Tag Archive: scraperwiki

Apr 14 2015

Book review: How Linux works by Brian Ward

 

hlw2e_cover-new_webThis review was first published at ScraperWiki.

A break since my last book review since I’ve been coding, rather than reading, on the commute into the ScraperWiki offices in Liverpool. Next up is How Linux Works by Brian Ward. In some senses this book follows on from Data Science at the Command Line by Jeroen Janssens. Data Science was about doing analysis with command line incantations, How Linux Works tells us about the system in which that command line exists and makes the incantations less mysterious.

I’ve had long experience with doing analysis on Windows machines, typically using Matlab, but over many years I have also dabbled with Unix systems including Silicon Graphics workstations, DEC Alphas and, more recently, Linux. These days I use Ubuntu to ensure compatibility with my colleagues and the systems we deploy to the internet. Increasingly I need to know more about the underlying operating system.

I’m looking to monitor system resources, manage devices and configure my environment. I’m not looking for a list of recipes, I’m looking for a mindset. How Linux Works is pretty good in this respect. I had a fair understanding of pipes in *nix operating systems before reading the book, another fundamental I learnt from How Linux Works was understanding that files are used to represent processes and memory. The book is also good on where these files live – although this varies a bit with distribution and time. Files are used liberally to provide configuration.

The book has 17 chapters covering the basics of Linux and the directory hierarchy, devices and disks, booting the kernel and user space, logging and user management, monitoring resource usage, networking and aspects of shell scripting and developing on Linux systems. They vary considerably in length with those on developing relatively short. There is an odd chapter on rsync.

I got a bit bogged down in the chapters on disks, how the kernel boots, how user space boots and networking. These chapters covered their topics in excruciating detail, much more than required for day to day operations. The user startup chapter tells us about systemd, Upstart and System V init – three alternative mechanisms for booting user space. Systemd is the way of the future, in case you were worried. Similarly, the chapters on booting the kernel and managing disks at a very low level provide more detail than you are ever likely to need. The author does suggest the more casual reader skip through the more advanced areas but frankly this is not a directive I can follow. I start at the beginning of a book and read through to the end, none of this “skipping bits” for me!

The user environments chapter has a nice section explaining clearly the sequence of files accessed for profile information when a terminal window is opened, or other login-like activity. Similarly the chapters on monitoring resources seem to be pitched at just the right level.

Ward’s task is made difficult by the complexity of the underlying system. Linux has an air of “If it’s broke, fix it and if ain’t broke, fix it anyway”. Ward mentions at one point that a service in Linux had not changed for a while therefore it was ripe for replacement! Each new distribution appears to have heard about standardisation (i.e. where to put config files) but has chosen to ignore it. And if there is consistency in the options to Linux commands it is purely co-incidental. I think this is my biggest bugbear in Linux, I know which command to use but the right option flags are more just blindly remembered.

The more Linux-oriented faction of ScraperWiki seemed impressed by the coverage of the book. The chapter on shell scripting is enlightening, providing the mindset rather than the detail, so that you can solve your own problems. It’s also pragmatic in highlighting where to to step in shell scripting and move to another language. I was disturbed to discover that the open-square bracket character in shell script is actually a command. This “explain the big picture rather than trying to answer a load of little questions”, is a mark of a good technical book.  The detail you can find on Stackoverflow or other Googling.

How Linux Works has a good bibliography, it could do with a glossary of commands and an appendix of the more in depth material. That said it’s exactly the book I was looking for, and the writing style is just right. For my next task I will be filleting it for useful commands, and if someone could see their way to giving me a Dell XPS Developer Edition for “review”, I’ll be made up.

Feb 10 2015

Book review: Data Science at the Command Line by Jeroen Janssens

 

datascienceatthecommandlineThis review was first published at ScraperWiki.

In the mixed environment of ScraperWiki we make use of a broad variety of tools for data analysis. Data Science at the Command Line by Jeroen Janssens covers tools available at the Linux command line for doing data analysis tasks. The book is divided thematically into chapters on Obtaining, Scrubbing, Modeling, Interpreting Data with “intermezzo” chapters on parameterising shell scripts, using the Drake workflow tool and parallelisation using GNU Parallel.

The original motivation for the book was a desire to move away from purely GUI based approaches to data analysis (I think he means Excel and the Windows ecosystem). This is a common desire for data analysts, GUIs are very good for a quick look-see but once you start wanting to repeat analysis or even repeat visualisation they become more troublesome. And launching Excel just to remove a column of data seems a bit laborious. Windows does have its own command line, PowerShell, but it’s little used by data scientists. This book is about the Linux command line, examples are all available on a virtual machine populated with all of the tools discussed in the book.

