> For the complete documentation index, see [llms.txt](https://katt.gitbook.io/wiki/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://katt.gitbook.io/wiki/sql/performance/initial-explorations.md).

# Initial Explorations

Keeping the total bandwidth for initial explorations low

To get familiar with a new database, roaming around and looking at the data is necessary. Unfortunately, large queries can slow down performance. Worse, large exports of data from a new user can flag your account as fraudulent by a SecOps AI. To avoid that, some of best practices are...

## Look at Small Data

Most of the time, you don't need a full table to get an idea of what the table is holding. Make use of the LIMIT operation.

```
SELECT * FROM table1 LIMIT 10;
```

Once you're relatively sure of the columns you'll need to use in the future, the next step is determining size.

```
SELECT COUNT(col1) FROM table1;
SELECT COUNT(*) FROM table1;
```

This confirms whether the table will be the right size to pull down, or a further trim down with a WHERE statement would be more helpful. Especially before looking at more interesting ways to directly pull data, know the size of the data on which SQL is being performed.

## Understand the Scope

With good business requirements, data pulls can be small and actionable. Pulling an extra month of data in each direction can give a report more context, but if your source has multiple years of data then pulling all dates for a monthly update is overkill.

```
SELECT min(timestamp), max(timestamp) FROM table1;
```

Then, trim the data to the dates needed.

```
SELECT col1, col2, col3 FROM table1 t
WHERE '2020-01-01' < t.timestamp < '2020-05-31'
```