Cursor-based incremental loading#

In most REST APIs (and other data sources, i.e., database tables), you can request new or updated data by passing a timestamp or ID of the "last" record to a query. The API/database returns just the new/updated records from which you take the maximum/minimum timestamp/ID for the next load.

To do incremental loading this way, we need to:

• Figure out which field is used to track changes (the so-called cursor field) (e.g., "inserted_at", "updated_at", etc.);
• Determine how to pass the "last" (maximum/minimum) value of the cursor field to an API to get just new or modified data (how we do this depends on the source API).

Once you've figured that out, dlt takes care of finding maximum/minimum cursor field values, removing duplicates, and managing the state with the last values of the cursor. Take a look at the GitHub example below, where we request recently created issues.

@dlt.resource(primary_key="id")
def repo_issues(
    access_token=dlt.secrets.value,
    repository=dlt.config.value,
    updated_at = dlt.sources.incremental("updated_at", initial_value="1970-01-01T00:00:00Z")
):
    # Get issues since "updated_at" stored in state on previous run (or initial_value on first run)
    for page in _get_issues_page(access_token, repository, since=updated_at.start_value):
        yield page
        # Last_value is updated after every page
        print(updated_at.last_value)

Here we add an updated_at argument that will receive incremental state, initialized to 1970-01-01T00:00:00Z. It is configured to track the updated_at field in issues yielded by the repo_issues resource. It will store the newest updated_at value in dlt state and make it available in updated_at.start_value on the next pipeline run. This value is inserted in the _get_issues_page function into the request query param since to the GitHub API.

In essence, the dlt.sources.incremental instance above:

• updated_at.initial_value which is always equal to "1970-01-01T00:00:00Z" passed in the constructor
• updated_at.start_value a maximum updated_at value from the previous run or the initial_value on the first run
• updated_at.last_value a "real-time" updated_at value updated with each yielded item or page. Before the first yield, it equals start_value
• updated_at.end_value (here not used) marking the end of the backfill range

When paginating, you probably need the start_value which does not change during the execution of the resource, however, most paginators will return a next page link which you should use.

Behind the scenes, dlt will deduplicate the results, i.e., in case the last issue is returned again (updated_at filter is inclusive) and skip already loaded ones.

In the example below, we incrementally load the GitHub events, where the API does not let us filter for the newest events - it always returns all of them. Nevertheless, dlt will load only the new items, filtering out all the duplicates and past issues.

# Use naming function in table name to generate separate tables for each event
@dlt.resource(primary_key="id", table_name=lambda i: i['type']) # type: ignore
def repo_events(
    last_created_at = dlt.sources.incremental("created_at", initial_value="1970-01-01T00:00:00Z", last_value_func=max), row_order="desc"
) -> Iterator[TDataItems]:
    repos_path = "/repos/%s/%s/events" % (urllib.parse.quote(owner), urllib.parse.quote(name))
    for page in _get_rest_pages(access_token, repos_path + "?per_page=100"):
        yield page

We just yield all the events and dlt does the filtering (using the id column declared as primary_key).

GitHub returns events ordered from newest to oldest. So we declare the rows_order as descending to stop requesting more pages once the incremental value is out of range. We stop requesting more data from the API after finding the first event with created_at earlier than initial_value.

Max, min, or custom last_value_func#

dlt.sources.incremental allows you to choose a function that orders (compares) cursor values to the current last_value.

• The default function is the built-in max, which returns the larger value of the two.
• Another built-in, min, returns the smaller value.

You can also pass your custom function. This lets you define
last_value on nested types, i.e., dictionaries, and store indexes of last values, not just simple
types. The last_value argument is a JSON Path
and lets you select nested data (including the whole data item when $ is used).
The example below creates a last value which is a dictionary holding a max created_at value for each
created table name:

def by_event_type(event):
    last_value = None
    if len(event) == 1:
        item, = event
    else:
        item, last_value = event

    if last_value is None:
        last_value = {}
    else:
        last_value = dict(last_value)
    item_type = item["type"]
    last_value[item_type] = max(item["created_at"], last_value.get(item_type, "1970-01-01T00:00:00Z"))
    return last_value

@dlt.resource(primary_key="id", table_name=lambda i: i['type'])
def get_events(last_created_at = dlt.sources.incremental("$", last_value_func=by_event_type)):
    with open("tests/normalize/cases/github.events.load_page_1_duck.json", "r", encoding="utf-8") as f:
        yield json.load(f)

Using end_value for backfill#

You can specify both initial and end dates when defining incremental loading. Let's go back to our Github example:

@dlt.resource(primary_key="id")
def repo_issues(
    access_token=dlt.secrets.value,
    repository=dlt.config.value,
    updated_at=dlt.sources.incremental("updated_at", initial_value="1970-01-01T00:00:00Z", end_value="2022-07-01T00:00:00Z")
):
    # get issues updated in range defined by incremental
    for page in _get_issues_page(access_token, repository, since=updated_at.start_value, until=updated_at.end_value):
        yield page

Above, we use the initial_value and end_value arguments of the incremental to define the range of issues that we want to retrieve
and pass this range to the Github API (since and until). As in the examples above, dlt will make sure that only the issues from
the defined range are returned.

Please note that when end_date is specified, dlt will not modify the existing incremental state. The backfill is stateless and:

1. You can run backfill and incremental load in parallel (i.e., in an Airflow DAG) in a single pipeline.
2. You can partition your backfill into several smaller chunks and run them in parallel as well.

To define specific ranges to load, you can simply override the incremental argument in the resource, for example:

july_issues = repo_issues(
    updated_at=dlt.sources.incremental(
        initial_value='2022-07-01T00:00:00Z', end_value='2022-08-01T00:00:00Z'
    )
)
august_issues = repo_issues(
    updated_at=dlt.sources.incremental(
        initial_value='2022-08-01T00:00:00Z', end_value='2022-09-01T00:00:00Z'
    )
)
...

Note that dlt's incremental filtering considers the ranges half-closed. initial_value is inclusive, end_value is exclusive, so chaining ranges like above works without overlaps. This behaviour can be changed with the range_start (default "closed") and range_end (default "open") arguments.

Partition large backfills#

You can execute a backfill on large amount of data by partitioning it into ranges. In best case you are able to
create partitions i.e. by day or week without additionally querying your data source as we do in sql database example. Ranges will be loaded using initial_value and end_value, each in separate pipeline run.

1. As mentioned above, each run of this kind is stateless and may be run in parallel.
2. However, we recommend that you load a single partition first (pick a small one) so dlt creates dataset and schema without risk of races and a need to retry
   i.e. when two runs created new dataset at the same time.
3. Such first load is also a good opportunity to begin a full refresh
4. After a backfill you can resume regular incremental loading with a state. You'll need to use end_value of final range as initial_value of incremental loading.

For a robust backfill of this kind you probably want to use an orchestrator to make sure that each partition is loaded and loaded only once.

Also check filesystem example.

Declare row order to not request unnecessary data#

With the row_order argument set, dlt will stop retrieving data from the data source (e.g., GitHub API) if it detects that the values of the cursor field are out of the range of start and end values.

In particular:

• dlt stops processing when the resource yields any item with a cursor value equal to or greater than the end_value and row_order is set to asc. (end_value is not included)
• dlt stops processing when the resource yields any item with a cursor value lower than the last_value and row_order is set to desc. (last_value is included)

Row order is most useful when:

1. The data source does not offer start/end filtering of results (e.g., there is no start_time/end_time query parameter or similar).
2. The source returns results ordered by the cursor field.

The GitHub events example is exactly such a case. The results are ordered on cursor value descending, but there's no way to tell the API to limit returned items to those created before a certain date. Without the row_order setting, we'd be getting all events, each time we extract the github_events resource.

