OpenLink Virtuoso (Product Blog)

I was asked for an oracular statement about the future of relational database (RDBMS) at the conference. The answer, without doubt or hesitation, is that this is forever. But this does not mean that the RDBMS world would be immutable, quite the opposite.

The specializations converge. The RDBMS becomes more adaptable and less schema-first. Of course the RDBMS also take new data models beside the relational. RDF and other property graph models, for instance.

The schema-last-ness is now well in evidence. For example, PostgreSQL has an hstore column type which is a list of key-value pairs. Vertica has a feature called flex tables where a column can be added on a row-by-row basis.

Specialized indexing for text and geometries is a well established practice. However, dedicated IR systems, often Lucene derivatives, can offer more transparency in the IR domain for things like vector-space-models and hit-scoring. There is specialized faceted search support which is quite good. I do not know of an RDBMS that would do the exact same trick as Lucene for facets, but, of course, in the forever expanding scope of RDB, this is added easily enough.

JSON is all the rage in the web developer world. Phil Archer even said in his keynote, as a parody of the web developer: " I will never touch that crap of RDF or the semantic web; this is a pipe dream of reality ignoring academics and I will not have it. I will only use JSON-LD."

XML and JSON are much the same thing. While most databases have had XML support for over a decade, there is a crop of specialized JSON systems like MongoDB. PostgreSQL also has a JSON datatype. Unsurprisingly, MarkLogic too has JSON, as this is pretty much the same thing as their core competence of XML.

Virtuoso, too, naturally has a JSON parser, and mapping this to the native XML data type is a non-issue. This should probably be done.

Stefano Bertolo of the EC, also LOD2 project officer, used the word Cambrian explosion when talking about the proliferation of new database approaches in recent years.

Hadoop is a big factor in some environments. Actian Vector (née VectorWise), for example, can use this as its file system. HDFS is singularly cumbersome for this but still not impossible and riding the Hadoop bandwagon makes this adaptation likely worthwhile.

Graphs are popular in database research. We have a good deal of exposure to this via LDBC. Going back to an API for database access, as is often done in graph database, can have its point, especially as a reaction to the opaque and sometimes hard to predict query optimization of declarative languages. This just keeps getting more complex, so a counter-reaction is understandable. APIs are good if crossed infrequently and bad otherwise. So, graph database APIs will develop vectoring, is my prediction and even recommendation in LDBC deliverables.

So, there are diverse responses to the same evolutionary pressures. These are of initial necessity one-off special-purpose systems, since the time to solution is manageable. Doing these things inside an RDBMS usually takes longer. The geek also likes to start from scratch. Well, not always, as there have been some cases of grafting some entirely non-MySQL-like functionality, e.g. Infobright and Kickfire, onto MySQL.

From the Virtuoso angle, adding new data and control structures has been done many times. There is no reason why this cannot continue. The next instances will consist of some graph processing (BSP, or Bulk Synchronous Processing) in the query languages. Another recent example is an interface for pluggable specialized content indices. One can make chemical structure indices, use alternate full text indices, etc., with this.

Most of this diversification has to do with physical design. The common logical side is a demand for more flexibility in schema and sometimes in scaling, e.g., various forms of elasticity in growing scale-out clusters, especially with the big web players.

The diversification is a fact, but the results tend to migrate into the RDBMS given enough time.

On the other hand, when a new species like the RDF store emerges, with products that do this and no other thing and are numerous enough to form a market, the RDBMS functionality seeps in. Bigdata has a sort of multicolumn table feature, if I am not mistaken. We just heard about the wish for strict schema, views, and triggers. By all means.

From the Virtuoso angle, with structure awareness, the difference of SQL and RDF gradually fades, and any advance can be exploited to equal effect on either side.

Right now, I would say we have convergence when all the experimental streams feel many of the same necessities.

Of course you cannot have a semantic tech conference without the matter of the public SPARQL end point coming up. The answer is very simple: If you have operational need for SPARQL accessible data, you must have your own infrastructure. No public end points. Public end points are for lookups and discovery; sort of a dataset demo. If operational data is in all other instances the responsibility of the one running the operation, why should it be otherwise here? Outsourcing is of course possible, either for platform (cloud) or software (SaaS). To outsource something with a service level, the service level must be specifiable. A service level cannot be specified in terms of throughput with arbitrary queries but in terms of well defined transactions; hence the services world runs via APIs, as in the case of Open PHACTS. For arbitrary queries (i.e., analytics on demand), with the huge variation in performance dependent on query plans and configuration of schema, the best is to try these things with platform on demand in a cloud. Like this, there can be a clear understanding of performance, which cannot be had with an entirely uncontrolled concurrent utilization. For systems in constant operation, having one's own equipment is cheaper, but still might be impossible to procure due to governance.

