From ca076334b6e61fc9694c874901d6b4e4474e408d Mon Sep 17 00:00:00 2001 From: Steinthor Palsson Date: Thu, 5 Dec 2024 16:57:31 -0500 Subject: [PATCH] Typo --- .../dlt-ecosystem/verified-sources/sql_database/advanced.md | 4 ++-- docs/website/docs/general-usage/incremental-loading.md | 6 +++--- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/docs/website/docs/dlt-ecosystem/verified-sources/sql_database/advanced.md b/docs/website/docs/dlt-ecosystem/verified-sources/sql_database/advanced.md index 9014ef3b9b..c532f6d357 100644 --- a/docs/website/docs/dlt-ecosystem/verified-sources/sql_database/advanced.md +++ b/docs/website/docs/dlt-ecosystem/verified-sources/sql_database/advanced.md @@ -98,7 +98,7 @@ This ensures there are no gaps in the extracted sequence. But it does come with both due to the deduplication processing and the cost of fetching redundant records from the database. This is not always needed. If you know that your data does not contain overlapping cursor values then you -can optimize extraction by passing `start_range="open"` to incremental. +can optimize extraction by passing `range_start="open"` to incremental. This both disables the deduplication process and changes the operator used in the SQL `WHERE` clause from `>=` (greater-or-equal) to `>` (greater than), so that no overlapping rows are fetched. @@ -110,7 +110,7 @@ table = sql_table( incremental=dlt.sources.incremental( 'last_modified', # Cursor column name initial_value=pendulum.DateTime(2024, 1, 1, 0, 0, 0), # Initial cursor value - start_range="open", # exclude the start value + range_start="open", # exclude the start value ) ) ``` diff --git a/docs/website/docs/general-usage/incremental-loading.md b/docs/website/docs/general-usage/incremental-loading.md index 98e9c4165f..5008795ed4 100644 --- a/docs/website/docs/general-usage/incremental-loading.md +++ b/docs/website/docs/general-usage/incremental-loading.md @@ -693,7 +693,7 @@ august_issues = repo_issues( ... ``` -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 `start_range` (default `"closed"`) and `end_range` (default `"open"`) arguments. +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. ### Declare row order to not request unnecessary data @@ -793,8 +793,8 @@ def some_data(last_timestamp=dlt.sources.incremental("item.ts", primary_key=())) yield {"delta": i, "item": {"ts": pendulum.now().timestamp()}} ``` -This deduplication process is always enabled when `start_range` is set to `"closed"` (default). -When you pass `start_range="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. +This deduplication process is always 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. ### Using `dlt.sources.incremental` with dynamically created resources