These are the main factors in the performance of Impala versus that of other Hadoop components and related technologies.

Impala AVOIDS MapReduce. While MapReduce is a great general parallel processing model with many benefits, it is not designed to execute SQL. Impala avoids the inefficiencies of MapReduce in these ways:

• Impala does not MATERIALIZE intermediate results to disk. SQL queries often map to multiple MapReduce jobs with all intermediate data sets written to disk.
• Impala avoids MapReduce start-up time. For interactive queries, the MapReduce start-up time becomes very noticeable. Impala runs as a service and essentially has no start-up time.
• Impala can more naturally disperse query plans instead of having to fit them into a pipeline of map and reduce jobs. This enables Impala to parallelize multiple stages of a query and avoid overheads such as sort and shuffle when unnecessary.
  
  Impala uses a more efficient execution engine by taking advantage of modern hardware and technologies:
  
  
• Impala generates runtime code. Impala uses LLVM to generate ASSEMBLY code for the query that is being run. Individual queries do not have to pay the overhead of running on a system that needs to be able to execute arbitrary queries.
• Impala uses AVAILABLE hardware instructions when possible. Impala uses the supplemental SSE3 (SSSE3) instructions which can OFFER tremendous speedups in some cases. (Impala 2.0 and 2.1 required the SSE4.1 instruction set; Impala 2.2 and higher relax the restriction again so only SSSE3 is required.)
• Impala uses better I/O scheduling. Impala is aware of the disk location of blocks and is able to schedule the order to process blocks to keep all disks busy.
• Impala is designed for performance. A lot of time has been spent in designing Impala with sound performance-oriented fundamentals, such as tight inner loops, inlined function calls, minimal branching, better use of cache, and minimal memory usage.

These are the main factors in the performance of Impala versus that of other Hadoop components and related technologies.

Impala avoids MapReduce. While MapReduce is a great general parallel processing model with many benefits, it is not designed to execute SQL. Impala avoids the inefficiencies of MapReduce in these ways: