"Microbatch" incremental strategy #6194
Draft
+172
−18
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
|
The latest updates on your projects. Learn more about Vercel for Git ↗︎
|
github-actions
bot
added
content
mirnawong1
reviewed
Oct 1, 2024
| @@ -42,6 +42,10 @@ exports.versions = [ | |||
| * @property {string} lastVersion The last version the page is visible in the sidebar | |||
| */ | |||
| exports.versionedPages = [ | |||
| { | |||
| page: "docs/build/incremental-microbatch", | |||
| lastVersion: "1.9", | |||
There was a problem hiding this comment.
i think its meant to show for 1.9 and higher right? it wasn't showing when i was on versionless, however it was showing for 1.8 and lower
Suggested change
| lastVersion: "1.9", | |
| firstVersion: "1.9", |
mirnawong1
reviewed
Oct 1, 2024
mirnawong1
reviewed
Oct 1, 2024
mirnawong1
reviewed
Oct 1, 2024
mirnawong1
reviewed
Oct 1, 2024
mirnawong1
reviewed
Oct 1, 2024
|
|
||
| Incremental models in dbt are a [materialization](/docs/build/materializations) designed to efficiently update your data warehouse tables by only transforming and loading _new or changed data_ since the last run. Instead of reprocessing an entire dataset every time, incremental models append, update, or replace rows in the existing table with the new data just processed. This can significantly reduce the time and resources required for your data transformations. | ||
|
|
||
| Microbatch incremental models make it possible to process transformations on large time-series datasets with efficiency and resiliency. When dbt runs a microbatch model — whether for the first time, during incremental runs, or during manual backfills — it will split the processing into multiple queries (or "batches"), based on the `event_time` column you configure. |
There was a problem hiding this comment.
Suggested change
| Microbatch incremental models make it possible to process transformations on large time-series datasets with efficiency and resiliency. When dbt runs a microbatch model — whether for the first time, during incremental runs, or during manual backfills — it will split the processing into multiple queries (or "batches"), based on the `event_time` column you configure. | |
| Microbatch incremental models make it possible to process transformations on large time-series datasets with efficiency and resiliency. When dbt runs a microbatch model — whether for the first time, during incremental runs, or during manual backfills — it will split the processing into multiple queries (or "batches"), based on the `event_time` column you configure. Instead of processing all of your data at once. Since each "batch" is based on a time period, like a single day, it makes it much faster and more efficient to update large datasets, especially when you're working with data that changes over time (like new records being added daily). |
mirnawong1
reviewed
Oct 1, 2024
|
|
||
| During standard incremental runs, dbt will process new batches and any according to the configured `lookback` (with one query per batch) | ||
|
|
||
| <Lightbox src="/img/docs/building-a-dbt-project/microbatch/microbatch_lookback.png" title="Configure a lookback to reprocess additional batches during standard incremental runs"/> |
graciegoheen
reviewed
Oct 1, 2024
|
|
||
| # About microbatch incremental models <Lifecycle status="beta" /> | ||
|
|
||
| :::info Microbatch |
There was a problem hiding this comment.
@jtcohen6 Thoughts on calling out that this feature is in beta - as I anticipate there will be some changes ahead of our 1.9 final release based on community feedback
|
|
||
| Incremental models in dbt are a [materialization](/docs/build/materializations) designed to efficiently update your data warehouse tables by only transforming and loading _new or changed data_ since the last run. Instead of reprocessing an entire dataset every time, incremental models append, update, or replace rows in the existing table with the new data just processed. This can significantly reduce the time and resources required for your data transformations. | ||
|
|
||
| Microbatch incremental models make it possible to process transformations on large time-series datasets with efficiency and resiliency. When dbt runs a microbatch model — whether for the first time, during incremental runs, or during manual backfills — it will split the processing into multiple queries (or "batches"), based on the `event_time` column you configure. |
There was a problem hiding this comment.
Suggested change
| Microbatch incremental models make it possible to process transformations on large time-series datasets with efficiency and resiliency. When dbt runs a microbatch model — whether for the first time, during incremental runs, or during manual backfills — it will split the processing into multiple queries (or "batches"), based on the `event_time` column you configure. | |
| Microbatch incremental models make it possible to process transformations on large time-series datasets with efficiency and resiliency. When dbt runs a microbatch model — whether for the first time, during incremental runs, or during manual backfills — it will split the processing into multiple queries (or "batches"), based on the `event_time` columns and `batch_size` you configure. |
|
|
||
| <Lightbox src="/img/docs/building-a-dbt-project/microbatch/event_time.png" title="The event_time column configures the real-world time of this record"/> | ||
|
|
||
| - `batch_size` (string, optional) - The granularity of your batches. The default is `day`, and currently that is the only granularity supported. |
There was a problem hiding this comment.
