> For the complete documentation index, see [llms.txt](https://docs.soda.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.soda.io/data-testing/failed-rows-check.md).
# Failed Rows check
{% hint style="info" %}
In the Soda Cloud no-code builder, Failed Rows Checks are labelled **SQL Expression**, **SQL Query – Failed Rows**, and **SQL Query – Failed Keys**
{% endhint %}
## `expression` vs `query` vs `keys_query`
A `failed_rows` check flags individual rows that break a rule and sends them to the Diagnostics Warehouse (if it's set up).
* You must provide **exactly one** of `expression`, `query`, or `keys_query`.
* The parser rejects zero or more than one (`failed_rows_check_yaml.py`).
* All three support `metric: count` (default, threshold `= 0`) or `metric: percent`.
| | **SQL Expression** (`expression`) | **SQL Query – Failed Rows** (`query`) | **SQL Query – Failed Keys** (`keys_query`) |
| -------------------------------- | -------------------------------------------------------- | ---------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------- |
| **What you write** | A **boolean predicate** that is TRUE for a failing row | A **full `SELECT`** returning the failing rows | A **full `SELECT`** returning only the key column(s) of the failing rows, which should be mapped to the Diagnostics Warehouse key column(s) |
| **How Soda counts failures** | `COUNT(CASE WHEN THEN 1)` — Soda builds the query | Counts rows returned by your query | Counts rows returned by your query (identical to `query`) |
| **Where failed rows are stored** | **Shared** diagnostics-warehouse tables (`fk_`/`fr_`) | **Per-check** table (`frq_`) | **Shared** diagnostics-warehouse tables (`fk_`/`fr_`) |
#### When to use
* **Boolean condition on one dataset?** → **`expression`**. Simplest; Soda applies filters, derives passing rows, and computes rows-tested for you.
* **Need custom SQL (joins/aggregations) and want the failing rows themselves, stored per-check?** → **`query`**.
* **Same, but you want only key identifiers stored (hashed, in the shared diagnostics warehouse) for privacy/correlation, or your rule is key-based?** → **`keys_query`**.
### 1. SQL Expression (`expression`)
A **boolean SQL expression that evaluates to TRUE for a failing row**. It functions similar to a `WHERE`-clause predicate. Soda counts a row as failing only when the expression is TRUE, via `COUNT(CASE WHEN THEN 1)`; rows where it's FALSE **or NULL/UNKNOWN** pass (`failed_rows.py`, `failed_rows_check.py`).
The expression is transparent to Soda, which means this is the most capable form:
* Soda can **derive the passing (complement) set** of rows.
* Soda **applies the dataset filter and check-level `filter:`** at scan time.
* Soda **computes `check_rows_tested` automatically**, so `metric: percent` needs no extra query.
{% code overflow="wrap" %}
```yaml
checks:
- failed_rows:
expression: "discount > list_price" # rows where this is TRUE are failures
```
{% endcode %}
{% hint style="success" %}
**Use it when** the rule is a row-level boolean condition on columns of the *same* dataset (ranges, allowed values, cross-column comparisons, null logic). It's the simplest and the only form where Soda understands and can scope the query.
{% endhint %}
### 2. SQL Query – Failed Rows (`query`)
A **verbatim, full `SELECT`** you supply; every row it returns is a failed row. Soda treats it as opaque SQL, which means it cannot compute the complement and **cannot express it as a dataset+filter AST**, so the **dataset filter is not applied** (`failed_rows.py`). The failing rows land in a **per-check diagnostics table** (`frq_`).
{% code overflow="wrap" %}
```yaml
checks:
- failed_rows:
query: |
SELECT o.* FROM orders o
LEFT JOIN customers c ON o.customer_id = c.id
WHERE c.id IS NULL -- orphaned orders
metric: percent
rows_tested_query: "SELECT COUNT(*) FROM orders" # required for percent
```
{% endcode %}
{% hint style="success" %}
**Use it when** the failing logic needs joins, aggregations, CTEs, or cross-dataset lookups **and you want the actual failing rows** captured/displayed. For `metric: percent` you must supply `rows_tested_query` for the denominator (the parser errors otherwise).
{% endhint %}
### 3. SQL Query – Failed Keys (`keys_query`)
Also a **verbatim, full `SELECT`**, but it returns only the **key/identifier column(s)** of the failing rows. **Counting is identical to `query`**. The returned keys are **hashed by Soda and routed into the *****shared***** diagnostics-warehouse tables (`fk_`/`fr_`)** rather than a per-check `frq_` table (`failed_rows_check.py`, `failed_rows_check_yaml.py`).
It additionally supports **`key_column_mapping`**, which maps each diagnostics-warehouse key-column name to the output column of your query that supplies it (identity mapping when omitted).
{% code overflow="wrap" %}
```yaml
checks:
- failed_rows:
keys_query: |
SELECT order_id, line_no -- composite key of failing rows
FROM order_lines
GROUP BY order_id, line_no
HAVING COUNT(*) > 1 -- duplicates
key_column_mapping:
dwh_order_id: order_id
dwh_line_no: line_no
metric: count
```
{% endcode %}
{% hint style="success" %}
**Use it when** you want **key-based diagnostics stored centrally.**
The keys are hashed and share a warehouse, so this suits **not persisting full row payloads** (privacy/sensitive tables), and identifier-centric rules (dedup, orphans, cross-source mismatches). Same limitations as `query`: opaque to Soda, dataset filter not applied, `rows_tested_query` required for `percent`.
{% endhint %}
## Shared behavior & limitations
* Diagnostics carry three SQL fields end-to-end: `failedRowsSourceQuery`, `failedRowsDataQuery`, and `failedRowsKeysQuery` (`soda_cloud_failed_rows_diagnostic.py`).
* **Sample collection must be enabled** organization wide (Organization Settings → *Allow Soda to collect sample data and failed row samples*); default sample volume is **100 rows**, adjustable via the samples limit (set `0` to disable).
* **Dataset filters do not apply** to `query`/`keys_query`. Scope with a `WHERE` clause inside the query yourself. Only `expression` respects the dataset filter and check `filter:`.
* `rows_tested_query` is **only** used with `query`/`keys_query`
* Supplying it with `expression` just logs a warning.
* `key_column_mapping` is only used with `keys_query`.
---
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