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Incremental loading

Incremental loading is the act of loading only new or changed data and not old records that we already loaded. It enables low-latency and low cost data transfer.

The challenge of incremental pipelines is that if we do not keep track of the state of the load (i.e. which increments were loaded and which are to be loaded). Read more about state here.

Choosing a write disposition

The 3 write dispositions:

  • Full load: replaces the destination dataset with whatever the source produced on this run. To achieve this, use write_disposition='replace' in your resources. Learn more in the full loading docs

  • Append: appends the new data to the destination. Use write_disposition='append'.

  • Merge: Merges new data to the destination using merge_key and/or deduplicates/upserts new data using primary_key. Use write_disposition='merge'.

Two simple questions determine the write disposition you use

write disposition flowchart

The "write disposition" you choose depends on the data set and how you can extract it.

To find the "write disposition" you should use, the first question you should ask yourself is "Is my data stateful or stateless"? Stateful data has a state that is subject to change - for example a user's profile Stateless data cannot change - for example, a recorded event, such as a page view.

Because stateless data does not need to be updated, we can just append it.

For stateful data, comes a second question - Can I extract it incrementally from the source? If not, then we need to replace the entire data set. If however we can request the data incrementally such as "all users added or modified since yesterday" then we can simply apply changes to our existing dataset with the merge write disposition.

Merge incremental loading

The merge write disposition is used in two scenarios:

  1. You want to keep only one instance of certain record i.e. you receive updates of the user state from an API and want to keep just one record per user_id.
  2. You receive data in daily batches, and you want to make sure that you always keep just a single instance of a record for each batch even in case you load an old batch or load the current batch several times a day (i.e. to receive "live" updates).

The merge write disposition loads data to a staging dataset, deduplicates the staging data if a primary_key is provided, deletes the data from the destination using merge_key and primary_key, and then inserts the new records. All of this happens in a single atomic transaction for a parent and all child tables.

Example below loads all the GitHub events and updates them in the destination using "id" as primary key, making sure that only a single copy of event is present in github_repo_events table:

@dlt.resource(primary_key="id", write_disposition="merge")
def github_repo_events():
yield from _get_event_pages()

You can use compound primary keys:

@dlt.resource(primary_key=("id", "url"), write_disposition="merge")

Example below merges on a column batch_day that holds the day for which given record is valid. Merge keys also can be compound:

@dlt.resource(merge_key="batch_day", write_disposition="merge")
def get_daily_batch(day):
yield _get_batch_from_bucket(day)

As with any other write disposition you can use it to load data ad hoc. Below we load issues with top reactions for duckdb repo. The lists have, obviously, many overlapping issues, but we want to keep just one instance of each.

p = dlt.pipeline(destination="bigquery", dataset_name="github")
issues = []
reactions = ["%2B1", "-1", "smile", "tada", "thinking_face", "heart", "rocket", "eyes"]
for reaction in reactions:
for page_no in range(1, 3):
page = requests.get(f"{repo}/issues?state=all&sort=reactions-{reaction}&per_page=100&page={page_no}", headers=headers)
print(f"got page for {reaction} page {page_no}, requests left", page.headers["x-ratelimit-remaining"])
issues.extend(page.json()), write_disposition="merge", primary_key="id", table_name="issues")

Example below dispatches GitHub events to several tables by event type, keeps one copy of each event by "id" and skips loading of past records using "last value" incremental. As you can see, all of this we can just declare in our resource.

@dlt.resource(primary_key="id", write_disposition="merge", table_name=lambda i: i['type'])
def github_repo_events(last_created_at = dlt.sources.incremental("created_at", "1970-01-01T00:00:00Z")):
"""A resource taking a stream of github events and dispatching them to tables named by event type. Deduplicates be 'id'. Loads incrementally by 'created_at' """
yield from _get_rest_pages("events")

Forcing root key propagation

Merge write disposition requires that the _dlt_id of top level table is propagated to child tables. This concept is similar to foreign key which references a parent table, and we call it a root key. Root key is automatically propagated for all tables that have merge write disposition set. We do not enable it everywhere because it takes storage space. Nevertheless, is some cases you may want to permanently enable root key propagation.

