PyAthena 2.0.0

Python DB API 2.0 (PEP 249) client for Amazon Athena

PyAthena

PyAthena is a Python DB API 2.0 (PEP 249) client for Amazon Athena.

Requirements

• Python

  • CPython 3.6, 3.7 3.8

Installation

$ pip install PyAthena

Extra packages:

Package	Install command	Version
Pandas	pip install PyAthena[Pandas]	>=1.0.0
SQLAlchemy	pip install PyAthena[SQLAlchemy]	>=1.0.0, <2.0.0

Usage

Basic usage

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()
cursor.execute("SELECT * FROM one_row")
print(cursor.description)
print(cursor.fetchall())

Cursor iteration

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()
cursor.execute("SELECT * FROM many_rows LIMIT 10")
for row in cursor:
    print(row)

Query with parameter

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(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()
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%%'

SQLAlchemy

Install SQLAlchemy with pip install "SQLAlchemy>=1.0.0, <2.0.0" or pip install PyAthena[SQLAlchemy]. Supported SQLAlchemy is 1.0.0 or higher and less than 2.0.0.

from urllib.parse import quote_plus  # PY2: from urllib import quote_plus
from sqlalchemy.engine import create_engine
from sqlalchemy.sql.expression import select
from sqlalchemy.sql.functions import func
from sqlalchemy.sql.schema import Table, MetaData

conn_str = "awsathena+rest://{aws_access_key_id}:{aws_secret_access_key}@athena.{region_name}.amazonaws.com:443/"\
           "{schema_name}?s3_staging_dir={s3_staging_dir}""
engine = create_engine(conn_str.format(
    aws_access_key_id=quote_plus("YOUR_ACCESS_KEY_ID"),
    aws_secret_access_key=quote_plus("YOUR_SECRET_ACCESS_KEY"),
    region_name="us-west-2",
    schema_name="default",
    s3_staging_dir=quote_plus("s3://YOUR_S3_BUCKET/path/to/")))
many_rows = Table("many_rows", MetaData(bind=engine), autoload=True)
print(select([func.count("*")], from_obj=many_rows).scalar())

The connection string has the following format:

awsathena+rest://{aws_access_key_id}:{aws_secret_access_key}@athena.{region_name}.amazonaws.com:443/{schema_name}?s3_staging_dir={s3_staging_dir}&...

If you do not specify aws_access_key_id and aws_secret_access_key using instance profile or boto3 configuration file:

awsathena+rest://:@athena.{region_name}.amazonaws.com:443/{schema_name}?s3_staging_dir={s3_staging_dir}&...

NOTE: s3_staging_dir requires quote. If aws_access_key_id, aws_secret_access_key and other parameter contain special characters, quote is also required.

Pandas

As DataFrame

You can use the pandas.read_sql to handle the query results as a DataFrame object.

from pyathena import connect
import pandas as pd

conn = 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")
df = pd.read_sql("SELECT * FROM many_rows", conn)
print(df.head())

The pyathena.util package also has helper methods.

from pyathena import connect
from pyathena.util import as_pandas

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()
cursor.execute("SELECT * FROM many_rows")
df = as_pandas(cursor)
print(df.describe())

If you want to use the query results output to S3 directly, you can use PandasCursor. This cursor fetches query results faster than the default cursor. (See benchmark results.)

To SQL

You can use pandas.DataFrame.to_sql to write records stored in DataFrame to Amazon Athena. pandas.DataFrame.to_sql uses SQLAlchemy, so you need to install it.

import pandas as pd
from urllib.parse import quote_plus
from sqlalchemy import create_engine

conn_str = "awsathena+rest://:@athena.{region_name}.amazonaws.com:443/"\
           "{schema_name}?s3_staging_dir={s3_staging_dir}&s3_dir={s3_dir}&compression=snappy"
engine = create_engine(conn_str.format(
    region_name="us-west-2",
    schema_name="YOUR_SCHEMA",
    s3_staging_dir=quote_plus("s3://YOUR_S3_BUCKET/path/to/"),
    s3_dir=quote_plus("s3://YOUR_S3_BUCKET/path/to/")))

df = pd.DataFrame({"a": [1, 2, 3, 4, 5]})
df.to_sql("YOUR_TABLE", engine, schema="YOUR_SCHEMA", index=False, if_exists="replace", method="multi")

The location of the Amazon S3 table is specified by the s3_dir parameter in the connection string. If s3_dir is not specified, s3_staging_dir parameter will be used. The following rules apply.

s3://{s3_dir or s3_staging_dir}/{schema}/{table}/

The data format only supports Parquet. The compression format is specified by the compression parameter in the connection string.

