使用pyspark连接很慢

qybjjes1  于 2021-05-29  发布在  Hadoop
关注(0)|答案(1)|浏览(707)

我正在使用pyspark玩以下代码:

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之后,大约有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

我预感在我的worker节点(?)中缺少资源,或者我遗漏了什么?

5f0d552i

5f0d552i1#

尝试增加执行器和内存的数量pyspark--num executors 5--executor memory 1g

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