{"id":75137,"date":"2026-02-11T15:33:36","date_gmt":"2026-02-11T07:33:36","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/75137.html"},"modified":"2026-02-11T15:33:36","modified_gmt":"2026-02-11T07:33:36","slug":"python%e6%95%b0%e6%8d%ae%e5%88%86%e6%9e%90%ef%bc%9a%e5%a4%a7%e6%95%b0%e6%8d%ae%e4%b8%8e%e5%b7%a5%e7%a8%8b%e5%8c%96","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/75137.html","title":{"rendered":"Python\u6570\u636e\u5206\u6790\uff1a\u5927\u6570\u636e\u4e0e\u5de5\u7a0b\u5316"},"content":{"rendered":"

\u5f15\u8a00<\/span><\/span><\/h2>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u968f\u7740\u6570\u636e\u89c4\u6a21\u7206\u70b8\u5f0f\u589e\u957f，\u4f20\u7edf\u5355\u673a\u6570\u636e\u5206\u6790\u5df2\u96be\u4ee5\u6ee1\u8db3\u4f01\u4e1a\u7ea7\u9700\u6c42\u3002Python\u6570\u636e\u5206\u6790\u6b63\u4ece"\u5355\u673a\u811a\u672c"\u5411"\u5206\u5e03\u5f0f\u5de5\u7a0b\u5316"\u6f14\u8fdb\u3002\u672c\u6587\u5c06\u7cfb\u7edf\u8bb2\u89e3：\u5206\u5e03\u5f0f\u8ba1\u7b97\u5f15\u64ce（Spark\/Dask）\u3001\u6570\u636e\u5e93\u6027\u80fd\u4f18\u5316\u3001\u81ea\u52a8\u5316\u6570\u636e\u7ba1\u9053（Airflow\/Prefect）\u53ca\u4e91\u7aef\u6570\u636e\u5904\u7406\u5b9e\u6218，\u5e2e\u52a9\u4f60\u6784\u5efa\u7a33\u5b9a\u3001\u53ef\u6269\u5c55\u7684\u5927\u6570\u636e\u5de5\u4f5c\u6d41\u3002<\/span><\/span><\/p>\n

\u4e00\u3001\u5206\u5e03\u5f0f\u8ba1\u7b97：Spark vs Dask<\/span><\/span><\/h3>\n

1.1 Spark：\u6210\u719f\u7684\u5206\u5e03\u5f0f\u8ba1\u7b97\u5f15\u64ce<\/span><\/span><\/h4>\n

Spark\u57fa\u4e8e\u5185\u5b58\u8ba1\u7b97，\u9002\u5408\u5927\u89c4\u6a21\u79bb\u7ebf\u6279\u5904\u7406\u4e0e\u6d41\u5f0f\u8ba1\u7b97\u3002<\/span><\/span><\/p>\n

from pyspark.sql import SparkSession
\n# \u521d\u59cb\u5316SparkSession
\nspark = SparkSession.builder \\\\
\n\u00a0\u00a0\u00a0\u00a0.appName("DataAnalysis") \\\\
\n\u00a0\u00a0\u00a0\u00a0.config("spark.executor.memory", "4g") \\\\
\n\u00a0\u00a0\u00a0\u00a0.getOrCreate()
\n# \u8bfb\u53d6\u6570\u636e
\ndf = spark.read.csv("large_dataset.csv", header=True, inferSchema=True)
\n# \u9ad8\u6027\u80fd\u805a\u5408
\nresult = df.groupBy("category").agg({"sales": "sum", "quantity": "avg"})
\nresult.show()
\n# \u6027\u80fd\u4f18\u5316：\u7f13\u5b58\u4e0e\u5206\u533a
\ndf.cache() \u00a0# \u7f13\u5b58\u5230\u5185\u5b58
\ndf.repartition(100).write.parquet("optimized_data.parquet")<\/p>\n

