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Real-time Query for Hadoop
C++ Java Python Thrift Shell CMake Other

Improve distinctpc/sa for small cardinalities.

Improving the cardinality estimate for Flajolet and Martin's algorithm
used in distinctpc and distinctpcsa. The estimate for small cardinalities
is improved by providing a correction hinted to in the original paper.

We use the correction constant 1.75 proposed by Scheuermann et al
DialM-POMC '07 [Near-Optimal Compression of Probabilistic Counting
Sketches for Networking Applications]

Change-Id: I90410328a1a01a72601e7e95ae719fb8caf1587f
Reviewed-on: http://gerrit.cloudera.org:8080/395
Reviewed-by: Ippokratis Pandis <ipandis@cloudera.com>
Tested-by: Internal Jenkins
latest commit 04d31883c0
@superdupershant superdupershant authored Internal Jenkins committed

README.md

Welcome to Impala

Lightning-fast, distributed SQL queries for petabytes of data stored in Apache Hadoop clusters.

Impala is a modern, massively-distributed, massively-parallel, C++ query engine that lets you analyze, transform and combine data from a variety of data sources:

  • Best of breed performance and scalability.
  • Support for data stored in HDFS, Apache HBase and Amazon S3.
  • Wide analytic SQL support, including window functions and subqueries.
  • On-the-fly code generation using LLVM to generate CPU-efficient code tailored specifically to each individual query.
  • Support for the most commonly-used Hadoop file formats, including the Apache Parquet (incubating) project.
  • Apache-licensed, 100% open source.

More about Impala

To learn more about Impala as a business user, or to try Impala live or in a VM, please visit the Impala homepage.

If you are interested in contributing to Impala as a developer, or learning more about Impala's internals and architecture, visit the Impala wiki.

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