超时错误:带有 400 状态代码的错误:“要求失败:会话未处于活动状态". [英] Timeout error: Error with 400 StatusCode: "requirement failed: Session isn't active."

问题描述

我正在使用 Zeppelin v0.7.3 笔记本来运行 Pyspark 脚本.在一个段落中，我正在运行脚本以将数据从 dataframe 写入 Blob 文件夹中的 parquet 文件.文件按国家/地区进行分区.数据帧的行数为 99,452,829.当脚本到达1小时时，遇到错误——

I'm using Zeppelin v0.7.3 notebook to run Pyspark scripts. In one paragraph, I am running script to write data from dataframe to a parquet file in a Blob folder. File is partitioned per country. Number of rows of dataframe is 99,452,829. When the script reaches 1 hour, an error is encountered -

400 状态码错误:要求失败:会话不是活跃.

Error with 400 StatusCode: "requirement failed: Session isn't active.

我的笔记本默认解释器是 jdbc.我已经阅读了 timeoutlifecyclemanager 并在解释器设置中添加了 zeppelin.interpreter.lifecyclemanager.timeout.threshold 并将其设置为 7200000 但仍然遇到了在处理完成 33% 时达到 1 小时的运行时间后出现错误.

My default interpreter for the notebook is jdbc. I have read about timeoutlifecyclemanager and added in the interpreter setting zeppelin.interpreter.lifecyclemanager.timeout.threshold and set it to 7200000 but still encountered the error after it reaches 1 hour runtime at 33% processing completion.

我在 1 小时超时后检查了 Blob 文件夹，并且镶木地板文件已成功写入 Blob，这些文件确实按国家/地区进行了分区.

I checked the Blob folder after the 1 hr timeout and parquet files were successfully written to Blob which are indeed partitioned per country.

我正在运行将 DF 写入 parquet Blob 的脚本如下:

The script I am running to write DF to parquet Blob is below:

trdpn_cntry_fct_denom_df.write.format("parquet").partitionBy("CNTRY_ID").mode("overwrite").save("wasbs://[e-mail]/cbls/hdi/trdpn_cntry_fct_denom_df.parquet")

这是 Zeppelin 超时问题吗?怎么能延长1小时以上的运行时间?感谢您的帮助.

Is this Zeppelin timeout issue? How can it be extended for more than 1 hour runtime? Thanks for the help.

推荐答案

来自 这个堆栈溢出问题的答案对我有用

根据输出判断，如果您的应用程序没有以 FAILED 状态结束，这听起来像是 Livy 超时错误:您的应用程序可能比 Livy 会话的定义超时(默认为 1 小时)花费的时间更长，因此即使尽管 Spark 应用程序成功，但如果应用程序花费的时间超过 Livy 会话的超时时间，您的笔记本将收到此错误.

Judging by the output, if your application is not finishing with a FAILED status, that sounds like a Livy timeout error: your application is likely taking longer than the defined timeout for a Livy session (which defaults to 1h), so even despite the Spark app succeeds your notebook will receive this error if the app takes longer than the Livy session's timeout.

如果是这种情况，以下是解决方法:

If that's the case, here's how to address it:

1. edit the /etc/livy/conf/livy.conf file (in the cluster's master node)
2. set the livy.server.session.timeout to a higher value, like 8h (or larger, depending on your app)
3. restart Livy to update the setting: sudo restart livy-server in the cluster's master
4. test your code again

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