The pyathena.util package also has helper methods.

import pandas as pd
from pyathena import connect
from pyathena.util import to_sql

conn = 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")
df = pd.DataFrame({"a": [1, 2, 3, 4, 5]})
to_sql(df, "YOUR_TABLE", conn, "s3://YOUR_S3_BUCKET/path/to/",
       schema="YOUR_SCHEMA", index=False, if_exists="replace")

This helper method supports partitioning.

import pandas as pd
from datetime import date
from pyathena import connect
from pyathena.util import to_sql

conn = 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")
df = pd.DataFrame({
    "a": [1, 2, 3, 4, 5],
    "dt": [
        date(2020, 1, 1), date(2020, 1, 1), date(2020, 1, 1),
        date(2020, 1, 2),
        date(2020, 1, 3)
    ],
})
to_sql(df, "YOUR_TABLE", conn, "s3://YOUR_S3_BUCKET/path/to/",
       schema="YOUR_SCHEMA", partitions=["dt"])

cursor = conn.cursor()
cursor.execute("SHOW PARTITIONS YOUR_TABLE")
print(cursor.fetchall())

Conversion to Parquet and upload to S3 use ThreadPoolExecutor by default. It is also possible to use ProcessPoolExecutor.

import pandas as pd
from concurrent.futures.process import ProcessPoolExecutor
from pyathena import connect
from pyathena.util import to_sql

conn = 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")
df = pd.DataFrame({"a": [1, 2, 3, 4, 5]})
to_sql(df, "YOUR_TABLE", conn, "s3://YOUR_S3_BUCKET/path/to/",
       schema="YOUR_SCHEMA", index=False, if_exists="replace",
       chunksize=1, executor_class=ProcessPoolExecutor, max_workers=5)

AsynchronousCursor

AsynchronousCursor is a simple implementation using the concurrent.futures package. Python 2.7 uses backport of the concurrent.futures package. This cursor does not follow the DB API 2.0 (PEP 249).

You can use the AsynchronousCursor by specifying the cursor_class with the connect method or connection object.

from pyathena import connect
from pyathena.async_cursor import AsyncCursor

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

from pyathena.connection import Connection
from pyathena.async_cursor import AsyncCursor

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

It can also be used by specifying the cursor class when calling the connection object’s cursor method.

from pyathena import connect
from pyathena.async_cursor import AsyncCursor

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

from pyathena.connection import Connection
from pyathena.async_cursor import AsyncCursor

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

The default number of workers is 5 or cpu number * 5. If you want to change the number of workers you can specify like the following.

from pyathena import connect
from pyathena.async_cursor import AsyncCursor

cursor = connect(s3_staging_dir="s3://YOUR_S3_BUCKET/path/to/",
                 region_name="us-west-2",
                 cursor_class=AsyncCursor).cursor(max_workers=10)

The execute method of the AsynchronousCursor returns the tuple of the query ID and the future object.

from pyathena import connect
from pyathena.async_cursor import AsyncCursor

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

query_id, future = cursor.execute("SELECT * FROM many_rows")

The return value of the future object is an AthenaResultSet object. This object has an interface that can fetch and iterate query results similar to synchronous cursors. It also has information on the result of query execution.

from pyathena import connect
from pyathena.async_cursor import AsyncCursor

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

query_id, future = cursor.execute("SELECT * FROM many_rows")
result_set = future.result()
print(result_set.state)
print(result_set.state_change_reason)
print(result_set.completion_date_time)
print(result_set.submission_date_time)
print(result_set.data_scanned_in_bytes)
print(result_set.engine_execution_time_in_millis)
print(result_set.query_queue_time_in_millis)
print(result_set.total_time_in_millis)
print(result_set.query_planning_time_in_millis)
print(result_set.service_processing_time_in_millis)
print(result_set.output_location)
print(result_set.description)
for row in result_set:
    print(row)

from pyathena import connect
from pyathena.async_cursor import AsyncCursor

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

query_id, future = cursor.execute("SELECT * FROM many_rows")
result_set = future.result()
print(result_set.fetchall())

A query ID is required to cancel a query with the AsynchronousCursor.

from pyathena import connect
from pyathena.async_cursor import AsyncCursor

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

query_id, future = cursor.execute("SELECT * FROM many_rows")
cursor.cancel(query_id)

NOTE: The cancel method of the future object does not cancel the query.

