# Find slow SQL in history and add indexes for your filters

Source: https://www.hotdata.dev/use-cases/index-tuning
Site index: https://www.hotdata.dev/llms.txt

Dashboards and tools usually hammer the same patterns. Pull execution time and SQL from run history, look at the tables involved, and add indexes that match how people actually query, not how you guessed they would.

## How it works

### Step 1: Mine query runs for slow or frequent SQL

**Claude**

```
Show my last 25 queries and how long each took. I’m trying to spot the slow ones.
```

**CLI**

```bash
hotdata databases queries list --limit 25
```

**Python SDK**

```python
import hotdata

runs = hotdata.QueryRunsApi(api_client)
runs.list_query_runs(limit=25)
```

Each run stores **`execution_time_ms`** and **`sql_text`** so you can sort by what's actually slow.

### Step 2: Align with the physical model

**Claude**

```
Our slow reports filter orders by status and when they were created. Show the structure of the orders table on our commerce database.
```

**CLI**

```bash
hotdata databases tables show commerce.public.orders
```

**Python SDK**

```python
import hotdata

api = hotdata.InformationSchemaApi(api_client)
api.information_schema(
    connection_id="commerce",
    var_schema="public",
    table="orders",
    include_columns=True,
)
```

### Step 3: Create indexes that match common access paths

**Claude**

```
Speed up those filters: add an index on status and created_at for orders. If the build can happen in the background, use that.
```

**Python SDK**

```python
import hotdata

indexes = hotdata.IndexesApi(api_client)
indexes.create_index(
    "commerce",
    "public",
    "orders",
    hotdata.CreateIndexRequest(
        index_name="orders_status_created_idx",
        index_type="sorted",
        columns=["status", "created_at"],
    ),
)
```

With **`--async`**, index creation returns a **job**. Poll **`hotdata jobs`** until it's done before you expect the faster path.

## Who uses this

- Platform engineers connecting production query patterns to concrete index changes.
- Analytics owners tuning tables that BI and agents hit hard.
- Teams whose agent-generated SQL volume keeps climbing and needs guardrails.