Usage¶

Basic usage¶

from pyathena import connect

cursor = connect(s3_staging_dir="s3://YOUR_S3_BUCKET/path/to/",
                 region_name="us-west-2").cursor()
cursor.execute("SELECT * FROM one_row")
print(cursor.description)
print(cursor.fetchall())

Cursor iteration¶

from pyathena import connect

cursor = connect(s3_staging_dir="s3://YOUR_S3_BUCKET/path/to/",
                 region_name="us-west-2").cursor()
cursor.execute("SELECT * FROM many_rows LIMIT 10")
for row in cursor:
    print(row)

Query with parameters¶

Supported DB API paramstyle is only PyFormat. PyFormat only supports named placeholders with old % operator style and parameters specify dictionary format.

from pyathena import connect

cursor = connect(s3_staging_dir="s3://YOUR_S3_BUCKET/path/to/",
                 region_name="us-west-2").cursor()
cursor.execute("""
               SELECT col_string FROM one_row_complex
               WHERE col_string = %(param)s
               """,
               {"param": "a string"})
print(cursor.fetchall())

if % character is contained in your query, it must be escaped with %% like the following:

SELECT col_string FROM one_row_complex
WHERE col_string = %(param)s OR col_string LIKE 'a%%'

Use parameterized queries¶

If you want to use Athena’s parameterized queries, you can do so by changing the paramstyle to qmark as follows.

from pyathena import connect

pyathena.paramstyle = "qmark"
cursor = connect(s3_staging_dir="s3://YOUR_S3_BUCKET/path/to/",
                 region_name="us-west-2").cursor()
cursor.execute("""
               SELECT col_string FROM one_row_complex
               WHERE col_string = ?
               """,
               ["'a string'"])
print(cursor.fetchall())

You can also specify the paramstyle using the execute method when executing a query.

from pyathena import connect

cursor = connect(s3_staging_dir="s3://YOUR_S3_BUCKET/path/to/",
                 region_name="us-west-2").cursor()
cursor.execute("""
               SELECT col_string FROM one_row_complex
               WHERE col_string = ?
               """,
               ["'a string'"],
               paramstyle="qmark")
print(cursor.fetchall())

You can find more information about the considerations and limitations of parameterized queries in the official documentation.

Quickly re-run queries¶

Result reuse configuration¶

Athena engine version 3 allows you to reuse the results of previous queries.

It is available by specifying the arguments result_reuse_enable and result_reuse_minutes in the connection object.

from pyathena import connect

conn = connect(s3_staging_dir="s3://YOUR_S3_BUCKET/path/to/",
               region_name="us-west-2",
               work_group="YOUR_WORK_GROUP",
               result_reuse_enable=True,
               result_reuse_minutes=60)

You can also specify result_reuse_enable and result_reuse_minutes when executing a query.

from pyathena import connect

cursor = connect(s3_staging_dir="s3://YOUR_S3_BUCKET/path/to/",
                 region_name="us-west-2").cursor()
cursor.execute("SELECT * FROM one_row",
               work_group="YOUR_WORK_GROUP",
               result_reuse_enable=True,
               result_reuse_minutes=60)

If the following error occurs, please use a workgroup configured with Athena engine version 3.

pyathena.error.DatabaseError: An error occurred (InvalidRequestException) when calling the StartQueryExecution operation: This functionality is not enabled in the selected engine version. Please check the engine version settings or contact AWS support for further assistance.

If for some reason you cannot use the reuse feature of Athena engine version 3, please use the Cache configuration implemented by PyAthena.

Cache configuration¶

Please use the Result reuse configuration.

You can attempt to re-use the results from a previously executed query to help save time and money in the cases where your underlying data isn’t changing. Set the cache_size or cache_expiration_time parameter of cursor.execute() to a number larger than 0 to enable caching.

from pyathena import connect

cursor = connect(s3_staging_dir="s3://YOUR_S3_BUCKET/path/to/",
                 region_name="us-west-2").cursor()
cursor.execute("SELECT * FROM one_row")  # run once
print(cursor.query_id)
cursor.execute("SELECT * FROM one_row", cache_size=10)  # re-use earlier results
print(cursor.query_id)  # You should expect to see the same Query ID

The unit of expiration_time is seconds. To use the results of queries executed up to one hour ago, specify like the following.

from pyathena import connect

cursor = connect(s3_staging_dir="s3://YOUR_S3_BUCKET/path/to/",
                 region_name="us-west-2").cursor()
cursor.execute("SELECT * FROM one_row", cache_expiration_time=3600)  # Use queries executed within 1 hour as cache.

If cache_size is not specified, the value of sys.maxsize will be automatically set and all query results executed up to one hour ago will be checked. Therefore, it is recommended to specify cache_expiration_time together with cache_size like the following.

from pyathena import connect

cursor = connect(s3_staging_dir="s3://YOUR_S3_BUCKET/path/to/",
                 region_name="us-west-2").cursor()
cursor.execute("SELECT * FROM one_row", cache_size=100, cache_expiration_time=3600)  # Use the last 100 queries within 1 hour as cache.

Results will only be re-used if the query strings match exactly, and the query was a DML statement (the assumption being that you always want to re-run queries like CREATE TABLE and DROP TABLE).

The S3 staging directory is not checked, so it’s possible that the location of the results is not in your provided s3_staging_dir.

Query Execution Callback¶

PyAthena provides a callback mechanism that allows you to get immediate access to the query ID as soon as the start_query_execution API call is made, before waiting for query completion. This is useful for monitoring, logging, or cancelling long-running queries from another thread.

The on_start_query_execution callback can be configured at both the connection level and the execute level. When both are set, both callbacks will be invoked.

Connection-level callback¶

You can set a default callback for all queries executed through a connection:

from pyathena import connect

def query_callback(query_id):
    print(f"Query started with ID: {query_id}")
    # You can use query_id for monitoring or cancellation

cursor = connect(
    s3_staging_dir="s3://YOUR_S3_BUCKET/path/to/",
    region_name="us-west-2",
    on_start_query_execution=query_callback
).cursor()

cursor.execute("SELECT * FROM many_rows")  # Callback will be invoked

Execute-level callback¶

You can also specify a callback for individual query executions:

from pyathena import connect

def specific_callback(query_id):
    print(f"Specific query started: {query_id}")

cursor = connect(
    s3_staging_dir="s3://YOUR_S3_BUCKET/path/to/",
    region_name="us-west-2"
).cursor()

cursor.execute(
    "SELECT * FROM many_rows",
    on_start_query_execution=specific_callback
)

Query cancellation example¶

A common use case is to cancel long-running analytical queries after a timeout:

import time
from concurrent.futures import ThreadPoolExecutor, TimeoutError
from pyathena import connect

def cancel_long_running_query():
    """Example: Cancel a complex analytical query after 10 minutes."""

    def track_query_start(query_id):
        print(f"Long-running analysis started: {query_id}")
        return query_id

    def monitor_and_cancel(cursor, timeout_minutes):
        """Monitor query and cancel if it exceeds timeout."""
        time.sleep(timeout_minutes * 60)  # Convert to seconds
        try:
            cursor.cancel()
            print(f"Query cancelled after {timeout_minutes} minutes timeout")
        except Exception as e:
            print(f"Cancellation failed: {e}")

    cursor = connect(
        s3_staging_dir="s3://YOUR_S3_BUCKET/path/to/",
        region_name="us-west-2",
        on_start_query_execution=track_query_start
    ).cursor()

    # Complex analytical query that might run for a long time
    long_query = """
    WITH daily_metrics AS (
        SELECT
            date_trunc('day', timestamp_col) as day,
            user_id,
            COUNT(*) as events,
            AVG(duration) as avg_duration
        FROM large_events_table
        WHERE timestamp_col >= current_date - interval '1' year
        GROUP BY 1, 2
    ),
    user_segments AS (
        SELECT
            user_id,
            CASE
                WHEN AVG(events) > 100 THEN 'high_activity'
                WHEN AVG(events) > 10 THEN 'medium_activity'
                ELSE 'low_activity'
            END as segment
        FROM daily_metrics
        GROUP BY user_id
    )
    SELECT
        segment,
        COUNT(DISTINCT user_id) as users,
        AVG(events) as avg_daily_events
    FROM daily_metrics dm
    JOIN user_segments us ON dm.user_id = us.user_id
    GROUP BY segment
    ORDER BY avg_daily_events DESC
    """

