So, I’m at code4lib 2013 right now, where side conversations and informal exchanges tend to be the most interesting part.

Last night I had an conversation with the inimitable Michael B. Klein, and after complaining about faculty members that keep books out for decades at a time, we ended up asking a simple question:

> How much more shelving would we need if everyone returned their books?

Assuming we could get them all checked in and such, well, where would we put them?

I’m looking at this in the simplest, most conservative way possible:

• Assume they’re all paperbacks, so we don’t worry about how thick a cover is (cover width = 0)
• Assume items for which we don’t have page count information are “average”

Starting data

What’s my current situation at Michigan?

• Total bibs: about 10M (but that includes a bunch of HathiTrust items and other electronic-only items that could never be checked out)
• Total items checked out right now: 162,080

The first problem I run into is that I don’t know how many pages are in a given book. Well, in theory I can look in MARC field 300$a, and it will tell me.

Finding the number of pages in a book

I went through a recent dump of all our records and pulled out page counts from the 300 (those that matched the regular expression $$a\d+\s+[pP].).

Problem solved, right? Well, kind of

• 3,085,433 total bibs with page count data (about 30%)
• 40,872 checked out items with page count data (about 25%)

OK, so I don’t have data for everything. Plus, some of those are multi-volume works that list the total page count, even though only a single volume may be checked out.

We’ll have to drop down into statistics:

• Average number of pages in a checked-out item: 270
• Median number of pages in a checked-out item: 244

The median is lower, so we’ll go with that. Being conservative, remember?

Bringing it all together

Obviously we need to make a lot of assumptions.

• All paperbacks (== no space allowance for covers)
• 244 pages per item (the median of checked out items for which we have data)
• Pages = 244 * 162,080 = 39,547,520 pages

So…what’s the damage?

But how to do the calculation?

It turns out that simply googling book spine width calculator a few come up.

I picked one and input 39,547,520 pages and assumed 50lb paper (the lightest paper in the tool).

Total width: 77,241.25 inches, or 6437 feet, or 1.22 miles

1.22 miles???

Well, we had a lot of assumptions,but most of them were pretty conservative. And I have no idea if the book spine calculator is at all accurate.

But…it’s gonna be a big number no matter what. Add in that many of them are hardcover, and this seems like a pretty good guess at a lower end.

What is this good for again?

Oh, nothing at all. Just a little fun while I’m at code4lib.

Next steps

Well, the best next step would be to walk away. This is a huge waste of time.

But…we could look in the 020s for a hint of whether it’s hardcover or paperback (which is really hard. And maybe try to figure out if multiple volumes of a multi-volume work are all checked out and take that into account.

But really: this is enough for me. Whether Michael wants to pursue it further on his own, well, that’s up to him.

Ross Singer recently updated ruby-marc to include a #to_hash method that creates a data structure that is (a) round-trippable without any data loss, and (b) amenable to serializing to JSON. He’s calling it marc-in-json (even though the serialization is up to the programmer, it’s expected most of us will use JSON), and I think it’s the way to go in terms of JSON-able MARC data.

I wanted to take a quick look at the space/speed tradeoffs of using various means to serialize MARC records in the marc-in-json format compared to using binary MARC-21.

Why bother?

Binary MARC-21 is “broken” in that a lot of us have records that are so long (more than 9999 bytes) it’s impossible to create a valid marc binary record. The standard alternative, MARC-XML, has huge filesizes (roughly 3 times as large) and runs a lot more slowly in every benchmark I’ve ever run. For ruby-marc, the penalty for using XML is further exaggerated because the serializer is based on REXML and is super-slow.

There have been a few proposals for a MARC data structure that can easily be serialized to JSON (I had my own, in fact), but the stuff Ross has done with marc-in-json is preferable in being (a) not a ton bigger in terms of file size, and (b) much easier to query from a NoSQL database using something like JSONPath or JSONQuery.

What I’m testing

For this test, I used:

• marc21 binary This is the stock serialization / deserialization provided by ruby-marc.
• YAJL for JSON YAJL is a very fast C-based JSON library. Here we’re using the Ruby bindings and calling Yajl::Encoder.encode(r.to_hash) to serialize and MARC::Record.new_from_hash(Yajl::Parser.parse(JSON)) to deserialize.
• Msgpack The Msgpack project is explicitly designed to be “binary JSON” — smaller, faster, etc — at the expense of human readability/editabilty . Again, this used the ruby bindings.

The benchmark and its results

I’m interested in how long it takes to serialize and deserialize a single record. My primary use-case is sticking a single record into Solr, and then pulling the string representation of that record out and turning it back into MARC.

It’s entirely possible that trying to deal with a whole set of MARC records — as a JSON array of marc-in-json objects, or as a set of newline-delimited JSON or Msgpack objects — would yield different results. The former is especially interesting, since to parse a large JSON array one needs to use a streaming parser, which will almost certainly have a different profile in both processing and memory use.

The ambitious can see the full source code of the benchmark.

