# The SQL Explorer
source: https://docs.chalk.ai/docs/chalksql/sql-explorer

## Chalk's built-in console for Chalk SQL

The SQL Explorer is an interface built into Chalk's web dashboard, which provides an easy-to-use interface for
making exploratory and experimental queries.

SQL Explorer Example

### Database Explorer

The Database Explorer is a direct overview of the various catalogs, schemas, and tables that Chalk SQL can integrate with.
This catalog of items is determined by your deployment, meaning that it will change as you make deployments to your
environment.

### Worksheets

Chalk SQL worksheets can be created in the SQL explorer via the Worksheets tab. You can create a "New Sheet" or choose
to use "Save Sheet" on an existing worksheet. Worksheets are tied to users and will be persisted across sessions,
providing an easy way to revisit queries within the explorer.

### Datasources

Each worksheet's toolbar includes a datasource dropdown, marked with a database icon. The default
selection, Chalk SQL, runs the worksheet on Chalk's query engine, which can read from and join
across every catalog listed in the Database Explorer.

Selecting any other datasource sends the worksheet's SQL to that datasource verbatim. Chalk does
not translate the query, so write it in the selected datasource's own SQL dialect: Snowflake SQL
for a Snowflake source, PostgreSQL for a Postgres source, and so on. Because the query executes
inside the selected datasource, reference tables by the names the datasource itself uses, without
a catalog prefix from the Database Explorer. To query across catalogs again, set the dropdown back
to Chalk SQL.

### Resource Groups

The resource group dropdown, marked with a server icon, selects which of your environment's
resource groups executes the query.
Every environment has a default group. Additional resource groups run on separate compute, which
keeps exploratory or analytical queries from contending with latency-sensitive workloads in
another group. The selected group applies to both synchronous and asynchronous runs of the
worksheet.

### Synchronous vs. Asynchronous Queries

Chalk SQL can be run in synchronous and asynchronous modes, which are tailored for transactional and
analytical workloads, respectively. Synchronous queries will hit the gRPC query servers directly, whereas async
queries will create an isolated environment but will take some time to provision.

Running synchronous Chalk SQL queries that are larger in nature can cause gRPC query servers to
go out-of-memory, which can impact any production traffic running on that engine.
Clients provide the ability to set memory limits for queries to mitigate this.