Pipeline#

A pipeline moves data from your Python code to a
destination. The pipeline accepts dlt sources or
resources, as well as generators, async generators, lists, and any iterables.
Once the pipeline runs, all resources are evaluated and the data is loaded at the destination.

Example:

This pipeline loads a list of objects into a DuckDB table named "three":

import dlt

pipeline = dlt.pipeline(destination="duckdb", dataset_name="sequence")

info = pipeline.run([{'id':1}, {'id':2}, {'id':3}], table_name="three")

print(info)

You instantiate a pipeline by calling the dlt.pipeline function with the following arguments:

• pipeline_name: a name of the pipeline that is used to identify it in trace and monitoring
  events and to restore its state and data schemas on subsequent runs. If not provided, dlt creates a pipeline name from the filename of the currently executing Python module.
• destination: a name of the destination to which dlt
  loads the data. It may also be provided to the run method of the pipeline and can be declared in various ways.
• dataset_name: a name of the dataset to which the data is loaded. A dataset is a logical
  group of tables, that is, schema in relational databases or a folder grouping many files. It may also be
  provided later to the run or load methods of the pipeline. If not provided, then
  it defaults to the {pipeline_name}_dataset on destinations that require datasets (most of the warehouses).
  It stays empty on destinations that don't separate tables into datasets (or database schemas), that is, on vector databases or ClickHouse.

To load the data, you call the run method and pass your data in the data argument.

Arguments:

• data (the first argument) may be a dlt source, resource, generator function, or any Iterator or
  Iterable (that is, a list or the result of the map function).
• write_disposition controls how to write data to a table. Defaults to "append".
  • append always adds new data at the end of the table.
  • replace replaces existing data with new data.
  • merge deduplicates and merges data based on primary_key and merge_key hints.
• table_name: specified in cases when the table name can't be inferred, that is, from the resources or name
  of the generator function.

Example: This pipeline loads the data the generator generate_rows(10) produces:

import dlt

def generate_rows(nr):
    for i in range(nr):
        yield {'id':1}

pipeline = dlt.pipeline(destination='bigquery', dataset_name='sql_database_data')

info = pipeline.run(generate_rows(10))

print(info)

Pipeline working directory#

Each pipeline that you create with dlt stores extracted files, load packages, inferred schemas,
execution traces, and the pipeline state in a folder in the local filesystem. The default
location for such folders is in the user's home directory: ~/.dlt/pipelines/<pipeline_name>.

You can inspect stored artifacts using the command
dlt pipeline info and
programmatically.

💡 A pipeline with a given name looks for its working directory in the location above - so if you have two
pipeline scripts that create a pipeline with the same name, they see the same working folder
and share all the possible state. You may override the default location using the pipelines_dir
argument when creating the pipeline.

💡 You can attach a Pipeline instance to an existing working folder, without creating a new
pipeline with dlt.attach.

Separate working environments with pipelines_dir#

You can run several pipelines with the same name but with different configurations, for example, to target development, staging, or production environments.
Set the pipelines_dir argument to store all the working folders in a specific place. For example:

import dlt
from dlt.common.pipeline import get_dlt_pipelines_dir

dev_pipelines_dir = os.path.join(get_dlt_pipelines_dir(), "dev")
pipeline = dlt.pipeline(destination="duckdb", dataset_name="sequence", pipelines_dir=dev_pipelines_dir)

This code stores the pipeline working folder in ~/.dlt/pipelines/dev/<pipeline_name>. Note that you need to pass this ~/.dlt/pipelines/dev/
into all CLI commands to get info/trace for that pipeline.

Do experiments with dev mode#

If you create a new pipeline script, you experiment
a lot. If you want each time the pipeline resets its state and loads data to a
new dataset, set the dev_mode argument of the dlt.pipeline method to True. Each time the
pipeline is created, dlt adds a datetime-based suffix to the dataset name.

Drop destination schema / dataset to start over#

If you drop the destination schema / dataset to which your pipeline loads data, the pipeline fully resets its
state and working directory and executes its first run.
If your pipeline doesn't share the dataset with any other pipeline and you don't keep any additional data in it - this
operation is also safe to be executed in production to trigger full refresh without a need to writing additional code.

Refresh pipeline data and state#

You can reset parts or all of your sources by using the refresh argument to dlt.pipeline or the pipeline's run or extract method.
That means when you run the pipeline, the sources/resources being processed have their state reset and their tables either dropped or truncated,
depending on which refresh mode is used.

The refresh option works with all relational or SQL destinations and cloud storages and files (filesystem). It doesn't work with vector databases (support is in progress) and with custom destinations.

