Timeplus Python Driver
=============================
Introduction
------------
`Timeplus <https://github.com/timeplus-io/proton>`_ is a unified streaming and historical data processing engine in a single binary.
This project provides python driver to interact with Timeplus Proton or Timeplus Enterprise, the code is based on https://github.com/mymarilyn/clickhouse-driver.
Installation
------------
Timeplus Python Driver currently supports the following versions of Python: 3.8, 3.9, 3.10, 3.11, 3.12 and 3.13.
Installing with pip
We recommend creating a virtual environment when installing Python dependencies. For more information on setting up a virtual environment, see the `Python documentation <https://docs.python.org/3.9/tutorial/venv.html>`_.
.. code-block:: shell
pip install proton-driver --extra-index-url https://d.timeplus.com/simple/
Quick Start
------------
1. Run Timeplus Proton with docker. Make sure the port 8463 is exposed.
.. code-block:: shell
docker run -d -p 8463:8463 --pull always --name proton d.timeplus.com/timeplus-io/proton:latest
2. Run following python code
.. code-block:: python
from proton_driver import connect
with connect("proton://default:@localhost:8463/default") as conn:
with conn.cursor() as cursor:
cursor.execute("select 1")
print(cursor.fetchone())
above code should return ``(1,)`` , which shows that everything is working fine now.
Streaming Query
----------------
.. code-block:: python
from proton_driver import client
c = client.Client(host='127.0.0.1', port=8463)
# create a random stream if not exist
c.execute("CREATE RANDOM STREAM IF NOT EXISTS"
" devices("
" device string default 'device'||to_string(rand()%4), "
" temperature float default rand()%1000/10"
")")
# query the stream and return in a iterator
rows = c.execute_iter(
"SELECT device, count(*), min(temperature), max(temperature) "
"FROM devices GROUP BY device",
)
for row in rows:
print(row)
the output of the code will be something like following, as for streaming query is unbounded, you can add your flow control to terminate the loop.
.. code-block:: shell
('device0', 747, 0.0, 99.5999984741211)
('device1', 723, 0.10000000149011612, 99.30000305175781)
('device3', 768, 0.30000001192092896, 99.9000015258789)
('device2', 762, 0.20000000298023224, 99.80000305175781)
('device0', 1258, 0.0, 99.5999984741211)
('device1', 1216, 0.10000000149011612, 99.69999694824219)
('device3', 1276, 0.30000001192092896, 99.9000015258789)
('device2', 1250, 0.20000000298023224, 99.80000305175781)
Insert Data
------------
.. code-block:: python
from proton_driver import client
c = client.Client(host='127.0.0.1', port=8463)
# create a random stream if not exist
c.execute("INSERT INTO proton_stream (raw) VALUES",rows) #rows is an array of arrays
Pandas DataFrame
----------------
Big fan of Pandas? We too! You can mix SQL and Pandas API together. Also you can converting query results to a variety of formats(e.g. Numpy Array, Pandas DataFrame, Polars DataFrame, Arrow Table) by DBAPI.
.. code-block:: python
import pandas as pd
import time
from proton_driver import client
if __name__ == "__main__":
c = client.Client(host='127.0.0.1', port=8463)
# setup the test stream
c.execute("drop stream if exists test")
c.execute("""create stream test (
year int16,
first_name string
)""")
# add some data
df = pd.DataFrame.from_records([
{'year': 1994, 'first_name': 'Vova'},
{'year': 1995, 'first_name': 'Anja'},
{'year': 1996, 'first_name': 'Vasja'},
{'year': 1997, 'first_name': 'Petja'},
])
c.insert_dataframe(
'INSERT INTO "test" (year, first_name) VALUES',
df,
settings=dict(use_numpy=True),
)
# or c.execute("INSERT INTO test(year, first_name) VALUES", df.to_dict('records'))
time.sleep(3) # wait for 3 sec to make sure data available in historical store
df = c.query_dataframe('SELECT * FROM table(test)')
print(df)
print(df.describe())
# Converting query results to a variety of formats with dbapi
with connect('proton://localhost') as conn:
with conn.cursor() as cur:
cur.execute('SELECT * FROM table(test)')
print(cur.df()) # Pandas DataFrame
cur.execute('SELECT * FROM table(test)')
print(cur.fetchnumpy()) # Numpy Arrays
cur.execute('SELECT * FROM table(test)')
print(cur.pl()) # Polars DataFrame
cur.execute('SELECT * FROM table(test)')
print(cur.arrow()) # Arrow Table
Raw data
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"maintainer": null,
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"requires_python": "<4,>=3.8",
"maintainer_email": null,
"keywords": "Proton db database cloud analytics",
"author": "Gang Tao",
"author_email": "gang@timeplus.com",
"download_url": null,
"platform": null,
