Spark報錯（二）：關於Spark-Streaming官方示例wordcount執行異常

關於Spark-Streaming官方示例：
https://github.com/apache/spark/tree/master/examples

本文采用kafka作為spark輸入源
執行時出現以下日誌：

18/09/12 11:15:28 INFO JobScheduler: Added jobs for time 1536722117000 ms
18/09/12 11:15:28 INFO JobScheduler: Added jobs for time 1536722118000 ms
18/09/12 11:15:28 INFO JobScheduler: Added jobs for time 1536722119000 ms
18/09/12 11:15:28 INFO JobScheduler: Added jobs for time 1536722120000 ms
18/09/12 11:15:28 INFO JobScheduler: Added jobs for time 1536722121000 ms
18/09/12 11:15:28 INFO JobScheduler: Added jobs for time 1536722122000 ms
18/09/12 11:15:29 INFO JobScheduler: Added jobs for time 1536722123000 ms
18/09/12 11:15:29 INFO JobScheduler: Added jobs for time 1536722124000 ms
18/09/12 11:15:29 INFO JobScheduler: Added jobs for time 1536722125000 ms
18/09/12 11:15:29 INFO JobScheduler: Added jobs for time 1536722126000 ms
18/09/12 11:15:29 INFO JobScheduler: Added jobs for time 1536722127000 ms
18/09/12 11:15:29 INFO JobScheduler: Added jobs for time 1536722128000 ms
18/09/12 11:15:29 INFO JobScheduler: Added jobs for time 1536722129000 ms
18/09/12 11:15:30 INFO JobScheduler: Added jobs for time 1536722130000 ms
18/09/12 11:15:31 INFO JobScheduler: Added jobs for time 1536722131000 ms
18/09/12 11:15:32 INFO JobScheduler: Added jobs for time 1536722132000 ms
18/09/12 11:15:33 INFO JobScheduler: Added jobs for time 1536722133000 ms
18/09/12 11:15:34 INFO JobScheduler: Added jobs for time 1536722134000 ms
18/09/12 11:15:35 INFO JobScheduler: Added jobs for time 1536722135000 ms
18/09/12 11:15:36 INFO JobScheduler: Added jobs for time 1536722136000 ms
18/09/12 11:15:37 INFO JobScheduler: Added jobs for time 1536722137000 ms
18/09/12 11:15:38 INFO JobScheduler: Added jobs for time 1536722138000 ms
18/09/12 11:15:39 INFO JobScheduler: Added jobs for time 1536722139000 ms
18/09/12 11:15:40 INFO JobScheduler: Added jobs for time 1536722140000 ms
18/09/12 11:15:41 INFO JobScheduler: Added jobs for time 1536722141000 ms
18/09/12 11:15:42 INFO JobScheduler: Added jobs for time 1536722142000 ms
18/09/12 11:15:43 INFO JobScheduler: Added jobs for time 1536722143000 ms
18/09/12 11:15:44 INFO JobScheduler: Added jobs for time 1536722144000 ms
18/09/12 11:15:45 INFO JobScheduler: Added jobs for time 1536722145000 ms

很顯然這並非正常日誌。檢視kafka端消費正常後，確認是spark的問題。最後在官網看到一段話：

簡單來說就是如果是本地執行，指定master不要指定local或local[1]，應該設定為local[n]，n>接收器數量。
如果是叢集模式執行，分配給Spark Streaming的核心數量必須大於接收者的數量。否則，spark就只能接受資料，無法處理資料了。

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更多：Spark專欄
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作者：桃花惜春風
轉載請標明出處，原文地址：
https://blog.csdn.net/xiaoyu_BD/article/details/82688001
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