That’s a worthwhile goal. Applications often grow organically, especially in the early, exploratory stages of development. For example, you may decide to track when a user last did something on the website, so that you can adapt news and notices for those users (e.g. “Did you know that we added feature XYZ since you last visited?”). Developers have a need to produce a prototype quickly to work out the edge cases (do we update that timestamp for all actions, or only certain ones?), and probably a need to put it in production so that the users can benefit sooner.

A common worry is that ALTER TABLE will be a major performance problem. That’s sometimes a problem, but in PostgreSQL, you can add a column to a table in constant time (not dependent on the size of the table) in most situations. I don’t think this is a good reason to avoid ALTER TABLE, at least in PostgreSQL (other systems may impose a greater burden).

There are good reasons to avoid ALTER TABLE, however. We’ve only defined one use case for this new “last updated” field, and it’s a fairly loose definition. If we use ALTER TABLE as a first reaction for tracking any new application state, we’d end up with lots of columns with overlapping meanings (all subtly different), and it would be challenging to keep them consistent with each other. More importantly, adding new columns without thinking through the meaning and the data migration strategy will surely cause confusion and bugs. For example, if you see the following table:

    CREATE TABLE users
    (
      name         TEXT,
      email        TEXT,
      ...,
      last_updated TIMESTAMPTZ
    );

you might (reasonably) assume that the following query makes sense:

    SELECT * FROM users
      WHERE last_updated < NOW() - '1 month'::INTERVAL;

Can you spot the problem? Old user records (before the ALTER TABLE) will have NULL for last_updated timestamps, and will not satisfy the WHERE condition even though they intuitively qualify. There are two parts to the problem:

1. The presence of the last_updated field fools the author of the SQL query into making assumptions about the data, because it seems so simple on the surface.
2. NULL semantics allow the query to be executed even without complete information, leading to a wrong result.

Let’s try changing the table definition:

    CREATE TABLE users
    (
      name       TEXT,
      email      TEXT,
      ...,
      properties HSTORE
    );

HSTORE is a set of key/value pairs. Some tuples might have the last_updated key in the properties attribute, and others may not. This accomplishes two things:

1. There’s no need for ALTER TABLE or cluttering of the namespace with a lot of nullable columns.
2. The name “properties” is vague enough that query writers would (hopefully) be on their guard, understanding that not all records will share the same properties.

You could still write the same (wrong) query against the second table with minor modification. Nothing has fundamentally changed. But we are using a different development strategy that’s easy on application developers during rapid development cycles, yet does not leave a series of pitfalls for users of the data. When a certain property becomes universally recorded and has a concrete meaning, you can plan a real data migration to turn it into a relation attribute instead.

Now, we need some guiding principles about when to use a complex type to represent complex information, and when to use separate columns in the table. To maximize utility and minimize confusion, I believe the best guiding principle is the meaning of the data you’re storing across all tuples. When defining the attributes of a relation, if you find yourself using vague nouns such as “properties,” or resorting to complex qualifications (lots of “if/then” branching in your definition), consider less constrained data types like HSTORE. Otherwise, it’s best to nail down the meaning in terms of appropriate nouns, which will help keep the DBMS smart and queries simple (and correct). See Choosing Data Types and further guidance in reference [1].

I believe there are three reasons why application developers feel that relational schemas are “inflexible”:

1. A reliance on NULL semantics to make things “magically work,” when in reality, it just makes queries succeed that should fail. See my previous posts: None, nil, Nothing, undef, NA, and SQL NULL and What is the deal with NULLs?.
2. The SQL database industry has avoided interesting types, like HSTORE, for a long time. See my previous post: Choosing Data Types.
3. ORMs make a fundamental false equivalence between an object attribute and a table column. There is a relationship between the two, of course; but they are simply not the same thing. This is a direct consequence of “The First Great Blunder”[2].

EDIT: I found a more concise way to express my fundamental point — During the early stages of application development, we only vaguely understand our data. The most important rule of database design is that the database should represent reality, not what we wish reality was like. Therefore, a database should be able to express that vagueness, and later be made more precise when we understand our data better. None of this should be read to imply that constraints are less important or that we need not understand our data. These ideas mostly apply only at very early stages of development, and even then, prudent use of constraints often makes that development much faster.

[1] Date, C.J.; Darwen, Hugh (2007). Databases, Types, and the Relational Model. pp. 377-380 (Appendix B, “A Design Dilemma”).

[2] Date, C.J. (2000). An Introduction To Database Systems, p. 865.

What is already done?

• PERIOD data type, which can represent anchored intervals of time; that is, a chunk of time with a definite beginning and a definite end (in contrast to a SQL INTERVAL, which is not anchored to any specific beginning or end time).
  • Critical for usability because it acts as a set of time, so you can easily test for containment and other operations without using awkward constructs like BETWEEN or lots of comparisons (and keeping track of inclusivity/exclusivity of boundary points).
  • Critical for performance because you can index the values for efficient “contains” and “overlaps” queries (among others).

• Temporal Keys (called Exclusion Constraints, and will be available in the next release of PostgreSQL, 9.0), which can enforce the constraint that no two periods of time (usually for a given resource, like a person) overlap. See the documentation (look for the word “EXCLUDE”), and see my previous articles (part 1 and part 2) on the subject.
  • Critical for usability to avoid procedural, error-prone hacks to enforce the constraint with triggers or by splitting time into big chunks.
  • Critical for performance because it performs comparably to a UNIQUE index, unlike the other procedural hacks which are generally too slow to use for most real systems.

What needs to be done?

• Range Types — Aside from PERIOD, which is based on TIMESTAMPTZ, it would also be useful to have very similar types based on, for example, DATE. It doesn’t stop there, so the natural conclusion is to generalize PERIOD into “range types” which could be based on almost any subtype.
• Range Keys, Foreign Range Keys — If Range Types are known to the Postgres engine, that means that we can have syntactic sugar for range keys (like temporal keys, except for any range type), etc., that would internally use Exclusion Constraints.
• Range Join — If Range Types are known to the Postgres engine, there could be syntactic sugar for a “range join,” that is, a join based on “overlaps” rather than “equals”. More importantly, there could be a new join type, a Range Merge Join, that could perform this join efficiently (without a Range Merge Join, a range join would always be a nested loop join).
• Simple table logs — The ability to easily create an effective “audit log” or similar trigger-based table log, that can record changes and be efficiently queried for historical state or state changes.

I’ll be speaking on this subject (specifically, the new Exclusion Constraints feature) in the upcoming PostgreSQL Conference EAST 2010 (my talk description) in Philadelphia later this month and PGCon 2010 (my talk description) in Ottawa this May. In the past, these conferences and others have been a great place to get ideas and help me move the temporal features forward.

The existing features have been picking up a little steam lately. The temporal-general mailing list has some traffic now — fairly low, but enough that others contribute to the discussions, which is a great start. I’ve also received some great feedback from a number of people, including the folks at PGX. There’s still a ways to go before we have all the features we want, but progress is being made.

So, right away, it’s meaningless to talk about the scalability of the relational model. Given a particular question, it might be difficult to break it down into bite-sized pieces and distribute it to N worker nodes. But going with MapReduce doesn’t solve that scalability problem — it just means that you will have a hard time defining a useful map or reduce operation, or you will have to change the question into something easier to answer.

There may exist scalability problems in:

• SQL, which defines requirements outside the scope of the relational model, such as ACID properties and transactional semantics.
• Traditional architectures and implementations of SQL, such as the “table is a file” equivalence, lack of sophisticated types, etc.
• Particular implementations of SQL — e.g. “MySQL can’t do it, so the relational model doesn’t scale”.

Why are these distinctions important? As with many debates, terminology confusion is at the core, and prevents us from dealing with the problems directly. If SQL is defined in a way that causes scalability problems, we need to identify precisely those requirements that cause a problem, so that we can proceed forward without losing all that has been gained. If the traditional architectures are not suitable for some important use-cases, they need to be adapted. If some particular implementations are not suitable, developers need to switch or demand that it be made competitive.

The NoSQL movement (or at least the hype surrounding it) is far too disorganized to make real progress. Usually, incremental progress is best; and sometimes a fresh start is best, after drawing on years of lessons learned. But it’s never a good idea to start over with complete disregard for the past. For instance, an article from Digg starts off great:

The fundamental problem is endemic to the relational database mindset, which places the burden of computation on reads rather than writes.

That’s good because he blames it on the mindset not the model, and then identifies a specific problem. But then the article completely falls flat:

Computing the intersection with a JOIN is much too slow in MySQL, so we have to do it in PHP.

A join is faster in PHP than MySQL? Why bother even discussing SQL versus NoSQL if your particular implementation of SQL — MySQL — can’t even do a hash join, the exact operation that you need? Particularly when almost every other implementation can (including PostgreSQL)? That kind of reasoning won’t lead to solutions.

So, where do we go from here?

1. Separate the SQL model from the other requirements (some of which may limit scalability) when discussing improvements.
2. Improve the SQL model (my readers know that I’ve criticized SQL’s logical problems many times in the past).
3. Improve the implementations of SQL, particularly how tables are physically stored.
4. If you’re particularly ambitious, come up with a relational alternative to SQL that takes into account what’s been learned after decades of SQL and can become the next general-purpose DBMS language.

EDIT 2010-03-09: I should have cited Josh Berkus’s talk on Relational vs. Non-Relational (complete list of PGX talks), which was part of the inspiration for this post.

• A schedule conflict is a typical business problem.
• The later you try to resolve a schedule conflict, the more costly it is to resolve.
• In particular, there is a big jump in the cost the moment after conflicting data is recorded.
• Therefore, it’s best for the DBMS itself to enforce the constraint, because only the DBMS can avoid the conflict effectively before the conflict is recorded.

Then, I opened up a discussion to see how people are dealing with these schedule conflicts. In the comments I received at the end of the article, as well as other anecdotes from conferences, user groups, mailing lists, and my own experience, the solutions fall into a few categories:

• The rate of conflicts is so low that the costs are not important. For instance, you may make 0.1% of your customers unhappy, and need to refund them, but perhaps that’s a cost you’re willing to pay.
• The application receives so few requests that performance is not an object, and serialization of all requests is a viable option. The serialization is done using big locks and a read-check-write cycle. Even if performance is not an object, these applications sometimes run into maintenance problems or unexpected outages because of the big locks required.
• You can break the time slices into manageable chunks, e.g. one day chunks aligned at midnight. This kind of solution is highly specific to the business, reduces the flexibility of the business, and often requires a substantial amount of custom, error-prone procedural code.
• Complex procedural code: usually a mix of application code, functions in the DBMS, row-level locking, static data in tables that only exists for the purposes of row-level locks, etc. This kind of solution is generally very specific to the application and the business, requires lots of very error-prone custom procedural code, is difficult to adequately test, and it’s hard to understand what’s going on in the system at any given time. Hunting down sporadic performance problems would be a nightmare.

Those solutions just aren’t good enough. We use relational database systems because they are smart, declarative, generally useful for many problems, and maintainable (Note: these principles contrast with NoSQL, which is moving in the opposite direction — more on that in another article).

[UPDATE: The following project has been committed for the next release of PostgreSQL; the feature is now called "Exclusion Constraints"; and the new version of PostgreSQL will be called 9.0 (not 8.5). See the documentation under the heading "EXCLUDE".]

The project that I’ve been working on for PostgreSQL 8.5 is called “Operator Exclusion Constraints“. These are a new type of constraint that most closely resembles the UNIQUE constraint, because one tuple can preclude the existence of other tuples. With a UNIQUE constraint on attribute A of a table with attributes (A, B, C), the existence of the tuple (5, 6, 7) precludes the existence of any tuple (5, _, _) in that table at the same time. This is different from a foreign key, which requires the existence of a tuple in another table; and different from a CHECK constraint which rejects tuples independently from any other tuple in any table (and the same goes for NOT NULL).

The same semantics as a UNIQUE constraint can be easily specified as an Operator Exclusion Constraint, with a minor performance penalty at insert time (one additional index search, usually only touching pages that are already in cache). Exclusion constraints are more general than UNIQUE, however. For instance, with a complex type such as CIRCLE, you can specify that no two circles in a table overlap — which is a constraint that is impossible to specify otherwise (without resorting to the poor solutions mentioned above).

This applies to temporal keys very nicely. First, get the PERIOD data type, which allows you a better way to work with periods of time (sets of time, really), rather than points in time. Second, you need to install the btree_gist contrib module. Then, use an exclusion constraint like so:

[UPDATE 2010-03-09: Syntax updated to reflect the version of this project committed for PostgreSQL 9.0. ]

CREATE TABLE room_reservation
(
  name   TEXT,
  room   TEXT,
  during PERIOD,
  EXCLUDE USING gist (room WITH =, during WITH &&)
);

That will prevent two reservations on the same room from overlapping. There are a few pieces to this that require explanation:

• && is the “overlaps” operator for the PERIOD data type.
• USING gist tells PostgreSQL what kind of index to create to enforce this constraint. The operators must map to search strategies for this index method, and searching for overlapping periods requires a GiST index.
• Because we are using GiST, we need GiST support for equality searches for the TEXT data type, which is the reason we need the btree_gist contrib module.
• Conflicts will only occur if two tuples have equal room numbers, and overlapping periods of time for the reservation.

