Continuing our series of PostgreSQL Data Types today we’re going to introduce the PostgreSQL ranges data type.

Range types are a unique feature of PostgreSQL, managing two dimensions of data in a single column, and allowing advanced processing. The main example is the daterange data type, which stores as a single value a lower and an upper bound of the range as a single value. This allows PostgreSQL to implement a concurrent safe check against overlapping ranges, as we’re going to see in this article.

Ranges

As usual, read the PostgreSQL documentation chapters with the titles Range Types and Range Functions and Operators for complete information.

The International Monetary Fund publishes exchange rate archives by month for lots of currencies. An exchange rate is relevant from its publication until the next rate is published, which makes a very good use case for our PostgreSQL range types.

The following SQL script is the main part of the ELT script that has been used for my book Mastering PostgreSQL in Application Development. Only missing from this book’s pages is the transformation script that pivots the available tsv file into the more interesting format we use here:

begin;

create schema if not exists raw;

-- Must be run as a Super User in your database instance
-- create extension if not exists btree_gist;

drop table if exists raw.rates, rates;

create table raw.rates
 (
  currency text,
  date     date,
  rate     numeric
 );

\copy raw.rates from 'rates.csv' with csv delimiter ';'

create table rates
 (
  currency text,
  validity daterange,
  rate     numeric,

  exclude using gist (currency with =,
                      validity with &&)
 );

insert into rates(currency, validity, rate)
     select currency,
            daterange(date,
                      lead(date) over(partition by currency
                                          order by date),
                      '[)'
                     )
            as validity,
            rate
       from raw.rates
   order by date;

commit;

In this SQL script, we first create a target table for loading the CSV file. The file contains lines with a currency name, a date of publication, and a rate as a numeric value. Once the data is loaded into this table, we can transform it into something more interesting to work with from an application, the rates table.

Ranges Exclusion Constraints

The rates table registers the rate value for a currency and a validity period, and uses an exclusion constraint that guarantees non-overlapping validity periods for any given currency:

exclude using gist (currency with =, validity with &&)

This expression reads: exclude any tuple where the currency is = to an existing currency in our table AND where the validity is overlapping with (&&) any existing validity in our table. This exclusion constraint is implemented in PostgreSQL using a GiST index.

By default, GiST in PostgreSQL doesn’t support one-dimensional data types that are meant to be covered by B-tree indexes. With exclusion constraints though, it’s very interesting to extend GiST support for one-dimensional data types, and so we install the btree_gist extension, provided in PostgreSQL contrib package.

The script then fills in the rates table from the raw.rates we’d been importing in the previous step. The query uses the lead() window function to implement the specification spelled out in English earlier: an exchange rate is relevant from its publication until the next rate is published.

Querying Ranges

Here’s how the data looks, with the following query targeting Euro rates:

  select currency, validity, rate
    from rates
   where currency = 'Euro'
order by validity
   limit 10;

We can see that the validity is a range of dates, and the standard output for this type is a closed range which includes the first entry and excludes the second one:

 currency │        validity         │   rate   
══════════╪═════════════════════════╪══════════
 Euro     │ [2017-05-02,2017-05-03) │ 1.254600
 Euro     │ [2017-05-03,2017-05-04) │ 1.254030
 Euro     │ [2017-05-04,2017-05-05) │ 1.252780
 Euro     │ [2017-05-05,2017-05-08) │ 1.250510
 Euro     │ [2017-05-08,2017-05-09) │ 1.252880
 Euro     │ [2017-05-09,2017-05-10) │ 1.255280
 Euro     │ [2017-05-10,2017-05-11) │ 1.255300
 Euro     │ [2017-05-11,2017-05-12) │ 1.257320
 Euro     │ [2017-05-12,2017-05-15) │ 1.255530
 Euro     │ [2017-05-15,2017-05-16) │ 1.248960
(10 rows)

Having this data set with the exclusion constraint means that we know we have at most a single rate available at any point in time, which allows an application needing the rate for a specific time to write the following query:

select rate
  from rates
 where currency = 'Euro' 
   and validity @> date '2017-05-18';

The operator @> reads contains, and PostgreSQL uses the exclusion constraint’s index to solve that query efficiently:

   rate   
══════════
 1.240740
(1 row)

Conclusion

PostgreSQL has support for more than date ranges, ranges of numbers are also suppored. While it’s possible to manually manage the lower and upper bounds of the ranges in one’s own application code, it’s almost impossible to deal with the concurrency issues right when dealing with non-overlapping ranges.

Think about it that way: a user inserts a new value in you table, and its lower and upper bounds are not overlapping any other value in your table, so it’s fine. But in between when you check for that, from the application code, and when you INSERT your new entry, a concurrent user does the same thing, with a range that overlapps the first one being inserted. As the first user’s new range is not inserted yet, the check passes. And now you insert two overlapping ranges. Ooops.

Rather than having to serialize all write access to the data set, PostgreSQL indexes are allowed special visibility rules in order to guarantee unicity at all times, and EXCLUDE USING is an extension of the unicity constraint: it benefits from this capacity and guarantee your data set even when dealing with concurrency updates to it!

This article is an extract from my book Mastering PostgreSQL in Application Development, which teaches SQL to developers so that they may replace thousands of lines of code with very simple queries. The book has a full chapter about data types in PostgreSQL, check it out!