The command line is at its strongest with the early steps of the data analysis process, getting data from places, carrying out relatively minor acts of tidying and answering the question “does my data look remotely how I expect it to look?”. Janssens introduces the battle tested tools sed, awk, and cut which we use around the office at ScraperWiki. He also introduces jq (the JSON parser), this is a more recent introduction but it’s great for poking around in JSON files as commonly delivered by web APIs. An addition I hadn’t seem before was csvkit which provides a suite of tools for processing CSV at the command line, I particularly like the look of csvstat. csvkit is a Python tool and I can imagine using it directly in Python as a library.

The style of the book is to provide a stream of practical examples for different command line tools, and illustrate their application when strung together. I must admit to finding shell commands deeply cryptic in their presentation with chunks of options effectively looking like someone typing a strong password. Data Science is not an attempt to clear the mystery of these options more an indication that you can work great wonders on finding the right incantation.

Next up is the Rio tool for using R at the command line, principally to generate plots. I suspect this is about where I part company with Janssens on his quest to use the command line for all the things. Systems like R, ipython and the ipython notebook all offer a decent REPL (read-evaluation-print-loop) which will convert seamlessly into an actual program. I find I use these REPLs for experimentation whilst I build a library of analysis functions for the job at hand. You can write an entire analysis program using the shell but it doesn’t mean you should!

Weka provides a nice example of smoothing the command line interface to an established package. Weka is a machine learning library written in Java, it is the code behind Data Mining: Practical Machine Learning Tools and techniques. The edges to be smoothed are that the bare command line for Weka is somewhat involved since it requires a whole pile of boilerplate. Janssens demonstrates nicely how to do this by developing automatically autocompletion hints for the parts of Weka which are accessible from the command line.

The book starts by pitching the command line as a substitute for GUI driven applications which is something I can agree with to at least some degree. It finishes by proposing the command line as a replacement for a conventional programming language with which I can’t agree. My tendency would be to move from the command line to Python fairly rapidly perhaps using ipython or ipython notebook as a stepping stone.

Data Science at the Command Line is definitely worth reading if not following religiously. It’s a showcase for what is possible rather than a reference book as to how exactly to do it.

Feb 09 2015

Book review: Remote Pairing by Joe Kutner

 

jkrp_xlargecoverThis review was first published at ScraperWiki.

Pair programming is an important part of the Agile process but sometimes the programmers are not physically co-located. At ScraperWiki we have staff who do both scheduled and ad hoc remote working therefore methods for working together remotely are important to us. A result of a casual comment on Twitter, I picked up “Remote Pairing” by Joe Kutner which covers just this subject.

Remote Pairing is a short volume, less than 100 pages. It starts for a motivation for pair programming with some presentation of the evidence for its effectiveness. It then goes on to cover some of the more social aspects of pairing – how do you tell your partner you need a “comfort break”? This theme makes a slight reprise in the final chapter with some case studies of remote pairing. And then into technical aspects.

The first systems mentioned are straightforward audio/visual packages including Skype and Google Hangouts. I’d not seen ScreenHero previously but it looks like it wouldn’t be an option for ScraperWiki since our developers work primarily in Ubuntu; ScreenHero only supports Windows and OS X currently. We use Skype regularly for customer calls, and Google Hangouts for our daily standup. For pairing we typically use appear.in which provides audio/visual connections and screensharing without the complexities of wrangling Google’s social ecosystem which come into play when we try to use Google Hangouts.

But these packages are not about shared interaction, for this Kutner starts with the vim/tmux combination. This is venerable technology built into Linux systems, or at least easily installable. Vim is the well-known editor, tmux allows a user to access multiple terminal sessions inside one terminal window. The combination allows programmers to work fully collaboratively on code, both partners can type into the same workspace. You might even want to use vim and tmux when you are standing next to one another. The next chapter covers proxy servers and tmate (a fork of tmux) which make the process of sharing a session easier by providing tunnels through the Cloud.

Remote Pairing then goes on to cover interactive screensharing using vnc and NoMachine, these look like pretty portable systems. Along with the chapter on collaborating using plugins for IDEs this is something we have not used at ScraperWiki. Around the office none of us currently make use of full blown IDEs despite having used them in the past. Several of us use Sublime Text for which there is a commercial sharing product (floobits) but we don’t feel sufficiently motivated to try this out.

