F# and Scala are now in more direct competition after Microsoft open sourced F# and .NET Core. They have many similarities but a distinctly different feel. It was hard for me to put my finger on the difference. This blog post investigates their design decisions and use cases. Starting with a brief overview of F# and Scala.
F# (F Sharp)
F# is a mature, open source, cross-platform, functional-first programming language. It was created by Don Syme in 2005 as a port of the OCaml language to .NET.
Core: Strict, strong, inferred, hybrid
Popularity: Some use in industry and backed by Microsoft
Complexity: Easy to learn, but part of a big ecosystem
Maturity: It is 14 years old and part of the .NET, so quite mature
Tooling: Very good, both .NET based and F# specialized
Cross platform: with Mono and .NET Core and JavaScript
IDE: Visual Studio, VS Code
Scala
Scala combines object-oriented and functional programming in one concise, high-level language. It was created by Martin Odersky in 2004.
Core: Strict and lazy, nominal and structural, hybrid, implicit for IoC
Popularity: Very popular. No 13 on Red Monk June 2019 list. Spark is written in Scala
Complexity: It is a quite complex language, but it is easy to get started with
Maturity: Very stable. Run on JVM, well integrated with JVM ecosystem
Tooling: Great build tool and package managers
Cross platform: JVM and JS. Also early work on native / LLVM version
IDE: IntelliJ, VS Code, Eclipse
Microsoft Open Source Bet
Choosing between F# and Scala used to be pretty easy. If you were doing Windows development you would use F# if you were on an open source stack you would choose Scala.
In 2012 Microsoft open sourced F# and started porting it to Mono a Microsoft supported cross platform version of the CLR. That was cool but not something I would run production code on.
However, in September 2019 Microsoft released .NET Core 3 an open source cross platform version of a big part of their SDK and also a first release of Apache Spark for .NET.
After this .NET and F# are serious contenders for being parts of an open source stack.
Relation to Java and C#
You might think that F# is just the .NET version of Scala and moving from Java to Scala is similar to moving from C# to F#. This is not the case.
Java was a small and simple language with a lot of innovations but some annoying problems. A big part of Scala appeal was that it was a better Java with more features.
C# was also made to be a better Java. It fixed some of the flaws in the original Java, e.g. auto boxing of integers, generics and lambdas. C# is a great but also very big language. F# is more like a leaner version of C#, with less features.
Collection Libraries
Scala has made a big effort to make a full set of immutable and mutable Scala collections and make different Java collections look like they are native Scala collections.
I tried Scala in 2007 it had generic and could use Java generic, but either you were programming in Java or in Scala. This took a long time to get this right and cost was that the standard library code was very complicated. This is not really a problem for the user who won't see this.
Generally, F# is using a few collections: Arrays, lists, seq, set and map.
It is a bit messy to bridge the OCaml and the C# heritage, especially map / dictionaries are clumsy.
Monads
A monad is an important part of functional programming. It is a general principle to express a sequence of operations and work on a lot of different data types:
List, Seq, Future, Option.
Scala monadic for comprehension
Scala's version is syntactic sugar over flatMap(). It is more flexible, it can mix two types of monads say List and Option. Scala's monad will also return the same type as the input type.
F# monadic for comprehension
F#'s version of the monad computation is called computational expression. It has more features than Scala's.
Classes
Classes are considered an anti-pattern by functional programming purist. Some problems with classes are:
A class maintains state
A class creates a custom language instead of reuse of operations
Inheritance is crating tight coupling
Scala has a very sophisticated type and class system and classes are central part of Scala.
F# has support for classes, but it is billing itself as object programming not object-oriented programming. It is made to use classes defined in C#, but will often define objects with methods without a full class definition.
I like that F# is exploring more lightweight alternatives but classes are easy to create and feels natural to use.
Type Classes, IoC, DI, Type Providers
Type class is a powerful abstraction that can make a third party class implement an interface. It plays an important role in Scala and are implemented with helper classes created by implicit.
Inversion of control and dependency injection, are first class in Scala with implicit. This is an advanced but very useful feature of Scala.
Scala has developed these ideas to the point where you can do logic style programming with implicit. A lot of the more sophisticated category theory like programming is based on this.
You can do inversion of control and dependency injection in F# using libraries.
F# has type providers that on the fly generated typed access to a lot of different data sources, e.g. a table on a webpage.
Design Decisions
F# is white space indentation-based language. Scala is a curly bracket language.
F# is a lightweight language with strong compose-ability.
Scala has sophisticated type system, including type classes, this unifies a lot of different classes and facilitating reuse.
F# program feel a little more like a loose collection of definitions while Scala program feels more like a carefully packaged system.
Conclusion
F# and Scala have a lot in common. To a large extent you would still chose F# or Scala based on your platform choice.
Both languages are very well suited for building back end programs that can interact with a universe of libraries written in C# or Java.
Scala has more momentum and a better niche. It is still having status as a better Java. Even after Java added some of the best constructs from Scala. Spark has made Scala a corner stone of data engineering.
F# is more lightweight than Scala. This makes it great for data exploration and great for building small scripts. It still remains to be seen how well supported Spark is going to be for .NET.
From a language evolution perspective, the object functional hybrid has been very successful. F# and Scala's different emphasis has produced different language from similar goals. I am very happy that we now can compare their design decisions on merit not just compare .NET and Java ecosystems.
This article is an elaboration on my last blog post Typed Functional Languages 2019. Disclaimer I have been a happy Scala user for years, and only occasionally use F#.
This post is a brief status of the state of typed functional languages in late 2019.
