SQL blocks

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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:

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Add SQL block to pipeline

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

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Configure SQL block

There are 4 - 5 fields that must be configured for each SQL block:
FieldRequiredDescription
Data providerYesThe database or data warehouse you want to execute your SQL commands in.
ProfileYesWhen 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.
Use raw SQLNoYou can write raw SQL and it’ll be executed as written in your data provider.
DatabaseDepends on data providerSome 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 saveYesEvery 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].
TableNoSQL 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 policyYesHow do you want to handle existing data with the same database, schema, and table name? See below for more information.

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Write policies

PolicyDescription
AppendAdd rows to the existing table.
ReplaceDelete the existing data.
FailRaise an error during execution.

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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:
FieldRequiredDescription
limitNoThe maximum number of rows to return in notebook.
limit_in_pipeline_runNoThe maximum number of rows to return when running the block in the pipeline run.
SQL block monsters

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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.

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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.

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Required SQL statements

When writing raw SQL, you must 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'

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Multiple SQL statements

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

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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.

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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.

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Variables

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

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{{ execution_date }}

The date and time the block is ran. 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:

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{{ 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
idusername
1Sorcerer supreme

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Variable interpolation

Available in versions >= 0.9.59
Interpolate values in the block’s code using data from:
  1. Upstream block output
  2. Variables
    1. Global variables
    2. Pipeline variables
    3. Runtime variables
  3. Environment variables

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Upstream block output

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

Interpolate block output dataz

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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✅
Typestr
Example'data_loader_block'

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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✅
Typefunction
Examplelambda 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❌
    TypeIf 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❌
    Typedict
    Example{ 'fire': 40 }

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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

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Variables

Interpolate values from a dictionary containing keys and values from:
  1. Global variables
  2. Pipeline variables
  3. Runtime variables

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Example

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

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Environment variables

Interpolate values from the environment variables.

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Example

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

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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