pytd

Quickly read/write your data directly from/to the Presto query engine and Plazma primary storage

Unlike the other official Treasure Data API libraries for Python, td-client-python and pandas-td, pytd gives a direct access to their back-end query and storage engines. The seamless connection allows your Python code to read and write a large volume of data in a shorter time. It eventually makes your day-to-day data analytics work more efficient and productive.

Project milestones

This project has been actively developed based on the milestones.

Installation

pip install pytd

Usage

• Sample usage on Google Colaboratory

Set your API key and endpoint to the environment variables, TD_API_KEY and TD_API_SERVER, respectively, and create a client instance:

import pytd

client = pytd.Client(database='sample_datasets')
# or, hard-code your API key, endpoint, and/or query engine:
# >>> pytd.Client(apikey='1/XXX', endpoint='https://api.treasuredata.com/', database='sample_datasets', engine='presto')

Issue Presto query and retrieve the result:

client.query('select symbol, count(1) as cnt from nasdaq group by 1 order by 1')
# {'columns': ['symbol', 'cnt'], 'data': [['AAIT', 590], ['AAL', 82], ['AAME', 9252], ..., ['ZUMZ', 2364]]}

In case of Hive:

client = pytd.Client(database='sample_datasets', engine='hive')
client.query('select hivemall_version()')
# {'columns': ['_c0'], 'data': [['0.6.0-SNAPSHOT-201901-r01']]} (as of Feb, 2019)

Once you install the package with PySpark dependencies, any data represented as pandas.DataFrame can directly be written to TD via td-spark:

pip install pytd[spark]

import pandas as pd

df = pd.DataFrame(data={'col1': [1, 2], 'col2': [3, 10]})
client.load_table_from_dataframe(df, 'takuti.foo', if_exists='overwrite')

DB-API

pytd implements Python Database API Specification v2.0 with the help of prestodb/presto-python-client.

Connect to the API first:

from pytd.dbapi import connect

conn = connect(pytd.Client(database='sample_datasets'))
# or, connect with Hive:
# >>> conn = connect(pytd.Client(database='sample_datasets', engine='hive'))

Cursor defined by the specification allows us to flexibly fetch query results from a custom function:

def query(sql, connection):
    cur = connection.cursor()
    cur.execute(sql)
    rows = cur.fetchall()
    columns = [desc[0] for desc in cur.description]
    return {'data': rows, 'columns': columns}

query('select symbol, count(1) as cnt from nasdaq group by 1 order by 1', conn)

Below is an example of generator-based iterative retrieval, just like pandas.DataFrame.iterrows:

def iterrows(sql, connection):
    cur = connection.cursor()
    cur.execute(sql)
    index = 0
    columns = None
    while True:
        row = cur.fetchone()
        if row is None:
            break
        if columns is None:
            columns = [desc[0] for desc in cur.description]
        yield index, dict(zip(columns, row))
        index += 1

for index, row in iterrows('select symbol, count(1) as cnt from nasdaq group by 1 order by 1', conn):
    print(index, row)
# 0 {'cnt': 590, 'symbol': 'AAIT'}
# 1 {'cnt': 82, 'symbol': 'AAL'}
# 2 {'cnt': 9252, 'symbol': 'AAME'}
# 3 {'cnt': 253, 'symbol': 'AAOI'}
# 4 {'cnt': 5980, 'symbol': 'AAON'}
# ...

How to replace pandas-td

pytd offers pandas-td-compatible functions that provide the same functionalities in a more efficient way. If you are still using pandas-td, we recommend you to switch to pytd as follows.

First, install the package from PyPI:

pip install pytd  
# or, `pip install pytd[spark]` if you wish to use `to_td`

Next, make the following modifications on the import statements.

Before:

import pandas_td as td

In [1]: %%load_ext pandas_td.ipython

After:

import pytd.pandas_td as td

In [1]: %%load_ext pytd.pandas_td.ipython

Consequently, all pandas_td code should keep running correctly with pytd. Report an issue from here if you noticed any incompatible behaviors.