• Stars
    star
    588
  • Rank 76,022 (Top 2 %)
  • Language
    C++
  • License
    Apache License 2.0
  • Created almost 9 years ago
  • Updated about 1 year ago

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

A modular query optimizer for big data
======================================================================
                 __________  ____  ____  _________
                / ____/ __ \/ __ \/ __ \/ ____/   |
               / / __/ /_/ / / / / /_/ / /   / /| |
              / /_/ / ____/ /_/ / _, _/ /___/ ___ |
              \____/_/    \____/_/ |_|\____/_/  |_|
                  The Greenplum Query Optimizer
              Copyright (c) 2015, Pivotal Software, Inc.
            Licensed under the Apache License, Version 2.0
======================================================================

Welcome to GPORCA, the Greenplum Next Generation Query Optimizer!

To understand the objectives and architecture of GPORCA please refer to the following articles:

Want to Contribute?

Questions? Connect with Greenplum on Slack.

GPORCA supports various build types: debug, release with debug info, release. You'll need CMake 3.1 or higher to build GPORCA. Get it from cmake.org, or your operating system's package manager.

Note: GPDB 6X and later contain their own copy of GPORCA, this version is for GPDB 5X and any other uses.

First Time Setup

Clone GPORCA

git clone https://github.com/greenplum-db/gporca.git
cd gporca

Pre-Requisites

GPORCA uses the following library:

  • GP-Xerces - Greenplum's patched version of Xerces-C 3.1.X

Installing GP-Xerces

GP-XERCES is available here. The GP-XERCES README gives instructions for building and installing.

Build and install GPORCA

ORCA is built with CMake, so any build system supported by CMake can be used. The team uses Ninja because it's really really fast and convenient.

Go into gporca directory:

cmake -GNinja -H. -Bbuild
ninja install -C build

Test GPORCA

To run all GPORCA tests, simply use the ctest command from the build directory after build finishes.

ctest

Much like make, ctest has a -j option that allows running multiple tests in parallel to save time. Using it is recommended for faster testing.

ctest -j8

By default, ctest does not print the output of failed tests. To print the output of failed tests, use the --output-on-failure flag like so (this is useful for debugging failed tests):

ctest -j8 --output-on-failure

To run only the previously failed ctests, use the --rerun-failed flag.

ctest -j8 --rerun-failed --output-on-failure

To run a specific individual test, use the gporca_test executable directly.

./server/gporca_test -U CAggTest

To run a specific minidump, for example for ../data/dxl/minidump/TVFRandom.mdp:

./server/gporca_test -d ../data/dxl/minidump/TVFRandom.mdp

Note that some tests use assertions that are only enabled for DEBUG builds, so DEBUG-mode tests tend to be more rigorous.

Adding tests

Most of the regression tests come in the form of a "minidump" file. A minidump is an XML file that contains all the input needed to plan a query, including information about all tables, datatypes, and functions used, as well as statistics. It also contains the resulting plan.

A new minidump can be created by running a query on a live GPDB server:

  1. Run these in a psql session:
set client_min_messages='log';
set optimizer=on;
set optimizer_enumerate_plans=on;
set optimizer_minidump=always;
set optimizer_enable_constant_expression_evaluation=off;
  1. Run the query in the same psql session. It will create a minidump file under the "minidumps" directory, in the master's data directory:
$ ls -l $MASTER_DATA_DIRECTORY/minidumps/
total 12
-rw------- 1 heikki heikki 10818 Jun 10 22:02 Minidump_20160610_220222_4_14.mdp
  1. Run xmllint on the minidump to format it better, and copy it under the data/dxl/minidump directory:
xmllint --format $MASTER_DATA_DIRECTORY/minidumps/Minidump_20160610_220222_4_14.mdp > data/dxl/minidump/MyTest.mdp
  1. Add it to the test suite, in server/src/unittest/gpopt/minidump/CICGTest.cpp
--- a/server/src/unittest/gpopt/minidump/CICGTest.cpp
+++ b/server/src/unittest/gpopt/minidump/CICGTest.cpp
@@ -217,6 +217,7 @@ const CHAR *rgszFileNames[] =
                "../data/dxl/minidump/EffectsOfJoinFilter.mdp",
                "../data/dxl/minidump/Join-IDF.mdp",
                "../data/dxl/minidump/CoerceToDomain.mdp",
+               "../data/dxl/minidump/Mytest.mdp",
                "../data/dxl/minidump/LeftOuter2InnerUnionAllAntiSemiJoin.mdp",
 #ifndef GPOS_DEBUG
                // TODO:  - Jul 14 2015; disabling it for debug build to reduce testing time

Alternatively, it could also be added to the proper test suite in server/CMakeLists.txt as follows:

--- a/server/CMakeLists.txt
+++ b/server/CMakeLists.txt
@@ -183,7 +183,8 @@ CPartTbl5Test:
 PartTbl-IsNullPredicate PartTbl-IsNotNullPredicate PartTbl-IndexOnDefPartOnly
 PartTbl-SubqueryOuterRef PartTbl-CSQ-PartKey PartTbl-CSQ-NonPartKey
 PartTbl-LeftOuterHashJoin-DPE-IsNull PartTbl-LeftOuterNLJoin-DPE-IsNull
-PartTbl-List-DPE-Varchar-Predicates PartTbl-List-DPE-Int-Predicates;
+PartTbl-List-DPE-Varchar-Predicates PartTbl-List-DPE-Int-Predicates
+Mytest;

Update tests

In some situations, a failing test does not necessarily imply that the fix is wrong. Occasionally, existing tests need to be updated. There is now a script that allows for users to quickly and easily update existing mdps. This script takes in a logfile that it will use to update the mdps. This logfile can be obtained from running ctest as shown below.

Existing minidumps can be updated by runing the following:

  1. Run ctest -j8.

  2. If there are failing tests, run

ctest -j8 --rerun-failed --output-on-failure | tee /tmp/failures.out
  1. The output file can then be used with the fix_mdps.py script.
gporca/scripts/fix_mdps.py --logFile /tmp/failures.out

Note: This will overwrite existing mdp files. This is best used after committing existing changes, so you can more easily see the diff. Alternatively, you can use gporca/scripts/fix_mdps.py --dryRun to not change mdp files

  1. Ensure that all changes are valid and as expected.

Concourse

GPORCA contains a series of pipeline and task files to run various sets of tests on concourse. You can learn more about deploying concourse with bosh at bosh.io.

Our concourse currently runs the following sets of tests:

  • build and ctest on centos6
  • build and ctest on centos7
  • build and ctest on ubuntu18

All configuration files for our concourse pipelines can be found in the concourse/ directory.

Note: concourse jobs and pipelines for GPORCA are currently experimental and should not be considered ready for use in production-level CI environments.

Advanced Setup

How to generate build files with different options

Here are a few build flavors (commands run from the ORCA checkout directory):

# debug build
cmake -GNinja -D CMAKE_BUILD_TYPE=Debug -H. -Bbuild.debug
# release build with debug info
cmake -GNinja -D CMAKE_BUILD_TYPE=RelWithDebInfo -H. -Bbuild.release

Explicitly Specifying GP-Xerces For Build

GP-XERCES

It is recommended to use the --prefix option to the Xerces-C configure script to install GP-Xerces in a location other than the default under /usr/local/, because you may have other software that depends on Xerces-C, and the changes introduced in the GP-Xerces patch make it incompatible with the upstream version. Installing in a non-default prefix allows you to have GP-Xerces installed side-by-side with unpatched Xerces without incompatibilities.

You can point cmake at your patched GP-Xerces installation using the XERCES_INCLUDE_DIR and XERCES_LIBRARY options like so:

However, to use the current build scripts in GPDB, Xerces with the gp_xerces patch will need to be placed on the /usr path.

cmake -GNinja -D XERCES_INCLUDE_DIR=/opt/gp_xerces/include -D XERCES_LIBRARY=/opt/gp_xerces/lib/libxerces-c.so ..

Again, on Mac OS X, the library name will end with .dylib instead of .so.

Cross-Compiling 32-bit or 64-bit libraries

GP-XERCES

Unless you intend to cross-compile a 32 or 64-bit version of GP-Orca, you can ignore these instructions. If you need to explicitly compile for the 32 or 64-bit version of your architecture, you need to set the CFLAGS and CXXFLAGS environment variables for the configure script like so (use -m32 for 32-bit, -m64 for 64-bit):

CFLAGS="-m32" CXXFLAGS="-m32" ../configure --prefix=/opt/gp_xerces_32

GPORCA

For the most part you should not need to explicitly compile a 32-bit or 64-bit version of the optimizer libraries. By default, a "native" version for your host platform will be compiled. However, if you are on x86 and want to, for example, build a 32-bit version of Optimizer libraries on a 64-bit machine, you can do so as described below. Note that you will need a "multilib" C++ compiler that supports the -m32/-m64 switches, and you may also need to install 32-bit ("i386") versions of the C and C++ standard libraries for your OS. Finally, you will need to build 32-bit or 64-bit versions of GP-Xerces as appropriate.

