The Internet-of-things: Architecting for the deluge of data

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The Internet-of-things: Architecting for the deluge of data CTO [email protected] Bryan Cantrill @bcantrill

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My presentation from Velocity 2014.

Transcript of The Internet-of-things: Architecting for the deluge of data

Page 1: The Internet-of-things: Architecting for the deluge of data

The Internet-of-things: Architecting for thedeluge of dataCTO

[email protected]

Bryan Cantrill

@bcantrill

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Big Data circa 1994: Pre-Internet

Source: BusinessWeek, September 5, 1994

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Aside: Internet circa 1994

Source: BusinessWeek, October 10, 1994

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Big Data circa 2004: Internet exhaust

• Through the 1990s, Moore’s Law + Kryder’s Law grew faster than transaction rates, and what was “overwhelming” in 1994 was manageable by 2004

• But large internet concerns (Google, Facebook, Yahoo!) encountered a new class of problem: analyzing massive amounts of data emitted as a byproduct of activity

• Data scaled with activity, not transactions — changing both data sizes and economics

• Data sizes were too large for extant data warehousing solutions — and were embarrassingly parallel besides

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Big Data circa 2004: MapReduce

• MapReduce, pioneered by Google and later emulated by Hadoop, pointed to a new paradigm where compute tasks are broken into map and reduce phases

• Serves to explicitly divide the work that can be parallelized from that which must be run sequentially

• Map phases are farmed out to a storage layer that attempts to co-locate them with the data being mapped

• Made for commodity scale-out systems; relatively cheap storage allowed for sloppy but effective solutions (e.g. storing data in triplicate)

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Big Data circa 2014

• Hadoop has become the de facto big data processing engine — and HDFS the de facto storage substrate

• But HDFS is designed around availability during/for computation; it is not designed to be authoritative

• HDFS is used primarily for data that is redundant, transient, replaceable or otherwise fungible

• Authoritative storage remains either enterprise storage (on premises) or object storage (in the cloud)

• For analysis of non-fungible data, pattern is to ingest data into a Hadoop cluster from authoritative storage

• But a new set of problems is poised to emerge...

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Big Data circa 2014: Internet-of-things

• IDC forecasts that the “digital universe” will grow from 130 exabytes in 2005 to 40,000 exabytes in 2020 — with as much of a third having “analytic value”

• This doesn’t even factor in the (long forecasted) rise of the internet-of-things/industrial internet...

• Machine-generated data at the edge will effect a step function in data sizes and processing methodologies

• No one really knows how much data will be generated by IoT, but the numbers are insane (e.g., HD camera generates 20 GB/hour; a Ford Energi engine generates 25 GB/hour; a GE jet engine generates 1TB/flight)

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How to cope with IoT-generated data?

• IoT presents so much more data that we will increasingly need data science to make sense of it

• To assure data, we need to retain as much raw data as possible, storing it once and authoritatively

• Storing data authoritatively has ramifications for the storage substrate

• To allow for science, we need to place an emphasis on hypothesis exploration: it must be quick to iterate from hypothesis to experiment to result to new hypothesis

• Emphasizing hypothesis exploration has ramifications for the compute abstractions and data movement

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The coming ramifications of IoT

• It will no longer be acceptable to discard data: all data will need to be retained to explore future hypotheses

• It will no longer be acceptable to store three copies: 3X on storage costs is too acute when data is massive

• It will no longer be acceptable to move data for analysis: in some cases, not even over the internet!

• It will no longer be acceptable to dictate the abstraction: we must accommodate anything that can process data

• These shifts are as significant as the shift from traditional data warehousing to scale-out MapReduce!

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IoT: Authoritative storage?

• “Filesystems” that are really just user-level programs layered on local filesystems lack device-level visibility, sacrificing reliability and performance

• Even in-kernel, we have seen the corrosiveness of an abstraction divide in the historic divide between logical volume management and the filesystem:

• The volume manager understands multiple disks, but nothing of the higher level semantics of the filesystem

• The filesystem understands the higher semantics of the data, but has no physical device understanding

• This divide became entrenched over the 1990s, and had devastating ramifications for reliability and performance

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The ZFS revolution

• Starting in 2001, Sun began a revolutionary new software effort: to unify storage and eliminate the divide

• In this model, filesystems would lose their one-to-one association with devices: many filesystems would be multiplexed on many devices

• By starting with a clean sheet of paper, ZFS opened up vistas of innovation — and by its architecture was able to solve many otherwise intractable problems

• Sun shipped ZFS in 2005, and used it as the foundation of its enterprise storage products starting in 2008

• ZFS was open sourced in 2005; it remains the only open source enterprise-grade filesystem

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ZFS advantages

• Copy-on-write design allows on-disk consistency to be always assured (eliminating file system check)

• Copy-on-write design allows constant-time snapshots in unlimited quantity — and writable clones!

