Redis + Structured Streaming—A Perfect Combination to Scale-Out Your Continuous Applications

WIFI SSID:SparkAISummit | Password: UnifiedAnalytics

Roshan Kumar, Redis Labs
@roshankumar
Redis + Structured Streaming:
A Perfect Combination to Scale-out Your
Continuous Applications
#UnifiedAnalytics #SparkAISummit

This Presentation is About….
How to collect and process data stream in real-time at scale
IoT
User Activity
Messages

Breaking up Our Solution into Functional Blocks
Click data
Record all clicks Count clicks in real-time Query clicks by assets
2. Data Processing1. Data Ingest 3. Data Querying

ClickAnalyzer
Redis Stream Redis Hash Spark SQLStructured Stream Processing
1. Data Ingest 2. Data Processing 3. Data Querying
The Actual Building Blocks of Our Solution
Click data

1. Data Ingest
8#UnifiedAnalytics #SparkAISummit

ClickAnalyzer
Data Ingest using Redis Streams

Redis Streams in its Simplest Form

Redis Streams Connects Many Producers and Consumers

Comparing Redis Streams with Redis Pub/Sub, Lists, Sorted Sets
Pub/Sub
• Fire and forget
• No persistence
• No lookback
queries
Lists
• Tight coupling between
producers and
consumers
• Persistence for
transient data only
• No lookback queries
Sorted
Sets
• Data ordering isn’t built-in;
producer controls the
order
• No maximum limit
• The data structure is not
designed to handle data
streams

What is Redis Streams?
Pub/Sub Lists Sorted Sets
It is like Pub/Sub, but
with persistence
It is like Lists, but decouples
producers and consumers
It is like Sorted Sets,
but asynchronous
+
• Lifecycle management of streaming data
• Built-in support for timeseries data
• A rich choice of options to the consumers to read streaming and static data
• Super fast lookback queries powered by radix trees
• Automatic eviction of data based on the upper limit

Redis Streams Benefits
It enables asynchronous data exchange between producers and
consumers and historical range queries

With consumer groups, you can scale out and avoid backlogs

Simplify data collection, processing and
distribution to support complex scenarios

Data Ingest Solution
Redis Stream
1. Data Ingest
Command
xadd clickstream * img [image_id]
Sample data
127.0.0.1:6379> xrange clickstream - +
1) 1) "1553536458910-0"
2) 1) ”image_1"
2) "1"
2) 1) "1553536469080-0"
2) 1) ”image_3"
2) "1"
3) 1) "1553536489620-0"
2) 1) ”image_3"
2) "1”
.
.
.
.

2. Data Processing

ClickAnalyzer
Data Processing using Spark’s Structured Streaming

“Structured Streaming provides fast, scalable, fault-
tolerant, end-to-end exactly-once stream processing
without the user having to reason about streaming.”
Definition

How Structured Streaming Works?
Micro-batches as
DataFrames (tables)
Source: Data Stream
DataFrame Operations
Selection: df.select(“xyz”).where(“a > 10”)
Filtering: df.filter(_.a > 10).map(_.b)
Aggregation: df.groupBy(”xyz").count()
Windowing: df.groupBy(
window($"timestamp", "10 minutes", "5 minutes"),
$"word"”
).count()
Deduplication: df.dropDuplicates("guid")
Output Sink
Spark Structured Streaming

ClickAnalyzer
Redis Stream Redis HashStructured Stream Processing
Redis Streams as data source
Spark-Redis Library
Redis as data sink
§ Developed using Scala
§ Compatible with Spark 2.3 and higher
§ Supports
• RDD
• DataFrames
• Structured Streaming

Redis Streams as Data Source
1. Connect to the Redis instance
2. Map Redis Stream to Structured Streaming schema
3. Create the query object
4. Run the query

Code Walkthrough: Redis Streams as Data Source
1. Connect to the Redis instance
val spark = SparkSession.builder()
.appName("redis-df")
.master("local[*]")
.config("spark.redis.host", "localhost")
.config("spark.redis.port", "6379")
.getOrCreate()
val clickstream = spark.readStream
.format("redis")
.option("stream.keys","clickstream")
.schema(StructType(Array(
StructField("img", StringType)
)))
.load()
val queryByImg = clickstream.groupBy("img").count

2. Map Redis Stream to Structured Streaming schema
.master("local[*]")
.getOrCreate()
.format("redis")
)))
.load()
xadd clickstream * img [image_id]

3. Create the query object
.master("local[*]")
.getOrCreate()
.format("redis")
)))
.load()

4. Run the query
.format("redis")
)))
.load()
val clickWriter: ClickForeachWriter = new ClickForeachWriter("localhost","6379")
val query = queryByImg.writeStream
.outputMode("update")
.foreach(clickWriter)
.start()
query.awaitTermination()
Custom output sink

Redis as Output Sink
override def process(record: Row) = {
var img = record.getString(0);
var count = record.getLong(1);
if(jedis == null){
connect()
}
jedis.hset("clicks:"+img, "img", img)
jedis.hset("clicks:"+img, "count", count.toString)
}
Create a custom class extending ForeachWriter and override the method, process()
Save as Hash with structure
clicks:[image]
img [image]
count [count]
Example
clicks:image_1001
img image_1001
count 1029
clicks:image_1002
img image_1002
count 392
.
.
.
.
img count
image_1001 1029
image_1002 392
. .
. .
Table: Clicks

3. Data Querying

ClickAnalyzer
Query Redis using Spark SQL

1. Initialize Spark Context with Redis
2. Create table
3. Run Query
3 Steps to Query Redis using Spark SQL
clicks:image_1001
img image_1001
count 1029
clicks:image_1002
img image_1002
count 392
.
.
.
.
img count
image_1001 1029
image_1002 392
. .
. .
Redis Hash to SQL mapping

1. Initialize
scala> import org.apache.spark.sql.SparkSession
scala> val spark = SparkSession.builder().appName("redis-
test").master("local[*]").config("spark.redis.host","localhost").config("spark.redis.port","6379").getOrCreate()
scala> val sc = spark.sparkContext
scala> import spark.sql
scala> import spark.implicits._
2. Create table
scala> sql("CREATE TABLE IF NOT EXISTS clicks(img STRING, count INT) USING org.apache.spark.sql.redis OPTIONS (table
'clicks’)”)
How to Query Redis using Spark SQL

3. Run Query
scala> sql("select * from clicks").show();
+----------+-----+
| img|count|
+----------+-----+
|image_1001| 1029|
|image_1002| 392|
|. | .|
|. | .|
|. | .|
|. | .|
+----------+-----+
How to Query Redis using Spark SQL

Recap

ClickAnalyzer
Building Blocks of our Solution
Redis Streams as data source; Redis as data sinkSpark-Redis Library is used for:

Questions
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roshan@redislabs.com
@roshankumar
Roshan Kumar

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Redis + Structured Streaming—A Perfect Combination to Scale-Out Your Continuous Applications

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Redis + Structured Streaming—A Perfect Combination to Scale-Out Your Continuous Applications