PandasCursor

PandasCursor directly handles the CSV file of the query execution result output to S3. This cursor is to download the CSV file after executing the query, and then loaded into DataFrame object. Performance is better than fetching data with a cursor.

You can use the PandasCursor by specifying the cursor_class with the connect method or connection object.

from pyathena import connect
from pyathena.pandas.cursor import PandasCursor

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

from pyathena.connection import Connection
from pyathena.pandas.cursor import PandasCursor

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

It can also be used by specifying the cursor class when calling the connection object’s cursor method.

from pyathena import connect
from pyathena.pandas.cursor import PandasCursor

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

from pyathena.connection import Connection
from pyathena.pandas.cursor import PandasCursor

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

The as_pandas method returns a DataFrame object.

from pyathena import connect
from pyathena.pandas.cursor import PandasCursor

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

df = cursor.execute("SELECT * FROM many_rows").as_pandas()
print(df.describe())
print(df.head())

Support fetch and iterate query results.

from pyathena import connect
from pyathena.pandas.cursor import PandasCursor

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

cursor.execute("SELECT * FROM many_rows")
print(cursor.fetchone())
print(cursor.fetchmany())
print(cursor.fetchall())

from pyathena import connect
from pyathena.pandas.cursor import PandasCursor

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

cursor.execute("SELECT * FROM many_rows")
for row in cursor:
    print(row)

The DATE and TIMESTAMP of Athena’s data type are returned as pandas.Timestamp type.

from pyathena import connect
from pyathena.pandas.cursor import PandasCursor

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

cursor.execute("SELECT col_timestamp FROM one_row_complex")
print(type(cursor.fetchone()[0]))  # <class 'pandas._libs.tslibs.timestamps.Timestamp'>

Execution information of the query can also be retrieved.

from pyathena import connect
from pyathena.pandas.cursor import PandasCursor

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

cursor.execute("SELECT * FROM many_rows")
print(cursor.state)
print(cursor.state_change_reason)
print(cursor.completion_date_time)
print(cursor.submission_date_time)
print(cursor.data_scanned_in_bytes)
print(cursor.engine_execution_time_in_millis)
print(cursor.query_queue_time_in_millis)
print(cursor.total_time_in_millis)
print(cursor.query_planning_time_in_millis)
print(cursor.service_processing_time_in_millis)
print(cursor.output_location)

If you want to customize the Dataframe object dtypes and converters, create a converter class like this:

from pyathena.converter import Converter

class CustomPandasTypeConverter(Converter):

    def __init__(self):
        super(CustomPandasTypeConverter, self).__init__(
            mappings=None,
            types={
                "boolean": object,
                "tinyint": float,
                "smallint": float,
                "integer": float,
                "bigint": float,
                "float": float,
                "real": float,
                "double": float,
                "decimal": float,
                "char": str,
                "varchar": str,
                "array": str,
                "map": str,
                "row": str,
                "varbinary": str,
                "json": str,
            }
        )

    def convert(self, type_, value):
        # Not used in PandasCursor.
        pass

Specify the combination of converter functions in the mappings argument and the dtypes combination in the types argument.

Then you simply specify an instance of this class in the convertes argument when creating a connection or cursor.

from pyathena import connect
from pyathena.pandas.cursor import PandasCursor

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

from pyathena import connect
from pyathena.pandas.cursor import PandasCursor

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

If you want to change the NaN behavior of Pandas Dataframe, you can do so by using the keep_default_na, na_values and quoting arguments of the cursor object’s execute method.

from pyathena import connect
from pyathena.pandas.cursor import PandasCursor

cursor = connect(s3_staging_dir="s3://YOUR_S3_BUCKET/path/to/",
                 region_name="us-west-2",
                 cursor_class=PandasCursor).cursor()
df = cursor.execute("SELECT * FROM many_rows",
                    keep_default_na=False,
                    na_values=[""]).as_pandas()

NOTE: PandasCursor handles the CSV file on memory. Pay attention to the memory capacity.

AsyncPandasCursor

AsyncPandasCursor is an AsyncCursor that can handle Pandas DataFrame. This cursor directly handles the CSV of query results output to S3 in the same way as PandasCursor.

