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Destination tables

When you run a pipeline, dlt creates tables in the destination database and loads the data from your source into these tables. In this section, we will take a closer look at what destination tables look like and how they are organized.

We start with a simple dlt pipeline:

import dlt

data = [
{'id': 1, 'name': 'Alice'},
{'id': 2, 'name': 'Bob'}
]

pipeline = dlt.pipeline(
pipeline_name='quick_start',
destination='duckdb',
dataset_name='mydata'
)
load_info = pipeline.run(data, table_name="users")
note

Here we are using the DuckDb destination, which is an in-memory database. Other database destinations will behave similarly and have similar concepts.

Running this pipeline will create a database schema in the destination database (DuckDB) along with a table named users. Quick tip: you can use the show command of the dlt pipeline CLI to see the tables in the destination database.

Database schemaโ€‹

The database schema is a collection of tables that represent the data you loaded into the database. The schema name is the same as the dataset_name you provided in the pipeline definition. In the example above, we explicitly set the dataset_name to mydata. If you don't set it, it will be set to the pipeline name with a suffix _dataset.

Be aware that the schema referred to in this section is distinct from the dlt Schema. The database schema pertains to the structure and organization of data within the database, including table definitions and relationships. On the other hand, the "dlt Schema" specifically refers to the format and structure of normalized data within the dlt pipeline.

Tablesโ€‹

Each resource in your pipeline definition will be represented by a table in the destination. In the example above, we have one resource, users, so we will have one table, mydata.users, in the destination. Where mydata is the schema name, and users is the table name. Here also, we explicitly set the table_name to users. When table_name is not set, the table name will be set to the resource name.

For example, we can rewrite the pipeline above as:

@dlt.resource
def users():
yield [
{'id': 1, 'name': 'Alice'},
{'id': 2, 'name': 'Bob'}
]

pipeline = dlt.pipeline(
pipeline_name='quick_start',
destination='duckdb',
dataset_name='mydata'
)
load_info = pipeline.run(users)

The result will be the same; note that we do not explicitly pass table_name="users" to pipeline.run, and the table is implicitly named users based on the resource name (e.g., users() decorated with @dlt.resource).

note

Special tables are created to track the pipeline state. These tables are prefixed with _dlt_ and are not shown in the show command of the dlt pipeline CLI. However, you can see them when connecting to the database directly.

Child and parent tablesโ€‹

Now let's look at a more complex example:

import dlt

data = [
{
'id': 1,
'name': 'Alice',
'pets': [
{'id': 1, 'name': 'Fluffy', 'type': 'cat'},
{'id': 2, 'name': 'Spot', 'type': 'dog'}
]
},
{
'id': 2,
'name': 'Bob',
'pets': [
{'id': 3, 'name': 'Fido', 'type': 'dog'}
]
}
]

pipeline = dlt.pipeline(
pipeline_name='quick_start',
destination='duckdb',
dataset_name='mydata'
)
load_info = pipeline.run(data, table_name="users")

Running this pipeline will create two tables in the destination, users and users__pets. The users table will contain the top-level data, and the users__pets table will contain the child data. Here is what the tables may look like:

mydata.users

idname_dlt_id_dlt_load_id
1AlicewX3f5vn801W16A1234562350.98417
2BobrX8ybgTeEmAmmA1234562350.98417

mydata.users__pets

idnametype_dlt_id_dlt_parent_id_dlt_list_idx
1Fluffycatw1n0PEDzuP3grwwX3f5vn801W16A0
2Spotdog9uxh36VU9lqKpwwX3f5vn801W16A1
3Fidodogpe3FVtCWz8VuNArX8ybgTeEmAmmA0

When creating a database schema, dlt recursively unpacks nested structures into relational tables, creating and linking children and parent tables.

This is how it works:

  1. Each row in all (top level and child) data tables created by dlt contains a UNIQUE column named _dlt_id.
  2. Each child table contains a FOREIGN KEY column _dlt_parent_id linking to a particular row (_dlt_id) of a parent table.
  3. Rows in child tables come from the lists: dlt stores the position of each item in the list in _dlt_list_idx.
  4. For tables that are loaded with the merge write disposition, we add a root key column _dlt_root_id, which links the child table to a row in the top-level table.
note

If you define your own primary key in a child table, it will be used to link to the parent table, and the _dlt_parent_id and _dlt_list_idx will not be added. _dlt_id is always added even if the primary key or other unique columns are defined.

Naming convention: tables and columnsโ€‹

During a pipeline run, dlt normalizes both table and column names to ensure compatibility with the destination database's accepted format. All names from your source data will be transformed into snake_case and will only include alphanumeric characters. Please be aware that the names in the destination database may differ somewhat from those in your original input.

Variant columnsโ€‹

If your data has inconsistent types, dlt will dispatch the data to several variant columns. For example, if you have a resource (i.e., JSON file) with a field with name answer and your data contains boolean values, you will get a column with name answer of type BOOLEAN in your destination. If for some reason, on the next load, you get integer and string values in answer, the inconsistent data will go to answer__v_bigint and answer__v_text columns respectively. The general naming rule for variant columns is <original name>__v_<type> where original_name is the existing column name (with data type clash) and type is the name of the data type stored in the variant.

