SQL blocks

Credentials

Before starting, you need to add credentials so Mage can execute your SQL commands. Follow the steps for the database or data warehouse of your choice:

BigQuery
ClickHouse
Databricks (Mage Pro only)
Druid
DuckDB
MySQL
Oracle (Mage Pro only)
PostgreSQL
Redshift
Snowflake
SQLite (Mage Pro only)
Microsoft SQL Server
Trino

Add SQL block to pipeline

Create a new pipeline or open an existing pipeline.
Add a data loader, transformer, or data exporter block.
Select SQL.

Configure SQL block

There are 4 - 5 fields that must be configured for each SQL block:

Field	Required	Description
Data provider	Yes	The database or data warehouse you want to execute your SQL commands in.
Profile	Yes	When you add your credentials to the `io_config.yaml` file, you added them under a key. That key is called the profile. Choose which set of credentials you want this SQL block to use. For detailed information about setting up profiles, see the IO Config Setup documentation.
Use raw SQL	No	You can write raw SQL and it’ll be executed as written in your data provider.
Database	Depends on data provider	Some data warehouses require that we explicitly state the name of the database we want to write to. If this is present, it’s required. The name of the table that is created follows this convention: `[database].[schema].[pipeline UUID]_[block UUID]`.
Schema to save	Yes	Every SQL block will save data to your data provider. The name of the table that is created follows this convention: `[schema].[pipeline UUID]_[block UUID]`.
Table	No	SQL blocks will automatically name your table for you using a naming convention (see the section `Automatic naming of tables` for more information). You can override this automatic naming convention by filling in a value in this field.
Write policy	Yes	How do you want to handle existing data with the same database, schema, and table name? See below for more information.

Write policies

Policy	Description
Append	Add rows to the existing table.
Replace	Delete the existing data.
Fail	Raise an error during execution.

YAML configuration

You can also modify block configuration in pipeline’s metadata.yaml file. Each block has a configuration field. Example configuration

  configuration:
    data_provider: sqlserver
    data_provider_profile: default
    data_provider_schema: ''
    export_write_policy: append
    limit: 1000
    limit_in_pipeline_run: 1
    use_raw_sql: false

In addition to the fields mentioned in the table above. Here are some extra fields that can be included in the configuration:

Field	Required	Description
limit	No	The maximum number of rows to return in notebook.
limit_in_pipeline_run	No	The maximum number of rows to return when running the block in the pipeline run.

Dynamic `data_provider_profile`

You can configure the data_provider_profile in a SQL block to resolve dynamically at runtime using Mage variable syntax. This is done by modifying the pipeline’s metadata.yaml directly. Example:

configuration:
  data_provider: snowflake
  data_provider_profile: '{{ variables("DB_PROFILE") }}'
  data_provider_schema: analytics
  export_write_policy: append

When the pipeline runs, the profile name will be resolved from the referenced variable (environment variable, pipeline variable, or secret variable).

Automatically created tables

Each SQL block will create a table in the data provider of your choice. When you run a block, it’ll execute your SQL command, then store the results in a table created in your database or data warehouse.

Using raw SQL

If you’re using version 0.9.69 or less and interpolating a SQL command from an upstream block that uses raw SQL: if the downstream block’s SQL command is referencing the upstream block using a variable such as {{ df_1 }} and ends the statement with a semi-colon (;), there is an interpolation error. To work around the error, remove the semi-colon ;. A fix will be available in 0.9.70.

If you toggle this setting, you’re responsible for writing the CREATE TABLE command and the INSERT command. For example, if a table already exists then you can write the INSERT statement:

INSERT INTO mage.users
SELECT 1 AS id, 'Urza' AS username;

If the table doesn’t exist yet, you can write both the CREATE TABLE statement and the INSERT statement:

CREATE TABLE IF NOT EXISTS mage.users (
    id BIGINT
    , username VARCHAR(255)
);

WITH users AS (
    SELECT
        1 AS id
        , 'Urza' AS username
)

INSERT INTO mage.users
SELECT
    *
FROM users;

This SQL query will create a table named mage.users with 2 columns: id as a BIGINT and username as a VARCHAR(255). Then, it’ll insert a single row into that table.

