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SCALABLE TIME SERIES AND STREAM PROCESSING FOR IOT APPLICATIONS

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Michael Hausenblas, Developer & Cloud Advocate | 2016-01-16

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MOTIVATION

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AIRLINES

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LOGISTICS

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HEALTH CARE

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TRADERS

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FARMERS

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CITIES

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© 2

014,

Wire

d m

agaz

ine

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YOU

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THETOOLBOX

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LET'S TALK ABOUT WORKLOADS* …

11*) kudos to Timothy St. Clair, @timothysc

batch streaming PaaS

MapReduce

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• Apache Kafka• ØMQ, RabbitMQ, Disque (Redis-based), etc.• fluentd, Logstash, Flume• Akka streams• cloud-only: AWS SQS, Google Cloud Pub/Sub• see also queues.io

MESSAGE QUEUES & ROUTERS

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APACHE KAFKA

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• High-throughput, distributed, persistent publish-subscribe messaging system

• Originates from LinkedIn

• Typically used as buffer/de-coupling layer in online stream processing

Message queues & routers

kafka.apache.org

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FLUENTD

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Message queues & routers

www.fluentd.org

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STREAM PROCESSING PLATFORMS

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• Apache Storm• Apache Spark• Apache Samza• Apache Flink• Concord• cloud-only: AWS Kinesis, Google Cloud Dataflow• see also my webinar on stream processing

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APACHE STORM

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• Distributed, fault-tolerant stream-processing platform

• Guaranteed message processing (replaying messages on failure)

• Concepts: tuples, streams, spouts, bolts, topologies

Stream processing platforms

storm.apache.org

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APACHE SPARK

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Stream processing platforms

spark.apache.org

Spark SQL Spark Streaming MLlib(machine learning)

Spark core (RDD)

GraphX(graph processing)

Mesos

Filesystem (local, HDFS, S3) or data store (HBase, Cassandra, Elasticsearch, etc.)

YARNStandalone

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TIME SERIES DATASTORES

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• InfluxDB• OpenTSDB• KairosDB• Prometheus• see also iot-a.info

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OPENTSDB

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• Distributed time series database on top HBase

• Store, index, query & plot metrics

• Extremely scalable

• Low-level monitoring

Time series datastores

opentsdb.net

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INFLUXDB

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• No-dependency, time series database written in Go

• SQLish query language (incl. regex, fan out)

• Single node or Raft-based distributed node mode

Time series datastores

influxdb.com

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CHALLENGES

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• Set up and operation of components

• Elasticity: static vs. dynamic partitioning

• Efficient usage of resources (TCO)

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MEET THE DATACENTER OPERATINGSYSTEM(DCOS)

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LOCAL OS VS. DISTRIBUTED OS

23http://bitly.com/os-vs-dcos

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DCOS IS A DISTRIBUTED OPERATING SYSTEM

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• local OS per node (+container enabled)• scheduling (long-lived, batch)• networking• service discovery• stateful services• security• monitoring, logging, debugging

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BENEFITS

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DCOS

• Run stateless services such as Web server or app server and Big Data services like Kafka, Spark, or Cassandra together on one cluster

• Dynamic partitioning of your cluster, depending on your business requirements

• Increased utilization (10% → 80%++)

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ANEXAMPLE

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https://mesosphere.com/blog/2015/11/18/dcos-time-series-demo

© 2015 Mesosphere, Inc. All Rights Reserved. 29https://github.com/mesosphere/time-series-demo

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Q & A

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• @mhausenblas

• mhausenblas.info

• @mesosphere

• mesosphere.io/product

• mesosphere.com/infinity