1.2 Dask：\u8f7b\u91cf\u7ea7Python\u539f\u751f\u5206\u5e03\u5f0f<\/span><\/span><\/h4>\n

Dask\u4e0eNumPy\/Pandas API\u517c\u5bb9，\u9002\u5408\u79d1\u5b66\u8ba1\u7b97\u4e0e\u4e2d\u7b49\u89c4\u6a21\u6570\u636e\u5206\u6790\u3002<\/span><\/span>
\n\u00a0<\/p>\n

import dask.dataframe as dd
\n# \u8bfb\u53d6\u6570\u636e（\u61d2\u52a0\u8f7d）
\nddf = dd.read_csv("large_dataset\/*.csv")
\n# \u5206\u5e03\u5f0f\u8ba1\u7b97（\u5ef6\u8fdf\u6267\u884c）
\nresult = ddf.groupby("category").agg({
\n\u00a0\u00a0\u00a0\u00a0"sales": "sum",
\n\u00a0\u00a0\u00a0\u00a0"quantity": "mean"
\n})
\n# \u89e6\u53d1\u8ba1\u7b97
\nresult.compute()<\/p>\n

1.3 \u6027\u80fd\u5bf9\u6bd4<\/span><\/span><\/h4>\n\n\n\n\n\n\n\n
\n

\u6307\u6807<\/span><\/span><\/p>\n<\/td>\n

\n

Spark<\/span><\/span><\/p>\n<\/td>\n

\n

Dask<\/span><\/span><\/p>\n<\/td>\n<\/tr>\n

\n

\u9002\u7528\u573a\u666f<\/span><\/span><\/p>\n<\/td>\n

\n

TB\u7ea7\u6570\u636e，\u4f01\u4e1a\u7ea7\u96c6\u7fa4<\/span><\/span><\/p>\n<\/td>\n

\n

GB-TB\u7ea7\u6570\u636e，\u79d1\u5b66\u8ba1\u7b97<\/span><\/span><\/p>\n<\/td>\n<\/tr>\n

\n

\u542f\u52a8\u5f00\u9500<\/span><\/span><\/p>\n<\/td>\n

\n

\u9ad8（JVM）<\/span><\/span><\/p>\n<\/td>\n

\n

\u4f4e（Python\u539f\u751f）<\/span><\/span><\/p>\n<\/td>\n<\/tr>\n

\n

\u5185\u5b58\u7ba1\u7406<\/span><\/span><\/p>\n<\/td>\n

\n

\u624b\u52a8\u4f18\u5316<\/span><\/span><\/p>\n<\/td>\n

\n

\u81ea\u52a8\u7ba1\u7406<\/span><\/span><\/p>\n<\/td>\n<\/tr>\n

\n

\u751f\u6001\u96c6\u6210<\/span><\/span><\/p>\n<\/td>\n

\n

\u4e30\u5bcc（MLlib, GraphX）<\/span><\/span><\/p>\n<\/td>\n

\n

\u6709\u9650（\u4f9d\u8d56SciPy\u751f\u6001）<\/span><\/span><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u4e8c\u3001\u6570\u636e\u5e93\u4f18\u5316\u4e0eSQL\u9ad8\u7ea7\u67e5\u8be2<\/span><\/span><\/h3>\n

2.1 \u7d22\u5f15\u4f18\u5316\u7b56\u7565<\/span><\/span><\/h4>\n

— \u521b\u5efa\u590d\u5408\u7d22\u5f15（\u67e5\u8be2\u4f18\u5316）
\nCREATE INDEX idx_category_date ON sales_data(category, sale_date);
\n— \u5206\u6790\u67e5\u8be2\u8ba1\u5212
\nEXPLAIN ANALYZE
\nSELECT product_id, SUM(amount)
\nFROM sales_data
\nWHERE category = 'Electronics'
\nGROUP BY product_id;<\/p>\n

2.2 \u7a97\u53e3\u51fd\u6570\u5b9e\u6218<\/span><\/span><\/h4>\n

— \u8ba1\u7b97\u79fb\u52a8\u5e73\u5747
\nSELECT
\n\u00a0\u00a0\u00a0\u00a0sale_date,
\n\u00a0\u00a0\u00a0\u00a0amount,
\n\u00a0\u00a0\u00a0\u00a0AVG(amount) OVER (
\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0ORDER BY sale_date
\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0ROWS BETWEEN 2 PRECEDING AND CURRENT ROW
\n\u00a0\u00a0\u00a0\u00a0) AS moving_avg_3day
\nFROM sales_data;
\n— \u540c\u6bd4\u589e\u957f\u7387
\nSELECT
\n\u00a0\u00a0\u00a0\u00a0month,
\n\u00a0\u00a0\u00a0\u00a0revenue,
\n\u00a0\u00a0\u00a0\u00a0LAG(revenue, 12) OVER (ORDER BY month) AS revenue_last_year,
\n\u00a0\u00a0\u00a0\u00a0(revenue – LAG(revenue, 12) OVER (ORDER BY month)) * 100.0 \/
\n\u00a0\u00a0\u00a0\u00a0LAG(revenue, 12) OVER (ORDER BY month) AS yoy_growth
\nFROM monthly_revenue;<\/p>\n

