Developer Guide

Red Hat Enterprise Linux 7

An introduction to application development tools in Red Hat Enterprise Linux 7

Vladimír Slávik

Red Hat Customer Content Services

Abstract

This document describes the different features and utilities that make Red Hat Enterprise Linux 7.5 an ideal enterprise platform for application development.

Preface

This document describes the different features and utilities that make Red Hat Enterprise Linux 7 an ideal enterprise platform for application development.

Part I. Setting Up a Development Workstation

Red Hat Enterprise Linux 7 supports development of custom applications. To allow developers to do so, the system must be set up with the required tools and utilities. This chapter lists the most common use cases for development and the items to install.

Chapter 1. Installing the Operating System

Before setting up for specific development needs, the underlying system must be set up.

  1. Install Red Hat Enterprise Linux in the Workstation variant. Follow the instructions in Red Hat Enterprise Linux Installation Guide.
  2. While installing, pay attention to software selection. Select the Development and Creative Workstation system profile and enable installation of Add-ons appropriate for your development needs. The relevant Add-ons are listed in each of the following sections focusing on various types of development.
  3. To develop applications that cooperate closely with the Linux kernel such as drivers, enable automatic crash dumping with kdump during the installation.
  4. After the system itself is installed, register it and attach the required subscriptions. The following sections focusing on various types of development list the particular subscriptions that must be attached for the respective type of development.
  5. More recent versions of development tools and utilities are available as Red Hat Software Collections. For instructions on accessing Red Hat Software Collections, see Red Hat Software Collections Release Notes, Chapter Installation.

Additional Resources

Chapter 2. Setting up to Manage Application Versions

Effective version control is essential to all multi-developer projects. Red Hat Enterprise Linux is distributed with Git, a distributed version control system.

  1. Select the Development Tools Add-on during system installation to install Git.
  2. Alternatively, install the git package from the Red Hat Enterprise Linux repositories after the system is installed.

    # yum install git
  3. To get the latest version of Git supported by Red Hat, install the rh-git29 component from Red Hat Software Collections.

    # yum install rh-git29
  4. Set the full name and email address associated with your Git commits:

    $ git config --global user.name "full name"
    $ git config --global user.email "email_address"

    Replace full name and email_address with your actual name and email address.

  5. To change the default text editor started by Git, set value of the core.editor configuration option:

    $ git config --global core.editor command

    Replace command with the command to be used to start the selected text editor.

Additional Resources

Chapter 3. Setting up to Develop Applications Using C and C++

Red Hat Enterprise Linux best supports development using the fully compiled C and C++ programming languages.

  1. Select the Development Tools and Debugging Tools Add-ons during system installation to install the GNU Compiler Collection (GCC) and GNU Debugger (GDB) as well as other development tools.
  2. Latest versions of GCC, GDB and the associated tools are available as a part of the Red Hat Developer Toolset toolchain component.

    # yum install devtoolset-7-toolchain
  3. The Red Hat Enterprise Linux repositories contain many libraries widely used for development of C and C++ applications. Install the development packages of the libraries needed for your application using the yum package manager.
  4. For graphical-based development, install the Eclipse integrated development environment. The C and C++ languages are directly supported. Eclipse is available as part of Red Hat Developer Tools. For the actual installation procedure, see Using Eclipse.

Additional Resources

Chapter 4. Setting up to Debug Applications

Red Hat Enterprise Linux offers multiple debugging and instrumentation tools to analyze and troubleshoot internal application behavior.

  1. Select the Debugging Tools and Desktop Debugging and Performance Tools Add-ons during system installation to install the GNU Debugger (GDB), Valgrind, SystemTap, ltrace, strace, and other tools.
  2. For the latest versions of GDB, Valgrind, SystemTap, strace, and ltrace, install Red Hat Developer Toolset. This installs memstomp, too.

    # yum install devtoolset-7
  3. The memstomp utility is available only as a part of Red Hat Developer Toolset. In case installing the whole Developer Toolset is not desirable and memstomp is required, install only its component from Red Hat Developer Toolset.

