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products: all
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# Typing and Deduping

This page refers to new functionality added by
[Destinations V2](/release_notes/upgrading_to_destinations_v2/). Typing and deduping is the default
method of transforming datasets within data warehouse and database destinations after they've been
replicated. Please check each destination to learn if Typing and Deduping is supported.

## What is Destinations V2?

[Airbyte Destinations V2](/release_notes/upgrading_to_destinations_v2) provide:

- One-to-one table mapping: Data in one stream will always be mapped to one table in your data
  warehouse. No more sub-tables.
- Improved per-row error/change handling with `_airbyte_meta`: Airbyte will now populate typing changes in
  the `_airbyte_meta` column instead of failing your sync. You can query these results to audit
  misformatted or unexpected data.
- Internal Airbyte tables in the `airbyte_internal` schema: Airbyte will now generate all raw tables
  in the `airbyte_internal` schema. We no longer clutter your desired schema with raw data tables.
- Incremental delivery for large syncs: Data will be incrementally delivered to your final tables
  when possible. No more waiting hours to see the first rows in your destination table.

## `_airbyte_meta` Changes

"Per-row change handling" is a new paradigm for Airbyte which provides greater flexibility for our
users. Airbyte now separates `data-moving problems` from `data-content problems`. Prior to
Destinations V2, both types of errors were handled the same way: by failing the sync. Now, a failing
sync means that Airbyte could not _move_ all of your data. You can query the `_airbyte_meta` column
to see which rows failed for _content_ reasons, and why. This is a more flexible approach, as you
can now decide how to handle rows with errors/changes on a case-by-case basis.

:::tip

When using data downstream from Airbyte, we generally recommend you only include rows which do not
have an change, e.g:

```sql
-- postgres syntax
SELECT COUNT(*) FROM _table_ WHERE json_array_length(_airbyte_meta ->> changes) = 0
```

:::

The types of changes which will be stored in `_airbyte_meta.changes` include:

- **Typing changes**: the source declared that the type of the column `id` should be an integer, but
  a string value was returned.
- **Size changes**: the source returned content which cannot be stored within this this
  row or column (e.g.
  [a Redshift Super column has a 16mb limit](https://docs.aws.amazon.com/redshift/latest/dg/limitations-super.html)).
  Destinations V2 will allow us to trim records which cannot fit into destinations, but retain the
  primary key(s) and cursors and include "too big" changes messages.

Also, sources can make use of the same tooling to denote that there was a problem emitting the Airbyte record to begin with, 
possibly also creating an entry in `_airbyte_meta.changes`.

Depending on your use-case, it may still be valuable to consider rows with changes, especially for
aggregations. For example, you may have a table `user_reviews`, and you would like to know the count
of new reviews received today. You can choose to include reviews regardless of whether your data
warehouse had difficulty storing the full contents of the `message` column. For this use case,
`SELECT COUNT(*) from user_reviews WHERE DATE(created_at) = DATE(NOW())` is still valid.

## Destinations V2 Example

Consider the following [source schema](/integrations/sources/faker) for stream `users`:

```json
{
  "id": "number",
  "first_name": "string",
  "age": "number",
  "address": {
    "city": "string",
    "zip": "string"
  }
}
```

The data from one stream will now be mapped to one table in your schema as below:

#### Final Destination Table Name: _public.users_

| _(note, not in actual table)_                | \_airbyte_raw_id | \_airbyte_extracted_at | \_airbyte_meta                                                 | id  | first_name | age  | address                                   |
| -------------------------------------------- | ---------------- | ---------------------- | -------------------------------------------------------------- | --- | ---------- | ---- | ----------------------------------------- |
| Successful typing and de-duping ⟶            | xxx-xxx-xxx      | 2022-01-01 12:00:00    | `{}`                                                           | 1   | sarah      | 39   | `{ city: “San Francisco”, zip: “94131” }` |
| Failed typing that didn’t break other rows ⟶ | yyy-yyy-yyy      | 2022-01-01 12:00:00    | `{ changes: {"field": "age", "change": "NULLED", "reason": "DESTINATION_TYPECAST_ERROR"}}` | 2   | evan       | NULL | `{ city: “Menlo Park”, zip: “94002” }`    |
| Not-yet-typed ⟶                              |                  |                        |                                                                |     |            |      |                                           |

In legacy normalization, columns of
[Airbyte type](/understanding-airbyte/supported-data-types/#the-types) `Object` in the Destination
were "unnested" into separate tables. In this example, with Destinations V2, the previously unnested
`public.users_address` table with columns `city` and `zip` will no longer be generated.

