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A Apache Doris client for the Python programming language.
Apache Doris is a high-performance, real-time analytical database based on MPP architecture, known for its extreme speed and ease of use. It only requires a sub-second response time to return query results under massive data and can support not only high-concurrent point query scenarios but also high-throughput complex analysis scenarios. All this makes Apache Doris an ideal tool for scenarios including report analysis, ad-hoc query, unified data warehouse, and data lake query acceleration. On Apache Doris, users can build various applications, such as user behavior analysis, AB test platform, log retrieval analysis, user portrait analysis, and order analysis.
pip install pydoris
To connect to doris using SQLAlchemy, use a connection string (URL) following this pattern:
Here's what the connection string looks like:
doris://<User>:<Password>@<Host>:<Port>/<Database>
pydoris://<User>:<Password>@<Host>:<Port>/<Database>
doris://<User>:<Password>@<Host>:<Port>/<Catalog>.<Database>
pydoris://<User>:<Password>@<Host>:<Port>/<Catalog>.<Database>
It is recommended to use python 3.x to connect to the doris database, eg:
from sqlalchemy import create_engine
from sqlalchemy.schema import Table, MetaData
from sqlalchemy.sql.expression import select, text
engine = create_engine('doris://root:xxx@localhost:9030/hive_catalog.hive_db')
connection = engine.connect()
rows = connection.execute(text("SELECT * FROM hive_table")).fetchall()
FAQs
Python interface to Doris
We found that pydoris demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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Socket now supports Scala and Kotlin, bringing AI-powered threat detection to JVM projects with easy manifest generation and fast, accurate scans.
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