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

Project description

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PyAthenaJDBC

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

Requirements

  • Python
    • CPython 3.6, 3.7, 3.8, 3.9
  • Java
    • Java >= 8 (JDBC 4.2)

JDBC driver compatibility

Version JDBC driver version Vendor
< 2.0.0 == 1.1.0 AWS (Early released JDBC driver. It is incompatible with Simba’s JDBC driver)
>= 2.0.0 >= 2.0.5 Simba

Installation

$ pip install PyAthenaJDBC

Extra packages:

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

Usage

Basic usage

from pyathenajdbc import connect

conn = connect(S3OutputLocation='s3://YOUR_S3_BUCKET/path/to/',
               AwsRegion='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(S3OutputLocation='s3://YOUR_S3_BUCKET/path/to/',
               AwsRegion='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(S3OutputLocation='s3://YOUR_S3_BUCKET/path/to/',
               AwsRegion='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(S3OutputLocation='s3://YOUR_S3_BUCKET/path/to/',
               AwsRegion='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()

JDBC 4.1

If you want to use JDBC 4.1, download the corresponding JDBC driver and specify the path of the downloaded JDBC driver as the argument driver_path of the connect method or connection object.

from pyathenajdbc import connect

conn = connect(S3OutputLocation='s3://YOUR_S3_BUCKET/path/to/',
               AwsRegion='us-west-2',
               driver_path='/path/to/AthenaJDBC41_2.0.7.jar')

JDBC driver configuration options

The connect method or connection object pass keyword arguments as options to the JDBC driver. If you want to change the behavior of the JDBC driver, specify the option as a keyword argument in the connect method or connection object.

from pyathenajdbc import connect

conn = connect(S3OutputLocation='s3://YOUR_S3_BUCKET/path/to/',
               AwsRegion='us-west-2',
               LogPath='/path/to/pyathenajdbc/log/',
               LogLevel='6')

For details of the JDBC driver options refer to the official documentation.

NOTE: Option names and values are case-sensitive. The option value is specified as a character string.

SQLAlchemy

Install SQLAlchemy with pip install SQLAlchemy>=1.0.0 or pip install PyAthenaJDBC[SQLAlchemy]. Supported SQLAlchemy is 1.0.0 or higher and less than 2.0.0.

import contextlib
from urllib.parse 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://{User}:{Password}@athena.{AwsRegion}.amazonaws.com:443/'\
           '{Schema}?S3OutputLocation={S3OutputLocation}'
engine = create_engine(conn_str.format(
    User=quote_plus('YOUR_ACCESS_KEY'),
    Password=quote_plus('YOUR_SECRET_ACCESS_KEY'),
    AwsRegion='us-west-2',
    Schema='default',
    S3OutputLocation=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://{User}:{Password}@athena.{AwsRegion}.amazonaws.com:443/{Schema}?S3OutputLocation={S3OutputLocation}&driver_path={driver_path}&...

If you do not specify User (i.e. AWSAccessKeyID) and Password (i.e. AWSSecretAccessKey) using instance profile credentials or credential profiles file:

awsathena+jdbc://:@athena.{Region}.amazonaws.com:443/{Schema}?S3OutputLocation={S3OutputLocation}&driver_path={driver_path}&...

NOTE: S3OutputLocation requires quote. If User, Password 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 pyathenajdbc import connect
import pandas as pd

conn = connect(User='YOUR_ACCESS_KEY_ID',
               Password='YOUR_SECRET_ACCESS_KEY',
               S3OutputLocation='s3://YOUR_S3_BUCKET/path/to/',
               AwsRegion='us-west-2',
               jvm_path='/path/to/jvm')
df = pd.read_sql("SELECT * FROM many_rows LIMIT 10", conn)

The pyathena.util package also has helper methods.

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

with contextlib.closing(
        connect(S3OutputLocation='s3://YOUR_S3_BUCKET/path/to/'
                AwsRegion='us-west-2'))) as conn:
    with conn.cursor() as cursor:
        cursor.execute("""
        SELECT * FROM many_rows
        """)
        df = as_pandas(cursor)
print(df.describe())
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+jdbc://:@athena.{AwsRegion}.amazonaws.com:443/'\
           '{Schema}?S3OutputLocation={S3OutputLocation}&S3Location={S3Location}&compression=snappy'
engine = create_engine(conn_str.format(
    AwsRegion='us-west-2',
    Schema_name='YOUR_SCHEMA',
    S3OutputLocation=quote_plus('s3://YOUR_S3_BUCKET/path/to/'),
    S3Location=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 S3Location parameter in the connection string. If S3Location is not specified, S3OutputLocation parameter will be used. The following rules apply.

s3://{S3Location or S3OutputLocation}/{schema}/{table}/

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

Credential

AWS credentials provider chain

See Supplying and retrieving AWS credentials

https://docs.aws.amazon.com/AWSJavaSDK/latest/javadoc/com/amazonaws/auth/DefaultAWSCredentialsProviderChain.html

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
  • Web Identity Token credentials from the environment or container
  • 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 S3OutputLocation and AwsRegion. User and Password are not required if environment variables, credential files, or instance profiles have been set.

from pyathenajdbc import connect

conn = connect(S3OutputLocation='s3://YOUR_S3_BUCKET/path/to/',
               AwsRegion='us-west-2')

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-jdbc.

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 3.9.0 3.8.6 3.7.9 3.6.12
$ 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

License

The license of all Python code except JDBC driver is MIT license.

JDBC driver

For the license of JDBC driver, please check the following link.

Project details


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