Next week we see two awesome PostgreSQL conferences in Europe, back to back, with a day in between just so that people may attend both! In chronological order we have first Nordic pgDay in Oslo where I will have the pleasure to talk about Data Modeling, Normalization and Denormalization. Then we have pgday.paris with an awesome schedule and a strong focus on the needs of application developers!
So in today’s article I wanted to share some of the bits I’m going to talk about next week at the Nordic pgDay conference: database modeling. To get your interest into database normalization, we see what could happen when you neglect to normalize properly, with a selection of three classic anti-patterns: the infamous EAV, using multiple values in a single column, and how using UUIDs might be an anti-pattern too.
The content of this article is extracted from my book Mastering PostgreSQL in Application Development, check it out!
Entity Attribute Values
The entity attribue values or EAV is a design that tries to accommodate with a lack of specifications. In our application, we have to deal with parameters and new parameters may be added at each release. It’s not clear which parameters we need, we just want a place to manage them easily, and we are already using a database server after all. So there we go:
begin; create schema if not exists eav; create table eav.params ( entity text not null, parameter text not null, value text not null, primary key(entity, parameter) ); commit;
You might have already seen this model or a variation of it in the field. The model makes it very easy to add things to it, and very difficult to make sense of the accumulated data, or to use them effectively in SQL, making it an anti-pattern.
insert into eav.params(entity, parameter, value) values ('backend', 'log_level', 'notice'), ('backend', 'loglevel', 'info'), ('api', 'timeout', '30'), ('api', 'timout', '40'), ('gold', 'response time', '60'), ('gold', 'escalation time', '90'), ('platinum', 'response time', '15'), ('platinum', 'escalation time', '30');
In this example we made some typos on purpose, to show the limits of the EAV model. It’s impossible to catch those errors, and you might have parts of your code that query one spelling or a different one.
Main problems of this EAV anti-pattern are:
The value attribute is of type text so as to be able to host about anything, where some parameters are going to be integer, interval, inet or boolean values.
The entity and parameter fields are likewise free-text, meaning that any typo will actually create new entries, which might not even be used anywhere in the application.
When fetching all the parameters of an entity to set up your application’s object, the parameter names are a value in each row rather than the name of the column where to find them, meaning extra work and loops.
When you need to process parameter in SQL queries, you need to add a join to the params table for each parameter you are interested in.
As an example of the last point, here’s a query that fetches the response time and the escalated time for support customers when using the previous params setup. First, we need a quick design for a customer and a support contract table:
begin; create table eav.support_contract_type ( id serial primary key, name text not null ); insert into eav.support_contract_type(name) values ('gold'), ('platinum'); create table eav.support_contract ( id serial primary key, type integer not null references eav.support_contract_type(id), validity daterange not null, contract text, exclude using gist(type with =, validity with &&) ); create table eav.customer ( id serial primary key, name text not null, address text ); create table eav.support ( customer integer not null, contract integer not null references eav.support_contract(id), instances integer not null, primary key(customer, contract), check(instances > 0) ); commit;
And now it’s possible to get customer support contract parameters such as response time and escalation time, each with its own join:
select customer.id, customer.name, ctype.name, rtime.value::interval as "resp. time", etime.value::interval as "esc. time" from eav.customer join eav.support on support.customer = customer.id join eav.support_contract as contract on support.contract = contract.id join eav.support_contract_type as ctype on ctype.id = contract.type join eav.params as rtime on rtime.entity = ctype.name and rtime.parameter = 'response time' join eav.params as etime on etime.entity = ctype.name and etime.parameter = 'escalation time';
Each parameter you add has to be added as an extra join operation in the previous query. Also, if someone enters a value for response time that isn’t compatible with the interval data type representation, then the query fails.
Never implement an EAV model, this anti-pattern makes everything more complex than it should for a very small gain at modeling time.
It might be that the business case your application is solving actually has an attribute volatility problem to solve. In that case, consider having as solid a model as possible and use jsonb columns as extension points.
Multiple Values per Column
In database normalization, we say that a table (or a relation) is in first normal form (1NF) if:
There are no duplicated rows in the table.
Each cell is single-valued (no repeating groups or arrays).
Entries in a column (field) are of the same kind.
