Introduction to Spark Structured Streaming. Presented at Current 22 conference.

1. Getting Started with
Spark Structured Streaming
Dustin Vannoy
dustinvannoy.com

2. Dustin
Consultant – Data Engineer
/in/dustinvannoy
youtube.com/DustinVannoy
dustinvannoy.com
Data Engineering SD meetup
Technologies
Azure & AWS
Apache Spark
Apache Kafka
Azure Synapse Analytics
Python & Scala
Vannoy
@dustinvannoy

3. Apache Spark
3

4. Why Spark?
Batch and Streaming
framework that has
large community
and wide adoption.
Spark is a leader in
data processing that
scales across many
machines. It is
relatively easy to get
started.

5. Yeah, but what is Spark?
Spark consists of a programming API and execution engine
Worker Worker Worker Worker
Master
from pyspark.sql import SparkSession
from pyspark.sql.functions import col
spark = SparkSession.builder.getOrCreate()
song_df = spark.read
.option('sep','t')
.option("inferSchema","true")
.csv("/databricks-datasets/songs/data-001/part-0000*")
tempo_df = song_df.select(
col('_c4').alias('artist_name'),
col('_c14').alias('tempo'),
)
avg_tempo_df = tempo_df
.groupBy('artist_name')
.avg('tempo')
.orderBy('avg(tempo)',ascending=False)
avg_tempo_df.show(truncate=False)

6. Simple code, parallel compute
Worker
Controller
Worker
Worker

7. What is Spark?
● Fast, general purpose engine for large-scale data processing
● Replaces MapReduce as Hadoop parallel programming API
● Many options:
○ Yarn / Spark Cluster / Local
○ Scala / Python / Java / R
○ Spark Core / SQL / Streaming / MLLib / Graph

8. What is Spark Structured Streaming?
● Alternative to traditional Spark Streaming which used
DStreams
● If you are familiar with Spark, it is best to think of
Structured Streaming as Spark SQL API but for streaming
● Use import spark.sql.streaming

9. What is Spark Structured Streaming?
Tathagata Das “TD” - Lead Developer on Spark Streaming
● “Fast, fault-tolerant, exactly-once stateful stream
processing without having to reason about streaming"
● "The simplest way to perform streaming analytics is not
having to reason about streaming at all"
● A table that is constantly appended with each micro-batch
Reference: https://youtu.be/rl8dIzTpxrI

10. Lazy Evaluation with Query Optimization
● Transformations not run immediately
● Builds up a DAG
● Optimizes steps
● Runs when an Action is called

11. Why use Spark Structured Streaming?
● Run Spark as batch or streaming
● Open-source with many connectors
● Stateful streaming and joins
● Kafka is not required for every stream

12. Apache Spark Options
12

13. Unified Analytics Platform
Managed Apache Spark
Performance optimizations
Auto-scale and auto-terminate
AWS, Azure, GCP
Databricks

14. Amazon EMR
Azure Synapse Spark
Google Cloud Proc
Cloud Specific Options

15. Install on Mac or Linux
Use WSL on Windows
Docker Container
PySpark package
Local

16. Spark and storage together
Many containers per worker
Use what you have
Hadoop / YARN

17. On-premises or multi-cloud
Scale size with familiar tools
Use what you know
Kubernetes

18. Apache Spark Syntax
18

19. Spark Example - Read
df = spark.read
.format("kafka")
.options(**config)
.load()
df = spark.readStream
.format("kafka")
.options(**config)
.load()
Batch Streaming

20. Spark Example - Write
df.write
.format("kafka")
…
df.writeStream
.format("kafka")
…
Batch Streaming

21. Structured Streaming - Read
df = spark.readStream
.format("delta")
.options(**delta_config)
.load(path)

22. Structured Streaming - Transformations
new_df = df.withColumn("new_column",
concat("col1", "col2" ))

23. Structured Streaming - Transformations
new_df = df.selectExpr(
"*",
"concat(col1, '-',col2) as new_col",
"current_timestamp() as load_tm")

