Tuesday, July 24, 2018

Mastering Object-Oriented Python -- 2nd Edition

It's time to revise Mastering Object-Oriented Python. While the previous edition is solidly focused on Python3, it lacks some important features:
  • F-Strings
  • Type Hints
  • types.NamedTuple
  • Data Classes
So. There's some stuff to add. I don't think there's too much to take away. I plan to make some things a little more tidy. I will remove all references to Python2 and all references to how things used to be and why they're better now.

It will be several months before this is available. Stand by for updates.

The earliest drafts of this book date back to 2002. Seriously. I've been over this material a lot in the past 1.5 decades.

The nascent form of this book took me years (maybe 10 years) to accumulate. It covered everything: data structures, statements, built-in functions, classes, and a bunch of libraries. It was beyond merely ambitious and off into some void of "cover all the things." 

I was motivated by my undergrad CS text books on the foundations of computer science. The idea of putting the language features into a parallel structure with boolean algebra, set theory, and number theory was too cool for words. And -- lacking the necessary formal background -- it was something I'm not able to present very well.

While I wanted to cover all of Computer Science, acquisition editors were pointed out how crazy that idea was. A focus on the object-oriented features of Python was sufficient to sell a distinctive book. And they were absolutely right.

As I rework the outline for the 2nd edition, there are some other topics that crop up. These are not going to wind up in the book, but they're an implicit feature of the topics being covered.

CS Foundations and Python

One of the best of the introductory books (which came out after I graduated) was Structured Concurrent Programming With Operating Systems Applications. They presented a nested collection of sub-languages: SP/k. The organization of the nested subsets can be helpful for exposing programming incrementally. There are issues, and we'll look at them in detail below. Here's the collection of subsets from the original book (and related articles.)

  • SP/1 expressions and output. The print() function.
  • SP/2 variables, assignment, and the input() function.
  • SP/3 selection and repetition. The Python if and while constructs are the logical minimum, but the for statement makes more sense because it's so widely used.
  • SP/4 character strings. 
  • SP/5 arrays. Python lists, really.
  • SP/6 procedures. Python function definition.
  • SP/7 formatted input-output. f-strings for output, and regular expressions for parsing.
  • SP/8 records and files.
There are a lot of gaps between this list of subsets and modern programming languages. SP/k was explicitly based on subset of PL/I, saving the complexity of implementing special compilers. It also reflects the mid-70's state of the art.

What didn't age well is the implicit understanding that numbers are the only built-in data types. Strings are so magical they're isolated into two separate subsets: SP/4 and SP/7. Arrays are called out, but sets and dictionaries didn't exist in PL/I and aren't part of this nested sequence.

Also. And even more fundamental.

There's a bias toward "procedural" programming. The SP/k subsets expose the statements of the language. There are few data structures, and it seems the data structures require some statements before they're useful.

This leads to my restructuring of this. It doesn't apply to the Mastering OO Python book. It's something I use for Python bootcamp training.

  • py/1 expressions and output: int, float, numeric built-in functions, and the print() function.
  • py/2 variables, assignment, and the input() function.
  • py/3 strings, formatting, and various built-in string parsing methods.
  • py/4 tuples and multiple assignment. (Since tuples are immutable, they're more like strings than they are like lists.) And yes, this is kind of short.
  • py/5 if statements and try/except statements. These are the two fundamental "selection" statements. The raise statement is deferred until the functions section.
  • py/6 sets and the for statement.
  • py/7 lists.
  • py/8 dictionaries.
  • py/9 functions (avoiding higher-order functions, decorators, and generator functions.)
  • py/10 contexts, with, and file I/O.
  • py/11 classes and objects.
  • py/12 modules and packages.
The point here is to expose the data structures as the central theme of Python. Statements follow as needed to work with the data structures. 

Note that some topics -- like break, continue, and while -- are advanced parts of working with data structures.

The standard library? Not included. Perhaps should be. But. It's technically separate from the language and all of this can be done without any imports. We would then cover a bunch of standard library modules. The order includes math, random, re, collections, typing, and pathlib

Tuesday, July 17, 2018

Patient Crawling and Possible Phishing

Once every few months I get an email like this. What is it? Phishing?

