> ## 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
export const Comments = ({children}) => {
return ;
};
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.
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)
```
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=""
)
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()
```
Replace \ 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.
...
```
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
```