目录
1. 说明
2. 服务器规划
3. 步骤
3.1 要点
3.2 配置文件
3.2 访问Spark Master
4. 使用测试
5. 参考
1. 说明
- 以docker容器方式实现apache spark计算集群，能灵活的增减配置与worker数目。
2. 服务器规划
| 服务器 (1master, 3workers) | ip | 开放端口 | 备注 |
|---|---|---|---|
| center01.dev.sb | 172.16.20.20 | 8080,7077 | 硬件配置：32核64G 软件配置：ubuntu22.04 + 宝塔面板 |
| host001.dev.sb | 172.16.20.60 | 8081,7077 | 8核16G |
| host002.dev.sb | 172.16.20.61 | 8081,7077 | ... |
| BEN-ZX-GZ-MH | 172.16.1.106 | 应用服务，发任务机器 |
3. 步骤
3.1 要点
- worker节点的网络模式用host，不然spark ui页面中获取的路径会是容器ip，里面的链接变得不可访问
- 测试前需保证任务发布机与Worker机的运行语言版本一致(如: 同是python10 / python12)，否则会报错 "Python in worker has different version (3, 12) than that in driver 3.10"。
- 确保发任务机器能被Worker节点访问，否则会出现诸如:
"WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources"
等莫名其妙的错误，观察工作机错误日志：
"Caused by: java.io.IOException: Failed to connect to BEN-ZX-GZ-MH/<unresolved>:10027"
由于访问不了发任务机器而导致的，目前采取的解决方法是在配置里写死映射IP
3.2 配置文件
docker-compose.spark-master.yml
services:spark:image: docker.io/bitnami/spark:latestcontainer_name: spark-masterrestart: alwaysenvironment:- SPARK_MODE=master- SPARK_RPC_AUTHENTICATION_ENABLED=no- SPARK_RPC_ENCRYPTION_ENABLED=no- SPARK_LOCAL_STORAGE_ENCRYPTION_ENABLED=no- SPARK_SSL_ENABLED=no- SPARK_USER=sparkports:- '8080:8080'- '7077:7077'docker-compose.spark-worker.yml
services:spark-worker:image: docker.io/bitnami/spark:latestcontainer_name: spark-workerrestart: alwaysenvironment:- SPARK_MODE=worker- SPARK_MASTER_URL=spark://spark-master:7077- SPARK_WORKER_MEMORY=2G- SPARK_WORKER_CORES=2- SPARK_RPC_AUTHENTICATION_ENABLED=no- SPARK_RPC_ENCRYPTION_ENABLED=no- SPARK_LOCAL_STORAGE_ENCRYPTION_ENABLED=no- SPARK_SSL_ENABLED=no- SPARK_USER=sparkports:- '8081:8081'- '7077:7077'extra_hosts:- "spark-master:172.16.20.20"- "BEN-ZX-GZ-MH:172.16.1.106"network_mode: host3.2 访问Spark Master
访问Spark Master，可见已有两台worker机可供驱使
4. 使用测试
t3.py
from pyspark.sql import SparkSessiondef main():# Initialize SparkSessionspark = (SparkSession.builder.appName("HelloSpark") # type: ignore.master("spark://center01.dev.sb:7077").config("spark.executor.memory", "512m").config("spark.cores.max", "1")# .config("spark.driver.bindAddress", "center01.dev.sb").getOrCreate())# Create an RDD containing numbers from 1 to 10numbers_rdd = spark.sparkContext.parallelize(range(1, 11))# Count the elements in the RDDcount = numbers_rdd.count()print(f"Count of numbers from 1 to 10 is: {count}")# Stop the SparkSessionspark.stop()if __name__ == "__main__":main()
运行监控 
结果
5. 参考
- containers/bitnami/spark at main · bitnami/containers · GitHub