Typed
functional languages like Clean, Haskell and OCaml were developed within
academia in the 1990s. Around 2010, languages like F# and Scala were
gaining some acceptance in industry. Today there are many great typed functional languages, several used in industry. I will give a brief side by side introduction to the following languages:
F#
F*
Haskell
OCaml
Rust
Scala
TypeStript
Concepts from typed functional languages have also spread into object oriented languages like C++, C# and Java. The distinction between OOP and typed functional is fluid, so that list might seem a little arbitrary.
These languages are best of breed so the point of this article is not to compare them by merits, but to explore what language to use for what purpose. Follow up post covers F# vs Scala.
F# (F Sharp)
F# is a mature, open source, cross-platform, functional-first programming language.
Core: Strict, strong, inferred, hybrid
Popularity: Some use in industry and backed by Microsoft
Complexity: Easy to learn, but part of a big ecosystem
Maturity: It is 14 years old and part of the .NET, so quite mature
Tooling: Very good
Cross platform: with Mono and .NET Core and JavaScript
IDE: Visual Studio, VS Code
Strengths
Simple, open source, cross platform with good integration with the whole .NET universe.
Well suited for backend programming, Azure, web-serving and finance.
Type providers give easy typed access to a lot of different data sources.
Issues
IDE, GUI programming and LINQ is not as well developed as for C#.
F* (F Star)
F* is a general-purpose functional programming language with effects aimed at program verification.
Maturity: Several researchers are working on it, but it is not used a lot
Tooling: Not super polished, but build on top of good tooling in OCaml and F#
Cross platform: OCaml, F#, C, WASM and ASM
IDE: Support for Emacs
Strengths
F*
has implemented a lot of powerful and interesting ideas that you can
try and actually use. It is a very well developed dependently typed
language.
Good for validating highly sensitive security programs, encryption protocols.
Issues
There is little adaptation and it has not stood the test of time yet.
Haskell
Haskell is an advanced purely-functional programming language.
Core: Lazy, pure, effect tracking using effect monads
Popularity: Prestigious research language with some industry adoption. Number 19 on Red Monk June 2019 list
Complexity: Very complex language
Maturity: It has been around for 30 years, used in industry, used for research
Tooling: New build tool Stack is quite nice
Cross platform: Runs on OS X, Linux and Windows
IDE: Several decent plugins for: VSCode, emacs, Spacemacs, SpaceVim and IntelliJ
Strengths
Very influential research language, test bed for a lot of language research and development.
It has been optimized for years and has some use in industry.
Type classes are built into the language so you can reuse code very broadly.
Aesthetically pleasing if you love math or category theory.
Issues
It is a very complex language and tracking effect in non pure computations is quite hard.
It has some use in industry, but is still very much a research language.
OCaml
OCaml is a strictly evaluated functional language with some imperative features.
Core: Strict, strong, inferred, hybrid
Popularity: Used as teaching language and by a few big companies
Complexity: It is a simple language to learn
Maturity: It has been around for 20 years and is used in industry so quite mature
Tooling: Recently it got a good build tool and package manager
Cross platform: Runs on a lot of different operating system, hardware
IDE: Language server with good integration with Eclipse, VS Code, Emacs and Vim
Strengths
Great REBL, very fast compiler, makes it suited for tooling. Facebook using it for web tooling.
Popular in theorem provers.
Issues
Concurrency is not great.
Rust
TM Mozilla
Rust is a multi-paradigm system programming language focused on safety, especially safe concurrency.
Core: Inferred, linear type, nominal, static, strict, strong, build around concurrency
Popularity: Quite popular and raising. No 21 on Red Monk June 2019 list
Complexity: Somewhat complex language
Maturity: Pretty new language, but used in Firefox and by AWS Firecracker
Tooling: Excellent build tool and package manager
Cross platform: Work on many different OSs
IDE: Good VS Code support
Strengths
Rust
is a combination of ideas from OCaml, Haskell, C++, linear types and
low level imperative control. It is very fast and well suited for system programming
and secure programming. There is no garbage collector and no runtime, this makes Rust great for writing libraries and WebAssembly. Rust has started to make inroads in cloud infrastructure.
Issues
Getting rid of the garbage collector makes the language harder to understand and program in.
It is a pretty new language, still developing, and there are fewer libraries.
Scala
Scala combines object-oriented and functional programming in one concise, high-level language.
Core: Strict and lazy, nominal and structural, hybrid, implicits for IoC
Popularity: Very popular. No 13 on Red Monk June 2019 list. Spark is written in Scala
Complexity: It is a quite complex language, but it is easy to get started with
Maturity: Very stable. Run on JVM, well integrated with JVM ecosystem
Tooling: Great build tool and package managers
Cross platform: JVM and JS. Also early work on native / LLVM version
IDE: IntelliJ, VS Code, Eclipse
Strengths
Back-end programming, data engineering, web serving.
It
is a great all around language. A lot of work has gone into creating
language constructs that makes Scala work well with Java libraries. In
Scala 2.0 this was not the case.
Spark is a cornerstone in data engineering.
Issues
There is quite a lot of complexity: Implicits, macros, type classes / ad hoc polymorphism is possible but it takes some work.
Not super easy to set up a small project.
GUI programming support is not that great.
TypeStript
TypeScript brings you optional static type-checking along with the latest ECMAScript features.
Core: Gradually typed, structural, many new sophisticated type constructs, data language
Popularity: Very popular. No 10 on Red Monk June 2019 list
Complexity: Pretty complex
Maturity: A lot of money has gone into JavaScript, it is improving but it still feels wonky
Tooling: NPM. There are a lot of tools in the Nodes ecosystem, too many
Cross platform: Runs in every browser and on Node.js
IDE: Amazing support in VS Code
Strengths
Typescript makes big JavaScript codebases a lot more robust.