I've finally looked into it, and learned two important lessons.

Here's the body of the email.
Hello there,
Your page http://www.itmaybeahack.com/homepage/iblog/C364310209/E20080407095503.html has some good references to cyber security so I wanted to get in touch with you. I've recently written an article The 6 Types Of Cyber Attacks To Protect Against In 2018 and was wondering if you thought my article could be a good addition to your page.
You can read my article right here: https://pagely.com/blog/cyber-attacks-in-2018/
I would like to hear your opinion on this article. Also, if you find it useful, please consider linking to it from your page I mentioned earlier. If you prefer you may republish the article. Let me know what you think.
Thank you very much,

The page they cited has three (3) external links. One is to actual cyber security content. Another now gets redirected to generic advertising, and the third (like the original blog post) is a decade old.

What does this mean?

Clearly, it means some bot found my page. One of the links was to something they're trying to SEO boost. (How do I know it's SEO? I don't. The email address is similar to an SEO boosting company, so it seems like that's what's going on here.)

I've been haphazard about responding to these because I'm a fundamentally charitable person.

Or I'm a total pushover to certain kinds of social engineering. You choose.

You see the appeal to my vanity in the email? They read my ancient content! Swoon!

The email looks personal. There's a name. Spelled consistently. With no digits in it. Someone read my content and reached out to me! I'm in love! Ah! Sweet Mystery of Life at last, I've found you!

The email makes me think -- somehow -- it's not a bot and there's a person involved. A person trying to make a buck selling content and advertising. I should help them, right? Amplify their signal and all?

What a chump I am! I should simply ignore these.

In the past, I have responded with a "Nope. That content is too old to do anything with. I should delete it but I'm too lazy." Once a bot found a link on live content, and I dutifully updated it. I now know any response is a mistake.

I checked out the page.ly site. It's a nice summary of cyber attacks. It seems to be a not-to-dangerous link to not-bad content. Except for the Unicode errors throughout the document. Like someone copied and pasted the original bytes -- intended for CP-1252 -- to a site explicitly using UTF-8.

That's not all.

The name on the email, and the author of the article don't match.  The email says "my article" but the article has a different author.

Red Flag.

After (finally) spending five minutes on this, I learned two things.

  • First: this is nonsense. It's some kind of phishing attack. Or some kind of SEO-boosting bot that doesn't check dates very well.
  • Second: I'm an easy mark when people appeal to my vanity. I need to stop responding, no matter how effusive the (inferred) praise I think I'm hearing.

Tuesday, July 10, 2018

10 common security gotchas in Python and how to avoid them

First, read this: 10 common security gotchas in Python and how to avoid them by Anthony Shaw

Of these, most are important, but not specific to Python at all. Only items 3, 4, 7, and 8 are pretty specific to Python. They talk about the assert statement, some timing vulnerabilities, and the bad idea of transmitting pickle files.

Item 5 is also specific to Python, but I quibble about it's relevance. It is at the very edge of "security." The PYTHONPATH environment variable is most definitely not "...one of the biggest security holes in Python." If the path is a security hole, then any code is a security hole. If we view code as a security hole, then the only truly secure system has no software.

(As someone who lived on a sailboat. I happen to subscribe the position that the only truly secure system has no software. Use line, shackles, and well-known knots if you want to stake your life on it. Use fancy electronics with software to make it simple and fun.)

Bad programming is the biggest security hole. Failure to prevent SQL injection. Failure to use CSRF tokens. Failure to properly handle credentials. These are security holes of epic proportions.

The PYTHONPATH cannot be changed through any kind of request handling. Even colossally dumb software that blindly uploads XML or JPEG files without vetting them won't change the PYTHONPATH.  You'd have to write code that changed sys.path. Or you'd have to write code that reset the os.environ and then started applications in the new environment. This is seriously bad code, and has nothing to do with Python.

Otherwise, the only way to change PYTHONPATH requires an Evil Super Genius who has your compromised credentials. Once your credentials are compromised anything is possible, including the setting the PATH environment variable, or deleting all the accounts, or rm -rf /. None of which is specific to Python.

Item 9 -- patching the system Python -- may be important, All OS's should have patches applied early and often. However. We strongly discourage our developers from using the system Python for anything. We always build environments. We always install our own Python 3 with our own packages. We generally ignore the system Python to the extent possible.

Item 7, though, is a huge deal. We use OAS (formerly known as swagger.) The old swagger.json end-point was -- clearly -- json. The new OAS 3, however, suggests the specifications be provided at  openapi.yaml. This week we're rolling out a cluster of microservices using our shiny new OAS 3 specifications. And we're using default yaml.load() instead of yaml.safe_load() as part of the contract hand-shake among the services. All internally-facing handshakes, but still unsafe with respect to a man-in-the-middle hacking our specifications.

While I can quibble about two of the ten items, the other eight are rock solid, and should be part of periodic in-house code reviews.

And number 7 is killer. 

Friday, June 22, 2018

Type Hinting Edge Case

Warning. I'm new to this. Yes, my book Functional Python Programming -- 2nd ed -- is full of type hints. But my examples are all (intentionally) relatively simple. There are edge cases that I do not pretend to understand.

Here's a fun one. Start here

This is a cool question.

Here's an essential clarification on what this structure is.

This is tricky and I think there are two reasons why it's hard.
1. We want to specify some details internal to instances of the np.array class.
2. We want to provide a size constraint, something that I don't think typing can do.

The size constraint may be handled by using Tuple, but it doesn't really fit in a general way. This three-tuple is Tuple[float, float, float]. You can see how that rapidly gets hideous for higher-dimension objects. You'd want Tuple[float*3], right?

The internal constraint, similarly, is challenging. However. An np.array() -- for the most part -- is a Sequence with extra features.

I have a suggestion.

1. A stubs/numpy.py file with this. I think this characterizes the array structure.

from typing import TypeVar, Sequence

_Base = TypeVar("_Base")

def array(*args: Sequence[_Base]) -> Sequence[_Base]: ...

2. Here's the target function.

import numpy as np
from typing import Sequence

Vector3 = Sequence[float]

def vec3(x: float, y: float, z: float) -> Vector3:
    return np.array((x, y, z))

This seems to capture part of the type definition. It doesn't capture the 3-ness of the vector.

Tuesday, June 12, 2018

Coping with a Spreadsheet Database

A common way to save persistent, important data is a spreadsheet. It provides a handy, potentially normalized store that's readily accessible with minimal tooling. It has a UI usable by people with a spectrum of skills.


There's a core conflict:
  • The advantages of spreadsheets-as-database are numerous. 
  • The disadvantage is the lack of any strict, formal control over the schema.
At the very best, the steward of the data has some discipline and they include column headers and assure they're used throughout the rows of data.

It goes downhill rapidly from that ideal.

Let's look at some scenarios. And. How to cope. And. Python to the Rescue.

Outliers, Special Cases, Anomalies, and other Irregularities

The whole point of a "normalized" view of the data is to identify a pattern, assign the lofty title of "Schema" to the pattern, and assure all of the data fits the schema. In rare cases, all of the data fits a simple schema. These cases are so rare they only exist in examples of SQL code in tutorials.

A far more common case is to have several subtypes which are so similar that optional attributes (or "nullable columns" in SQL parlance) allow one schema description to encompass all of the cases. If you're a JSON Schema person, this is the "OneOf" or "AnyOf" type definition.

Some folks will try argue that optional attributes don't always mean that there are several subtypes. They'll ramble on for a while and eventually land on "state change" as a reason for optional attributes. The distinct states are distinct subtypes. Read up on the State design pattern for OO programming. Optional attributes is the definition of subtype.

