PyAthenaJDBC-qubole 1.3.4

Python DB API 2.0 (PEP 249) compliant wrapper for Amazon Athena JDBC driver

PyAthenaJDBC

PyAthenaJDBC is a Python DB API 2.0 (PEP 249) compliant wrapper for Amazon Athena JDBC driver.

Requirements

• Python

  • CPython 2,7, 3,4, 3.5, 3.6

• Java

  • Java >= 8

Installation

$ pip install PyAthenaJDBC

Extra packages:

Package	Install command	Version
Pandas	pip install PyAthenaJDBC[Pandas]	>=0.19.0
SQLAlchemy	pip install PyAthenaJDBC[SQLAlchemy]	>=1.0.0

Usage

Basic usage

from pyathenajdbc import connect

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

Cursor iteration

from pyathenajdbc import connect

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

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 pyathenajdbc import connect

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

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%%'

JVM Options

In the connect method or connection object, you can specify JVM options with a string array.

You can increase the JVM heap size like the following:

from pyathenajdbc import connect

conn = connect(s3_staging_dir='s3://YOUR_S3_BUCKET/path/to/',
               region_name='us-west-2',
               jvm_options=['-Xms1024m', '-Xmx4096m'])
try:
    with conn.cursor() as cursor:
        cursor.execute("""
        SELECT * FROM many_rows
        """)
        print(cursor.fetchall())
finally:
    conn.close()

SQLAlchemy

Install SQLAlchemy with pip install SQLAlchemy>=1.0.0 or pip install PyAthenaJDBC[SQLAlchemy]. Supported SQLAlchemy is 1.0.0 or higher.

import contextlib
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+jdbc://{access_key}:{secret_key}@athena.{region_name}.amazonaws.com:443/'\
           '{schema_name}?s3_staging_dir={s3_staging_dir}'
engine = create_engine(conn_str.format(
    access_key=quote_plus('YOUR_ACCESS_KEY'),
    secret_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/')))
try:
    with contextlib.closing(engine.connect()) as conn:
        many_rows = Table('many_rows', MetaData(bind=engine), autoload=True)
        print(select([func.count('*')], from_obj=many_rows).scalar())
finally:
    engine.dispose()

The connection string has the following format:

awsathena+jdbc://{access_key}:{secret_key}@athena.{region_name}.amazonaws.com:443/{schema_name}?s3_staging_dir={s3_staging_dir}&driver_path={driver_path}&...

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

Pandas

Minimal example for Pandas DataFrame:

from pyathenajdbc import connect
import pandas as pd

conn = connect(access_key='YOUR_ACCESS_KEY_ID',
               secret_key='YOUR_SECRET_ACCESS_KEY',
               s3_staging_dir='s3://YOUR_S3_BUCKET/path/to/',
               region_name='us-west-2',
               jvm_path='/path/to/jvm')  # optional, as used by JPype
df = pd.read_sql("SELECT * FROM many_rows LIMIT 10", conn)

As Pandas DataFrame:

import contextlib
from pyathenajdbc import connect
from pyathenajdbc.util import as_pandas

with contextlib.closing(
        connect(s3_staging_dir='s3://YOUR_S3_BUCKET/path/to/'
                region_name='us-west-2'))) as conn:
    with conn.cursor() as cursor:
        cursor.execute("""
        SELECT * FROM many_rows
        """)
        df = as_pandas(cursor)
print(df.describe())

Examples

Redash query runner example

See examples/redash/athena.py

Credential

Support AWS CLI credentials, Properties file credentials and AWS credentials provider chain.

Credential Files

~/.aws/credentials

[default]
aws_access_key_id=YOUR_ACCESS_KEY_ID
aws_secret_access_key=YOUR_SECRET_ACCESS_KEY

~/.aws/config

[default]
region=us-west-2
output=json

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

Additional environment variable:

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

Properties file credentials

Create a property file of the following format.

/path/to/AWSCredentials.properties

accessKeyId:YOUR_ACCESS_KEY_ID
secretKey:YOUR_SECRET_ACCESS_KEY

Specify the property file path with credential_file of the connect method or connection object.

from pyathenajdbc import connect

conn = connect(credential_file='/path/to/AWSCredentials.properties',
               s3_staging_dir='s3://YOUR_S3_BUCKET/path/to/',
               region_name='us-west-2')

PyAthenaJDBC uses the property file to authenticate Amazon Athena.

AWS credentials provider chain

See AWS credentials provider chain

AWS credentials provider chain that looks for credentials in this order:

• Environment Variables - AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY (RECOMMENDED since they are recognized by all the AWS SDKs and CLI except for .NET), or AWS_ACCESS_KEY and AWS_SECRET_KEY (only recognized by Java SDK)

• Java System Properties - aws.accessKeyId and aws.secretKey

• Credential profiles file at the default location (~/.aws/credentials) shared by all AWS SDKs and the AWS CLI

• Credentials delivered through the Amazon EC2 container service if AWS_CONTAINER_CREDENTIALS_RELATIVE_URI” environment variable is set and security manager has permission to access the variable,

• Instance profile credentials delivered through the Amazon EC2 metadata service

In the connect method or connection object, you can connect by specifying at least s3_staging_dir and region_name. It is not necessary to specify access_key and secret_key.

from pyathenajdbc import connect

conn = connect(s3_staging_dir='s3://YOUR_S3_BUCKET/path/to/',
               region_name='us-west-2')

Terraform Instance profile example:

See examples/terraform/

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/

Run test

$ 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

$ pip install pipenv
$ pipenv install --dev
$ pipenv run scripts/test_data/upload_test_data.sh
$ pyenv local 3.6.5 3.5.5 3.4.8 2.7.14
$ pipenv run tox
$ pipenv run scripts/test_data/delete_test_data.sh