Most interesting links of May ’12

This was a rich month, bringing some hope for ORM, providing a peep-hole into the bright and awesome future with in-browser video and other cool web-stuff presented at WebRebels 2012 and IDEs providing immediate feedback and visualisation. There were valuable articles about simplicity and quality in software and good talks about the lean startup (i.e. enabling innovation) and other topics.

Recommended Readings

• M.Fowler: ORM Hate – Why ORM is actually a good solution – a very valuable article where Fowler opposes the popular trend of criticising Object-Relational Mappers such as Hibernate. Yes, using an ORM is difficult and a leaky abstraction – but that’s because the problem of mapping from a rich in-memory object model to a relational store is inherently difficult (and you need to do it with or w/o an ORM tool) and because those last 10-20% of DB access require human intelligence. If you can avoid the need for ORM by using the relational model also in memory or by using a NoSQL database with a data model that fits your in-memory model then it’s great to do so but often you can’t and then using ORM is the best solution. You certainly don’t want to code your own “lightweight” ORM.
• Alan Quayle on WebRTC, the HTML5 standard for in-browser video/text communication – intro & status – this is an exciting technology coming to our browsers. Some quotes: “WebRTC enables applications such as voice calls, video chat, file sharing, messaging, white-boarding, gaming, human computer interaction, etc. without any client or plug-in download to run from a browser using simple HTML and JavaScript APIs.  Real time communications becomes pervasive on the internet. … Essentially any browser becomes a SIP end point, a telephone, an ‘open’ Skype client, an end point for any real-time communication and control. … Likely by the end of this year we’ll see Chrome and Firefox running WebRTC.“
• Communicating Sequential Processes: Theory for reasoning about concurrent, interacting processes – an inspirational reading about a much better way to do concurrency than Java threads; “… [CSP] is a language for describing patterns of interaction between concurrent objects. It is supported by an elegant, mathematical theory, a set of proof tools, and an extensive literature.” The beauty is that thanks to the theory behind, you can actually reason about the interactions and verify their correctness, contrary to the feared mess of Java threads. CSP is broadly similar to the popular Actors model and is implemented in Occam while it also influenced Erlang’s concurrency model and Go. The library JSCP brings it to Java. I guess we’re better of using Actors due to their popularity and maturity though the mathematical backing of CSP with the potential of formal proofs of correctness is indeed attractive. Any of the two is better than using threads directly because:
  

  The monitor-threads model provided by Java, whilst easy to understand, proves very difficult to apply safely in any system above a modest level of complexity. One problem is that monitor methods are tightly interdependent, so that their semantics compose in non-trivial ways [...]

• Rich Hickey introduces the Reducers library: simplicity in practice – a beautiful example of simplifying something by taking appart all the unrelated but mingled concerns and focus only on those really needed. Whether you’re interested in Clojure or not, you should read the beginning of the post where Hickey explains how the current collection functions based on first (returns 1st element) and rest (returns the remaining ones) mix too many things (ordering, output representation, etc.) and how this “new super-generalized and minimal abstraction for collections” avoids that and thus provides e.g. for doing things in parallel and composing transformation without producing intermediate collections. Beautiful! (PS: I’ve blogged about more examples of pursuing simplicity & gaining power.)
• M. Fowler: Cannot Measure Productivity – a thoughful discussion of why the productivity of programmers is hard/impossible to measure (i.e. you should concentrate on measuring other, more useful metrics) “[..] false measures only make things worse.”
• Gojko Adzic: Redefining software quality – an obligatory read that introduces a holistic view of SW quality and the quality pyramid. The key idea is that there are multiple, vertically organized facets of quality and once a more basic facet is saturated, you should move and and concentrate on the next facet and level of quality. The quality pyramid: Deployable & functionally OK > Performant & secure > Usable > Useful > Successful. Once a particular level is satisfied, it is wasteful to put more effort into it and you’ll bring much more value to the customer by focusing on the next higher level. Gojko: “Yet from what I see most software teams invest, build and test only at the lowest two levels, gold-plating things without a way to explain why that is bad.”
• Is Pair Programming for Me? – the author, who claims to have taught pair programming to 200+500 people, points out that pair programming is a skill that must be (consciously) learned, or actually a number of inter-personal skills. He also describes the cycle people go through when learning it, including a temporary downswing in productivity and negative view of pairing (therefore people should do it at least for 3-4 weeks to overcome the problems and gain the benefits).