#### Raw Destination Table Name: _airbyte_internal.raw_public\_\_users_ (`airbyte_internal.raw_{namespace}__{stream}`)

| _(note, not in actual table)_                | \_airbyte_raw_id | \_airbyte_data                                                                             | \_airbyte_loaded_at  | \_airbyte_extracted_at |
| -------------------------------------------- | ---------------- | ------------------------------------------------------------------------------------------- | -------------------- | ---------------------- |
| Successful typing and de-duping ⟶            | xxx-xxx-xxx      | `{ id: 1, first_name: “sarah”, age: 39, address: { city: “San Francisco”, zip: “94131” } }` | 2022-01-01 12:00:001 | 2022-01-01 12:00:00   |
| Failed typing that didn’t break other rows ⟶ | yyy-yyy-yyy      | `{ id: 2, first_name: “evan”, age: “fish”, address: { city: “Menlo Park”, zip: “94002” } }` | 2022-01-01 12:00:001 | 2022-01-01 12:00:00   |
| Not-yet-typed ⟶                              | zzz-zzz-zzz      | `{ id: 3, first_name: “edward”, age: 35, address: { city: “Sunnyvale”, zip: “94003” } }`    | NULL                 | 2022-01-01 13:00:00   |

You also now see the following changes in Airbyte-provided columns:

![Airbyte Destinations V2 Column Changes](../../release_notes/assets/updated_table_columns.png)

## On final table re-creation

From time to time, Airbyte will drop and re-create the final table produced by a sync (sometimes
called a "soft reset"). This is done as transactionally as possible, and should be invisible to most
observers. This is done for a number of reasons, including:

- **Schema Migrations** - Many destinations lack the ability to control column order, or cannot
  alter one data type to another. Re-creating the table allows Airbyte to strictly control the
  column order and data types of each column.
- **Transactional Full Refresh** - In order to keep your final tables consistently useful, when a
  refresh or reset occurs, airbyte will erase the raw tables, and then build a new tmp final table
  first. Airbyte attempts to do an atomic swap of old and tmp final tables, usually via a
  rename at the last second. Otherwise, there would be a period of time where the final table is empty, which could
  cause downstream issues.

This means that additional permissions, constraints, views, or other rules you apply to the final
table outside of Airbyte could be lost during a sync. Many destinations provide ways to use roles or
wildcards to grant permissions to tables, which are better suited for this ELT process. We do not
recommend altering the final tables (e.g. adding constraints) as it may cause issues with the sync.

### Manually triggering a final table re-creation

In some cases, you need to manually run a soft reset - for example, if you accidentally delete some
records from the final table and want to repopulate them from the raw data. This can be done by:

1. Dropping the final table entirely (`DROP TABLE <your_final_table>`)
1. Unsetting the raw table's `_airbyte_loaded_at` column
   (`UPDATE airbyte_internal.<your_raw_table> SET _airbyte_loaded_at = NULL`)
   1. If you are using a nonstandard raw table schema, replace `airbyte_internal` with that schema.
1. And then running a sync.

After the sync completes, your final table will be restored to its correct state.

## Loading Data Incrementally to Final Tables

:::note

Typing and Deduping may cause an increase in your destination's compute cost. This cost will vary
depending on the amount of data that is transformed and is not related to Airbyte credit usage.
Enabling loading data incrementally to final tables may further increase this cost.

:::

V2 destinations may include the option "Enable Loading Data Incrementally to Final Tables". When
enabled your data will load into your final tables incrementally while your data is still being
synced. When Disabled (the default), your data loads into your final tables once at the end of a
sync. Note that this option only applies if you elect to create Final tables.