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.. image:: https://img.shields.io/pypi/dm/PyAthena.svg :target: https://pypistats.org/packages/pyathena
PyAthena
PyAthena is a Python DB API 2.0 (PEP 249)_ compliant client for Amazon Athena_.
.. _DB API 2.0 (PEP 249): https://www.python.org/dev/peps/pep-0249/
.. _Amazon Athena: http://docs.aws.amazon.com/athena/latest/APIReference/Welcome.html
lambda-pyathena
lambda-pyathena is a fork of PyAthena that simply removes boto3 and botocore from the install-requires, resulting in an AWS Lambda friendly package.
Requirements
-
Python
- CPython 2,7, 3.5, 3.6, 3.7
Installation
.. code:: bash
$ pip install lambda-pyathena
Extra packages:
+---------------+--------------------------------------+------------------+
| Package | Install command | Version |
+===============+======================================+==================+
| Pandas | pip install PyAthena[Pandas] | >=0.24.0 |
+---------------+--------------------------------------+------------------+
| SQLAlchemy | pip install PyAthena[SQLAlchemy] | >=1.0.0, <2.0.0 |
+---------------+--------------------------------------+------------------+
Usage
Basic usage
.. code:: python
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
.. code:: python
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.
.. code:: python
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:
.. code:: sql
SELECT col_string FROM one_row_complex
WHERE col_string = %(param)s OR col_string LIKE 'a%%'
.. _`DB API paramstyle`: https://www.python.org/dev/peps/pep-0249/#paramstyle
.. _`named placeholders`: https://pyformat.info/#named_placeholders
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.
.. code:: python
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:
.. code:: python
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:
.. code:: python
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
~~~~~~
Minimal example for Pandas DataFrame:
.. code:: python
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())
As Pandas DataFrame:
.. code:: python
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 Pandas `DataFrame object`_ directly, you can use `PandasCursor`_.
AsynchronousCursor
~~~~~~~~~~~~~~~~~~
AsynchronousCursor is a simple implementation using the concurrent.futures package.
Python 2.7 uses `backport of the concurrent.futures`_ package.
This cursor is not `DB API 2.0 (PEP 249)`_ compliant.
You can use the AsynchronousCursor by specifying the ``cursor_class``
with the connect method or connection object.
.. code:: python
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()
.. code:: python
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.
.. code:: python
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)
.. code:: python
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.
.. code:: python
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`_.
.. code:: python
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.
.. code:: python
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.execution_time_in_millis)
print(result_set.output_location)
print(result_set.description)
for row in result_set:
print(row)
.. code:: python
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.
.. code:: python
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.
.. _`backport of the concurrent.futures`: https://pypi.python.org/pypi/futures
.. _`future object`: https://docs.python.org/3/library/concurrent.futures.html#future-objects
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.
.. code:: python
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()
.. code:: python
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.
.. code:: python
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)
.. code:: python
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`_.
.. code:: python
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.
.. code:: python
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())
.. code:: python
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.
.. code:: python
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.
.. code:: python
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.execution_time_in_millis)
print(cursor.output_location)
If you want to customize the Dataframe object dtypes and converters, create a converter class like this:
.. code:: python
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.
.. code:: python
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())
.. code:: python
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)
NOTE: PandasCursor handles the CSV file on memory. Pay attention to the memory capacity.
.. _`DataFrame object`: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html
.. _`pandas.Timestamp`: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Timestamp.html
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.
.. code:: python
from pyathena import connect
from pyathena.async_pandas_cursor import AsyncPandasCursor
cursor = connect(s3_staging_dir='s3://YOUR_S3_BUCKET/path/to/',
region_name='us-west-2',
cursor_class=AsyncPandasCursor).cursor()
.. code:: python
from pyathena.connection import Connection
from pyathena.async_pandas_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.
.. code:: python
from pyathena import connect
from pyathena.async_pandas_cursor import AsyncPandasCursor
cursor = connect(s3_staging_dir='s3://YOUR_S3_BUCKET/path/to/',
region_name='us-west-2').cursor(AsyncPandasCursor)
.. code:: python
from pyathena.connection import Connection
from pyathena.async_pandas_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.
.. code:: python
from pyathena import connect
from pyathena.async_pandas_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`_.
.. code:: python
from pyathena import connect
from pyathena.async_pandas_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``.
.. code:: python
from pyathena import connect
from pyathena.async_pandas_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.execution_time_in_millis)
print(result_set.output_location)
print(result_set.description)
for row in result_set:
print(row)
.. code:: python
from pyathena import connect
from pyathena.async_pandas_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.
.. code:: python
from pyathena import connect
from pyathena.async_pandas_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.
.. code:: python
from pyathena import connect
from pyathena.async_pandas_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.
.. code:: python
from pyathena import connect
from pyathena.async_pandas_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.
.. code:: python
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_.
.. _Boto3 credentials: http://boto3.readthedocs.io/en/latest/guide/configuration.html
Additional environment variable:
.. code:: bash
$ 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:
.. code:: bash
$ 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.
Run test
.. code:: bash
$ pip install pipenv
$ pipenv install --dev
$ pipenv run scripts/test_data/upload_test_data.sh
$ pipenv run pytest
$ pipenv run scripts/test_data/delete_test_data.sh
Run test multiple Python versions
.. code:: bash
$ pip install pipenv
$ pipenv install --dev
$ pipenv run scripts/test_data/upload_test_data.sh
$ pyenv local 3.7.2 3.6.8 3.5.7 2.7.16
$ pipenv run tox
$ pipenv run scripts/test_data/delete_test_data.sh
FAQs
Python DB API 2.0 (PEP 249) compliant client for Amazon Athena
We found that lambda-pyathena demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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