Videos

• Bret Victor: Inventing on Principle (55 min, see at least the first 5 min) – very inspiring! Victor firmly believes that “creators need an immediate connection to what they create” and demonstrates how this can be achieve when coding image rendering, a game, an algorithm, when designing a circuit. After watching it for few minutes you will think: How could we have been working with such crappy tools without realizing how limited they are? Fortunately people started to apply the idea of an immediate connection between code and the result, f.ex. in LightTable and Bikeshed‘s IDE. On the other hand, there is an evidence that this may be too hard with the current programming languages.
• Eric Ries: Evangelizing for the Lean Startup – entertaining and enriching introduction into an approach for bringing innovation to life – f.ex. in startups – withou failing unnecessary, demonstrated on the example of the author’s failed and successful startup. Many innovators fail because they don’t realize that their key challenge is that they don’t now neither the problem (who are our customers and what they need) nor the solution (the product to satisfy the need) and thus what they need to do is to experiment and learn in the shortest cycles possible. If you wander what the buzz about lean startup is or how to build innovations, this is the ultimite source you should watch. The video has 1h but the first 20-30 min will give you a sufficient overview. The key points summarized by the Iterate lean guru Anders Haugeto:
  dd

  - There is only one way to measure progress: Progress == The amount of things you have learned from your real customers
  - Hence, you need to work a continuous loop to build, measure and learn as fast as possible. Typical iterations, like sprints, are too long, hence inefficient
  - Until you have an established product, even recognized engineering practices like TDD, sprints, refactoring, and all the XP-stuff are less important than this feedback cycle
  - Even the perfect agile method is nothing, if you’re using it to build the wrong product: How can you know you are heading in the right direction?

• WebRebels 2012 conference talks – I’d especially recommend the awakening talk by Zed Shaw pointing out that we’re building amazing things – but on top of crapy technologies without realizing anymore that the technologies are crappy and could/should be much better. Erlend Oftedal’s talk about webapp security was an (scary) eye-opener for me. If you’re considering offline webapps with HTML5′s webapp cache and/or local storage then you must listen to Jake Archibald’s painful story of various pitfalls hidden there. Christian Johansen’s Pure, functional JavaScript is a pleasure to listen to. Check out the program.

Links to Keep

• E. King: Maximizing the Value of Your Stand-up – interesting techniques to try out at your stand-ups – Speed Scrum, Pass-the-Conch Scrum (passing a token randomly to define the order), Time-Box Scrum, Challenge Scrum (the team may ask 1 question each presenter), Impediments-Only Scrum, Award Scrum (reward for best articulation of his/her information), Business Value-Focused Scrum, No-Board Scrum, Whiteboard Scrum, Buddy Scrum (report for sb. else)

Useful Tools

• puppet-lint – check code style of your Puppet files
• Guard – cross-platform tool that can watch for file changes and execute actions (“guards”) when a file is changed, useful e.g. to execute tests/style checks only on the files being modified. Includes support for many testing/checking tools and multiple notification means such as Growl.
• ThreadLogic – Thread dump analyzer that understands common patterns found in application servers and enabling the definition of custom patterns. Supports Sun, IBM, and JRockit.
• Dumbster – mock SMTP server for unit testing (start in @Before, get sent messages in the test, stop afterwards)

Quotes

Kai Thomas Gilb, in a talk proposal for JavaZone 2012:

Accurate estimation is impossible for complex technical projects, but keeping to agreed budgets, and deadlines is achievable by using feedback and change.

