Migrate from DDL, DML, and stored procedures

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Introduction

One of the more common situations that new dbt adopters encounter is a historical codebase of transformations written as a hodgepodge of DDL and DML statements, or stored procedures. Going from DML statements to dbt models is often a challenging hump for new users to get over, because the process involves a significant paradigm shift between a procedural flow of building a dataset (e.g. a series of DDL and DML statements) to a declarative approach to defining a dataset (e.g. how dbt uses SELECT statements to express data models). This guide aims to provide tips, tricks, and common patterns for converting DML statements to dbt models.

Preparing to migrate

Before getting into the meat of conversion, it’s worth noting that DML statements will not always illustrate a comprehensive set of columns and column types that an original table might contain. Without knowing the DDL to create the table, it’s impossible to know precisely if your conversion effort is apples-to-apples, but you can generally get close.

If your data warehouse supports SHOW CREATE TABLE, that can be a quick way to get a comprehensive set of columns you’ll want to recreate. If you don’t have the DDL, but are working on a substantial stored procedure, one approach that can work is to pull column lists out of any DML statements that modify the table, and build up a full set of the columns that appear.

As for ensuring that you have the right column types, since models materialized by dbt generally use CREATE TABLE AS SELECT or CREATE VIEW AS SELECT as the driver for object creation, tables can end up with unintended column types if the queries aren’t explicit. For example, if you care about INT versus DECIMAL versus NUMERIC, it’s generally going to be best to be explicit. The good news is that this is easy with dbt: you just cast the column to the type you intend.

We also generally recommend that column renaming and type casting happen as close to the source tables as possible, typically in a layer of staging transformations, which helps ensure that future dbt modelers will know where to look for those transformations! See How we structure our dbt projects for more guidance on overall project structure.

Operations we need to map

There are four primary DML statements that you are likely to have to convert to dbt operations while migrating a procedure:

• INSERT
• UPDATE
• DELETE
• MERGE

Each of these can be addressed using various techniques in dbt. Handling MERGEs is a bit more involved than the rest, but can be handled effectively via dbt. The first three, however, are fairly simple to convert.

Map INSERTs

An INSERT statement is functionally the same as using dbt to SELECT from an existing source or other dbt model. If you are faced with an INSERT-SELECT statement, the easiest way to convert the statement is to just create a new dbt model, and pull the SELECT portion of the INSERT statement out of the procedure and into the model. That’s basically it!

To really break it down, let’s consider a simple example:

INSERT INTO returned_orders (order_id, order_date, total_return)

SELECT order_id, order_date, total FROM orders WHERE type = 'return'

Converting this with a first pass to a dbt model (in a file called returned_orders.sql) might look something like:

SELECT
    order_id as order_id,
    order_date as order_date,
    total as total_return

FROM {{ ref('orders') }}

WHERE type = 'return'

Functionally, this would create a model (which could be materialized as a table or view depending on needs) called returned_orders that contains three columns: order_id, order_date, total_return) predicated on the type column. It achieves the same end as the INSERT, just in a declarative fashion, using dbt.

A note on FROM clauses

In dbt, using a hard-coded table or view name in a FROM clause is one of the most serious mistakes new users make. dbt uses the ref and source macros to discover the ordering that transformations need to execute in, and if you don’t use them, you’ll be unable to benefit from dbt’s built-in lineage generation and pipeline execution. In the sample code throughout the remainder of this article, we’ll use ref statements in the dbt-converted versions of SQL statements, but it is an exercise for the reader to ensure that those models exist in their dbt projects.

Sequential INSERTs to an existing table can be UNION ALL’ed together

Since dbt models effectively perform a single CREATE TABLE AS SELECT (or if you break it down into steps, CREATE, then an INSERT), you may run into complexities if there are multiple INSERT statements in your transformation that all insert data into the same table. Fortunately, this is a simple thing to handle in dbt. Effectively, the logic is performing a UNION ALL between the INSERT queries. If I have a transformation flow that looks something like (ignore the contrived nature of the scenario):

CREATE TABLE all_customers

INSERT INTO all_customers SELECT * FROM us_customers

INSERT INTO all_customers SELECT * FROM eu_customers

The dbt-ified version of this would end up looking something like:

SELECT * FROM {{ ref('us_customers') }}

UNION ALL

SELECT * FROM {{ ref('eu_customers') }}

The logic is functionally equivalent. So if there’s another statement that INSERTs into a model that I’ve already created, I can just add that logic into a second SELECT statement that is just UNION ALL'ed with the first. Easy!

Map UPDATEs

UPDATEs start to increase the complexity of your transformations, but fortunately, they’re pretty darn simple to migrate, as well. The thought process that you go through when translating an UPDATE is quite similar to how an INSERT works, but the logic for the SELECT list in the dbt model is primarily sourced from the content in the SET section of the UPDATE statement. Let’s look at a simple example:

UPDATE orders

SET type = 'return'

WHERE total < 0

The way to look at this is similar to an INSERT-SELECT statement. The table being updated is the model you want to modify, and since this is an UPDATE, that model has likely already been created, and you can either:

• add to it with subsequent transformations
• create an intermediate model that builds off of the original model – perhaps naming it something like int_[entity]_[verb].sql.