• Filesystem architecture allows filesystems to be created instantly and expanded — or shrunk! — on-the-fly

• Integrated volume management allows for intelligent device behavior with respect to disk failure and recovery

• Adaptive replacement cache (ARC) allows for optimal use of DRAM — especially on high DRAM systems

• Support for dedicated log and cache devices allows for optimal use of flash-based SSDs

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ZFS at Joyent

• Joyent was the earliest ZFS adopter: becoming (in 2005) the first production user of ZFS outside of Sun

• ZFS is one of the four foundational technologies of Joyent’s SmartOS, our illumos derivative

• The other three foundational technologies in SmartOS are DTrace, Zones and KVM

• Search “fork yeah illumos” for the (uncensored) history of OpenSolaris, illumos, SmartOS and derivatives

• Joyent has extended ZFS to provide better support multi-tenant operation with I/O throttling

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ZFS as the basis for IoT?

• ZFS offers commodity hardware economics with enterprise-grade reliability — and obviates the need for cross-machine mirroring for durability

• But ZFS is not itself a scale-out distributed system, and is ill suited to become one

• Conclusion: ZFS is a good building block for the data explosion from IoT, but not the whole puzzle

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IoT: Compute abstraction?

• To facilitate hypothesis exploration, we need to carefully consider the abstraction for computation

• How is data exploration programmatically expressed?

• How can this be made to be multi-tenant?

• The key enabling technology for multi-tenancy is virtualization — but where in the stack to virtualize?

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• The historical answer — since the 1960s — has been to virtualize at the level of the hardware:

• A virtual machine is presented upon which each tenant runs an operating system of their choosing

• There are as many operating systems as tenants

• The historical motivation for hardware virtualization remains its advantage today: it can run entire legacy stacks unmodified

• However, hardware virtualization exacts a heavy tolls: operating systems are not designed to share resources like DRAM, CPU, I/O devices or the network

• Hardware virtualization limits tenancy and inhibits performance!

Hardware-level virtualization?

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• Virtualizing at the application platform layer addresses the tenancy challenges of hardware virtualization…

• ...but at the cost of dictating abstraction to the developer

• With IoT, this is especially problematic: we can expect much more analog data and much deeper numerical analysis — and dependencies on native libraries and/or domain-specific languages

• Virtualizing at the application platform layer poses many other challenges:

• Security, resource containment, language specificity, environment-specific engineering costs

Platform-level virtualization?

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• Containers virtualizing the OS and hit the sweet spot:

• Single OS (single kernel) allows for efficient use of hardware resources, and therefore allows load factors to be high

• Disjoint instances are securely compartmentalized by the operating system

• Gives customers what appears to be a virtual machine (albeit a very fast one) on which to run higher-level software

• Gives customers PaaS when the abstractions work for them, IaaS when they need more generality

• OS-level virtualization allows for high levels of tenancy without dictating abstraction or sacrificing efficiency

• Zones is a bullet-proof implementation of OS-level virtualization — and is the core abstraction in Joyent’s SmartOS

Joyent’s solution: OS containers

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Idea: ZFS + Containers?

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• Building a sophisticated distributed system on top of ZFS and zones, we have built Manta, an internet-facing object storage system offering in situ compute

• That is, the description of compute can be brought to where objects reside instead of having to backhaul objects to transient compute

• The abstractions made available for computation are anything that can run on the OS...

• ...and as a reminder, the OS — Unix — was built around the notion of ad hoc unstructured data processing, and allows for remarkably terse expressions of computation

Manta: ZFS + Containers!

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Aside: Unix

• When Unix appeared in the early 1970s, it was not just a new system, but a new way of thinking about systems

• Instead of a sealed monolith, the operating system was a collection of small, easily understood programs

• First Edition Unix (1971) contained many programs that we still use today (ls, rm, cat, mv)

• Its very name conveyed this minimalist aesthetic: Unix is a homophone of “eunuchs” — a castrated Multics

We were a bit oppressed by the big system mentality. Ken wanted to do something simple. — Dennis Ritchie

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Unix: Let there be light

• In 1969, Doug McIlroy had the idea of connecting different components:

At the same time that Thompson and Ritchie were sketching out a file system, I was sketching out how to do data processing on the blackboard by connecting together cascades of processes

• This was the primordial pipe, but it took three years to persuade Thompson to adopt it:

And one day I came up with a syntax for the shell that went along with the piping, and Ken said, “I’m going to do it!”

