Spark Streaming-as-a-Service with Kafka and YARN

Jim DowlingKTH Royal Institute of Technology, StockholmSenior Researcher, SICSCEO, Logical Clocks AB

Spark Streaming-as-a-Service in Sweden

• SICS ICE: datacenter research environment• Hopsworks: Spark/Flink/Kafka/Tensorflow/Hadoop• -as-a-service

– Built on Hops Hadoop (www.hops.io)– >130 active users

Hadoop is not a cool kid anymore!

Hadoop’s Evolution

2009 2016

?

Hadoop’s Evolution

2009 2016

?Tiny Brain

(NameNode, ResourceMgr)

Huge Body (DataNodes)

Build out Hadoop’s Brain with External Weakly Consistent MetaData Services

Google-Glass Approach to Intelligence

NameNodes

NDB

HDFS Client

DataNodes

>37X Capacity

>16 XThroughput

HopsFS

Larger Brains => Bigger, Faster*

16xPerformance on Spotify Workload

*Usenix FAST 2017, HopsFS: Scaling Hierarchical File System Metadata Using NewSQL Databases

Hopsworks• Projects

– Datasets/Files– Topics– Jobs/Notebooks

Hadoop• Clusters• Users• Jobs/Applications• Files• ACLs• Sys Admins• Kerberos

Larger Brains => More Intelligent*

*HMGA2 gene mutations correlated with increased intracranial volume as well as enhanced IQ.http://newsroom.ucla.edu/releases/international-team-uncovers-new-231989

User-Friendly Concepts

http://www.ibtimes.co.uk/embargoed-8pm-25th-jan-size-matters-brain-size-relative-body-size-indicates-animals-ability-1539994

YARN Spark Streaming Support

• Apache Kafka• ELK Stack

– Real-time Logs• Grafana/InfluxDB

– Monitoring

Hopsworks

YARN aggregates logs on job completion

http://mkuthan.github.io/blog/2016/09/30/spark-streaming-on-yarn/

Kafka Self-Service UI

Manage & Share• Topics• ACLs• Avro Schemas

Manage & Share• Topics• ACLs• Avro Schemas

Logs

Elasticsearch, Logstash,

Kibana(ELK Stack)

Elasticsearch, Logstash,

Kibana(ELK Stack)

Monitoring/Alerting

InfluxDB and

Grafana

InfluxDB and

Grafana

metrics.properties: StreamingMetrics.streaming.lastReceivedBatch_records == 0

Zeppelin for Prototyping Streaming Apps

[https://github.com/knockdata/spark-highcharts]

Debugging Spark with Dr. Elephant

• Analyzes Spark Jobs for errors and common using pluggable heuristics

• Doesn’t show killed jobs

• No online support for streaming apps yet

Integration as Microservices in Hopsworks

• Project-based Multi-tenancy

• Self-Service UI

• Simplifying Spark Streaming Apps

Proj-All

Proj-X

Projects in Hopsworks•

Proj-42

Shared TopicTopic /Projs/My/Data

CompanyDB

User roles

18

Data Owner- Import/Export data- Manage Membership- Share DataSets, Topics

Data Scientist- Write and Run code

Self-Service Administration – No Administrator Needed

Notebooks, Data sharing and Quotas

• Zeppelin Notebooks in HDFS, Jobs launcher UI.

• Sharing is not Copying– Datasets/Topics

• Per-Project quotas– Storage in HDFS– CPU in YARN (Uber-style Pricing)

Dynamic roles

alice@gmail.com

ProjectA

Authenticate

ProjectB

HopsFS

YARN

Kafka

SSL/TLSCertificates

Secure Impersonation

ProjectA__alice

ProjectB__alice

Look Ma, no Kerberos• Each project-specific user issued with a SSL/TLS

(X.509) certificate for both authentication and encryption.

• Services also issued with SSL/TLS certificates.– Same root CA as user certs

Simplifying Spark Streaming Apps

• Spark Streaming Applications need to know– Credentials

• Hadoop, Kafka, InfluxDb, Logstash– Endpoints

• Kafka Broker, Kafka SchemaRegistry, ResourceManager, NameNode, InfluxDB, Logstash

• The HopsUtil API hides this complexity.– Location/security transparent Spark applications

Secure Streaming App with Kafka

Developer

1.Discover: Schema Registry and Kafka/InfluxDB/ELK Endpoints2.Create: Kafka Properties file with certs and broker details3.Create: Producer/Consumer using Kafka Properties

4.Download: the Schema for the Topic from the Schema Registry5.Distribute: X.509 certs to all hosts on the cluster6.Cleanup securely

These steps are replaced by calls to the HopsUtil API

Operations

https://github.com/hopshadoop/hops-kafka-examples

Streaming Producer in HopsWorks

JavaSparkContext jsc = new JavaSparkContext(sparkConf);String topic = HopsUtil.getTopic(); //Optional

SparkProducer producer = HopsUtil.getSparkProducer();

Map<String, String> message = …sparkProducer.produce(message);

Streaming Consumer in HopsWorks

JavaStreamingContext jssc = new JavaStreamingContext(sparkConf,Durations.seconds(2));

String topic = HopsUtil.getTopic(); //OptionalString consumerGroup = HopsUtil.getConsumerGroup(); //Optional

SparkConsumer consumer = HopsUtil.getSparkConsumer(jssc);