Required SQL statements

When writing raw SQL, you must use at least 1 of the following statements:

SELECT
```
SELECT 1;
```
INSERT
```
INSERT INTO some_table
SELECT 1;
```
CREATE TABLE
```
CREATE TABLE some_table (id BIGINT);
```
DROP TABLE
```
DROP TABLE some_table;
```

UPDATE

UPDATE some_table
SET col1 = 'value1'
WHERE col2 = 'value2'

Multiple SQL statements

You can execute multiple SQL statements in a SQL block. Separate your SQL statements using a semi-colon (;).

Automatic naming of tables

If you don’t choose the setting for using raw SQL, the name of this automatically created table follows these conventions:

If Database field is configured: [database].[schema].[pipeline UUID]_[block UUID]
If no Database field is configured: [schema].[pipeline UUID]_[block UUID]

Where pipeline UUID is the name of the current pipeline you’re editing. Where block UUID is the name of the SQL block you are running.

Upstream blocks

If your SQL block depends on upstream blocks that aren’t SQL blocks (e.g. Python code blocks), then those blocks will also automatically create tables. The name of those tables follows the same naming convention mentioned above.

Variables

All SQL blocks have the following variables they can access in their query:

`{{ execution_date }}`

The date and time the block is run. Example

SELECT '{{ execution_date }}' AS today

Result

`today`
`2022-09-24 23:01:08.376057`

If a SQL block has 1 or more upstream blocks, then they have access to their parent blocks’ output using the following variable:

`{{ df_1 }}`

Depending on how many upstream blocks there are, the variable name changes. For example, if there are 3 upstream blocks then there are 3 variables that can be accessed:

{{ df_1 }}
{{ df_2 }}
{{ df_3 }}

The SQL block UI will display which variable maps to which upstream block. By convention, the 1st added upstream block will be {{ df_1 }}, and every upstream block added after that will have an incrementing number in the variable name after the prefix df_. Example

SELECT a.id , b.username FROM {{ df_1 }} AS a LEFT JOIN {{ df_2 }} AS b ON
a.id = b.user_id LIMIT 1

Result

`id`	`username`
`1`	`Sorcerer supreme`

Variable interpolation

Available in versions >= 0.9.59

Interpolate values in the block’s code using data from:

Upstream block output
Variables
1. Global variables
2. Pipeline variables
3. Runtime variables
Environment variables

Upstream block output

Use the data from 1 or more upstream block’s output by using the block_output function.

Interpolate block output dataz

`block_uuid`

The UUID of the upstream block to get data from. If argument isn’t present, data from all upstream blocks will be fetched.


Optional	✅
Type	`str`
Example	`'data_loader_block'`

`parse`

A lambda function to parse the data from an upstream block’s output. If the parse argument isn’t present, then the fetched data from the upstream block’s output will be interpolated as is.


Optional	✅
Type	`function`
Example	`lambda data, variables: data['runtime'] * variables['tries']`

Arguments

data If the block_uuid argument isn’t present, then the 1st argument in the lambda function is a list of objects. The list of objects contain the data from an upstream block’s output. The positional order of the data in the list corresponds to the current block’s upstream blocks order. For example, if the current block has the following upstream blocks with the following output:
1. load_api_data: [1, 2, 3]
2. load_users_data: { 'mage': 'powerful' }
Then the 1st argument in the lambda function will be the following list:
```
[
    [1, 2, 3],
    { 'mage': 'powerful' },
]
```
Optional ❌
Type If block_uuid argument is present, then the type depends on the output from that block. If block_uuid isn’t present, then the type is list.
Example { 'mage': 'powerful' }
variables A dictionary containing pipeline variables and runtime variables.
Optional ❌
Type dict
Example { 'fire': 40 }


