# The SQL Language

## Introduction

This chapter provides an overview of how to use SQL to perform simple operations. This tutorial is only intended to give you an introduction and is in no way a complete tutorial on SQL. Numerous books have been written on SQL, including melt93 and date97. You should be aware that some PostgreSQL language features are extensions to the standard.

In the examples that follow, we assume that you have created a database named `mydb`, as described in the previous chapter, and have been able to start psql.

Examples in this manual can also be found in the PostgreSQL source distribution in the directory `src/tutorial/`. (Binary distributions of PostgreSQL might not provide those files.) To use those files, first change to that directory and run make:

    $ cd .../src/tutorial
    $ make

This creates the scripts and compiles the C files containing user-defined functions and types. Then, to start the tutorial, do the following:

    $ psql -s mydb

    ...

    mydb=> \i basics.sql

The `\i` command reads in commands from the specified file. `psql`'s `-s` option puts you in single step mode which pauses before sending each statement to the server. The commands used in this section are in the file `basics.sql`.

## Concepts

PostgreSQL is a relational database management system (RDBMS). That means it is a system for managing data stored in relations. Relation is essentially a mathematical term for table. The notion of storing data in tables is so commonplace today that it might seem inherently obvious, but there are a number of other ways of organizing databases. Files and directories on Unix-like operating systems form an example of a hierarchical database. A more modern development is the object-oriented database.

Each table is a named collection of rows. Each row of a given table has the same set of named columns, and each column is of a specific data type. Whereas columns have a fixed order in each row, it is important to remember that SQL does not guarantee the order of the rows within the table in any way (although they can be explicitly sorted for display).

Tables are grouped into databases, and a collection of databases managed by a single PostgreSQL server instance constitutes a database cluster.

## Creating a New Table

You can create a new table by specifying the table name, along with all column names and their types:

    CREATE TABLE weather (
        city            varchar(80),
        temp_lo         int,           -- low temperature
        temp_hi         int,           -- high temperature
        prcp            real,          -- precipitation
        date            date
    );

You can enter this into `psql` with the line breaks. `psql` will recognize that the command is not terminated until the semicolon.

White space (i.e., spaces, tabs, and newlines) can be used freely in SQL commands. That means you can type the command aligned differently than above, or even all on one line. Two dashes (`--`) introduce comments. Whatever follows them is ignored up to the end of the line. SQL is case-insensitive about key words and identifiers, except when identifiers are double-quoted to preserve the case (not done above).

`varchar(80)` specifies a data type that can store arbitrary character strings up to 80 characters in length. `int` is the normal integer type. `real` is a type for storing single precision floating-point numbers. `date` should be self-explanatory. (Yes, the column of type `date` is also named date. This might be convenient or confusing — you choose.)

PostgreSQL supports the standard SQL types `int`, `smallint`, `real`, `double precision`, `char(`N`)`, `varchar(`N`)`, `date`, `time`, `timestamp`, and `interval`, as well as other types of general utility and a rich set of geometric types. PostgreSQL can be customized with an arbitrary number of user-defined data types. Consequently, type names are not key words in the syntax, except where required to support special cases in the SQL standard.

The second example will store cities and their associated geographical location:

    CREATE TABLE cities (
        name            varchar(80),
        location        point
    );

The `point` type is an example of a PostgreSQL-specific data type.

Finally, it should be mentioned that if you don't need a table any longer or want to recreate it differently you can remove it using the following command:

    DROP TABLE tablename;

## Populating a Table With Rows

The `INSERT` statement is used to populate a table with rows:

    INSERT INTO weather VALUES ('San Francisco', 46, 50, 0.25, '1994-11-27');

Note that all data types use rather obvious input formats. Constants that are not simple numeric values usually must be surrounded by single quotes (`'`), as in the example. The `date` type is actually quite flexible in what it accepts, but for this tutorial we will stick to the unambiguous format shown here.

The `point` type requires a coordinate pair as input, as shown here:

    INSERT INTO cities VALUES ('San Francisco', '(-194.0, 53.0)');

The syntax used so far requires you to remember the order of the columns. An alternative syntax allows you to list the columns explicitly:

    INSERT INTO weather (city, temp_lo, temp_hi, prcp, date)
        VALUES ('San Francisco', 43, 57, 0.0, '1994-11-29');

You can list the columns in a different order if you wish or even omit some columns, e.g., if the precipitation is unknown:

    INSERT INTO weather (date, city, temp_hi, temp_lo)
        VALUES ('1994-11-29', 'Hayward', 54, 37);

Many developers consider explicitly listing the columns better style than relying on the order implicitly.

Please enter all the commands shown above so you have some data to work with in the following sections.

