Spark SQL 入门

PySpark 入门

Benjamin Schmidt

Data Engineer

什么是 Spark SQL

Apache Spark 中用于结构化数据处理的模块
可在数据处理任务中同时运行 SQL 查询
在同一应用中无缝结合 Python 与 SQL

DataFrame 接口：提供对结构化数据的编程访问

创建临时表

# Initialize Spark session
spark = SparkSession.builder.appName("Spark SQL Example").getOrCreate()

# Sample DataFrame
data = [("Alice", "HR", 30), ("Bob", "IT", 40), ("Cathy", "HR", 28)]
columns = ["Name", "Department", "Age"]
df = spark.createDataFrame(data, schema=columns)

# Register DataFrame as a temporary view
df.createOrReplaceTempView("people")

# Query using SQL
result = spark.sql("SELECT Name, Age FROM people WHERE Age > 30")
result.show()

深入临时视图

临时视图在分析时保护底层数据

从 CSV 加载使用我们已掌握的方法

df = spark.read.csv("path/to/your/file.csv", header=True, inferSchema=True)

# Register DataFrame as a temporary view
df.createOrReplaceTempView("employees")

结合 SQL 与 DataFrame 操作

# SQL query result
query_result = spark.sql("SELECT Name, Salary FROM employees WHERE Salary > 3000")

# DataFrame transformation
high_earners = query_result.withColumn("Bonus", query_result.Salary * 0.1)
high_earners.show()

让我们来练习！

PySpark 入门