RDF propaganda, time for change

I’m not supposed to but I’m proud. It’s not only me who’s doing it.

Adrien is one of the new guys on the block. He’s working on integration with Tracker’s RDF service and various web services like Flickr, Facebook, Twitter, picasaweb and RSS. This is the kind of guy several companies should be afraid of. His work is competing with what they are trying to do do: integrating the social web with mobile.

Oh come on Steve, stop pretending that you aren’t. And you better come up with something good, because we are.

Not only that, Adrien is implementing so-called writeback. It means that when you change a local resource’s properties, that this integration will update Flickr, Facebook, picasaweb and Twitter.

You change a piece of info about a photo on your phone, and it’ll be replicated to Flickr. It’ll also be synchronized onto your phone as soon as somebody else made a change.

This is the future of computing and information technology. Integration with social networking and the phone is what people want. Dear Mark, it’s unstoppable. You better keep your eyes open, because we are going fast. Faster than your business.

I’m not somebody trying to guess how technology will look in a few years. I try to be in the middle of the technical challenge of actually doing it. Talking about it is telling history before your lip’s muscles moved.

At the Tracker project we are building a SPARQL endpoint that uses D-Bus as IPC. This is ideal on Nokia’s Meego. It’ll be a centerpiece for information gathering. On Meego you wont ask the filesystem, instead you’ll ask Tracker using SPARQL and RDF.

To be challenged is likely the most beautiful state of mind.

I invite everybody to watch this demo by Adrien. It’s just the beginning. It’s going to get better.

Tracker writeback & web service integration demo / MeegoTouch UI from Adrien Bustany on Vimeo.

I tagged this as ‘extremely controversial’. That’s fine, Adrien told me that “people are used to me anyway”.

Performance DBus handling of the query results in Tracker’s RDF service

Before

For returning the results of a SPARQL SELECT query we used to have a callback like this. I removed error handling, you can find the original here.

We need to marshal a database result_set to a GPtrArray because dbus-glib fancies that. This is a lot of boxing the strings into GValue and GStrv. It does allocations, so not good.

static void
query_callback(TrackerDBResultSet *result_set,GError *error,gpointer user_data)
{
  TrackerDBusMethodInfo *info = user_data;
  GPtrArray *values = tracker_dbus_query_result_to_ptr_array (result_set);
  dbus_g_method_return (info->context, values);
  tracker_dbus_results_ptr_array_free (&values);
}

void
tracker_resources_sparql_query (TrackerResources *self, const gchar *query,
                                DBusGMethodInvocation *context, GError **error)
{
  TrackerDBusMethodInfo *info = ...; guint request_id;
  TrackerResourcesPrivate *priv= ...; gchar *sender;
  info->context = context;
  tracker_store_sparql_query (query, TRACKER_STORE_PRIORITY_HIGH,
                              query_callback, ...,
                              info, destroy_method_info);
}

After

Last week I changed the asynchronous callback to return a database cursor. In SQLite that means an sqlite3_step(). SQLite returns const pointers to the data in the cell with its sqlite3_column_* APIs.

This means that now we’re not even copying the strings out of SQLite. Instead, we’re using them as const to fill in a raw DBusMessage:

static void
query_callback(TrackerDBCursor *cursor,GError *error,gpointer user_data)
{
  TrackerDBusMethodInfo *info = user_data;
  DBusMessage *reply; DBusMessageIter iter, rows_iter;
  guint cols; guint length = 0;
  reply = dbus_g_method_get_reply (info->context);
  dbus_message_iter_init_append (reply, &iter);
  cols = tracker_db_cursor_get_n_columns (cursor);
  dbus_message_iter_open_container (&iter, DBUS_TYPE_ARRAY,
                                    "as", &rows_iter);
  while (tracker_db_cursor_iter_next (cursor, NULL)) {
    DBusMessageIter cols_iter; guint i;
    dbus_message_iter_open_container (&rows_iter, DBUS_TYPE_ARRAY,
                                      "s", &cols_iter);
    for (i = 0; i < cols; i++, length++) {
      const gchar *result_str = tracker_db_cursor_get_string (cursor, i);
      dbus_message_iter_append_basic (&cols_iter,
                                      DBUS_TYPE_STRING,
                                      &result_str);
    }
    dbus_message_iter_close_container (&rows_iter, &cols_iter);
  }
  dbus_message_iter_close_container (&iter, &rows_iter);
  dbus_g_method_send_reply (info->context, reply);
}