Load Packages and Load IDsโ€‹

Each execution of the pipeline generates one or more load packages. A load package typically contains data retrieved from all the resources of a particular source. These packages are uniquely identified by a load_id. The load_id of a particular package is added to the top data tables (referenced as _dlt_load_id column in the example above) and to the special _dlt_loads table with a status of 0 (when the load process is fully completed).

To illustrate this, let's load more data into the same destination:

data = [
{
'id': 3,
'name': 'Charlie',
'pets': []
},
]

The rest of the pipeline definition remains the same. Running this pipeline will create a new load package with a new load_id and add the data to the existing tables. The users table will now look like this:

mydata.users

idname_dlt_id_dlt_load_id
1AlicewX3f5vn801W16A1234562350.98417
2BobrX8ybgTeEmAmmA1234562350.98417
3Charlieh8lehZEvT3fASQ1234563456.12345

The _dlt_loads table will look like this:

mydata._dlt_loads

load_idschema_namestatusinserted_atschema_version_hash
1234562350.98417quick_start02023-09-12 16:45:51.17865+00aOEb...Qekd/58=
1234563456.12345quick_start02023-09-12 16:46:03.10662+00aOEb...Qekd/58=

The _dlt_loads table tracks complete loads and allows chaining transformations on top of them. Many destinations do not support distributed and long-running transactions (e.g., Amazon Redshift). In that case, the user may see the partially loaded data. It is possible to filter such data out: any row with a load_id that does not exist in _dlt_loads is not yet completed. The same procedure may be used to identify and delete data for packages that never got completed.

For each load, you can test and alert on anomalies (e.g., no data, too much loaded to a table). There are also some useful load stats in the Load info tab of the Streamlit app mentioned above.

You can add transformations and chain them together using the status column. You start the transformation for all the data with a particular load_id with a status of 0 and then update it to 1. The next transformation starts with the status of 1 and is then updated to 2. This can be repeated for every additional transformation.

Data lineageโ€‹

Data lineage can be super relevant for architectures like the data vault architecture or when troubleshooting. The data vault architecture is a data warehouse that large organizations use when representing the same process across multiple systems, which adds data lineage requirements. Using the pipeline name and load_id provided out of the box by dlt, you are able to identify the source and time of data.

You can save complete lineage info for a particular load_id including a list of loaded files, error messages (if any), elapsed times, schema changes. This can be helpful, for example, when troubleshooting problems.

Staging datasetโ€‹

So far we've been using the append write disposition in our example pipeline. This means that each time we run the pipeline, the data is appended to the existing tables. When you use the merge write disposition, dlt creates a staging database schema for staging data. This schema is named <dataset_name>_staging and contains the same tables as the destination schema. When you run the pipeline, the data from the staging tables is loaded into the destination tables in a single atomic transaction.

Let's illustrate this with an example. We change our pipeline to use the merge write disposition:

import dlt

@dlt.resource(primary_key="id", write_disposition="merge")
def users():
yield [
{'id': 1, 'name': 'Alice 2'},
{'id': 2, 'name': 'Bob 2'}
]

pipeline = dlt.pipeline(
pipeline_name='quick_start',
destination='duckdb',
dataset_name='mydata'
)

load_info = pipeline.run(users)

Running this pipeline will create a schema in the destination database with the name mydata_staging. If you inspect the tables in this schema, you will find the mydata_staging.users table identical to themydata.users table in the previous example.

Here is what the tables may look like after running the pipeline:

mydata_staging.users

idname_dlt_id_dlt_load_id
1Alice 2wX3f5vn801W16A2345672350.98417
2Bob 2rX8ybgTeEmAmmA2345672350.98417

mydata.users

idname_dlt_id_dlt_load_id
1Alice 2wX3f5vn801W16A2345672350.98417
2Bob 2rX8ybgTeEmAmmA2345672350.98417
3Charlieh8lehZEvT3fASQ1234563456.12345

Notice that the mydata.users table now contains the data from both the previous pipeline run and the current one.

Versioned datasetsโ€‹

When you set the full_refresh argument to True in dlt.pipeline call, dlt creates a versioned dataset. This means that each time you run the pipeline, the data is loaded into a new dataset (a new database schema). The dataset name is the same as the dataset_name you provided in the pipeline definition with a datetime-based suffix.

We modify our pipeline to use the full_refresh option to see how this works:

import dlt

data = [
{'id': 1, 'name': 'Alice'},
{'id': 2, 'name': 'Bob'}
]

pipeline = dlt.pipeline(
pipeline_name='quick_start',
destination='duckdb',
dataset_name='mydata',
full_refresh=True # <-- add this line
)
load_info = pipeline.run(data, table_name="users")

Every time you run this pipeline, a new schema will be created in the destination database with a datetime-based suffix. The data will be loaded into tables in this schema. For example, the first time you run the pipeline, the schema will be named mydata_20230912064403, the second time it will be named mydata_20230912064407, and so on.

This demo works on codespaces. Codespaces is a development environment available for free to anyone with a Github account. You'll be asked to fork the demo repository and from there the README guides you with further steps.
The demo uses the Continue VSCode extension.

Off to codespaces!

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