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Book review: Spark GraphX in Action by Michael S. Malak and Robin East

malak-meapI wrote about Spark not so long ago when I reviewed Learning Spark, at the time I noted that Learning Spark did not cover the graph processing component of Spark, GraphX. Spark GraphX in Action by Michael S. Malak and Robin East fills that gap.

I read the book via Mannings Early Access Program (MEAP), they approached me and gave me access to the whole book for free, this meant I read it on my Kindle which I tend not to do these days for technical books because I still find paper a more flexible medium. Early Access means the book is still a little rough around the edges but it is complete.

The authors suggest that readers should be comfortable reading Scala code to enjoy the book. Scala is the language Spark is written in, and the best way to access GraphX. In fact access via Python (my favoured route) is impossible and using Java it sounds ugly. Scala is a functional language which runs on the Java virtual machine. It seems to be motivated by a desire to remove Java’s verbosity but perhaps goes a little too far. There is no `return` keyword for identifying the return value of a function. Its affectation is to overload the meaning of the underscore _. As it was I felt comfortable enough reading Scala code. I was interested to read that the two “variable” definitions are `val` and `var`, `val` is immutable and is preferred – var is mutable. This is probably a lesson for my Python programming – immutable “variables” can provide higher performance (and using immutable for things that you intend to be immutable aids clarity and debugging).

From the point of view of someone who has read about Spark and graph theory in the past the book is pitched at the right level, there is some introductory material about Spark and also about graph theory and then a set of examples. The book finishes with some material on inspecting running jobs in Spark using the Spark web interface. If you have never heard of Spark, then this book probably isn’t a good place to start.

The examples start with basic algorithms on measuring shortest paths across a graph, connectedness and the Page Rank algorithm on which Google was originally built. These are followed by simple implementations of some further algorithms including shortest paths with weighted edges (essential for route finding) and the travelling salesman problem. There then follows a chapter on some machine learning algorithms including recommendation engines, spam detection, and document clustering. Where appropriate the authors cite the original papers for algorithms including PageRank, Pregel (Google’s graph processing framework) and SVD++ (which was a key component of the winning entry for the Netflix recommendation prize) which is very welcome. The examples are outlines rather than full implementations of these sophisticated algorithms.

Finally, there is a chapter titled “The Missing Algorithms”, this is more a discussion of utility functions for GraphX in terms of import from other schemes such as RDF, operations such as merging two graphs or trimming away stray vertices.

The book gives the impression that GraphX is not ready for the big time yet, in a couple of places the authors said “this bit has only just started working”, and when they move on to talking about using SVD++ in GraphX they explain how the algorithm is only half implemented in GraphX. Full implementations are available in other languages.

It seemed to me on my original reading about Spark that the big benefit was that you could write machine learning systems in a familiar language which ran on a single machine in Spark, and then scale up effortlessly to a computing cluster, if required. Those benefits are not currently present in GraphX, you need to worry about coding in a foreign language and about the quality of the underlying implementation. It feels like the appropriate approach (for me) should be to prototype using Python/Neo4J, and likely discover that that is all that is needed. Only if you have a very large graph do you need to consider switching to a Spark based solution, and I’m not convinced GraphX is how you would do it even then.

The code samples are poorly formatted but you can fix this by downloading the source code and viewing it in the editor of your choice with nice syntax highlighting and consistent indenting – this makes things much clearer. The figures are clear enough but I find the Kindle approach of embedding thumbnail scale figures unhelpful – you need to double click them to make them readable. A reasonable solution would be to make figures full page by default, if that is possible.

This is one of the better “* in Action” books I’ve read, it’s not convinced me to use GraphX – quite the reverse – but that’s no bad thing and I’ve learnt a little about recommender algorithms and Scala.

Book review: The Invention of Nature by Andrea Wulf

inventionofnatureThe Invention of Nature by Andrea Wulf is subtitled The Adventures of Alexander von Humboldt – this is his biography.

Alexander von Humboldt was born in Berlin in 1769, he died in 1859. The year in which On the Origin of Species was published. He was a naturalist of a Romantic tendency, born into an aristocratic family, giving him access to the Prussian court.