In the same fashion, the row_order can be used to optimize backfill so we don't continue
making unnecessary API requests after the end of the range is reached. For example:

@dlt.resource(primary_key="id")
def tickets(
    zendesk_client,
    updated_at=dlt.sources.incremental(
        "updated_at",
        initial_value="2023-01-01T00:00:00Z",
        end_value="2023-02-01T00:00:00Z",
        row_order="asc"
    ),
):
    for page in zendesk_client.get_pages(
        "/api/v2/incremental/tickets", "tickets", start_time=updated_at.start_value
    ):
        yield page

In this example, we're loading tickets from Zendesk. The Zendesk API yields items paginated and ordered from oldest to newest,
but only offers a start_time parameter for filtering, so we cannot tell it to
stop retrieving data at end_value. Instead, we set row_order to asc and dlt will stop
getting more pages from the API after the first page with a cursor value updated_at is found older
than end_value.

Split large loads into chunks#

You can split large incremental resources into smaller chunks and load them sequentially. This way you'll see the data quicker and
in case of loading error you are able to retry a single chunk. This method works only if your source returns data in deterministic order, for example:

• you can request your REST API endpoint to return data ordered by updated_at.
• you use row_order on one of supported sources like sql_database or filesystem.

Below we go for the second option and load data from messages table that we order on created_at column.

import dlt
from dlt.sources.sql_database import sql_table

pipeline = dlt.pipeline("test_load_sql_table_split_loading", destination="duckdb")

messages = sql_table(
    table="chat_message",
    incremental=dlt.sources.incremental(
        "created_at",
        row_order="asc", # critical to set row_order when doing split loading
        range_start="open", # use open range to disable deduplication
    ),
)

# produce chunk each minute, stop when empty
while not pipeline.run(messages.add_limit(max_time=60)).is_empty:
    pass

Note how we combine incremental and add_limit to generate chunk each minute. If you create and index on created_at, the database
engine will be able to stream data using the index without the need to scan the whole table.

Check two other examples: filesystem and
sql_database.

Deduplicate overlapping ranges#

Incremental does not deduplicate datasets like the merge write disposition does. However, it ensures that when another portion of data is extracted, records that were previously loaded at the end of range won't be included again. dlt assumes that you load a range of data, where the lower bound is inclusive by default (i.e., greater than or equal). This ensures that you never lose any data but will also re-acquire some rows. For example, if you have a database table with a cursor field on updated_at which has a day resolution, then there's a high chance that after you extract data on a given day, more records will still be added. When you extract on the next day, you should reacquire data from the last day to ensure all records are present; however, this will create overlap with data from the previous extract.

By default, a content hash (a hash of the JSON representation of a row) will be used to deduplicate. This may be slow, so dlt.sources.incremental will inherit the primary key that is set on the resource. You can optionally set a primary_key that is used exclusively to deduplicate and which does not become a table hint. The same setting lets you disable the deduplication altogether when an empty tuple is passed. Below, we pass primary_key directly to incremental to disable deduplication. That overrides the delta primary_key set in the resource:

@dlt.resource(primary_key="delta")
# disable the unique value check by passing () as primary key to incremental
def some_data(last_timestamp=dlt.sources.incremental("item.ts", primary_key=())):
    for i in range(-10, 10):
        yield {"delta": i, "item": {"ts": pendulum.now().timestamp()}}

This deduplication process is enabled when range_start is set to "closed" (default).
When you pass range_start="open" no deduplication is done as it is not needed as rows with the previous cursor value are excluded. This can be a useful optimization to avoid the performance overhead of deduplication if the cursor field is guaranteed to be unique.
Deduplication is also disabled when lag is used or when end_value is specified as in this case, state is disabled and no hashes from previous runs will be present.

Use dlt.sources.incremental with dynamically created resources#

When resources are created dynamically, it is possible to use the dlt.sources.incremental definition as well.

@dlt.source
def stripe():
    # declare a generator function
    def get_resource(
        endpoints: List[str] = ENDPOINTS,
        created: dlt.sources.incremental=dlt.sources.incremental("created")
    ):
        ...