The hoped-for simple case is a superclass extended by subclasses used to add new attributes. In this case, they're all polymorphic with respect to the superclass. In a spreadsheet page, the column names reflect the union of all of the various attributes. There are two minor variants in the way people use this:

  • An attribute value is a discriminator among the subtypes. We like this in SQL processing because it's fast. It also allows for some validation of the discriminator value and the pattern of attributes present vs. attributes omitted. Of course, the pattern of empty cells may disagree with the discriminator value provided.
  • The pattern of attributes provided versus omitted is used to identify the subtype. This is a more reliable way to detect subtypes. There can, of course, be problems here with values provided accidentally, or omitted accidentally.
The less desirable case is disjoint classes with few common attributes. Worse, the common attributes are not part of the problem domain, but are things that feel databasey, like made-up surrogate keys. There's an "ID" in column A or some other such implementation detail. Some of the rows use column A and columns B to G. The other rows use column A and columns H to L. The only common attributes are the surrogate keys, perhaps mixed with foreign key references to rows in other spreadsheet tables or pages.)

This is a collection of disjoint types, slapped together for no good reason. SQL folks like to call it "multiple master-detail relationships". The master record has children of multiple types. In some cases, the only thing the children have in common is the foreign key relationship with the parent. If you want a concrete example, think of customer contact information: multiple email addresses, multiple phone numbers. The two contacts have nothing in common except belonging to one customer. 

These don't belong in a single spreadsheet table. But. There they are. Our code must disentangle the subtypes.


A lot of spreadsheet data is a two-dimensional grid. Budgets, for example, might have categories down the page and months across the page. 

This is handy for visualization. But. It's not the right way to process the data at all. 

This extends, of course, to higher orders. Each tab of a spreadsheet may be a dimension of visualization. There may be groups of tabs with a complex naming convention to include multiple dimensions into tab names. Rows may have multiple-part names, or use bullets and indentation to show a hierarchy.

All of these techniques are ways to provide a number of dimensions around a fact that's crammed into a cell. The budget amount is the fact. The category and the month information are the two dimensions of that cell. In many cases, Star-Schema techniques are helpful for understanding the underlying data, separate from the visualization as a spreadsheet.

Our code must disentangle the dimensions of the meaningful facts. 


There are tiers of normalization. The normalization described above is part of First Normal Form (1NF): all rows are the same and all data items are atomic. Pragmatically, it's rare that all spreadsheet rows are the same, because it's common to bundle multiple subtypes into a single table.
Sidebar Rant. Yes, the presence of nullable columns in a SQL table *is* a normalization error. There, I said it. Error. We can always partition the rows of table into a number of separate tables; in each of those tables, all columns are required. We can rebuild the original table (with optional fields) via a union of the various decompositions (none of which have optional fields). The SQL folks prefer nullable columns and 1NF violations over unions and 1NF absolutism. I'm a fan of 1NF absolutism to understand each and every nullable attribute because casual abuse of nulls is a common design error.
The other part of 1NF is each value is atomic: there's no internal structure to the value. In manually-prepared spreadsheet data, this is difficult to insist on.  Stuff gets combined into a single cell because -- well -- it seemed helpful to the people entering it. They put all the lines of an address into a single cell because they like to see it that way.

Third Normal Form (3NF) forbids derived data (and transitive dependencies). In a spreadsheet, we might have a row-level computation. It helps the person confirm the data is correct. It's not "essential". It breaks the 3NF rule because the computed attribute depends on other field values; a change to one attribute will also change the derived attribute.

When we first encounter spreadsheet data, this isn't always obvious. In some cases, the derived data is computed "off-line" -- i.e., manually -- and entered into the spreadsheet. Really. People pull up a calculator app (or whip out their phone), compute a value, and type it in. In other cases, they look something up manually and enter it.

These kinds of data entry weirdnesses require code to normalize the manually-prepared data. We'll have to decompose non-atomic fields. And we'll have to handle derived data gracefully. (Reject it? Fix it? Warn them about it? Handle it as an exception?)


Let's talk about Second Normal Form (2NF). We really want to have a row in a table represent a single thing. The SQL folks require all of the attributes to be dependent on the row's key. In spreadsheet world, we may have a jumble of attributes with a jumble of dependencies. We may have multiple relationships in a single row.  Look at the Second Normal Form page on Wikipedia for examples of multiple relationships mashed together into a single row.