He made a four year journey to South America in 1800 which he reported (in part) in his book Personal Narratives, which were highly influential – inspiring Charles Darwin amongst many others. On this South American trip he made a huge number of observations across the natural and social sciences and was sought after by the newly formed US government as the Spanish colonies started to gain independence. Humboldt was a bit of a revolutionary at heart, looking for the liberation of countries, and also of slaves. This was one of his bones of contention with his American friends.

His key scientific insight was to see nature as an interconnected web, a system, rather than a menagerie of animals created somewhat arbitrarily by God. As part of this insight he saw the impact that man made on the environment, and in some ways inspired what was to become the environmentalist movement.

For Humboldt the poetry and art of his observations were as important as the observations themselves. He was a close friend of Goethe who found him a great inspiration, as did Henry David Thoreau. This was at the time when Erasmus Darwin was publishing his “scientific poems”. This is curious to the eye of the modern working scientist, modern science is not seen as a literary exercise. Perhaps a little more effort is spent on the technical method of presentation for visualisations but in large part scientific presentations are not works of beauty.

Humboldt was to go voyaging again in 1829, conducting a whistle-stop 15,000 mile 25 week journey across Russia sponsored by the government. On this trip he built on his earlier observations in South America as well as carrying out some mineral prospecting observations for his employers.

Despite a paid position in the Prussian court in Berlin he much preferred to spend his time in Paris, only pulled back to Berlin as the climate in Paris became less liberal and his paymaster more keen to see value for money.

Personally he seemed to be a mixed bag, he was generous in his support of other scientists but in conversation seems to have been a force of nature, Darwin came away from a meeting with him rather depressed – he had not managed to get a word in edgewise!

I’m increasingly conscious of how the climate of the time influences the way we write about the past. This seems particularly the case  with The Invention of Nature. Humboldt’s work on what we would now call environmentalism and ecology are highly relevant today. He was the first to talk so explicitly about nature as a system, rather than a garden created by God. He pre-figures the study of ecology, and the more radical Gaia Hypothesis of James Lovelock. He was already alert to the damage man could do to the environment, and potentially how he could influence the weather if not the climate. There is a brief discussion of his potential homosexuality which seems to me another theme in keeping with modern times.

The Invention of Nature is sub-subtitled “The Lost Hero of Science”, this type of claim is always a little difficult. Humboldt was not lost, he was famous in his lifetime. His name is captured in the Humboldt Current, the Humboldt Penguin plus many further plants, animals and geographic features. He is not as well-known as he might be for his theories of the interconnectedness of nature, in this area he was eclipsed by Charles Darwin. In the epilogue Wulf suggests that part of his obscurity is due to anti-German sentiment in the aftermath of two World Wars. I suspect the area of the “appropriate renownedness of scientific figures of the past” is ripe for investigation.

The Invention of Nature is very readable. There are seven chapters illustrating Humboldt’s interactions with particular people (Johann Wolfgang von Goethe, Thomas Jefferson, Simon Bolivar, Charles Darwin, Henry David Thoreau, George Perkins Marsh, Ernst Haeckel and John Muir). Marsh was involved in the early environmental movement in the US, Muir in the founding of the Yosemite National Park (and other National Parks). At first I was a little offended by this: I bought a book on Humboldt, not these other chaps! However, then I remembered I actually prefer biographies which drift beyond the core character and this approach is very much in the style of Humboldt himself.

Parsing XML and HTML using xpath and lxml in Python

For the last few years my life has been full of the processing of HTML and XML using the lxml library for Python and the xpath query language. xpath is a query language designed specifically to search XML, unlike regular expressions which should definitely not be used to process XML related languages. Typically this has involved a lot of searching my own code to remind me how to do stuff. This blog post captures some handy snippets to avoid the inevitable Googling, and solidify for me exactly what I’ve been doing for the last few years!

But what does it {xml, html} look like?

xml and html are made up of “elements”, delimited by pointy brackets and attributes which are equal to things:

<element1 attribute1=”thing”>content</element1>

Elements can be nested inside other elements to make a tree structure. A wrinkle to be aware of is the so-called “tail” of an element. This is most often seen with <br/> tags (I think it is general):

<element1 attribute1=”thing”>content</br>tail</element1>

The “content” is accessed using text(), whilst the tail is accessed using .tail.