Having clarified this, the incentives for operators also become clearer. A public end point is a free evaluation; a SaaS deal or product sale is the commercial offering.

Anyway, common datasets like DBpedia are available preconfigured on AWS with a Virtuoso server. For larger data, there is a point to making ready-to-run cluster configurations available for evaluation, now that AWS has suitable equipment (e.g., dual E5 2670 with 240 GB RAM and SSD for USD 2.8 an hour). According to Amazon, up to five of these are available at a time without special request. We will try this during the fall and make the images available.

SEMANTiCS 2014 Series

• SEMANTiCS 2014 (part 1): Keynote
• SEMANTiCS 2014 (part 2): RDF Data Shapes
• SEMANTiCS 2014 (part 3): Conversations (this post)

The first keynote of Semantics 2014 was by Phil Archer of the W3C, entitled "10 Years of Achievement." After my talk, in the questions, Phil brought up the matter of the upcoming W3C work group charter on RDF Data Shapes. We had discussed this already at the reception the night before and I will here give some ideas about this.

After the talk, my answer was that naturally the existence of something that expressed the same sort of thing as SQL DDL, with W3C backing, can only be a good thing and will give the structure awareness work by OpenLink in Virtuoso and probably others a more official seal of approval. Quite importantly, this will be a facilitator of interoperability and will raise this from a product specific optimization trick to a respectable, generally-approved piece of functionality.

This is the general gist of the matter and can hardly be otherwise. But underneath is a whole world of details, which we discussed at the reception.

Phil noted that there was controversy around whether a lightweight OWL-style representation or SPIN should function as the basis for data shapes.

Phil stated in the keynote that the W3C considered the RDF series of standards as good and complete, but would still have working groups for filling in gaps as these came up. This is what I had understood from my previous talks with him at the Linking Geospatial Data workshop in London earlier this year.

So, against this backdrop, as well as what I had discussed with Ralph Hodgson of Top Quadrant at a previous LDBC TUC meeting in Amsterdam, SPIN seems to me a good fit.

Now, it turns out that we are talking about two different use cases. Phil said that the RDF Data Shapes use case was about making explicit what applications required of data. For example, all products should have a unit price, and this should have one value that is a number.

The SPIN proposition on the other hand, as Ralph himself put it in the LDBC meeting, is providing to the linked data space functionality that roughly corresponds to SQL views. Well, this is one major point, but SPIN involves more than this.

So, is it DDL or views? These are quite different. I proposed to Phil that there was in fact little point in fighting over this; best to just have two profiles.

To be quite exact, even SQL DDL equivalence is tricky, since enforcing this requires a DBMS; consider, for instance, foreign key and check constraints. At the reception, Phil stressed that SPIN was certainly good but since it could not be conceived without a SPARQL implementation, it was too heavy to use as a filter for an application that, for example, just processed a stream of triples.

The point, as I see it, is that there is a wish to have data shape enforcement, at least to a level, in a form that can apply to a stream without random access capability or general purpose query language. This can make sense for some big data style applications, like an ETL-stage pre-cooking of data before the application. Applications mostly run against a DBMS, but in some cases, this could be a specialized map-reduce or graph analytics job also, so no low cost random access.

My own take is that views are quite necessary, especially for complex query; this is why Virtuoso has the SPARQL macro extension. This will do, by query expansion, a large part of what general purpose inference will do, except for complex recursive cases. Simple recursive cases come down to transitivity and still fit the profile. SPIN is a more generic thing, but has a large intersection with SPARQL macro functionality.

My other take is that structure awareness needs a way of talking about structure. This is a use case that is clearly distinct from views.

A favorite example of mine is the business rule that a good customer is one that has ordered more than 5 times in the last year, for a total of more than so much, and has no returns or complaints. This can be stated as a macro or SPIN rule with some aggregates and existences. This cannot be stated in any of the OWL profiles. When presented with this, Phil said that this was not the use case. Fair enough. I would not want to describe what amounts to SQL DDL in these terms either.