    # yum install devtoolset-7-memstomp
  4. Install the yum-utils package in order to use the debuginfo-install tool:

    # yum install yum-utils
  5. To debug applications and libraries available as part of Red Hat Enterprise Linux, install their respective debuginfo and source packages from the Red Hat Enterprise Linux repositories using the debuginfo-install tool. This applies to core dump file analysis, too.
  6. Install kernel debuginfo and source packages required by the SystemTap application. See SystemTap Beginners Guide, section Installing SystemTap.
  7. To capture kernel dumps, install and configure kdump. Follow the instructions in Kernel Crash Dump Guide, Chapter Installing and Configuring kdump.
  8. Make sure SELinux policies allow the relevant applications to run not only normally, but in the debugging situations, too. See SELinux User’s and Administrator’s Guide, section Fixing Problems.

Additional Resources

Chapter 5. Setting up to Measure Performance of Applications

Red Hat Enterprise Linux includes several applications that can help a developer identify the causes of application performance loss.

  1. Select the Debugging Tools, Development Tools, and Performance Tools Add-ons during system installation to install the tools OProfile, perf, and pcp.
  2. Install the tools SystemTap which allows some types of performance analysis, and Valgrind which includes modules for performance measurement.

    # yum install valgrind systemtap systemtap-runtime
  3. Run a SystemTap helper script for setting up the environment.

    # stap-prep
    Note

    Running this script installs very large kernel debuginfo packages.

  4. For more frequently updated versions of SystemTap, OProfile, and Valgrind, install the Red Hat Developer Toolset package perftools.

    # yum install devtoolset-7-perftools

Additional Resources

Chapter 6. Setting up to Develop Applications Using Java

Red Hat Enterprise Linux supports development of applications in Java.

  1. During system installation, select the Java Platform add-on to install OpenJDK as the default Java version.

    Alternatively, follow the instructions in Red Hat JBoss Developer Studio Installation Guide, Section Installing OpenJDK on Red Hat Enterprise Linux to install OpenJDK separately.

  2. For an integrated graphical development environment, install the Eclipse-based Red Hat JBoss Developer Studio offering extensive support for Java development. Follow the instructions in Red Hat JBoss Developer Studio Installation Guide.

Chapter 7. Setting up to Develop Applications Using Python

The Python language version 2.7.5 is available as a part of Red Hat Enterprise Linux.

  1. Newer versions of the Python interpreter and libraries are available as Red Hat Software Collections packages. Install the package with desired version according to the table below.

    # yum install package

    Python versions corresponding to Red Hat Software Collections packages

    VersionPackage

    Python 2.7.13

    python27

    Python 3.4.2

    rh-python34

    Python 3.5.1

    rh-python35

    Python 3.6.3

    rh-python36

  2. Install the Eclipse integrated development environment which supports development in the Python language. Eclipse is available as part of Red Hat Developer Tools. For the actual installation procedure, see Using Eclipse.

Additional Resources

Chapter 8. Setting up to Develop Applications Using C# and .NET Core

Red Hat supports development of applications using the C# language targeting the .NET Core runtime environment.

Apart from C#, the .NET Core 2.0 for Red Hat Enterprise Linux supports development in ASP.NET, F# and Visual Basic.

Additional Resources

Chapter 9. Setting up to Develop Containerized Applications

Red Hat supports development of containerized applications based on Red Hat Enterprise Linux, Red Hat OpenShift and a number of other Red Hat products.

Additional Resources

Chapter 10. Setting up to Develop Web Applications

Red Hat Enterprise Linux supports development of web applications and being the platform for their deployment.

The topic of web development is too broad to capture it with a few simple instructions. This section offers only the best supported paths to development of web applications on Red Hat Enterprise Linux.

  • To set up your environment for developing traditional web applications, install the Apache web server, PHP runtime, and MariaDB database server and tools.

    # yum install httpd mariadb-server php-mysql php

    Alternatively, more recent versions of these applications are available as components of Red Hat Software Collections.

    # yum install httpd24 rh-mariadb102 rh-php71
  • For development of web applications using Ruby on Rails, install the package from Red Hat Software Collections containing the desired version according to the table below.

    # yum install package

    Ruby on Rails versions corresponding to Red Hat Software Collections packages

    VersionPackage

    Ruby on Rails 4.1.5

    rh-ror41

    Ruby on Rails 4.2.6

    rh-ror42

    Ruby on Rails 5.0.1

    rh-ror50

Additional Resources

Part II. Collaborating on Applications with Other Developers

Chapter 11. Using Git

Effective revision control is essential to all multi-developer projects. It allows all developers in a team to create, review, revise, and document code in a systematic and orderly manner. Red Hat Enterprise Linux 7.5 is distributed with an open-source revision control system, Git.