The refresh argument should have one of the following string values to decide the refresh mode:

Drop tables and pipeline state for a source with drop_sources#

All sources being processed in pipeline.run or pipeline.extract are refreshed.
That means all tables listed in their schemas are dropped and the state belonging to those sources and all their resources is completely wiped.
The tables are deleted both from the pipeline's schema and from the destination database.

If you only have one source or run with all your sources together, then this is practically like running the pipeline again for the first time.

import dlt

pipeline = dlt.pipeline("airtable_demo", destination="duckdb")
pipeline.run(airtable_emojis(), refresh="drop_sources")

The preceding example instructs dlt to wipe the pipeline state belonging to the airtable_emojis source and drop all the database tables in duckdb to
which data was loaded. The airtable_emojis source had two resources named "📆 Schedule" and "💰 Budget" loading to tables "_schedule" and "_budget". Here's
what dlt does step by step:

1. Collects a list of tables to drop by looking for all the tables in the schema that are created in the destination.
2. Removes existing pipeline state associated with the airtable_emojis source.
3. Resets the schema associated with the airtable_emojis source.
4. Executes extract and normalize steps. These create fresh pipeline state and a schema.
5. Before it executes the load step, the collected tables are dropped from staging and regular dataset.
6. Schema airtable_emojis (associated with the source) is removed from the _dlt_version table.
7. Executes the load step as usual so tables are recreated and fresh schema and pipeline state are stored.

Selectively drop tables and resource state with drop_resources#

Limits the refresh to the resources being processed in pipeline.run or pipeline.extract (for example, by using source.with_resources(...)).
Tables belonging to those resources are dropped, and their resource state is wiped (that includes incremental state).
The tables are deleted both from the pipeline's schema and from the destination database.

Source level state keys aren't deleted in this mode (that is, dlt.current.source_state()[<'my_key>'] = '<my_value>')

import dlt

pipeline = dlt.pipeline("airtable_demo", destination="duckdb")
pipeline.run(airtable_emojis().with_resources("📆 Schedule"), refresh="drop_resources")

Above, the state associated with the "📆 Schedule" resource is reset, and the table generated by it ("_schedule") is dropped. Other resources,
tables, and state aren't affected. Please check drop_sources for a step-by-step description of what dlt does internally.

Selectively truncate tables and reset resource state with drop_data#

Same as drop_resources, but instead of dropping tables from the schema, only the data is deleted from them (that is, by TRUNCATE <table_name> in SQL destinations). Resource state for selected resources is also wiped. In the case of incremental resources, this resets the cursor state and fully reloads the data from the initial_value.

The schema remains unmodified in this case.

import dlt

pipeline = dlt.pipeline("airtable_demo", destination="duckdb")
pipeline.run(airtable_emojis().with_resources("📆 Schedule"), refresh="drop_data")

Above, the incremental state of the "📆 Schedule" is reset before the extract step so data is fully reacquired. Just before the load step starts,
the "_schedule" is truncated, and new (full) table data is inserted/copied.

Monitor the loading progress#

You can add a progress monitor to the pipeline. Typically, its role is to visually assure the user that
the pipeline run is progressing. dlt supports 4 progress monitors out of the box:

• enlighten - a status bar with progress
  bars that also allows for logging.
• tqdm - the most popular Python progress bar lib, proven to work in
  Notebooks.
• alive_progress - with the fanciest animations.
• log - dumps the progress information to log, console, or text stream. The most useful on
  production optionally adds memory and CPU usage stats.

💡 You must install the required progress bar library yourself.

You pass the progress monitor in the progress argument of the pipeline. You can use a name from the
preceding list as in the following example:

# create a pipeline loading chess data that dumps
# progress to stdout every 10 seconds (the default)
pipeline = dlt.pipeline(
    pipeline_name="chess_pipeline",
    destination='duckdb',
    dataset_name="chess_players_games_data",
    progress="log"
)

You can fully configure the progress monitor. See two examples below:

from airflow.operators.python import get_current_context # noqa

# log each minute to Airflow task logger
ti = get_current_context()["ti"]
pipeline = dlt.pipeline(
    pipeline_name="chess_pipeline",
    destination='duckdb',
    dataset_name="chess_players_games_data",
    progress=dlt.progress.log(60, ti.log)
)

# set tqdm bar color to yellow
pipeline = dlt.pipeline(
    pipeline_name="chess_pipeline",
    destination='duckdb',
    dataset_name="chess_players_games_data",
    progress=dlt.progress.tqdm(colour="yellow")
)

Note that the value of the progress argument is
configurable.