Optional	❌
Type	If `block_uuid` argument is present, then the type depends on the output from that block. If `block_uuid` isn’t present, then the type is `list`.
Example	`{ 'mage': 'powerful' }`


Optional	❌
Type	`dict`
Example	`{ 'fire': 40 }`

Example

With block_uuid

SELECT
    '{{ block_output("magic_config", parse=lambda data, _vars: data["power"]) }}' AS power
    , '{{ block_output("magic_config", parse=lambda data, _vars: data["spell"]) }}' AS spell
FROM mage.core_spells

SELECT
    '{{ block_output("data_loader_block", parse=lambda data, variables: data["runtime"] * variables["tries"]) }}' AS data
FROM mage.core_spells

SELECT
    '{{ block_output("load_recent_user", parse=lambda user, _variables: user["id"]) }}'
FROM mage.core_spells

Without block_uuid

SELECT
    '{{ block_output(parse=lambda data, _vars: data[0]["power"]) }}' AS power
    , '{{ block_output(parse=lambda data, _vars: data[0]["spell"]) }}' AS spell
FROM mage.core_spells

SELECT
    '{{ block_output(parse=lambda data, variables: data[0]["runtime"] * variables[0]["tries"]) }}' AS data
FROM mage.core_spells

SELECT
    '{{ block_output(parse=lambda user, _variables: user[0]["id"]) }}'
FROM mage.core_spells

Variables

Interpolate values from a dictionary containing keys and values from:

Global variables
Pipeline variables
Runtime variables

Example

SELECT
    '{{ variables("mana") }}' AS mana
FROM mage.dual_lands

Environment variables

Interpolate values from the environment variables.

Example

SELECT
    '{{ env_var("ALLOW_POWER_9") }}' AS vintage
FROM mage.mox

Executing pure SQL

Available in versions >= 0.9.59

Mage automatically pre-processes your SQL command before sending it to the final destination. The pre-process extracts and structures the command so that Mage can build upstream tables and properly display preview data. However, there are scenarios where the SQL command you write must be executed as is. You can disable Mage’s pre-processing by opening a SQL block and checking the box labeled Use raw SQL and Disable query preprocessing.

Execute pure SQl commands

Quickstart

Developer experience

Version control

Blocks

Pipelines

dbt

Credentials

Add SQL block to pipeline

Configure SQL block

Write policies

YAML configuration

Dynamic `data_provider_profile`

Automatically created tables

Using raw SQL

Required SQL statements

Multiple SQL statements

Automatic naming of tables

Upstream blocks

Variables

`{{ execution_date }}`

`{{ df_1 }}`

Variable interpolation

Upstream block output

`block_uuid`

`parse`

Example

Variables

Example

Environment variables

Example

Executing pure SQL

Quickstart

Developer experience

Version control

Blocks

Pipelines

dbt

Documentation Index

​Credentials

​Add SQL block to pipeline

​Configure SQL block

​Write policies

​YAML configuration

​Dynamic data_provider_profile

​Automatically created tables

​Using raw SQL

​Required SQL statements

​Multiple SQL statements

​Automatic naming of tables

​Upstream blocks

​Variables

​{{ execution_date }}

​{{ df_1 }}

​Variable interpolation

​Upstream block output

​block_uuid

​parse

​Example

​Variables

​Example

​Environment variables

​Example

​Executing pure SQL

Credentials

Add SQL block to pipeline

Configure SQL block

Write policies

YAML configuration

Dynamic `data_provider_profile`

Automatically created tables

Using raw SQL

Required SQL statements

Multiple SQL statements

Automatic naming of tables

Upstream blocks

Variables

`{{ execution_date }}`

`{{ df_1 }}`

Variable interpolation

Upstream block output

`block_uuid`

`parse`

Example

Variables

Example

Environment variables

Example

Executing pure SQL