hadoop - Hadoop HDFS: Read/Write parallelism?

Question

Couldn't find enough information on internet so asking here:

Assuming I'm writing a huge file to disk, hundreds of Terabytes, which is a result of mapreduce (or spark or whatever). How would mapreduce write such a file to HDFS efficiently (potentially parallel?) which could be read later in a parallel way as well?

My understanding is that HDFS is simply block based (128MB e.g.). so in order to write the second block, you must have wrote the first block (or at least determine what content will go to block 1). Let's say it's a CSV file, it is quite possible that a line in the file will span two blocks -- how could we read such CSV to different mapper in mapreduce? Does it have to do some smart logic to read two blocks, concat them and read the proper line?

标签： hadoophdfs

Answer 1

Hadoop uses RecordReaders and InputFormats as the two interfaces which read and understand bytes within blocks.

By default, in Hadoop MapReduce each record ends on a new line with TextInputFormat, and for the scenario where just one line crosses the end of a block, the next block must be read, even if it's just literally the \r\n characters

Writing data is done from reduce tasks, or Spark executors, etc, in that each task is responsible for writing only a subset of the entire output. You'll generally never get a single file for non-small jobs, and this isn't an issue because the input arguments to most Hadoop processing engines are meant to scan directories, not point at single files