Clojure Corner

• StackOverflow: Clojure Performance Benchmarks – links to various discussions and benchmarks (beware that older results and facts are likely to be outdated). And of course you must keep in mind that 1) benchmark only measure what they measure, e.g. the outcomes cannot be generalized and that 2) benchmark results aren’t relevant for your problem unless you’re doing exactly the kind of operations being benchmarked (e.g. who cares that X is 100 ms slower if your code spends 1s waiting for a XML file download?) (Craig Andera had a pretty good experience report from webapp performance testing including what (not) to do)
• A good wrapup of the EuroClojure conference by Deon Garrett. Such a pity I missed it!
• Goldberg (at GitHub) – Johann Sebastian Bach’s Goldberg Variations in Overtone by @ctford; using Overtone and Clojure to build up mathematical and functional definitions of canons. Deon Garrett: “Go right now and download the code from Chris’ talk. If you don’t know Clojure, use this as an excuse to learn it – it’s that good.”

Rich Hickey interviewed by M. Fogus:

Reducing incidental complexity is a primary focus of Clojure, and you could dig into how it does that in every area.

…

In particular, the use of objects to represent simple informational data is almost criminal in its generation of per-piece-of-information micro-languages, i.e. the class methods, versus far more powerful, declarative, and generic methods like relational algebra. Inventing a class with its own interface to hold a piece of information is like inventing a new language to write every short story. This is anti-reuse, and, I think, results in an explosion of code in typical OO applications.

Bad Code: Too Many Object Conversions Between Application Layers And How to Avoid Them

Have you ever worked with an application where you had to copy data from one object to another and another and so on before you actually could do something with it? Have you ever written code to convert data from XML to a DTO to a Business Object to a JDBC Statement? Again and again for each of the different data types being processed? Then you have encountered an all too common antipattern of many “enterprise” (read “overdesigned”) applications, which we could call The Endless Mapping Death March. Let’s look at an application suffering from this antipattern and how to rewrite it in a much nicer, leaner and easier to maintain form.

The application, The World of Thrilling Fashion (or WTF for short) collects and stores information about newly designed dresses and makes it available via a REST API. Every poor dress has to go through the following conversions before reaching a devoted fashion fan:

1. Parsing from XML into a XML-specific XDress object
2. Processing and conversion to an application-specific Dress object
3. Conversion to a MongoDB’s DBObject so that it can be stored in the DB (as JSON)
4. Conversion from the DBObject back to the Dress object
5. Conversion from Dress to a JSON string

Uff, that’s lot of work! Each of the conversions is coded manually and if we want to extend WTF to provide information also about trendy shoes, we will need to code all of them again. (Plus couple of methods in our MongoDAO, such as getAllShoes and storeShoes.) But we can do much better than that!

Beautiful Code: Simplicity Yields Power

In Simple Made Easy argues Rich Hickey that mixing orthogonal concerns introduces unnecessary complexity and that we should keep them separate. This mixing sometimes occurs on such a basic level that we believe that there is no other way to do it, an example being the interleaving of polymorphism and hierarchical namespacing represented by OO class hierarchies. Taking those “complected” concerns apart and dealing with them separately yields cleaner, simpler solutions and sometimes also more powerful ones because you are free to combine them as you need and not as the author decided.

Creating On-Demand Clusters in EC2 with Puppet

For a recent project I needed to be able to start on-demand clusters of machines in Amazon EC2. We needed each instance in a cluster to allow SSH and sudo access for all team members and to install and configure the software appropriate for that cluster (“database” node or “testclient” node).

You can see the results of my effort using EC2 command-line tools, Puppet etc. at the project’s Puppet GitHub repository, the setup is described in detail in its README.

(Tips for improvements are welcome. And not, Star Cluster isn’t what we needed.)