The SELECT list should contain all of the columns for the table, but for the specific columns being updated by the DML, you’ll use the computation on the right side of the equals sign as the SELECTed value. Then, you can use the target column name on the left of the equals sign as the column alias.

If I were building an intermediate transformation from the above query would translate to something along the lines of:

SELECT
    CASE
        WHEN total < 0 THEN 'return'
        ELSE type
    END AS type,

    order_id,
    order_date

FROM {{ ref('stg_orders') }}

Since the UPDATE statement doesn’t modify every value of the type column, we use a CASE statement to apply the contents’ WHERE clause. We still want to select all of the columns that should end up in the target table. If we left one of the columns out, it wouldn’t be passed through to the target table at all due to dbt’s declarative approach.

Sometimes, you may not be sure what all the columns are in a table, or in the situation as above, you’re only modifying a small number of columns relative to the total number of columns in the table. It can be cumbersome to list out every column in the table, but fortunately dbt contains some useful utility macros that can help list out the full column list of a table.

Another way I could have written the model a bit more dynamically might be:

SELECT
    {{ dbt_utils.star(from=ref('stg_orders'), except=['type']) }},
    CASE
        WHEN total < 0 THEN 'return'
        ELSE type
    END AS type,

FROM {{ ref('stg_orders') }}

The dbt_utils.star() macro will print out the full list of columns in the table, but skip the ones I’ve listed in the except list, which allows me to perform the same logic while writing fewer lines of code. This is a simple example of using dbt macros to simplify and shorten your code, and dbt can get a lot more sophisticated as you learn more techniques. Read more about the dbt_utils package and the star macro.

Map DELETEs

One of the biggest differences between a procedural transformation and how dbt models data is that dbt, in general, will never destroy data. While there are ways to execute hard DELETEs in dbt that are outside of the scope of this article, the general best practice for handling deleted data is to just use soft deletes, and filter out soft-deleted data in a final transformation.

Let’s consider a simple example query:

DELETE FROM stg_orders WHERE order_status IS NULL

In a dbt model, you’ll need to first identify the records that should be deleted and then filter them out. There are really two primary ways you might translate this query:

SELECT * FROM {{ ref('stg_orders') }} WHERE order_status IS NOT NULL

This first approach just inverts the logic of the DELETE to describe the set of records that should remain, instead of the set of records that should be removed. This ties back to the way dbt declaratively describes datasets. You reference the data that should be in a dataset, and the table or view gets created with that set of data.

Another way you could achieve this is by marking the deleted records, and then filtering them out. For example:

WITH

soft_deletes AS (

    SELECT
        *,
        CASE
            WHEN order_status IS NULL THEN true
            ELSE false
        END AS to_delete

    FROM {{ ref('stg_orders') }}

)

SELECT * FROM soft_deletes WHERE to_delete = false

This approach flags all of the deleted records, and the final SELECT filters out any deleted data, so the resulting table contains only the remaining records. It’s a lot more verbose than just inverting the DELETE logic, but for complex DELETE logic, this ends up being a very effective way of performing the DELETE that retains historical context.

It’s worth calling out that while this doesn’t enable a hard delete, hard deletes can be executed a number of ways, the most common being to execute a dbt macros via as a run-operation, or by using a post-hook to perform a DELETE statement after the records to-be-deleted have been marked. These are advanced approaches outside the scope of this guide.

Map MERGEs

dbt has a concept called materialization, which determines how a model is physically or logically represented in the warehouse. INSERTs, UPDATEs, and DELETEs will typically be accomplished using table or view materializations. For incremental workloads accomplished via commands like MERGE or UPSERT, dbt has a particular materialization called incremental. The incremental materialization is specifically used to handle incremental loads and updates to a table without recreating the entire table from scratch on every run.

Step 1: Map the MERGE like an INSERT/UPDATE to start

Before we get into the exact details of how to implement an incremental materialization, let’s talk about logic conversion. Extracting the logic of the MERGE and handling it as you would an INSERT or an UPDATE is the easiest way to get started migrating a MERGE command. .

To see how the logic conversion works, we’ll start with an example MERGE. In this scenario, imagine a ride sharing app where rides are loaded into a details table daily, and tips may be updated at some later date, and need to be kept up-to-date:

MERGE INTO ride_details USING (
    SELECT
        ride_id,
        subtotal,
        tip

    FROM rides_to_load AS rtl

    ON ride_details.ride_id = rtl.ride_id

    WHEN MATCHED THEN UPDATE

    SET ride_details.tip = rtl.tip

    WHEN NOT MATCHED THEN INSERT (ride_id, subtotal, tip)
    VALUES (rtl.ride_id, rtl.subtotal, NVL(rtl.tip, 0, rtl.tip)
);

The content of the USING clause is a useful piece of code because that can easily be placed in a CTE as a starting point for handling the match statement. I find that the easiest way to break this apart is to treat each match statement as a separate CTE that builds on the previous match statements.