Note that the following represent only the performance of ruby-marc and the particular serializers used. Other platforms or other libraries will certainly give different results!

Total of 18880 records run 20 times (377,600 serialize/deserialize cycles per method) on my Mac OSX desktop; comparisons are to MARC21-Binary.

 SERIALIZING
   MARC Binary       357.02 s (100%)
   YAJL              312.65 s ( 88%)
   Msgpack           266.26 s ( 75%)

 DESERIALIZING
   MARC Binary       648.91 s (100%)
   YAJL              507.64 s ( 78%)
   Msgpack           459.73 s ( 71%)

 SERIALIZE + DESERIALIZE
   MARC Binary      1005.93 s (100%)
   YAJL              820.29 s ( 82%)
   Msgpack           725.99 s ( 72%)

 SIZE
   MARC Binary   31.15 MBytes (100%)
   Msgpack       42.00 MBytes (135%)
   JSON          55.99 MBytes (180%)
   XML           93.42 MBytes (300%)

Analysis, such as it is

Obviously, there are size/speed tradeoffs. Nothing is as small as binary MARC21, but both YAJL and Msgpack are faster — significantly so for deserialization, which happens to be where I want the speed for my uses.

At 80% larger, the JSON serialization is quite a big bigger, but it’s a hell of a lot smaller than MARC-XML and suffers none of the limitations of binary MARC.

For a closed system (i.e., you’re not worried about anyone else being able to read your data) such as a Blacklight installation, I’d be tempted to move to using JSON sooner rather than later.

A couple weeks ago, representatives from UMich (that’d be me), Purdue, Notre Dame, UChicago, and our hosts at Western Michigan got together in lovely Kalamazoo to talk about our VuFind implementations.

Eric Lease Morgan already wrote up his notes about the meeting, and I encourage you to go there for more info, but I’ll add my two cents here.

So, in light of that meeting, here’s what I’m thinking about VuFind of late:

• None of us are running VuFuind 1.0 as released with full catalog data. Eric has a special purpose portal running the current code over an aggregated special collection and hasn’t done much to the underlying PHP. The rest of us were running heavily modified versions of RC1. An issue we had in common was that the changes from RC1 to RC2 to 1.0 release were so significant, including some complete architectual change (some based on the stuff I’ve done with mirlyn) that the effort required to get up with 1.0 would be no less significant than the effort to switch wholesale to something else (e.g., Blacklight).

• A point that I made that was echoed by others is that we need to remember that these new discovery systems are all just thin wrappers over Solr. They basically have two jobs: to get a query and format it in a way that Solr can handle, and then to take the Solr results and display them. There’s some sugar on top of that (exporting, tagging, etc) but that’s really it. The heavy lifting is all done by your indexer (Solrmarc for most, although watch this space for my announcement of my JRuby-based stuff today) and Solr itself. It’s not a hard problem, although it is occasionally a messy one.

• VuFind has, in my mind, fundamental architectural issues mostly based on the inability to easily separate local code from core code. A re-architecture to base everything on subclasses of the core code would help, but at some point you start to run up against fundamental limitations of PHP and Smarty to do things cleanly. Without the ability to update core code and know it won’t affect your local code, there’s no good way to keep on track with the trunk of the code and do upgrades; for the same reason, it’s almost impossible to send changes back to trunk.

• Coupled tightly to the architectural issues is the lack of tests. The code is potentially very brittle; there’s no good way to know if you’re breaking anything until you notice it’s broken. It’s not at all clear how to write good tests for the code, because there’s a lot of inter-dependencies.

• The second big problem is one of community; to wit, there isn’t much of one. There are some active players, and there’s what seems like a great conference going on right now, so this may change. But — especially because of the technical difficulties in contributing local changes back –VuFind could use a benevolent dictator, someone who has organizing and administrating VuFind be a part of his/her job. The last bit is important.

All of these are surmountable issues. The reason they’re at the top of my head, of course, is that the Blacklight community has, in many ways, already taken care of most of them.

If I were starting from scratch tomorrow, we’d already decided to do something locally, and I could convince my systems people to run a ruby implementation (I like JRuby myself), I’d go with Blacklight. If we were already looking at something like Summon, I’d take a hard, hard look at the build-vs-buy numbers. Summon and Primo both give you APIs to program an interface against, and boy, it might be worth the effort to do so and leave everything else alone.

[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.

Jonathan Rochkind, in response to a long (and, IMHO, mostly ridiculous) thread on NGC4Lib, has been exploring the boundaries between a data model and its expression/serialization ( see here, here, and here ) and I thought I’d jump in.

What this post is not

There’s a lot to be said about a good domain model for bibliographic data. I’m so not the guy to say it. I know there are arguments for and against various aspects of the AACR2 and RDA and FRBR, and I’m unable to go into them.

What I am comfortable saying is this:

Anyone advocating or dismissing a data model based on the data structure or serialization most-often associated with that model is missing the goddamn point.

