s-expressions

Amit Rathore blogs about software development

Posts Tagged ‘clojure’

Why Datomic?

Posted by Amit Rathore on March 29, 2013

Cross-posted from Zololabs.

Many of you know we’re using Datomic for all our storage needs for Zolodeck. It’s an extremely new database (not even version 1.0 yet), and is not open-source. So why would we want to base our startup on something like it, especially when we have to pay for it? I’ve been asked this question a number of  times, so I figured I’d blog about my reasons:

  • I’m an unabashed fan of Clojure and Rich Hickey
  • I’ve always believed that databases (and the insane number of optimization options) could be simpler
  • We get basically unlimited read scalability (by upping read throughput in Amazon DynamoDB)
  • Automatic built-in caching (no more code to use memcached (makes DB effectively local))
  • Datalog-as-query language (declarative logic programming (and no explicit joins))
  • Datalog is extensible through user-defined functions
  • Full-text search (via Lucene) is built right in
  • Query engine on client-side, so no danger from long-running or computation-heavy queries
  • Immutable data – audits all versions everything automatically
  • “As of” queries and “time-window” queries are possible
  • Minimal schema (think RDF triples (except Datomic tuples also include the notion of time)
  • Supports cardinality out of the box (has-many or has-one)
  • These reference relationships are bi-directional, so you can traverse the relationship graph in either direction
  • Transactions are first-class (can be queried or “subscribed to” (for db-event-driven designs))
  • Transactions can be annotated (with custom meta-data) 
  • Elastic 
  • Write scaling without sharding (hundreds of thousands of facts (tuples) per second)
  • Supports “speculative” transactions that don’t actually persist to datastore
  • Out of the box support for in-memory version (great for unit-testing)
  • All this, and not even v1.0
  • It’s a particularly good fit with Clojure (and with Storm)

This is a long list, but perhaps begins to explain why Datomic is such an amazing step forward. Ping me with questions if you have ‘em! And as far as the last point goes, I’ve talked about our technology choices and how they fit in with each other at the Strange Loop conference last year. Here’s a video of that talk.

Posted in Uncategorized | Tagged: , , , , , | 3 Comments »

Pretty-printing in Clojure logs

Posted by Amit Rathore on January 15, 2013

Cross-posted from Zolo Labs.

Logging is an obvious requirement when it comes to being able to debug non-trivial systems. We’ve been thinking a lot about logging, thanks to the large-scale, distributed nature of the Zolodeck architecture. Unfortunately, when logging larger Clojure data-structures, I often find some kinds of log statements a bit hard to decipher. For instance, consider a map m that looked like this:

When you log things like m (shown here with println for simplicity), you may end up needing to understand this:

Aaugh, look at that second line! Where does the data-structure begin and end? What is nested, and what’s top-level? And this problem gets progressively worse as the size and nested-ness of such data-structures grow. I wrote this following function to help alleviate some of the pain:

Remember to include clojure.pprint. And here’s how you use it:

That’s it, really. Not a big deal, not a particularly clever function. But it’s much better to see this structured and formatted log statement when you’re poring over log files in the middle of the night.

Just note that you want to use this sparingly. I first modified things to make ALL log statements automatically wrap everything being logged with pp-str: it immediately halved the performance of everything. pp-str isn’t cheap (actually, pprint isn’t cheap). So use with caution, where you really need it!

Now go sign-up for Zolodeck!

Posted in Uncategorized | Tagged: , , , | Leave a Comment »

Why Java programmers have an advantage when learning Clojure

Posted by Amit Rathore on December 20, 2012

Cross-posted from Zolo Labs.

There is a spectrum of productivity when it comes to programming languages. I don’t really care to argue how much more productive dynamic languages are… but for those who buy that premise and want to learn a hyper-productive language, Clojure is a good choice. And for someone who has a Java background, the choice Clojure becomes the best one. Here’s why:

  • Knowing Java – obviously useful: class-paths, class loaders, constructors, methods, static methods, standard libraries, jar files, etc. etc.
  • Understanding of the JVM – heap, garbage collection, perm-gen space, debugging, profiling, performance tuning, etc.
  • The Java library ecosystem – what logging framework to use? what web-server? database drivers? And on and on….
  • The Maven situation – sometimes you have to know what’s going on underneath lein
  • Understanding of how to structure large code-bases – Clojure codebases also grow
  • OO Analysis and Design – similar to figuring out what functions go where

I’m sure there’s a lot more here, and I’ll elaborate on a few of these in future blog posts.

