator snapshots (RocksDB files)
  metadata          # query metadata

WAL (Write-Ahead Log): Spark writes source offsets before processing. On restart, it re-reads from the last uncommitted offset → at-least-once for sources + idempotent sink = exactly-once end-to-end.

# Schema changes: after changing schema, must either:
# 1. Migrate state (complex) — use same checkpoint with mergeSchema
# 2. Start fresh — change checkpoint path or delete state/ dir only

---

Managing Streaming Queries

# Start query and keep reference
query = stream.writeStream.format("delta").option(...).start()

# Check status
print(query.status)
# {'message': 'Processing new data', 'isDataAvailable': True, 'isTriggerActive': True}

# Last progress (metrics, watermark, lag)
import json
print(json.dumps(query.lastProgress, indent=2))
# {
#   "batchId": 42,
#   "numInputRows": 15000,
#   "inputRowsPerSecond": 1250.0,
#   "processedRowsPerSecond": 2100.0,
#   "eventTime": {"max": "2024-01-15T10:30:00", "watermark": "2024-01-15T10:20:00"},
#   "stateOperators": [{"numRowsTotal": 42000, "numRowsDroppedByWatermark": 120}],
#   "sources": [{"description": "KafkaV2[..orders..]", "endOffset": {"orders": {"0": 8150}}}]
# }

# Block until termination
query.awaitTermination()
query.awaitTermination(timeout=300)   # timeout in seconds

# Stop gracefully
query.stop()

# List all active queries
spark.streams.active

# Restart on failure pattern
while True:
    try:
        query.awaitTermination()
        break
    except Exception as e:
        logger.error(f"Query failed: {e}")
        query = stream.writeStream.option(...).start()

---

RocksDB State Store (Production Stateful Pipelines)

Default in-memory state store exhausts executor memory for large key spaces. RocksDB offloads state to local SSD.

spark.conf.set(
    "spark.sql.streaming.stateStore.providerClass",
    "org.apache.spark.sql.execution.streaming.state.RocksDBStateStoreProvider",
)
# Block cache (default 8 MB — increase for large state)
spark.conf.set("spark.sql.streaming.stateStore.rocksdb.blockCacheSizeMB", "512")
# Write buffer (default 64 MB)
spark.conf.set("spark.sql.streaming.stateStore.rocksdb.writeBufferSizeMB", "128")

---

Production Pipeline Pattern — Kafka → Silver Delta Lake

import pendulum
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, from_json, current_timestamp, to_date
from pyspark.sql.types import StructType, StructField, LongType, StringType, TimestampType, DoubleType
from delta.tables import DeltaTable

spark = (
    SparkSession.builder
    .appName("orders-kafka-to-silver")
    .config("spark.jars.packages",
            "org.apache.spark:spark-sql-kafka-0-10_2.13:3.5.0,"
            "io.delta:delta-spark_2.13:3.1.0")
    .config("spark.sql.extensions", "io.delta.sql.DeltaSparkSessionExtension")
    .config("spark.sql.catalog.spark_catalog", "org.apache.spark.sql.delta.catalog.DeltaCatalog")
    .config("spark.sql.streaming.stateStore.providerClass",
            "org.apache.spark.sql.execution.streaming.state.RocksDBStateStoreProvider")
    .getOrCreate()
)

ORDER_SCHEMA = StructType([
    StructField("order_id",   LongType(),      False),
    StructField("user_id",    LongType(),      False),
    StructField("amount",     DoubleType(),    True),
    StructField("status",     StringType(),    True),
    StructField("event_time", TimestampType(), True),
])