Most interesting links of April ’12

Recommended Readings

• V. Duarte: Story Points Considered Harmful – Or why the future of estimation is really in our past… (also as 1h video) – thoughtful and data-backed claim that there is a much cheaper way for estimating work throughput than estimating each story in story points (SP) and that is simply counting the stories. Even though their sizes differ, over (not that much) longer periods, where it really matters, these differences will even out. The author argues that estimating in number of stories provides the same reliability and benefits as SP and is much easier. (Keep in mind that estimation is just an attempt at predicting the future and humans are proved to be terrible at doing that; why to pretend that we can do it?) I’d recommand this to anybody doing Scrum and similar.
• M. Fowler: Test Coverage – it’s obvious that increasing test coverage for the sake of test coverage it’s a nonsense but some people still need to be reminded of it . Fowler explains what the real benefit of test coverage measurements is and how to use it for good instead of for evil.
• Brian Marick: How to Misuse Code Coverage (pdf) – cited a lot by Fowler in his article, this is really a good paper. Marick has participated in the development of several code coverage tools and understands well their limitations. One of the key points is that code coverage tools can discover only one class of test weakness (not testing some paths through your code) but cannot discover that you are missing some code you should have (e.g. when you check only for two of three possible return values). Thus the code coverage metric tells you “this code isn’t well tested, are you sure you don’t to look more into it”? It’s crucial not to write tests so as to increase the code coverage; look at the code and improve the test without any regard for coverage. You may thus decrease the likeliness of both the class of problems.
• A Year with MongoDB – Kiip has found out that Mongo isn’t the best choice for them (having 240GB, 500+ operations/s, 85M docs and their specific usage of the store) and migrated to the combination of Riak (key-value store) and PostgreSQL. Some of the issues they hit are slow counts and limit/offset queries due to using non-counting B-trees for indexing, memory management that could be more intelligent and tuned for the use to make sure the data needed is indeed in RAM, no built-in support for compressing key names (their size adds up as they’re repeated in each document; you’ve to compress them [user -> u etc.] in the client if you want to), limited concurrency due to process-wide write lock (which becomes a problem if the write’s aren’t short enough w.r.t. number of ops/s, e.g. because data isn’t in RAM and/or the query is complicated), safe settings (waiting for a write to finish, …) off by default, offline-only table compaction (w/o it the disk usage grows unbounded). The lessons learnt for me: Know your storage, its weaknesses and intended way of usage, and make sure it matches your needs.
• Rudolf Winestock: The Lisp Curse – Lisp’s expressive power is actually a cause of its lack of momentum because it’s so easy to implement anything that people have no need to join forces and thus there are many half-baked (“works-for-me”) solutions for anything – but no complete, generally accepted one. An interesting essay. “Lisp is so powerful that problems which are technical issues in other programming languages are social issues in Lisp.”
• Understanding JDBC Internals & Timeout Configuration – the article itself could have been written better but it conveys the important information that configuring timeouts for JDBC isn’t trivial because they need to be set correctly at different levels and without a socket timeout set in a driver-specific way it can hang forever if the DB cannot be reached due to network/system failure
• Circos: An Amazing Tool for Visualizing Big Data – this article is interesting primarily for its combination of Google Analytics API, Neo4J and an unusual data visualization with circular graphs

Tools

• CRaSH: Extensible shell for the JVM (docs) – a shell that you can embedd into a web server as a WAR, run standalone or attach to a running JVM, connect to it via SSH or Telnet, and use it to execute commands against the JVM. Some commands: configure loggers, control threads, monitor the system (mem, threads, ..), connect/issue queries via JDBC. More commands can be written in Groovy. There is a whole set of commands for working with JCR. Pluggable authentication.

Clojure Corner

• Clojure 1.4 Released – What’s new? – support for custom reader literals => literals for instants and uuid, aligned dot property access with ClojureScript, added mapv, filterv, reduce-kv
• Running REPL simplified with lein2 (lein2 repl) – no more need for rlwrap, completion

