Robot Librarian

[Note: edited for clarity thanks to rsinger's comment, below]

Doomed, I say! DOOOOOOOOOOMMMMMMMED!

My reasoning is simple: RDA will fail because it’s not “better enough.”

Now, those of you who know me might be saying to yourselves, “Waitjustaminute. Bill doesn’t know anything at all about cataloging, or semantic representations, or the relative merits of various encapsulations of bibliographic metadata. I mean, sure, he knows a lot about…err….hmmm…well, in any case, he’s definitely talking out of his ass on this one.”

First off, thanks for having such a long-winded internal monologue about me; it’s good to be thought of.

And, of course, you’re right on all counts. I don’t know what I’m talking about in any of those realms.

And yet I’m still willing to make a strong statement?

Yes. I am. Here’s why.

[Oh, and if you're convinced I'm wrong -- please say so. I'd love to be wrong about this.]

First, an assertion

The purpose of any bibliographic metadata is to facilitate three things:

• Description/Identification. If you know what you want, does the metadata give you enough information to determine if the described item is what you want? Alternately, if you’re holding an item (or an alternate metadata representation of it), can you find the record that describes it?
• Machine finding. Can a machine, given a good-enough query, find a work via a search of the metadata?
• Machine grouping. Given the metadata, can a machine help a person find items “like this one”?

Take issue with one or more of those statements. I don’t care. The point I’m really trying to make is that any standard that doesn’t put unmediated machine reasoning at the forefront of what the metadata needs to support is living in a deep, deep hole.

Computer cycles are pretty cheap, and programmers are pretty smart. We can figure out how to do useful things with virtually any data, but only if we can reliably get at those data.

Getting 75% of the way there

Three-fourths of the problem can be addressed with one simple concept.

A solid equality relationship.

By this I mean that “=” had better damn well mean “equal,” as opposed to “probably the same, but there might be other representations, too.” If I want to say “A = B” (where A and B are authors, or works, or subjects, or anything that can be nailed down) there’s better be no false positives and no false negatives. Ever. MARC’s use of “hopefully-unique strings” is ridiculously insufficient in the modern era.

RDA does pretty well with this, with URIs for appropriate concepts, so that’s good.

What’s wrong with it?

Well, it’s gonna cost money to access the spec, for starters. That’s just dumb.

But it’s also not flexible/extensible enough. It’s true that I’m not a cataloger. I do have an MS in computer science, though, and there is stuff in the various versions of the RDA spec which lead me to believe that the committee desperately, desperately needed some hardcore geeks on it. Computer science has basically done nothing but develop methods for abstraction and composition for decades, and that isn’t reflected enough here.

Language such as, “If it is determined that a mechanism for providing a direct link between a note and the instance of the element to which it relates is required,…” worries me. if? IF????? That’s not a spec. That’s a guideline. Nail it down, for god’s sake. When is it appropriate or inappropriate? How do you add links to multiple (but not all) instances of the element?

The spec also seems to describe at least half a dozen kinds of titles. One of these is “Abbreviated title.” Do we really want an abbreviated title? No. We want a title with an “abbreviated” modifier, so we can use that same modifier for, say, a corporate name or publisher or anything else. [Note: see rsinger's comment below, indicating this was a piss-poor example on my part.]

Well, sure, but it’s still better than the AACR2!

[This section updated to disabiguate my use of 'MARC' when I really meant 'AACR2 as commonly talked about in term of MARC tags']

Of course it is. It’s just not better enough!

We’re not just talking about writing a spec. We’re talking about replacing every single tool in the library toolchain, from the ILS to editing software to OPACs to scripts that keep it all put together. We’ll be asking programmers to learn new skills and new ways of thinking, vendors to produce functional software for untested data formats, and catalogers to essentially take their whole brain out of their heads and get a new one.

But that, frankly, is the easy part. The entire culture of the library is built around AACR2 concepts and MARC data structures. The thought processes, nomenclature — everything sometimes feels as if it’s built around three-digit tags. The majority of the (crucial!) specialized vocabulary librarians, and experts and specialists, use to communicate with each other is directly or indirectly tied to MARC

So, yeah, RDA is a hellofa lot better than AACR2/MARC. But in my view, it’s not better enough to justify all the pain. Switching is incredibly, astoundingly expensive both in terms of cost and in terms of the devaluation of institutional knowledge. We can’t do it every few years. We need to be damn sure we’re getting it right.