We can ignore the ON clause for now, as that will only come into play once we get to a point where we’re ready to turn this into an incremental.

As with UPDATEs and INSERTs, you can use the SELECT list and aliases to name columns appropriately for the target table, and UNION together INSERT statements (taking care to use UNION, rather than UNION ALL to avoid duplicates).

The MERGE would end up translating to something like this:

WITH

using_clause AS (

    SELECT
        ride_id,
        subtotal,
        tip

    FROM {{ ref('rides_to_load') }}

),

updates AS (

    SELECT
        ride_id,
        subtotal,
        tip

    FROM using_clause

),

inserts AS (

    SELECT
        ride_id,
        subtotal,
        NVL(tip, 0, tip)

    FROM using_clause

)

SELECT *

FROM updates

UNION inserts

To be clear, this transformation isn’t complete. The logic here is similar to the MERGE, but will not actually do the same thing, since the updates and inserts CTEs are both selecting from the same source query. We’ll need to ensure we grab the separate sets of data as we transition to the incremental materialization.

One important caveat is that dbt does not natively support DELETE as a MATCH action. If you have a line in your MERGE statement that uses WHEN MATCHED THEN DELETE, you’ll want to treat it like an update and add a soft-delete flag, which is then filtered out in a follow-on transformation.

Step 2: Convert to incremental materialization

As mentioned above, incremental materializations are a little special in that when the target table does not exist, the materialization functions in nearly the same way as a standard table materialization, and executes a CREATE TABLE AS SELECT statement. If the target table does exist, however, the materialization instead executes a MERGE statement.

Since a MERGE requires a JOIN condition between the USING clause and the target table, we need a way to specify how dbt determines whether or not a record triggers a match or not. That particular piece of information is specified in the dbt model configuration.

We can add the following config() block to the top of our model to specify how it should build incrementally:

{{
    config(
        materialized='incremental',
        unique_key='ride_id',
        incremental_strategy='merge'
    )
}}

The three configuration fields in this example are the most important ones.

• Setting materialized='incremental' tells dbt to apply UPSERT logic to the target table.
• The unique_key should be a primary key of the target table. This is used to match records with the existing table.
• incremental_strategy here is set to MERGE any existing rows in the target table with a value for the unique_key which matches the incoming batch of data. There are various incremental strategies for different situations and warehouses.

The bulk of the work in converting a model to an incremental materialization comes in determining how the logic should change for incremental loads versus full backfills or initial loads. dbt offers a special macro, is_incremental(), which evaluates false for initial loads or for backfills (called full refreshes in dbt parlance), but true for incremental loads.

This macro can be used to augment the model code to adjust how data is loaded for subsequent loads. How that logic should be added will depend a little bit on how data is received. Some common ways might be:

1. The source table is truncated ahead of incremental loads, and only contains the data to be loaded in that increment.
2. The source table contains all historical data, and there is a load timestamp column that identifies new data to be loaded.

In the first case, the work is essentially done already. Since the source table always contains only the new data to be loaded, the query doesn’t have to change for incremental loads. The second case, however, requires the use of the is_incremental() macro to correctly handle the logic.

Taking the converted MERGE statement that we’d put together previously, we’d augment it to add this additional logic:

WITH

using_clause AS (

    SELECT
        ride_id,
        subtotal,
        tip,
        max(load_timestamp) as load_timestamp

    FROM {{ ref('rides_to_load') }}


    {% if is_incremental() %}

        WHERE load_timestamp > (SELECT max(load_timestamp) FROM {{ this }})

    {% endif %}

),

updates AS (

    SELECT
        ride_id,
        subtotal,
        tip,
        load_timestamp

    FROM using_clause

    {% if is_incremental() %}

        WHERE ride_id IN (SELECT ride_id FROM {{ this }})

    {% endif %}

),

inserts AS (

    SELECT
        ride_id,
        subtotal,
        NVL(tip, 0, tip),
        load_timestamp

    FROM using_clause

    WHERE ride_id NOT IN (SELECT ride_id FROM updates)

)

SELECT * FROM updates UNION inserts

There are a couple important concepts to understand here:

1. The code in the is_incremental() conditional block only executes for incremental executions of this model code. If the target table doesn’t exist, or if the --full-refresh option is used, that code will not execute.
2. {{ this }} is a special keyword in dbt that when used in a Jinja block, self-refers to the model for which the code is executing. So if you have a model in a file called my_incremental_model.sql, {{ this }} will refer to my_incremental_model (fully qualified with database and schema name if necessary). By using that keyword, we can leverage the current state of the target table to inform the source query.

Migrate Stores procedures

The techniques shared above are useful ways to get started converting the individual DML statements that are often found in stored procedures. Using these types of patterns, legacy procedural code can be rapidly transitioned to dbt models that are much more readable, maintainable, and benefit from software engineering best practices like DRY principles. Additionally, once transformations are rewritten as dbt models, it becomes much easier to test the transformations to ensure that the data being used downstream is high-quality and trustworthy.