A related topic that has come up in other conversations is the equivalent of the trigger. One use case of this is enforcement of business rules and complex access rights for updates. So, we see that the whole RDBMS repertoire is getting recreated.

Now, talking from the viewpoint of the structure-aware RDF store, or the triple-stream application for that matter, I will outline some of what data shapes should do. The triggers and views matter is left out, here.

The commonality of bulk-load, ETL, and stream processing, is that they should not rely on arbitrary database access. This would slow them down. Still, they must check the following sorts of things:

• Data types
• Presence of some required attributes
• Cardinality — e.g., a person has no more than one date of birth
• Ranges — e.g., a product's price is a positive number; gender is male/female; etc.
• Limited referential integrity — e.g., a product has one product type, and this is a subject of the RDF type product type.
• Limited intra-subject checks — e.g.. delivery date is greater-than-or-equal-to ship date.

All these checks depend on previous triples about the subject; for example, these checks may be conditional on the subject having a certain RDF type. In a data model with a join per attribute, some joining cannot be excluded. Checking conditions that can be resolved one triple at a time is probably not enough, at least not for the structure-aware RDF store case.

But, to avoid arbitrary joins which would require a DBMS, we have to introduce a processing window. The triples in the window must be cross-checkable within the window. With RDF set semantics, some reference data may be replicated among processing windows (e.g., files) with no ill effect.

A version of foreign key declarations is useful. To fit within a processing window, complete enforcement may not be possible but the declaration should still be possible, a little like in SQL where one can turn off checking.

In SQL, it is conventional to name columns by prefixing them with an abbreviation of the table name. All the TPC schemas are like that, for example. Generally in coding, it is good to prefix names with data type or subsystem abbreviation. In RDF, this is not the practice. For reuse of vocabularies, where a property may occur in anything, the namespace or other prefix denotes where the property comes from, not where it occurs.

So, in TPC-H, l_partkey and ps_partkey are both foreign keys that refer to part, plus that l_partkey is also a part of a composite foreign key to partsupp. By RDF practices, these would be called rdfh:hasPart. So, depending on which subject type we have, rdfh:hasPart is 30:1 or 4:1. (distinct subjects:distinct objects) Due to this usage, the property's features are not dependent only on the property, but on the property plus the subject/object where it occurs.

In the relational model, when there is a parent and a child item (one to many), the child item usually has a composite key prefixed with the parent's key, with a distinguishing column appended, e.g., l_orderkey, l_linenumber. In RDF, this is rdfh:hasOrder as a property of the lineitem subject. In SQL, there is no single part lineitem subject at all, but in RDF, one must be made since everything must be referenceable with a single value. This does not have to matter very much, as long as it is possible to declare that lineitems will be primarily accessed via their order. It is either this or a scan of all lineitems. Sometimes a group of lineitems are accessed by the composite foreign key of l_partkey, l_suppkey. There could be a composite index on these. Furthermore, for each l_partkey, l_suppkey in lineitem there exists a partsupp. In an RDF translation, the rdfh:hasPart and rdfh:hasSupplier, when they occur in a lineitem subject, specify exactly one subject of type partsupp. When they occur in a partsupp subject, they are unique as a pair. Again, because names are not explicit as to where they occur and what role they play, the referential properties do not depend only on the name, but on the name plus included data shape. Declaring and checking all this is conventional in the mainstream and actually useful for query optimization also.

Take the other example of a social network where the foaf:knows edge is qualified by a date when this edge was created. This may be by reification, or more usually by an "entitized" relationship where the foaf:knows is made into a subject with the persons who know each other and the date of acquaintance as properties. In a SQL schema, this is a key person1, person2 -> date. In RDF, there are two join steps to go from person1 to person2; in SQL, 1. This is eliminated by saying that the foaf:knows entity is usually referenced by the person1 Object or person2 Object, not the Subject identifier of the foaf:knows.

This allows making the physical storage by O, S, G -> O2, O3, …. A secondary index with S, G, O still allows access by the mandatory subject identifier. In SQL, a structure like this is called a clustered table. In other words, the row is arranged contiguous with a key that is not necessarily the primary key.

So, identifying a clustering key in RDF can be important.