    # create resources for several endpoints on a single decorator function
    for endpoint in endpoints:
        yield dlt.resource(
            get_resource,
            name=endpoint.value,
            write_disposition="merge",
            primary_key="id"
        )(endpoint)

Please note that in the example above, get_resource is passed as a function to dlt.resource to which we bind the endpoint: dlt.resource(...)(endpoint).

Using Airflow schedule for backfill and incremental loading#

When running an Airflow task, you can opt-in your resource to get the initial_value/start_value and end_value from the Airflow schedule associated with your DAG. Let's assume that the Zendesk tickets resource contains a year of data with thousands of tickets. We want to backfill the last year of data week by week and then continue with incremental loading daily.

@dlt.resource(primary_key="id")
def tickets(
    zendesk_client,
    updated_at=dlt.sources.incremental[int](
        "updated_at",
        allow_external_schedulers=True
    ),
):
    for page in zendesk_client.get_pages(
        "/api/v2/incremental/tickets", "tickets", start_time=updated_at.start_value
    ):
        yield page

We opt-in to the Airflow scheduler by setting allow_external_schedulers to True:

1. When running on Airflow, the start and end values are controlled by Airflow and the dlt state is not used.
2. In all other environments, the incremental behaves as usual, maintaining the dlt state.

Let's generate a deployment with dlt deploy zendesk_pipeline.py airflow-composer and customize the DAG:

from dlt.helpers.airflow_helper import PipelineTasksGroup

@dag(
    schedule_interval='@weekly',
    start_date=pendulum.DateTime(2023, 2, 1),
    end_date=pendulum.DateTime(2023, 8, 1),
    catchup=True,
    max_active_runs=1,
    default_args=default_task_args
)
def zendesk_backfill_bigquery():
    tasks = PipelineTasksGroup("zendesk_support_backfill", use_data_folder=False, wipe_local_data=True)

    # import zendesk like in the demo script
    from zendesk import zendesk_support

    pipeline = dlt.pipeline(
        pipeline_name="zendesk_support_backfill",
        dataset_name="zendesk_support_data",
        destination='bigquery',
    )
    # select only incremental endpoints in support api
    data = zendesk_support().with_resources("tickets", "ticket_events", "ticket_metric_events")
    # create the source, the "serialize" decompose option will convert dlt resources into Airflow tasks. use "none" to disable it
    tasks.add_run(pipeline, data, decompose="serialize", trigger_rule="all_done", retries=0, provide_context=True)

zendesk_backfill_bigquery()

What got customized:

1. We use a weekly schedule and want to get the data from February 2023 (start_date) until the end of July (end_date).
2. We make Airflow generate all weekly runs (catchup is True).
3. We create zendesk_support resources where we select only the incremental resources we want to backfill.

When you enable the DAG in Airflow, it will generate several runs and start executing them, starting in February and ending in August. Your resource will receive subsequent weekly intervals starting with 2023-02-12, 00:00:00 UTC to 2023-02-19, 00:00:00 UTC.

You can repurpose the DAG above to start loading new data incrementally after (or during) the backfill:

@dag(
    schedule_interval='@daily',
    start_date=pendulum.DateTime(2023, 2, 1),
    catchup=False,
    max_active_runs=1,
    default_args=default_task_args
)
def zendesk_new_bigquery():
    tasks = PipelineTasksGroup("zendesk_support_new", use_data_folder=False, wipe_local_data=True)

    # import your source from pipeline script
    from zendesk import zendesk_support

    pipeline = dlt.pipeline(
        pipeline_name="zendesk_support_new",
        dataset_name="zendesk_support_data",
        destination='bigquery',
    )
    tasks.add_run(pipeline, zendesk_support(), decompose="serialize", trigger_rule="all_done", retries=0, provide_context=True)

Above, we switch to a daily schedule and disable catchup and end date. We also load all the support resources to the same dataset as backfill (zendesk_support_data).
If you want to run this DAG parallel with the backfill DAG, change the pipeline name, for example, to zendesk_support_new as above.