When a spreadsheet has 2NF problems, there will be situations were some collection of attributes is repeated -- verbatim -- in multiple places. The most common example in US-based data is City-State-ZIP Code. These three *always* form a consistent triple of data, and should be repeated as part of an address. In SQL terms, City and State have a functional dependency on the ZIP Code. In an Object-Oriented database, we might have a separate City-State-Zip class definition. In a document datastore, we might combine these items into a sub-document.

In any 2NF problem area, we're forced to write code which normalizes this internal relationship.

And. When we do that we'll find the kinds of problems we find with derived data: The ZIP code 22102 might be McLean or Tysons Corner. One of them is "right" and the other is "wrong", Or perhaps there needs to be an exception to handle this. Or perhaps a correction applied to coerce the wrong values to be right.

The "Association" Table

There's a SQL design pattern called an association table. This is used to handle a many-to-many relationship between two entities. Consider Boats and Owners. A boat will have multiple owners. An owner may have multiple boats. In SQL world, this requires a special table with two foreign keys. In the degenerate case, there are no other attributes. In the boat-owner relationship case, however, there's often a range of dates that specifies when an owner was associated with a boat. The range of dates applies to the relationship itself, not to boat nor to owner.

In a spreadsheet there are numerous ways to represent this. Numerous. A list of boat rows after each owner.  A list of owner rows after each boat. A number of owner columns for each boat.  A block of text with a list of owner names in a single cell. Creative people will create many creative solutions to this data representation problem.

Note that the association table is a SQL hack. It's an implementation detail, not an essential feature of the problem domain. In Python, for example, we'll need to use weakref objects to handle this cleanly. 

When Owner O1 refers to Vessel V1 it's easy to have a list of vessel references under the owner. When the Owner O1 object is no longer needed, it can be removed from memory. This decrements the references count for Vessel V1 to zero, and it will also be removed from memory, too. 

When we have mutual references, we have a problem, solved by weakrefs.

If Owner O1 refers to Vessel V1 and we also have Vessel V1 referring to Owner O1, we have mutual references. O1 has a list that includes V1.  V1 also has a list that includes O1. This means there are two strong references to O1: some variable, owner, and Vessel V1 also refers to O1. When the variable owner is no longer needed, then the reference count to O1 is decremented from two to one. And the object can't be deleted yet. 

If V1 has a weak reference to O1, then the strong reference count -- based on the variable owner -- is only one. The weak reference from V1 doesn't count for memory management purposes. O1 can be removed from memory, references to V1 will be decremented, and it, too, can be removed.

Our code will have to parse and populate the relationships. And we'll need to use weakref to be sure we can cleanly remove objects.

Coping Strategies

As noted above, we have to cope with manually-prepared spreadsheet data. It looks like this:
  1. Figure out what the likely data structure is. This isn't simple. We'll look at Pythonic techniques below. When starting, it helps to draw UML class diagrams (or ER diagrams) over and over again to try and depict the data. I'm a fan of using https://yuml.me to draw the pictures because they have a super-handy text notation for the relationships and attributes.
  2. Leverage the Extract-Transform-Load design pattern.

    • The "extract" reads the source spreadsheet data. A first version will be trivial use of xlrd or csv module. Or any of the modules listed here: http://www.python-excel.org
    • The "transform" should be implemented as a function to transform source to the target model. Pragmatically, this single function will leverage a number of other functions to validate, cleanse, convert, and normalize the data.
    • The "load" may not be anything more than creating instances of the underlying model classes. In some cases, the instances of the model classes may wind up in an in-memory dictionary. In other cases, the "load" might be a simple use of pickle or shelve to persist the useful data.

