> ## Documentation Index
> Fetch the complete documentation index at: https://anaconda.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Run SQL query with AWS Athena

You can query data in S3 with SQL directly from a Metaflow task, using the same AWS tools you would use in any Python script. This page uses [AWS Glue](https://aws.amazon.com/glue/) and [AWS Athena](https://aws.amazon.com/athena/): AWS Glue is a managed extract, transform, and load (ETL) service, and AWS Athena is a serverless SQL service that runs queries against Glue databases.

**1. Add parquet files to an AWS Glue database**

The following utility function creates the Glue database and writes a small dataset to S3 as `.parquet` files. AWS Glue works with many other data formats as well.

```py title="create_glue_db.py" expandable theme={null}
import pandas as pd
import awswrangler as wr

def create_db(database_name, bucket_uri, table_name):
    dataset = pd.DataFrame({
        "id": [1, 2],
        "feature_1": ["foo", "bar"],
        "feature_2": ["fizz", "buzz"]}
    )

    try: 
        # Create an AWS Glue database to query S3 data
        wr.catalog.create_database(name=database_name)
    except wr.exceptions.AlreadyExists as error:
        # If the database exists, ignore this step
        print(f"{database_name} exists!")

    # Store data in the AWS data lake.
    # This example uses .parquet files, but
    # AWS Glue works with many other data formats.
    _ = wr.s3.to_parquet(df=dataset, 
                         path=f"{bucket_uri}/dataset/",
                         dataset=True, 
                         database=database_name,
                         table=table_name)
```

**2. Run the flow**

This flow shows how to:

* Access parquet data with a SQL query using AWS Athena.
* Transform a dataset.
* Write a pandas dataframe to AWS S3 as `.parquet` files.

```py title="sql_query_athena.py" expandable theme={null}
from metaflow import FlowSpec, step, Parameter
import awswrangler as wr
from create_glue_db import create_db

class AWSQueryFlow(FlowSpec):
    
    bucket_uri = Parameter(
                    "bucket_uri", 
                    default="<BUCKET_URI>"
                 )
    db_name = Parameter("database_name", 
                        default="test_db")
    table_name = Parameter("table_name", 
                           default="test_table")

    @step
    def start(self):
        create_db(self.db_name, self.bucket_uri, 
                  self.table_name)
        self.next(self.query)

    @step
    def query(self):
        QUERY = f"SELECT * FROM {self.table_name}"
        result = wr.athena.read_sql_query(
            QUERY, 
            database=self.db_name
        )
        self.dataset = result
        self.next(self.transform)
        
    @step
    def transform(self):
        concat = lambda x: x["feature_1"] + x["feature_2"]
        self.dataset["feature_12"] = self.dataset.apply(
            concat, 
            axis=1
        )
        self.next(self.write)
        
    @step
    def write(self):
        path = f"{self.bucket_uri}/dataset/"
        _ = wr.s3.to_parquet(df=self.dataset, 
                             mode="overwrite",
                             path=path,
                             dataset=True, 
                             database=self.db_name,
                             table=self.table_name)
        self.next(self.end)
        
    @step
    def end(self):
        print("Database is updated!")

if __name__ == '__main__':
    AWSQueryFlow()
```

Comments

Replace \<BUCKET\_URI> with the URI of your S3 bucket, such as `s3://my-bucket`.

```bash theme={null}
python sql_query_athena.py run
```

```text theme={null}
    ...
     [106/extract/481 (pid 8240)] Task is starting.
     [106/extract/481 (pid 8240)] Task finished successfully.
    ...
     [106/transform/482 (pid 8244)] Task is starting.
     [106/transform/482 (pid 8244)] Task finished successfully.
    ...
     [106/write/483 (pid 8249)] Task is starting.
     [106/write/483 (pid 8249)] Task finished successfully.
    ...
     [106/end/484 (pid 8253)] Task is starting.
     [106/end/484 (pid 8253)] Database is updated!
     [106/end/484 (pid 8253)] Task finished successfully.
    ...
```

**3. Access artifacts outside of the flow**

Run the following in any script or notebook to access the contents of the dataframe that was stored as a flow artifact with `self.dataset`:

```python theme={null}
from metaflow import Flow
run_data = Flow('AWSQueryFlow').latest_run.data
run_data.dataset
```

```text theme={null}
   id feature_1 feature_2 feature_12
0   1       foo      fizz    foofizz
1   2       bar      buzz    barbuzz
```