JavaInputDStream<ConsumerRecord<String, byte[]>> messages = consumer.createDirectStream();jssc.start();

Less code to write

https://github.com/hopshadoop/hops-kafka-examples

Properties props = new Properties();props.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, brokerList);props.put(SCHEMA_REGISTRY_URL, restApp.restConnect);props.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, org.apache.kafka.common.serialization.StringSerializer.class);props.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, io.confluent.kafka.serializers.KafkaAvroSerializer.class);props.put("producer.type", "sync");props.put("serializer.class","kafka.serializer.StringEncoder");props.put("request.required.acks", "1");props.put("ssl.keystore.location","/var/ssl/kafka.client.keystore.jks")props.put("ssl.keystore.password","test1234")props.put("ssl.key.password","test1234")ProducerConfig config = new ProducerConfig(props);String userSchema = "{\"namespace\": \"example.avro\", \"type\": \"record\", \"name\": \"User\"," + "\"fields\": [{\"name\": \"name\", \"type\": \"string\"}]}";Schema.Parser parser = new Schema.Parser();Schema schema = parser.parse(userSchema);GenericRecord avroRecord = new GenericData.Record(schema);avroRecord.put("name", "testUser");Producer<String, String> producer = new Producer<String, String>(config);ProducerRecord<String, Object> message = new ProducerRecord<>(“topicName”, avroRecord );producer.send(data);

Lots of Hard-Coded Endpoints Here!

SparkProducer producer = HopsUtil.getSparkProducer();Map<String, String> message = …sparkProducer.produce(message);

Massively Simplified Code for Secure Spark Streaming/Kafka

Distributing Certs for Spark Streaming

Alice@gmail.com

1. Launch Spark Job

Distributed Database

2. Get certs, service endpoints

YARN Private LocalResources

Spark Streaming App

4. Materialize certs

3. YARN Job, config

6. Get Schema

7. Consume Produce

5. Read Certs

HopsworksHopsUtil

8. Read ACLs for authentication

Multi-Tenant IoT Scenario

Sensor Node

Sensor Node

Sensor Node

Sensor Node

Sensor Node

Sensor Node

Field Gateway

StorageStorage

AnalysisAnalysis

IngestionIngestion

ACMEACME

Evil CorpEvil Corp

IoT Cloud Platform

DontBeEvil Corp

DontBeEvil Corp

IoT Scenario

ACME DontBeEvil Corp Evil-Corp

AWS Google Cloud

OracleCloud

User Apps control IoT Devices

IoT Company:Analyze Data,Data Services for Clients

ACME DontBeEvil Corp Evil Corp

Cloud-Native Analytics Solution

ACME S3S3[Authorization]

GCSGCS

OracleOracleIoT Company

Each customer needs its own Analytics Infrastructure

Each customer needs its own Analytics Infrastructure

Spark Streaming App

IoT CompanyProject

GatewayTopic

Hopsworks Solution using Projects

ACMEProjectACMETopic

ACME Dataset

Data Stream

Analytics Reports

Hopsworks Solution

ACME

Spark Streaming App

[Authorized]

ACMEDatasetACMEDataset

ACME Topic

ACME Analytics Reports

ACME Analytics Reports

Spark Batch Job

ACME Project

Karamel/Chef for Automated Installation

Google Compute Engine BareMetal

DEMO

Hops Roadmap

• HopsFS– HA support for Multi-Data-Center– Small files, 2-Level Erasure Coding

• HopsYARN– Tensorflow with isolated GPUs

• Hopsworks– P2P Dataset Sharing– Jupyter, Presto, Hive

Summary

• Hops is a new distribution of Hadoop– Tinker-friendly and open-source.

• Hopsworks provides first-class support for Spark-Streaming-as-a-Service – With support services like Kafka, ELK Stack,

Zeppelin, Grafana/InfluxDB.

Hops Team

Jim Dowling, Seif Haridi, Tor Björn Minde, Gautier Berthou, Salman Niazi, Mahmoud Ismail, Theofilos Kakantousis, Ermias Gebremeskel, Antonios Kouzoupis, Alex Ormenisan, Roberto Bampi, Fabio Buso, Fanti Machmount Al Samisti, Braulio Grana, Adam Alpire, Zahin Azher Rashid, Robin Andersso, ArunaKumari Yedurupaka, Tobias Johansson, August Bonds, Tiago Brito, Filotas Siskos.

Active:

Alumni:

Vasileios Giannokostas, Johan Svedlund Nordström,Rizvi Hasan, Paul Mälzer, Bram Leenders, Juan Roca, Misganu Dessalegn, K “Sri” Srijeyanthan, Jude D’Souza, Alberto Lorente, Andre Moré, Ali Gholami, Davis Jaunzems, Stig Viaene, Hooman Peiro, Evangelos Savvidis, Steffen Grohsschmiedt, Qi Qi, Gayana Chandrasekara, Nikolaos Stanogias, Daniel Bali, Ioannis Kerkinos, Peter Buechler, Pushparaj Motamari, Hamid Afzali, Wasif Malik, Lalith Suresh, Mariano Valles, Ying Lieu.

Hops

Thank You.We totally understand it’s going to be America First Spark Streaming first, but can we take this chance to say Hopsworks second!

http://www.hops.io@hopshadoop

Hops