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

Project description

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PyAthena

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

Requirements

  • Python
    • CPython 3.7 3.8 3.9 3.10

Installation

$ pip install PyAthena

Extra packages:

Package Install command Version
SQLAlchemy pip install PyAthena[SQLAlchemy] >=1.0.0, <2.0.0
Pandas pip install PyAthena[Pandas] >=1.3.0
Arrow pip install PyAthena[Arrow] >=7.0.0

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 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(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
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/")))
with engine.connect() as connection:
    many_rows = Table("many_rows", MetaData(), autoload_with=connection)
    result = connection.execute(select([func.count("*")], from_obj=many_rows))
    print(result.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.

Dialect & driver
Dialect Driver Schema Cursor
awsathena   awsathena DefaultCursor
awsathena rest awsathena+rest DefaultCursor
awsathena pandas awsathena+pandas PandasCursor
awsathena arrow awsathena+arrow ArrowCursor
awsathena jdbc awsathena+jdbc PyAthenaJDBC
Dialect options
Table options
location
Type:
str
Description:
Specifies the location of the underlying data in the Amazon S3 from which the table is created.
value:
s3://bucket/path/to/
Example:
Table("some_table", metadata, ..., awsathena_location="s3://bucket/path/to/")
compression
Type:
str
Description:
Specifies the compression format.
Value:
  • BZIP2
  • DEFLATE
  • GZIP
  • LZ4
  • LZO
  • SNAPPY
  • ZLIB
  • ZSTD
  • NONE|UNCOMPRESSED
Example:
Table("some_table", metadata, ..., awsathena_compression="SNAPPY")
row_format
Type:
str
Description:
Specifies the row format of the table and its underlying source data if applicable.
Value:
  • [DELIMITED FIELDS TERMINATED BY char [ESCAPED BY char]]
  • [DELIMITED COLLECTION ITEMS TERMINATED BY char]
  • [MAP KEYS TERMINATED BY char]
  • [LINES TERMINATED BY char]
  • [NULL DEFINED AS char]
  • SERDE ‘serde_name’
Example:
Table("some_table", metadata, ..., awsathena_row_format="SERDE 'org.openx.data.jsonserde.JsonSerDe'")
file_format
Type:
str
Description:
Specifies the file format for table data.
Value:
  • SEQUENCEFILE
  • TEXTFILE
  • RCFILE
  • ORC
  • PARQUET
  • AVRO
  • ION
  • INPUTFORMAT input_format_classname OUTPUTFORMAT output_format_classname
Example:
Table("some_table", metadata, ..., awsathena_file_format="PARQUET")
Table("some_table", metadata, ..., awsathena_file_format="INPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat' OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat'")
serdeproperties
Type:
dict[str, str]
Description:
Specifies one or more custom properties allowed in SerDe.
Value:
{ "property_name": "property_value", "property_name": "property_value", ... }
Example:
Table("some_table", metadata, ..., awsathena_serdeproperties={
    "separatorChar": ",", "escapeChar": "\\\\"
})
tblproperties
Type:
dict[str, str]
Description:
Specifies custom metadata key-value pairs for the table definition in addition to predefined table properties.
Value:
{ "property_name": "property_value", "property_name": "property_value", ... }
Example:
Table("some_table", metadata, ..., awsathena_tblproperties={
    "projection.enabled": "true",
    "projection.dt.type": "date",
    "projection.dt.range": "NOW-1YEARS,NOW",
    "projection.dt.format": "yyyy-MM-dd",
})
bucket_count
Type:
int
Description:
The number of buckets for bucketing your data.
Value:
Integer value greater than or equal to 0
Example:
Table("some_table", metadata, ..., awsathena_bucket_count=5)

All table options can also be configured with the connection string as follows:

awsathena+rest://:@athena.us-west-2.amazonaws.com:443/default?s3_staging_dir=s3%3A%2F%2Fbucket%2Fpath%2Fto%2F&location=s3%3A%2F%2Fbucket%2Fpath%2Fto%2F&file_format=parquet&compression=snappy&...

serdeproperties and tblproperties must be converted to strings in the 'key'='value','key'='value' format and url encoded. If single quotes are included, escape them with a backslash.