The chapter on “building a pairing server” seems a bit out of place to me, the content is quite generic. Perhaps because at ScraperWiki we have always written code in the Cloud we take it for granted. The scheme Kutner follows uses vagrant and Puppet to configure servers in the Cloud. This is a fairly effective scheme. We have been using Docker extensively which is a slightly different thing, since a Docker container is not a virtual machine.

Are we doing anything different in the office as a result of this book? Yes – we’ve got a good quality external microphone (a Blue Snowball), and it’s so good I’ve got one for myself. Managing audio is still something that seems a challenge for modern operating systems. To a human it seems obvious that if we’ve plugged in a headset and opened up Google Hangouts then we might want to talk to someone and that we might want to hear their voice too. To a computer this seems unimaginable. I’m looking to try out NoMachine when a suitable occasion arises.

Remote Pairing is a handy guide for those getting started with remote working, and it’s a useful summary for those wanting to see if they are missing any tricks.

Jan 03 2015

Book review: Graph Databases by Ian Robinson, Jim Webber and Emil Eifrem

graphdatabases

This review was first posted at ScraperWiki.

Regular readers will know I am on a bit of a graph binge at the moment. In computer science and mathematics graphs are collections of nodes joined by edges, they have all sorts of applications including the study of social networks and route finding. Having covered graph theory and visualisation, I now move on to graph databases. I started on this path with Seven Databases in Seven Weeks which introduces the Neo4j graph database.

And so to Graph Databases by Ian Robinson, Jim Webber and Emil Eifrem which, despite its general title, is really a book about Neo4j. This is no big deal since Neo4j is the leading open source graph database.

This is not just random reading, we’re working on an EU project, NewsReader, which makes significant use of RDF – a type of graph-shaped data. We’re also working on a project for a customer which involves traversing a hierarchy of several thousand nodes. This leads to some rather convoluted joining operations when done on a SQL database, a graph database might be better suited to the problem.

The book starts with some definitions, identifying the types of graph database (property graph, hypergraph, RDF). Neo4j uses property graphs where nodes and edges are distinct items and each can hold properties. In contrast RDF graphs are expressed as triples which encompass both edges and nodes. In hypergraphs multiple edges can be expressed as a single item. A second set of definitions are regarding the types of graph processing system: graph databases and graph analytical engines. Neo4j is designed to provide good performance for database-like queries, acting as a backing store for a web application rather than an analytical engine to carry out offline calculations. There’s also an Appendix comparing NoSQL databases which feels like it should be part of the introduction.

A key feature of native graph databases, such as Neo4j, is “index-free adjacency”. The authors don’t seem to define this well early in the book but later on whilst discussing the internals of Neo4j it is all made clear: nodes and edges are stored as fixed length records with references to a list of nodes to which they are connected. This means its very fast to visit a node, and then iterate over all of its attached neighbours. The alternative index-based lookups may involve scanning a whole table to find all links to a particular node. It is in the area of traversing networks that Neo4j shines in performance terms compared to SQL.

As Robinson et al emphasise in motivating the use of graph databases: Other types of NoSQL database and SQL databases are not built fundamentally around the idea of relationships between data except in quite a constrained sense. For SQL databases there is an overhead to carrying out join queries which are SQLs way of introducing relationships. As I hinted earlier storing hierarchies in SQL databases leads to some nasty looking, slow queries. In practice SQL databases are denormalised for performance reasons to address these cases. Graph databases, on the other hand, are all about relationships.

Schema are an important concept in SQL databases, they are used to enforce constraints on a database i.e. “this thing must be a string” or “this thing must be in this set”. Neo4j describes itself as “schema optional”, the schema functionality seems relatively recently introduced and is not discussed in this book although it is alluded to. As someone with a small background in SQL the absence of schema in NoSQL databases is always the cause of some anxiety and distress.

A chapter on data modelling and the Cypher query language feels like the heart of the book. People say that Neo4j is “whiteboard friendly” in that if you can draw a relationship structure on a whiteboard then you can implement it in Neo4j without going through the rigmarole of making some normalised schema that doesn’t look like what you’ve drawn. This seems fair up to a point, your whiteboard scribbles do tend to be guided to a degree by what your target system is, and you can go wrong with your data model going from whiteboard to data model, even in Neo4j.