Results

The test is a query on 13500 resources where we ask for two strings, repeated eleven times. I removed a first repeat from each round, because the first time the sqlite3_stmt still has to be created. This means that our measurement would get a few more milliseconds. I also directed the standard out to /dev/null to avoid the overhead created by the terminal. The results you see below are the value for “real”.

There is of course an overhead created by the “tracker-sparql” program. It does demarshaling using normal dbus-glib. If your application uses DBusMessage directly, then it can avoid the same overhead. But since for both rounds I used the same “tracker-sparql” it doesn’t matter for the measurement.

$ time tracker-sparql -q "SELECT ?u  ?m { ?u a rdfs:Resource ;
          tracker:modified ?m }" > /dev/null

Without the optimization:

0.361s, 0.399s, 0.327s, 0.355s, 0.340s, 0.377s, 0.346s, 0.380s, 0.381s, 0.393s, 0.345s

With the optimization:

0.279s, 0.271s, 0.305s, 0.296s, 0.295s, 0.294s, 0.295s, 0.244s, 0.289s, 0.237s, 0.307s

The improvement ranges between 7% and 40% with average improvement of 22%.

Focus on query performance

Every (good) developer knows that copying of memory and boxing, especially when dealing with a large amount of pieces like members of collections and the cells in a table, are a bad thing for your performance.

More experienced developers also know that novice developers tend to focus on just their algorithms to improve performance, while often the single biggest bottleneck is needless boxing and allocating. Experienced developers come up with algorithms that avoid boxing and copying; they master clever pragmatical engineering and know how to improve algorithms. A lot of newcomers use virtual machines and script languages that are terrible at giving you the tools to control this and then they start endless religious debates about how great their programming language is (as if it matters). (Anti-.NET people don’t get on your horses too soon: if you know what you are doing, C# is actually quite good here).

We were of course doing some silly copying ourselves. Apparently it had a significant impact on performance.

Once Jürg and Carlos have finished the work on parallelizing SELECT queries we plan to let the code that walks the SQLite statement fill in the DBusMessage directly without any memory copying or boxing (for marshalling to DBus). We found the get_reply and send_reply functions; they sound useful for this purpose.

I still don’t really like DBus as IPC for data transfer of Tracker’s RDF store’s query results. Personally I think I would go for a custom Unix socket here. But Jürg so far isn’t convinced. Admittedly he’s probably right; he’s always right. Still, DBus to me doesn’t feel like a good IPC for this data transfer..

We know about the requests to have direct access to the SQLite database from your own process. I explained in the bug that SQLite3 isn’t MVCC and that this means that your process will often get blocked for a long time on our transaction. A longer time than any IPC overhead takes.

Supporting ontology changes in Tracker

It used to be in Tracker that you couldn’t just change the ontology. When you did, you had to reboot the database. This means loosing all the non-embedded data. For example your tags or other such information that’s uniquely stored in Tracker’s RDF store.

This was of course utterly unacceptable and this was among the reasons why we kept 0.8 from being released for so long: we were afraid that we would need to make ontology changes during the 0.8 series.

So during 0.7 I added support for what I call modest ontology changes. This means adding a class, adding a property. But just that. Not changing an existing property. This was sufficient for 0.8 because now we could at least do some changes like adding a property to a class, or adding a new class. You know, making implementing the standard feature requests possible.

Last two weeks I worked on supporting more intrusive ontology changes. The branch that I’m working on currently supports changing tracker:notify for the signals on changes feature, tracker:writeback for the writeback features and tracker:indexed which controls the indexes in the SQLite tables.

But also certain range changes are supported. For example integer to string, double and boolean. String to integer, double and boolean. Double to integer, string and boolean. Range changes will sometimes of course mean data loss.