For example, if you configure a projection setting 'projection.enabled'='true','projection.dt.type'='date','projection.dt.range'='NOW-1YEARS,NOW','projection.dt.format'= 'yyyy-MM-dd' in tblproperties, it would look like this

awsathena+rest://:@athena.us-west-2.amazonaws.com:443/default?s3_staging_dir=s3%3A%2F%2Fbucket%2Fpath%2Fto%2F&tblproperties=%27projection.enabled%27%3D%27true%27%2C%27projection.dt.type%27%3D%27date%27%2C%27projection.dt.range%27%3D%27NOW-1YEARS%2CNOW%27%2C%27projection.dt.format%27%3D+%27yyyy-MM-dd%27
Column options
partition
Type:
bool
Description:
Specifies a key for partitioning data.
Value:
True / False
Example:
Column("some_column", types.String, ..., awsathena_partition=True)
cluster
Type:
bool
Description:
Divides the data in the specified column into data subsets called buckets, with or without partitioning.
Value:
True / False
Example:
Column("some_column", types.String, ..., awsathena_cluster=True)

To configure column options from the connection string, specify the column name as a comma-separated string.

awsathena+rest://:@athena.us-west-2.amazonaws.com:443/default?partition=column1%2Ccolumn2&cluster=column1%2Ccolumn2&...

If you want to limit the column options to specific table names only, specify the table and column names connected by dots as a comma-separated string.

awsathena+rest://:@athena.us-west-2.amazonaws.com:443/default?partition=table1.column1%2Ctable1.column2&cluster=table2.column1%2Ctable2.column2&...

Pandas

As DataFrame

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

from pyathena import connect
import pandas as pd

conn = connect(s3_staging_dir="s3://YOUR_S3_BUCKET/path/to/",
               region_name="us-west-2")
df = pd.read_sql_query("SELECT * FROM many_rows", conn)
print(df.head())

NOTE: Poor performance when using pandas.read_sql #222

The pyathena.pandas.util package also has helper methods.

from pyathena import connect
from pyathena.pandas.util import as_pandas

cursor = connect(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}&location={location}&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/"),
    location=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 location parameter in the connection string. If location is not specified, s3_staging_dir parameter will be used. The following rules apply.

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

The file format, row format, and compression settings are specified in the connection string, see Table options.

The pyathena.pandas.util package also has helper methods.

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

conn = connect(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.pandas.util import to_sql

conn = connect(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.pandas.util import to_sql

conn = connect(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)

DictCursor

DictCursor retrieve the query execution result as a dictionary type with column names and values.

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

from pyathena import connect
from pyathena.cursor import DictCursor

cursor = connect(s3_staging_dir="s3://YOUR_S3_BUCKET/path/to/",
                 region_name="us-west-2",
                 cursor_class=DictCursor).cursor()
from pyathena.connection import Connection
from pyathena.cursor import DictCursor

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

cursor = connect(s3_staging_dir="s3://YOUR_S3_BUCKET/path/to/",
                 region_name="us-west-2").cursor(DictCursor)
from pyathena.connection import Connection
from pyathena.cursor import DictCursor

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

The basic usage is the same as the Cursor.

from pyathena.connection import Connection
from pyathena.cursor import DictCursor

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

If you want to change the dictionary type (e.g., use OrderedDict), you can specify like the following.

from collections import OrderedDict
from pyathena import connect
from pyathena.cursor import DictCursor

cursor = connect(s3_staging_dir="s3://YOUR_S3_BUCKET/path/to/",
                 region_name="us-west-2",
                 cursor_class=DictCursor).cursor(dict_type=OrderedDict)
from collections import OrderedDict
from pyathena import connect
from pyathena.cursor import DictCursor

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

AsyncCursor

AsyncCursor is a simple implementation using the concurrent.futures package. This cursor does not follow the DB API 2.0 (PEP 249).

You can use the AsyncCursor 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 AsyncCursor 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_execution_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 AsyncCursor.

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.

AsyncDictCursor

AsyncDIctCursor is an AsyncCursor that can retrieve the query execution result as a dictionary type with column names and values.