Groovy Grape: Troubleshooting Failed Download

If you use the Grape’s @Grab annotation to get dependencies for your Groovy scripts at runtime and their retrieval fails with the exception “General error during conversion: Error grabbing Grapes — [unresolved dependency: ...not found]” and a useless stack trace then you migth want to know that you can configure Ivy to log all the details of what is going on (what it is trying to download, where from, …), for example in the interactive groovysh shell:

groovy:000> org.apache.ivy.util.Message.setDefaultLogger(new org.apache.ivy.util.DefaultMessageLogger(org.apache.ivy.util.Message.MSG_DEBUG))
groovy:000> groovy.grape.Grape.grab(autoDownload: true, group: 'org.eclipse.jetty.orbit', module: 'javax.servlet', version: '3.0.0.v201112011016')
...
WARN: ==== ibiblio: tried
WARN:   http://repo1.maven.org/maven2/org/eclipse/jetty/orbit/javax.servlet/3.0.0.v201112011016/javax.servlet-3.0.0.v201112011016.orbi
...

You can likely also increase the log level by setting the system property ivy.message.logger.level to 4 (debug, see the Ivy Message class.)

(For the list of arguments that grab supports see GrapeIvy, namely the method createGrabRecord [btw, ext and type are ignored unless you also set classifier])

Most interesting links of Mars ’12

Recommended Readings

• ThoughtWorks Technology Radar 3/2012 – including apps with embedded servlet containers (assess), health check pages for webapp monitoring, testing at the appropriate level (adopt), JavaScript micro-framewors (trial, see Microjs.com), Gradle over Maven (e.g. thanks to flexibility), OpenSocial for data & content sharing between (enterprise) apps (assess), Clojure (before in asses) and CoffeeScript on trial (Scala very close to adopt), JavaScript as a 1st class language (adopt), single-threaded servers with aync I/O (Node.js, Webbit for Java [http/websocket], …; assess).
• Jez Humble: Four Principles of Low-Risk Software Releases – how to make your releases safer by making them incremental (versioned artifacts instead of overwritting, expand & contract DB scripts, versioned APIs, releasing to a subset of customers first), separating software deployment from releasing it so that end-users can use it (=> you can do smoke tests, canary releasing, dark launching [feature in place but not visible to users, already doing something]; includes feature toggles [toggle on only for somebody, switch off new buggy feature, ...]), delivering features in smaller batches (=> more frequently, smaller risk of any individual release thanks to less stuff and easier roll-back/forward), and optimizing for resiliance (=> ability to provision a running production system to a known good state in predictable time – crucial when stuff fails).
• The Game of Distributed Systems Programming. Which Level Are You? (via Kent Beck) – we start with a naive approach to distributed systems, treating them as just a little different local systems, then (painfully) come to understand the fallacies of distributed programming and start to program explicitely for the distributed environment leveraging asynchronous messaging and (often functional) languages with good support for concurrency and distribution. We suffer by random, subtle, non-deterministic defects and try to separate and restrict non-determinism by becoming purely functional … . Much recommended to anybody dealing with distributed systems (i.e. everybody, nowadays). The discussion is worth reading as well.
• Shapes Don’t Draw – thought-provoking criticism of inappropriate use of OOP, which leads to bad and inflexible code. Simplification is OK as long as the domain is equally simple – but in the real world shapes do not draw themselves. (And Trades don’t decide their price and certainly shouldn’t reference services and a database.)
• Capability Im-Maturity Model (via Markus Krüger) – everybody knows CMMI, but it’s useful to know also the negative directions an organization can develop in. Defined by Capt. Tom Schorsch in 1996, building on Anthony Finkelstein’s paper A Software Process Immaturity Model.
• Cynefin: A Leader’s Framework for Decision Making – an introduction into the Cynefin cognitive framework – the key point is that we encounter 5 types of contexts differing by the predictability of effects and each of them requires a different management style, using the wrong one is a recipe for a disaster. Quote:
  

  The framework sorts the issues facing leaders into five contexts defined by the nature of the relationship between cause and effect. Four of these—simple, complicated, complex, and chaotic—require leaders to diagnose situations and to act in contextually appropriate ways. The fifth—disorder—applies when it is unclear which of the other four contexts is predominant.