Web pages are made from HTML which is a “relaxed” XML format. XML is the basis of many other file formats found in the wild (such as GPX and GML). Dealing with XML is very similar to dealing with HTML except for namespaces, which I discuss in more detail at the end of this post.

XPath Helper

Before I get onto xpath I should introduce xpath helper – which is a plugin for Google Chrome which helps you develop xpath queries.

You can find XPath Helper in the Chrome Store, it is free. I use it in combination with the Google Chrome Developer tools, particular the “Inspect Element” functionality. XPath helper allows you to see the results of an xpath query live. You open up the XPath console (Ctrl+shift+x), type in your xpath and you see the results in both in the xpath helper console, and also as highlighting on the page.

You can get automatically generated xpath queries, however typically I have used these just as inspiration since they tend to be rather long and “brittle”.

Loading up the data

My Python scripts nearly always start with the following imports:

import lxml.html
import requests
import requests_cache
requests_cache.install_cache('demo_cache')

requests and requests_cache to access data on the web and lxml.html to parse the HTML. Then I can get a webpage using:

r = requests.get(url)
root = lxml.html.fromstring(r.content)

You might want to make any URLs absolute rather than relative:

root.make_links_absolute(base_url)

If I’m dealing with XML rather than HTML then I might do:

from lxml import etree

And then when it came to loading in a local XML file:

with open(input_file, "rb") as f:
	root = etree.XML(f.read())

XPath queries

With your root element in hand you can now get on with querying. Xpath queries are designed to extract a set of elements or attributes from an XML/HTML document by the name of the element, the value of an attribute on an element, by the relationship an element has with another element or by the content of an element.

Quite often xpath will return elements or lists of elements which, when printed in Python, don’t show you the content you want to see. To get the text content of an element you need to use .text, text_content(), or .tail, and make sure you ask for an array element rather than the whole array.

The follow examples show the key features of xpath. I’m using this blog (http:/www.ianhopkinson.org.uk/) as an example website so you can play along with xpath:

Specifying a complete path with / as separator

title = root.xpath('/html/body/div/div/div[2]/h1')

is the full path to my blog title. Notice how we request the 2nd element of the third set of div elements using div[2] – xpath arrays are one-based, not zero-based.

Specifying a path with wildcards using //

This expression also finds the title but the preamble of /html/body/div/div is absorbed by the // wildcard match:

title = root.xpath('//div[2]/h1')

To obtain the text of the title in Python, rather than an element object, we would do:

title_text = title[0].text.strip() or maybe title_text = title[0].text_content().strip()

text_content() would pick up any tail content, and any text in child elements. I use strip() here to remove leading and trailing whitespace

Selecting attribute values

we’ve seen that //element selects all of the elements of type “element”. We select attribute values like this:

ids = root.xpath('//li/@id')

which selects the id attribute from the list elements (li) on my blog

Specifying an element by attribute

We can select elements which have particular attribute values:

tagcloud = root.xpath('//*[@class="tagcloud"]')

this selects the tag cloud on my blog by selecting elements which having the class attribute “tagcloud”.

Select an element containing some specified text

We can do something similar with the text content of an element:

title = root.xpath(‘//h1[contains(., ‘SomeBeans’)]’)

This selects h1 elements which contain the text “SomeBeans”.

Select via a parent or sibling relationship

Sometimes we want to select elements by their relationship to another element, for example:

subtitle = root.xpath('//h1[contains(@class,"header_title")]/../h2')

this selects the h1 title of my blog (SomeBeans) then navigates to the parent with .. and selects the sibling h2 element (the subtitle “the makings of a small casserole”).

The same effect can be achieved with the following-sibling keyword:

subtitle = root.xpath('//h1[contains(@class,"header_title")]/following-sibling::h2')

XML Namespaces

When dealing with XML, we need to worry about namespaces. In principle the elements of an XML document are described in a schema which can be looked up and is universally unique. In practice the use of namespaces in XML documents can lead to much banging head against wall! This is largely because trivial examples of XML wrangling don’t use namespaces, except as a “special” example.