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

from pyathena import connect
from pyathena.async_cursor import AsyncDictCursor

cursor = connect(s3_staging_dir="s3://YOUR_S3_BUCKET/path/to/",
                 region_name="us-west-2",
                 cursor_class=AsyncDictCursor).cursor()
from pyathena.connection import Connection
from pyathena.async_cursor import AsyncDictCursor

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

cursor = connect(s3_staging_dir="s3://YOUR_S3_BUCKET/path/to/",
                 region_name="us-west-2").cursor(AsyncDictCursor)
from pyathena.connection import Connection
from pyathena.async_cursor import AsyncDictCursor

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

The basic usage is the same as the AsyncCursor.

from pyathena.connection import Connection
from pyathena.cursor import DictCursor

cursor = Connection(s3_staging_dir="s3://YOUR_S3_BUCKET/path/to/",
                    region_name="us-west-2").cursor(AsyncDictCursor)
query_id, future = cursor.execute("SELECT * FROM many_rows LIMIT 10")
result_set = future.result()
for row in result_set:
    print(row["a"])

If you want to change the dictionary type (e.g., use OrderedDict), you can specify like the following.

from collections import OrderedDict
from pyathena import connect
from pyathena.async_cursor import AsyncDictCursor

cursor = connect(s3_staging_dir="s3://YOUR_S3_BUCKET/path/to/",
                 region_name="us-west-2",
                 cursor_class=AsyncDictCursor).cursor(dict_type=OrderedDict)
from collections import OrderedDict
from pyathena import connect
from pyathena.async_cursor import AsyncDictCursor

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

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 pandas.DataFrame object. Performance is better than fetching data with 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 pandas.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_execution_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 pandas.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())

If the unload option is enabled, the Parquet file itself has a schema, so the conversion is done to the dtypes according to that schema, and the mappings and types settings of the Converter class are not used.

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.

[PandasCursor] Unload options

PandasCursor also supports the UNLOAD option, as does ArrowCursor.

See [ArrowCursor] Unload options for more information.

The UNLOAD option can be enabled by specifying it in the cursor_kwargs argument of the connect method or as an argument to the 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_class=PandasCursor,
                 cursor_kwargs={
                     "unload": True
                 }).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_class=PandasCursor).cursor(unload=True)

SQLAlchemy allows this option to be specified in the connection string.

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

AsyncPandasCursor

AsyncPandasCursor is an AsyncCursor that can handle pandas.DataFrame object. 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 AsyncPandasCursor 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_execution_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 pandas.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 AsyncCursor, 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)

As with PandasCursor, the UNLOAD option is also available.

from pyathena import connect
from pyathena.pandas.cursor import AsyncPandasCursor

cursor = connect(s3_staging_dir="s3://YOUR_S3_BUCKET/path/to/",
                 region_name="us-west-2",
                 cursor_class=AsyncPandasCursor,
                 cursor_kwargs={
                     "unload": True
                 }).cursor()
from pyathena import connect
from pyathena.pandas.cursor import AsyncPandasCursor

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

ArrowCursor

ArrowCursor 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 pyarrow.Table object. Performance is better than fetching data with Cursor.

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

from pyathena import connect
from pyathena.arrow.cursor import ArrowCursor

cursor = connect(s3_staging_dir="s3://YOUR_S3_BUCKET/path/to/",
                 region_name="us-west-2",
                 cursor_class=ArrowCursor).cursor()
from pyathena.connection import Connection
from pyathena.arrow.cursor import ArrowCursor

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

cursor = connect(s3_staging_dir="s3://YOUR_S3_BUCKET/path/to/",
                 region_name="us-west-2").cursor(ArrowCursor)
from pyathena.connection import Connection
from pyathena.arrow.cursor import ArrowCursor

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

The as_arrow method returns a pyarrow.Table object.

from pyathena import connect
from pyathena.arrow.cursor import ArrowCursor

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

table = cursor.execute("SELECT * FROM many_rows").as_arrow()
print(table)
print(table.column_names)
print(table.columns)
print(table.nbytes)
print(table.num_columns)
print(table.num_rows)
print(table.schema)
print(table.shape)

Support fetch and iterate query results.

from pyathena import connect
from pyathena.arrow.cursor import ArrowCursor

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

cursor.execute("SELECT * FROM many_rows")
print(cursor.fetchone())
print(cursor.fetchmany())
print(cursor.fetchall())
from pyathena import connect
from pyathena.arrow.cursor import ArrowCursor

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

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

Execution information of the query can also be retrieved.

