Big Data is changing abruptly, and where it is likely heading

Big Data Spain 2014-11-17 bigdataspain.org

Paco Nathan @pacoid

The economics of datacenters has shifted toward warehouse scale, based on commodity hardware that includes multicore and large memory spaces. !Incumbent technologies for Big Data do not embrace those changes, however newer OSS projects have emerged to leverage them.

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Some questions, and an emerging idea…

Also, the data requirements are changing abruptly as sensor data, microsatellites, and other IoT use cases boost data rates by orders of magnitude. !We have math available to address those industrial needs, but have software frameworks kept up?

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Some questions, and an emerging idea…

1. Containers

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If you’re not using them, you’re missing out… !See recent announcements by Google, Amazon, etc. !Containers are the tip of the iceberg: OS have evolved quietly, while practices near to customers have perhaps not kept pace

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Containers

Key benefits based on Mesos case studies:

• mixed workloads • higher utilization rates • lower latency for data products • significantly lower Ops overhead !Examples: Mesos @Twitter, eBay, Netflix, many startups, etc.; Borg/Omega @Google, Symphony @IBM, Autopilot @Microsoft, etc. !Major cloud users understand this shift; however much of the world still thinks in terms of VMware

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Containers

We need much more than Docker, we need better definitions for what is to be used outside of the container as well – e.g., for microservices

• Intro to Mesos: goo.gl/PbUfNe

• https://mesosphere.com/

• A project to watch: Weave

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Containers

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Containers

One problem: how does an institution, such as a major bank, navigate this kind of change?

2. Clouds

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Speaking of clouds, forgive me for being biased…

For nearly a decade, I’ve been talking with people about whether cloud economics makes sense?

Consider: datacenter util rates in single-digits (~8%)

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Clouds

Companies accustomed to cheap energy rates, with staff who thinking in terms of individual VMs… these will not find clouds to be a friendly territory.

Not so much about software, as it is about sophisticated engineering practice.

The change recalls the shift of BI ⇒ Data Science That was not simple…

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Clouds

The majors understand this new world: Google, Amazon, Microsoft, IBM, Apple, etc.

Sub-majors can create their own clouds: Facebook, Twitter, LinkedIn, etc.

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Clouds

What about industries approaching Big Data now which cannot afford to buy small armies of expert Ops people? How they must proceed becomes a key question.

Current research points to significant changes ahead, where machine learning plays a key role in the context of advanced cluster scheduling:

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Clouds

Improving Resource Efficiency with Apache Mesos Christina Delimitrouyoutu.be/YpmElyi94AA

Experiences with Quasar+Mesos showed:

• 88% apps get >95% performance

• ~10% overprovisioning instead of 500%

• up to 70% cluster util at steady state

• 23% shorter scenario completion

3. Abstract Algebra

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A question on StackOverflow in 2013 challenged Twitter’s use of Abstract Algebra libraries for their revenue apps…

cs.stackexchange.com/questions/9648/what-use-are-groups-monoids-and-rings-in-database-computations

The answers are enlightening: think of a kind of containerization for business logic – leading to exponential increase in performance, particularly in the context of streaming cases.

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Abstract Algebra

A cheat-sheet:

Abstract Algebra

non-empty set

Semigroup

Group

has a binary associative operation, with closure

each element has an inverse

Ring

has two binary associative operations:

addition and multiplicationMonoid

has an identity element

Add ALL the Things: Abstract Algebra Meets Analyticsinfoq.com/presentations/abstract-algebra-analyticsAvi Bryant, Strange Loop (2013)

• grouping doesn’t matter (associativity)

• ordering doesn’t matter (commutativity)

• zeros get ignored

In other words, while partitioning data at scale is quite difficult, you can let the math allow your code to be flexible at scale

Avi Bryant@avibryant

Abstract Algebra

Algebra for Analyticsspeakerdeck.com/johnynek/algebra-for-analyticsOscar Boykin, Strata SC (2014)

• “Associativity allows parallelism in reducing” by letting you put the () where you want

• “Lack of associativity increases latency exponentially”

Oscar Boykin @posco

Abstract Algebra

4. Functional Programming

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Theory, eight decades ago: what can be computed?

Haskell Curry haskell.org

Alonso Churchwikipedia.org

Functional Programming for Big Data

John Backusacm.org

David Turnerwikipedia.org

Praxis, four decades ago: algebra for applicative systems

Pattie MaesMIT Media Lab

Reality, two decades ago: machine data from web apps

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circa 2002: mitigate risk of large distributed workloads lost due to disk failures on commodity hardware…

Google File System Sanjay Ghemawat, Howard Gobioff, Shun-Tak Leung research.google.com/archive/gfs.html !MapReduce: Simplified Data Processing on Large Clusters Jeffrey Dean, Sanjay Ghemawat research.google.com/archive/mapreduce.html

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Functional Programming for Big Data

DryadLINQ influenced a new class of workflow abstractions based on functional programming: Spark, Flink, Scalding, Scoobi, Scrunch, etc.