Thoughts on making this a table instead of a bulleted list?
|
|
||
| - `batch_size` (string, optional) - The granularity of your batches. The default is `day`, and currently that is the only granularity supported. | ||
| - `lookback` (integer, optional) - Process X batches prior to the latest bookmark, in order to capture late-arriving records. The default value is `0`. | ||
| - `begin` (date, optional) - The "beginning of time" for your data. This is the starting point for any initial or full-refresh builds. For example, a daily-grain microbatch model run on `2024-10-01` with `begin = '2023-10-01` will process 366 batches. (It's a leap year!) |
There was a problem hiding this comment.
I don't think this is optional
|
|
||
| <Lightbox src="/img/docs/building-a-dbt-project/microbatch/microbatch_lookback.png" title="Configure a lookback to reprocess additional batches during standard incremental runs"/> | ||
|
|
||
| If there’s an upstream model that configures `event_time`, but you *don’t* want the reference to it to be filtered, you can specify `ref('upstream_model').render()` to opt-out of auto-filtering. |
There was a problem hiding this comment.
Thoughts on nesting this under the sentence "dbt will automatically filter upstream inputs (source or ref) that define event_time, based on the lookback and batch_size configs for this model."
|
|
||
| ### Available configs | ||
|
|
||
| - `event_time` - The column indicating "at what time did the row occur" (for both your microbatch model and its direct parents) |
There was a problem hiding this comment.
maybe a callout that event_time and begin need to be in UTC? (also true of the CLI args --event-time-start/end
There was a problem hiding this comment.
or maybe having the "timezone"s section is better!
| select * from {{ ref('stg_events') }} | ||
| where my_time_field >= '2024-10-01 00:00:00' | ||
| and my_time_field < '2024-10-02 00:00:00' | ||
| ) # this ref will be auto-filtered |
There was a problem hiding this comment.
Suggested change
| ) # this ref will be auto-filtered | |
| ) |
|
|
||
| ### Supported incremental strategies by adapter |
There was a problem hiding this comment.
do we need versioning here? (since microbatch is versionless + core 1.9+)
|
|
||
| Microbatch incremental models make it possible to process transformations on large time-series datasets with efficiency and resiliency. When dbt runs a microbatch model — whether for the first time, during incremental runs, or during manual backfills — it will split the processing into multiple queries (or "batches"), based on the `event_time` column you configure. | ||
|
|
||
| Where other incremental strategies operate only on "old" and "new" data, microbatch models treat each "batch" of data as a unit that can be built or replaced on its own. Each batch is independent and <Term id="idempotent" />. This is a powerful abstraction that makes it possible for dbt to run batches separately — in the future, concurrently — and to retry them independently. |
There was a problem hiding this comment.
I know we have an example in the "## How does microbatch compare to other incremental strategies?" section - but maybe we should have one here as well?
|
|
||
| dbt has changed the default materialization for incremental table merges from `temporary table` to `view`. For more information about this change and instructions for setting the configuration to a temp table, please read about [Snowflake temporary tables](/reference/resource-configs/snowflake-configs#temporary-tables). | ||
| The `merge` strategy is available in dbt-postgres and dbt-redshift beginning in dbt v1.6. |
There was a problem hiding this comment.
Do we need this? Or can we add it to the table somehow?
graciegoheen
reviewed
Oct 1, 2024
|
|
||
| ## How does `microbatch` compare to other incremental strategies? | ||
|
|
||
| Most incremental models rely on the end user (you) to explicitly tell dbt what "new" means, in the context of each model, by writing a filter in an `is_incremental()` block. You are responsibly for crafting this SQL in a way that queries `this` to check when the most recent record was last loaded, with an optional look-back window for late-arriving records. Other incremental strategies will control _how_ the data is being added into the table — whether append-only `insert`, `delete` + `insert`, `merge`, `insert overwrite`, etc — but they all have this in common. |
There was a problem hiding this comment.
"Other incremental strategies will control how the data is being added into the table"
maybe add a callout of how microbatch choose which of these "how"s is best?
|
Currently
with more adapters to come! |
|
Currently, as microbatch is still in "beta", this functionality is still gated behind an env var (will be swapped to behavior change flag ahead of the final 1.9 release) - so you need to set |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Resolves #6136
What are you changing in this pull request and why?
microbatchincremental strategy (v1.9+)microbatchto table of incremental strategiesChecklist
website/sidebars.js