You could also have used `COPY` to load large amounts of data from flat-text files. This is usually faster because the `COPY` command is optimized for this application while allowing less flexibility than `INSERT`. An example would be:

    COPY weather FROM '/home/user/weather.txt';

where the file name for the source file must be available on the machine running the backend process, not the client, since the backend process reads the file directly. The data inserted above into the weather table could also be inserted from a file containing (values are separated by a tab character):

    San Francisco    46    50    0.25    1994-11-27
    San Francisco    43    57    0.0    1994-11-29
    Hayward    37    54    \N    1994-11-29

You can read more about the `COPY` command in sql-copy.

## Querying a Table

To retrieve data from a table, the table is queried. An SQL `SELECT` statement is used to do this. The statement is divided into a select list (the part that lists the columns to be returned), a table list (the part that lists the tables from which to retrieve the data), and an optional qualification (the part that specifies any restrictions). For example, to retrieve all the rows of table weather, type:

    SELECT * FROM weather;

Here `*` is a shorthand for all columns.

While `SELECT *` is useful for off-the-cuff queries, it is widely considered bad style in production code, since adding a column to the table would change the results.

So the same result would be had with:

    SELECT city, temp_lo, temp_hi, prcp, date FROM weather;

The output should be:

         city      | temp_lo | temp_hi | prcp |    date
    ---------------+---------+---------+------+------------
     San Francisco |      46 |      50 | 0.25 | 1994-11-27
     San Francisco |      43 |      57 |    0 | 1994-11-29
     Hayward       |      37 |      54 |      | 1994-11-29
    (3 rows)

You can write expressions, not just simple column references, in the select list. For example, you can do:

    SELECT city, (temp_hi+temp_lo)/2 AS temp_avg, date FROM weather;

This should give:

         city      | temp_avg |    date
    ---------------+----------+------------
     San Francisco |       48 | 1994-11-27
     San Francisco |       50 | 1994-11-29
     Hayward       |       45 | 1994-11-29
    (3 rows)

Notice how the `AS` clause is used to relabel the output column. (The `AS` clause is optional.)

A query can be qualified by adding a `WHERE` clause that specifies which rows are wanted. The `WHERE` clause contains a Boolean (truth value) expression, and only rows for which the Boolean expression is true are returned. The usual Boolean operators (`AND`, `OR`, and `NOT`) are allowed in the qualification. For example, the following retrieves the weather of San Francisco on rainy days:

    SELECT * FROM weather
        WHERE city = 'San Francisco' AND prcp > 0.0;

Result:

         city      | temp_lo | temp_hi | prcp |    date
    ---------------+---------+---------+------+------------
     San Francisco |      46 |      50 | 0.25 | 1994-11-27
    (1 row)

You can request that the results of a query be returned in sorted order:

    SELECT * FROM weather
        ORDER BY city;

         city      | temp_lo | temp_hi | prcp |    date
    ---------------+---------+---------+------+------------
     Hayward       |      37 |      54 |      | 1994-11-29
     San Francisco |      43 |      57 |    0 | 1994-11-29
     San Francisco |      46 |      50 | 0.25 | 1994-11-27

In this example, the sort order isn't fully specified, and so you might get the San Francisco rows in either order. But you'd always get the results shown above if you do:

    SELECT * FROM weather
        ORDER BY city, temp_lo;

You can request that duplicate rows be removed from the result of a query:

    SELECT DISTINCT city
        FROM weather;

         city
    ---------------
     Hayward
     San Francisco
    (2 rows)

Here again, the result row ordering might vary. You can ensure consistent results by using `DISTINCT` and `ORDER BY` together:

In some database systems, including older versions of PostgreSQL, the implementation of `DISTINCT` automatically orders the rows and so `ORDER BY` is unnecessary. But this is not required by the SQL standard, and current PostgreSQL does not guarantee that `DISTINCT` causes the rows to be ordered.

    SELECT DISTINCT city
        FROM weather
        ORDER BY city;

## Joins Between Tables

Thus far, our queries have only accessed one table at a time. Queries can access multiple tables at once, or access the same table in such a way that multiple rows of the table are being processed at the same time. Queries that access multiple tables (or multiple instances of the same table) at one time are called join queries. They combine rows from one table with rows from a second table, with an expression specifying which rows are to be paired. For example, to return all the weather records together with the location of the associated city, the database needs to compare the city column of each row of the weather table with the name column of all rows in the cities table, and select the pairs of rows where these values match.

This is only a conceptual model. The join is usually performed in a more efficient manner than actually comparing each possible pair of rows, but this is invisible to the user.

