Data Query Language (DQL) in ClickHouse

Data Query Language, commonly abbreviated as DQL, represents the part of SQL that is focused entirely on reading, retrieving, and analyzing data from the database, and in a system like ClickHouse—whose primary goal is fast analytical querying—DQL is not just a feature but the central purpose of the database itself. When you work with ClickHouse in real analytical scenarios such as log analysis, metrics aggregation, or business intelligence workloads, the majority of your interaction with the system will happen through DQL, and therefore developing a deep understanding of it directly translates into practical effectiveness.

DQL commands do not modify the structure of the database and do not change stored data; instead, they allow you to ask questions about the data and obtain results that help in decision-making, reporting, and exploration. Because ClickHouse is optimized for large-scale reads and aggregations, its DQL capabilities are both familiar to SQL users and enhanced for analytical efficiency.

At the heart of DQL lies the SELECT statement, which is the most frequently used SQL statement in ClickHouse.

1. The SELECT Statement as the Core of DQL

The SELECT statement is the primary tool for querying data in ClickHouse, and conceptually it allows you to specify:

What data you want
From where you want it
Under what conditions it should be filtered
How it should be grouped or sorted
How much of it should be returned

The simplest form of a SELECT statement looks like this:

SELECT column1, column2
FROM table_name;

This structure already captures the declarative nature of SQL, because you describe the desired result while ClickHouse internally decides how to scan columns, apply filters, and produce output efficiently.

If you want all columns, you can use:

SELECT *
FROM table_name;

Although SELECT * is convenient for exploration, in production analytics it is often better to explicitly select only required columns so that ClickHouse reads less data from disk, which aligns with columnar performance principles.

2. Common SELECT Clauses in ClickHouse

ClickHouse SELECT statements support several clauses that refine how data is retrieved, and understanding how these clauses interact is crucial for writing effective queries.

Key clauses include:

LIMIT
DISTINCT
WHERE
GROUP BY
ORDER BY

Each clause serves a specific purpose in shaping the result set.

3. LIMIT Clause

The LIMIT clause restricts the number of rows returned by a query, which is extremely useful during exploration because large analytical tables can contain millions or billions of rows.

Example:

SELECT *
FROM system.functions
LIMIT 10;

This query reads from the system.functions table and returns only ten rows, which allows you to quickly preview data without scanning or returning an overwhelming result set.

In practice, LIMIT is often used during development, debugging, and sampling.

4. DISTINCT Clause

The DISTINCT clause ensures that duplicate values are removed from the result set for the specified columns.

Example:

SELECT DISTINCT name
FROM system.functions
LIMIT 100;

This query retrieves unique values from the name column and limits the output to 100 rows.

DISTINCT is useful when you want to understand the variety of values in a dataset, such as listing unique users, categories, or event types. However, because DISTINCT may require additional processing, it should be used thoughtfully on very large datasets.

5. WHERE Clause

The WHERE clause filters rows based on conditions, and it is one of the most powerful tools in querying because it allows you to narrow down results to only the data that satisfies certain criteria.

Example:

SELECT name
FROM system.functions
WHERE name = 'sum';

Here, the condition name = 'sum' is an expression that evaluates to true or false for each row, and only rows where it evaluates to true are returned.

WHERE clauses can include:

Comparisons (=, !=, <, >)
Logical operators (AND, OR)
Functions and expressions

Filtering early is especially beneficial in ClickHouse because it reduces the amount of data processed in later stages of the query.

6. GROUP BY Clause

The GROUP BY clause is used to aggregate data by grouping rows that share the same values in specified columns, and this clause is central to analytical workloads where summaries and aggregates are more important than raw rows.

Example:

SELECT 
    is_aggregate,
    COUNT(*) AS total_rows
FROM system.functions
WHERE origin = 'system'
GROUP BY is_aggregate;

In this query:

Rows are filtered where origin = 'system'
Rows are grouped by is_aggregate
COUNT(*) calculates how many rows fall into each group

This kind of query answers questions like “How many items fall into each category?” and is a typical analytical pattern.

7. ORDER BY Clause

The ORDER BY clause sorts the result set based on one or more columns, either in ascending or descending order.

Example:

SELECT *
FROM system.functions
ORDER BY is_aggregate DESC;

Here, results are sorted in descending order based on is_aggregate.

Sorting is useful for ranking, prioritizing, and presenting data in a meaningful order, such as showing highest values first or arranging results chronologically.

8. Clause Execution Order and Logical Flow

Although a SELECT statement is written in a certain order, it is important to understand the logical processing order of clauses.

In ClickHouse queries:

WHERE comes before GROUP BY
GROUP BY comes before ORDER BY
LIMIT is applied at the end to restrict output

A typical structured query therefore looks like:

SELECT column, COUNT(*)
FROM table_name
WHERE condition
GROUP BY column
ORDER BY column
LIMIT 10;

Understanding this logical flow helps avoid mistakes and ensures that aggregations and filters behave as expected.