It is really easy to process semi structured data in json.
Starting to see some use of TS in machine learning e.g. with TensorFlow.js.
Issues
The
JavaScript modules seem simple like in Python or Java, but there are
many different module systems and it is pretty complicated. There are a
lot of NPM packages but it still feels less mature. Getting setup with a
small project with unit tests is more work than it should be.
Concurrency: Async await dramatically simplified call back style of programming, but still not great.
Golden Age Programming Languages
For many years I was puzzled about why language evolution
seems to favor bloated and hacky development, while ignoring more principled computer science ideas. Twenty years ago I got very
excited to read about these new functional languages with strong
types. Unfortunately they were only popular in academia.
We are finally living in the golden age of programming languages. It just took some time. Development is moving quickly now and not slowing down.
Apologies in advance for omissions, outdated information and other mistakes.
There are at least 5 popular ways to install Python on OS X / Mac.
OS X default Python installation, currently Python 2.7.10
Use brew install python
Use brew install pyenv
Anaconda
Python pkg installer from python.org
I have used all of these distributions. They are all high quality and easy to install, but you run into conflicts later. You think that you are installing a library into one Python distribution but it get installed into another distribution so you cannot use it. This causes many frustrating errors.
Every time I install a Mac I have to decide what is the best Python distribution for my use case and there is no simple choice. It has been hard to find good documentation on the trade offs between the Python distributions. I have a compiled a short list of benefits and issues and where I think that the different distribution make sense.
OS X Default Python Installation
You don't have to install anything
If you only want to have one Python distribution this will be the one
It is a pretty recent version of Python 2.7 currently 2.7.10
Issue
Not supporting Python 3 which is now in common use
If you only are doing light Python 2 scripting this is probably the easiest choice.
brew install python
Brew is the de facto package manager on OS X so most software is installed with brew
Very up to date versions of Python 2 and Python 3
Works well when you want to install many Python libraries
Python 3 is the default, but brew install python@2 will install Python 2
It takes precedence over the OS X default Python by being in earlier on PATH env
Brew will probably install Python as a requirement for other packages so you get it whether you want it or not
Good for more demanding programming and installing libraries.
brew install pyenv
pyenv is a tool to have different versions of Python to chose from
It has no dependencies on either Python 2 or 3 but manipulate PATH env
It can co exists with brew install python
It can also work with virtual environments
Issues
You have to install other libraries say gzip before you can install this
Python is compiled from scratch and you easily run into compile problems
Good if you are a serious programmer who need many different versions of Python possibly with conflicting versions of libraries.
Use Anaconda
Anaconda installs different version of Python with high quality curated packages specialized for data science libraries
It can be hard to get data science libraries working with manual installs
It is a whole ecosystem of software
Includes good Python GUI called Spyder
Great support for Jupyter notebook
Has good built in support for Python's virtual environments
Issue
It is a pretty heavy distribution taking up around 3GB
I usually need the data science libraries so I install Anaconda but also end up with the brew version of Python.
Machine learning and data technology are changing fast and the big cloud providers compete with new offerings. This blog is a short introduction to what this looks like in 2019. It is focused on cloud providers Amazon Web Services, Microsoft Azure and Google Compute Platform.
A few things I will discuss -
Most data in an organization can be put into a data lake to query and combine
We now have very powerful, user friendly open source ML libraries
We have NLP and computer vision REST APIs from cloud providers
Let me start with a little history of both ML and data.
History of Machine Learning Libraries
Simplified timeline for languages, libs and APIs
1960 Lisp since ML was a small part of A.I.
1986 C++ neural network software on a floppy disk in the back of book
1997 Open source Java ML like WEKA, good but hard to integrate with you data and code
2010 Modern Python open source libs NumPy, Pandas, Scikit-learn easy to use and integrate
2015 Spark ML, attempts to make a fast ML pipeline as easy to use as Scikit-learn
2017 Deeplearning open source libraries Tensorflow, Keras and PyTorch
2017 Cloud Vision API and Natural Language API
We now have several strong contenders to build or buy production quality ML functionality.
Convergence of Data
Recently I talked with a DBA and was surprised how much the DBA profession has changed. He told me big organizations used to have a big database such as Oracle, SQL Server, Sybase or DB2 and a lot of data stored in different files.
Now maintaining the data lake is one of his main responsibilities. The data lake is a system that allows you to store log files, structured, semi structured and unstructured data files in cheap cloud blob storage and still query and join it with SQL.
He was also in charge of an Oracle database and a few open source databases running, MySQL and Postgres and a MongoDB.
Data Lake Fundamentals
Uniform data that can be joined is very powerful. Here are a few underlying technologies that makes this possible.
In 2004 Google released the famous MapReduce paper, describing how you can do distributed computation using functional programming operations. The idea is that you send your computation to were you data is.
In 2010 Hadoop was released. Hadoop is an open source Java implementation of MapReduce. It turned out of be very hard to program in. Two new technologies made it easier to program MapReduce: Hive and Spark.
Hive
A lot of MapReduce job was just queries on data. Hive is a tool that lets you write these queries as simple SQL. Hive will translate the SQL to a MapReduce job, all you had to do was to add schemas definition describing the files with your data.
Spark
With Spark you can write more complicated MapReduce jobs. Spark is written in Scala which is a natural language to write MapReduce in. Spark is often use to ingest data into the data lake.
All the cloud providers have great support for Spark, AWS has EMR, Azure has HDInsight and GCP has Dataproc.