Toolchain files for building 32 or 64-bit x86 libraries are located in the cmake directory. Here is an example of building for 32-bit x86:

cmake -GNinja -D CMAKE_TOOLCHAIN_FILE=../cmake/i386.toolchain.cmake ../

And for 64-bit x86:

cmake -GNinja -D CMAKE_TOOLCHAIN_FILE=../cmake/x86_64.toolchain.cmake ../

How to debug the build

Show all command lines while building (for debugging purpose)

ninja -v -C build

Extended Tests

Debug builds of GPORCA include a couple of "extended" tests for features like fault-simulation and time-slicing that work by running the entire test suite in combination with the feature being tested. These tests can take a long time to run and are not enabled by default. To turn extended tests on, add the cmake arguments -D ENABLE_EXTENDED_TESTS=1.

Installation Details

GPORCA has four libraries:

  1. libnaucrates --- has all DXL related classes, and statistics related classes
  2. libgpopt --- has all the code related to the optimization engine, meta-data accessor, logical / physical operators, transformation rules, and translators (DXL to expression and vice versa).
  3. libgpdbcost --- cost model for GPDB.
  4. libgpos --- abstraction of memory allocation, scheduling, error handling, and testing framework.

By default, GPORCA will be installed under /usr/local. You can change this by setting CMAKE_INSTALL_PREFIX when running cmake, for example:

cmake -GNinja -D CMAKE_INSTALL_PREFIX=/home/user/gporca -H. -Bbuild

By default, the header files are located in:

/usr/local/include/naucrates
/usr/local/include/gpdbcost
/usr/local/include/gpopt
/usr/local/include/gpos

the library is located at:

/usr/local/lib/libnaucrates.so*
/usr/local/lib/libgpdbcost.so*
/usr/local/lib/libgpopt.so*
/usr/local/lib/libgpos.so*

Build and install:

ninja install -C build

Common Issues

Note that because Red Hat-based systems do not normally look for shared libraries in /usr/local/lib, it is suggested to add /usr/local/lib to the /etc/ld.so.conf and run ldconfig to rebuild the shared library cache if developing on one of these Linux distributions.

Cleanup

Remove the cmake files generated under build folder of gporca repo:

rm -fr build/*

Remove gporca header files and library, (assuming the default install prefix /usr/local)

rm -rf /usr/local/include/naucrates
rm -rf /usr/local/include/gpdbcost
rm -rf /usr/local/include/gpopt
rm -rf /usr/local/include/gpos
rm -rf /usr/local/lib/libnaucrates.so*
rm -rf /usr/local/lib/libgpdbcost.so*
rm -rf /usr/local/lib/libgpopt.so*
rm -rf /usr/local/lib/libgpos.so*

How to Contribute

ORCA has a style guide, try to follow the existing style in your contribution to be consistent.

A set of clang-format-based rules are enforced in CI. Your editor or IDE may automatically support it. When in doubt, check formatting locally before submitting your PR:

CLANG_FORMAT=clang-format scripts/fmt chk

For more information, head over to the formatting README.

Please see the CONTRIBUTING file for details.

More Repositories

1

gpdb

Greenplum Database - Massively Parallel PostgreSQL for Analytics. An open-source massively parallel data platform for analytics, machine learning and AI.
C
6,009
star
2

PivotalR

An convenient R tool for manipulating tables in PostgreSQL type databases and a wrapper of Apache MADlib.
R
122
star
3

postgres

86
star
4

pxf

Platform Extension Framework: Federated Query Engine
Java
69
star
5

gpbackup

GPDB Backup Utility
Go
52
star
6

diskquota

PostgreSQL disk quota extension
C
48
star
7

GreenplumPython-archive

Python
43
star
8

plcontainer

PL/Container - GPDB execution sandboxing for Python and R
C
43
star
9

go-gpdb

Pivotal Greenplum Database downloader and installer based in golang
Go
27
star
10

geospatial

PostGIS for Greenplum
PLpgSQL
26
star
11

gpupgrade

GPDB major version upgrade utility
Go
22
star
12

greenplum-for-kubernetes

Deploy Greenplum cluster on Kubernetes
Go
22
star
13

gp-xerces

C++
18
star
14

TPC-DS

Greenplum TPC-DS benchmark
C
15
star
15

gpbackup-s3-plugin

S3 plugin for use with GPDB backup utility
Go
10
star
16

pljava

PL/Java GPDB Package
Java
10
star
17

gp-common-go-libs

Go
9
star
18

greenplum-database-release

A repository for code related to creating packages of Greenplum Database
Ruby
9
star
19

gpdb-postgres-merge

Scratch repository for merging upstream Postgres into GPDB
C
9
star
20

pgbouncer

pgbouncer repo
C
8
star
21

filedump

Greenplum Database Filedump is a utility to format Greenplum heap/index/control files into a human-readable form. You can format/dump the files several ways, as listed in the Invocation section, as well as dumping straight binary.
C
5
star
22

plr

plr
C
4
star
23

GreenplumR

R
3
star
24

gssapi

Go
3
star
25

libusual

libusual
C
2
star