A detailed description of Git and its features is beyond the scope of this book. For more information about this revision control system, see the resources listed below.

Installed Documentation

  • Linux manual pages for Git and tutorials:

    $ man git
    $ man gittutorial
    $ man gittutorial-2

    Note that many Git commands have their own manual pages.

  • Git User’s Manual — HTML documentation for Git is located at /usr/share/doc/git-1.8.3/user-manual.html.

Online Documentation

  • Pro Git — The online version of the Pro Git book provides a detailed description of Git, its concepts and its usage.
  • Reference — Online version of the Linux manual pages for Git

Part III. Making an Application available to Users

There are multiple ways of making an application available to its users. This guide describes the most common methods:

  • Packaging an application as a RPM package
  • Packaging an application as a software collection
  • Packaging an application as a container

Chapter 12. Distribution Options

Red Hat Enterprise Linux offers three methods of distribution for third-party applications.

RPM Packages

RPM Packages are the traditional method of distributing and installing software.

  • A mature technology with multiple tools and widely disseminated knowledge.
  • Applications are installed as part of the system.
  • The installation tools greatly assist in resolving dependencies.
  • Only one version of a package can be installed, making multiple application version installations difficult.

To create a RPM package, follow the instructions in RPM Packaging Guide, Chapter Packaging Software.

Software Collections

A Software Collection is a specially prepared RPM package for an alternative version of an application.

  • A packaging method used and supported by Red Hat.
  • Built on top of the RPM package mechanism.
  • Multiple versions of an application can be installed at once.

For more information, see Red Hat Software Collections Packaging Guide, 1.2 What Are Software Collections?

To create a software collection package, follow the instructions in Red Hat Software Collections Packaging Guide, Chapter Packaging Software Collections.

Containers

Docker-formatted containers are a lightweight virtualization method.

  • Application can be present in multiple independent versions and instances.
  • Can be prepared easily from an RPM package or Software Collection.
  • Interaction with the system can be precisely controlled.
  • Isolation of the application increases security.
  • Containerizing applications or their components enables orchestration of multiple instances.

Additional Resources

Chapter 13. Creating a Container with an Application

This section describes creating a docker-formatted container image from a locally built application. Making your application available as a container is advantageous when you wish to use orchestration for deployment. Alternatively, containerizing effectively solves conflicts of dependencies.

Prerequisites

  • Understanding containers
  • An application built locally from sources

Steps

  1. Decide which base image to use.

    Note

    Red Hat recommends starting with a base image that uses Red Hat Enterprise Linux as its foundation. Refer to Base Image in the Red Hat Container Catalog for further information.

  2. Create a workspace directory.
  3. Prepare your application as a directory containing all of the application’s required files. Place this directory inside the workspace directory.
  4. Write a Dockerfile that describes the steps required to create the container.

    Refer to the Dockerfile Reference for information about how to create a Dockerfile that includes your content, sets default commands to run, and opens necessary ports and other features.

    An example of a minimal Dockerfile that contains the my-program/ directory:

    FROM registry.access.redhat.com/rhel7
    USER root
    ADD my-program/ .

    Place this Dockerfile into the workspace directory.

  5. Build a container image from the Dockerfile:

    # docker build .
    (...)
    Successfully built container-id

    During this step, note the container-id of the newly created container image.

  6. Add a tag to the image, to identify the registry where you want the container image to be stored. See Getting Started with Containers — Tagging Images.

    # docker tag container-id registry:port/name

    Replace container-id with the value shown in the output of the previous step.

    Replace registry with address of the registry where you want to push the image, port with the port of the registry (omit if not needed), and name with the name of the image.

    For example, if you are running a registry using the docker-distribution service on your local system with an image named myimage, the tag localhost:5000/myimage would make that image ready to put to the registry.

  7. Push the image to the registry so it can be pulled from that registry later by someone who wants to use it.

    # docker push registry:port/name

    Replace the tag parts with the same values as these used in the previous step.

    To run your own Docker registry, see Getting Started with Containers — Working with Docker registries

Additional Resources

Chapter 14. Containerizing an Application from Packages

For multiple reasons, it may be advantageous to distribute an application packaged in an RPM package as a container, too.

Prerequisites

  • Understanding containers
  • An application packaged as one or more RPM packages

Steps

To containerize an application from RPM packages, see Getting Started with Containers — Creating Docker images.

