Adding Simplicity to Complexity

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The document discusses abstractions in software and their boundaries. It notes that abstractions aim to simplify and separate concerns but often have "leaky" boundaries where performance needs require crossing abstraction layers. Abstractions can interfere with holistic system analysis by bisecting common analysis tasks. The document advocates that abstractions be used intentionally and that violation of abstraction boundaries is sometimes necessary and carries an understood cost burden.

Technology

A concrete talk about abstraction.
Theo Schlossnagle @postwait 1

 Founder: Circonus, OmniTI, Sparkpost, Fontdeck
 Distinguish ACM Member
 ACM Member at Large
 Avid Open Source Contributor
 Gentleman Farmer
Theo Schlossnagle @postwait 2

Boundaries in the software/hardware and software/human interface.
Also in the software/software interface.
Theo Schlossnagle @postwait 3

Transistors
Hardware Function
Micro Architecture
Instruction Set Architecture
Machine Code
Assembly Languages
Programming Languages
Algorithms/Data Structures
Applications
Theo Schlossnagle @postwait 4
Hardware
Software

Abstractions boundaries are at a
significantly lower dimension
(typically one)
than the space they separate
(typically greater than four).
Theo Schlossnagle @postwait 5
Task /
Problem
Skills
Efficiency /
Resources
Safety /
Correctness
Availability
Security /
Compliance

Time Series Data
Histogram
RocksDB
Raw Data
Column Families
Rollups
Tiers
Time Shards
LMDB
ZFS
Files
Abstractions are divides,
and the world isn’t always clean.
Performance requirements are
often a witness.
Result:
“Leaky Abstractions” or
“Abstraction Violations”
Theo Schlossnagle @postwait 6
Time Series Data
(for rebuild)
Time Series Data
(for rollup ETL)

Constraints Design
Performanc
e
AudienceScale
Resources
Techniques
API
Boundary
Abstraction
Core Problem
Abstractions aim to be generically useful.
The fewer initial consumers, the less likely this will be satisfied.
Theo Schlossnagle @postwait 7

Abstractions interfere with holistic
systems thinking.
Problems span layers.
Therefore analysis spans layers.
Abstractions are loans.
If abstraction boundaries bisect
common analysis tasks, those end
up being high-interest loans.
Theo Schlossnagle @postwait 8
Layer 1
client
communications
Layer 2
distributed
coordination
Layer 3
storage
layer
Expensive analysis
Inexpensiveanalysis

Abstractions in real-life can win, can lose, but most often do both.
Theo Schlossnagle @postwait 9

 Time Series Database
 Distributed
 Scalable
 High performance
• HTTP
• Lua
• CRDTs
• Math
• Resource Pools
• RocksDB
• C/C++/Asm
Speed Scale
AccessibilityCorrectness
Theo Schlossnagle @postwait 10

A sordid love story of layer violations and compromise.
Theo Schlossnagle @postwait 11

 libmtev is a multi-threaded, event-driven framework for
building high-performance service applications in C
 It makes it “easy”… for C developers that understand event-driven
code.
 Lua is an elegant and accessible functional language
 LuaJIT (is lua, but fast)
 LuaJIT’s embedding API make violating boundaries
convenient.
Theo Schlossnagle @postwait 12

Mistakes layered on mistakes layered on mistakes.
Theo Schlossnagle @postwait 13

 updating, appending, deleting
 reading randomly, sequentially, bulk
 It’s basically impossible to excel at all without layering
violations.
Theo Schlossnagle @postwait 14

Concurrency limitations and fair scheduling.
Theo Schlossnagle @postwait 15

 Resources (CPU, memory, disk, L2 cache, network, etc.) are
limited.
 Concurrency can help until… it hurts.
 For performance, we abstract on resource usage
boundaries/workload characteristics.
Theo Schlossnagle @postwait 16

Concrete lessons about abstractions.
Theo Schlossnagle @postwait 17

No one would get anything done. Ever.
Theo Schlossnagle @postwait 18

Microservices (in particular) afford
isolation choices beyond code
complexity: security, compliance,
availability, ownership, etc.
The dimension on which the abstraction
boundary is drawn should be selected to
yield the most benefit.
Theo Schlossnagle @postwait 19

The more things (and ultimately people)
that use the abstraction, the better it will
be designed (or subsequently
redesigned).
Never build an API without a real
consumer (TDD doesn’t count). This
should apply to all simplifications.
Theo Schlossnagle @postwait 20

They are not a goal, but they are not
evil. It should be intentional.
Some things behind an abstraction
should intuitively be hidden. Intuition
isn’t always right. When these leaks
consistently benefit multiple
stakeholders, it simply means the line of
abstraction has been redrawn.
Theo Schlossnagle @postwait 21

They happen.
They happen all the time.
I am unapologetic about this.
Violation carries cost burden.
Assess it, accept it, and move on.
Theo Schlossnagle @postwait 22

https://irondb.io/
https://github.com/circonus-labs/libmtev
Theo Schlossnagle @postwait 23

What we see in the modern data store world is a race between different approaches to achieve a distributed and resilient storage of data. Most applications need a stateful layer which holds the data. There are at least three necessary ingredients which are everything else than trivial to combine and of course even more challenging when heading for an acceptable performance. Over the past years there has been significant progress in respect in both the science and practical implementations of such data stores. In his talk Max Neunhoeffer will introduce the audience to some of the needed ingredients, address the difficulties of their interplay and show four modern approaches of distributed open-source data stores. Topics are: – Challenges in developing a distributed, resilient data store – Consensus, distributed transactions, distributed query optimization and execution – The inner workings of ArangoDB, Cassandra, Cockroach and RethinkDB The talk will touch complex and difficult computer science, but will at the same time be accessible to and enjoyable by a wide range of developers.

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What we see in the modern data store world is a race between different approaches to achieve a distributed and resilient storage of data. Every application needs a stateful layer which holds the data. There are several different necessary components which are anything but trivial to combine, and, of course, even more challenging when attempting to optimize for performance. Over the past years there has been significant progress in both the science and practical implementations of such data stores. In this talk Dan Larkin-York will introduce the audience to some of the challenges, address the difficulties of their interplay, and cover key approaches taken by some of the industry’s leaders (ArangoDB, Cassandra, CockroachDB, MarkLogic, and more).

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What we see in the modern data store world is a race between different approaches to achieve a distributed and resilient storage of data. Every application needs a stateful layer which holds the data. There are at least three necessary components which are everything else than trivial to combine, and, of course, even more challenging when heading for an acceptable performance. Over the past years there has been significant progress in both the science and practical implementations of such data stores. In his talk Max Neunhoeffer will introduce the audience to some of the needed ingredients, address the difficulties of their interplay and show four modern approaches of distributed open-source data stores (ArangoDB, Cassandra, Cockroach and RethinkDB).

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