Building a Streaming Microservice Architecture: with Apache Spark Structured Streaming and Friends
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The document outlines a streaming microservice architecture utilizing Spark Structured Streaming, gRPC, and Protocol Buffers. It explains the benefits of Protocol Buffers for data serialization and the integration of gRPC for efficient client-server communication, enabling real-time data pipelining and robust data lineage. Key topics include the architecture's components, strict data typing, and end-to-end use cases for improved data handling in various programming languages.
Building a Streaming Microservice Architecture: with Apache Spark Structured Streaming and Friends
- 2. Building a Streaming Microservice Architecture: With Spark Structured Streaming and Friends Scott Haines Senior Principal Software Engineer
- 3. Introductions ▪ I work at Twilio ▪ Over 10 years working on Streaming Architectures ▪ Helped Bring Streaming-First Spark Architecture to Voice & Voice Insights ▪ Leads Spark Office Hours @ Twilio ▪ Loves Distributed Systems About Me Scott Haines: Senior Principal Software Engineer @newfront
- 4. Agenda The Big Picture What the Architecture looks like Protocol Buffers What they are. Why they rule! GRPC / Protocol Streams Versioned Data Lineage as a Service How this fits into Spark Structured Streaming with Protobuf support
- 6. Streaming Microservice Architecture GRPC Client GRPC Server GRPC Server GRPC Server 1 2 3 Kafka Broker 4 Kafka Broker 5 6 Spark Application 7 8 HDFS S39 HTTP /2
- 7. Streaming Microservice Architecture Kafka Topic Kafka Topic Spark Application Spark Application Spark Application Kafka Topic Data Table Data Table Spark Application GRPC Server
- 8. Protocol Buffers aka protobuf
- 9. Protocol Buffers ▪ Strict Types ▪ Enforce structure at compile time ▪ Similar to StructType in Apache Spark ▪ Interoperable with Spark via ExpressionEncoding extension ▪ Versioning API / Data Pipeline ▪ Compiled protobuf (*.proto) can be released like normal code ▪ Interoperable ▪ Pick your favorite programming language and compile and release. ▪ Supports Java, Scala, C++, Go, Obj-C, Node-JS, Python and more Why use them?
- 10. Protocol Buffers ▪ Code Gen ▪ Automatically generate Builder classes ▪ Being lazy is okay! ▪ Optimized ▪ Messages are optimized and ship with their own Serialization/Deserialization mechanics (SerDe) Why use them?
- 11. GRPC and Protocol Streams
- 12. gRPC ▪ High Performance ▪ Compact Binary Exchange Format ▪ Make API Calls to the Server like they were Client local ▪ Cross Language/Cross Platform ▪ Autogenerate API definitions for idiomatic client and server – just implement the interfaces ▪ Bi-Directional Streaming ▪ Pluggable support for streaming with HTTP/2 transport What is it? GRPC Client GRPC Server GRPC Server GRPC Server HTTP /2
- 14. GRPC ▪ Define Messages ▪ What kind of Data are your sending? ▪ Example: Click Tracking / Impression Tracking ▪ What is necessary for the public interface? ▪ Example: AdImpression and Response How it works?
- 15. GRPC ▪ Service Definition ▪ Compile your rpc definition to generate Service Interfaces ▪ Uses the Same protobuf definition (service.proto) as your Client/Server request and response objects ▪ Can be used to create a binding Service Contract within your organization or publicly How it works?
- 16. GRPC ▪ Implement the Service ▪ Compilation of the Service auto-generates your interfaces. ▪ Just implement the service contracts. How it works?
- 17. GRPC ▪ Protocol Streams ▪ Messages (protobuf) are emitted to Kafka topic(s) from the Server Layer ▪ Protocol Streams are now available from the Kafka Topics bound to a given Service / Collection of Messages ▪ Sets up Spark for the Hand-Off How it works?
- 18. GRPC System Architecture GRPC Client GRPC Server GRPC Server GRPC Server Kafka Broker Kafka Broker 6 HTTP /2 Topic: ads.click.stream Client: service.adTrack(trackedAd) Server: ClickTrackService.adTrack(trackedAd)
- 19. Structuring Protocol Streams: with Structured Streaming and protobuf
- 20. Structured Streaming with Protobuf ▪ Expression Encoding ▪ Natively Interop with Protobuf in Apache Spark. ▪ Protobuf to Case Class conversion from scalapb. ▪ Product encoding comes for free via import sparkSession.implicits._ From Protocol Buffer to StructType through ExpressionEncoders
- 21. Structured Streaming with Protobuf ▪ Native is Better ▪ Strict Native Kafka to DataFrame conversion with no need for transformation to intermediary types ▪ Mutations and Joins can be done across DataFrame or Datasets API. ▪ Create RealTime Data Pipelines, Machine Learning Pipelines and More. ▪ Rest at Night knowing the pipelines are safe! From Protocol Buffer to StructType through ExpressionEncoders
- 22. Structured Streaming with Protobuf ▪ Strict Data Writer ▪ Compiled / Versioned Protobuf can be used to strictly enforce the format of your Writers even ▪ Use Protobuf to define the StructType that can be used in your conversions to *Parquet. (* must abide by parquet nesting rules ) ▪ Declarative Input / Output means that Streaming Applications don’t go down due to incompatible Data Streams ▪ Can also be used with Delta so that the version of the schema lines up with compiled Protobuf. From Protocol Buffer to StructType through ExpressionEncoders
- 23. Structured Streaming with Protobuf ▪ Real World Use Case ▪ Close of Books Data Lineage Job ▪ Uses End to End Protobuf ▪ Enables teams to move quick with guarantees regarding the Data being published and at what Frequency ▪ Can be emitted at different speeds to different locations based on configuration Example: Streaming Transformation Pipeline
- 24. Streaming Microservice Architecture GRPC Client GRPC Server GRPC Server GRPC Server 1 2 3 Kafka Broker 4 Kafka Broker 5 6 Spark Application 7 8 HDFS S39 HTTP /2
- 25. Recap
- 26. What We Learned ▪ Language Agnostic Structured Data ▪ Compile Time Guarantees ▪ Lightning Fast Serialization/Dese rialization ▪ Language Agnostic Binary Services ▪ Low-Latency ▪ Compile Time Guarantees ▪ Smart Framework GRPCProtobuf ▪ Highly Available ▪ Native Connector for Spark ▪ Topic Based Binary Protobuf Store ▪ Use to Pass Records to one or more Downstream Services Kafka ▪ Handle Data Reliably ▪ Protobuf to Dataset / DataFrames is awesome ▪ Parquet / Delta plays nice as Columnar Data Exchange format Structured Streaming
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