Here is a fragment of XML defining two namespaces:

<foo:Results xmlns:foo="http://www.foo.com" xmlns="http://www.bah.com">

xmlns:foo defines a namespace whose short form is “foo”, we select elements in this space using a namespace parameter to the xpath query:

records = root.xpath('//foo:Title', namespaces = {"foo": "http://www.foo.com"})

The “catch” here is we also define a default namespace xmlns = “http://www.bah.com”, which means that elements which don’t have a prefix cannot be selected unless we define the namespace in our xpath:

records = root.xpath('//bah:Title', namespaces = {"bah": http://www.bah.com})

Worse than that we need to include our namespace prefix in the query, even though it doesn’t appear in the file!

Conclusion

These snippets cover the majority of the xpath queries I’ve needed over the past few years, I’ll add any others as I find them. I’ve put all the code used here in a GitHub gist.

Xpath is the right tool for the job of extracting information from XML documents, including HTML – do not accept inferior alternatives!

Book Review: Canals: The making of a nation by Liz McIvor

canalsCanals: The making of a Nation by Liz McIvor is a tie-in with a BBC series of the same name, presented by the author. It is about canals in England from the mid-18th century through to the present day although most of the action takes place before the end of the 19th century.

The chapters of the book match the episodes of the series which are thematic, rather than chronological. Each chapter introduces a different topic, loosely tied to a particular canal.

The book starts with a discussion of the growth of London, and the Grand Junction canal linking it to Birmingham. The guild system was a factor in limiting the growth of the capital until the mid-18th century. The “Bubble Act” of 1720, enacted in the aftermath of the South Sea Bubble likely also had an impact. It prevented the formation of any joint stock company without an act of parliament to approve. It was repealed in 1825 before the railways saw their enormous growth. The Grand Junction canal was built as Birmingham became a manufacturing hub and London a great city with many requirements for daily life, and also a showroom to at least the United Kingdom, if not the world.

I was chastened to discover that the Bridgewater canal, one of the earliest of “canal boom” projects of the 18th century is only just up the road from me in Chester. I’d always assumed it was close to the town of a very similar name in Somerset! Bridgewater is named for the Duke of Bridgewater, Francis Egerton, for which the pub just over the road from me is presumably named. The Bridgewater canal was built around 1760, linking the Duke’s coal mines at Worsley to Manchester. With this revelation I realise that the Bridgewater canal and the Liverpool to Manchester railway, the first exclusively steam railway, are sited very close to each other.

Support for manufacture was the theme of canal building in the North of England, and also around Birmingham with canals built to move bulky raw materials to factories placed to benefit from hydraulic power, and benevolent climates for the processing of materials such as cotton. Manufacturers such as Josiah Wedgewood were keen to see their fragile wares safely make the outward journey to the showrooms of London.

The Kennet and Avon canal was built to provide navigable water access from Bristol to London. William Smith, who produced the first geological map of Great Britain is introduced in this chapter. I read more about him in The Map that Changed the World by Simon Winchester. The digging of canal cuts and tunnels reveal the local geology. Nowadays we see canals as bucolic thoroughfares but when they were built they were raw cuts indicating industrialisation.

The Manchester Ship Canal was opened in 1894 to bypass the port of Liverpool, these were the dying days of canal building. 154 died in its construction and 1404 were seriously injured from a workforce of 16,361. For comparison, projects such as the 2012 London Olympics and the close-to-completion Crossrail project are of similar scale yet have casualty numbers hovering around zero although these are best-in-class projects for health and safety. In this chapter McIvor talks more of the Irish “navigators” who built the canals, and something of the early trade union movement.

The families that worked the canals were seen as outsiders, once the long networks were set up they led an itinerant lifestyle with no fixed church or school for their children. The Victorian moralists arguing for improved conditions for the boat families seem to do so from the point of view of pointing out how bloody awful they were!

It’s interesting to see the likes of Thomas Telford and John Rennie cropping up repeatedly in this book. They have the air of rockstar engineers, not a niche found these days. Perhaps this is a result of the work of the Victorian writer, Samuel Smiles, who was very keen on self-improvement and wrote biographies of these men to promote his ideas.

To me the book lacks a little prehistory, the great boom for canal building in the UK was at the end of the 18th century but the very first “pound lock” in England was built in 1566 on the Exeter canal. What went on between these two times? And what was happening elsewhere in the world? Perhaps the answer here is that the canals in Britain never represented a technological revolution, they were always about the social and commercial climate being right.