Combining Data Lake and Normal Database
Combine a data lake with a RDBMS is not easy. There are several approaches.
You can copy over all your relational data to your data lake every day. It takes work to build and operate, but when it is working everything is unified and it is easy to do any kind of analytic queries. Some data lake products have specialized functionality to do this in an easier way, see below.
Data Lake on AWS, Azure and GCP
AWS, Azure and GCP have different data lake solution.
AWS Redshift and Redshift Spectrum
AWS Redshift is a proprietary columnar database build on Postgres 8.
Redshift Spectrum is a query engine that can read files from S3 in these formats: avro, csv, json, parquet, orc and txt and treat them as database tables. First you have to make a Hive table definition in Glue Data Catalog.
Azure Data Lake Store
Microsoft data lake is called Azure Data Lake Storage works with blob storage and is compliant with HDFS the Hadoop distributed file system.
U-SQL is a query tool to combine Azure SQL DB and your data lake.
Google BigQuery
GCP's data lake is called BigQuery works with blob storage and stores native data in proprietary columnar format called Capacitor.
BigQuery is very fast and has a nice web GUI for SQL queries. It is very easy to get started with, since it can do schema auto-detection of your blob data, unlike Hive that needs a table definition before it can process the data.
New Cloud ML APIs
In 2017 Google released their Cloud Vision API and Natural Language API. I heard from several data scientists that instead of building their own computer vision system, named entity or sentiment analysis system, they just use APIs.
It feels like cheating, but ML APIs are here to stay.
When you should build your own ML models and when you you use the APIs?
If you have a hard problem in computer vision or NLP that is not essential to your goal, then using API seems like a good idea. Here are a few reasons why it can be problematic:
It is not free
Sometimes it works badly
There are privacy and compliance issues
Are you helping train a model that your competitor is going to use next
Speed e.g. if you are doing live computer vision
Working with ML APIs
If you decide to use the ML API your job will be quite different than if you chose to build and train your own models. Your challenges will be:
Transparency of data
Evolution or your data sources
Transparency of ML models
ML model evolution
QA of ML models
Interaction between ML models
The 2014 book Linked Data is a great source of techniques to use for data transparency and evolution. It describes linked data as transparent data with enough meta data that it can be linked from other data sources. It is advocating using self describing data technologies like RDF and SPARQL.
The response to a Cloud Vision query is nested and complex. I think that schemas or a gradual type system, similar to TypeScript's could give stability when working with semi structured evolving data. Some of the Google's Node API wrappers are already written in TypeScript and so they already have the type definitions.
Cloud ML Developments
There are a few minor cloud ML developments that deserve a mention.
I
find Jupyter notebooks a natural place to combine code, data and
presentation. One problem I have had when programming on cloud is that
there are so many places where you can put programming logic.
Model Deployment
Model deployment has traditionally received less attention than other part of the ML pipeline. Azure and GCP
have done a great job of optimizing model deployment into something that can be done
in few line of code. It will train a model, save it in a bucket and
spin up a serverless function that serves up the model as a REST
call.
Auto ML
ML tools that help find best ML models there are now available for GCP, AutoML, Amazon SageMaker and Azure, Automated Machine Learning.
These will help you to chose the best model and tune hyper parameters. This seems like a natural expansion of current ML techniques. It does involve using cloud specific
libraries.
Transfer Learning
If you have an image categorization task, you could build a classifier
from scratch by training a deep convolutional neural network. This can take a long time. With
transfer learning you will start with a trained CNN for example
Inception or ResNet network. It should be trained on data that
is similar to the data that you will be processing.
You train your classifier
model by taking the second to last layer in the trained CNN as input. This is much less
work than staring to build a 100 layered CNN from scratch. While transfer learning is not specific to the cloud it is easy to do it on the cloud where you have easy access to the per-trained models.
AWS vs Azure vs GCP
The cloud service market is projected to be worth $200 billion in 2019. There is a healthy competition despite AWS head start. Let me end with a very brief general comparison.
AWS was the first cloud service. It started in 2006 and has biggest market share. It is very mature offering both Linux and Windows VMs. They continue to innovate, but the number of services they have are a little overwhelming.
Azure is a very slick experience. Microsoft has embraced open source, offering both Linux and Windows VMs. It has great integration with the Microsoft and Windows ecosystem: SQL Server, .net, C#, F#, Office 365 and SharePoint.
Google Cloud Platform is polished. Is easy to get started with BigQuery and do data exploration in it. GCP has hosted Apache Airflow workflow system. GCP shines with machine learning offering great ML, vision and NLP APIs.
This post contains a short introduction to these technologies and my best practices for which cloud technology to use in different situations.
Virtualization Technologies
Here is a quick history and brief summary of difference.
A Highly Abbreviated Virtualization History
2006 Amazon released EC2 a cloud VM you could spin up fast on demand.
2013 Docker. Describes everything VM needs in a small file, used to build lightweight image.
2014 Google open sourced Kubernetes a system to run Docker images together.
2015 Serverless functions / lambdas. Code independent of VM.
2018 Firecracker. A microVM with 125ms start time used for AWS lambda and Fargate.
VM vs Containers vs Lambdas
Main difference
VM has a full operating system that run on a hypervisor.
Docker / Kubernetes runs as layers on top of a guest Linux OS.
Lambda serverless function running in a minimal VM with a good sandbox separation.
There has been a development from heavyweight VM to super lightweight VM.
Recently AWS lambdas started running in a microVM called Firecracker that can spin up in around 125ms with only 5MB memory overhead.
Best Practices for Virtualization
When should you use full VMs, Docker, Kubernetes or lambdas?