An anti-pattern that fails to comply with those rules means having a multi-valued field in a database schema:
create table tweet ( id bigint primary key, date timestamptz, message text, tags text );
Data would then be added with a semicolon separator, for instance, or maybe
| char, or in some cases with a fancy Unicode separator char such
¦. Here we find a classic semicolon:
id │ date │ message │ tags ════════════════════╪══════╪═════════╪════════════════════════ 720553530088669185 │ ... │ ... │ #NY17 720553531665682434 │ ... │ ... │ #Endomondo;#endorphins (2 rows)
Using PostgreSQL makes it possible to use the regexp_split_to_array() and regexp_split_to_table() functions in order to process the data in a relatively sane way. The problem with going against 1NF is that it’s nearly impossible to maintain the data set as the model offers all the database anomalies at once.
Several things are very hard to do when you have several tags hidden in a text column using a separator:
To implement searching for a list of messages containing a single given tag, this model forces a substring search which is much less efficient than direct search.
A normalized model would have a separate tags table and an association table in between the tweet and the tags reference table that we could name tweet_tags. Then search for tweets using a given tag is easy, as it’s a simple join operation with a restriction that can be expressed either as a where clause or in the join condition directly.
It is even possible to implement more complex searches of tweets containing several tags, or at least one tag in a list. Doing that on top of the CSV inspired anti-pattern is much more complex, if even possible at all.
Rather than trying, we would fix the model!
Usage Statistics per Tag
For the same reasons that implementing search is difficult, this CSV model anti-pattern makes it hard to compute per-tag statistics, because the tags column is considered as a whole.
Normalization of Tags
People make typos or use different spellings for the tags, so we might want to normalize them in our database. As we keep the message unaltered in a different column, we would not lose any data doing so.
While normalizing the tags at input time is trivial when using a tags reference table, it is now an intense computation, as it requires looping over all messages and splitting the tags each time.
This example looks a lot like a case of premature optimization, which per Donald Knuth is the root of all evil… in most cases. The exact quote reads:
Programmers waste enormous amounts of time thinking about, or worrying about, the speed of noncritical parts of their programs, and these attempts at efficiency actually have a strong negative impact when debugging and maintenance are considered. We should forget about small efficiencies, say about 97% of the time: premature optimization is the root of all evil. Yet we should not pass up our opportunities in that critical 3%.
“Structured Programming with Goto Statements”. Computing Surveys 6:4 (December 1974), pp. 261–301, §1.
Database modeling has a non-trivial impact on query performance and as such is part of making attempts at upping efficiency. Using a CSV formatted attribute rather than two additional tables looks like optimization, but actually it will make just about everything worse: debugging, maintenance, search, statistics, normalization, and other use cases.
The PostgreSQL data type UUID allows for 128 bits synthetic keys rather than 32 bits with serial or 64 bits with bigserial.
The serial family of data types is built on a sequence with a standard defined behavior for collision. A sequence is non-transactional to allow several concurrent transactions to each get their own number, and each transaction might then commit or fail to commit with a rollback. It means that sequence numbers are delivered in a monotonous way, always incrementally, and will be assigned and used without any ordering known in advance, and with holes in between delivered values.
Still, sequences and their usage as a default value for synthetic keys offer a guarantee against collisions.
UUIDs on the other hand rely on a way to produce random numbers in a 128 bits space that offers a strong theoretical guarantee against collision. You might have to retry producing a number, though very rarely.
UUIDs are useful in distributed computing where you can’t synchronize every concurrent and distributed transaction against a common centralized sequence, which would then act as a Single Point Of Failure, or SPOF.
That said, neither sequences nor UUID provides a natural primary key for your data, as seen in the Primary Keys section.
Good database modeling is always a trade-off between normalization theories and denormalization techniques. PostgreSQL offers many denormalization techniques and some of them are quite advanced, so it’s quite tempting to put them all in good use.
My advice is to always normalize your database model first, and then only fix the problems you have with that when you actually have them. Well except in those 3% of cases where really, really, it should be done in the design phase of the project. It’s quite hard to recognize those 3% though, and that ability is hard gained with experience.
The best way to gain experience and grow your gut feeling and know instinctively when to denormalize before it gets to be a production problem, well, as usual, get lots of deliberate practice. Which means, learn to normalize, and force you to doing it always. Then create a data set as in my article Simple Data Modeling with a Test Data Set and write some queries using your normalized database model.
And only when you find problems then see about denormalizing. Maybe before the problems happen in production, of course.