24. Structured Streaming - Actions
● writeStream.format(“console”).start()
● writeStream.format(“kafka”).start()
● writeStream.foreach(…).start()
● writeStream.foreachBatch(…).start()

25. Structured Streaming - Write
df.writeStream
.format("kafka")
.options(**producer_config)
.option("checkpointLocation","/tmp/cp001")
.start()

26. Structured Streaming – Output Mode
How to output data when triggered
● Append - Just keep adding newest records
● Update – Only those updated since last trigger
● Complete mode - Output latest state of table
(useful for aggregation results)

27. Example from docs (Structured Streaming Programming Guide)

28. Structured Streaming – Write with Output Mode
df.writeStream
.format("kafka")
.outputMode("complete")
.options(**producer_config)
.option("checkpointLocation","/tmp/cp001")
.start()

29. Structured Streaming - Triggers
Specify how often to check for a new data
● Default: micro-batch mode, as soon as possible
● Fixed: micro-batch mode, wait specified time
● Only once: just one micro-batch then job stops
● Continuous: continuously read from source,
experimental and only works for certain types of queries

30. Structured Streaming – Write with Trigger
df.writeStream
.format("kafka")
.trigger(processingTime="5 seconds")
.options(**producer_config)
.option("checkpointLocation","/tmp/cp001")
.start()

31. Structured Streaming - Windows
Calculate over time using windows -> Avg trips every 5 minutes
● Tumbling window: specific period of time, such as 1:00 to 1:30
● Sliding windows: fixed window, but can track multiple groups...
1:00 – 1:30, 1:05 – 1:35, 1:10 – 1:40, etc
● Session windows: similar to an inactive session timeout…
group together until a long enough gap in time between events

32. Comparing windows (Structured Streaming Programming Guide)

33. Window Example
df2.groupBy(
col("VendorID"),
window(col("pickup_dt"), "10 min"))
.avg("trip_distance")

34. Streaming Joins
Stream-static
Simple join
Does not tracking state
Options: Inner, Left Outer,
Left Semi
Stream-stream
Complex join
Data may arrive early or
late
Options: Inner, Left/Right
Outer, Full Outer, Left Semi

35. Stream to Static DataFrame Join
zdf = spark.read.format("delta").load(path)
trip_df = (
tdf.join(zdf,
tdf["PULocId"] == zdf["LocID"],
how="left")

36. Stream to Stream Join
trip_df = (
tdf.withWatermark("eventTime","1 minute")
.join(pmt.withWatermark("pTime", "1 hour"),
expr("""
LocId = PULocId AND
zTime >= eventTime AND
zTime <= eventTime + interval 1 hour
"""),
how="leftOuter")

37. Stateful Streaming
Aggregations, dropDuplicates, and Joins will require storing state
● Custom stateful operations possible
● HDFS or RocksDB State Store (Spark 3.2)
● If retaining many keys, HDFSBackedStateStore is likely to have JVM
memory issues

38. Example Structured
Streaming Application
38

39. Databricks Notebook - Setup

40. Databricks Notebook – Confluent Cloud Config

41. Databricks Notebook – Spark Schema

42. Databricks Notebook – Read Kafka Record

43. Databricks Notebook – Convert JSON

44. Databricks Notebook – Read Lookup Table

45. Databricks Notebook – Join and write

46. Databricks Notebook – Check destination

47. Monitoring
47

48. SQL UI

49. SQL UI - Detail

50. Structured Streaming UI

51. Structured Streaming UI

52. Notebook Streaming UI - Timeseries

53. Notebook Streaming UI – Query Progress

54. Cloud Logging Services

55. References
● https://spark.apache.org/docs/latest/structured-streaming-programming-
guide.html
● https://databricks.com/blog/2016/01/04/introducing-apache-spark-
datasets.html
● https://databricks.com/blog/2016/07/14/a-tale-of-three-apache-spark-
apis-rdds-dataframes-and-datasets.html

56. Website: dustinvannoy.com
Twitter: @dustinvannoy
YouTube: youtube.com/DustinVannoy
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