  3. Separate Model, ETL, and "Real Work" from each other. The model should evolve very slowly. It's the essential problem we're solving. The ETL may vary with each major revision to the spreadsheet database. Users add columns, they change meanings, their understanding evolves. The final work is based on the model -- and only the model -- ignoring the vagaries of ETL.
  4. Plan for change. Each manually-prepared spreadsheet is a unique snowflake, precious and distinct. This leads to an important lesson based on the Open/Closed Principle: Code Must Be Closed To Modification and Open To Extension. Each version of the source data means adding new functions or classes to cope with each bizarre new spreadsheet issue. When the source data changes, don't modify any old code; Always Be Adding. This means planning for multiple versions of functions: validate_1(), validate_2(), validate_3().  It's essential to be able process *all* old versions of the data and get meaningful, useful results for regression testing.

Python To The Rescue

Data modeling must be done slowly and reluctantly. Don't overfit the model to the first spreadsheet.

Here's the place to start

from typing import SimpleNamespace
class Model(SimpleNamespace ):

This is *enough* modeling to get started. Don't over-engineer the model. We can then do things like this.

class Owner(Model):

This defines the class Owner as an instance of some abstract Model class. The SimpleNamespace allows us to have any attributes we think we need.

owner = Owner(vessel=some_id, name=row['name'])

We can leverage the SimpleNamespace to build useful objects with minimal code. This can be replaced with a typing.NamedTuple or a @dataclass class definition when the definition is more mature.

The "extract" code needs to gather row-like objects. Ideally, this is a generator function. Because normalization and dereferencing may require multiple passes through the data, a list can be slightly easier to deal with. We'll come back to normalization and dereferencing below.

For some background in the classes used here, see https://sourceforge.net/projects/stingrayreader/. (Yes, this is old; I'm thinking of moving it to GitHub and updating it to Python 3.7.)

def load_live_rows(workbook, sheet_name):
    sheet1 = sheet.EmbeddedSchemaSheet(workbook, sheet_name, schema.loader.HeadingRowSchemaLoader)
    dict_rows = sheet1.schema.rows_as_dict_iter(sheet1)
    clean_data = filter(lambda row:not row['Hull No.'].is_empty(), dict_rows)
    initial_data = take_until(lambda row:row['Hull No.'].to_str() == 'Definitely WB Owners:', clean_data)
    return list(initial_data)

  1. We're working with a sheet that has the schema embedded in it. That means using the heading rows as column information. The HeadingRowSchemaLoader will be grabbing the first few rows from the EmbeddedSchemaSheet. Sometimes we need more complex loaders to read multiple rows. If the schema is separate from the sheet, then the loader doesn't interact with the source of data. 
  2. Each row is modeled as a simple dictionary in this example code.
  3. A filter locates rows that have hull numbers. Other rows are quietly discarded.
  4. The take_until() function reads rows until the matching row is found, then stops. This chops off the bottom of the spreadsheet where manual notes were kept.
The resulting list of rows can be validated, cleansed, and normalized to create the useful instances of the various Model subclasses.

Here's the "transform" portion.

def make_owner_1(row: Dict[str, Cell]) -> Owner:
    return Owner(
        last_name=null_strip(row["Owner's Last Name"].to_str()),
        first_name=null_strip(row["Owner's First Name"].to_str()),
        display_name=null_strip(row["Display Name"].to_str()),

We've built an instance of the Owner subclass of Model by extracting a number of attributes from the row. There are other columns not extracted; they are part of various normalizations and dereferencing.

The owner_vessel attribute is a parent-child relationship that can't be trivially populated from the row. The SQL folks would include a foreign key in each child that refers to the parent. The vessel page of the spreadsheet has this information, and it's used to populate the owner's details. This is one of the dereferencing activities that needs to be done as part of "loading".

The to_str() method is feature of the Stingray Reader's cell definitions. Conversion methods like this are not typical of idiomatic Python code. If we were only creating built-in str, float, or int, the bunch of conversion methods would be A Bad Idea. To be useful, we also need to create Decimal objects, and that leads us to embracing a grid of conversion methods for each cell source to desired resulting objects. We could use decimal(str(cell)), but it seems cleaner to use cell.to_decimal().