When Should You Use Serverless / Lambdas
There are many names for the same concept: AWS Lambdas, Azure Functions and Cloud Functions on GCP.
Good use cases for serverless functions
RESTful call with no state.
RESTful call that only interact with a database.
Database maintenance tasks.
Logging operation.
On Azure and GCP they are used to server up machine learning models when they are trained.
Lambdas / serverless functions don't need to have a VM running and they scales from no use to massive use. They are very cheap and flexible.
Serverless functions have been marketed as the future of cloud computing and are clearly going to play a big role.
When Should You Use VMs or Kubernetes
Good use cases for VM or Kubernetes
Your program has to load a lot of data on startup.
Web application with a lot of functionality that are naturally grouped together.
Your program has to do a long sequence of operations.
You could use lambdas for a long sequence of operations. You would just push messages along from one lambda to the next. This is similar to Erlang or Akka actors model. I find that this gives you little control and it makes error handling hard.
When Should You Use Kubernetes
Good use cases for Kubernetes
If you are running a lot of daily tasks from some scheduling system, say Airflow or Luigi, it is faster to start them in Kubernetes than to spin up a new full VM instance for each.
You find a Docker image with a program that does what you need.
If you have several programs that needs to run together one program might need to be installed on Debian another on Ubuntu and one on CentOS. Kubernetes handles this very well. You can actually deploy all 3 containers to the same Kubernetes pod that share a hard disk.
When Should You Use a Full VM
There is overhead with setting up Kubernetes. You also need to have a Kubernetes master node running which cost money. So sometimes the simplest solution is to use a full VM.
Should You Run Docker Inside a VM?
The advantage of Docker is that
you package up the Docker image and you can test it locally running in
the same way as it will run on the VM.
The disadvantages are
that you still have an extra step of creating the Docker file, build and
deploy the Docker image to DockerHub or some other repository. You have
to install Docker on your VM. There can be some performance hit by an extra level of virtualization.
I use Docker on my laptop and on Kubernetes but I usually do not use Docker in full VM.
Terraform
Terraform is a
new tool for infrastructure as code, released by Hashicorp in 2014. It is a small
functional programming language focused of configuration.
In your Terraform program you define the state you want to put your cloud
system in. You run these commands from command line in the directory where you
have your program:
terraform init terraform plan terraform apply
This will start a VM or create your infrastructure for you, and Terraform stores the state of your system in what is called a Terraform state file. This state file can be stored locally
or shared in a cloud bucket.
When you want to make changes to your cloud infrastructure you change your Terraform program and you run another:
terraform plan terraform apply
Terraform is declarative it
will compare the state of your system with the state you want it to be
in find out what changes it need to make.
I have used Terraform a lot with AWS to spin up EC2 and EMR clusters, but also to create IAM roles, policies, VPNs and security groups.
Terraform has a concept called a module.
It enables code reuse. It is an advanced topic, but I find it
absolutely essential in writing maintainable code. Especially if you
have multiple environment say dev, staging and prod.
Terraform Version Problem
A problem that I experienced
several times is that one team member accidentally updates Terraform to the current
version, the next time somebody runs an update script they get
this message:
Terraform doesn't allow running any operations against a state that was written by a future Terraform version. The state is reporting it is written by Terraform '0.11.8'.
The
good news is that the Terraform state file is written in json and is
somewhat robust. So you can download the state file and change the
version number back to the old version and there is a good chance that
it will work. Still this is not the kind of error message that you want
to see when you are doing a prod release.
Issues with Terraform
Terraform is a nice declarative framework, but Terraform state file is stored either locally or in cloud bucket.
Local state file makes is hard for a team to collaborate. They will get a different state file.
Cloud storage allows you to collaborate but now you are still dealing with a shared mutable state that is susceptible to the version problem mentioned above.
I used Terraform to create a lambda function with IAM roles, policies and code. When I tried to update lambda to newer version. Terraform did not sense the changed program files so I had to destroy everything and recreate it.
Using Terraform is often safter than
making manual changes in a web console, but I would hesitate to
update a database using Terraform.
There is an enterprise version of Terraform that might alleviate some of these problems, but I have only used the open source version.
Kubernetes
Kubernetes is container orchestration framework. It was open sources by Google in 2014 and it works very well on GCP, Google Compute Platform. Many cloud providers has Kubernetes offerings e.g. AWS, Azure and DigitalOcean.
Kubernetes uses declarative cloud definition. I a yaml file you define how many instances of a web server do you want to run. If a web server crashes Kumbernetes will start a new one without intervention.
Kubernetes was one of the most active developed open source framework in 2018. It feels mature.
The state is part of the Kubernetes system not a file living locally or in an S3 bucket.
Issues with Kubernetes
It is quite complicated to set Kubernetes up in a private cloud. You need highly dedicated DevOps staff to do this. A lot of things can and do go bad. I have many memories of DNS server going missing and the block storage / hard disks disappearing after running programs for hours.
Terraform or Kubernetes
When should you use Terraform and when should you use Kubernetes?
They are both declarative tools that you can use to start programs and define things like security groups in your cloud environment.
Terraform is a good option if you want to define your infrastructure and spin up VMs, EMR clusters etc. It is not AWS specific but works very well with AWS.
Kubernetes is a good option if you chose to use containers and you are working on a cloud that has good Kubernetes support. AWS has a competing technology Fargate and AWS integration with Kubernetes is less mature.
You can divide functional programming languages into 2 groups: Static and dynamic.
Dynamic functional languages: Clojure, Common Lisp, Racket and Scheme. They have few types often only known at run time.
