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Author: hsrasheed

articles/hdinsight/spark/apache-spark-perf.md

@@ -194,7 +194,7 @@ When running concurrent queries, consider the following:
 
 For more information on using Ambari to configure executors, see [Apache Spark settings - Spark executors](apache-spark-settings.md#configuring-spark-executors).
 
-Monitor your query performance for outliers or other performance issues, by looking at the timeline view, SQL graph, job statistics, and so forth. For information on debugging Spark jobs using YARN and the Spark History server, see [Debug Apache Spark jobs running on Azure HDInsight](apache-spark-job-debugging). For tips on using YARN Timeline Server, see [Access Apache Hadoop YARN application logs](../hdinsight-hadoop-access-yarn-app-logs-linux.md).
+Monitor your query performance for outliers or other performance issues, by looking at the timeline view, SQL graph, job statistics, and so forth. For information on debugging Spark jobs using YARN and the Spark History server, see [Debug Apache Spark jobs running on Azure HDInsight](apache-spark-job-debugging.md). For tips on using YARN Timeline Server, see [Access Apache Hadoop YARN application logs](../hdinsight-hadoop-access-yarn-app-logs-linux.md).
 
 Sometimes one or a few of the executors are slower than the others, and tasks take much longer to execute. This frequently happens on larger clusters (> 30 nodes). In this case, divide the work into a larger number of tasks so the scheduler can compensate for slow tasks. For example, have at least twice as many tasks as the number of executor cores in the application. You can also enable speculative execution of tasks with `conf: spark.speculation = true`.