This solution:

• Performs well under light and heavy contention. Not quite as well as a UNIQUE constraint, but much better than the alternatives, and without the surprises you might get from using big locks. Note that the constraint will be enforced at some point, so ignoring the problem is not a high-performance alternative (interpersonal communication has higher latency than a computer).
• Is declarative. The implementation shows through a little bit — the user will know that an index is being used, for instance — but it’s a relatively simple declaration. As a consequence, it’s not very error-prone from the schema designer’s standpoint.
• Is not specific to the business. You don’t have to decide on an appropriate time slice (e.g. one hour, one day, etc.); you don’t have to try to partition locks in creative ways; you don’t have to write procedural code (in the database system or application); and you don’t have to come up with interesting ways to detect a conflict or notify the user.

Temporal keys are just one part of the support required for effective temporal data management inside the DBMS. However, it’s one of the most important pieces that requires support from the core engine, and cannot be implemented as a module.

In the past, I’ve worked on a temporal extension to PostgreSQL that implements the PERIOD data type. This is a data type that offers both a definite beginning and a definite end time, which is important for describing things that happen over a period of time, rather than instantaneously. Trying to use separate attributes for “start” and “end” is bad for a number of reasons, and will certainly be addressed in a subsequent blog entry. For now, I’ll just say that I believe the PERIOD data type is fundamentally important for handling all kinds of time data, which I believe is a common problem.

At WEST, I’ll be presenting my progress on temporal keys. Temporal keys are used to prevent overlapping periods of time — a schedule conflict — by using an index and following the same concurrent behavior as UNIQUE with minimal performance cost (one extra index search, to be precise).

Temporal keys cannot be expressed in PostgreSQL 8.4, unless you resort to triggers and a full table lock (ouch!). So, additional backend support is required. This is accomplished in my patch for operator exclusion constraints, which are a more general way of using arbitrary operators and index searches to enforce a constraint. I plan to do what’s required for the patch to be accepted in PostgreSQL 8.5.

Temporal modeling is a common problem. It seems like almost every PostgreSQL conference has had at least one talk on the matter, so we know there is some demand for improvement. If you’re interested, I hope you come to WEST and chat with Scott or I, and let’s see if we can come up with some real solutions.

First off, I’m strictly discussing SQL NULL here. SQL NULL is peculiar in a number of ways, and the general excuse for this is that there is a need to represent “missing information” — which may be true. But there are lots of ways to represent missing information, as I pointed out in a previous post, and SQL’s approach to missing information is, well, “unique”.

