Configured API

In many cases, the option to configure an entity at runtime is more distracting than helpful, and it's preferable to supply the entity's configuration at definition time.

The configured API offers a way to do this. When invoked on a ResourceDefinition, ExecutorDefinition, SolidDefinition, CompositeSolidDefinition, LoggerDefinition, it returns an interchangeable object with the given configuration "baked in".

from dagster import resource

@resource(config_schema={"region": str, "use_unsigned_session": bool})
def s3_session(_init_context):
    """Connect to S3"""

east_unsigned_s3_session = s3_session.configured(
    {"region": "us-east-1", "use_unsigned_session": False}

In other cases, it's useful to partially fill out configuration at definition time and leave other configuration for runtime. For these cases, configured can be used as a decorator, accepting a function that translates from runtime config to config that satisfies the entity's config schema. It returns an entity with the "outer" config schema as its schema.

from dagster import configured, resource

@resource(config_schema={"region": str, "use_unsigned_session": bool})
def s3_session(_init_context):
    """Connect to S3"""

@configured(s3_session, config_schema={"region": str})
def unsigned_s3_session(config):
    return {"region": config["region"], "use_unsigned_session": False}

The configured API can be used with any definition type in the same way. To configure a solid, for example, simply invoke configured on the solid definition:

from dagster import Field, configured, solid

    config_schema={"iterations": int, "word": Field(str, is_required=False, default_value="hello")}
def example_solid(context):
    for _ in range(context.solid_config["iterations"]):["word"])

# This example is fully configured. With this syntax, a name must be explicitly provided.
configured_example = configured(example_solid, name="configured_example")(
    {"iterations": 6, "word": "wheaties"}

# This example is partially configured: `iterations` is passed through
# The decorator yields a solid named 'another_configured_example' (from the decorated function)
# with `int` as the `config_schema`.
@configured(example_solid, int)
def another_configured_example(config):
    return {"iterations": config, "word": "wheaties"}

When using the decorator syntax (@configured), the resulting solid definition will inherit the name of the function being decorated (like another_configured_example in the above example). When configuring a solid completely with a config dictionary rather than with a function (as with configured_example), you must add a keyword argument name in the call to configured. When naming solids, remember that solid definitions must have unique names within a repository or pipeline.

from dagster import Field, InputDefinition, Int, List, configured, execute_pipeline, pipeline, solid

# start_configured_named
    config_schema={"is_sample": Field(bool, is_required=False, default_value=False),},
    input_defs=[InputDefinition("xs", List[Int])],
def variance(context, xs):
    n = len(xs)
    mean = sum(xs) / n
    summed = sum((mean - x) ** 2 for x in xs)
    result = summed / (n - 1) if context.solid_config["is_sample"] else summed / n
    return result ** (1 / 2)

# If we want to use the same solid configured in multiple ways in the same pipeline,
# we have to specify unique names when configuring them:
sample_variance = configured(variance, name="sample_variance")({"is_sample": True})
population_variance = configured(variance, name="population_variance")({"is_sample": False})

def stats_pipeline():

# end_configured_named

def run_pipeline():
    result = execute_pipeline(
            "solids": {
                "sample_variance": {"inputs": {"xs": [4, 8, 15, 16, 23, 42]}},
                "population_variance": {
                    "inputs": {"xs": [33, 30, 27, 29, 32, 30, 27, 28, 30, 30, 30, 31]}
    return result