Identifying whether there are value-based accesses on a given Object without making the Object a clustering key is also important. This is equivalent to creating a secondary index in SQL. In the tradition of homogenous access by anything, such indexing may be on by default, except if the property is explicitly declared of low cardinality. For example, an index on gender makes no sense. The same is most often true of rdfs:type. Some properties may have many distinct values (e.g., price), but are still not good for indexing, as this makes for the extreme difference in load time between SQL and the all-indexing RDF.

Identifying whether a column will be frequently updated is another useful thing. This will turn off indexing and use an easy-to-update physical representation. Plus, properties which are frequently updated are best put physically together. This may, for example, guide the choice between row-wise and column-wise representation. A customer's account balance and orders year-to-date would be an example of such properties.

Some short string valued properties may be frequently returned or used as sorting keys. This requires accessing the literal via an ID in the dictionary table. Non-string literals, numbers, dates, etc., are always inlined (at least in most implementations), but strings are a special question. Bigdata and early versions of Virtuoso would inline short ones; later versions of Virtuoso would not. So specifying, per property/class combination, a length limit for an inlined string is very high gain and trivial to do. The BSBM explore score at large scales can get a factor of 2 gain just from inlining one label. BSBM is out of its league here, but this is still really true and yields benefits across the board. The simpler the application, the greater the win.

If there are foreign keys, then data should be loaded with the referenced entities first. This makes dimensional clustering possible at load time. If the foreign key is frequently used for accessing the referencing item (for example, if customers are often accessed by country), then loading customers so that customers of the same country end up next to each other can result in great gains. The same applies to a time dimension, which in SQL is often done as a dimension table, but rarely so in linked data. Anyhow, if date is a frequent selection criterion, physically putting items in certain date ranges together can give great gains.

The trick here is not necessarily to index on date, but rather to use zone maps (aka min/max index). If nearby values are together, then just storing a min-max value for thousands of consecutive column values is very compact and fast to check, provided that the rows have nearby values. Actian Vector's (VectorWise) prowess in TPC-H is in part from smart use of date order in this style.

To recap, the data shapes desiderata from the viewpoint of guiding physical storage is as follows:

(I will use "data shape" to mean "characteristic set," or "set of Subjects subject to the same set of constraints." A Subject belonging to a data shape may be determined either by its rdfs:type or by the fact of it having, within the processing window, all or some of a set of properties.)

• All normal range, domain, cardinality, optionality, etc. — Specifically, declaring something as single valued (as with SQL's UNIQUE constraint) and mandatory (as with SQL's NOT NULL constraint) is good.
• Primary access path — The Properties whose Objects are dominant access criteria is important
• No-index — Declare that no index will be made on the Object of a Property within a data shape.
• Inlined string — String values of up to so many characters in this data shape are inlined
• Clustering key — The Subject identifiers will be picked to be correlated with the Object of this Property in this data shape. This can be qualified by a number of buckets (e.g., if dates are from 2000 to 2020, then this interval may be 100 buckets), with an exception bucket for out of range values.
• No full text index — A string value will not need to be full text indexed in this Property even if full text indexing is generally on.
• Full text index desired — This means that if the value of the property is a string, then the row must be locatable via this string. The string may or may not be inlined, but an index will exist on the literal ID of the string, e.g., POSG.
• Co-location — This is akin to clustering but specifies, for a high cardinality Object, that the Subject identifier should be picked to fall in the same partition as the Object. The Object is typically a parent of the Subject being loaded; for example, the containing assembly of a sub-assembly. Traversing the assembly created in this way will be local on a scale-out system. This can also apply to geometries or text values: If primary access is by text or geo index, then the metadata represented as triples should be in the same partition as the entry in the full text/geo index.
• Update group — A set of properties that will often change together. Implies no index and some form of co-location, plus update-friendly physical representation. Many update groups may exist, in which case they may or may not be collocated.
• Composite foreign/primary key — A data shape can have a multicolumn foreign key, e.g., l_partkey, l_suppkey in lineitem with the matching primary key of ps_partkey, ps_suppkey in partsupp. This can be used for checking and for query optimization: Looking at l_partkey and l_suppkey as independent properties, the guess would be that there hardly ever exists a partsupp, whereas one does always exist. The XML standards stack also has a notion of a composite key for random access on multiple attributes.

These things have the semantic of "hint for physical storage" and may all be ignored without effect on semantics, at least if the data is constraint-compliant to start with.