"description": "Timeplus Python Driver\n=============================\n\nIntroduction\n------------\n\n`Timeplus <https://github.com/timeplus-io/proton>`_ is a unified streaming and historical data processing engine in a single binary.\n\nThis project provides python driver to interact with Timeplus Proton or Timeplus Enterprise, the code is based on https://github.com/mymarilyn/clickhouse-driver. \n\n\nInstallation\n------------\nTimeplus Python Driver currently supports the following versions of Python: 3.8, 3.9, 3.10, 3.11, 3.12 and 3.13.\n\nInstalling with pip\nWe recommend creating a virtual environment when installing Python dependencies. For more information on setting up a virtual environment, see the `Python documentation <https://docs.python.org/3.9/tutorial/venv.html>`_.\n\n.. code-block:: shell\n\n pip install proton-driver --extra-index-url https://d.timeplus.com/simple/\n\n\nQuick Start\n------------\n\n1. Run Timeplus Proton with docker. Make sure the port 8463 is exposed.\n\n.. code-block:: shell\n\n docker run -d -p 8463:8463 --pull always --name proton d.timeplus.com/timeplus-io/proton:latest\n\n2. Run following python code \n\n.. code-block:: python\n\n from proton_driver import connect\n with connect(\"proton://default:@localhost:8463/default\") as conn:\n with conn.cursor() as cursor:\n cursor.execute(\"select 1\")\n print(cursor.fetchone())\n\nabove code should return ``(1,)`` , which shows that everything is working fine now.\n\nStreaming Query\n----------------\n\n.. code-block:: python\n\n from proton_driver import client\n\n c = client.Client(host='127.0.0.1', port=8463)\n\n # create a random stream if not exist\n c.execute(\"CREATE RANDOM STREAM IF NOT EXISTS\"\n \" devices(\"\n \" device string default 'device'||to_string(rand()%4), \"\n \" temperature float default rand()%1000/10\"\n \")\")\n # query the stream and return in a iterator\n rows = c.execute_iter(\n \"SELECT device, count(*), min(temperature), max(temperature) \"\n \"FROM devices GROUP BY device\",\n )\n for row in rows:\n print(row)\n\n\nthe output of the code will be something like following, as for streaming query is unbounded, you can add your flow control to terminate the loop.\n\n.. code-block:: shell\n\n ('device0', 747, 0.0, 99.5999984741211)\n ('device1', 723, 0.10000000149011612, 99.30000305175781)\n ('device3', 768, 0.30000001192092896, 99.9000015258789)\n ('device2', 762, 0.20000000298023224, 99.80000305175781)\n ('device0', 1258, 0.0, 99.5999984741211)\n ('device1', 1216, 0.10000000149011612, 99.69999694824219)\n ('device3', 1276, 0.30000001192092896, 99.9000015258789)\n ('device2', 1250, 0.20000000298023224, 99.80000305175781)\n\nInsert Data\n------------\n.. code-block:: python\n\n from proton_driver import client\n\n c = client.Client(host='127.0.0.1', port=8463)\n\n # create a random stream if not exist\n c.execute(\"INSERT INTO proton_stream (raw) VALUES\",rows) #rows is an array of arrays\n\nPandas DataFrame\n----------------\nBig fan of Pandas? We too! You can mix SQL and Pandas API together. Also you can converting query results to a variety of formats(e.g. Numpy Array, Pandas DataFrame, Polars DataFrame, Arrow Table) by DBAPI.\n\n\n.. code-block:: python\n\n import pandas as pd\n import time\n \n from proton_driver import client\n \n if __name__ == \"__main__\":\n c = client.Client(host='127.0.0.1', port=8463)\n \n # setup the test stream\n c.execute(\"drop stream if exists test\")\n c.execute(\"\"\"create stream test (\n year int16,\n first_name string\n )\"\"\")\n # add some data\n df = pd.DataFrame.from_records([\n {'year': 1994, 'first_name': 'Vova'},\n {'year': 1995, 'first_name': 'Anja'},\n {'year': 1996, 'first_name': 'Vasja'},\n {'year': 1997, 'first_name': 'Petja'},\n ])\n c.insert_dataframe(\n 'INSERT INTO \"test\" (year, first_name) VALUES',\n df,\n settings=dict(use_numpy=True),\n )\n # or c.execute(\"INSERT INTO test(year, first_name) VALUES\", df.to_dict('records'))\n time.sleep(3) # wait for 3 sec to make sure data available in historical store\n \n df = c.query_dataframe('SELECT * FROM table(test)')\n print(df)\n print(df.describe())\n\n # Converting query results to a variety of formats with dbapi\n with connect('proton://localhost') as conn:\n with conn.cursor() as cur:\n cur.execute('SELECT * FROM table(test)')\n print(cur.df()) # Pandas DataFrame\n\n cur.execute('SELECT * FROM table(test)')\n print(cur.fetchnumpy()) # Numpy Arrays\n\n cur.execute('SELECT * FROM table(test)')\n print(cur.pl()) # Polars DataFrame\n\n cur.execute('SELECT * FROM table(test)')\n print(cur.arrow()) # Arrow Table\n",
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