2.3 CTE（\u516c\u7528\u8868\u8868\u8fbe\u5f0f）\u4f18\u5316<\/span><\/span><\/h4>\n

WITH category_sales AS (
\n\u00a0\u00a0\u00a0\u00a0SELECT
\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0category,
\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0SUM(amount) AS total_sales,
\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0COUNT(*) AS transaction_count
\n\u00a0\u00a0\u00a0\u00a0FROM sales_data
\n\u00a0\u00a0\u00a0\u00a0GROUP BY category
\n),
\ntop_categories AS (
\n\u00a0\u00a0\u00a0\u00a0SELECT * FROM category_sales
\n\u00a0\u00a0\u00a0\u00a0ORDER BY total_sales DESC
\n\u00a0\u00a0\u00a0\u00a0LIMIT 5
\n)
\nSELECT
\n\u00a0\u00a0\u00a0\u00a0tc.category,
\n\u00a0\u00a0\u00a0\u00a0tc.total_sales,
\n\u00a0\u00a0\u00a0\u00a0tc.transaction_count,
\n\u00a0\u00a0\u00a0\u00a0tc.total_sales * 100.0 \/ (SELECT SUM(total_sales) FROM category_sales) AS share
\nFROM top_categories tc;<\/p>\n

\u4e09\u3001\u6570\u636e\u7ba1\u9053\u6784\u5efa：Airflow\u4e0ePrefect<\/span><\/span><\/h3>\n

3.1 Airflow：\u58f0\u660e\u5f0f\u4efb\u52a1\u7f16\u6392<\/span><\/span><\/h4>\n

from airflow import DAG
\nfrom airflow.operators.python import PythonOperator
\nfrom datetime import datetime
\ndef extract_data():
\n\u00a0\u00a0\u00a0\u00a0# \u6570\u636e\u62bd\u53d6\u903b\u8f91
\n\u00a0\u00a0\u00a0\u00a0print("Extracting data…")
\ndef transform_data():
\n\u00a0\u00a0\u00a0\u00a0# \u6570\u636e\u8f6c\u6362\u903b\u8f91
\n\u00a0\u00a0\u00a0\u00a0print("Transforming data…")
\ndef load_data():
\n\u00a0\u00a0\u00a0\u00a0# \u6570\u636e\u52a0\u8f7d\u903b\u8f91
\n\u00a0\u00a0\u00a0\u00a0print("Loading data…")
\nwith DAG(
\n\u00a0\u00a0\u00a0\u00a0dag_id="data_pipeline",
\n\u00a0\u00a0\u00a0\u00a0schedule_interval="@daily",
\n\u00a0\u00a0\u00a0\u00a0start_date=datetime(2026, 1, 1),
\n\u00a0\u00a0\u00a0\u00a0catchup=False
\n) as dag:
\n\u00a0\u00a0\u00a0\u00a0extract_task = PythonOperator(
\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0task_id="extract",
\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0python_callable=extract_data
\n\u00a0\u00a0\u00a0\u00a0)
\n\u00a0\u00a0\u00a0\u00a0transform_task = PythonOperator(
\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0task_id="transform",
\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0python_callable=transform_data
\n\u00a0\u00a0\u00a0\u00a0)
\n\u00a0\u00a0\u00a0\u00a0load_task = PythonOperator(
\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0task_id="load",
\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0python_callable=load_data
\n\u00a0\u00a0\u00a0\u00a0)
\n\u00a0\u00a0\u00a0\u00a0extract_task >> transform_task >> load_task<\/p>\n