Statically typed functional languages: F#, ML, Haskell, Idris and Scala. They have advanced types that are known at compile time.
Functional programming languages have similarities but are very different from one another. Some are quite hard to learn. What should you pick?
Slogans
Here are two extreme positions in functional programming reduced to slogans:
Lisp: Everything is Data
Lisp has a great story: Everything is data.
Lisp is homoiconic. There is one datatype: The S-expression in Lisp or an EDN in Clojure. This encodes:
Records
List
Map
Stream
Programs
Everything is unified and first class. This makes Lisp very elastic and adaptable to handle open ended problems like AI. It also leaves a lot of room for mistakes when dealing with complex data structures since everything sticks to everything.
Haskell: Everything is a Computation
Computation sounds like an equally strong unifying foundation. This is a strong counter argument to Lisp.
Haskell turns the world into mathematics by giving strong guarantees and the ability to reason about programs. It is fast, elegant and remarkably safe.
Most of the world is messy so programming in Haskell is both an art and a science.
A Type System is a Must For Production Code
In my experience:
A complex production server application demands a static type system for stability
The type system is doing at least half of my work when I work alone and prevents total anarchy when working in teams.
History of Static and Dynamic Type System
Like many other programmers, I have gone back and forth between preferring static and dynamic languages several times.
C++ and Java
I started using and loving the sophisticated statically typed object oriented languages: C++ and Java.
Why would anybody want to program in Basic?
Perl, Python and Ruby
At some point I had to make a small script for text processing. I realized that dynamically typed languages Perl, Python and Ruby are much simpler and faster to work with.
They borrow a lot of ideas form functional languages and saves you a lot of boiler plate. Programming became fun again.
I never wanted to go back.
F#, Scala and Haskell
Then came the raise of F#, Scala and Haskell.
I thought that you got the best of both worlds:
There are few visible types due to type inference
They look like dynamic language
Still you get strong safety from the invisible type system
They are fast
My stability concern for production application ruled out dynamic languages. The future belongs to F#, Scala and Haskell.
Living with Static Types
For the last 4 years I
have been happy programming in Scala. It really improved my productivity.
I mainly deal with stable data types. Each data structure get immutable case class and they flow beautifully and it even works well in a concurrent system.
I am a little concerned about the amount of black magic going on at the type level in Scala and Haskell.
Web, Scripting and Data Exploration
Some fields continue to be dominated by dynamic languages:
Data exploration
Data science
Scripting
Web front end work in JavaScript, PHP and Ruby
I do data mining in Scala and can quickly add a new data source with unit tests to a stable functional reactive ingestion pipeline, but during a hackaton I had to explore a lot of different data sources and my normal startup time was too slow for the deadline.
Dynamic languages have an edge for small systems.
Problems Using Scala for NLP
Idiomatic Scala has been great for NLP.
I had to extract all the hidden and visible
information on a html page and had to parse the DOM tree for
everything: elements, attributes, code and json data.
The DOM tree is
similar to an S-expression.
The best idiomatic Scala
representation I could find was Play JSON. The DOM tree and Play JSON
are not that similar and processing json in dynamic languages is more
natural than in strongly typed languages.
Dynamic languages have an edge for some complex systems.
Lisp Revisited
I used Lisp in school. It was the cool AI language and my first functional
language. I loved it, it blew my mind but I had a very shallow
understanding.
Impressions from revisiting Lisp after using statically typed functional languages:
Lisp is small and elegant
Easy learning curve
Great at traversing dynamic data
Well suited for exploration
A lot of the principles of statically typed functional programming translate directly
I still think in Scala like types making my Clojure code better organized
Macros feel natural unlike in C++ and Scala
Lisp is really fluid combining in so many crazy ways
You lose a lot of safety
Going from Haskell to Clojure left me with the feeling I had
when moving from C++ to Python. You get a lot of value for less
effort.
Raise of the Gradual Type Systems
There has been slow movement towards gradual types. Here are a few place where they have popped up:
Ambrose Bonnaire-Sergeant on gradual typing in Clojure
The type systems in Typed Clojure and Typed Racket are pretty different than in Scala and Haskell. Generally weaker, but Typed Clojure and Typed Racket have union types that are only now investigated in Scala's experimental new type system Dotty.
These advances in gradual types make it possible to harden Lisp code to improve stability.
Data or Calculation
I was puzzled by the Lisp and Haskell slogans:
Everything is data
Everything is a calculation
It was a paradox. Which is a better foundation for computer science?
I could not easily dismiss either. For now I have accepted that we are stuck with both.
For a long time I suffered from the misunderstanding that F#, Scala
and Haskell are like dynamic languages, with the addition of speed and
safety. But they are fundamentally different.
Functional programming has moved from academia to industry in the last few years. It is theoretical with a steep learning curve. I have worked with strongly typed functional programming for 4 years. I took the normal progression, first Scala then Haskell and ended with category theory.
What practical results does functional programming give me?
I typically do data mining, NLP and back end programming. How does functional programming help me with NLP, AI and math?
Scala
Scala is a complex language that can take quite some time to learn. For a couple of years I was unsure if it really improved my productivity compared to Java or Python.
After 2 years my productivity in Scala went up. I find that Scala is an excellent choice for creating data mining pipelines because it is:
Before Scala I did NLP in Python. I used NLTK the Natural Language Toolkit for 3 years.
