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Impala has his own execution engine or it works on MapR in Hadoop eco system?

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Tags : BigData MapReduce hadoop2 Impala
  • 461

    I never got a chance to work on Impala. I have just started reading about Impala. But i have one basic question which i am not clear about Impala. Impala has its own demons so it also has its own execution engine or it works on MapR or other execution engine. Thanks in advance

     
      October 7, 2021 1:24 PM IST
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  • 319 1

    Yes Impala daemons runs the SQL in memory with the resident pool of resources available and managed by YARN or any other resource scheduler. This can be tweaked.

    MapR is a hadoop distribution package - and yes it does offer Impala as part of larger bundle.

      October 9, 2021 1:15 PM IST
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  • 317
    MapReduce is a design pattern for processing large data sets in a distributed and parallel mode.

    Impala is an open source Massively Parallel Processing (MPP) query engine that runs on Apache Hadoop. Impala is more of a warehouse like Hive with its own pro-cons vs Hive.

    Major differences between Imapala and mapreduce are:

    Impala does not use mapreduce. It runs separate Impala daemon which splits the query and runs them in parallel and merge result set at the end.

    Impala does most of its operation in-memory and disk I/O is limited.

    Impala uses hdfs for its storage which brings in reliability with efficiency. It caches in-memory as much as possible results from queries.

    Impala supports new file format like parquet, which is columnar file format. So if you use this format it will be faster for queries where you are accessing only few columns most of the time.
      October 26, 2021 12:51 PM IST
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  • 259
    Impala uses the distributed filesystem HDFS as its primary data storage medium. Impala relies on the redundancy provided by HDFS to guard against hardware or network outages on individual nodes. Impala table data is physically represented as data files in HDFS, using familiar HDFS file formats and compression codecs.
     
      February 2, 2022 1:44 PM IST
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