用 PySpark 和 SQL 整形数据

在 Databricks 中使用 Spark SQL 进行数据转换

Disha Mukherjee

Lead Data Engineer

整形您的数据

path = "/Volumes/.../default/transactions.csv"
df = spark.read.csv(path, header=True, inferSchema=True)
df.show(5, truncate=False)

在 Databricks 预览输出

select() → 选列
filter() → 选行

选择列

df.select("ID", "Customer_ID", "Transaction_Amount", "Category").show(5)

+---+-----------+------------------+-----------+
| ID|Customer_ID|Transaction_Amount|   Category|
+---+-----------+------------------+-----------+
|  1|    CUST001|          27337.49|Electronics|
|  2|    CUST002|           97716.6|Electronics|
|  3|    CUST003|           5752.36|Electronics|
|  4|    CUST004|          93443.22|    Savings|
|  5|    CUST005|          15109.98|Electronics|
+---+-----------+------------------+-----------+

筛选行

from pyspark.sql import functions as F


filtered_df = df.filter(F.col("Transaction_Status") == "Completed")
filtered_df.show(5)

+---+-------------------+-----------+------------------+------------------+
| ID|               Date|Customer_ID|Transaction_Amount|Transaction_Status|
+---+-------------------+-----------+------------------+------------------+
|  3|2023-01-03 00:00:00|    CUST003|           5752.36|         Completed|
|  9|2023-01-09 00:00:00|    CUST009|          28959.12|         Completed|
| 10|2023-01-10 00:00:00|    CUST010|          72098.18|         Completed|
| 11|2023-01-11 00:00:00|    CUST011|          49771.05|         Completed|
| 20|2023-01-20 00:00:00|    CUST020|          69825.14|         Completed|
+---+-------------------+-----------+------------------+------------------+

组合筛选条件

high_value_df = df.filter(

    (F.col("Transaction_Amount") > 20000) &

    (F.col("Transaction_Status") == "Completed")

)
high_value_df.show(3)

+---+-------------------+-----------+------------------+------------------+
| ID|               Date|Customer_ID|Transaction_Amount|Transaction_Status|
+---+-------------------+-----------+------------------+------------------+
|  9|2023-01-09 00:00:00|    CUST009|          28959.12|         Completed|
| 10|2023-01-10 00:00:00|    CUST010|          72098.18|         Completed|
| 11|2023-01-11 00:00:00|    CUST011|          49771.05|         Completed|
+---+-------------------+-----------+------------------+------------------+

Python 方法还是 SQL

Python 方法：select()、filter()
- 逐步构建转换

SQL 查询：标准 SQL 语法
- 汇总与分组更易读

Python 对比 SQL

创建临时视图

df.createOrReplaceTempView("transactions")

临时视图 = 指向 DataFrame 的名称
仅在当前 Spark 会话中有效

使用 SQL 查询

%sql
SELECT Category, COUNT(*) AS transaction_count

FROM transactions

WHERE Transaction_Status = 'Completed'
GROUP BY Category
ORDER BY transaction_count DESC

+-----------+-----------------+
|   Category|transaction_count|
+-----------+-----------------+
|   Clothing|             6764|
|     Dining|             6732|
|Electronics|             6640|
|    Savings|             6629|
|  Groceries|             6557|
+-----------+-----------------+

用 display() 校验

display(filtered_df)

演示可交互表格的排序与搜索的 GIF

排序、分页查看结果
发现空值与异常值

检查查询计划

filtered_df.explain()

== Physical Plan ==
*(1) Filter (Transaction_Status = Completed)
+- FileScan csv [ID, Date, Customer_ID, ...]

也可用于 SQL：EXPLAIN SELECT ...
有助于定位性能问题

阅读 Spark 错误信息

df.select("Account").show()

[UNRESOLVED_COLUMN.WITH_SUGGESTION] A column with name `Account` 
cannot be resolved. Did you mean one of the following? 
[`Date`, `Location`, `Customer_ID`, `Transaction_Amount`]

让我们一起练习吧！

在 Databricks 中使用 Spark SQL 进行数据转换