Stream, Stream, Stream - Different Streaming Methods with Apache Spark & Kafka

At NMC (Nielsen Marketing Cloud) we provide our customers (marketers and publishers) real-time analytics tools to profile their target audiences. To achieve that, we need to ingest billions of events per day into our big data stores, and we need to do it in a scalable yet cost-efficient manner.

In this session, we will discuss how we continuously transform our data infrastructure to support these goals. Specifically, we will review how we went from CSV files and standalone Java applications all the way to multiple Kafka and Spark clusters, performing a mixture of Streaming and Batch ETLs, and supporting 10x data growth We will share our experience as early-adopters of Spark Streaming and Spark Structured Streaming, and how we overcame technical barriers (and there were plenty). We will present a rather unique solution of using Kafka to imitate streaming over our Data Lake, while significantly reducing our cloud services’ costs. Topics include:

Kafka and Spark Streaming for stateless and stateful use-cases
Spark Structured Streaming as a possible alternative
Combining Spark Streaming with batch ETLs
”Streaming” over Data Lake using Kafka

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1.WIFI SSID:Spark+AISummit | Password: UnifiedDataAnalytics

2.Title of Your Presentation Stream, Stream, Stream Goes Here Different Streaming methods with Spark and Kafka Your Name,Nielsen Itai Yaffe, Your Organization #UnifiedDataAnalytics #SparkAISummit #UnifiedDataAnalytics #SparkAISummit

3.Introduction Itai Yaffe ● Tech Lead, Big Data group ● Dealing with Big Data challenges since 2012

4.Introduction - part 2 (or: “your turn…”) ● Data engineers? Data architects? Something else? ● Working with Spark? Planning to? ● Working Kafka? Planning to?

5.Agenda Nielsen Marketing Cloud (NMC) ○ About ○ High-level architecture Data flow - past and present Spark Streaming ○ ”Stateless” and ”stateful” use-cases Spark Structured Streaming ”Streaming” over our Data Lake

6.Nielsen Marketing Cloud (NMC) ● eXelate was acquired by Nielsen on March 2015 ● A Data company ● Machine learning models for insights ● Targeting ● Business decisions

7.Nielsen Marketing Cloud - questions we try to answer 1. How many unique users of a certain profile can we reach? E.g campaign for young women who love tech 2. How many impressions a campaign received?

8.Nielsen Marketing Cloud - high-level architecture

9.Data flow in the old days... OLAP In-DB aggregation

10.Data flow in the old days… What’s wrong with that? ● CSV-related issues, e.g: ○ Truncated lines in input files ○ Can’t enforce schema ● Scale-related issues, e.g: ○ Had to “manually” scale the processes

11.That's one small step for [a] man… (2014) OLAP In-DB aggregation “Apache Spark is the Taylor Swift of big data software" (Derrick Harris, Fortune.com, 2015)

12.Why just a small step? ● Solved the scaling issues ● Still faced the CSV-related issues

13.Data flow - the modern way + Photography Copyright: NBC

14.Spark Streaming - “stateless” app use-case (2015) Read Messages OLAP In-DB aggregation

15.The need for stateful streaming Fast forward a few months... ● New requirements were being raised ● Specific use-case : ○ To take the load off of the operational DB (used both as OLTP and OLAP), we wanted to move most of the aggregative operations to our Spark Streaming app

16.Stateful streaming via “local” aggregations .5 .1 Read Messages Upsert aggregated data (every X micro-batches) OLAP .2 Aggregate current micro-batch .3 .4 Write combined aggregated data Read aggregated data of previous micro-batches from HDFS

17.Stateful streaming via “local” aggregations ● Required us to manage the state on our own ● Error-prone ○ E.g what if my cluster is terminated and data on HDFS is lost? ● Complicates the code ○ Mixed input sources for the same app (Kafka + files) ● Possible performance impact ○ Might cause the Kafka consumer to lag

18.Structured Streaming - to the rescue? Spark 2.0 introduced Structured Streaming ● Enables running continuous, incremental processes ○ Basically manages the state for you ● Built on Spark SQL ○ DataFrame/Dataset API ○ Catalyst Optimizer ● Many other features ● Was in ALPHA mode in 2.0 and 2.1 Structured Streaming

19.Structured Streaming - stateful app use-case .2 Aggregate current window .4 .1 Upsert aggregated data Read Messages (on window end) OLAP Structured streaming .3 Checkpoint (state and offsets) handled internally by Spark

20.Structured Streaming - known issues & tips ● 3 major issues we had in 2.1.0 (solved in 2.1.1) : ○ https://issues.apache.org/jira/browse/SPARK-19517 ○ https://issues.apache.org/jira/browse/SPARK-19677 ○ https://issues.apache.org/jira/browse/SPARK-19407 ● Checkpointing to S3 wasn’t straight-forward ○ Tried using EMRFS consistent view ■ Worked for stateless apps ■ Encountered sporadic issues for stateful apps

21.Structured Streaming - strengths and weaknesses (IMO) ● Strengths include: ○ Running incremental, continuous processing ○ Increased performance (e.g via Catalyst SQL optimizer) ○ Massive efforts are invested in it ● Weaknesses were mostly related to maturity

22.Back to the future - Spark Streaming revived for “stateful” app use-case .1 .3 .4 Read Messages Write Files Load Data OLAP .2 Aggregate current micro-batch

23.Cool, so… Why can’t we stop here? ● Significantly underutilized cluster resources = wasted $$$

24.Cool, so… Why can’t we stop here? (cont.) ● Extreme load of Kafka brokers’ disks ○ Each micro-batch needs to read ~300M messages, Kafka can’t store it all in memory ● ConcurrentModificationException when using Spark Streaming + Kafka 0.10 integration ○ Forced us to use 1 core per executor to avoid it ○ https://issues.apache.org/jira/browse/SPARK-19185 supposedly solved in 2.4.0 (possibly solving https://issues.apache.org/jira/browse/SPARK-22562 as well) ● We wish we could run it even less frequently ○ Remember - longer micro-batches result in a better aggregation ratio

25.Introducing RDR RDR (or Raw Data Repository) is our Data Lake ● Kafka topic messages are stored on S3 in Parquet format partitioned by date (date=2019-10-17) ● RDR Loaders - stateless Spark Streaming applications ● Applications can read data from RDR for various use-cases ○ E.g analyzing data of the last 1 day or 30 days Can we leverage our Data Lake and use it as the data source (instead of Kafka)?

26.Potentially yes... S3 RDR date=2019-10-14 date=2019-10-15 .1 date=2019-10-16 Read RDR files from last day .2 Process files

27.... but ● This ignores late arriving events

28.Enter “streaming” over RDR + +

29.How do we “stream” RDR files - producer side RDR Loaders S3 RDR .1 .2 Read Messages Write files .3 Write files’ paths Topics with files’ paths as messages