Additional Information

Part IV. Creating C or C++ Applications

Red Hat offers multiple tools for creating applications using the C and C++ languages. This part of the book lists some of the most common development tasks.

Chapter 15. Building Code with GCC

This chapter deals with situations where source code must be transformed into executable code.

15.1. Relationship between Code Forms

Prerequisites

  • Understanding the concepts of compiling and linking

Possible Code Forms

When using the C and C++ languages, there are three forms of code:

  • Source code written in the C or C++ language, present as plain text files.

    The files typically use extensions such as .c, .cc, .cpp, .h, .hpp, .i, .inc. For a complete list of supported extensions and their interpretation, see the gcc manual pages:

    $ man gcc
  • Object code, created by compiling the source code with a compiler. This is an intermediate form.

    The object code files use the .o extension.

  • Executable code, created by linking object code with a linker.

    Linux application executable files do not use any file name extension. Shared object (library) executable files use the .so file name extension.

Note

Library archive files for static linking also exist. This is a variant of object code and uses the .a file name extension.

Handling of Code Forms in GCC

Producing executable code from source code requires two steps, which require different applications or tools. GCC can be used as an intelligent driver for both compilers and linkers. This allows you to use a single command gcc for any of the required actions. GCC automatically selects the actions required (compiling and linking), as well as their sequence:

  1. Source files are compiled to object files.
  2. Object files and libraries are linked (including the previously compiled sources).

It is possible to run GCC such that only step 1 happens, only step 2 happens, or both steps 1 and 2 happen. This is determined by the types of inputs and requested type of output(s).

Because larger projects require a build system which usually runs GCC separately for each action, it is better to always consider compilation and linking as two distinct actions, even if GCC can perform both at once.

Additional Resources

15.2. Compiling Source Files to Object Code

To create object code files from source files and not an executable file immediately, GCC must be instructed to create only object code files as its output. This action represents the basic operation of the build process for larger projects.

Prerequisites

Steps

  1. Change to the directory containing the source code file(s).
  2. Run gcc with the -c option:

    $ gcc -c source.c another_source.c

    Object files are created, with their file names reflecting the original source code files: source.c results in source.o.

    Note

    With C++ source code, replace the gcc command with g++ for convenient handling of C++ Standard Library dependencies.

Additional Resources

15.3. Enabling Debugging of C and C++ Applications with GCC

Because debugging information is large, it is not included in executable files by default. To enable debugging of your C and C++ applications with it, you must explicitly instruct the compiler to create it.

Enabling Creation of Debugging Information with GCC

To enable creation of debugging information with GCC when compiling and linking code, use the -g option:

$ gcc ... -g ...
  • Optimizations performed by the compiler and linker can result in executable code which is hard to relate to the original source code: Variables may be optimized out, loops unrolled, operations merged into the surrounding ones etc. This affects debugging negatively. For improved debuging experience, consider setting the optimization with the -Og option. However, changing the optimization level changes the executable code and may change the actual behaviour so as to remove some bugs.
  • The -fcompare-debug GCC option tests code compiled by GCC with debug information and without debug information. The test passes if the resulting two binary files are identical. This test ensures that executable code is not affected by any debugging options, which further ensures that there are no hidden bugs in the debug code. Note that using the -fcompare-debug option significantly increases compilation time. See the GCC manual page for details about this option.

Additional Resources

15.4. Code Optimization with GCC

A single program can be transformed into more than one sequence of machine instructions. A more optimal result can be achieved if more resources are allocated for analysis of the code during compilation.

Code Optimization with GCC

With GCC, it is possible to set the optimization level using the -Olevel option. This option accepts a set of values in place of the level.

LevelDescription

0

Optimize for compilation speed - no code optimization (default)

1, 2, 3

Increasing optimization effort for code execution speed

s

Optimize for resulting file size

fast

Level 3 plus disregard for strict standards compliance to allow for additional optimizations

g

Optimize for debugging experience

For release builds, the optimization option -O2 is recommended.

During development, the -Og option is more useful for debugging the program or library in some situations. Because some bugs manifest only with certain optimization levels, ensure to test the program or library with the release optimization level.

GCC offers a large number of options to enable individual optimizations. For more information, see the following Additional Resources.

Additional Resources

15.5. Hardening Code with GCC

When the compiler transforms source code to object code, it can add various checks to prevent commonly exploited situations and thus increase security. Choosing the right set of compiler options can help produce more secure programs and libraries, without changes to the source code.