Under the hood

When allow_external_schedulers=True, dlt looks up the active TimeIntervalContext while binding the resource. The context resolves an interval from, in order:

1. An interval passed directly to its constructor (programmatic injection - see Injecting and reading the current interval below).
2. DLT_INTERVAL_START / DLT_INTERVAL_END environment variables (UTC ISO 8601). An optional DLT_INTERVAL_TIMEZONE (IANA name) is applied after string parsing.
3. Airflow's data_interval_start / data_interval_end from the current task context (both the modern airflow.sdk.get_current_context() and the older airflow.operators.python.get_current_context() are supported).

The resolved interval is mapped onto initial_value and end_value of the Incremental class:

• dlt converts the datetimes to your cursor type. In the example above we instantiate dlt.sources.incremental[int], so dlt converts the interval to Unix timestamps. Other supported cursor types are timestamp, date, double, and bigint. String cursors are rejected - convert them on the resource with add_map first.
• If you also configured initial_value / end_value directly on the Incremental, the scheduler interval is clipped against your bounds using last_value_func, so your configured limits always win.

When allow_external_schedulers=True but no interval can be resolved, dlt raises ExternalSchedulerNotAvailable instead of silently falling back to dlt state. If the cursor type is Any or not coercible to a timestamp, dlt raises JoinSchedulerError.

Manual runs

When you trigger an Airflow DAG manually, the run uses whatever data_interval Airflow assigns it - typically (now, now), which yields no data. To backfill manually, set the logical date in the past (use the Run with Config option) so Airflow sets a real interval.

Injecting and reading the current interval#

dlt exposes the active interval as an injectable context, TimeIntervalContext, so you can drive Incremental from your own scheduler, a plain cron job, a backfill script - without depending on Airflow or environment variables.

Inject an interval from your code:

from dlt.common.configuration.container import Container
from dlt.extract.incremental.context import TimeIntervalContext

start = pendulum.datetime(2024, 1, 15, tz="UTC")
end = pendulum.datetime(2024, 1, 16, tz="UTC")

with Container().injectable_context(TimeIntervalContext(interval=(start, end))):
    # any Incremental with allow_external_schedulers=True picks up [start, end) from the context
    pipeline.run(my_source)

Incremental consults the active context automatically - you don't need to wire anything else. Resources without allow_external_schedulers=True ignore the context and behave as usual (state-driven incremental).

TimeIntervalContext also accepts an allow_external_schedulers field that overrides the per-incremental setting. Useful when you want a single switch at the runtime layer:

ctx = TimeIntervalContext(interval=(start, end), allow_external_schedulers=True)
with Container().injectable_context(ctx):
    # joins the context even if individual resources didn't opt in
    pipeline.run(my_source)

To read whatever the runtime resolved from inside a resource, use dlt.current.interval(). This is handy for resources that don't use Incremental but still need to know the active window (custom paginators, validation, logging):

@dlt.resource
def my_resource():
    iv = dlt.current.interval()
    if iv is not None:
        start, end = iv
        # use start/end to scope your requests
    yield {}

dlt.current.interval() returns None when no context is active.

Reading incremental loading parameters from configuration#

Consider the example below for reading incremental loading parameters from "config.toml". We create a generate_incremental_records resource that yields "id", "idAfter", and "name". This resource retrieves cursor_path and initial_value from "config.toml".

1. In "config.toml", define the cursor_path and initial_value as:

   ⧉

   # Configuration snippet for an incremental resource
   [pipeline_with_incremental.sources.id_after]
   cursor_path = "idAfter"
   initial_value = 10
   

   cursor_path is assigned the value "idAfter" with an initial value of 10.

2. Here's how the generate_incremental_records resource uses the cursor_path defined in "config.toml":

   ⧉

   @dlt.resource(table_name="incremental_records")
   def generate_incremental_records(id_after: dlt.sources.incremental = dlt.config.value):
       for i in range(150):
           yield {"id": i, "idAfter": i, "name": "name-" + str(i)}
   
   pipeline = dlt.pipeline(
       pipeline_name="pipeline_with_incremental",
       destination="duckdb",
   )
   
   pipeline.run(generate_incremental_records)
   

   id_after incrementally stores the latest cursor_path value for future pipeline runs.