pipeline = dlt.pipeline(
fb_ads = facebook_ads_source()
# enable root key propagation on a source that is not a merge one by default.
# this is not required if you always use merge but below we start with replace
fb_ads.root_key = True
# load only disapproved ads"DISAPPROVED", ))
info ="ads"), write_disposition="replace")
# merge the paused ads. the disapproved ads stay there!
fb_ads = facebook_ads_source()"PAUSED", ))
info ="ads"), write_disposition="merge")

In example above we enforce the root key propagation with fb_ads.root_key = True. This ensures that correct data is propagated on initial replace load so the future merge load can be executed. You can achieve the same in the decorator @dlt.source(root_key=True).

Incremental loading with last value

In most of the APIs (and other data sources i.e. database tables) you can request only 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 "last value" timestamp/id for the next load.

To do incremental loading this way, we need to

  • figure which data element is used to get new/updated records (e.g. “last value”, “last updated at”, etc.);
  • request the new part only (how we do this depends on the source API).

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

def repo_issues(
created_at = dlt.sources.incremental("created_at", initial_value="1970-01-01T00:00:00Z")
# get issues since "created_at" stored in state on previous run (or initial_value on first run)
for page in _get_issues_page(access_token, repository, since=created_at.start_value):
yield page
# last_value is updated after every page

Here we add created_at argument that will receive incremental state, initialized to 1970-01-01T00:00:00Z. It is configured to track created_at field in issues returned by _get_issues_page and then yielded. It will store the newest created_at value in dlt state and make it available in created_at.start_value on next pipeline run. This value is used to request only issues newer (or equal) via GitHub API.

In essence, dlt.sources.incremental instance above

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

When paginating you probably need 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 ie. in case the last issue is returned again (created_at filter is inclusive) and skip already loaded ones. In the example below we incrementally load the GitHub events, where 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)
) -> 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

# ---> part below is an optional optimization
# Stop requesting more pages when we encounter an element that
# is older than the incremental value at the beginning of the run.
# The start_out_of_range boolean flag is set in this case
if last_created_at.start_out_of_range:

We just yield all the events and dlt does the filtering (using id column declared as primary_key). As an optimization we stop requesting more pages once the incremental value is out of range, in this case that means we got an element which has a smaller created_at than the the last_created_at.start_value. The start_out_of_range boolean flag is set when the first such element is yielded from the resource, and since we know that github returns results ordered from newest to oldest, we know that all subsequent items will be filtered out anyway and there's no need to fetch more data.

max, min or custom last_value_func

dlt.sources.incremental allows to choose a function that orders (compares) values coming from the items to current last_value.

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

You can pass your custom function as well. This lets you define last_value on complex 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 and complex data (including the whole data item when $ is used). Example below creates 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
item, last_value = event

if last_value is None:
last_value = {}
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/", "r", encoding="utf-8") as f:
yield json.load(f)

Deduplication primary_key

dlt.sources.incremental let's you 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 empty tuple is passed. Below we pass primary_key directly to incremental to disable deduplication. That overrides delta primary_key set in the resource:

# 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":}}

Using dlt.sources.incremental with dynamically created resources

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

def stripe():
# declare a generator function
def get_resource(
endpoint: Endpoints,
created: dlt.sources.incremental=dlt.sources.incremental("created")
yield data

# create resources for several endpoints on a single decorator function
for endpoint in Endpoints:
yield dlt.resource(

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).

🛑 The typical mistake is to pass a generator (not a function) as below:

yield dlt.resource(get_resource(endpoint), name=endpoint.value, write_disposition="merge", primary_key="id").

Here we call get_resource(endpoint) and that creates un-evaluated generator on which resource is created. That prevents dlt from controlling the created argument during runtime and will result in IncrementalUnboundError exception.

