January 2018
Beginner to intermediate
412 pages
9h 33m
English
Content preview from Practical Big Data Analytics
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Limitations of Hadoop
A few of the common limitations with Hadoop were as follows:
- I/O Bound operations: Due to the reliance on local disk storage for saving and retrieving data, any operation performed in Hadoop incurred an I/O overhead. The problem became more acute in cases of larger datasets that involved thousands of blocks of data across hundreds of servers. To be fair, the ability to co-ordinate concurrent I/O operations (via HDFS) formed the foundation of distributed computing in Hadoop world. However, leveraging the capability and tuning the Hadoop cluster in an efficient manner across different use cases and datasets required an immense and perhaps disproportionate level of expertise. Consequently, the I/O bound nature of workloads ...
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