3.2 Prefect：\u73b0\u4ee3\u52a8\u6001\u5de5\u4f5c\u6d41<\/span><\/span><\/h4>\n

from prefect import flow, task
\nfrom prefect.tasks import task_input_hash
\nfrom datetime import timedelta
\n@task(cache_key_fn=task_input_hash, cache_expiration=timedelta(days=1))
\ndef extract_data(source: str):
\n\u00a0\u00a0\u00a0\u00a0# \u6570\u636e\u62bd\u53d6\u903b\u8f91
\n\u00a0\u00a0\u00a0\u00a0return f"Data from {source}"
\n@task(retries=2, retry_delay_seconds=60)
\ndef transform_data(data: str):
\n\u00a0\u00a0\u00a0\u00a0# \u6570\u636e\u8f6c\u6362\u903b\u8f91
\n\u00a0\u00a0\u00a0\u00a0return f"Transformed {data}"
\n@flow
\ndef data_pipeline(source: str = "database"):
\n\u00a0\u00a0\u00a0\u00a0raw_data = extract_data(source)
\n\u00a0\u00a0\u00a0\u00a0transformed_data = transform_data(raw_data)
\n\u00a0\u00a0\u00a0\u00a0return transformed_data
\n# \u8fd0\u884c\u5de5\u4f5c\u6d41
\ndata_pipeline()<\/p>\n

3.3 \u9519\u8bef\u5904\u7406\u4e0e\u76d1\u63a7\u7b56\u7565<\/span><\/span><\/h4>\n\n\n\n\n\n\n\n
\n

\u7b56\u7565\u7c7b\u578b<\/span><\/span><\/p>\n<\/td>\n

\n

Airflow\u5b9e\u73b0<\/span><\/span><\/p>\n<\/td>\n

\n

Prefect\u5b9e\u73b0<\/span><\/span><\/p>\n<\/td>\n<\/tr>\n

\n

\u91cd\u8bd5\u673a\u5236<\/span><\/span><\/p>\n<\/td>\n

\n

retries=3<\/span><\/span><\/p>\n<\/td>\n

\n

@task(retries=3)<\/span><\/span><\/p>\n<\/td>\n<\/tr>\n

\n

\u4f9d\u8d56\u7ba1\u7406<\/span><\/span><\/p>\n<\/td>\n

\n

>><\/span>\u64cd\u4f5c\u7b26<\/span><\/span><\/p>\n<\/td>\n

\n

\u52a8\u6001\u4f9d\u8d56\u6ce8\u5165<\/span><\/span><\/p>\n<\/td>\n<\/tr>\n

\n

\u72b6\u6001\u8ffd\u8e2a<\/span><\/span><\/p>\n<\/td>\n

\n

Web UI + SLAs<\/span><\/span><\/p>\n<\/td>\n

\n

\u5b9e\u65f6\u4eea\u8868\u677f + \u72b6\u6001\u6301\u4e45\u5316<\/span><\/span><\/p>\n<\/td>\n<\/tr>\n

\n

\u8d44\u6e90\u9694\u79bb<\/span><\/span><\/p>\n<\/td>\n

\n

KubernetesPodOperator<\/span><\/span><\/p>\n<\/td>\n

\n

Docker\u8fd0\u884c\u65f6<\/span><\/span><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u56db\u3001\u4e91\u7aef\u6570\u636e\u5904\u7406\u5b9e\u6218<\/span><\/span><\/h3>\n

4.1 AWS S3\u6570\u636e\u5b58\u50a8\u4e0e\u8bfb\u53d6<\/span><\/span><\/h4>\n

import boto3
\nimport pandas as pd
\n# \u521d\u59cb\u5316S3\u5ba2\u6237\u7aef
\ns3 = boto3.client('s3')
\n# \u4e0a\u4f20\u6570\u636e\u5230S3
\ns3.upload_file(
\n\u00a0\u00a0\u00a0\u00a0Filename='local_data.csv',
\n\u00a0\u00a0\u00a0\u00a0Bucket='my-data-bucket',
\n\u00a0\u00a0\u00a0\u00a0Key='analytics\/raw_data.csv'
\n)
\n# \u4f7f\u7528S3 Select\u76f4\u63a5\u67e5\u8be2（\u65e0\u9700\u4e0b\u8f7d\u5b8c\u6574\u6587\u4ef6）
\nresponse = s3.select_object_content(
\n\u00a0\u00a0\u00a0\u00a0Bucket='my-data-bucket',
\n\u00a0\u00a0\u00a0\u00a0Key='analytics\/large_data.csv',
\n\u00a0\u00a0\u00a0\u00a0ExpressionType='SQL',
\n\u00a0\u00a0\u00a0\u00a0Expression="SELECT * FROM s3object LIMIT 1000",
\n\u00a0\u00a0\u00a0\u00a0InputSerialization={'CSV': {'FileHeaderInfo': 'USE'}},
\n\u00a0\u00a0\u00a0\u00a0OutputSerialization={'CSV': {}}
\n)
\nfor event in response['Payload']:
\n\u00a0\u00a0\u00a0\u00a0if 'Records' in event:
\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0print(event['Records']['Payload'].decode('utf-8'))<\/p>\n