Release Version Options

The following list of options is the recommended minimum for developers targeting Red Hat Enterprise Linux:

$ gcc ... -O2 -g -Wall -Wl,-z,now,-z,relro -fstack-protector-strong -D_FORTIFY_SOURCE=2 ...
  • For programs, add the -fPIE and -pie Position Independent Executable options.
  • For dynamically linked libraries, the mandatory -fPIC (Position Independent Code) option indirectly increases security.

Development Options

The following options are recommended to detect security flaws during development. Use these options in conjunction with the options for the release version:

$ gcc ... -Walloc-zero -Walloca-larger-than -Wextra -Wformat-security -Wvla-larger-than ...

Additional Resources

15.6. Linking Code to Create Executable Files

Linking is the final step when building a C or C++ application. Linking combines all object files and libraries into an executable file.

Prerequisites

Steps

  1. Change to the directory containing the object code file(s).
  2. Run gcc:

    $ gcc ... objfile.o another_object.o ... -o executable-file

    An executable file named executable-file is created from the supplied object files and libraries.

    To link additional libraries, add the required options before the list of object files. See Chapter 16, Using Libraries with GCC.

    Note

    With C++ source code, replace the gcc command with g++ for convenient handling of C++ Standard Library dependencies.

Additional Resources

15.7. C++ Compatibility of Various Red Hat Products

The Red Hat ecosystem includes several versions of Red Hat Enterprise Linux and Red Hat Developer Toolset. The C++ ABI compatibility between these is as follows:

  • Any C++98-compliant binaries or libraries built explicitly with options -std=C++98 or -std=gnu++98 can be freely mixed. This is the recommended setting for production software development.
  • The default setting for Red Hat Enterprise Linux 6 and 7 and Red Hat Developer Toolset up to 4.1 is -std=gnu++98. For Red Hat Developer Toolset 6, 6.1, and 7, the default is -std=gnu++14.
  • Using and mixing the C++11 and C++14 language versions is supported in Red Hat Developer Toolset only when all C++ objects compiled with the respective flag have been built using the same major version of GCC.
  • When linking C++ files built with both Red Hat Developer Toolset and Red Hat Enterprise Linux toolchain, prefer the Red Hat Developer Toolset version of GCC and linker.

Additional Resources

15.8. Example: Building a C Program with GCC

This example shows the exact steps to build a sample minimal C program.

Prerequisites

  • Understanding use of GCC

Steps

  1. Create a directory hello-c and change to it:

    $ mkdir hello-c
    $ cd hello-c
  2. Create file hello.c with the following contents:

    #include <stdio.h>
    
    int main(int argc, char *argv[]) {
      printf("Hello, World!\n");
      return 0;
    }
  3. Compile the code with GCC:

    $ gcc -c hello.c

    The object file hello.o is created.

  4. Link an executable file helloworld from the object file:

    $ gcc hello.o -o helloworld
  5. Run the resulting executable file:

    $ ./helloworld
    Hello, World!

Additional Resources

15.9. Example: Building a C++ Program with GCC

This example shows the exact steps to build a sample minimal C++ program.

Prerequisites

  • Understanding the use of GCC
  • Understanding the difference between gcc and g++

Steps

  1. Create a directory hello-cpp and change to it:

    $ mkdir hello-cpp
    $ cd hello-cpp
  2. Create file hello.cpp with the following contents:

    #include <iostream>
    
    int main() {
      std::cout << "Hello, World!\n";
      return 0;
    }
  3. Compile the code with g++:

    $ g++ -c hello.cpp

    The object file hello.o is created.

  4. Link an executable file helloworld from the object file:

    $ g++ hello.o -o helloworld
  5. Run the resulting executable file:

    $ ./helloworld
    Hello, World!

Chapter 16. Using Libraries with GCC

This chapter describes using libraries in code.

16.1. Library Naming Conventions

A special file name convention is used for libraries: A library known as foo is expected to exist as file libfoo.so or libfoo.a. This convention is automatically understood by the linking input options of GCC, but not by the output options:

  • When linking against the library, the library can be specified only by its name foo with the -l option as -lfoo:

    $ gcc ... -lfoo ...
  • When creating the library, the full file name libfoo.so or libfoo.a must be specified.

Additional Resources

16.2. Using a Library with GCC

A library is a package of code which can be reused in your program. A C or C++ library consists of two parts:

  • The library code
  • Header files

Compiling Code That Uses a Library

The header files describe the interface of the library: The functions and variables available in the library. Information from the header files is needed for compiling the code.