Canals: The Making of a Nation is an unchallenging read, well-suited to a holiday. If you’re on a canal boat it won’t tell you much about the particular bridges and tunnels you pass over but it will give you a strong feeling for the lives of the people that built and used the canals, and why they were built in the first place.

Book review: Effective Computation in Physics by Anthony Scopatz & Kathryn D. Huff

ecipThis next review, of “Effective Computation in Physics” by Anthony Scopatz & Kathryn D. Huff, arose after a brief discussion on twitter with Mike Croucher after my review of “High Performance Python” by Ian Micha Gorelick and Ian Ozsvald. This in the context of introducing students, primarily in the sciences, to programming and software development.

I use the term “software development” deliberately. Scientists have been taught programming (badly, in my view*) for many years. Typically they are given a short course in the first year of their undergraduate training, where they are taught the crude mechanics of a programming language (typically FORTRAN, C, Matlab or Python). They are then left to it, perhaps taking up projects requiring significant coding as final year projects or in PhDs. The thing they have lacked is the wider skillset around programming – what you might call “software development”. The value of this is two-fold – firstly, it is a good training for a scientist to have for careers in science. Secondly, the wider software industry is full of scientists, providing students with a good grounding in this field is no bad thing for their future employability.

The book covers in at least outline all the things a scientist or engineer needs to know about software development. It is inspired by the Software Carpentry and The Hacker Within programmes.

The restriction to physics in the title seems needless to me. The material presented is mostly applicable to any science, and those working in the digital humanities, undertaking programming work. The examples have a physics basis but not to any great depth, and the decorative historical anecdotes are all physics based. Perhaps the only exception to this is the chapter on HDF5 which is a specialised data storage system, some coverage of SQL databases would make a reasonable substitute for a more general course. The chapter on parallel computing could likewise be dropped for a wider audience.

The book is divided into four broad sections. Including in these are chapters on:

  • Command line operations;
  • Programming in Python;
  • Build systems, version control, debugging and testing;
  • Documentation, publication, collaboration and licensing;

Command line operations are covered in two chunks, firstly in the basic navigation of the file system and files followed by a second chapter on “Regular Expressions” which covers find, grep, sed and awk – at a very basic level.

The introduction to Python is similarly staged with initial chapters covering the fundamentals of the core language, with sufficient detail and explanation that I learnt some new things**. Further chapters introduce core Python libraries for data analysis including NumPy, Pandas and matplotlib.

Beyond these core chapters on Python those on version control, debugging and testing are a welcome addition. Our dearest wish at ScraperWiki, a small software company where I worked until recently, was that new recruits and interns would come with at least some knowledge and habit for using source control (preferably Git). It is also nice to see some wider discussion of GitHub and the culture of Pull Requests and issue tracking. Systematic testing is also a useful skill to have, in fact my experience has been that formal testing is most useful for those most physics-like functions.

The final section covers documentation, publication and licensing. I found the short chapter on licensing rather useful, I’ve been working on some code to analyse LIDAR data and have made it public on GitHub, which helpfully asks which license I would like to use. As it turns out I chose the MIT license and this seems to be the correct one for the application. On publication the authors are Latex evangelists but students can chose to ignore their monomania on this point. Latex has a cult-like following in physics which I’ve never understood. I have written papers in Latex but much prefer Microsoft Word for creating documents, although Google Docs is nice for collaborative work. The view that a source control repository issue tracker might work for collaboration beyond coding is optimistic unless academics have changed radically in the last few years.

I’d say the only thing lacking was any mention of pair programming, although to be fair that is more a teaching method than course material. I found I learnt most when I had a goal of my own to work towards, and I had the opportunity to pair with people with more knowledge than I had. Actually, pairing with someone equally clueless in a particular technology can work pretty well.

There is a degree to which the book, particularly in this section strays into a fantasy of how the authors wish computational physics was undertaken, rather than describing how it is actually undertaken.

To me this is the ideal “Software development for scientists” undergraduate text, it is opinionated in places and I occasionally I found the style grating but nevertheless it covers the right bases.

*I’m happy to say this since I taught programming badly to physics undergraduates some years ago!

**People who know my Python skills will realise this is not an earthshattering claim.