Loading when incremental cursor path is missing or value is None/NULL#

You can customize the incremental processing of dlt by setting the parameter on_cursor_value_missing.

When loading incrementally with the default settings, there are two assumptions:

1. Each row contains the cursor path.
2. Each row is expected to contain a value at the cursor path that is not None.

For example, the two following source data will raise an error:

@dlt.resource
def some_data_without_cursor_path(updated_at=dlt.sources.incremental("updated_at")):
    yield [
        {"id": 1, "created_at": 1, "updated_at": 1},
        {"id": 2, "created_at": 2}, # cursor field is missing
    ]

list(some_data_without_cursor_path())

@dlt.resource
def some_data_without_cursor_value(updated_at=dlt.sources.incremental("updated_at")):
    yield [
        {"id": 1, "created_at": 1, "updated_at": 1},
        {"id": 3, "created_at": 4, "updated_at": None}, # value at cursor field is None
    ]

list(some_data_without_cursor_value())

To process a data set where some records do not include the incremental cursor path or where the values at the cursor path are None, there are the following four options:

1. Configure the incremental load to raise an exception in case there is a row where the cursor path is missing or has the value None using incremental(..., on_cursor_value_missing="raise"). This is the default behavior.
2. Configure the incremental load to tolerate the missing cursor path and None values using incremental(..., on_cursor_value_missing="include").
3. Configure the incremental load to exclude the missing cursor path and None values using incremental(..., on_cursor_value_missing="exclude").
4. Before the incremental processing begins: Ensure that the incremental field is present and transform the values at the incremental cursor to a value different from None. See docs below

Here is an example of including rows where the incremental cursor value is missing or None:

@dlt.resource
def some_data(updated_at=dlt.sources.incremental("updated_at", on_cursor_value_missing="include")):
    yield [
        {"id": 1, "created_at": 1, "updated_at": 1},
        {"id": 2, "created_at": 2},
        {"id": 3, "created_at": 4, "updated_at": None},
    ]

result = list(some_data())
assert len(result) == 3
assert result[1] == {"id": 2, "created_at": 2}
assert result[2] == {"id": 3, "created_at": 4, "updated_at": None}

If you do not want to import records without the cursor path or where the value at the cursor path is None, use the following incremental configuration:

@dlt.resource
def some_data(updated_at=dlt.sources.incremental("updated_at", on_cursor_value_missing="exclude")):
    yield [
        {"id": 1, "created_at": 1, "updated_at": 1},
        {"id": 2, "created_at": 2},
        {"id": 3, "created_at": 4, "updated_at": None},
    ]

result = list(some_data())
assert len(result) == 1

Transform records before incremental processing#

If you want to load data that includes None values, you can transform the records before the incremental processing.
You can add steps to the pipeline that filter, transform, or pivot your data.

See below how you can modify rows before the incremental processing using add_map() and filter rows using add_filter().

@dlt.resource
def some_data(updated_at=dlt.sources.incremental("updated_at")):
    yield [
        {"id": 1, "created_at": 1, "updated_at": 1},
        {"id": 2, "created_at": 2, "updated_at": 2},
        {"id": 3, "created_at": 4, "updated_at": None},
    ]

def set_default_updated_at(record):
    if record.get("updated_at") is None:
        record["updated_at"] = record.get("created_at")
    return record

# Modifies records before the incremental processing
with_default_values = some_data().add_map(set_default_updated_at, insert_at=1)
result = list(with_default_values)
assert len(result) == 3
assert result[2]["updated_at"] == 4

# Removes records before the incremental processing
without_none = some_data().add_filter(lambda r: r.get("updated_at") is not None, insert_at=1)
result_filtered = list(without_none)
assert len(result_filtered) == 2