• needed for leveraging the newer hardware: multicore, large memory spaces, etc.

• significant code volume reduction ⇒ eng costs

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Functional Programming for Big Data

2002

2002MapReduce @ Google

2004MapReduce paper

2006Hadoop @ Yahoo!

2004 2006 2008 2010 2012 2014

2014Apache Spark top-level

2010Spark paper

2008Hadoop Summit

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Functional Programming for Big Data

MR doesn’t compose well for large applications, and so specialized systems emerged as workarounds

MapReduce

General Batch Processing Specialized Systems: iterative, interactive, streaming, graph, etc.

Pregel Giraph

Dremel Drill

TezImpala

GraphLab

StormS4

F1

MillWheel

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Functional Programming for Big Data

Spark: Cluster Computing with Working Sets Matei Zaharia, Mosharaf Chowdhury, Michael Franklin, Scott Shenker, Ion Stoica people.csail.mit.edu/matei/papers/2010/hotcloud_spark.pdf !Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing Matei Zaharia, Mosharaf Chowdhury, Tathagata Das, Ankur Dave, Justin Ma, Murphy McCauley, Michael Franklin, Scott Shenker, Ion Stoica usenix.org/system/files/conference/nsdi12/nsdi12-final138.pdf

circa 2010: a unified engine for enterprise data workflows, based on commodity hardware a decade later…

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Functional Programming for Big Data

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Functional Programming for Big Data

One minor problem: where do we find many graduating students who are good at Scala, Clojure, F#, etc.?

4. Databases

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+1 generations of computer scientists has grown up thinking that Data implies SQL…

Excellent as a kind of declarative, functional language for describing data transformations

However, in the long run we should move away from thinking in terms of SQL

Computational Thinking as a rubric in general would be preferred

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Databases

A step further is to move away from thinking of data as an engineering issue – it is about business

So much of the US curricula for math is still stuck in the Cold War: years of calculus to identify the best people to build ICBMs

We need more business people thinking in terms of math, beyond calculus, to contend with difficult problems in supply chain, maintenance schedules, energy needs, transportation routes, etc.

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Databases

1. starting with real-world data ⇒

2. leverage graph queries for representation ⇒

3. convert to sparse matrix, use FP and abstract algebra ⇒

4. achieve high-ROI parallelism at scale, mostly about optimization ⇒

Databases

6. Approximations

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Approximations

19-20c. statistics emphasized defensibility in lieu of predictability, based on analytic variance and goodness-of-fit tests !That approach inherently led toward a manner of computational thinking based on batch windows !They missed a subtle point…

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21c. shift towards modeling based on probabilistic approximations: trade bounded errors for greatly reduced resource costs

highlyscalable.wordpress.com/2012/05/01/probabilistic-structures-web-analytics-data-mining/

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Approximations

21c. shift towards modeling based on probabilapproximations: trade bounded errors for greatly reduced resource costs

highlyscalable.wordpress.com/2012/05/01/probabilistic-structures-web-analytics-data-mining/

Twitter catch-phrase:

“Hash, don’t sample”

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Approximations

a fascinating and relatively new area, pioneered by relatively few people – e.g., Philippe Flajolet

provides approximation, with error bounds – in general uses significantly less resources (RAM, CPU, etc.)

many algorithms can be constructed from combinations of read and write monoids

aggregate different ranges by composing hashes, instead of repeating full-queries

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Approximations

algorithm use case example

Count-Min Sketch frequency summaries code

HyperLogLog set cardinality code

Bloom Filter set membership

MinHash set similarity

DSQ streaming quantiles

SkipList ordered sequence search

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Approximations

algorithm use case example

Count-Min Sketch frequency summaries code

HyperLogLog set cardinality code

Bloom Filter set membership

MinHash set similarity

DSQ streaming quantiles

SkipList ordered sequence search

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suggestion: consider these as your most quintessential collections data types at scale

Approximations

• sketch algorithms: trade bounded errors for orders of magnitude less required resources, e.g., fit more complex apps in memory

• multicore + large memory spaces (off heap) are increasing the resources per node in a cluster

• containers allow for finer-grain allocation of cluster resources and multi-tenancy

• monoids, etc.: guarantees of associativity within the code allow for more effective distributed computing, e.g., partial aggregates

• less resources must be spent sorting/windowing data prior to working with a data set

• real-time apps, which don’t have the luxury of anticipating data partitions, can respond quickly

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Approximations

Probabilistic Data Structures for Web Analytics and Data MiningIlya Katsov (2012-05-01)

A collection of links for streaming algorithms and data structures Debasish Ghosh

Aggregate Knowledge blog (now Neustar) Timon Karnezos, Matt Curcio, et al.