• Et spørsmål om kompleksitet (Norwegian). Key ideas mixed with my own: Command & control management in the traditional Ford way works very well – but only in stable domains with clear cause-and-effect relationships (i.e. the Simple context of Cynefin). But many tasks today have lot of uncertanity and complexity and deal with creating new, never before seen things. We try to lead projects as if they were automobile factories while often they are more like research – and researchers cannot plan when they will make a breakthrough. Most of the new development of IT systems falls into the Complex context of Cynefin – there is lot of uncertanity, no clear answers, we cannot forsee problems, and have to base our progress on empirical experience and leverage emergence (emergent design, ..).
• The Economics of Developer Testing – a very interesting reflection on the cost and value of testing and what is enough tests. Tests cost to develop and maintain (and different tests cost differently, the more complex the more expensive). Not having tests costs too – usually quite a lot. To find the right ballance between tests and code and different types of tests we must be aware of their cost and benefits, both short & long term. Worth reading, good links. (Note: We often tend to underestimate the cost of not having good tests. Much more then you might think.)

Links to Keep

• Proved Patterns for Creating Responsive Web UIs (adapting to the user device size – PC, mobile, ..): Mostly Fluid (fluid grids, down-scaling images,decreasing margins and eventually stacking columns below each other on smaller screens), Column Drop, Layout Shifter (diff. layout for large/medium/small screens), … .
• 16 Linux Server Monitoring Commands You Really Need To Know – some elementary such as top, iostat, and netstat and some more dashboard-like such as nmon (Nigel’s Monitor) and the stats collecting sar.

Quotes

Kent Beck answering a question about how much testing to do (highlighted by me):

I get paid for code that works, not for tests, so my philosophy is to test as little as possible to reach a given level of confidence (I suspect this level of confidence is high compared to industry standards, but that could just be hubris). If I don’t typically make a kind of mistake (like setting the wrong variables in a constructor), I don’t test for it. I do tend to make sense of test errors, so I’m extra careful when I have logic with complicated conditionals. When coding on a team, I modify my strategy to carefully test code that we, collectively, tend to get wrong.

Different people will have different testing strategies based on this philosophy, but that seems reasonable to me given the immature state of understanding of how tests can best fit into the inner loop of coding. Ten or twenty years from now we’ll likely have a more universal theory of which tests to write, which tests not to write, and how to tell the difference. In the meantime, experimentation seems in order.

Kent Beck: Best Practices for Software Design with Low Feature Latency and High Throughput

I was fortunate to attend Kent Beck’s lecture summarizing his experiences and thoughts regarding efficient software design. Traditionally there have been two schools of thought about design: Predictive design, trying to design everything upfront (and making lot of wrong decisions) and reactive design, where any design is only done if it is absolutely necessary for implementing a feature (thus developing often on top of an insufficient design). Kent tried hard to discover such a design method that really delivers on the promises of both while avoiding their failures. This method is based on evolving design frequently in small, safe steps and focusing on learning while following some key best practices. It doesn’t really matter what scope of design we are are speaking about, the method and principles are the same whether you’re redesigning a class or a complex system.

Link: Benchmark and Scaling of Amazon RDS (MySQL)

Performance and scaling of the Amazon-managed MySQL, Relational Data Store (RDS):

Scaling options:

• Horizontal scaling
  • Sharding (distribute data [tables or rows] among multiple RDS instances; Tumblr uses sharded MySQL and it worked well for them) – there is no explicit support so the applications have to handle it themselves, i.e. know which table/rows to read from which instance
  • Read-replicas: RDS supports set up of read-only replicas using MySQL’s own replication; the replicas are evidently only usable for reading and may contain little stale data
• Vertical scaling (stronger EC2 instances) – there are interesting results from a benchmark of RDS with various instances/DB sizes (6/2011, complete report); key observations:
  • “With hardly any dependency on the database size, MySQL reaches its optimal throughput at around 64 concurrent users. Anything above that causes throughput degradation.”
  • “Throughput is improving as machines get stronger. However, there is a sweet-spot, a point where adding hardware doesn’t help performance. The sweet spot is around the XL machine, which reaches a [max] throughput of around 7000 tpm.” (transactions per minute => ~ 110 tx/sec)

Disclaimer: No banchmark proves anything generally applicable, it’s always necessary to run one’s own production load and measure that to see how in reality a DB performs for one’s actual needs.

