Here we provide an overview of how to perform simple operations in SQL. This tutorial is only intended to give you an introduction and is in no way a complete tutorial on SQL. This tutorial is adapted from the PostgreSQL tutorial.
In the examples that follow, we assume that you have installed the DuckDB Command Line Interface (CLI) shell. See here for information on how to install the CLI. If you build from the source tree, you can launch the CLI from the build directory
build/release/duckdb. Launching the shell should give you the following prompt:
SQLite version DuckDB cbf92c4 Enter ".help" for usage hints. Connected to a transient in-memory database. Use ".open FILENAME" to reopen on a persistent database. sqlite>
By launching the database like this, an in-memory database is launched. That means that no data is persisted on disk. To persist data on disk you should also pass a database path to the shell. The database will then be stored at that path and can be reloaded from disk later. Also please don’t be confused that it says “SQLite” in the shell, we re-used SQLite’s shell as-is and only replaced the backend with DuckDB.
DuckDB is a relational database management system (RDBMS). That means it is a system for managing data stored in relations. A relation is essentially a mathematical term for a table.
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. Tables themselves are stored inside schemas, and a collection of schemas constitutes the entire database that you can access.
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, temp_lo INTEGER, -- minimum temperature on a day temp_hi INTEGER, -- maximum temperature on a day prcp REAL, date DATE );
You can enter this into the shell with the line breaks. 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).
In the SQL command, we first specify the type of command that we want to perform:
CREATE TABLE. After that follows the parameters for the command. First, the table name, weather, is given. Then the column names and column types follow.
VARCHAR specifies that the table has a column called city that is of type
VARCHAR specifies a data type that can store text of arbitrary length. The temperature fields are stored in an
INTEGER type, a type that stores integer numbers (i.e. whole numbers without a decimal point).
REAL columns store single precision floating-point numbers (i.e. numbers with a decimal point).
DATE stores a date (i.e. year, month, day combination).
DATE only stores the specific day, not a time associated with that day.
DuckDB supports the standard SQL types
The second example will store cities and their associated geographical location:
CREATE TABLE cities ( name VARCHAR, lat DECIMAL, lon DECIMAL );
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');
Constants that are not numeric values (e.g. text and dates) must be surrounded by single quotes (‘’), as in the example. Input dates for the date type must be formatted as ‘YYYY-MM-DD’.
We can insert into the cities table in the same manner.
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 prcp 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 CSV 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.csv';
Where the file name for the source file must be available on the machine running the process. There are many other ways of loading data into DuckDB, see the corresponding documentation section for more information.
Querying a Table
To retrieve data from a table, the table is queried. A 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”. 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;
city | temp_lo | temp_hi | prcp | date ---------------+----------+----------+---------------+------------ San Francisco | 46 | 50 | 0.25 | 1994-11-27
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:
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. A query that accesses multiple rows of the same or different tables at one time is called a join query. As an example, say you wish to list all the weather records together with the location of the associated city. To do that, we need 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 would be accomplished by the following query:
SELECT * FROM weather, cities WHERE city = name;
city | temp_lo | temp_hi | prcp | date | name | lon | lat ---------------+---------+---------+------+------------+---------------+-----+---- 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, lon, lat FROM weather, cities WHERE 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.lon, cities.lat FROM weather, cities WHERE cities.name = weather.city;
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 alternative form:
SELECT * FROM weather INNER JOIN cities ON (weather.city = cities.name);
This syntax is not as commonly used as the one above, but we show it here to help you understand the following topics.
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 | lon | lat ---------------+---------+---------+------+------------+---------------+-----+---- 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 Hayward | 37 | 54 | | 1994-11-29 | | | (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.
Like most other relational database products, DuckDB 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 maximum low temperature observed in each city with:
SELECT city, max(temp_lo) FROM weather GROUP BY city;
city | max ---------------+----- Hayward | 37 San Francisco | 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, max(temp_lo) FROM weather GROUP BY city HAVING max(temp_lo) < 40;
city | max ---------+----- Hayward | 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 can use the LIKE operator:
SELECT city, max(temp_lo) FROM weather WHERE city LIKE 'S%' -- (1) GROUP BY city HAVING max(temp_lo) < 40;
More information about the LIKE operator can be found here.
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.
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.
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)
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!