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python - 使用 PySpark 连接速度很慢

问题描述

我正在使用以下代码玩 PySpark：

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("Scoring System").getOrCreate()

df = spark.read.csv('output.csv')

df.show()

我在命令行上运行 python trial.py 后大约 5 到 10 分钟，没有进展：

To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
2019-05-05 22:58:31 WARN  Utils:66 - Service 'SparkUI' could not bind on port 4040. Attempting port 4041.
2019-05-05 22:58:32 WARN  Client:66 - Neither spark.yarn.jars nor spark.yarn.archive is set, falling back to uploading libraries under SPARK_HOME.
[Stage 0:>                                                          (0 + 0) / 1]2019-05-05 23:00:08 WARN  YarnScheduler:66 - Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
2019-05-05 23:00:23 WARN  YarnScheduler:66 - Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
2019-05-05 23:00:38 WARN  YarnScheduler:66 - Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
2019-05-05 23:00:53 WARN  YarnScheduler:66 - Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
[Stage 0:>                                                          (0 + 0) / 1]2019-05-05 23:01:08 WARN  YarnScheduler:66 - Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
2019-05-05 23:01:23 WARN  YarnScheduler:66 - Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
2019-05-05 23:01:38 WARN  YarnScheduler:66 - Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources

我预感我的工作节点中缺少资源（？），或者我错过了什么？

标签： pythonhadooppysparkpyspark-sql

解决方案

尝试增加Executor的数量和内存 pyspark --num-executors 5 --executor-memory 1G

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