I’ve not used Java itself in a fairly long time (we’re using Clojure for Zolodeck). Even so, I’m getting a bit tired of some folks looking down on Java devs, when I’ve seen so many Clojure programmers struggle from not understanding the Java landscape.

So, hey Java Devs! Given that there are so many good reasons to learn Clojure – it’s a modern LISP with a full macro system, it’s a functional programming language, it has concurrency semantics, it sits on the JVM and has access to all those libraries, it makes a lot of sense for you to look at it. And if you’re already looking at something more dynamic than Java itself (say Groovy, or JRuby, or something similar), why not just take that extra step to something truly amazing? Especially when you have such an incredible advantage (your knowledge of the Java ecosystem) on your side already?

Posted in Uncategorized | Tagged: , , , , | 3 Comments »

Clojure utility functions – part II

Posted by Amit Rathore on December 3, 2012

Cross-posted from Zolo Labs

 

Here’s another useful function I keep around:

Everyone knows what map does, and what concat does. And what mapcat does. 

The function definition for pmapcat above, does what mapcat does, except that by using pmap underneath, it does so in parallel. The semantics are a bit different: first off, the first parameter is called batches (and not, say, coll, for collection). This means that instead of passing in a simple collection of items, you have to pass in a collection of collections, where each is a batch of items. 

Correspondingly, the parameter f is the function that will be applied not to each item, but to each batch of items.

Usage of this might look something like this:

One thing to remember is that pmap uses the Clojure send-off pool to do it’s thing, so the usual caveats will apply wrt to how f should behave.

Posted in Uncategorized | Tagged: , , , | Leave a Comment »

Clojure utility functions – part I

Posted by Amit Rathore on November 22, 2012

Cross-posted from Zolo Labs.

 

I kept using an extra line of code for this, so I decided to create the following function:

https://gist.github.com/4123530

Another extra line of code can similarly be removed using this function:

https://gist.github.com/4123531

Obviously, the raw forms (i.e. using doseq or map) can be far more powerful when used with more arguments. Still, these simple versions cover 99.9% of my use-cases.

I keep both these (and a few more) in a handy utils.clojure namespace I created for just such functions.

Posted in Uncategorized | Tagged: , , , , | Leave a Comment »

Make it right, then make it fast

Posted by Amit Rathore on November 5, 2012

Cross-posted to Zolo Labs

Alan Perlis once said: A Lisp programmer knows the value of everything, but the cost of nothing.

I re-discovered this maxim this past week. 

As many of you may know, we’re using Clojure, Datomic, and Storm to build Zolodeck. (I’ve described my ideal tech stack here). I’m quite excited about the leverage these technologies can provide. And I’m a big believer in getting something to work whichever way I can, as fast as I can, and then worrying about performance and so on. I never want to fall under the evil of premature optimization and all that… In fact, on this project, I keep telling my colleague (and everyone else who listens) how awesome (and fast) Datomic is, and how its built-in cache will make us stop worrying about database calls. 

A function I wrote (that does some fairly involved computation involving relationship graphs and so on) was taking 910 seconds to complete. Yes, more than 15 minutes. Of course, I immediately suspected the database calls, thinking my enthusiasm was somehow misplaced or that I didn’t really understand the costs. As it turned out, Datomic is plenty fast. And my algorithm was naive and basically sucked… I had knowingly  glossed over a lot of functions that weren’t exactly performant, and when called within an intensive set of tight loops, they added up fast.

After profiling with Yourkit, I was able to bring down the time to about 900 ms. At nearly a second, this is still quite an expensive call, but certainly less so than when it was ~ 1000x slower earlier.

I relearnt that tools are great and can help in many ways, just not in making up for my stupidity :-)

Posted in Uncategorized | Tagged: , , , , | Leave a Comment »

Rathore’s 10th Rule of Programming

Posted by Amit Rathore on August 5, 2012

Any sufficiently complicated Clojure (or Lisp) program contains an ad-hoc, informally-specified, bug-ridden, slow implementation of half of Haskell.