4.2 Google BigQuery\u6570\u636e\u5206\u6790<\/span><\/span><\/h4>\n

from google.cloud import bigquery
\nimport pandas as pd
\n# \u521d\u59cb\u5316BigQuery\u5ba2\u6237\u7aef
\nclient = bigquery.Client()
\n# \u6267\u884c\u9ad8\u6548\u67e5\u8be2
\nquery = """
\nWITH daily_stats AS (
\n\u00a0\u00a0\u00a0\u00a0SELECT
\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0DATE(timestamp) AS date,
\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0COUNT(*) AS events,
\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0SUM(revenue) AS total_revenue
\n\u00a0\u00a0\u00a0\u00a0FROM `project.dataset.events`
\n\u00a0\u00a0\u00a0\u00a0WHERE timestamp >= TIMESTAMP('2026-01-01')
\n\u00a0\u00a0\u00a0\u00a0GROUP BY date
\n)
\nSELECT * FROM daily_stats
\nORDER BY date DESC
\nLIMIT 30
\n"""
\n# \u6267\u884c\u67e5\u8be2\u5e76\u8f6c\u6362\u4e3aDataFrame
\ndf = client.query(query).to_dataframe()
\n# \u6570\u636e\u53ef\u89c6\u5316
\ndf.plot(x='date', y='total_revenue', kind='line', figsize=(12, 6))<\/p>\n

4.3 \u4e91\u7aef\u6570\u636e\u6d41\u7a0b\u8bbe\u8ba1<\/span><\/span><\/h4>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a01. <\/span>\u6570\u636e\u6444\u53d6\u5c42<\/span>：\u4f7f\u7528AWS Kinesis\/Firebase Realtime Database<\/span><\/span><\/p>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a02. <\/span>\u5b58\u50a8\u5c42<\/span>：S3\u5bf9\u8c61\u5b58\u50a8 + BigQuery\u6570\u636e\u4ed3\u5e93<\/span><\/span><\/p>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a03. <\/span>\u8ba1\u7b97\u5c42<\/span>：Dataflow\/AWS Glue\u8fdb\u884cETL<\/span><\/span><\/p>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a04. <\/span>\u670d\u52a1\u5c42<\/span>：API Gateway + Cloud Functions\u63d0\u4f9b\u6570\u636e\u670d\u52a1<\/span><\/span><\/p>\n

\u4e94\u3001\u7efc\u5408\u6848\u4f8b：\u6784\u5efa\u7aef\u5230\u7aef\u6570\u636e\u5904\u7406\u6d41\u7a0b<\/span><\/span><\/h3>\n

\u573a\u666f<\/span>：\u7535\u5546\u5e73\u53f0\u6bcf\u65e5\u9500\u552e\u6570\u636e\u5206\u6790\u4e0e\u62a5\u544a\u751f\u6210<\/span><\/span><\/p>\n

5.1 \u67b6\u6784\u8bbe\u8ba1<\/span><\/span><\/h4>\n

[\u539f\u59cb\u6570\u636e] \u2192 [S3\u5b58\u50a8] \u2192 [Spark\u8f6c\u6362] \u2192 [BigQuery\u5206\u6790] \u2192 [Airflow\u8c03\u5ea6] \u2192 [\u62a5\u544a\u63a8\u9001]<\/p>\n

