Databend 开源周报第 110 期

root@localhost:8000/default> PUT fs:///books.parquet @~
PUT fs:///books.parquet @~
┌───────────────────────────────────────────────┐│                 file                │  status ││                String               │  String │├─────────────────────────────────────┼─────────┤│ /books.parquet                       │ SUCCESS │└───────────────────────────────────────────────┘
GET @~/ fs:///fromStage/
┌─────────────────────────────────────────────────────────┐│                      file                     │  status ││                     String                    │  String │├───────────────────────────────────────────────┼─────────┤│ /fromStage/books.parquet                      │ SUCCESS │└─────────────────────────────────────────────────────────┘

pip install databend

# Create a table in DataBendctx.sql("CREATE TABLE IF NOT EXISTS user (created_at Date, count Int32)")
# Create a table in DataBendctx.sql("CREATE TABLE IF NOT EXISTS user (created_at Date, count Int32)")
# Insert multiple rows of data into the tablectx.sql("INSERT INTO user VALUES ('2022-04-01', 5), ('2022-04-01', 3), ('2022-04-03', 4), ('2022-04-03', 1), ('2022-04-04', 10)")
# Execute a queryresult = ctx.sql("SELECT created_at as date, count(*) as count FROM user GROUP BY created_at")
# Display the query resultresult.show()
# Import libraries for data visualizationimport matplotlib.pyplot as plt
# Convert the query result to a Pandas DataFramedf = result.to_pandas()
# Create a bar chart to visualize the datadf.plot.bar(x='date', y='count')plt.show()

CREATE FUNCTION [IF NOT EXISTS] <udf_name> (<arg_type>, ...) RETURNS <return_type> LANGUAGE <language> HANDLER=<handler> ADDRESS=<udf_server_address>