These things will have some degree of reference implementation through the evolution of Virtuoso structure awareness, though not necessarily immediately. These are, to the semanticist, surely dirty low-level disgraceful un-abstractions, some of the very abominations the early semanticists abhorred or were blissfully ignorant of when they first raised their revolutionary standard.

Still, these are well-established principles of the broader science of database. SQL does not standardize some of these, nor does it have much need to, as the use of these features is system-specific. The support varies widely and the performance impacts are diverse. However, since RDF excels as a reference model and as a data interchange format, giving these indications as hints to back-end systems cannot hurt, and can make a difference of night and day in load and query time.

As Phil Archer said, the idea of RDF Data Shapes is for an application to say that "it will barf if it gets data that is not like this." An extension is for the data to say what the intended usage pattern is so that the system may optimize for this.

All these things may be learned from static analysis and workload traces. The danger of this is over-fitting a particular profile. This enters a gray area in benchmarking. For big data, if RDF is to be used as the logical model and the race is about highest absolute performance, never mind what the physical model ends up being, all this and more is necessary. And if one is stretching the envelope for scale, the race is always about highest absolute performance. For this reason, these things will figure at the leading edge with or without standardization. I would say that the build-up of experience in the RDBMS world is sufficient for these things to be included as hints in a profile of data shapes. The compliance cost will be nil if these are ignored, so for the W3C, these will not make the implementation effort for compliance with an eventual data shapes recommendation prohibitive.

The use case is primarily the data warehouse to go. If many departments or organizations publish data for eventual use by their peers, users within the organization may compose different combinations of extractions for different purposes. Exhaustive indexing of everything by default makes the process slow and needlessly expensive, as we have seen. Much of such exploration is bounded by load time. Federated approaches for analytics are just not good, even though they may work for infrequent lookups. If datasets are a commodity to be plugged in and out, the load and query investment must be minimized without the user/DBA having to run workload analysis and manual schema optimization. Therefore, bundling guidelines such as these with data shapes in a dataset manifest can do no harm and can in cases provide 10-50x gains in load speeds and 2-4x in space consumption, not to mention unbounded gains in query time, as good and bad plans easily differ by 10-100x, especially in analytics.

So, here is the pitch:

• Dramatic gains in ad hoc user experience
• Minimal effort by data publishers, as much of the physical guidelines can be made from workload trace and dataset; the point is that the ad hoc user does not have to do this.
• Great optimization potential for system vendors; low cost for initial compliance
• Better understanding of the science of performance by the semantic community

To be continued...

SEMANTiCS 2014 Series

• SEMANTiCS 2014 (part 1): Keynote
• SEMANTiCS 2014 (part 2): RDF Data Shapes (this post)
• SEMANTiCS 2014 (part 3): Conversation

I was invited to give a keynote at SEMANTiCS 2014 in Leipzig, Germany last Thursday. I will here recap some of the main points, and comment on some of the ensuing controversy. The talk was initially titled Virtuoso, the Prometheus of RDF. Well, mythical Prometheus did perform a service but ended up paying for it. Still, the mythical reference is sometimes used when talking of major breakthroughs and big-gain ambitions. In the first slide, I changed it to Linked Data at Dawn, which is less product specific and more a reflection on the state of the linked data enterprise at large.

The first part of the talk was under the heading of the promise and the practice. The promise we know well and find no fault with: Schema-last-ness, persistent unique identifiers, self-describing data, some but not too much inference. The applications usually involve some form of integration and often have a mix of strictly structured content with semi-structured or textual content.

These values are by now uncontroversial and embraced by many; however, most instances of this embracing do not occur in the context of RDF as such. For example, the big online systems on the web: all have some schema-last (key-value) functionality. Applications involving long-term data retention have diverse means of having persistent IDs and self description, from UUIDs to having the table name in a column so that one can tell where a CSV dump came from.

The practice involves competing with diverse alternative technologies: SQL, key-value, information retrieval (often Lucene-derived). In some instances, graph databases occur as alternatives: Young semanticist, do or die.

In this race, linked data is often the prettiest and most flexible, but gets a hit on different aspects of performance and scalability. This is a database gig, and database is a performance game; make no mistake.

After these preliminaries we come to the "RDF tax," or the more or less intrinsic overheads of describing all as triples. The word "triple" is used by habit. In fact, we nearly always talk about quads, i.e., subject-predicate-object-graph (SPOG). The next slide is provocatively titled the Bane of the Triple, and is about why having all as triples is, on the surface, much like relational, except it makes life hard, where tables make it at least manageable, if still not altogether trivial.