    # Use ThreadPoolExecutor for timeout management
    with ThreadPoolExecutor(max_workers=1) as executor:
        # Start timeout monitor (cancel after 10 minutes)
        timeout_future = executor.submit(monitor_and_cancel, cursor, 10)

        try:
            print("Starting complex analytical query (10-minute timeout)...")
            cursor.execute(long_query)

            # Process results
            results = cursor.fetchall()
            print(f"Analysis completed successfully: {len(results)} segments found")
            for row in results:
                print(f"  {row[0]}: {row[1]} users, {row[2]:.1f} avg events")

        except Exception as e:
            print(f"Query failed or was cancelled: {e}")
        finally:
            # Clean up timeout monitor
            try:
                timeout_future.result(timeout=1)
            except TimeoutError:
                pass  # Monitor is still running, which is fine

# Run the example
cancel_long_running_query()

Multiple callbacks¶

When both connection-level and execute-level callbacks are specified, both callbacks will be invoked:

from pyathena import connect

def connection_callback(query_id):
    print(f"Connection callback: {query_id}")
    # Log to monitoring system

def execute_callback(query_id):
    print(f"Execute callback: {query_id}")
    # Store for cancellation if needed

cursor = connect(
    s3_staging_dir="s3://YOUR_S3_BUCKET/path/to/",
    region_name="us-west-2",
    on_start_query_execution=connection_callback
).cursor()

# This will invoke both connection_callback and execute_callback
cursor.execute(
    "SELECT 1",
    on_start_query_execution=execute_callback
)

Supported cursor types¶

The on_start_query_execution callback is supported by the following cursor types:

Cursor (default cursor)
DictCursor
ArrowCursor
PandasCursor

Note: AsyncCursor and its variants do not support this callback as they already return the query ID immediately through their different execution model.

Environment variables¶

Support Boto3 environment variables.

Additional environment variables¶

AWS_ATHENA_S3_STAGING_DIR: The S3 location where Athena automatically stores the query results and metadata information. Required if you have not set up workgroups. Not required if a workgroup has been set up.
AWS_ATHENA_WORK_GROUP: The setting of the workgroup to execute the query.

Credentials¶

Support Boto3 credentials.

Examples¶

Passing credentials as parameters¶

from pyathena import connect

cursor = connect(aws_access_key_id="YOUR_ACCESS_KEY_ID",
                 aws_secret_access_key="YOUR_SECRET_ACCESS_KEY",
                 s3_staging_dir="s3://YOUR_S3_BUCKET/path/to/",
                 region_name="us-west-2").cursor()

from pyathena import connect

cursor = connect(aws_access_key_id="YOUR_ACCESS_KEY_ID",
                 aws_secret_access_key="YOUR_SECRET_ACCESS_KEY",
                 aws_session_token="YOUR_SESSION_TOKEN",
                 s3_staging_dir="s3://YOUR_S3_BUCKET/path/to/",
                 region_name="us-west-2").cursor()

Multi-factor authentication¶

You will be prompted to enter the MFA code. The program execution will be blocked until the MFA code is entered.

from pyathena import connect

cursor = connect(duration_seconds=3600,
                 serial_number="arn:aws:iam::ACCOUNT_NUMBER_WITHOUT_HYPHENS:mfa/MFA_DEVICE_ID",
                 s3_staging_dir="s3://YOUR_S3_BUCKET/path/to/",
                 region_name="us-west-2").cursor()

Shared credentials file¶

The shared credentials file has a default location of ~/.aws/credentials.

If you use the default profile, there is no need to specify credential information.

from pyathena import connect

cursor = connect(s3_staging_dir="s3://YOUR_S3_BUCKET/path/to/",
                 region_name="us-west-2").cursor()

You can also specify a profile other than the default.

from pyathena import connect

cursor = connect(profile_name="YOUR_PROFILE_NAME",
                 s3_staging_dir="s3://YOUR_S3_BUCKET/path/to/",
                 region_name="us-west-2").cursor()

Assume role provider¶

from pyathena import connect

cursor = connect(role_arn="YOUR_ASSUME_ROLE_ARN",
                 role_session_name="PyAthena-session",
                 duration_seconds=3600,
                 s3_staging_dir="s3://YOUR_S3_BUCKET/path/to/",
                 region_name="us-west-2").cursor()

Assume role provider with MFA¶