Pat Patterson and Ted Malaska talk about current and emerging technologies. They evaluate each and understand how they are useful in solving problems related to large scale data processing, joining and combining streams. They also talk about the various ways of achieving "at least once" and "exactly once" processing and how we can make sure that data is processed in a timely fashion.

1. 1© Cloudera, Inc. All rights reserved.
13 June2016
Ted Malaska| Principle Solutions Architect @ Cloudera,
Pat Patterson| Community Champion @ StreamSets
Ingest and Stream Processing -
What will you choose?

2. 2© Cloudera, Inc. All rights reserved.
About Ted and Pat
Ted Malaska
• Principal Solutions Architect
@ Cloudera
• Apache HBase SparkOnHBase
Contributor
• Contact
• ted.malaska@cloudera.com
• @TedMalaska
Pat Patterson
• Community Champion @
StreamSets
• Formerly Developer Evangelist at
Salesforce
• Contact
• pat@streamsets.com
• @metadaddy

3. 3© Cloudera, Inc. All rights reserved.
Streaming Patterns
•Ingestion
•Low Millisecond Actions
•Near Real Time Complex Actions

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Parts Of Streaming
Producer Kafka Engine Destination

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Parts Of Streaming
Producer Kafka Engine Destination
At Least once
Ordered
Partitioned
At Least Once Depends
Depends

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Destinations
• File Systems: example HDFS
• Batch is good
• Only can do exactly once is a file is closed in a single ack.
• Good for Scans
• Solr
• Everything is Document based making exactly once
• Batch is still good
• Good for Search Queries

7. 7© Cloudera, Inc. All rights reserved.
Destinations
• NoSQL: example HBase
• Everything has a row key making exactly once for writes
• Increments can be applied twice is so be careful
• Good for gets and puts
• Kudu
• Everything has a row key making exactly once for writes
• Good for gets, puts, and scans

8. 8© Cloudera, Inc. All rights reserved.
Ingestion Destinations
• File Systems: example HDFS
• Flume
• Kafka Connect
• Solr
• Flume
• Any Streaming Engine

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Ingestion Destinations
• NoSQL: example HBase
• Flume
• Any Streaming Engine: Storm and Spark Streaming Tested
• Kudu
• Flume
• Kafka Connect
• Any Streaming Engine: Spark Streaming Tested

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Tricks With Producers
• Send Source ID (requires Partitioning In Kafka)
• Seq
• UUID
• UUID plus time
• Partition on SourceID
• Watch out for repartitions and partition fail overs

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Streaming Engines
• Consumer
• Flume, KafkaConnect, Streaming Engine
• Storm
• Spark Streaming
• Flink
• Kafka Streams

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Consumer: Flume, KafkaConnect
• Simple and Works
• Low latency
• High throughput
• Interceptors
• Transformations
• Alerting
• Ingestions

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Consumer: Streaming Engines
• Not so great at HDFS Ingestion
• But great for record storage systems
• HBase
• Cassandra
• Kudu
• SolR
• Elastic Search

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Storm
• Old Gen
• Low latency
• Low throughput
• At least once
• Around for ever
• Topology Based

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Spark Streaming
• The Juggernaut
• Higher Latency
• High Through Put
• Exactly Once
• SQL
• MlLib
• Highly used
• Easy to Debug/Unit Test
• Easy to transition from
Batch
• Flow Language
• 600 commits in a month
and about 100 meetups

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Spark Streaming
DStream
DStream
DStream
Single Pass
Source Receiver RDD
Source Receiver RDD
RDD
Filter Count Print
Source Receiver RDD
RDD
RDD
Single Pass
Filter Count Print
First
Batch
Second
Batch

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DStream
DStream
DStream
Single Pass
Source Receiver RDD
Source Receiver RDD
RDD
Filter Count
Print
Source Receiver
RDD
partitions
RDD
Parition
RDD
Single Pass
Filter Count
Pre-first
Batch
First
Batch
Second
Batch
Stateful
RDD 1
Print
Stateful
RDD 2
Stateful
RDD 1
Spark Streaming

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Flink
• I’m Better Than Spark Why Doesn’t Anyone use me
• Very much like Spark but not as feature rich
• Lower Latency
• Micro Batch -> ABS
• Asynchronous Barrier Snapshotting
• Flow Language
• ~1/6th the comments and meetups
• But Slim loves it 

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Flink - ABS
Operator
Buffer

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Operator
Buffer
Operator
Buffer
Flink - ABS
Barrier 1A
Hit
Barrier 1B
Still Behind

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Operator
Buffer
Flink - ABS
Both
Barriers Hit
Operator
Buffer
Barrier 1A
Hit
Barrier 1B
Still Behind

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Operator
Buffer
Flink - ABS
Both
Barriers Hit
Operator
Buffer
Barrier is
combined
and can
move on
Buffer can
be flushed
out

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Kafka Streams
• The new Kid on the Block
• When you only have Kafka
• Low Latency
• High Throughput
• Not exactly once
• Very Young
• Flow Language
• Very different hardware profile then others
• Not widely supported
• Not widely used
• Worries about separation of concern

24. 24© Cloudera, Inc. All rights reserved.
Summary about Engines
• Ingestion
• Flume and KafkaConnect
• Super Real Time and Special
• Consumer
• Counting, MlLib, SQL
• Spark
• Maybe future and cool
• Flink and KafkaStreams
• Odd man out
• Storm

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Abstractions
Code Abstractions
Beam
SQL Abstraction
SQL
UI Abstraction
StreamSets
Streaming Engines

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StreamSets Data Collector
Building a Higher Level, Open Source Tool

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Traditional and Big Data
Founders
StreamSets Company Background
Top tier Investors
Momentum to Date
Strategic Partners
• Founded 2014; exited stealth 9/15
• ~30 employees
• Double-digit enterprise customers
• 10,000 downloads

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Thank you!