from pyathena import connect
from pyathena.arrow.cursor import ArrowCursor

cursor = connect(s3_staging_dir="s3://YOUR_S3_BUCKET/path/to/",
                 region_name="us-west-2",
                 cursor_class=ArrowCursor).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_execution_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 pyarrow.Table object types, create a converter class like this:

import pyarrow as pa
from pyathena.arrow.converter import _to_date
from pyathena.converter import Converter

class CustomArrowTypeConverter(Converter):
    def __init__(self) -> None:
        super(CustomArrowTypeConverter, self).__init__(
            mappings={
                "date": _to_date,
            },
            types={
                "boolean": pa.bool_(),
                "tinyint": pa.int8(),
                "smallint": pa.int16(),
                "integer": pa.int32(),
                "bigint": pa.int64(),
                "float": pa.float32(),
                "real": pa.float64(),
                "double": pa.float64(),
                "char": pa.string(),
                "varchar": pa.string(),
                "string": pa.string(),
                "timestamp": pa.timestamp("ms"),
                "date": pa.timestamp("ms"),
                "time": pa.string(),
                "varbinary": pa.string(),
                "array": pa.string(),
                "map": pa.string(),
                "row": pa.string(),
                "decimal": pa.string(),
                "json": pa.string(),
            },
        )

def convert(self, type_, value):
    converter = self.get(type_)
    return converter(value)

types is used to explicitly specify the Arrow type when reading CSV files. mappings is used as a conversion method when fetching data from a cursor object.

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.arrow.cursor import ArrowCursor

cursor = connect(s3_staging_dir="s3://YOUR_S3_BUCKET/path/to/",
                 region_name="us-west-2").cursor(ArrowCursor, converter=CustomArrowTypeConverter())
from pyathena import connect
from pyathena.arrow.cursor import ArrowCursor

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

If the unload option is enabled, the Parquet file itself has a schema, so the conversion is done to the Arrow type according to that schema, and the types setting of the Converter class is not used.

[ArrowCursor] Unload options

ArrowCurosr supports the UNLOAD option. When this option is enabled, queries with SELECT statements are automatically converted to UNLOAD statements and executed to Athena, and the results are output in Parquet format (Snappy compressed) to s3_staging_dir. The cursor reads the output Parquet file directly.

The output of query results with the UNLOAD statement is faster than normal query execution. In addition, the output Parquet file is split and can be read faster than a CSV file. We recommend trying this option if you are concerned about the time it takes to execute the query and retrieve the results.

However, UNLOAD has some limitations. Please refer to the official UNLOAD documentation for more information on limitations. As per the limitations of the official documentation, the results of UNLOAD will be written to multiple files in parallel, and the contents of each file will be in sort order, but the relative order of the files to each other will not be sorted. Note that specifying ORDER BY with this option enabled does not guarantee the sort order of the data.

The UNLOAD option can be enabled by specifying it in the cursor_kwargs argument of the connect method or as an argument to the cursor method.

from pyathena import connect
from pyathena.arrow.cursor import ArrowCursor

cursor = connect(s3_staging_dir="s3://YOUR_S3_BUCKET/path/to/",
                 region_name="us-west-2",
                 cursor_class=ArrowCursor,
                 cursor_kwargs={
                     "unload": True
                 }).cursor()
from pyathena import connect
from pyathena.arrow.cursor import ArrowCursor

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

SQLAlchemy allows this option to be specified in the connection string.

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

If a NOT_SUPPORTED occurs, a type not supported by UNLOAD is included in the result of the SELECT. Try converting to another type, such as SELECT CAST(1 AS VARCHAR) AS name.

pyathena.error.OperationalError: NOT_SUPPORTED: Unsupported Hive type: time

In most cases of SYNTAX_ERROR, you forgot to alias the column in the SELECT result. Try adding an alias to the SELECTed column, such as SELECT 1 AS name.

pyathena.error.OperationalError: SYNTAX_ERROR: line 1:1: Column name not specified at position 1

AsyncArrowCursor

AsyncArrowCursor is an AsyncCursor that can handle pyarrow.Table object. This cursor directly handles the CSV of query results output to S3 in the same way as ArrowCursor.