CHECKPOINT = "s3a://checkpoints/orders-silver/"
SILVER_PATH = "s3a://datalake/silver/orders/"
KAFKA_SERVERS = "broker1:9092,broker2:9092"

raw = (
    spark.readStream
    .format("kafka")
    .option("kafka.bootstrap.servers", KAFKA_SERVERS)
    .option("subscribe", "orders")
    .option("startingOffsets", "latest")
    .option("maxOffsetsPerTrigger", 100_000)
    .option("failOnDataLoss", "false")
    .load()
)

parsed = (
    raw
    .selectExpr("CAST(value AS STRING) AS json_str", "timestamp AS kafka_ts",
                "partition", "offset")
    .withColumn("d", from_json(col("json_str"), ORDER_SCHEMA))
    .select("kafka_ts", "partition", "offset", "d.*")
    .withColumn("_ingest_ts", current_timestamp())
    .withColumn("dt", to_date("event_time"))
    .filter(col("order_id").isNotNull())
)

def merge_to_silver(batch_df, batch_id):
    if batch_df.isEmpty():
        return
    deduped = batch_df.dropDuplicates(["order_id"])

    if DeltaTable.isDeltaTable(spark, SILVER_PATH):
        target = DeltaTable.forPath(spark, SILVER_PATH)
        (
            target.alias("t")
            .merge(deduped.alias("s"), "t.order_id = s.order_id")
            .whenMatchedUpdate(condition="s.event_time > t.event_time", set={
                "status": "s.status", "amount": "s.amount",
                "event_time": "s.event_time", "_ingest_ts": "s._ingest_ts",
            })
            .whenNotMatchedInsertAll()
            .execute()
        )
    else:
        deduped.write.format("delta").mode("overwrite").partitionBy("dt").save(SILVER_PATH)

query = (
    parsed.writeStream
    .outputMode("update")
    .foreachBatch(merge_to_silver)
    .option("checkpointLocation", CHECKPOINT)
    .trigger(processingTime="1 minute")
    .start()
)
query.awaitTermination()

---

Best Practices

1. Always set checkpointLocation on persistent storage (S3/HDFS). Without it, a restart reprocesses from beginning.
2. Use foreachBatch for any non-trivial sink: upserts, multi-sink writes, custom error handling, Delta MERGE.
3. Use withWatermark + dropDuplicates together for deduplication with bounded state.
4. Set maxOffsetsPerTrigger to cap batch size and keep processing latency predictable.
5. Enable RocksDB state store for stateful aggregations with large key spaces (> 1M keys).
6. Monitor stateOperators[].numRowsDroppedByWatermark — high numbers = watermark delay too short for your late data distribution.
7. Trigger availableNow=True for Airflow-scheduled jobs — processes all backlog and terminates cleanly.
8. Persist batch_df at the start of foreachBatch if using it more than once (multiple sinks).
9. Separate checkpoint paths per query — never share checkpoints between different queries.
10. Schema evolution: use mergeSchema=true on Delta sink; restart with new checkpoint path for breaking schema changes.
11. Backpressure: Structured Streaming has no built-in backpressure — control via maxOffsetsPerTrigger and horizontal scaling.

---

Anti-Patterns

Anti-Pattern	Problem	Fix
No checkpoint location	Full replay on restart, duplicated writes	Always set persistent checkpointLocation
outputMode("complete") on large stream	Full result table written every trigger — OOM	Use append + watermark or update
Windowed agg without watermark in append mode	AnalysisException at runtime	Add withWatermark() before groupBy(window(...))
Collecting results with df.collect() inside foreachBatch	Brings all data to driver	Never collect; always write via distributed APIs
Local file path for checkpoint	Fails in cluster mode (executor ≠ driver filesystem)	Use HDFS/S3/GCS paths
Missing dropDuplicates in foreachBatch	Kafka at-least-once delivery causes duplicates in Delta	Dedup on natural key before merge
trigger(once=True) on large backlog	Processes entire backlog in a single batch — OOM	Use trigger(availableNow=True) instead
Stream-stream join without time constraint	State grows unbounded	Always add event-time constraint in join condition
Same checkpoint path for different queries	Corrupted checkpoint metadata	One checkpoint path per unique query
Using mapPartitions / foreach instead of foreachBatch	Per-record overhead, no access to SparkSession	Use foreachBatch for batch semantics

---

References to Consult When Needed

• Spark Structured Streaming Guide
• Spark + Kafka Integration
• Databricks Watermarking Deep Dive
• Delta Lake Streaming