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apache / hawq / refs/tags/2.0.0-beta-incubating-RC3
tage57b11b5cdb717fe6d207fb530609c5b9e99f533
taggerGoden Yao <tyao@office-5-173.pa.pivotal.io>Wed Jan 13 16:57:46 2016 -0800
object91c45cab0e5844abfe0f398f2378637f4028fe45 
Remove external Python Module, gradle-wrapper.jar,more RAT cleanup with license header injection
commit91c45cab0e5844abfe0f398f2378637f4028fe45[log] [tgz]
authorRoman Shaposhnik <rvs@apache.org>Mon Jan 04 15:36:29 2016 -0800
committerRoman Shaposhnik <rvs@apache.org>Tue Jan 12 19:13:19 2016 -0800
tree0af2a021b9663edbaa0f0ad2e73fe8bbd343e3dc
parent8ccff7b2f0b0c4e272428a8ebecb31b4218744b2 [diff]
HAWQ-184. pass RAT check
289 files changed
tree: 0af2a021b9663edbaa0f0ad2e73fe8bbd343e3dc
  1. config/
  2. contrib/
  3. depends/
  4. doc/
  5. pxf/
  6. src/
  7. tools/
  8. .gitignore
  9. aclocal.m4
  10. BUILD_INSTRUCTIONS.md
  11. configure
  12. configure.in
  13. COPYRIGHT
  14. DISCLAIMER
  15. getversion
  16. GNUmakefile.in
  17. LICENSE
  18. Makefile
  19. NOTICE
  20. pom.xml
  21. putversion
  22. README-PostgreSQL
  23. 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.