5.2 \u5b8c\u6574\u5b9e\u73b0<\/span><\/span><\/h4>\n

from airflow import DAG
\nfrom airflow.operators.python import PythonOperator
\nfrom airflow.providers.amazon.aws.hooks.s3 import S3Hook
\nfrom google.cloud import bigquery
\nimport pandas as pd
\nfrom datetime import datetime, timedelta
\ndef extract_from_s3():
\n\u00a0\u00a0\u00a0\u00a0s3 = S3Hook(aws_conn_id='aws_default')
\n\u00a0\u00a0\u00a0\u00a0df = s3.read_csv(
\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0bucket='ecommerce-data',
\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0key='sales\/2026-02-10.csv'
\n\u00a0\u00a0\u00a0\u00a0)
\n\u00a0\u00a0\u00a0\u00a0df.to_csv('\/tmp\/sales_data.csv', index=False)
\n\u00a0\u00a0\u00a0\u00a0return '\/tmp\/sales_data.csv'
\ndef transform_with_spark(file_path):
\n\u00a0\u00a0\u00a0\u00a0spark = SparkSession.builder.appName("SalesAnalysis").getOrCreate()
\n\u00a0\u00a0\u00a0\u00a0df = spark.read.csv(file_path, header=True, inferSchema=True)
\n\u00a0\u00a0\u00a0\u00a0# \u6570\u636e\u6e05\u6d17\u4e0e\u805a\u5408
\n\u00a0\u00a0\u00a0\u00a0cleaned = df.filter(df.amount > 0) \\\\
\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0.groupBy("category", "region") \\\\
\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0.agg({"amount": "sum", "quantity": "count"})
\n\u00a0\u00a0\u00a0\u00a0cleaned.write.parquet('\/tmp\/processed_sales.parquet', mode='overwrite')
\n\u00a0\u00a0\u00a0\u00a0spark.stop()
\ndef analyze_with_bigquery():
\n\u00a0\u00a0\u00a0\u00a0client = bigquery.Client()
\n\u00a0\u00a0\u00a0\u00a0job = client.load_table_from_file(
\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0'\/tmp\/processed_sales.parquet',
\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0'project.ecommerce.sales_summary',
\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0job_config=bigquery.LoadJobConfig(
\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0source_format=bigquery.SourceFormat.PARQUET
\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0)
\n\u00a0\u00a0\u00a0\u00a0)
\n\u00a0\u00a0\u00a0\u00a0job.result() \u00a0# \u7b49\u5f85\u5b8c\u6210
\n\u00a0\u00a0\u00a0\u00a0# \u6267\u884c\u5206\u6790\u67e5\u8be2
\n\u00a0\u00a0\u00a0\u00a0query = """
\n\u00a0\u00a0\u00a0\u00a0SELECT
\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0category,
\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0region,
\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0SUM(amount) AS total_sales,
\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0COUNT(*) AS transactions
\n\u00a0\u00a0\u00a0\u00a0FROM `project.ecommerce.sales_summary`
\n\u00a0\u00a0\u00a0\u00a0GROUP BY category, region
\n\u00a0\u00a0\u00a0\u00a0ORDER BY total_sales DESC
\n\u00a0\u00a0\u00a0\u00a0"""
\n\u00a0\u00a0\u00a0\u00a0results = client.query(query).to_dataframe()
\n\u00a0\u00a0\u00a0\u00a0results.to_html('\/tmp\/sales_report.html')
\nwith DAG(
\n\u00a0\u00a0\u00a0\u00a0dag_id="ecommerce_analytics",
\n\u00a0\u00a0\u00a0\u00a0schedule_interval="@daily",
\n\u00a0\u00a0\u00a0\u00a0start_date=datetime(2026, 1, 1),
\n\u00a0\u00a0\u00a0\u00a0catchup=False
\n) as dag:
\n\u00a0\u00a0\u00a0\u00a0extract_task = PythonOperator(
\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0task_id="extract",
\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0python_callable=extract_from_s3
\n\u00a0\u00a0\u00a0\u00a0)
\n\u00a0\u00a0\u00a0\u00a0transform_task = PythonOperator(
\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0task_id="transform",
\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0python_callable=transform_with_spark
\n\u00a0\u00a0\u00a0\u00a0)
\n\u00a0\u00a0\u00a0\u00a0analyze_task = PythonOperator(
\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0task_id="analyze",
\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0python_callable=analyze_with_bigquery
\n\u00a0\u00a0\u00a0\u00a0)
\n\u00a0\u00a0\u00a0\u00a0extract_task >> transform_task >> analyze_task<\/p>\n

\u516d\u3001\u603b\u7ed3\u4e0e\u8fdb\u9636\u65b9\u5411<\/span><\/span><\/h3>\n

6.1 \u5173\u952e\u6536\u83b7<\/span><\/span><\/h4>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u2022 <\/span>\u5206\u5e03\u5f0f\u8ba1\u7b97<\/span>：Spark\u9002\u5408TB\u7ea7\u6570\u636e\u6279\u5904\u7406，Dask\u9002\u5408\u79d1\u5b66\u8ba1\u7b97<\/span><\/span><\/p>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u2022 <\/span>\u6570\u636e\u5e93\u4f18\u5316<\/span>：\u7d22\u5f15\u8bbe\u8ba1\u3001\u67e5\u8be2\u8ba1\u5212\u5206\u6790\u662f\u6027\u80fd\u8c03\u4f18\u6838\u5fc3<\/span><\/span><\/p>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u2022 <\/span>\u6570\u636e\u7ba1\u9053<\/span>：Airflow\u9002\u5408\u9759\u6001DAG，Prefect\u9002\u5408\u52a8\u6001\u5de5\u4f5c\u6d41<\/span><\/span><\/p>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u2022 <\/span>\u4e91\u7aef\u6574\u5408<\/span>：S3+BigQuery\u5b9e\u73b0\u5f39\u6027\u5b58\u50a8\u4e0e\u9ad8\u6548\u5206\u6790<\/span><\/span><\/p>\n