You can use the AsyncPandasCursor by specifying the cursor_class with the connect method or connection object.

from pyathena import connect
from pyathena.pandas.async_cursor import AsyncPandasCursor

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

from pyathena.connection import Connection
from pyathena.pandas.async_cursor import AsyncPandasCursor

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

It can also be used by specifying the cursor class when calling the connection object’s cursor method.

from pyathena import connect
from pyathena.pandas.async_cursor import AsyncPandasCursor

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

from pyathena.connection import Connection
from pyathena.pandas.async_cursor import AsyncPandasCursor

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

The default number of workers is 5 or cpu number * 5. If you want to change the number of workers you can specify like the following.

from pyathena import connect
from pyathena.pandas.async_cursor import AsyncPandasCursor

cursor = connect(s3_staging_dir="s3://YOUR_S3_BUCKET/path/to/",
                 region_name="us-west-2",
                 cursor_class=AsyncPandasCursor).cursor(max_workers=10)

The execute method of the AsynchronousPandasCursor returns the tuple of the query ID and the future object.

from pyathena import connect
from pyathena.pandas.async_cursor import AsyncPandasCursor

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

query_id, future = cursor.execute("SELECT * FROM many_rows")

The return value of the future object is an AthenaPandasResultSet object. This object has an interface similar to AthenaResultSetObject.

from pyathena import connect
from pyathena.pandas.async_cursor import AsyncPandasCursor

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

query_id, future = cursor.execute("SELECT * FROM many_rows")
result_set = future.result()
print(result_set.state)
print(result_set.state_change_reason)
print(result_set.completion_date_time)
print(result_set.submission_date_time)
print(result_set.data_scanned_in_bytes)
print(result_set.engine_execution_time_in_millis)
print(result_set.query_queue_time_in_millis)
print(result_set.total_time_in_millis)
print(result_set.query_planning_time_in_millis)
print(result_set.service_processing_time_in_millis)
print(result_set.output_location)
print(result_set.description)
for row in result_set:
    print(row)

from pyathena import connect
from pyathena.pandas.async_cursor import AsyncPandasCursor

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

query_id, future = cursor.execute("SELECT * FROM many_rows")
result_set = future.result()
print(result_set.fetchall())

This object also has an as_pandas method that returns a DataFrame object similar to the PandasCursor.

from pyathena import connect
from pyathena.pandas.async_cursor import AsyncPandasCursor

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

query_id, future = cursor.execute("SELECT * FROM many_rows")
result_set = future.result()
df = result_set.as_pandas()
print(df.describe())
print(df.head())

The DATE and TIMESTAMP of Athena’s data type are returned as pandas.Timestamp type.

from pyathena import connect
from pyathena.pandas.async_cursor import AsyncPandasCursor

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

query_id, future = cursor.execute("SELECT col_timestamp FROM one_row_complex")
result_set = future.result()
print(type(result_set.fetchone()[0]))  # <class 'pandas._libs.tslibs.timestamps.Timestamp'>

As with AsynchronousCursor, you need a query ID to cancel a query.

from pyathena import connect
from pyathena.pandas.async_cursor import AsyncPandasCursor

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

query_id, future = cursor.execute("SELECT * FROM many_rows")
cursor.cancel(query_id)

Quickly re-run queries

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

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()
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

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.

Credentials

Support Boto3 credentials.

Additional environment variable:

$ export AWS_ATHENA_S3_STAGING_DIR=s3://YOUR_S3_BUCKET/path/to/
$ export AWS_ATHENA_WORK_GROUP=YOUR_WORK_GROUP

Testing

Depends on the following environment variables:

$ export AWS_ACCESS_KEY_ID=YOUR_ACCESS_KEY_ID
$ export AWS_SECRET_ACCESS_KEY=YOUR_SECRET_ACCESS_KEY
$ export AWS_DEFAULT_REGION=us-west-2
$ export AWS_ATHENA_S3_STAGING_DIR=s3://YOUR_S3_BUCKET/path/to/

And you need to create a workgroup named test-pyathena with the Query result location configuration.

Run test

$ pip install poetry
$ poetry install -v
$ poetry run scripts/test_data/upload_test_data.sh
$ poetry run pytest
$ poetry run scripts/test_data/delete_test_data.sh

Run test multiple Python versions

$ pip install poetry
$ poetry install -v
$ poetry run scripts/test_data/upload_test_data.sh
$ pyenv local 3.8.2 3.7.2 3.6.8 3.5.7 2.7.16
$ poetry run tox
$ poetry run scripts/test_data/delete_test_data.sh

Code formatting

The code formatting uses black and isort.

Appy format

$ make fmt

Check format

$ make chk