Posted in Uncategorized | Tagged: , , , , , | 19 Comments »

demonic v0.1 – utilities for Datomic

Posted by Amit Rathore on April 18, 2012

Announcing a new blog: blog.zolodeck.com. Just wrote the first post on my work with Datomic. I’ve put some of it into a project called demonic, and hopefully, you’ll find it of some use!

Posted in Uncategorized | Tagged: , , , , | Leave a Comment »

calling recur from catch or finally

Posted by Amit Rathore on August 15, 2010

Clojure doesn’t have tail recursion, but does support the recur form. Let’s take a quick look at how it’s used. Consider a function that sums up a list of numbers to an accumulator:

(defn add-numbers [acc numbers]
  (if (empty? numbers)
    acc
    (add-numbers (+ acc (first numbers)) (rest numbers))))

Lets ignore all the ways this can be done without the silly implementation above. Here it is in action:

user> (add-numbers 10 (range 10))
55

And here’s the problem with it:

user> (add-numbers 10 (range 10000))
; Evaluation aborted.
No message.
  [Thrown class java.lang.StackOverflowError]

The reason, of course, is that being a self-recursive function that calls itself explicitly, it blows the stack. Clojure has a way to get around this, via the recur form:

(defn add-numbers [acc numbers]
  (if (empty? numbers)
    acc
    (recur (+ acc (first numbers)) (rest numbers))))

And here is proof that it works:

user> (add-numbers 10 (range 10000))
49995010

Now, let’s look at a case where one might want to recurse from inside a catch or finally block. A use-case is a function like connect-to-service, that must retry the connection if the service is unavailable. An easy way to implement it is to catch the exception thrown when the attempt at connecting fails, then wait a few seconds, and try again by recursing. Here’s a contrived example of a function that recurs from catch:

(defn catch-recurse [n i]
  (try
    (if (> n i)
      (/ i 0)
      n)
    (catch Exception e
      (recur n (inc i)))))

The problem, of course, is that Clojure complains:

Cannot recur from catch/finally
  [Thrown class java.lang.UnsupportedOperationException]

So what to do? One way is to make the call explicitly, and hope that it won’t blow the stack:

(defn catch-recurse [n i]
  (try
    (if (> n i)
      (/ i 0)
      n)
    (catch Exception e
      (catch-recurse n (inc i)))))

It could blow the stack, though, depending:

user> (catch-recurse 100 1)
100
user> (catch-recurse 10000 1)
; Evaluation aborted.
No message.
  [Thrown class java.lang.StackOverflowError]

As pointed out, this may blow the stack, but it may not, depending on your situation. If you know it won’t, then this may be OK. Here’s a way to avoid this situation completely, using trampoline. First, a minor change to catch-recurse:

(defn catch-recurse [n i]
  (try
    (if (> n i)
      (/ i 0)
      n)
    (catch Exception e
      #(catch-recurse n (inc i)))))

Notice that in the case of an exception, we return a thunk. Now, to use our new function:

user> (trampoline catch-recurse 100 1)
100
user> (trampoline catch-recurse 10000 1)
10000

And there you have it. The common use-case of trampoline is to handle mutually recursive functions where recur isn’t useful. It checks to see if the return value of the function it’s passed in is another function. If so, it calls it. It repeats the process until a non-function value is returned, which it then itself returns. Very useful!

Posted in Uncategorized | Tagged: , , , | Leave a Comment »

Announcing WorkAtRuna.com

Posted by Amit Rathore on July 24, 2010

We’ve put a bunch of content together about our work here at Runa, and about what it’s like to work here. If you’re a Clojure developer, or a DevOps extraordinaire, drop us a line!

“In 1995, Paul Graham and Robert Morris used Common Lisp to help online merchants. Now, 15 years later, we’re doing the same with Clojure.”

Read more at www.workatruna.com

Posted in Uncategorized | Tagged: , , , , | Leave a Comment »

 
Follow

Get every new post delivered to your Inbox.

Join 1,585 other followers