• “NULL is not a value” — If you hear this one, beware: it’s in direct contradiction to the SQL standard, which uses the phrase “null value” dozens of times. It’s hard to imagine that NULL is not any kind of value at all; because it’s routinely passed to functions and operators, predicates can evaluate to NULL, and SQL uses a kind of three-valued logic (3VL) in some contexts. The phrase “NULL is not a value” also raises the question: “what is it, then?”.
• NULL means “unknown” (i.e. the third truth value) — This doesn’t hold up either. SUM of no tuples returns NULL, but clearly the SUM of no tuples is not unknown! SQL will happily generate NULLs from aggregates or outer joins without any NULLs at all in the database. Do you not know something you did know before, or do you now know that you don’t know something that you didn’t know you didn’t know before? Also, if NULL means “unknown”, how do you differentiate a boolean field for which you do not know the value, and a boolean field for which you do know the value, and it happens to be “unknown” (perhaps this is why boolean columns are a PostgreSQL extension and not part of the core SQL standard)?
• “NULL is false-like” — Don’t think of NULL as false-like, or “more false than true”. It’s a tempting rule of thumb, but it’s misleading. For instance, in a WHERE clause, a NULL predicate is treated like FALSE. However, in a constraint (like a CHECK constraint), NULL is treated like TRUE. Perhaps most importantly, when in a 3VL context (like a boolean expression), this misconception leads to problems when you try to invert the logic, e.g., use NOT.
• “Oh, that makes sense” — When you see individual behaviors of NULL, they look systematic, and your brain quickly sees a pattern and extrapolates what might happen in other situations. Often, that extrapolation is wrong, because NULL semantics are a mix of behaviors. I think the best way to think about NULL is as a Frankenstein monster of several philosophies and systems stitched together by a series of special cases.
• p OR NOT p — Everyone should know that this is not always true in SQL. But most people tend to reason assuming that this is always true, so you have to be very careful, and work against your intuition very deliberately, in order to form a correct SQL query.
• SUM() versus + (addition) — SUM is not repeated addition. SUM of 1 and NULL is 1, but 1 + NULL is NULL.
• Aggregates ignore NULLs — According to the standard, aggregates are supposed to ignore NULLs, because the information is missing. But why is it OK to ignore the missing information in an aggregate, but not, say, with the + operator? Is it really OK to just ignore it?
• Aggregates return NULL — According to the standard, aggregates are supposed to return NULL when they have no non-NULL input. Just because you don’t have any input tuples, does that really mean that the result is undefined, missing, or unknown? It’s certainly not unknown! What about SUM over zero tuples, wouldn’t the most useful result be zero?
• SQL breaks its own rules — The aforementioned aggregate rules don’t work very well for COUNT(), the simplest of all aggregates. So, they have two versions of count: COUNT(*) breaks the “aggregates ignore nulls” rule and the “aggregates return null” rule, and COUNT(x) only breaks the latter. But wait! There’s more: ARRAY_AGG() breaks the former but not the latter. But no exception is made for SUM — it still returns NULL when there are no input tuples.
• NULLs appear even when you have complete information — Because of OUTER JOIN and aggregates, NULLs can appear even when you don’t have any NULLs in your database! As a thought experiment, try to reconcile this fact with the various “definitions” of NULL.
• WHERE NOT IN (SELECT ...) — This one gets everyone at one point or another. If the subselect produces any NULLs, then NOT IN can only evaluate to FALSE or NULL, meaning you get no tuples. Because it’s in a WHERE clause, it will return no results. You are less likely to have a bunch of NULLs in your data while testing, so chances are everything will work great until you get into production.
• x >= 10 or x <= 10 — Not a tautology in SQL.
• x IS NULL AND x IS DISTINCT FROM NULL — You probably don’t know this, but this expression can evaluate to TRUE! That is, if x = ROW(NULL).
• NOT x IS NULL is not the same as x IS NOT NULL — If x is ROW(1,NULL), then the former will evaluate to TRUE, and the latter will evaluate to FALSE. Enjoy.
• NOT x IS NULL AND NOT x IS NOT NULL — Want to know if you have a value like ROW(1, NULL)? To distinguish it from NULL and also from values like ROW(1,1) and ROW(NULL,NULL), this expression might help you.
• NULLs can exist inside some things, but not others — If you concatenate: firstname || mi || lastname, and “mi” happens to be null, the entire result will be null. So strings cannot contain a NULL, but as we see above, a record can.

I believe the above shows, beyond a reasonable doubt, that NULL semantics are unintuitive, and if viewed according to most of the “standard explanations,” highly inconsistent. This may seem minor; that is, if you’re writing SQL you can overcome these things with training. But it is not minor, because NULL semantics are designed to make you think you understand them, and think that the semantics are intuitive, and think that it’s part of some ingenious consistent system for managing missing information. But none of those things are true.

I have seen lots of discussions about NULL in various forums and mailing lists. Many of the participants are obviously intelligent and experienced, and yet make bold statements that are, quite simply, false. I’m writing this article to make two important points:

1. There is a good case to be made that NULL semantics are very counterproductive; as opposed to a simple “error early” system that forces you to write queries that explicitly account for missing information (e.g. with COALESCE). “Error early” is a more mainstream approach, similar to null pointers in java or None in python. If you want compile-time checking, you can use a construct like Maybe in haskell. SQL attempts to pass along the problem, hoping the next operator will turn ignorance into knowledge — but it does not appear that anyone thought through this idea, quite frankly.
2. You should not attempt to apply your intellect to NULL, it will lead you in the wrong direction. If you need to understand it, understand it, but always treat it with skepticism. Test the queries, read the standard, do what you need to do, but do not attempt to extrapolate.

http://www.rubyforge.org/projects/ruby-pg

or install the gem directly:

# gem install –remote postgres

The previous project has gone unmaintained for a long time, which lead
to the fork.

This gem includes some important fixes, most notably the ability to
compile against PostgreSQL 8.3.

The real impedance mismatch is a set of very fundamental differences between application data and data in a well-designed relational database.

Application data:

• is ephemeral in nature (that is, temporary)
• is subject to changes in structure between revisions of the same application
• is subject to changes in semantics (meaning) between revisions of the same application
• the meaning is highly context-sensitive
• the data is highly dependent on physical structure
• not all of the data that has been processed by the application is held at once, meaning that contradictions are often impossible to detect (for instance, something as simple as a duplicate serial number).

While data in a well-designed database:

• has a definite predicate that maps the data to real world facts independent of the application
• these facts are kept as long as the fact is still believed to be true and relevant
• these facts are context-insensitive
• these facts are independent of any physical structure or implementation
• is held all at once, allowing detection of contradictions through complex logical relationships, enforced by declarative constraints