Plenty of code was also added to detect an unsupported ontology change and to ensure that we just abort the process and don’t do any changes in that case.

It’s all quite complex so it might take a while before the other team members have tested and reviewed all this. It should probably take even longer before it hits the stable 0.8 branch.

We wont yet open the doors to custom ontologies. Several reasons:

  • We want more testing on the support for ontology changes. We know that once we open the doors to custom ontologies that we’ll see usage of this rather sooner than later.
  • We don’t yet support removing properties and classes. This would be easy (drop the table and columns away and log the event in the journal) but it’s not yet supported. Mostly because we don’t need it ourselves (which is a good reason).
  • We don’t want you to meddle with the standard ontologies (we’ll do that, don’t worry). So we need a bit of ontology management code to also look in other directories, etc.
  • The error handling of unsupported ontology changes shouldn’t abort like mentioned above. Another piece of software shouldn’t make Tracker unusable just because they install junk ontologies.
  • We actually want to start using OSCAF‘s ontology format. Perhaps it’s better that we wait for this instead of later asking everybody to convert their custom ontologies?
  • We’re a bunch of pussies who are afraid of the can of worms that you guys’ custom ontologies will open.

But yes, you could say that the basics are being put in place as we speak.

Zürichsee

Today after I brought Tinne to the airport I drove around Zürichsee. She can’t stay in Switzerland the entire month; she has to go back to school on Monday.

While driving on the Seestrasse I started counting luxury cars. After I reached two for Lamborgini and three for Ferrari I started thinking: Zimmerberg Sihltal and Pfannenstiel must be expensive districts tooAnd yes, they are.

I was lucky today that it was nice weather. But wow, what a nice view on the mountain tops when you look south over Zürichsee. People from Zürich, you guys are so lucky! Such immense calming feeling the view gives me! For me, it beats sauna. And I’m a real sauna fan.

I’m thinking to check it out south of Zürich. But not the canton. I think the house prices are just exaggerated high in the canton of Zürich. I was thinking Sankt Gallen, Toggenburg. I’ve never been there; I’ll check it out tomorrow.

Hmmr, meteoswiss gives rain for tomorrow. Doesn’t matter.

Actually, when I came back from the airport the first thing I really did was fix coping with property changes in ontologies for Tracker. Yesterday it wasn’t my day, I think. I couldn’t find this damn problem in my code! And in the evening I lost three chess games in a row against Tinne. That’s really a bad score for me. Maybe after two weeks of playing chess almost every evening, she got better than me? Hmmrr, that’s a troubling idea.

Anyway, so when I got back from the airport I couldn’t resist beating the code problem that I didn’t find on Friday. I found it! It works!

I guess I’m both a dreamer and a realist programmer. But don’t tell my customers that I’m such a dreamer.

Reporting busy status

We’re nearing our first release since very long, so I’ll do another technical blog post about Tracker ;)

When the RDF store is replaying its journal at startup and when the RDF store is restoring a backup it can be in busy state. This means that we can’t handle your DBus requests during that time; your DBus method will be returned late.

Because that’s not very nice from a UI perspective (the uh, what is going on?? -syndrome kicks in) we’re adding a signal emission that emits the progression and status. You can also ask it using DBus methods GetProgress and GetStatus.

The miners already had something like this, so I kept the API more or less the same.

signal sender=:1.99 -> dest=(null destination) serial=1454
  path=/org/freedesktop/Tracker1/Status;
  interface=org.freedesktop.Tracker1.Status; member=Progress
   string "Journal replaying"
   double 0.197824
signal sender=:1.99 -> dest=(null destination) serial=1455
  path=/org/freedesktop/Tracker1/Status;
  interface=org.freedesktop.Tracker1.Status; member=Progress
   string "Journal replaying"
   double 0.698153

Jürg just reviewed the SPARQL regex performance improvement of yesterday, so that’s now in master. If you want this busy status notifying today already you can test with the busy-notifications branch.

Performance improvements for SPARQL’s regex in Tracker

The original SPARQL regex support of Tracker is using a custom SQLite function. But of course back when we wrote it we didn’t yet think much about optimizing. As a result, we were using g_regex_match_simple which of course recompiles the regular expression each time.

