SoundCloud's microservice architecture is built mostly in Scala, using Finagle as its distributed systems workhorse. Finagle is an RPC system for the JVM, and it is based on a pipes-and-filters architecture that maps very nicely to functional programming concepts of higher-order functions and combinators. Over the past few years we have found that it is extremely useful to go even a step further and think of microservices as functions themselves. In this talk let's explore how SoundCloud uses Scala and Finagle, and how we started thinking of a microservices architecture as a special case of a functional system.

1. Your microservice
as a Function
Phil Calçado
SoundCloud

4. > 11 hours of audio uploaded
every minute
~ 300 million people every month

5. for this talk:
implementation: code
design: how you abstract
concepts in your system
architecture: how systems talk
to each other

6. how I got started with
object-oriented
programming

9. Relationships Between BOUNDED CONTEXTS
Shared Kernel

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69ACMSIGPLANNotices,Volume28,No.3March1993
😧😆😄😃😀😧

11. how I got started with
functional programming

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lillcofthepublicationanditsdareappear.andnoticeisgiven
thatcopyingisbypermissionoftheAssociationforComputing
Machinery.Tocopyothcrwisc,ortorepublish.requiresBfee
and/orspecificpornlission.
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Programming R. Morris
Techniques Editor
On the Criteria To Be
Used in Decomposing
Systems into Modules
D.L. Parnas
Carnegie-Mellon University
This paper discusses modularization as a mechanism
for improving the flexibility and comprehensibility of a
system while allowing the shortening of its development
time. The effectiveness of a "modularization" is
dependent upon the criteria used in dividing the system
into modules. A system design problem is presented and
both a conventional and unconventional decomposition
are described. It is shown that the unconventional
decompositions have distinct advantages for the goals
outlined. The criteria used in arriving at the decom-
positions are discussed. The unconventional decomposi-
tion, if implemented with the conventional assumption
that a module consists of one or more subroutines, will
be less efficient in most cases. An alternative approach
to implementation which does not have this effect is
sketched.
Key Words and Phrases: software, modules,
modularity, software engineering, KWIC index,
software design
CR Categories: 4.0
Introduction
A lucid statement of the philosophy of modular
programming can be found in a 1970 textbook on the
design of system programs by Gouthier and Pont [1,
¶I0.23], which we quote below: 1
A well-defined segmentation of the project effort ensures
system modularity. Each task forms a separate, distinct program
module. At implementation time each module and its inputs and
outputs are well-defined, there is no confusion in the intended
interface with other system modules. At checkout time the in-
tegrity of the module is tested independently; there are few sche-
duling problems in synchronizing the completion of several tasks
before checkout can begin. Finally, the system is maintained in
modular fashion; system errors and deficiencies can be traced to
specific system modules, thus limiting the scope of detailed error
searching.
Usually nothing is said about the criteria to be used
in dividing the system into modules. This paper will
discuss that issue and, by means of examples, suggest
some criteria which can be used in decomposing a
system into modules.
Copyright @ 1972,Association for Computing Machinery, Inc.
General permission to republish, but not for profit, all or part
of this material is granted, provided that reference is made to this
publication, to its date of issue, and to the fact that reprinting
privileges were granted by permission of the Association for Com-
puting Machinery.
Author's address: Department of Computer Science, Carnegie-
Mellon University, Pittsburgh, PA 15213.
1053
A Brief Status Report
The major advancement in the area of modular
programming has been the development of coding
techniques and assemblers which (l) allow one module
to be written with little knowledge of the code in
another module, and (2) allow modules to be reas-
sembled and replaced without reassembly of the whole
system. This facility is extremely valuable for the
production of large pieces of code, but the systems most
often used as examples of problem systems are highly-
modularized programs and make use of the techniques
mentioned above.
1Reprinted by permission of Prentice-Hall, Englewood
Cliffs, N.J.
Communications December 1972
of Volume 15
the ACM Number 12
1 Introduction
The fundamental purpose of a type system is to prevent the occurrence of execution errors dur-
ing the running of a program. This informal statement motivates the study of type systems, but
requires clarification. Its accuracy depends, first of all, on the rather subtle issue of what consti-
tutes an execution error, which we will discuss in detail. Even when that is settled, the absence
of execution errors is a nontrivial property. When such a property holds for all of the program
runs that can be expressed within a programming language, we say that the language is type
sound. It turns out that a fair amount of careful analysis is required to avoid false and embar-
rassing claims of type soundness for programming languages. As a consequence, the classifica-
tion, description, and study of type systems has emerged as a formal discipline.
The formalization of type systems requires the development of precise notations and defi-
nitions, and the detailed proof of formal properties that give confidence in the appropriateness
of the definitions. Sometimes the discipline becomes rather abstract. One should always remem-
ber, though, that the basic motivation is pragmatic: the abstractions have arisen out of necessity
and can usually be related directly to concrete intuitions. Moreover, formal techniques need not
be applied in full in order to be useful and influential. A knowledge of the main principles of
type systems can help in avoiding obvious and not so obvious pitfalls, and can inspire regularity
and orthogonality in language design.
When properly developed, type systems provide conceptual tools with which to judge the
adequacy of important aspects of language definitions. Informal language descriptions often fail
to specify the type structure of a language in sufficient detail to allow unambiguous implemen-
tation. It often happens that different compilers for the same language implement slightly dif-
ferent type systems. Moreover, many language definitions have been found to be type unsound,
allowing a program to crash even though it is judged acceptable by a typechecker. Ideally, for-
mal type systems should be part of the definition of all typed programming languages. This way,
typechecking algorithms could be measured unambiguously against precise specifications and,
if at all possible and feasible, whole languages could be shown to be type sound.
In this introductory section we present an informal nomenclature for typing, execution er-
rors, and related concepts. We discuss the expected properties and benefits of type systems, and
we review how type systems can be formalized. The terminology used in the introduction is not
completely standard; this is due to the inherent inconsistency of standard terminology arising
from various sources. In general, we avoid the words type and typing when referring to run time
concepts; for example we replace dynamic typing with dynamic checking and avoid common
but ambiguous terms such as strong typing. The terminology is summarized in the Defining
Terms section.
Type Systems
Luca Cardelli
Microsoft Research

15. 😧😠😐😒😓😧

16. there isn’t a lot out
there on engineering for
functional programming

18. HTTP
HTTP
MySQL

19. what if we apply
function composition?

21. turns out functional
principles correlate with
good implementation

22. going up one level

23. life of a request to
SoundCloud.com

24. Actual
Feature
Request Validator
User

25. what if we apply
function composition?

27. basically

28. basically

29. Actual
Feature
Request Validator
User

30. Authentication Geo IP Rate Limiting
Available
Features
Actual
Feature
User

31. Actual
Feature
User
Filters
Service
}

32. we have heaps of filters

33. turns out functional
principles correlate
with good design

34. going up one level

35. metadata
track
credentials
user
track
pageplayback groups
facebook
integration
database api …

36. what if we apply
function composition?

39. you want your services to
be stateless as possible

40. you want to be careful
where you store stuff

41. 💽
💽
💽
💽
💽
💽
💽
💽
💽

42. 💽
💽
💽
💽
💽Filters
}
Services

44. track page
<track <track
metadata>stats>
<user
metadata>
💽 💽 💽
<content
filtering>
<monetisation
rules>

45. turns out functional
principles correlate
with good architecture

46. we still lack material
around engineering in
functional languages, but
the basic principles already
offer a good framework

47. tl;dr
small functions +
small combinators all
the way up

48. Q&A