Using dlt.sources.incremental for backfill

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

def repo_issues(
created_at = dlt.sources.incremental("created_at", initial_value="1970-01-01T00:00:00Z", end_value="2022-07-01T00:00:00Z")
# get issues from created from last "created_at" value
for page in _get_issues_page(access_token, repository, since=created_at.start_value, until=created_at.end_value):
yield page

Above we use 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 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 (ie. in 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(
initial_value='2022-07-01T00:00:00Z', end_value='2022-08-01T00:00:00Z'
august_issues = repo_issues(
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.

Using Airflow schedule for backfill and incremental loading

When running in Airflow task, you can opt-in your resource to get the initial_value/start_value and end_value from Airflow schedule associated with your DAG. Let's assume that 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 incremental loading daily.

def tickets(
for page in zendesk_client.get_pages(
"/api/v2/incremental/tickets", "tickets", start_time=updated_at.start_value
yield page

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

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

Let's generate a deployment with dlt deploy airflow-composer and customize the dag:

start_date=pendulum.datetime(2023, 2, 1),
end_date=pendulum.datetime(2023, 8, 1),
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(
# 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 converts 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)


What got customized:

  1. We use weekly schedule, and want to get the data from February 2023 (start_date) until end of July ('end_date').
  2. We make Airflow to 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:

start_date=pendulum.datetime(2023, 2, 1),
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(
tasks.add_run(pipeline, zendesk_support(), decompose="serialize", trigger_rule="all_done", retries=0, provide_context=True)

Above, we switch to 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 ie. to zendesk_support_new as above.

Under the hood Before dlt starts executing incremental resources, it looks for data_interval_start and data_interval_end Airflow task context variables. Those got mapped to initial_value and end_value of the Incremental class:

  1. dlt is smart enough to convert Airflow datetime to iso strings or unix timestamps if your resource is using them. In our example we instantiate updated_at=dlt.sources.incremental[int], where we declare the last value type to be int. dlt can also infer type if you provide initial_value argument.
  2. If data_interval_end is in the future or is None, dlt sets the end_value to now.
  3. If data_interval_start == data_interval_end we have a manually triggered DAG run. In that case data_interval_end will also be set to now.

Manual runs You can run DAGs manually but you must remember to specify the Airflow logical date of the run in the past (use Run with config option). For such run dlt will load all data from that past date until now. If you do not specify the past date, a run with a range (now, now) will happen yielding no data.

Using start/end_out_of_range flags with incremental resources

The dlt.sources.incremental instance provides start_out_of_range and end_out_of_range attributes which are set when the resource yields an element with a higher/lower cursor value than the initial or end values. This makes it convenient to optimize resources in some cases.

  • start_out_of_range is True when the resource yields any item with a lower cursor value than the initial_value
  • end_out_of_range is True when the resource yields any item with an equal or higher cursor value than the end_value

Note: "higher" and "lower" here refers to when the default last_value_func is used (max()), when using min() "higher" and "lower" are inverted.

You can use these flags when both:

  1. The 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

Note: These flags should not be used for unordered sources, e.g. if an API returns results both higher and lower than the given end_value in no particular order, the end_out_of_range flag can be True but you'll still want to keep loading.

The github events example above demonstrates how to use start_out_of_range as a stop condition. This approach works in any case where the API returns items in descending order and we're incrementally loading newer data.

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

def tickets(
for page in zendesk_client.get_pages(
"/api/v2/incremental/tickets", "tickets", start_time=updated_at.start_value
yield page

# Optimization: Stop loading when we reach the end value
if updated_at.end_out_of_range:

In this example we're loading tickets from Zendesk. The Zendesk API yields items paginated and ordered by oldest to newest, but only offers a start_time parameter for filtering. The incremental end_out_of_range flag is set on the first item which has a timestamp equal or higher than end_value. All subsequent items get filtered out so there's no need to request more data.

Doing a full refresh

You may force a full refresh of a merge and append pipelines:

  1. In case of a merge the data in the destination is deleted and loaded fresh. Currently we do not deduplicate data during the full refresh.
  2. In case of dlt.sources.incremental the data is deleted and loaded from scratch. The state of the incremental is reset to the initial value.


p = dlt.pipeline(destination="bigquery", dataset_name="dataset_name")
# do a full refresh, write_disposition="replace")
# do a full refresh of just one table"merge_table"), write_disposition="replace")
# run a normal merge

Passing write disposition to replace will change write disposition on all the resources in repo_events during the run of the pipeline.

