January 2019
Beginner to intermediate
154 pages
4h 31m
English
Content preview from Apache Spark Quick Start Guide
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Memory
Efficient use of memory is critical for good performance. In the earlier versions of Spark, the memory was used for three main purposes:
- RDD storage
- Shuffle and aggregation storage
- User code
Memory was divided among them with some fixed proportions. For example, RDD storage had 60%, shuffle had 20%, and the user code had 20% by default. These properties could be changed by users, depending on the nature of the Spark applications. In the current version of Spark (1.6+), memory management is done automatically.
In the case of caching, we need to be careful when choosing the storage level. The default caching level is MEMORY_ONLY, which keeps the data in memory. If the RDD is big enough and can't fit in memory, then Spark fits as many ...
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