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pyspark实现ETL操作

利用PySpark处理大数据，执行数据提取、转换、加载操作，实现数据清洗、整合与存储，支持分布式计算

1. Environment Setup and SparkSession Creation

  • Install PySpark: pip install pyspark
  • Start a SparkSession: 
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    from pyspark.sql import SparkSession
spark = SparkSession.builder.appName('ETL Process').getOrCreate()

2. Data Extraction

  • Read Data from CSV: 
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    df = spark.read.csv('path/to/csv', inferSchema=True, header=True)
  • Read Data from JSON: 
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    df = spark.read.json('path/to/json')
  • Read Data from Parquet: 
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    df = spark.read.parquet('path/to/parquet')
  • Read Data from a Database: 
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    df = spark.read.format("jdbc").option("url", jdbc_url).option("dbtable", "table_name").option("user", "username").option("password", "password").load()

3. Data Transformation

  • Selecting Columns: 
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    df.select('column1', 'column2')
  • Filtering Data: 
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    df.filter(df['column'] > value)
  • Adding New Columns: 
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    df.withColumn('new_column', df['column'] + 10)
  • Renaming Columns: 
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    df.withColumnRenamed('old_name', 'new_name')
  • Grouping and Aggregating Data: 
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    df.groupBy('column').agg({'column2': 'sum'})
  • Joining DataFrames: 
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    df1.join(df2, df1['id'] == df2['id'])
  • Sorting Data: 
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    df.orderBy(df['column'].desc())
  • Removing Duplicates: 
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    df.dropDuplicates()

4. Handling Missing Values

  • Dropping Rows with Missing Values: 
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    df.na.drop()
  • Filling Missing Values: 
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    df.na.fill(value)
  • Replacing Values: 
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    df.na.replace(['old_value'], ['new_value'])

5. Data Type Conversion

  • Changing Column Types: 
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    df.withColumn('column', df['column'].cast('new_type'))
  • Parsing Dates: 
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    from pyspark.sql.functions import to_date
df.withColumn('date', to_date(df['date_string']))

Advanced Data Manipulations

  • Using SQL Queries: 
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    df.createOrReplaceTempView('table')
spark.sql('SELECT * FROM table WHERE column > value')
  • Window Functions: 
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    from pyspark.sql.window import Window
from pyspark.sql.functions import row_number
df.withColumn('row', row_number().over(Window.partitionBy('column').orderBy('other_column')))
  • Pivot Tables: 
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    df.groupBy('column').pivot('pivot_column').agg({'column2': 'sum'})

7. Data Loading

  • Writing to CSV: 
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    df.write.csv('path/to/output')
  • Writing to JSON: 
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    df.write.json('path/to/output')
  • Writing to Parquet: 
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    df.write.parquet('path/to/output')
  • Writing to a Database: 
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    df.write.format("jdbc").option("url", jdbc_url).option("dbtable", "table_name").option("user", "username").option("password", "password").save()

8. Performance Tuning

  • Caching Data: 
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    df.cache()
  • Broadcasting a DataFrame for Join Optimization: 
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    from pyspark.sql.functions import broadcast
df1.join(broadcast(df2), df1['id'] == df2['id'])
  • Repartitioning Data: 
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    df.repartition(10)
  • Coalescing Partitions: 
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    df.coalesce(1)

9. Debugging and Error Handling

  • Showing Execution Plan: 
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    df.explain()

Data Reading Exception Handling

  • Catching Exceptions during Read: Implement try-except blocks during data reading operations.

10. Working with Complex Data Types

  • Exploding Arrays: 
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    from pyspark.sql.functions import explode
df.select(explode(df['array_column']))
  • Handling Struct Fields: 
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    df.select('struct_column.field1', 'struct_column.field2')

11. Custom Transformations with UDFs

  • Defining a UDF: 
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    from pyspark.sql.functions import udf
from pyspark.sql.types import StringType

@udf('return_type')
def my_udf(column):
    return transformation
  • Applying UDF on DataFrame: 
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    df.withColumn('new_column', my_udf(df['column']))

12. Working with Large Text Data

  • Tokenizing Text Data: 
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    from pyspark.ml.feature import Tokenizer
Tokenizer(inputCol='text_column', outputCol='words').transform(df)
  • TF-IDF on Text Data: 
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    from pyspark.ml.feature import HashingTF, IDF
HashingTF(inputCol='words', outputCol='rawFeatures').transform(df)

13. Machine Learning Integration

  • Using MLlib for Predictive Modeling: Building and training machine learning models using PySpark's MLlib.
  • Model Evaluation and Tuning: 
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    from pyspark.ml.evaluation import MulticlassClassificationEvaluator
MulticlassClassificationEvaluator().evaluate(predictions)

14. Stream Processing

  • Reading from a Stream: 
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    dfStream = spark.readStream.format('source').load()
  • Writing to a Stream: 
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    dfStream.writeStream.format('console').start()

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