I imagine it is no accident that more recent query languages like Cypher and SPARQL look a bit like SQL. Although that said, Cypher relies on ASCII art to MATCH nodes wrapped in round brackets and edges (relationships) wrapped in square brackets with arrows –>  indicating the direction of relationships:

MATCH (node1)-[rel:TYPE]->(node2)
RETURN rel.property

which is pretty un-SQL-like!

Graph databases goes on to describe implementing an application using Neo4j. The example code in the book is in Java but there appears, in py2neo, to be a relatively mature Python client. The situation here seems to be in flux since searching the web brings up references to an older python-embedded library which is now deprecated. The book pre-dates Neo4j 2.0 which introduced some significant changes.

The book finishes with some examples from the real world and some demonstrations of popular graph theory analysis. I liked the real world examples of a social recommendation system, access control and parcel routing. The coverage of graph theory analysis was rather brief, and didn’t explicit use Cypher which would have made the presentation different from what you find in the usual graph theory textbooks.

Overall I have mixed feelings about this book: the introduction and overview sections are good, as is the part on Neo4j internals. It’s a rather slim volume, feels a bit disjointed and is not up to date with Ne04j 2.0 which has significant new functionality.  Perhaps this is not the arena for a dead-tree publication – the Neo4j website has a comprehensive set of reference and tutorial material, and if you are happy with a purely electronic version than you can get Graph Databases for free (here).

Dec 09 2014

NewsReader – the developers story

newsreader

This post was first published at ScraperWiki.

ScraperWiki has been a partner in NewsReader, an EU Framework 7 research project, for the last couple of years. The aim of NewsReader is to give computers the power to “understand” the news; to extract from a myriad of news articles the underlying events which gave rise to those articles; the who, the where, the why and the what of those events. The project is comprised of academic researchers specialising in computational linguistics (VUA in Amsterdam, EHU in the Basque Country and FBK in Trento), Lexis Nexis – a major news aggregator, and a couple of small technology companies: ourselves at ScraperWiki and SynerScope – a Dutch startup specialising in the visualisation of complex networks.

Our role at ScraperWiki is in providing mechanisms to enable developers to exploit the NewsReader technology, and to feed news into the system. As part of this work we have developed a simple REST API which gives access to the KnowledgeStore, the system which underpins NewsReader. The native query language of the KnowledgeStore is SPARQL – the query language of the semantic web. The Simple API provides a set of predefined queries which are easier for end users to work with than raw SPARQL, and help us as service managers by providing a predictable set of optimised queries. If you want to know more technical detail then we’ve written a paper about it (here).

The Simple API has seen live action at a Hack Day on World Cup news which we held in London in the summer. Attendees were able to develop a range of applications which probed violence, money and corruption in the realm of the World Cup. I blogged about our previous Hack Day here and here. The Simple API, and the Hack Day helped us shake out some bugs and add features which will make it even better next time.

“Next time” is another Hack Day to be held in the Amsterdam on 21st January 2015, and London on the 30th January 2015. This time we have processed 6,000,000 articles relating to the car industry over the period 2005-2014. The motor industry is a trillion dollar a year business, so we can anticipate finding lots of valuable information in this horde.

From our previous experience the three things that NewsReader excels at are:

  1. Finding networks of interactions, identifying important players. For the World Cup Hack Day we at ScraperWiki were handicapped slightly by having no interest in football! But the NewsReader technology enabled us to quickly identify that “Sepp Blatter”, “Jack Warner” and “Mohammed bin Hammam” were important in world football. This is illustrated in this slightly cryptic visualisation made using Gephi:beckham_and_blatter
  2. Finding events of a particular type. the NewsReader technology carries out semantic role labeling: taking sentences and identifying what type of event is described in that sentence and what roles the participants took. This information is then aggregated and exposed using semantic web technology. In the World Cup Hack Day participants used this functionality to identify events involving violence, bribery, gambling, and other financial transactions;
  3. Establishing timelines. In the World Cup data we could track the events involving “Mohammed bin Hammam” through time and the type of events he was involved in. This enabled us to quickly navigate to pertinent news articles.Timeline

You can see fragments of code used to extract these data using the Simple API in these GitHub Gists (here and here), and dynamic visualisations illustrating these three features here and here.

The Simple API is up and running already, you can find it (here). It is self-documenting, simply visit the root URL and you’ll see query examples with optional and compulsory parameters. Be aware though: the Simple API is under active development, and the underlying data in the KnowledgeStore is being optimised for the Hack Days so it may not be available when you visit.

If you want to join our automotive Hack Day then you can sign up for the Amsterdam event (here) and the London event (here).

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