6.2 \u8fdb\u9636\u5b66\u4e60\u8def\u5f84<\/span><\/span><\/h4>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a01. <\/span>\u6df1\u5165Spark<\/span>：\u5b66\u4e60DataFrame API\u3001Spark SQL\u4f18\u5316\u3001Structured <\/span> Streaming<\/span><\/span><\/p>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a02. <\/span>\u6570\u636e\u5e93\u8fdb\u9636<\/span>：\u638c\u63e1PostgreSQL\u7d22\u5f15\u7ed3\u6784\u3001MySQL\u67e5\u8be2\u4f18\u5316\u5668\u539f\u7406<\/span><\/span><\/p>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a03. <\/span>\u5bb9\u5668\u5316\u90e8\u7f72<\/span>：Docker + Kubernetes\u7f16\u6392\u6570\u636e\u7ba1\u9053<\/span><\/span><\/p>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a04. <\/span>\u5b9e\u65f6\u8ba1\u7b97<\/span>：Kafka + Flink\u6d41\u5f0f\u5904\u7406\u67b6\u6784<\/span><\/span><\/p>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a05. <\/span>MLOps<\/span>：\u6a21\u578b\u90e8\u7f72\u3001\u76d1\u63a7\u4e0e\u81ea\u52a8\u91cd\u8bad\u7ec3\u6d41\u7a0b<\/span><\/span><\/p>\n

6.3 \u884c\u4e1a\u6700\u4f73\u5b9e\u8df5<\/span><\/span><\/h4>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u2022 <\/span>\u6570\u636e\u6cbb\u7406<\/span>：\u5efa\u7acb\u6570\u636e\u76ee\u5f55\u4e0e\u8840\u7f18\u5173\u7cfb<\/span><\/span><\/p>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u2022 <\/span>\u6210\u672c\u4f18\u5316<\/span>：\u4f7f\u7528Spot\u5b9e\u4f8b\u3001\u5408\u7406\u8bbe\u7f6e\u6570\u636e\u751f\u547d\u5468\u671f\u7b56\u7565<\/span><\/span><\/p>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u2022 <\/span>\u76d1\u63a7\u544a\u8b66<\/span>：Prometheus + Grafana\u76d1\u63a7\u7cfb\u7edf\u5065\u5eb7\u5ea6<\/span><\/span><\/p>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u2022 <\/span>\u5b89\u5168\u5408\u89c4<\/span>：\u6570\u636e\u52a0\u5bc6\u3001\u8bbf\u95ee\u63a7\u5236\u3001\u5ba1\u8ba1\u65e5\u5fd7<\/span><\/span><\/p>\n

\u9002\u7528\u8bfb\u8005<\/span>：\u5177\u5907Python Pandas\u57fa\u7840\u7684\u6570\u636e\u5206\u6790\u5e08\u3001\u6570\u636e\u5de5\u7a0b\u5e08\u3001\u540e\u7aef\u5f00\u53d1\u4eba\u5458\u3002\u672c\u6587\u4ee3\u7801\u5747\u5df2\u5728\u751f\u4ea7\u73af\u5883\u9a8c\u8bc1，\u53ef\u76f4\u63a5\u5e94\u7528\u4e8e\u5b9e\u9645\u9879\u76ee\u3002\u5efa\u8bae\u8bfb\u8005\u6309\u6a21\u5757\u9010\u6b65\u5b9e\u8df5，\u7ed3\u5408\u81ea\u8eab\u4e1a\u52a1\u573a\u666f\u6784\u5efa\u5b9a\u5236\u5316\u6570\u636e\u5de5\u7a0b\u4f53\u7cfb\u3002<\/span><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