Notes

• The number of concurrent connections is by default derived from the memory, namely 150 for a small 1.5GB instance and 650 for a large 7.5GB instance. According to one expert it’s completely OK to set it to 1000 connections without regard to memory; MySQL should handle it.

Most interesting links of February ’12

Recommended Readings

• List of open source projects at Twitter including e.g. their scala_school – Lessons in the Fundamentals of Scala and effectivescala – Twitter’s Effective Scala Guide
• M. Fowler & P. Sadalage: Introduction into NoSQL and Polyglot Persistence (pdf, 11 slides) – what RDBMS offer and why it sometimes isn’t enough, what the different NoSQL incarnations offer, how and on which projects to mix and match them
• Two phase release planning – the best way to plan something somehow reliably is to just start doing it, i.e. just start the project with the objective of answering “Can this team produce a respectable implementation of that system by that date?” in as short time as possible (i.e. few weeks). Then: “Phase 2: At this point, there’s a commitment: a respectable product will be released on a particular date. Now those paying for the product have to accept a brute fact: they will not know, until close to that date, just what that product will look like (its feature list). What they do know is that it will be the best product this development team can produce by that date.” Final words: “My success selling this approach has been mixed. People really like the feeling of certainty, even if it’s based on nothing more than a grand collective pretending.”
• Tumblr Architecture – 15 Billion Page Views A Month And Harder To Scale Than Twitter – what SW (Scala, Finagle, heavily partitioned MySQL, …) and HW they use, the architecture (Firehose – event bus, cell design), lessons learned (incl. “MySQL (plus sharding) scales, apps don’t.”
• Jay Fields’ Thoughts: Compatible Opinions on Software – about teams and opinion conflicts – there are some areas where no opinion is really right (e.g. powerful language vs. powerful IDE) yet people may have very strong feeling about them. Be aware of what your opinions are and how strong they are – and compose teams so that they include more less people with compatible (not same!) opinions – because if you team people with strong opposing opinions, they’ll loose lot of productivity. Quotes: “I also believe that you can have two technically excellent people who have vastly different opinions on the most effective way to deliver software.” “I suggest that you do your best to avoid working with someone who has both an opposing view and is as inflexible as you are on the subject. The more central the subject is to the project, the more likely it is that productivity will be lost.”
• Jay Fields’ Thoughts: Lessons Learned while Introducing a New Programming Language (namely Clojure) – introducing a new language and winning the hearts of (sufficient subset of) the people is difficult and requires lot of extra effort. This is both an experience report and a pretty good guide for doing it.
• Jay Fields’ Thoughts: Life After Pair Programming – a proponent of pair-programming comes to the conclusion that in some contexts pairing may not be beneficial, i.e. the benefits of pair-programming don’t overweight the costs (for a small team, small software, …)
• The Why Monitoring Sucks (and what we’re doing about it) – the #monitoringsucks initiative- what tools there are, why they suck, what to do, new tools, what metrics to collect, blogs, …
  • Related: Getting started with the Sensu monitoring framework
• JBoss Byteman 2.0.0: Bytecode Manipulation, Testing, Fault Injection, Logging – a Java agent which helps testing, tracing, and monitoring code, code is injected based on simple scripts (rules) in the event-condition-action form (the conditions may use counters, timers etc.). Contrary to AOP, there is no need to create classes or compile code. “Byteman is also simpler to use and easier to change, especially for testing and ad hoc logging purposes.” “Byteman was invented primarily to support automation of tests for multi-threaded and multi-JVM Java applications using a technique called fault injection.” It was used e.g. to orchestrate the timing of activities performed by independent threads, for monitoring and statistics gathering, for application testing via fault injection. Contains a JUnit4 Runner for easily instrumenting the code under test, it can automatically load a rule before a test and unload it afterwards:

  @Test
  @BMRule(name="throw IOException at 1st call",
  targetClass = "TextLineProcessor",
  targetMethod = "processPipeline",
  action = "throw new java.io.IOException()")
  public void testErrorInPipeline() throws Exception { ... }

• How should code search work? – a thought-provoking article about how much better code completion could be if it profited more from patterns of usage in existing source codes – and how to achieve that. Intermediate results available in the Code Recommenders Eclipse plugin.