This would be accomplished by the following query:

    SELECT * FROM weather JOIN cities ON city = name;

         city      | temp_lo | temp_hi | prcp |    date    |     name      | location
    ---------------+---------+---------+------+------------+---------------+-----------
     San Francisco |      46 |      50 | 0.25 | 1994-11-27 | San Francisco | (-194,53)
     San Francisco |      43 |      57 |    0 | 1994-11-29 | San Francisco | (-194,53)
    (2 rows)

Observe two things about the result set:

- There is no result row for the city of Hayward. This is because there is no matching entry in the cities table for Hayward, so the join ignores the unmatched rows in the weather table. We will see shortly how this can be fixed.

- There are two columns containing the city name. This is correct because the lists of columns from the weather and cities tables are concatenated. In practice this is undesirable, though, so you will probably want to list the output columns explicitly rather than using `*`:

      SELECT city, temp_lo, temp_hi, prcp, date, location
          FROM weather JOIN cities ON city = name;

Since the columns all had different names, the parser automatically found which table they belong to. If there were duplicate column names in the two tables you'd need to qualify the column names to show which one you meant, as in:

    SELECT weather.city, weather.temp_lo, weather.temp_hi,
           weather.prcp, weather.date, cities.location
        FROM weather JOIN cities ON weather.city = cities.name;

It is widely considered good style to qualify all column names in a join query, so that the query won't fail if a duplicate column name is later added to one of the tables.

Join queries of the kind seen thus far can also be written in this form:

    SELECT *
        FROM weather, cities
        WHERE city = name;

This syntax pre-dates the `JOIN`/`ON` syntax, which was introduced in SQL-92. The tables are simply listed in the `FROM` clause, and the comparison expression is added to the `WHERE` clause. The results from this older implicit syntax and the newer explicit `JOIN`/`ON` syntax are identical. But for a reader of the query, the explicit syntax makes its meaning easier to understand: The join condition is introduced by its own key word whereas previously the condition was mixed into the `WHERE` clause together with other conditions.

Now we will figure out how we can get the Hayward records back in. What we want the query to do is to scan the weather table and for each row to find the matching cities row(s). If no matching row is found we want some empty values to be substituted for the cities table's columns. This kind of query is called an outer join. (The joins we have seen so far are inner joins.) The command looks like this:

    SELECT *
        FROM weather LEFT OUTER JOIN cities ON weather.city = cities.name;

         city      | temp_lo | temp_hi | prcp |    date    |     name      | location
    ---------------+---------+---------+------+------------+---------------+-----------
     Hayward       |      37 |      54 |      | 1994-11-29 |               |
     San Francisco |      46 |      50 | 0.25 | 1994-11-27 | San Francisco | (-194,53)
     San Francisco |      43 |      57 |    0 | 1994-11-29 | San Francisco | (-194,53)
    (3 rows)

This query is called a left outer join because the table mentioned on the left of the join operator will have each of its rows in the output at least once, whereas the table on the right will only have those rows output that match some row of the left table. When outputting a left-table row for which there is no right-table match, empty (null) values are substituted for the right-table columns.

## Exercise:

There are also right outer joins and full outer joins. Try to find out what those do.

We can also join a table against itself. This is called a self join. As an example, suppose we wish to find all the weather records that are in the temperature range of other weather records. So we need to compare the temp_lo and temp_hi columns of each weather row to the temp_lo and temp_hi columns of all other weather rows. We can do this with the following query:

    SELECT w1.city, w1.temp_lo AS low, w1.temp_hi AS high,
           w2.city, w2.temp_lo AS low, w2.temp_hi AS high
        FROM weather w1 JOIN weather w2
            ON w1.temp_lo < w2.temp_lo AND w1.temp_hi > w2.temp_hi;

         city      | low | high |     city      | low | high
    ---------------+-----+------+---------------+-----+------
     San Francisco |  43 |   57 | San Francisco |  46 |   50
     Hayward       |  37 |   54 | San Francisco |  46 |   50
    (2 rows)

Here we have relabeled the weather table as `w1` and `w2` to be able to distinguish the left and right side of the join. You can also use these kinds of aliases in other queries to save some typing, e.g.:

    SELECT *
        FROM weather w JOIN cities c ON w.city = c.name;

You will encounter this style of abbreviating quite frequently.

## Aggregate Functions

Like most other relational database products, PostgreSQL supports aggregate functions. An aggregate function computes a single result from multiple input rows. For example, there are aggregates to compute the `count`, `sum`, `avg` (average), `max` (maximum) and `min` (minimum) over a set of rows.