Today Jürg and me found out about sqlite3_get_auxdata and sqlite3_set_auxdata which allows us to cache a compiled value for a specific custom SQLite function for the duration of the query.

This is much better:

static void
function_sparql_regex (sqlite3_context *context,
                       int              argc,
                       sqlite3_value   *argv[])
{
  gboolean ret;
  const gchar *text, *pattern, *flags;
  GRegexCompileFlags regex_flags;
  GRegex *regex;

  if (argc != 3) {
    sqlite3_result_error (context, "Invalid argument count", -1);
    return;
  }

  regex = sqlite3_get_auxdata (context, 1);
  text = sqlite3_value_text (argv[0]);
  flags = sqlite3_value_text (argv[2]);
  if (regex == NULL) {
    gchar *err_str;
    GError *error = NULL;
    pattern = sqlite3_value_text (argv[1]);
    regex_flags = 0;
    while (*flags) {
      switch (*flags) {
      case 's': regex_flags |= G_REGEX_DOTALL; break;
      case 'm': regex_flags |= G_REGEX_MULTILINE; break;
      case 'i': regex_flags |= G_REGEX_CASELESS; break;
      case 'x': regex_flags |= G_REGEX_EXTENDED; break;
      default:
        err_str = g_strdup_printf ("Invalid SPARQL regex flag '%c'", *flags);
        sqlite3_result_error (context, err_str, -1);
        g_free (err_str);
        return;
      }
      flags++;
    }
    regex = g_regex_new (pattern, regex_flags, 0, &error);
    if (error) {
      sqlite3_result_error (context, error->message, error->code);
      g_clear_error (&error);
      return;
    }
    sqlite3_set_auxdata (context, 1, regex, (void (*) (void*)) g_regex_unref);
  }
  ret = g_regex_match (regex, text, 0, NULL);
  sqlite3_result_int (context, ret);
  return;
}

Before (this was a test on a huge amount of resources):

$ time tracker-sparql -q "select ?u { ?u a rdfs:Resource . FILTER (regex(?u, '^titl', 'i')) }"
real	0m3.337s
user	0m0.004s
sys	0m0.008s

After:

$ time tracker-sparql -q "select ?u { ?u a rdfs:Resource . FILTER (regex(?u, '^titl', 'i')) }"
real	0m1.887s
user	0m0.008s
sys	0m0.008s

This will hit Tracker’s master today or tomorrow.

Working hard at the Tracker project

Today we improved journal replaying from 1050s for my test of 25249 resources to 58s.

Journal replaying happens when your cache database gets corrupted. Also when you restore a backup: restore uses the same code the journal replaying uses, backup just makes a copy of your journal.

During the performance improvements we of course found other areas related to data entry. It looks like we’re entering a period of focus on performance, as we have a few interesting ideas for next week already. The ideas for next week will focus on performance of some SPARQL functions like regex.

Meanwhile are Michele Tameni and Roberto Guido working on a RSS miner for Tracker and has Adrien Bustany been working on other web miners like for Flickr, GData, Twitter and Facebook.

I think the first pieces of the RSS- and the other web miners will start becoming available in this week’s unstable 0.7 release. Martyn is still reviewing the branches of the guys, but we’re very lucky with such good software developers as contributors. Very nice work Michele, Roberto and Adrien!

Tinymail 1.0!

Tinymail‘s co-maintainer Sergio Villar just released Tinymail’s first release.

psst. I have inside information that I might not be allowed to share that 1.2 is being prepared already, and will have bodystructure and envelope summary fetch. And it’ll fetch E-mail body content per requested MIME part, instead of always entire E-mails. Whoohoo!

An ode to our testers

You know about those guys that use your software against huge datasets like their entire filesystem, with thousands of files?

We do. His name is Tshepang Lekhonkhobe and we owe him a few beers for reporting to us many scalability issues.

Today we found and fixed such a scalability issue: the update query to reset the availability of file resources (this is for support for removable media) was causing at least a linear increase of VmRss usage per amount of file resources. For Tshepang’s situation that meant 600 MB of VmRss. Jürg reduced this to 30 MB of peak VmRss in the same use-case, and a performance improvement from minutes to a second or two, three. Without memory fragmentation as glibc is returning almost all of the VmRss back to the kernel.

