Joel Spolsky complains that architecture astronauts are taking over at big, rich companies like Google and Microsoft, pushing out elaborate architectural systems that don’t solve actual problems.

He’s right in that smart, technical people like to take on any large, abstract problem that is, as he puts it, “a fun programming exercise that you’re doing because it’s just hard enough to be interesting but not so hard that you can’t figure it out.”

But I think the constant reinvention of (to use Spolsky’s examples) Lotus Notes or file synchronization represents a failure of leadership more than a failure of the astronauts themselves. If you don’t give smart engineers something interesting to work on, they’ll invent something themselves. But all they know is what they studied in school — abstract architectures that don’t solve actual problems — so that’s what they invent. It takes a rare, creative individual to come up with an idea — spreadsheets, Napster, eBay — that is both an interesting technical challenge and a desirable product.

So my question to Mr. Spolsky is: what should all those architecture astronauts be working on instead?

My PC-oriented brain says it’s easier to work with a million small files than one gigantic file. Hadoop says the opposite — big files are stored contiguously on disk, so they can be read/written efficiently. UNIX tar files work on the same principle, but Hadoop can’t read them directly because they don’t contain enough information for efficient splitting. So, I wrote a program to convert tar files into Hadoop sequence files.

Here’s some code (Apache license), including all the Apache jars needed to make it work:

tar-to-seq.tar.gz (6.1 MB)

Unpack it and run:

java -jar tar-to-seq.jar tar-file sequence-file

The output sequence file is BLOCK-compressed, about 1.4 times the size of a bzip2-compressed tar file. Each key is the name of a file (a Hadoop “Text”), the value is the binary contents of the file (a BytesWritable).

It took about an hour and a half to convert a 615MB tar.bz2 file to an 868MB sequence file. That’s slow, but it only has to be done once.

I just discovered the paper The End of an Architectural Era (It’s Time for a Complete Rewrite), about re-designing database software from the ground up.  It contains some unsurprising predictions — “the next decade will bring domination by shared-nothing computer systems, often called grid computing” — and some interesting ideas:

• Any database smaller than 1 TB will fit entirely in main memory, distributed across multiple machines.
• We should scrap SQL in favor of “modifying little languages [Ruby, Python, etc.] to include clean embeddings of DBMS access.”  (CouchDB is a good example of this.)
• A database shouldn’t require an expert to tune and optimize it; instead it should all be automated to “produce a system with no visible knobs.”

In their implementation, H-store, they claim to run over 70,000 transactions per second on a standard benchmark on a modest server, compared to 850/second from a commercial DB tuned by an expert.  They also plan to “move from C++ to Ruby on Rails as our stored procedure language.”  (!)

I just ran my first Amazon EC2 instance.  Kind of a heady feeling, having nearly unlimited computing power just a few keystrokes away.  I got the same feeling the first time I logged in as root on a dedicated web server.

I gotta say, though, that the ticking meter — even at just $0.10/hour — will make me think real hard about how I use it.  I guess I’ll get used to it in time.  I’ll have to, since I’m running out of disk space on my local hard drive again.

I think I’m starting to get a handle on how Hadoop is supposed to work. The MapReduce model isn’t what troubles me.  The mind-bending part is that there is no database. Everything happens by scanning big files from beginning to end. It’s like everything I learned about data structures with O(log n) access no longer applies, and I’m back to writing giant for-each loops. It’s perl -n gone mad.

I’ve been trying for months to find the most efficient database for AltLaw — SQL, Lucene, RDF, even Berkeley DB.  But it still takes hours and hours to process things.  Maybe the secret is to get rid of the databases and just mash together some giant files and throw them at Hadoop.

An interesting scenario from Doug Cutting: Say you have a terabyte of data, on a disk with 10ms seek time and 100MB/s max throughput. You want to update 1% of the records. If you do it with random-access seeks, it takes 35 days to finish. On the other hand, if you scan the entire database sequentially and write it back out again, it takes 5.6 hours.

This is why Hadoop only supports linear access to the filesystem. It’s also why Hadoop coder Tom White says disks have become tapes.  All this is contrary to the way I think about data — building hash tables and indexes to do fast random-access lookups.  I’m still trying to get my head around this concept of “linear” data processing.  But I have found that I can do some things faster by reading sequentially through a batch of files rather than trying to stuff everything in a database (RDF or SQL) and doing big join queries.

As I told a friend recently, I’m pretty happy with the front-end code of AltLaw.  It’s just a simple Ruby on Rails app that uses Solr for search and storage.  The code is small and easy to maintain.

What I’m not happy with is the back-end code, the data extraction, formatting, and indexing.  It’s a hodge-podge of Ruby, Perl, Clojure, C, shell scripts, SQL, XML, RDF, and text files that could make the most dedicated Unix hacker blanch.  It works, but just barely, and I panic every time I think about implementing a new feature.

This is a software engineering problem rather than a pure computer science problem, and I never pretended to be a software engineer.  (I never pretended to be a computer scientist, either.)  It might also be a problem for a larger team than an army of one (plus a few volunteers).

But given that I can get more processing power (via Amazon) more easily than I can get more programmers, how can I make use of the resources I have to enhance my own productivity?

I’m studying Hadoop and Cascading in the hopes that they will help.  But those systems are inherently batch-oriented.  I’d like to move away from a batch processing model if I can.  Given that AltLaw acquires 50 to 100 new cases per day, adding to a growing database of over half a million, what I would really like to have is a kind of “continuous integration” process for data.  I want a server that runs continuously, accepting new data and new code and automatically re-running processes as needed to keep up with dependencies.  Perhaps given a year of free time I could invent this myself, but I’m too busy debugging my shell scripts.

I recently read the expression “going non-linear” describing a person, where most people would say something like “going nuts.”  Incredibly geeky; I like it.

Epigrams on Programming

… as explained by Sir Kenny,

From: Ken Tilton
Newsgroups: comp.lang.lisp
Date: Tue, 01 Apr 2008 14:53:07 -0400
Subject: Re: Newbie FAQ #2: Where’s the GUI?

Jonathan Gardner wrote:
> I know this is a FAQ, but I still don’t have any answers, at least answers that I like.

That’s because you missed FAQ #1 (”Where are the damn libraries?”) and the answer (”The Open Source Fairy has left the building. Do them your own damn self.”)

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