Data serializations

…are boring. They’re unimportant at the data modeling stage, and only barely important when thinking about data structures. For any given data structure there are lots of ways you can serialize it. A standard programming-language hash can be represented in a zillion ways, for example: yaml, json, various programming languages, .ini files, etc. Even MARC has two standard serializations (binary and xml) with several more actually in use (Aleph Sequential, for example).

So, let me repeat again, serializations are boring and not worth talking about until you’ve got everything else nailed down. Any format you can round-trip your data structure to/from is fine.

Serializations are measured from “less pain” to “more pain”, but all have the exact same expressiveness. Data structures, on the other hand, do not.

A hierarchy of data structures

Think about the following data structures:

• An ordered list
• key-value pairs
• A hierarchy (e.g., an XML document)
• An undirected graph
• A directed graph
• A labeled, directed multigraph (e.g., a set of RDF Triples)

You don’t have to think very hard to see that any of these can be viewed as a restricted version of the data structures above it. An ordered list (array) is just a set of key-value pairs where the keys represent each item’s sequence. A set of key-value pairs is a very, very flat hierarchy. A hierarchy is an undirected graph without cycles. An undirected graph is a directed graph where you’re careful to make links both ways. And a directed graph can easily be represented as a set of RDF triples (where you may, for example, only have one label for your relationships: “links to”).

[Note that I didn't say any of these would be efficient implementations!]

The reverse is not true — or, at least, not without an incredible amount of “out of band” information in another layer somewhere.

The structures at the end of the list have more expressiveness. You can just plain model more things in them (give-or-take the out-of-band stuff, composition, etc) per unit of screwing around. I’m not going to try to model my set of key=value pairs in an array. I could do it, but it would take so much of my attention that the data modeling would suffer.

Don’t handicap yourself

Don’t start with the data structure.

DON’T START WITH THE DATA STRUCTURE!

GET THAT MOTHER-FREAKIN’ DATA STRUCTURE OFF MY MOTHER-FREAKIN’ PLANE!

Seriously. Don’t be stupid. If all you’ve got is a hammer, everything starts to look like a thumb.

If you start off with a restrictive data structure before you even fully define the domain you’re trying to model, you may hose yourself. You may end up making stupid decisions based on the toolchain you’re imagining in your head.

Domain modeling is ridiculously hard for any domain worth modeling. If you start with a handicap (a restrictive data structure) it’s going to be even harder.

No one would think of trying to model bibliographic data using only arrays. That’s premature optimization on an epic scale.

The appeal of RDF Triples

Even if you ignore all the semantics and rules that make RDF Triples a value-added instance of a labeled, directed multigraph, the appeal (to me, anyway) is that any semantic model based on RDF Triples has enormous expressive power at its disposal.

Does it turn out that after you’ve fully satisfied the necessary model for the domain, the semantics you need can actually be accomplished with something lower down in the list? Awesome. Go with it. You’ll get great implementations with good real-life computing characteristics. A database can often usefully be thought of an implementation of an undirected graph with typed nodes (and, perhaps, some typed links, if you use the column name in the calling table a “type” of sorts, and add some out-of-band knowledge). And lord knows RDBMS’s have great performance characteristics.

But don’t start there. Start with the domain. Model it. Figure out what you need to describe and derive. Then pick the most appropriate data structure.

The nightmare that is MARC

MARC-the-data-structure (not to be confused with a serialization of that data structure, on the one hand, or with the AACR2 on the other) can incompletely (but usefully, I think) be described as:

• A set of key-value pairs
• …that have a defined order
• …where keys can be repeated
• …and values are strings
• …and keys are a concatenation of tag/ind1/ind2/code

Control fields are especially restricted (ind1, ind2, and code are all ‘null’). There’s been some bullshit attempts at links (e.g., the 880 fields) but really, this is it.

It doesn’t give us much to work with. It’s restricted. And, sadly, so is our thinking.

Putting the cart before the horse

As Jonathan (and zillions of others) rightly point out, a huge problem in the library world is that there are generations (plural) of working librarians who, because of years of practice, find it incredibly hard to think about bibliographic data as modeled outside the constraints inherent in the MARC data structure. It’s a handicap. It’s an anchor around our necks.

MARC-the-data-model (nee AACR2) is not inherently bad because it’s built on an impoverished data structure. It’s bad because it does a shitty job at modeling the bibliographic data space. If we could produce a good model in a crappy data structure like that, well, that’d be awesome because it would indicate that things are simple.

Things, of course, aren’t simple. They’re hard.

So, if you want to complain about MARC or RDA or FRBR, figure out what its trying to model and talk about the fidelity of the model with respect to the problem space. But don’t conflate data models, data structures, and serializations.

Oh, and don’t say “PIN Number” or “ATM Machine.” That drives me crazy, too.

[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.

I’ve been messing with easier ways of adding parsers to ruby-marc’s MARC::Reader object. The idea is that you can do this:

A parser need only implement #each and a module-level method #decode_from_string.

Read all about it on the github page.