Thursday is our usual release day. I invite all of the 0.7 pioneers to test us with your huge filesystems, just like Tshepang always does.

So long and thanks for all the testing, Tshepang! I’m glad we finally found it.

Invisible costs


We would rather suffer the visible costs of a few bad decisions than incur the many invisible costs that come from decisions made too slowly – or not at all – because of a stifling bureaucracy.

Letter by Warren E. Buffett to the shareholders of Berkshire, February 26, 2010

Working hard

I don’t decide about Tracker‘s release. The team of course does.

But when you look at our roadmap you notice one remaining ‘big feature’. That’s coping with modest ontology changes.

Right now if we’d make even a small ontology change all of our users would have to recreate their databases. We also don’t support restoring a backup of your metadata over a modified ontology.

This is about to change. This week I started working in a branch on supporting class and property ontology additions.

I finished it today and it appears to be working. The patches obviously need a thorough review by the other team members, and then testing of course. I invite all the contributors and people who have been testing Tracker 0.7’s releases to tryout the branch. It only supports additions, so don’t try to change properties or classes, or remove them. You can only add new ones. You might have noticed the nao:deprecated property in the ontology files? That’s what we do with deleted properties.

Anyway

Meanwhile are Martyn and Carlos working on a bugfix in the miner about duplicate entries for file resources and on a timeout for the extractor so that extraction of large or complicated documents doesn’t block the entire filesystem miner.

Jürg is working on timezone storage for xsd:dateTime fields and last few days he implemented limited support for named graphs.

By the looks of it, one would almost believe that Tracker’s first new stable release is almost ready!

Please don’t rewrite softwares (that are) written in .NET

This (super) cool .NET developer and good friend came to me at the FOSDEM bar to tell me he was confused about why during the Tracker presentation I was asking people to replace F-Spot and Banshee.

I hope I didn’t say it like that, I would never intent to say that. But I’ll review the video of the presentation as soon as Rob publishes it.

Anyway, to ensure everybody understood correctly what I did wanted to say (whether or not I did, is another question):

The call was to inspire people to reimplement or to provide different implementations of F-Spot’s and Banshee’s data backends, so that they would use an RDF store like tracker-store instead of each app its own metadata database.

I think I also mentioned Rhythmbox in the same sentence because the last thing I would want is to turn this into a .NET vs. anti-.NET debate. It just happens to be that the best GNOME softwares for photo and music management are written in .NET (and that has a good reason).

People who know me also know that I think those anti-.NET people are disruptive ignorable people. I also actively and willingly ignore them (and they should know this). I’m actually a big fan of the Mono platform.

I’ll try to ensure that I don’t create this confusion during presentations anymore.

FWD: [Tracker] tracker-miner-rss 0.3

This is the kind of stuff that needs a forward on the planets:

From: Roberto -MadBob- Guido

This is just an update about tracker-miner-rss effort, already mentioned in this list some time ago.

Website, SVN, Last release (0.3)

Since 0.2 we (Michele and me) have just dropped dependency from rss-glib due some limitation found, and created our own Glib-oriented feeds handling library, libgrss, starting from the code of Liferea and adding nice stuffs such as a PubSub subscriber implementation. At the moment it is shipped with tracker-miner-rss itself, in the future may be splitted so to easy usage by other developers.

Next will come integration with libchamplain to describe geographic points found in geo-rss enabled feeds, integration with libedataserver to better handle “person” rappresentation (suggestions for a better PIM-like shared library with useful objects?), and perhaps a first full-featured feed reader using Tracker as backend.

Enjoy :-)

Roberto is doing a demo on FSter at FOSDEM during our presentation. My role in the presentation will be light this year. I decided to give most of the talk away to Rob Taylor and Roberto. I will probably demo Debarshi Ray‘s Solang and if time permits his work on the Nautilus integration. Regretfully Debarshi can’t come and so he asked me to do the demo.