REST

• What Makes Jersey Interesting: Parameter Classes (by Coda Hale, 5/2009) – brief yet rich and very practical introduction into Jersey (the reference implementation of JAX-RS. i.e. REST, for Java) including error handling, parameter classes (automatic wrapping of primitive values). The following article, What Makes Jersey Interesting: Injection Providers, might be of interest too.
• How to GET a Cup of Coffee, 10/2008 – good introduction into creating applications based on REST, explained on an example of building REST workflow for the ordering process in Starbucks – a “self-describing state machine”. The advantage of this article is that it presents the whole REST workflow with GET, OPTIONS, POST, PUT and “advanced” features such as the use of If-Unmodified-Since/If-Match, Precondition Failed, Conflict. The workflow steps are connected via the Location header and a custom <next> link tag with rel and uri. Other keywords: etag, microformats, HATEOS (-> derive the next resource to access from the links in the previous one), Atom and AtomPub, caching (web trades latency for scaleability; if 1+s latency isn’t acceptable than web isn’t the right platform), URI templates (-> more coupling than links in responses), evolution (-> links from responses, new transitions), idempotency. “The Web is a robust framework for integrating systems at local, enterprise, and Internet scale.”

Links to Keep

Tools, Libraries etc.

• ClusterSSH – whatever commands you execute in the master SSH session are also execute in the slave sessions – useful if you often need to execute the same thing on multiple machines (requires Perl); to install on Mac: “brew install csshx”
• HTML5 Boilerplate (H5BP) – customizable initial HTML5 project template for a website; can be combined e.g. with Bootstrap, the HTML/JS/CSS toolkit (there is even a script to set them both up). Includes server configs for optimal performance, “delivers best practices, standard elements”.
• High performance libraries in Java – disruptor, Java Chronicle (ultra-fast in-memory db), Colt Matrix library (scientific computations), Javolution (RT Java), Trove collections for primitives, MG4J (free full-text search engine for large document collections), some serialization & other banchmarks links.
• Twitter Finagle – “library to implement asynchronous Remote Procedure Call (RPC) clients and servers. Finagle is flexible enough to support a variety of RPC styles, including request-response, streaming, and pipelining; for example, HTTP pipelining and Redis pipelining. It also makes it easy to work with stateful RPC styles; for example, RPCs that require authentication and those that support transactions.” Supports also failover/retry, service discovery, multiple protocol (e.g. http, thrift). Build on Netty, Java NIO. See the overview and architecture.
• Eclipse Code Recommenders – interesting plugin in incubation that tries to bring more more intelligent completion based more on context and the wisdom of the crowds (i.e. patterns of usage in existing source codes) to Eclipse

Clojure Corner

• Clojure/huh? – Clojure’s Governance and How It Got That Way – an interesting description how the development of Clojure and inclusion of new libraries is managed. “Rich is extremely conservative about adding features to the language, and he has impressed this view on Clojure/core for the purpose of screening tickets.” E.g. it took two years to get support for named arguments – but the result is a much better and cleaner way of doing it.
• Clojure Monads Series – comprehensive explanations of monads starting with Monads In Clojure

Quotes

A language that doesn’t affect the way you think about programming, is not worth knowing-

 - Alan Perlis

Lisp is worth learning for the profound enlightenment experience you will have when you finally get it; that experience will make you a better programmer for the rest of your days, even if you never actually use Lisp itself a lot.

Eric S. Raymond, “How to Become a Hacker”