As an example, we can find the highest low-temperature reading anywhere with:

    SELECT max(temp_lo) FROM weather;

     max
    -----
      46
    (1 row)

If we wanted to know what city (or cities) that reading occurred in, we might try:

    SELECT city FROM weather WHERE temp_lo = max(temp_lo);     WRONG

but this will not work since the aggregate `max` cannot be used in the `WHERE` clause. (This restriction exists because the `WHERE` clause determines which rows will be included in the aggregate calculation; so obviously it has to be evaluated before aggregate functions are computed.) However, as is often the case the query can be restated to accomplish the desired result, here by using a subquery:

    SELECT city FROM weather
        WHERE temp_lo = (SELECT max(temp_lo) FROM weather);

         city
    ---------------
     San Francisco
    (1 row)

This is OK because the subquery is an independent computation that computes its own aggregate separately from what is happening in the outer query.

Aggregates are also very useful in combination with `GROUP BY` clauses. For example, we can get the number of readings and the maximum low temperature observed in each city with:

    SELECT city, count(*), max(temp_lo)
        FROM weather
        GROUP BY city;

         city      | count | max
    ---------------+-------+-----
     Hayward       |     1 |  37
     San Francisco |     2 |  46
    (2 rows)

which gives us one output row per city. Each aggregate result is computed over the table rows matching that city. We can filter these grouped rows using `HAVING`:

    SELECT city, count(*), max(temp_lo)
        FROM weather
        GROUP BY city
        HAVING max(temp_lo) < 40;

      city   | count | max
    ---------+-------+-----
     Hayward |     1 |  37
    (1 row)

which gives us the same results for only the cities that have all temp_lo values below 40. Finally, if we only care about cities whose names begin with `S`, we might do:

    SELECT city, count(*), max(temp_lo)
        FROM weather
        WHERE city LIKE 'S%'            --
        GROUP BY city;

         city      | count | max
    ---------------+-------+-----
     San Francisco |     2 |  46
    (1 row)

- The `LIKE` operator does pattern matching and is explained in functions-matching.

It is important to understand the interaction between aggregates and SQL's `WHERE` and `HAVING` clauses. The fundamental difference between `WHERE` and `HAVING` is this: `WHERE` selects input rows before groups and aggregates are computed (thus, it controls which rows go into the aggregate computation), whereas `HAVING` selects group rows after groups and aggregates are computed. Thus, the `WHERE` clause must not contain aggregate functions; it makes no sense to try to use an aggregate to determine which rows will be inputs to the aggregates. On the other hand, the `HAVING` clause always contains aggregate functions. (Strictly speaking, you are allowed to write a `HAVING` clause that doesn't use aggregates, but it's seldom useful. The same condition could be used more efficiently at the `WHERE` stage.)

In the previous example, we can apply the city name restriction in `WHERE`, since it needs no aggregate. This is more efficient than adding the restriction to `HAVING`, because we avoid doing the grouping and aggregate calculations for all rows that fail the `WHERE` check.

Another way to select the rows that go into an aggregate computation is to use `FILTER`, which is a per-aggregate option:

    SELECT city, count(*) FILTER (WHERE temp_lo < 45), max(temp_lo)
        FROM weather
        GROUP BY city;

         city      | count | max
    ---------------+-------+-----
     Hayward       |     1 |  37
     San Francisco |     1 |  46
    (2 rows)

`FILTER` is much like `WHERE`, except that it removes rows only from the input of the particular aggregate function that it is attached to. Here, the `count` aggregate counts only rows with `temp_lo` below 45; but the `max` aggregate is still applied to all rows, so it still finds the reading of 46.

## Updates

You can update existing rows using the `UPDATE` command. Suppose you discover the temperature readings are all off by 2 degrees after November 28. You can correct the data as follows:

    UPDATE weather
        SET temp_hi = temp_hi - 2,  temp_lo = temp_lo - 2
        WHERE date > '1994-11-28';

Look at the new state of the data:

    SELECT * FROM weather;

         city      | temp_lo | temp_hi | prcp |    date
    ---------------+---------+---------+------+------------
     San Francisco |      46 |      50 | 0.25 | 1994-11-27
     San Francisco |      41 |      55 |    0 | 1994-11-29
     Hayward       |      35 |      52 |      | 1994-11-29
    (3 rows)

## Deletions

Rows can be removed from a table using the `DELETE` command. Suppose you are no longer interested in the weather of Hayward. Then you can do the following to delete those rows from the table:

    DELETE FROM weather WHERE city = 'Hayward';

All weather records belonging to Hayward are removed.

    SELECT * FROM weather;

         city      | temp_lo | temp_hi | prcp |    date
    ---------------+---------+---------+------+------------
     San Francisco |      46 |      50 | 0.25 | 1994-11-27
     San Francisco |      41 |      55 |    0 | 1994-11-29
    (2 rows)

One should be wary of statements of the form

    DELETE FROM tablename;

Without a qualification, `DELETE` will remove all rows from the given table, leaving it empty. The system will not request confirmation before doing this!