Solang, a photo manager

For the last few weeks has Debarshi Ray contributed to Tracker’s Nautilus plugin and worked on Solang, a photo manager that will start using Tracker’s SPARQL capability to get a language to query for metadata about the photos and the photos themselves.

Debarshi explains it all very well himself on his own blog.

We’ll probably do a lightening demo during our Tracker presentation at FOSDEM about how Solang did this integration. We’re also planning to demo the code of a few other applications that are working on integrating with Tracker’s store.

Somebody should port Solang to the next version of Maemo!

SPARQL subqueries

This style of subqueries will also work (you can do this one without a subquery too, but it’s just an example of course):

SELECT ?name COUNT(?msg)
WHERE {
	?from a nco:Contact  ;
	          nco:hasEmailAddress ?name . {
		SELECT ?from
		WHERE {
			?msg a nmo:Email ;
			         nmo:from ?from .
		}
	}
} GROUP BY ?from  

The same query in QtTracker will look like this (I have not tested this, let me know if it’s wrong Iridian):

#include <QObject>
#include <QtTracker/Tracker>
#include <QtTracker/ontologies/nco.h>
#include <QtTracker/ontologies/nmo.h>

void someFunction () {
	RDFSelect outer;
	RDFVariable from;
	RDFVariable name = outer.newColumn<nco::Contact>("name");
	from.isOfType<nco::Contact>();
	from.property<nco::hasEmailAddress>(name);
	RDFSelect inner = outer.subQuery();
	RDFVariable in_from = inner.newColumn("from");
	RDFVariable msg;
	msg.property<nmo::from>(in_from);
	msg.isOfType<nmo::Email>();
	outer.addCountColumn("total messages", msg);
	outer.groupBy(from);
	LiveNodes from_and_count = ::tracker()->modelQuery(outer);
}

What you find in this branch already supports it. You can find early support for subqueries in QtTracker in this branch.

To quickly put some stuff about Emails into your RDF store, read this page (copypaste the turtle examples in a file and use the tracker-import tool). You can also enable our Evolution Tracker plugin, of course.

ps. Yes, somebody should while building a GLib/GObject based client library for Tracker copy ideas from QtTracker.

Bla bla bla, subqueries in SPARQL, bla bla

Coming to you in a few days is what Jürg has been working on for last week.

Yeah, you guess it right by looking at the query below: subqueries!

This example shows you the amount of E-mails each contact has ever sent to you:

SELECT ?address
    (SELECT COUNT(?msg) AS ?msgcnt WHERE { ?msg nmo:from ?from })
WHERE {
    ?from a nco:Contact ;
          nco:hasEmailAddress ?address .
}

The usual warnings apply here: I’m way early with this announcement. It’s somewhat implemented but insanely experimental. The SPARQL spec has something for this in a draft wiki page. Due to lack of error reporting and detection it’s easy to make stuff crash or to get it to generate wrong native SQL queries.

But then again, you guys are developers. You like that!

Why are we doing this? Ah, some team at an undisclosed company was worried about performance and D-Bus overhead: They had to do a lot of small queries after doing a parent query. You know, a bunch of aggregate functions for counts, showing the last message of somebody, stuff like that.

I should probably not mention this feature yet. It’s too experimental. But so exciting!

Anyway, here’s the messy branch and here’s the reviewed stuff for bringing this feature into master.

ps. I wish I could show you guys the query that we support for that team. It’s awesome. I’ll ask around.

Tracker’s write back support now in master

Whoohoo!

We just committed the support for write back in master.

What is it?

Tracker has a limited capability to write metadata back into the data resource. In case of a file that means writing it back into the file. For example writing some of the metadata the user sets using a SPARQL Update back into an MP3 file as ID3 tags.

Which ones do we support already?

Right now the write back capability is under development and only supports a bunch of fields for a few XMP formats (JPEG, PNG and TIFF) and the Title of MP3 files. In near future we will start supporting increasingly more fields.

Documentation?

For people who want to write support for their properties and file formats, read the documentation.

Party like it’s 2009!