Probabilistic Data Structures and Breaking Down Big Sequence DataC. Titus Brown, O'Reilly (2010-11-10)

Algebird Avi Bryant, Oscar Boykin, et al. Twitter (2012)

Mining of Massive DatasetsJure Leskovec, Anand Rajaraman, Jeff Ullman, Cambridge (2011)

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Approximations

7. Notebooks

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Spreadsheets represented a revolution in how to think about computing !What Fernando Perez, et al., have done with IPython is subtle and powerful !Technology is not just about writing code; it is about people using that code !Enterprise data workflows are moving toward cloud-based notebooks

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Notebooks

Early emphasis on tools in Big Data now gives way to workflows: people, automation, process, integration, test, maintenance, scale

Google had the brilliant approach of shared documents…

Now we see cloud-based notebooks, which begin to displace web apps

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Notebooks

http://nbviewer.ipython.org/ http://jupyter.org/

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Notebooks

nature.com/news/ipython-interactive-demo-7.21492

What is Spark?

Developed in 2009 at UC Berkeley AMPLab, then open sourced in 2010, Spark has since become one of the largest OSS communities in big data, with over 200 contributors in 50+ organizations

What is Spark?

spark.apache.org

“Organizations that are looking at big data challenges – including collection, ETL, storage, exploration and analytics – should consider Spark for its in-memory performance and the breadth of its model. It supports advanced analytics solutions on Hadoop clusters, including the iterative model required for machine learning and graph analysis.”

Gartner, Advanced Analytics and Data Science (2014)

What is Spark?

Spark Core is the general execution engine for the Spark platform that other functionality is built atop: !• in-memory computing capabilities deliver speed

• general execution model supports wide variety of use cases

• ease of development – native APIs in Java, Scala, Python (+ SQL, Clojure, R)

What is Spark?

What is Spark?

WordCount in 3 lines of Spark

WordCount in 50+ lines of Java MR

Sustained exponential growth, as one of the most active Apache projects ohloh.net/orgs/apache

What is Spark?

• generalized patterns ⇒ unified engine for many use cases

• lazy evaluation of the lineage graph ⇒ reduces wait states, better pipelining

• generational differences in hardware ⇒ off-heap use of large memory spaces

• functional programming / ease of use ⇒ reduction in cost to maintain large apps

• lower overhead for starting jobs

• less expensive shuffles

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What is Spark?

unified compute

Kafka + Spark + Cassandra datastax.com/documentation/datastax_enterprise/4.5/datastax_enterprise/spark/sparkIntro.html http://helenaedelson.com/?p=991

github.com/datastax/spark-cassandra-connector

github.com/dibbhatt/kafka-spark-consumer

columnar key-valuedata streams

What is Spark?

databricks.com/blog/2014/11/05/spark-officially-sets-a-new-record-in-large-scale-sorting.html

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What is Spark?

Databricks Cloud databricks.com/blog/2014/07/14/databricks-cloud-making-big-data-easy.html youtube.com/watch?v=dJQ5lV5Tldw#t=883

What is Spark?

Resources

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Apache Spark developer certificate program

• http://oreilly.com/go/sparkcert

• defined by Spark experts @Databricks

• assessed by O’Reilly Media

• establishes the bar for Spark expertise

certification:

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community:

spark.apache.org/community.html

video+slide archives: spark-summit.org

events worldwide: goo.gl/2YqJZK

resources: databricks.com/spark-training-resources

workshops: databricks.com/spark-training

new: MOOCs via edX and University of California

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books:

Fast Data Processing with Spark Holden Karau Packt (2013) shop.oreilly.com/product/9781782167068.do

Spark in Action Chris FreglyManning (2015*) sparkinaction.com/

Learning Spark Holden Karau, Andy Konwinski, Matei ZahariaO’Reilly (2015*) shop.oreilly.com/product/0636920028512.do

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events:Strata EUBarcelona, Nov 19-21 strataconf.com/strataeu2014 Data Day Texas Austin, Jan 10 datadaytexas.com Strata CA San Jose, Feb 18-20 strataconf.com/strata2015 Spark Summit East NYC, Mar 18-19 spark-summit.org/east Strata EULondon, May 5-7 strataconf.com/big-data-conference-uk-2015 Spark Summit 2015 SF, Jun 15-17 spark-summit.org

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presenter:

Just Enough Math O’Reilly, 2014

justenoughmath.compreview: youtu.be/TQ58cWgdCpA

monthly newsletter for updates, events, conf summaries, etc.: liber118.com/pxn/

Enterprise Data Workflows with Cascading O’Reilly, 2013

shop.oreilly.com/product/0636920028536.do

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