Monday, September 26, 2016

An ast "Hello World": Getting Started with Python's Abstract Syntax Trees

I've been working on a Python library which - for a number of reasons - needs to dynamically alter itself. Essentially, I want it to parse a document and to generate some code based on that parsed file.


It turns out that Python's ast module lets me do exactly what I need. I came across some quite useful supplementary documentation on ast. But, to get started, I needed something simpler that those advanced examples. I therefore wrote a "Hello world!" program using ast. Here it is, in case you were looking for that, too.

Hello world!

Since I've become test infected, I wanted to structure my "Hello world!" ast program using unit tests.

So, to start, I tracked down a suitable "Hello world!" unit test in Python.

Hello world! in ast

Then I rewrote the class to use ast. My version constructs an abstract syntax tree for an Assignment. Specifically, it assigns the string "Hello world!" to the variable "m". The code then fixes the locations, compiles the code and executes it dynamically.

Obviously, the above code is a lot more work than simply assigning the string value to the variable directly. But it meant I now had the world's simplest ast program.


Armed with this most basic of unit tests, I was then in a position to work out how to support various other types of code in ast.

For example, here's a snippet of ast code which uses ast to generate an abstract syntax tree to assign an empty type to a variable named "nothing". In other words, equivalent to nothing = ()

Invoking Methods

One of the hardest things for me to figure out was how to invoke a method of a class.

First, I worked out to call a function - one not attached to an instance of a class. But to call a method of a class, I needed to understand a bit more about how Python itself is implemented.

Calling Functions

Here's some Python ast code to call a function _foo() and assign the returned value to a variable called "result", i.e. equivalent to result = foo() And here's a variant where you pass in a value, i.e. equivalent to result = bar("some value")

In Python, Methods are Attributes of Classes

Having figured out how to call functions and pass parameters to them, I reckoned that calling a method on a class would be similar.
And it sort of was - I still needed to use ast.Call to invoke the method. But it took me quite a while to figure out how to tell it which class method to call. For example, if I wanted to call

result = self._baz(theResult)

should I pass in a function name of "self._baz"? (I tried that - it didn't work). Eventually, I worked out that self._baz is an attribute of the instance object referred to as "self". In Python, instance objects have two kinds of valid attribute names, data attributes and methods. Which meant that the code to call one method of an instance from another method looks like this:
I had never thought that profoundly about how Python is really implemented behind the scenes. Although many of the Python design decisions are actually quite well documented.

An ast Short Cut

In the process of working out how to invoke instance object methods, I came up with a general-purpose shortcut. It turns out that - since Python 2.6 - ast has a very handy helper method called "ast.parse()". This - in combination with ast.dump() - will let you very quickly figure out the correct ast pattern to use for a given bit of Python code. For example, here's how to figure out how to invoke an instance object method

005-Syntax by vicdunk
Hopefully, that will be enough to get you going on your own Python ast adventures!

Monday, September 19, 2016

Falcor and GraphQL: Querying JSON APIs (Part Five of Three)

What's the best way to query a REST API which returns JSON? I look at two popular libraries - Netflix's Falcor and Facebook's GraphQL - which aim to overcome problems with API performance and "chattiness".

Querying JSON

XML (and related standards such as XSLT and XQuery) benefit from the power of XPath for selecting and querying XML. However, JSON has no direct equivalent to XPath. (Although there are a lot of projects which have named them selves [jJ][Pp]ath!)

I still like the approach taken by JSONiq- it is essentially XQuery for JSON. However, in this post, I want to talk about two libraries - Falcor and GraphQL - which address the problem in a somewhat different way: how to get just the JSON you want from an API?
2009APR101606 by Peter Renshaw

Trade Offs: Speed and Complexity

When you write a client for a typical REST API, you have to confront two basic problems: performance and complexity. On the one hand, if a REST API contains more data than you need, then you're paying a penalty for every unnecessary byte (being transferred over the network and parsed by your code). On the other hand, if a given API response doesn't have everything you need, then you will need to make follow-up calls, which adds complexity and, of course, more latency as you choreograph the back-and-forth.

The designer of the REST API should try to anticipate likely uses, so that they can provide just the right information, in the right ways. And, as I've previously recommended, it is a good idea to build in support for full or partial API responses. However, part of what is exciting about APIs is that they unlock innovation. So, if your API is a success, you will - by definition - have hard-to-anticipate uses of your design.
Sit! by Craig Sunter

Sitting in the Middle

Rather than rely on the REST API perfectly fitting your needs (or supporting a powerful query language) why not have an adapter which sits in-between your client and the REST API? Both Netflix's Falcor and Facebook's GraphQL take this approach: they are each implemented as servers which you configure to turn the REST API you have to work with into one that you want to work with. They differ somewhat in their philosophy and power, however.

Falcor - All of the Data in One Giant Model

Netflix has open-sourced their Falcor library, which they use to power their UIs. At the time of writing, it is still in "Developer Preview", however, many people outside of Netflix are using Falcor. You can try out the demo Falcor application or read the Falcor documentation for more details.

Falcor adds some capabilities to the standard JSON model - such as "virtual JSON models" and a "JSON Graph" - to make it easier to cache data on the client side. Using Falcor, you can

  • eliminate the need for multiple HTTP requests to get all the data you need
  • cache the data locally for better performance
  • deal with data using graphs, which are more flexible than the standard tree-model used in JSON
  • adapt JSON or non-JSON APIs into a JSON model customized for your application

Falcor is a server-side Javascript library run within a nodejs server. You construct a Falcor data model and define how each component maps to the actual APIs you need to use via "paths". Your application then interacts with the Falcor data model you've defined, while the Falcor server takes care of interacting with the APIs to get you the data you need, including handling caching for greater performance - particularly when you have multiple instances of your application querying a single data model.

A nice overview of working with Falcor is provided by Auth0. And you can find a lot more documentation on the Falcor website.

GraphQL - a Schema and Resolve Functions

Facebook has open-sourced their GraphQL library, which they developed to power their mobile and web apps. At the time of writing, Facebook has released a working draft of the GraphQL spec and a reference implementation in Javascript. They have also created an implementation you can actually download and use. Various people have started to build GraphQL tools and implementations, including GraphQL support in Python (one of my favourite languages). Check out the GraphQL documentation for more details.

As a GraphQL client, you send the server a query, which defines what data you want back. For example

  user(id: "1") {

Which says "give me back the name of the user who has an id=1".

On the GraphQL server side, you need to configure the schema and the resolve functions. The schema defines the data model which may be fetched from the server. The resolve functions map the fields in the schema into the backend services. A GraphQL resolve function therefore contains whatever code is necessary to fetch and transform data from a backend service - such as a REST API, a MongoDB or a SQL RDBMS - into the form promised by the schema.

There's a nice overview of working with GraphQL on RisingStack. And you can find Facebook's full documentation on GraphQL.

"Choice" by Jeremy Brooks

Which One Should You Choose?

Falcor is somewhat simpler to learn than GraphQL. In part, this is because GraphQL is more powerful - in particular it has a much a more sophisticated query capability. Both libraries have been implemented in Javascript, but only GraphQL is designed to be implemented in other languages, too.

Finally, it is worth considering whether you want to adopt either one at all: the REST architecture (when implemented correctly) has tremendous support for caching and scalability. So, rather than abandon a REST API altogether, consider whether you have the option of instead tuning it to perform better (tip: look at the granularity of the resources you've defined).

Designing JSON

This is part of my occasional series on designing and working with JSON:

This post - the fifth in the trilogy - picks up on a topic I discussed in Part 3 - Lessons Learnt - how to select and query the JSON you get back from an API.