Multiple Passes

We often touch the source more than once.
  1. There's a "validate and load" pass to get rows that are sensible to process. A generator might make sense here. 
  2. There may be a "cleanse and convert" pass to reformat the source data, perhaps parsing complex cells into components or combining multiple source rows into a single entity description. This, too, might involve a generator to restructure the spreadsheet rows into something sensible.
  3. There will be multiple "normalization" passes. Any 2NF relationships need to be extracted to create model objects. Any restructuring of complex dimensions should be handled via restructuring source data from grid to rows, or from multiple sheets to a single, long, sequence of rows with the various dimensions as explicit attributes of each row.
  4. There may be multiple "load" passes to build final objects from the source rows. This will often lead to including the built objects as part of the source data.
  5. There will be some final "dereferencing" passes where foreign key relationships are turned into proper references among the objects. These should be weakref references to permit proper garbage collection.
At this point, the application will have tidy collections of Python objects that can be used for the real work.

What's essential is finding a balance between end-user visualization of the data in a spreadsheet and schema validation in Python. It's often helpful to be flexible when trying to automate processing of complex, irregular, manually gathered data.

Letting candidate users work with spreadsheets lowers the barrier to automation.

Coping with irregularity gets the process started.

As the work matures, some schema controls will tend to evolve. People tend to recognize the cost and complexity of irregular data. They will try to identify the patterns and impose some order on those patterns. As they uncover patterns in the data, the "schema" will evolve. This is a good thing, and Python lets this proceed at a human pace.

We can -- easily-- create flexible tools that let people understand and organize their data.

Tuesday, May 15, 2018

PyCon 2018 Highlights

And yes, this is truncated because I left early, and missed some important things. I'm going to have to catch on YouTube https://www.youtube.com/channel/UCsX05-2sVSH7Nx3zuk3NYuQ/videos

Of course, you'll also need to see the keynotes.

And there's a HUGE number of talks I didn't get to. 

Tuesday, May 1, 2018

Misunderstanding OO Programming

Read this. Goodbye, Object Oriented Programming

I like this because parts of it are wrong, and parts are based on peculiarities of specific languages which aren’t problems in other languages.

The “wrong” things are on a spectrum. At one end are things almost right. The other end is hoped-for things which — frankly — were never true.

The most important piece of nonsense is class-level reuse across projects. Class-level reuse in a new project was not a thing in OO programming. The monkey-banana-jungle “problem” only exists in a strange world were someone made up the idea of single classes being reused in isolation. The rest of us knew the scope of reuse was within a project or a narrow family of projects aimed at a single problem domain.

"Utility" classes that could be reused and generic data structures were always available as frameworks and libraries. Things built to solve a specific problem were going to be tailored to the problem. Most OO designers knew this and knew that making something generic would be hard. Making something reusable and installable by others was even harder. (Especially in compiled languages where you wanted to hide intellectual property by keeping the source secret.)

The "OO promised me reuse and lied" is a misstatement. Please rephrase this is "I imagined there could be class-level reuse and discovered it was hard."

Multiple inheritance does work in a number of languages, so I’ll skip the complaints centered on single inheritance.

I don't fully understand the complained about encapsulation. There are lots of books on separating interface from implementation to more fully isolate implementation details. If references need to be treated more opaquely, there are lots of techniques for this. It’s not broken. Indeed, it’s really well understood. ("But I won't want to introduce wrapper classes to insulate the references." Sigh. That's how it's done.)

I think the "references leak details about encapsulation" requires rephrasing as "I imagined some kind of perfectly isolated programming where references were not usable in spite of me making them usable." Or perhaps "I wish references had special treatment to make them not work as references except in a limited context which I get to imagine."

The polymorphism complaint appears to be “okay, this actually works.”  I guess. Or. “There are other ways to do this in other languages.” I'm sure it's an important point, but I can't quite discern what OO principle is allegedly broken here.


No one was lied to. If someone was "burned" by some OO hype, I’d like to see the actual quote of the actual hype. The “I was told there would be X”, requires some substantiation.

And. Stop griping about encapsulation. When the source is available (as it is in many languages) there's no enforcement other than public shaming.

Also. Use Python. Most of the original post seems to be complaints about C++ weirdness.