Custom incremental loading with pipeline state

The pipeline state is a Python dictionary which gets committed atomically with the data; you can set values in it in your resources and on next pipeline run, request them back.

The pipeline state is in principle scoped to the resource - all values of the state set by resource are private and isolated from any other resource. You can also access the source-scoped state which can be shared across resources. You can find more information on pipeline state here.

Preserving the last value in resource state.

For the purpose of preserving the “last value” or similar loading checkpoints, we can open a dlt state dictionary with a key and a default value as below. When the resource is executed and the data is loaded, the yielded resource data will be loaded at the same time with the update to the state.

In the two examples below you see how the dlt.sources.incremental is working under the hood.

def tweets():
# Get a last value from loaded metadata. If not exist, get None
last_val = dlt.current.resource_state().setdefault("last_updated", None)
# get data and yield it
data = get_data(start_from=last_val)
yield data
# change the state to the new value
dlt.current.state()["last_updated"] = data["last_timestamp"]

If we keep a list or a dictionary in the state, we can modify the underlying values in the objects, and thus we do not need to set the state back explicitly.

def tweets():
# Get a last value from loaded metadata. If not exist, get None
loaded_dates = dlt.current.resource_state().setdefault("days_loaded", [])
# do stuff: get data and add new values to the list
# `loaded_date` is a reference to the `dlt.current.resource_state()["days_loaded"]` list
# and thus modifying it modifies the state
yield data

Step by step explanation of how to get or set the state:

  1. We can use the function var = dlt.current.resource_state().setdefault("key", []). This allows us to retrieve the values of key. If key was not set yet, we will get the default value [] instead.
  2. We now can treat var as a python list - We can append new values to it, or if applicable we can read the values from previous loads.
  3. On pipeline run, the data will load, and the new var's value will get saved in the state. The state is stored at the destination, so it will be available on subsequent runs.

Advanced state usage: storing a list of processed entities

Let’s look at the player_games resource from the chess pipeline. The chess API has a method to request games archives for a given month. The task is to prevent the user to load the same month data twice - even if the user makes a mistake and requests the same months range again:

  • Our data is requested in 2 steps:
    • Get all available archives URLs.
    • Get the data from each URL.
  • We will add the “chess archives” URLs to this list we created.
  • This will allow us to track what data we have loaded.
  • When the data is loaded, the list of archives is loaded with it.
  • Later we can read this list and know what data has already been loaded.

In the following example, we initialize a variable with an empty list as a default:

def players_games(chess_url, players, start_month=None, end_month=None):
loaded_archives_cache = dlt.current.resource_state().setdefault("archives", [])

# as far as python is concerned, this variable behaves like
# loaded_archives_cache = state['archives'] or []
# afterwards we can modify list, and finally
# when the data is loaded, the cache is updated with our loaded_archives_cache

# get archives for a given player
archives = get_players_archives(chess_url, players)
for url in archives:
# if not in cache, yield the data and cache the URL
if url not in loaded_archives_cache:
# add URL to cache and yield the associated data
r = requests.get(url)
yield r.json().get("games", [])
print(f"skipping archive {url}")

Advanced state usage: tracking the last value for all search terms in Twitter API

def search_tweets(twitter_bearer_token=dlt.secrets.value, search_terms=None, start_time=None, end_time=None, last_value=None):
headers = _headers(twitter_bearer_token)
for search_term in search_terms:
# make cache for each term
last_value_cache = dlt.current.state().setdefault(f"last_value_{search_term}", None)
print(f'last_value_cache: {last_value_cache}')
params = {...}
url = ""
response = _paginated_get(url, headers=headers, params=params)
for page in response:
page['search_term'] = search_term
last_id = page.get('meta', {}).get('newest_id', 0)
#set it back - not needed if we
dlt.current.state()[f"last_value_{search_term}"] = max(last_value_cache or 0, int(last_id))
# print the value for each search term
print(f'new_last_value_cache for term {search_term}: {last_value_cache}')

yield page

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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