Library of Congress Subject Headings (LCSH) in particular.

I’ve always been down on LCSH because I don’t understand them. They kinda look like a hierarchy, but they’re not really. Things get modifiers. Geography is inline and …weird.

And, of course, in our faceting catalog when you click on a linked LCSH to do an automatic search, you often get nothing but the record you started from. Which is super-annoying.

So, just for kicks, I ran some numbers.

The process

I extracted all the field 650, indicator2=”0″ from our catalog, threw away the subfield 6’s, and threw away any trailing punctuation in any of the subfields. I called the concatenation of what was left a unique LCSH.

Then I printed them out and put them all onto index cards, using tick-marks to indicate…

No, of course not. I used sort, uniq -c, and wc -l. Here’s what I found.

Counts of LCSH

…in round numbers.

In our catalog, there are:

• 8.50M subject headings (using the definition above)
• 1.87M unique subject headings
• …66% of which (1.23M) appear exactly once

We only have to go out to 30K subjects to account for half of all subject entries. The top 1000 most-used subjects account for 14.5% of all 8.5M subject entries.

The top ten subjects by count are:

• 6029 $$aSermons, American
• 6131 $$aPhilosophy
• 7224 $$aFeature films
• 7591 $$aPiano music
• 7968 $$aSocialism
• 8796 $$aEconomics
• 9185 $$aCommunism
• 12440 $$aSermons, English$$y17th century
• 13539 $$aBills, Private$$zUnited States
• 58823 $$aEconomics$$xHistory$$vSources

From a record’s point of view

Our catalog has:

• 7M records
• 4.4M records with at least one subject (as defined above)
• 2.4M records with more than one subject
• 2.0M records with exactly one subject
• 2.6M records with zero subjects

The records with the most subject headings tend to be collections of stuff (theses, photos, etc). Our local standout is the Dept. of Medicine and Surgery (University of Michigan) theses, 1851-1878 with 208 subject entries. 14 records have at least 30 subject entries.

What it means

Gee, lady, I don’t know.

One way to look at it: suppose you’re considering defining subjects in this way, and making them “hot” in the catalog interface. For our data, 2/3 of records would have either no subjects or a subject that found only the record you’re at. So…think again.

In real life, we index lots of possible subject fields, and we additionally index the $$a as well as the whole string, so ours are a little bit more useful. A little.

[This is in response to a thread on the blacklight mailing list about getting MARC data into Solr.]

What’s the question?

The question came up, “How much time do we spend processing the MARC vs trying to push it into Solr?”. Bob Haschart found that even with a pretty damn complicated processing stage, pushing the data to solr was still, at best, taking at least as long as the processing stage.

I’m interested because I’ve been struggling to write a solrmarc-like system that runs under JRuby. Architecturally, the big difference between my stuff and solrmac is that I use the StreamingUpdateSolrServer (on Erik Hatcher’s suggestion). So I thought I’d check how things break down for me.

Here are my numbers running under JRuby (using MARC4J as the marc implementation) with the Solr StreamingUpdateSolrServer. Obviously, there are a lot of differences between this and solrmarc, but I’m hoping that while it’s not comparing apples to apples, it’s at least comparing apples to some sort of processed cheese-like product.

What work is being done on what?

The data set is a file of 18,881 MARC records in marc-binary format. It’s probably not big enough to get a great idea of how things will run over the long (many millions of records) haul, but it’ll do for this rough-cut stuff.

I break my processing down into five categories:

• Read the records into marc4j objects and do nothing. This is a baseline of sorts.
• The “normal” fields are anything that you could do with SolrMarc without a custom routine; the actual processing is done in JRuby.
• Custom fields are generated with JRuby code, but these are things that in solmarc would require a custom routine.
• The big “allfields” field is text from tags 100 through 900.
• The “to_xml” routine is just calling the underlying marc4j XML output and stuffing it into a string.