The very first statement on the tax slide reads "90% of bad performance comes from non-optimal query plans." If one does triples in the customary way (i.e., a table of quads plus dictionary tables to map URIs and literal strings to internal IDs), one incurs certain fixed costs.

These costs are deemed acceptable by users who deploy linked data. If these costs were not acceptable, the proof of concept would have already disqualified linked data.

The support cases that come my way are nearly always about things taking too much time. Much less frequently, are these about something unambiguously not working. Database has well defined semantics, so whether something works or not is clear cut.

So, support cases are overwhelmingly about query optimization. The problems fall in two categories:

• The plan is good in the end, but it takes much longer to make the plan than to execute it.
• The plan either does the wrong things or does things in the wrong order, but produces a correct result.

Getting no plan at all or getting a clearly wrong result is much less frequent.

If the RDF overheads incurred with a good query plan were show stoppers, the show would have already stopped.

So, let's look at this in more detail; then we will talk about the fixed overheads.

The join selectivity of triple patterns is correlated. Some properties occur together all the time; some occur rarely; some not at all. Some property values can be correlated, i.e., order number and order date. Capturing these by sampling in a multicolumn table is easy; capturing this in triples would require doing the join in the cost model, which is not done since it would further extend compilation times. When everything is a join, selectivity estimation errors build up fast. When everything is a join, the space of possible graph query plans explodes as opposed to tables; thus, while the full plan space can be covered with 7 tables, it cannot be covered with 18 triple patterns. This is not factorial (number of permutations). For different join types (index/hash) and the different compositions of the hash build side, this is much worse, in some nameless outer space fringe of non-polynomiality.

TPC-H can be run with success because the cost model hits the right plan every time. The primary reason for this is the fact that the schema and queries unambiguously suggest the structure, even without foreign key declarations. The other reason is that with a handful of tables, all plans can be reviewed, and the cost model reliably tells how many rows will result from each sequence of operations.

Try this with triples; you will know what I mean.

Now, some people have suggested purely rule-based models of SPARQL query compilation. These are arguably faster to run and more predictable. But the thing that must be done, yet will not be done with these, is the right trade-off between index and hash. This is the crux of the matter, and without this, one can forget about anything but lookups. The choice depends on reliable estimation of cardinality (number of rows, number of distinct keys) on either side of the join. Quantity, not pattern matching.

Well, many linked data applications are lookups. The graph database API world is sometimes attractive because it gives manual control. Map reduce in the analytical space is sometimes attractive for the same reason.

On the other hand, query languages also give manual control, but then this depends on system specific hints and cheats. People are often black and white: Either all declarative or all imperative. We stand for declarative, but still allow physical control of plan, like most DBMS.

To round off, I will give a concrete example:

{  ?thing  rdfs:label    ?lbl         . 
   ?thing  dc:title      ?title       . 
   ?lbl    bif:contains  "gizmo"      . 
   ?title  bif:contains  "widget"     . 
   ?thing  a             xx:Document  . 
   ?thing  dc:date       ?dt          . 
   FILTER  ( ?dt  > "2014-01-01"^^xsd:date ) 
}

There are two full text conditions, one date, and one class, all on the same subject. How do you do this? Most selective text first, then get the data and check, then check the second full text given the literal and the condition, then check the class? Wrong. If widgets and gizmos are both frequent and most documents new, this is very bad because using a text index to check for a specific ID having a specific string is not easily vectorable. So, the right plan is: Take the more selective text expression, then check the date and class for the results, put the ?things in a hash table. Then do the less selective text condition, and drop the ones that are not in the hash table. Easily 10x better. Simple? In the end yes, but you do not know this unless you know the quantities.

This gives the general flavor of the problem. Doing this with TPC-H in RDF is way harder, but you catch my drift.

Each individual instance is do-able. Having closer and closer alignment between reality and prediction will improve the situation indefinitely, but since the space is as good as infinite there cannot be a guarantee of optimality except for toy cases.

The Gordian Knot shall not be defeated with pincers but by the sword.

We will come to this in a bit.

Now, let us talk of the fixed overheads. The embarrassments are in the query optimization domain; the daily grind, relative cost, and provisioning are in this one.

