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apache / hawq / HAWQ-1075
commit96288ee1d186da6bc5ec594c8c1829c7b8648a01[log] [tgz]
authorShivram Mani <shivram.mani@gmail.com>Thu Sep 29 12:21:12 2016 -0700
committerShivram Mani <shivram.mani@gmail.com>Thu Sep 29 12:21:12 2016 -0700
tree961eef0085293ab0b0d7222fac65f58a3751f64b
parent6910a42d6a9ddaf8f360de2c3cdec56ff6f84de4 [diff]
HAWQ-1045. PXF version update and update pxf classpaths to default to hdp paths
2 files changed
tree: 961eef0085293ab0b0d7222fac65f58a3751f64b
  1. config/
  2. contrib/
  3. depends/
  4. doc/
  5. pxf/
  6. src/
  7. tools/
  8. .gitignore
  9. .travis.yml
  10. aclocal.m4
  11. BUILD_INSTRUCTIONS.md
  12. configure
  13. configure.in
  14. COPYRIGHT
  15. DISCLAIMER
  16. getversion
  17. GNUmakefile.in
  18. LICENSE
  19. Makefile
  20. NOTICE
  21. pom.xml
  22. putversion
  23. README-PostgreSQL
  24. README.md
README.md

Apache HAWQ

Apache HAWQ is a Hadoop native SQL query engine that combines the key technological advantages of MPP database with the scalability and convenience of Hadoop. HAWQ reads data from and writes data to HDFS natively. HAWQ delivers industry-leading performance and linear scalability. It provides users the tools to confidently and successfully interact with petabyte range data sets. HAWQ provides users with a complete, standards compliant SQL interface. More specifically, HAWQ has the following features:

  • On-premise or cloud deployment
  • Robust ANSI SQL compliance: SQL-92, SQL-99, SQL-2003, OLAP extension
  • Extremely high performance. many times faster than other Hadoop SQL engine
  • World-class parallel optimizer
  • Full transaction capability and consistency guarantee: ACID
  • Dynamic data flow engine through high speed UDP based interconnect
  • Elastic execution engine based on virtual segment & data locality
  • Support multiple level partitioning and List/Range based partitioned tables
  • Multiple compression method support: snappy, gzip, quicklz, RLE
  • Multi-language user defined function support: Python, Perl, Java, C/C++, R
  • Advanced machine learning and data mining functionalities through MADLib
  • Dynamic node expansion: in seconds
  • Most advanced three level resource management: Integrate with YARN and hierarchical resource queues.
  • Easy access of all HDFS data and external system data (for example, HBase)
  • Hadoop Native: from storage (HDFS), resource management (YARN) to deployment (Ambari).
  • Authentication & Granular authorization: Kerberos, SSL and role based access
  • Advanced C/C++ access library to HDFS and YARN: libhdfs3 & libYARN
  • Support most third party tools: Tableau, SAS et al.
  • Standard connectivity: JDBC/ODBC

Build & Install & Test

Please refer to the BUILD_INSTRUCTIONS file.