NLTK vs. ScalaNLP
NLTK
Easy to learn and very flexible
Gives you a lot of functionality out of the box
Very adaptable, handles a lot of different structured file formats
What I did not like about NLTK was:
It had a very inefficient representation of a text features as a Dictionary
The file format readers were not producing exactly matching structures and this did not get caught by the type system
You have to jump between Python, NumPy and C or Fortran for low level work
ScalaNLP
ScalaNLP merged different Scala numeric and NLP libraries. It is a very active parent project of Breeze and Eric.
ScalaNLP Breeze
Breeze is a full featured, fast numeric library that uses the type system to great effect.
Linear algebra
Probability Distribution
Regression algorithms
You can drop down to the bottom level without having to program in C or Fortran
ScalaNLP Eric
Eric is the natural language processing part of ScalaNLP. It has become a competitive NLP library with many algorithms for several human languages:
Reader for text corpora
Tokenizer
Sentence splitter
Part-of-speech tagger
Named entity recognition
Statistical parser
Video lecture by David Hall the Eric lead
Machine Learning in Scala
The most active open source Scala machine learning library is MLib which is part of the Spark project.
Spark now has data frames like R and Pandas.
It is easy to set up machine learning pipelines, do cross validation and optimization of hyper parameters.
I did text classification and set it up in Spark MLib in only 100 lines of code. The result had satisfactory accuracy.
AI Search Problem in Scala vs. in Lisp
I loved Lisp when I learned it at the university. You could do all these cool Artificial Intelligence tree search problems. For many years I suffered from Lisp envy.
Tree search works a little differently in Scala, let me illustrate by 2 examples.
Example 1: Simple Tree Search for Bird Flu
You have an input HTML page and parsed into a DOM tree. Look for the word bird and flu in a paragraph that is not part of the advertisement section.
I can visualize what a search tree for this would look like.
Example2: Realistic Bird Flu Medication Search
The problems I deal with at work are often more complex:
Given a list of medical websites, search for HTML pages with bird flu and doctor recommendations for medications to take. Then do a secondary web search to see if the doctors are credible.
Parts of Algorithm for Example 2
This is a composite search problem:
Search HTML pages for the words bird and flu close to each other in DOM structure
Search individual match to ensure this is not in advertisement section
Search for Dr names
Find what Dr name candidates could be matched up with the section about bird flu
Web search for Dr to determine popularity and credentials
Visualizing this as a tree search is hard for me.
Lazy Streams to the Rescue
Implementing solutions to the Example 2 bird flu medication problem takes:
Feature extractors
Machine learning on top of that
Correlation of a disease and a doctor
This lends itself well to using Scala's lazy streams. Scala makes it easy to use the lazy streams and the type system gives a lot of support, especially when plugging together various streams.
Outline of Lazy Streams Algorithm for Example 2
Stream of all web pages
Stream of tokenized trees
Steam of potential text matches e.g. avian influenza, H5N1
Filter Stream 3 if it is an advertisement part of the DOM tree, (no Dr Mom)
Stream of potential Dr text matches from Stream 2
Stream of good Dr names. Detected with machine learning
Merge Stream 3 and Stream 6 to get bird flu and doctor name combination
Web search stream for the doctor names from Stream 7 for ranking of result
AI Search Problem in Lisp
Tree search is Lisp's natural domain. Lisp could certainly handle Example 2 the more complex bird flu medication search. Even using a similar lazy stream algorithm.
Additionally, Lisp has the ability to do very advanced meta programming:
Rules that create other rules or work on multiple levels. Things I do not know how to do in Scala.
Lisp gives you a lot of power to handle open ended problems and it is great for knowledge representation. When you try to do the same in Scala you end up either writing Lisp or Prolog style code or using RDF or graph databases.
Some Scala Technical Details
Here are a few observations on working with Scala.
Scala's Low Rent Monads
Monads are a general way to compose functionality. They are a very important organizing principle in Scala. Except is not really monads it is just syntactic sugar.
You give us a map and a flatMap function and we don't ask any questions.
Due to the organization of the standard library and subtyping you can even combine an Option and a List, which should strictly not be possible. Still this give you a lot of power.
I do use Scala monads with no shame.
Akka and Concurrency
Scala's monads make it convenient to work with two concurrency constructs: Futures and Promises.
Akka is a library implementing an Erlang style actor model in Scala.
I have used Akka for years and it is a good framework to organize a lot of concurrent computation that requires communication.
The
type system does not help you with the creation of parent actors so you
are not sure that they exist. This makes it hard to write unit tests
for actors.
Akka is good but the whole Erlang actor idea is rather low level.
Scalaz and Cake Patterns
Scalaz is a very impressive library that implements big parts of Haskell’s standard library in Scala.
Scalaz’s monad typeclass is invariant, which fixes the violations allowed in the standard library.
Cake Patterns allows for recursive modules, which make dependency injection easier. This is used in the Scala compiler.
Both of these libraries got me into trouble as a beginner Scala programmer. I would not recommend them for beginners.
How do you determine if you should use this heavy artillery?
Once you feel that you are spending a lot of time repeating code due to insufficient abstraction you can consider it. Otherwise:
Keep It Simple.
Dependent Types and Category Theory in Scala
There are many new theoretical developments in Scala:
Dotty - a new compiler built on DOT a new type-theoretic foundation of Scala
Cats library - a simplified version of Scalaz implementing concepts from category theory
Shapeless library for dependent types. I am using this in my production code since Shapeless is used in Slick and Parboiled2
Haskell
Haskell is a research language from 1990. In 2008 its popularity started to rise. You can now find real jobs working in Haskell. Most publicized is that Facebook wrote their spam filter in Haskell.
Why is Haskell so Hard to Learn?
It took me around 2 years to learn to program in Haskell, which is exceptionally long. I have spoken to other people at Haskell meetups who have told me the same.
