{"library":"sparkorm","title":"SparkORM","type":"library","description":"SparkORM is a Python library for schema management and basic Object Relational Mapping for PySpark SQL and DataFrames. Current version: 1.2.29 (stable, monthly releases).","language":"python","status":"active","last_verified":"Sat May 09","install":{"commands":["pip install sparkorm"],"cli":null},"imports":["from sparkorm import SparkSessionSingleton","from sparkorm import SparkDataFrame","from sparkorm import Structure"],"auth":{"required":false,"env_vars":[]},"links":{"homepage":null,"github":"https://github.com/asuiu/sparkorm","docs":null,"changelog":null,"pypi":"https://pypi.org/project/sparkorm/","npm":null,"openapi_spec":null,"status_page":null,"smithery":null},"quickstart":{"code":"from sparkorm import SparkSessionSingleton, SparkDataFrame, Structure\nfrom pyspark.sql.types import StructType, StructField, StringType, IntegerType\n\n# Define schema model\nclass Employee(Structure):\n    name: str\n    age: int\n\n# Get or create SparkSession\nspark = SparkSessionSingleton().get_or_create()\n\n# Create sample data\nrows = [(\"Alice\", 30), (\"Bob\", 25)]\nschema = StructType([\n    StructField(\"name\", StringType(), True),\n    StructField(\"age\", IntegerType(), True)\n])\ndf = spark.createDataFrame(rows, schema)\n\n# Wrap with SparkDataFrame\nsdf = SparkDataFrame(df, Employee)\nsdf.show()","lang":"python","description":"Define a schema model, create a SparkDataFrame, and display it.","tag":null,"tag_description":null,"last_tested":null,"results":[]},"compatibility":null}