The schema used is our normal UMICH schema except for High Level Browse (which appear in the our catalog as “Academic Discipline”). The code for that is written in Java, and I just call it from JRuby when I’m using it. I excluded it because it’s incredibly expensive, both at startup time (when it loads a giant database of call-number ranges and associated categories) and for processing — there’s a lot of call-number normalization, long-string comparisons, some modified binary searches, etc. etc. etc. It’s expensive. Trust me.

The Solr server itself is on a different, incredibly-beefy machine, and is emptied out before each invocation that involves actually pushing data to it (with a delete-by-query :).

How fast were things on my desktop?

• 18,881 records in marc-binary format
• Times are in seconds, run on my desktop
• Remember, you can’t compare these numbers to Bob’s because we’re doing different things to different data.

We can also break the same numbers down as:

Or like this:

Why does solr processing seem so much faster for me?

There are a lot of reasons why my submit-to-solr might seem like less of a burden. The ones I can think of off the top of my head are:

• SUSS is just faster than whatever solrmarc does.
• My processing stage is so much slower than solrmac’s (due to algorithms or jruby-vs-java, I don’t know) that the “push to solr” portion of it gets swallowed up by the slowness of the of overall code.
• The Solr server is so much faster than my desktop that my poor little desktop can’t send it data fast enough to work it.

For my setup, obviously adding a processing thread is a lot more beneficial than adding a SUSS thread. My desktop doesn’t have that many threads lying around (adding a third processing thread actually slowed things down), so I moved the code to a beefier machine to see what happened.

Trying the same thing on a beefy machine

This is the exact same code and data, but on a beefy machine (16 cores, gobs of memory).

So, on my hardware anyway, there’s a sweet spot with one suss thread and three processing threads. YMMV, of course.

What have we learned?

I’m not sure, to be honest. It’s logistically difficult for me to do the same process in solrmarc because I’d have to rebuild everything without the HLB stuff. I guess for me, what I’ve learned that if I’m going to continue working on my code, the places to focus my attention are threading (obviously) and MARC-XML generation.

For reasons I’m still not entirely clear on (I wasn’t there), the Code4Lib 2010 conference this week inspired renewed interest in a JSON-based format for MARC data.

When I initially looked at MARC-HASH almost a year ago, I was mostly looking for something that wasn’t such a pain in the butt to work with, something that would marshall into multiple formats easily and would simply and easily round-trip.

Now, though, a lot of us are looking for a MARC format that (a) doesn’t suffer from the length limitations of binary MARC, but (b) is less painful (both in code and processing time) than MARC-XML, and it’s worth re-visiting.

For at least a few folks, un-marshaling time is a factor, since no matter what you’re doing, processing XML is slower than most other options. Much of that expense is due to functionality and data-safety features that just aren’t a big win with a brain-dead format like MARC, so it’s worth looking at alternatives.

What is MARC-HASH?

At some point, we’ll want a real spec, but right now it’s just this:

So, a short example:

How's the speed?

I think it's important to separate the format marc-hash from the eventual marshaling format -- partly because someone might actually want to use something other than JSON as a final format, but mostly because it forces implementations to separate hash-creation from json-creation and makes it easy to swap in different json libraries when a faster one comes along.

Having said that, in real life people are mostly concerned about JSON. So, let's look at JSON performance.

The MARC-Binary and MARC-XML files are normal files, as you'd expect. The JSON file is "Newline-Delimited JSON" -- a single JSON record on each line.

The benchmark code looks like this:

  # Unmarshal
  x.report("MARC Binary") do
    reader = MARC::Reader.new('test.mrc')
    reader.each do |r|
      title = r['245']['a']
    end
  end


# Marshal
  x.report("MARC Binary") do 
    reader = MARC::Reader.new('test.mrc')
    writer = MARC::Writer.new('benchout.mrc')
    reader.each do |r|
      writer.write(r)
    end
    writer.close
  end

Under MRI, I used the nokogiri XML parser and the yajl JSON gem. Under JRUby, it was the jstax XML parser and the json-jruby JSON gem.

The test file is a set of 18,831 records I've been using for all my benchmarking of late. It's nothing special; just a nice size.