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

from pyathena import connect
from pyathena.arrow.async_cursor import AsyncArrowCursor

cursor = connect(s3_staging_dir="s3://YOUR_S3_BUCKET/path/to/",
                 region_name="us-west-2",
                 cursor_class=AsyncArrowCursor).cursor()
from pyathena.connection import Connection
from pyathena.arrow.async_cursor import AsyncArrowCursor

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

cursor = connect(s3_staging_dir="s3://YOUR_S3_BUCKET/path/to/",
                 region_name="us-west-2").cursor(AsyncArrowCursor)
from pyathena.connection import Connection
from pyathena.arrow.async_cursor import AsyncArrowCursor

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

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.arrow.async_cursor import AsyncArrowCursor

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

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

from pyathena import connect
from pyathena.arrow.async_cursor import AsyncArrowCursor

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

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

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

from pyathena import connect
from pyathena.arrow.async_cursor import AsyncArrowCursor

cursor = connect(s3_staging_dir="s3://YOUR_S3_BUCKET/path/to/",
                 region_name="us-west-2",
                 cursor_class=AsyncArrowCursor).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_execution_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.arrow.async_cursor import AsyncArrowCursor

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

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

This object also has an as_arrow method that returns a pyarrow.Table object similar to the ArrowCursor.

from pyathena import connect
from pyathena.arrow.async_cursor import AsyncArrowCursor

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

query_id, future = cursor.execute("SELECT * FROM many_rows")
result_set = future.result()
table = result_set.as_arrow()
print(table)
print(table.column_names)
print(table.columns)
print(table.nbytes)
print(table.num_columns)
print(table.num_rows)
print(table.schema)
print(table.shape)

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

from pyathena import connect
from pyathena.arrow.async_cursor import AsyncArrowCursor

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

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

As with ArrowCursor, the UNLOAD option is also available.

from pyathena import connect
from pyathena.arrow.cursor import AsyncArrowCursor

cursor = connect(s3_staging_dir="s3://YOUR_S3_BUCKET/path/to/",
                 region_name="us-west-2",
                 cursor_class=AsyncArrowCursor,
                 cursor_kwargs={
                     "unload": True
                 }).cursor()
from pyathena import connect
from pyathena.arrow.cursor import AsyncArrowCursor

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

Quickly re-run queries

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.

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

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

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(role_arn="YOUR_ASSUME_ROLE_ARN",
                 role_session_name="PyAthena-session",
                 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()
Instance profiles

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()

Testing

Depends on the following environment variables:

$ export AWS_DEFAULT_REGION=us-west-2
$ export AWS_ATHENA_S3_STAGING_DIR=s3://YOUR_S3_BUCKET/path/to/
$ export AWS_ATHENA_WORKGROUP=pyathena-test

In addition, you need to create a workgroup with the Query result location set to the name specified in the AWS_ATHENA_WORKGROUP environment variable. If primary is not available as the default workgroup, specify an alternative workgroup name for the default in the environment variable AWS_ATHENA_DEFAULT_WORKGROUP.

$ export AWS_ATHENA_DEFAULT_WORKGROUP=DEFAULT_WORKGROUP

Run test

$ pip install poetry
$ poetry install -v
$ poetry run pytest

Run test multiple Python versions

$ pip install poetry
$ poetry install -v
$ pyenv local 3.10.1 3.9.1 3.8.2 3.7.2
$ poetry run tox

GitHub Actions

GitHub Actions uses OpenID Connect (OIDC) to access AWS resources. You will need to refer to the GitHub Actions documentation to configure it.

The CloudFormation templates for creating GitHub OIDC Provider and IAM Role can be found in the aws-actions/configure-aws-credentials repository.

Under scripts/cloudformation you will also find a CloudFormation template with additional permissions and workgroup settings needed for testing.

The example of the CloudFormation execution command is the following:

$ aws --region us-west-2 \
    cloudformation create-stack \
    --stack-name github-actions-oidc-pyathena \
    --capabilities CAPABILITY_NAMED_IAM \
    --template-body file://./scripts/cloudformation/github_actions_oidc.yaml \
    --parameters ParameterKey=GitHubOrg,ParameterValue=laughingman7743 \
      ParameterKey=RepositoryName,ParameterValue=PyAthena \
      ParameterKey=BucketName,ParameterValue=laughingman7743-athena \
      ParameterKey=RoleName,ParameterValue=github-actions-oidc-pyathena-test \
      ParameterKey=WorkGroupName,ParameterValue=pyathena-test

Code formatting

The code formatting uses black and isort.

Appy format

$ make fmt

Check format

$ make chk

License

MIT license

Many of the implementations in this library are based on PyHive, thanks for PyHive.

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