The overheads come from:

• Indexing everything
• Having literals and URI strings via dictionary
• Having a join for every attribute

These all fall under the category of having little to no physical design room.

In the indexing everything department, we load 100 GB TPC-H in 15 minutes in SQL with ordering only on primary keys and almost no other indexing. The equivalent with triples is around 12 hours. This data can be found on this blog (TPC-H series and Meeting the Challenges of Linked Data in the Enterprise). This is on the order of confusing a screwdriver with a hammer. If the nail is not too big, the wood not too hard, and you hit it just right — the nail might still go in. The RDF bulk load is close to the fastest possible given the general constraints of what it does. The same logic is used for the record-breaking 15 minutes of TPC-H bulk load, so the code is good. But indexing everything is just silly.

The second, namely the dictionary of URIs and literals, is a dual edge. I talked to Bryan Thompson of SYSTAP (Bigdata RDF store) in D.C. at the ICDE there. He said that they do short strings inline and long ones via dictionary. I said we used to do the same but stopped in the interest of better compression. What is best depends on workload and working-set-to-memory ratio. But if you must make the choice once and for all, or at least as a database-wide global setting, you are between a rock and a hard place. Physical vs. logical design, again.

The other aspect of this is the applications that do regexps on URI strings or literals. Doing this is like driving a Formula 1 race in reverse gear. Use a text index. Always. This is why most implementations have one even though SPARQL itself makes no provisions for this. If you really need regexps, and on supposedly opaque URIs at that, tokenize them and put them in a text index as a text literal. Or if an inverted-file-word index is really not what you need, use a trigram one. So far, nobody has wanted one hard enough for us to offer this, even though this is easy enough. But special indices for special data types (e.g., chemical structure) are sometimes wanted, and we have a generic solution for all this, to be introduced shortly on this blog. Again, physical design.

I deliberately name the self-join-per-attribute point last, even though this is often the first and only intrinsic overhead that is named. True, if the physical model is triples, each attribute is a join against the triple table. Vectored execution and right use of hash-join help, though. The Star Schema Benchmark SQL to SPARQL gap is only 2.5x, as documented last year on this blog. This makes SPARQL win by 100+x against MySQL and lose by only 0.8x against column store pioneer MonetDB. Let it be said that this is so far the best case and that the gap is wider in pretty much all other cases. This gap is well and truly due to the self-join matter, even after the self-joins are done vectored, local, ordered; in one word, right. The literal and URI translation matter plays no role here. The needless indexing hurts at load but has no effect at query time, since none of the bloat participates in the running. Again, physical design.

Triples are done right, so?

In the summer of 2013, after the Star Schema results, it became clear that maybe further gains could be had and query optimization made smoother and more predictable, but that these would be paths of certain progress but with diminishing returns per effort. No, not the pincers; give me the sword. So, between fall 2013 and spring 2014, aside from doing diverse maintenance, I did the TPC-H series. This is the proficiency run for big league databases; the America's Cup, not a regatta on the semantic lake.

Even if the audience is principally Linked Data, the baseline must be that of the senior science of SQL.

It stands to reason and has been demonstrated by extensive experimentation at CWI that RDF data, by and large, has structure. This structure will carry linked data through the last mile to being a real runner against the alternative technologies (SQL, IR, key value) mentioned earlier.

The operative principles have been mentioned earlier and are set forth on the slides. In forthcoming articles I will display some results.

One important proposal for structure awareness was by Thomas Neumann in an RDF3X paper introducing characteristic sets. There, the application was creation of more predictable cost estimates. Neumann correctly saw this as possibly the greatest barrier to predictable RDF performance. Peter Boncz and I discussed the use of this for physical optimization once when driving back to Amsterdam from a LOD2 review in Luxembourg. Pham Minh Duc of CWI did much of the schema discovery research, documented in the now published LOD2 book (Linked Open Data -- Creating Knowledge Out of Interlinked Data). The initial Virtuoso implementation had to wait for the TPC-H and general squeezing of the quads model to be near complete. It will likely turn out that the greatest gain of all with structure awareness will be bringing optimization predictability to SQL levels. This will open the whole bag of tricks known to data warehousing to safe deployment for linked data. Of course, much of this has to do with exploiting physical layout; hence it also needs the physical model to be adapted. Many of these techniques have high negative impact if used in the wrong place; hence the cost model must guess right. But they work in SQL and, as per Thomas Neumann's initial vision, there is no reason why these would not do so in a schema-less model if adapted in a smart enough manner.