Handling triplets arriving in tracker-store, CouchDB integration as use-case

At GCDS Jamie told us that he wants to make a plugin for tracker-store that writes all the triplets to a CouchDB instance.

Letting a CouchDB be a sort of offline backup isn’t very interesting. You want triples to go into the CouchDB at the moment of guaranteed storage: at commit time.

For the purpose of developing this we provide the following internal API.

typedef void (*TrackerStatementCallback) (const gchar *graph,
                                          const gchar *subject,
                                          const gchar *predicate,
                                          const gchar *object,
                                          GPtrArray   *rdf_types,
                                          gpointer     user_data);
typedef void (*TrackerCommitCallback)    (gpointer     user_data);

tracker_data_add_insert_statement_callback (TrackerStatementCallback callback,
                                            gpointer                 user_data);
tracker_data_add_delete_statement_callback (TrackerStatementCallback callback,
                                            gpointer                 user_data);
tracker_data_add_commit_statement_callback (TrackerCommitCallback callback,
                                            gpointer              user_data);

You’ll need to make a plugin for tracker-store and make the hook at the initialization of your plugin.

Current behaviour is when graph is NULL, it means that the default graph is being used. If it’s not NULL, it means that you probably don’t want the data in CouchDB: it’s data that’s coming from a miner. You probably only want to store data that is coming from the user. His applications won’t use FROM and INTO for their SPARQL Update queries, meaning that graph is NULL.

Very important is that your callback handler works with bottom halves: put your expensive task on a queue and handle the queued item somewhere else. You can for example use a GThreadPool or a GQueue plus a g_idle_add_full with G_PRIORITY_LOW callback picking items one by one on the mainloop. You should never have a TrackerStatementCallback or a TrackerCommitCallback that blocks. Not even a tiny tiny bit of blocking: it’ll bring everything in tracker-store on its knees. It’s why we aren’t giving you a public plugin API with a way to install your own plugins outside of the Tracker project.

By the way: we want to see code instead of talk before we further optimize things for this purpose.

Writeback, writing metadata back into your files

Today, I feel like exposing you to some bleeding edge development going on as we speak at the Tracker team. I know you’re scared of that and that’s precisely why I want to expose you! Hah.

We are prototyping writeback support for Tracker.

With writeback we mean writing metadata that the user passes to us via SPARQL UPDATE into the file that he’s describing.

This means that it must be about a thing that is stored, that it must update a property that we want to writeback and it means that we need to support the format.

OK, that’s three requirements before we write anything back. Let’s explain how this stuff works in the prototype!

In our prototype you mark properties that are eligible for being written into the files using tracker:writeback.

It goes like this:

nie:title a rdf:Property ;
   rdfs:label "Title" ;
   rdfs:comment "The title of the document" ;
   rdfs:subPropertyOf dc:title ;
   nrl:maxCardinality 1 ;
   rdfs:domain nie:InformationElement ;
   rdfs:range xsd:string ;
   tracker:fulltextIndexed true ;
   tracker:weight 10 ;
   tracker:writeback true .

Next you need a writeback module for tracker-writeback. We implemented a prototype one that can only write the title of MP3 files. It uses ID3lib‘s C API.

When the user is describing a file, the resource must have nie:isStoredAs. The property being changed ‘s tracker:writeback must be true. We want the value of the property too. That’s simple in SPARQL, right? Sure it is!

SELECT ?url ?predicate ?object {
    <$subject> ?predicate ?object ;
               nie:isStoredAs ?url .
    ?predicate tracker:writeback true
 }

You’ll find this query in the code, go look!

Now it’s simple: using ID3lib we map Nepomuk to ID3 and write it.

No don’t be afraid, we’re not going to writeback metadata that we found ourselves. We’ll only writeback data that the user provided in the form of a SPARQL Update on the default graph. No panic. Besides, using tracker-writeback is going to be completely optional (just don’t run it).

This is a prototype, I repeat, this is a prototype. No expectations yet please. Just feel exposed to scary stuff, get overly excited and then join us by contributing. It’s all public what we’re doing in the branch ‘writeback’.

ps. Whether this will be Maemo’s future metadata-write stuff? Hmm, I don’t know. Do you know? ;-)