Mathematical Precision
Python effectively uses the Pareto principle: 20% of the features will give give you 80% of the functionality; Python has very few structures in the core language and reuses them.
Haskell uses many more constructs. E.g. exception handling can be done in many different ways each with small advantages. You can chose the optimal exception monad transformer that has least dependencies for your problem.
Cabal Hell and Stack
Haskell is a fast developing language with a very deep stack of interdependent libraries.
When I started programming in it, it was hard to set up even a simple project since you could not get the libraries to compile with versions that were compatible with each other.
The build system is called cabal, and this phenomenon is called Cabal Hell.
If you have been reading mailing list there are a lot of references to Cabal Hell.
The Haskell consulting company FPComplete first released Stackage a curated list of libraries that works together. In 2015 they went further and released Stack which is a system that installs different versions of Haskell to work with Stackage versions.
This has really made Haskell development easier.
Dependently Typed Constructs in Haskell
Dependently typed languages are the next step after Haskell. In
normal languages the type system and the objects of the language are
different systems. In dependently typed languages the objects and the
types inhabits the same space. This gives more safety and greater
flexibility but also makes it harder to program in.
The type checker has to be replaced with a theorem-prover.
You have to prove that the program is correct, and the proofs are part of the program and first order constructs.
Haskell has a lot of activities towards emulating dependently typed languages.
The next version of the Haskell compiler GHC 8 is making a big push for more uniform handling of types and kinds.
Practical Haskell
Haskell is a pioneering language and still introducing new ideas. It has clearly shown that it is production ready by being able to handle Facebook's spam filter.
Aesthetically I prefer terse programming and like to use Haskell for non work related programming.
There is a great Haskell community in New York City. Haskell feels like a subculture where Scala has now become the establishment. That said I do not feel Haskell envy when I program in Scala on a daily basis.
Learning Haskell is a little like running a marathon. You get in good mental shape.
Category Theory
Category theory is often called Abstract Nonsense both by practitioners and detractors.
It is a very abstract field of mathematics and its utility is pretty controversial.
It abstracts internal properties of objects away and instead looks at relations between objects.
Categories require very little structure and so there are categories everywhere. Many mathematical objects can be turned into categories in many different ways. This high level of abstraction makes it hard to learn.
There is a category Hask of Haskell types and functions.
Steve Awodey lecture series on category theory
Vector Spaces Described With a Few String Diagrams
To give a glimpse of the power of category theory: In this video lecture John Baez shows how you can express the axioms of finite dimensional vector spaces with a few string diagrams.
Video lecture by John Baez
With 2 more simple operations you can extend it to control theory.
Quest For a Solid Foundation of Mathematics
At the university I embarked on a long quest for a solid scientific foundation. Fist I studied chemistry and physics. Quantum physics drove me to studying mathematics for more clarity. For higher clarity and a solid foundation I studied mathematical logic.
I did not find clarity in mathematical logic. Instead I found:
Some random badly motivated axioms and inference rules
The Dirty Secret About the Foundation of Mathematics
My next stop was the normal foundation for modern mathematics: ZFC, Zermelo–Fraenkel set theory with the axiom of choice.
This was even less intuitive than logic. There were more non intuitive axioms. This was like learning computer science from a reference of x86 assembly: A big random mess. There were also an uncertain connection between the axioms of logic and the axioms set theory.
ZFC and first order logic makes 2 strong assumptions:
Law of Excluded Middle
Axiom of Choice
Law of Excluded Middle is saying that every mathematical sentence is either true or false. This is a very strong assumption that was not motivated at all. And it certainly does not extend to other sentences.
Constructive Mathematics / Intuitionistic Logic
There was actually a debate about what should be a foundation for mathematics at the beginning of the 20th century.
A competing foundation of mathematics was Brouwer's constructive mathematics. In order to prove something about a mathematical object you need to be able to construct it and via the Curry-Howard correspondence this is equivalent to writing a program constructing a particular type.
This was barely mentioned at the university. I had one professor who once briefly said that there was this other thing called intuitionistic logic, but it was so much harder to prove things in it, why should we bother.
Recently constructive mathematics have had a revival with Homotopy Type Theory. HoTT is based on category theory, type theory, homotopy theory and intuitionistic logic.
This holds a lot of promise and is another reason why category theory is practical for me.
Robert Harper's lectures on type theory end with an introduction to HoTT
Future of Intelligent Software
There are roughly 2 main approaches to artificial intelligence
Top down or symbolic techniques e.g. logic or Lisp
Bottom up or machine learning techniques e.g. neural networks
The symbolic approach was favored for a long time but did not deliver on its promise. Now machine learning is everywhere and has created many advances in modern software.
To
me it seems obvious that more intelligent software needs both. But
combining them has been an elusive goal since they are very different by
nature.
Databases created a revolution in data management. They reduce data
retrieval to simplified first order logic, you just write a logic
expression for what you want.
Dependently typed language is the level of abstraction where programs and logic merge.
I think that intelligent software of the future will be a combination of dependently typed languages and machine learning.
A promising approach is: Discovery of Bayesian network models from data. This finds causality in a form that can be combined with logic reasoning.
Conclusion
I invested a lot of time in statically typed functional languages and was not sure how much this would help me in my daily work. It helped a lot, especially with reuse and stability.
Scala has made it substantially easier to create production quality software.
MLib and ScalaNLP are 2 popular open source projects. They show me that Scala is a good environment for NLP and machine learning.
I am only starting to see an outline of category theory, dependently typed languages and HoTT. It looks like computer science and mathematics are not mainly done, but we still have some big changes ahead of us.
