Member since
05-21-2016
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06-12-2016
11:32 AM
Thank you for your response @Timothy Spann
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06-12-2016
07:57 AM
2 Kudos
As Spark has much more advantages over the Map-Reduce framework like in-memory processing, Faster Real-time data analysis and also it claims to process data 100x faster than MapReduce, while 10x faster with the disks. So, Will it be replacing the Map-Reduce? or will it be used paralelly?
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05-23-2016
05:03 PM
1 Kudo
Lets assume a NameNode crashes in the HDFS HA (High Availability) setup and the Secondary/Fallback NameNode takes over, What will happen to the currently running MapReduce Jobs? Will they also fail? > Also will the secondary NameNode takes over for the failed Primary NameNode automatically or do we need any administrator to manually redirect the client requests to the Secondary NameNode? Thanks.
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05-22-2016
05:54 AM
Thank you for the response @Benjamin Leonhardi... This explains the types of Joins clearly, So does this mean that 'Distributed Cache' is only used for Broadcast join (Mapside join)?
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05-21-2016
10:45 AM
Hi All, I am new to the Hadoop and just started learning about it. While some research i have come across some concepts like Mapper & Reducer joins and Distributed Cache. > Are these joins similar to traditional database joins? > Under what scenarios/conditions these joins are used? > Also need some information regarding the "Distributed Cache" concept and exactly how this is used in Mapper & Reducer Joins. Thanks.
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