Why in Hadoop do we have default block size as 128 and replication as 3?

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Qwikmind

Having block size as 128 or greater than 128 based on your input file will make the smallest data unit as 128MB in your cluster. Your input files will be divided into N number of 128 MB blocks which will help you to reduce the network overhead. How it does ? For example, consider the block size is...
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Having block size as 128 or greater than 128 based on your input file will make the smallest data unit as 128MB in your cluster. Your input files will be divided into N number of 128 MB blocks which will help you to reduce the network overhead. How it does ? For example, consider the block size is reduced to 20MB. Now how many spilts will be there ? How many times your data will be transmitted between network for aggregations ? How many number of replication files will get created?. Hence the default 128MB and replication factor of 3 is used to reduce the network overhead and your cluster jobs will be fast. 128 and 3RF is by default which is already benchmàrked w.r.t performance. You can change these values based on your input file. read less
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Big Data Architect

A large chunk size offers several important advantages. First, it reduces clients’ need to interact with the master because reads and writes on the same chunk require only one initial request to the master for chunk location information. The reduction is especially significant for our workloads because...
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A large chunk size offers several important advantages. First, it reduces clients’ need to interact with the master because reads and writes on the same chunk require only one initial request to the master for chunk location information. The reduction is especially significant for our workloads because applications mostly read and write large files sequentially. Second, since on a large chunk, a client is more likely to perform many operations on a given chunk, it can reduce network overhead by keeping a persistent TCP connection to the chunk server over an extended period of time. Third, it reduces the size of the metadata stored on the master. This allows us to keep the metadata in memory, which in turn brings other advantages read less
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