When encountering 'Out of Memory' errors in Hadoop, which configuration parameter is crucial to inspect?
- mapreduce.map.java.opts
- yarn.scheduler.maximum-allocation-mb
- io.sort.mb
- dfs.datanode.handler.count
When facing 'Out of Memory' errors in Hadoop, it's crucial to inspect the 'mapreduce.map.java.opts' configuration parameter. This parameter determines the Java options for map tasks and can be adjusted to allocate more memory, helping to address memory-related issues in MapReduce jobs.
Loading...
Related Quiz
- In Crunch, a ____ is used to represent a distributed dataset in Hadoop.
- The ____ architecture in Hadoop is designed to avoid a single point of failure in the filesystem.
- Apache ____ is a scripting language in Hadoop used for complex data transformations.
- Hive supports ____ as a form of dynamic partitioning, which optimizes data storage based on query patterns.
- What is the primary purpose of Hadoop Streaming API in the context of processing data?