\u5f15\u8a00\u968f\u7740\u6570\u636e\u89c4\u6a21\u7206\u70b8\u5f0f\u589e\u957f，\u4f20\u7edf\u5355\u673a\u6570\u636e\u5206\u6790\u5df2\u96be\u4ee5\u6ee1\u8db3\u4f01\u4e1a\u7ea7\u9700\u6c42\u3002Python\u6570\u636e\u5206\u6790\u6b63\u4ece\\”\u5355\u673a\u811a\u672c\\”\u5411\\”\u5206\u5e03\u5f0f\u5de5\u7a0b\u5316\\”\u6f14\u8fdb\u3002\u672c\u6587\u5c06\u7cfb\u7edf\u8bb2\u89e3：\u5206\u5e03\u5f0f\u8ba1\u7b97\u5f15\u64ce（Spark\/Dask）\u3001\u6570\u636e\u5e93\u6027\u80fd\u4f18\u5316\u3001\u81ea\u52a8\u5316\u6570\u636e\u7ba1\u9053（Airflow\/Prefect）\u53ca\u4e91\u7aef\u6570\u636e\u5904\u7406\u5b9e\u6218，\u5e2e\u52a9\u4f60\u6784\u5efa\u7a33\u5b9a\u3001\u53ef\u6269\u5c55\u7684\u5927\u6570\u636e\u5de5\u4f5c\u6d41\u3002\u4e00\u3001\u5206\u5e03\u5f0f\u8ba1\u7b97：Spark vs Dask1.1 Spark&#<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[81,1224,323,801,62],"topic":[],"class_list":["post-75137","post","type-post","status-publish","format-standard","hentry","category-server","tag-python","tag-1224","tag-323","tag-801","tag-62"],"yoast_head":"\nPython\u6570\u636e\u5206\u6790\uff1a\u5927\u6570\u636e\u4e0e\u5de5\u7a0b\u5316 - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.wsisp.com\/helps\/75137.html\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Python\u6570\u636e\u5206\u6790\uff1a\u5927\u6570\u636e\u4e0e\u5de5\u7a0b\u5316 - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3\" \/>\n<meta property=\"og:description\" content=\"\u5f15\u8a00\u968f\u7740\u6570\u636e\u89c4\u6a21\u7206\u70b8\u5f0f\u589e\u957f，\u4f20\u7edf\u5355\u673a\u6570\u636e\u5206\u6790\u5df2\u96be\u4ee5\u6ee1\u8db3\u4f01\u4e1a\u7ea7\u9700\u6c42\u3002Python\u6570\u636e\u5206\u6790\u6b63\u4ece"\u5355\u673a\u811a\u672c"\u5411"\u5206\u5e03\u5f0f\u5de5\u7a0b\u5316"\u6f14\u8fdb\u3002\u672c\u6587\u5c06\u7cfb\u7edf\u8bb2\u89e3：\u5206\u5e03\u5f0f\u8ba1\u7b97\u5f15\u64ce（Spark\/Dask）\u3001\u6570\u636e\u5e93\u6027\u80fd\u4f18\u5316\u3001\u81ea\u52a8\u5316\u6570\u636e\u7ba1\u9053（Airflow\/Prefect）\u53ca\u4e91\u7aef\u6570\u636e\u5904\u7406\u5b9e\u6218，\u5e2e\u52a9\u4f60\u6784\u5efa\u7a33\u5b9a\u3001\u53ef\u6269\u5c55\u7684\u5927\u6570\u636e\u5de5\u4f5c\u6d41\u3002\u4e00\u3001\u5206\u5e03\u5f0f\u8ba1\u7b97：Spark vs Dask1.1 Spark&#\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.wsisp.com\/helps\/75137.html\" \/>\n<meta property=\"og:site_name\" content=\"\u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3\" \/>\n<meta property=\"article:published_time\" content=\"2026-02-11T07:33:36+00:00\" \/>\n<meta name=\"author\" content=\"admin\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"\u4f5c\u8005\" \/>\n\t<meta name=\"twitter:data1\" content=\"admin\" \/>\n\t<meta name=\"twitter:label2\" content=\"\u9884\u8ba1\u9605\u8bfb\u65f6\u95f4\" \/>\n\t<meta name=\"twitter:data2\" content=\"5 \u5206\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.wsisp.com\/helps\/75137.html\",\"url\":\"https:\/\/www.wsisp.com\/helps\/75137.html\",\"name\":\"Python\u6570\u636e\u5206\u6790\uff1a\u5927\u6570\u636e\u4e0e\u5de5\u7a0b\u5316 - 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