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Unix: ...and there was light

And the next morning we had this orgy of one-liners. — Doug McIlroy

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The Unix philosophy

• The pipe — coupled with the small-system aesthetic — gave rise to the Unix philosophy, as articulated by Doug McIlroy:

• Write programs that do one thing and do it well

• Write programs to work together

• Write programs that handle text streams, because that is a universal interface

• Four decades later, this philosophy remains the single most important revolution in software systems thinking!

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• In 1986, Jon Bentley posed the challenge that became the Epic Rap Battle of computer science history:

Read a file of text, determine the n most frequently used words, and print out a sorted list of those words along with their frequencies.

• Don Knuth’s solution: an elaborate program in WEB, a Pascal-like literate programming system of his own invention, using a purpose-built algorithm

• Doug McIlroy’s solution shows the power of the Unix philosophy:

tr -cs A-Za-z '\n' | tr A-Z a-z | \ sort | uniq -c | sort -rn | sed ${1}q

Doug McIlroy v. Don Knuth: FIGHT!

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Big Data: History repeats itself?

• The original Google MapReduce paper (Dean et al., OSDI ’04) poses a problem disturbingly similar to Bentley’s challenge nearly two decades prior:

Count of URL Access Frequency: The function processes logs of web page requests and outputs ⟨URL, 1⟩. The reduce function adds together all values for the same URL and emits a ⟨URL, total count⟩ pair

• But the solutions do not adhere to the Unix philosophy...

• ...and nor do they make use of the substantial Unix foundation for data processing

• e.g., Appendix A of the OSDI ’04 paper has a 71 line word count in C++ — with nary a wc in sight

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• Manta allows for an arbitrarily scalable variant of McIlroy’s solution to Bentley’s challenge: mfind -t o /bcantrill/public/v7/usr/man | \ mjob create -o -m "tr -cs A-Za-z '\n' | \ tr A-Z a-z | sort | uniq -c" -r \ "awk '{ x[\$2] += \$1 } END { for (w in x) { print x[w] \" \" w } }' | \ sort -rn | sed ${1}q"

• This description not only terse, it is high performing: data is left at rest — with the “map” phase doing heavy reduction of the data stream

• As such, Manta — like Unix — is not merely syntactic sugar; it converges compute and data in a new way

Manta: Unix for Big Data — and IoT

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• Eventual consistency represents the wrong CAP tradeoffs for most; we prefer consistency over availability for writes (but still availability for reads)

• Many more details:http://dtrace.org/blogs/dap/2013/07/03/fault-tolerance-in-manta/

• Celebrity endorsement:

Manta: CAP tradeoffs

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• Hierarchical storage is an excellent idea (ht: Multics); Manta implements proper directories, delimited with a forward slash

• Manta implements a snapshot/link hybrid dubbed a snaplink; can be used to effect versioning

• Manta has full support for CORS headers

• Manta uses SSH-based HTTP auth for client-side tooling (IETF draft-cavage-http-signatures-00)

• Manta SDKs exist for node.js, R, Go, Java, Ruby, Python — and of course, compute jobs may be in any of these (plus Perl, Clojure, Lisp, Erlang, Forth, Prolog, Fortran, Haskell, Lua, Mono, COBOL, Fortran, etc.)

• “npm install manta” for command line interface

Manta: Other design principles

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• We believe compute/data convergence to be a constraint imposed by IoT: stores of record must support computation as a first-class, in situ operation

• We believe that some (and perhaps many) IoT workloads will require computing at the edge — internet transit may be prohibitive for certain applications

• We believe that Unix is a natural way of expressing this computation — and that OS containers are the right way to support this securely

• We believe that ZFS is the only sane storage underpinning for such a system

• Manta will surely not be the only system to represent the confluence of these — but it is the first

Manta and IoT

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• Product page:

http://joyent.com/products/manta

• node.js module:

https://github.com/joyent/node-manta

• Manta documentation:

http://apidocs.joyent.com/manta/

• IRC, e-mail, Twitter, etc.:

#manta on freenode, [email protected], @mcavage, @dapsays, @yunongx, @joyent

Manta: More information