All this gives rise to some sociological or psychological observations. Jens Lehmann asked me why now, why not earlier; after all, over the years many people have suggested property tables and other structured representations. This is now because there is no further breakthroughs within an undifferentiated physical model.

For completeness, we must here mention other approaches to alternative, if still undifferentiated, physical models. A number of research papers mention memory-only, pointer-based (i.e., no index, no hash-join) implementations of triples or quads. Some of these are on graph processing frameworks, some stand-alone. Yarc Data is a commercial implementation that falls in this category. These may have higher top speeds than column stores, even after all vectoring and related optimizations. However the space utilization is perforce larger than with optimum column compression and this plus the requirement of 100% in memory makes these more expensive to scale. The linked data proposition is usually about integration, and this implies initially large data even if not all ends up being used.

The graph analytics, pointer-based item will be specially good for a per-application extraction, as suggested by Oracle in their paper at GRADES 13. No doubt this will come under discussion at LDBC, where Oracle Labs is now a participant.

But back to physical model. What we have in mind is relational column store — multicolumn-ordered column-wise compressed tables — a bit like Vertica and Virtuoso in SQL mode for the regular parts and quads for the rest. What is big is regular, since a big thing perforce comes from something that happens a lot, like click streams, commercial transactions, instrument readings. For the 8-lane-motorway of regular data, you get the F1 racer with the hardcore best in column store tech. When the autobahn ends and turns into the mountain trail, the engine morphs into a dirt bike.

This is complex enough, and until all the easy gains have been extracted from quads, there is little incentive. Plus this has the prerequisite of quads done right, plus the need for top of the line relational capability for not falling on your face once the speedway begins.

Steve Buxton of MarkLogic gave a talk right before mine. Coming from a document-centric world, it stands to reason that MarkLogic would have a whole continuum of different mixes between SPARQL and document oriented queries. Steve correctly observed that some users found this great; others found this a near blasphemy, an unholy heterodoxy of confusing distinct principles.

This is our experience as well, since usage of XML fragments in SPARQL with XPath and such things in Virtuoso is possible but very seldom practiced. This is not the same as MarkLogic, though, as MarkLogic is about triples-in-documents, and the Virtuoso take is more like documents-in-triples. Not to mention that use of SQL and stored procedures in Virtuoso is rare among the SPARQL users.

The whole thing about the absence of physical design in RDF is a related, but broader instance of such purism.

In my talk, I had a slide titled The Cycle of Adventure, generally philosophizing on the dynamics of innovation. All progress begins with an irritation with the status quo; to mention a few examples: the No-SQL rebellion; the rejection of parallel SQL database in favor of key-value and map-reduce; the admission that central schema authority at web scale is impossible; the anti-ACID stance when having wide-area geographies to deal with. The stage of radicalism tends to discard the baby with the bathwater. But when the purists have their own enclave, free of the noxious corruption of the rejected world, they find that life is hard and defects of human character persist, even when all subscribe to the same religion. Of course, here we may have further splinter groups. After this, the dogma adapts to reality: the truly valuable insights of the original rebellion gain in appreciation, and the extremism becomes more moderate. Finally there is integration with mainstream, which becomes enriched by new content.

By the time the term Linked Data came to broad use, the RDF enterprise had its break-away colonies that started to shed some of the initial zeal. By now, we have the last phase of reconciliation in its early stages.

This process is in principle complete when linked data is no longer a radical bet, but a technology to be routinely applied to data when the nature of the data fits the profile. The structure awareness and other technology discussed here will mostly eliminate the differential in deployment cost.

The spreading perception of an expertise gap in this domain will even-out the cost in terms of personnel. The flexibility gains that were the initial drive for the movement will be more widely enjoyed when these factors fuel broader adoption.

To help this along, we have LDBC, the Linked Data Benchmark Council, with the agenda of creating industry consensus on measuring progress across the linked data and graph DB frontiers. I duly invited MarkLogic to join.

There were many other interesting conversations at the conference, I will later comment on these.

To be continued...

SEMANTiCS 2014 Series

• SEMANTiCS 2014 (part 1): Keynote (this post)
• SEMANTiCS 2014 (part 2): RDF Data Shapes
• SEMANTiCS 2014 (part 3): Conversation