Marshalling Speed (read from binary marc, dump to given format)

Times are in seconds on my Macbook laptop, using ruby-marc.

Unmarshalling speed (from pre-created file)

Again, times are in seconds

And so...

I'm not sure what else to say. The format is totally brain-dead. It round-trips. It's fast enough. It has no length limitations. We define it to have to be UTF-8. The NDJ format is easy to understand and allows streaming of large document collections.

If folks are interested in implementing this across other libraries, that'd be great. Any thoughts?

[Note: in this post I'm just going to focus on the "get stuff into Solr" part. My normal focus -- MARC data -- will make an appearance in the next post when I talk about using this in addition to / instead of solrmarc.]

Working with Solr

I love me the Solr. I love everything about it except that the best way to interact with it is via Java. I don’t so much love me the java.

So…taking Erik Hatcher’s lead and advice, as I will do whenever he offers either, I wrote some code to work within JRuby to deal with Solr.

Getting the code

I’ve added the gems to gemcutter, if you want to play along at home:

• jruby_producer_consumer (github, rdoc.info) Ruby syntax for threaded operations under jruby
• jruby_streaming_update_solr_server (github, rdoc.info) Ruby syntax on top of the Java class of the same name
• marc4j4r (github, rdoc.info) Ruby syntax on top of the marc4j java library.

WARNING: None of these gems have a 1.0 version tag on them, and that means that the API may change a titch in the future. Also, the fact that they’re released as gems means that it’s easy to release gems, not that I’m not an idiot.

The basics: Using SolrInputDocument and StreamingUpdateSolrServer

OK, with the disclaimer out of the way, let’s look at some code.

Nothing really fancy in there — just a few things worth noting:

• An suss object will take a hash (again, anything that responds to #each_pair) or a SolrInputDoc
• You can use either strings or symbols to represent Solr field names
• Values can be either a single value, or an array of multiple values

And there are three ways to get data into a doc:

• Via << [field, value(s)] (additive)
• Via doc.merge! hash (additive)
• Via doc[field] = value (replaces)

Adding Threads

I also went down the garden path of threading things. There are an awful lot of operations that are not threadsafe (e.g., reading a line from a file) but once you’ve got a bunch of records to worth with, turning them into Solr documents is usually thread-safe.

My model is that there’s a producer (usually the method #each) from an underlying data object. A thread takes whatever that method yields and sticks the values into a java BlockingQueue awaiting consumption. You then use ProdcuerConsumer#threaded_each (or ProducerConsumer#threaded_each_with_index) to pull items out of the queue and do something useful with them.

I extracted stuff into a library (jruby_producer_consumer) for your viewing pleasure.

CONFUSION ALERT: It’s perhaps unfortunate that the object you send to ProducerConsumer.new(obj) must implement #each and that the ProducerConsumer method #threaded_each calls that underlying #each…well there’s a lot of #each’s floating around. Keep them straight.

So…let’s look at some code to work with consumer threads.

Again, not a lot happening here.

• The “producer” is always one thread, because so little is thread-safe at the ‘each’ level. In this case, there’s a single thread pulling data out of the file and turning it into MARC records, which are added to the internal BlockingQueue. I buffer 5 of these at a pop (the default) so the consumer threads don’t starve. I presume that producing items is cheaper than consuming them, or else this library won’t help you much.
• ProducerConsumer#threaded_each calls the #each method of the underlying object. You can substitute anything that yields, though, as in this example where I call #each_line instead of the default #each

• Keep track of your threads. In this last example, there is one thread getting MARC records and putting them into the PC buffer (no way to change that), three threads consuming those records and sticking them into the suss object, and another two pulling stuff out of the suss object and sending things to Sorl. And, of course, there’s other stuff running on the computer, too. Experiment and figure out what works best for your hardware.
• See the docs for how to mess with what goes into a ProducerConsumer object. It’s entirely possible to use, say, #each_slice. There’s also a convenience method #threaded_each_with_index, but it does not call the underlying #each_with_index, it produces its own index as things are read.

Feedback not only welcome but necessary!

I’ve done a lot of messing around with Ruby in the last 10 days or so, but I’m still basically converting from Perl in my head. Any comments, bugs reports, or whatnot are definitely welcome!