Typically, header files of a library will be placed in a different directory than your application’s code. To tell GCC where the header files are, use the -I option:

$ gcc ... -Iinclude_path ...

Replace include_path with the actual path to the header file directory.

The -I option can be used multiple times to add multiple directories with header files. When looking for a header file, these directories are searched in the order of their appearance in the -I options.

Linking Code That Uses a Library

When linking the executable file, both the object code of your application and the binary code of the library must be available. The code for static and dynamic libraries is present in different forms:

  • Static libraries are available as archive files. They contain a group of object files. The archive file has an file name extension .a.
  • Dynamic libraries are available as shared objects. They are a form of an executable file. A shared object has an file name extension .so.

To tell GCC where the archives or shared object files of a are, use the -L option:

$ gcc ... -Llibrary_path -lfoo ...

Replace library_path with the actual path to the library directory.

The -L option can be used multiple times to add multiple directories. When looking for a library, these directories are searched in the order of their -L options.

The order of options matters: GCC cannot link against a library foo unless it knows the directory with this library. Therefore, use the -L options to specify library directories before using the -l options for linking against libraries.

Compiling and Linking Code Which Uses a Library in One Step

When the situation allows the code to be compiled and linked in one gcc command, use the options for both situations mentioned above at once.

Additional Resources

16.3. Using a Static Library with GCC

Static libraries are available as archives containing object files. After linking, they become an integral part of the resulting executable file.

Prerequisites

  • GCC installed on your system
  • A set of source or object files forming a valid program, requiring some static library foo and no other libraries
  • The foo library available as a file libfoo.a

Steps

To link a program from source and object files, adding a statically linked library foo, which is to be found as a file libfoo.a:

  1. Change to the directory containing your code.
  2. Compile the program source files with headers of the foo library:

    $ gcc ... -Iheader_path -c ...

    Replace header_path with a path to a directory containing the header files for the foo library.

  3. Link the program with the foo library:

    $ gcc ... -Llibrary_path -lfoo ...

    Replace library_path with a path to a directory containing the file libfoo.a.

  4. To run the program later, simply:

    $ ./program
Caution

The -static GCC option related to static linking forbids all dynamic linking. Instead, use the -Wl option to more precisely control linker behavior. See Section 16.5, “Using Both Static and Dynamic Libraries with GCC”.

16.4. Using a Dynamic Library with GCC

Dynamic libraries are available as standalone executable files, required at both linking time and run time. They stay independent of your application’s executable file.

Prerequisites

  • GCC installed on the system
  • A set of source or object files forming a valid program, requiring some dynamic library foo and no other libraries
  • The foo library available as a file libfoo.so

Linking a Program Against a Dynamic Library

To link a program against a dynamic library foo:

$ gcc ... -Llibrary_path -lfoo ...

When a program is linked against a dynamic library, the resulting program must always load the library at run time. There are two options for locating the library:

  • Using a rpath value stored in the executable file itself
  • Using the LD_LIBRARY_PATH variable at runtime

Using a rpath Value Stored in the Executable File

The rpath is a special value saved as a part of an executable file when it is being linked. Later, when the program is loaded from its executable file, the runtime linker will use the rpath value to locate the library files.

While linking with GCC, to store the path library_path as rpath:

$ gcc ... -Llibrary_path -lfoo -Wl,-rpath=library_path ...

The path library_path must point to a directory containing the file libfoo.so.

Caution

There is no space after the comma in the -Wl,-rpath= option!

To run the program later:

$ ./program

Using the LD_LIBRARY_PATH Environment Variable

If no rpath is found in the program’s executable file, the runtime linker will use the LD_LIBRARY_PATH environment variable. The value of this variable must be changed for each program according to the path where the shared library objects are to be found.

To run the program without rpath set, with libraries present in path library_path:

$ export LD_LIBRARY_PATH=library_path:$LD_LIBRARY_PATH
$ ./program

Leaving out the rpath value offers flexibility, but requires setting the LD_LIBRARY_PATH variable every time the program is to run.

Placing the Library into the Default Directories

The runtime linker configuration specifies a number of directories as a default location of dynamic library files. To use this default behaviour, copy your library to the appropriate directory.

A full description of the dynamic linker behavior is out of scope of this document. For more information, see the following resources:

  • Linux manual pages for the dynamic linker:

    $ man ld.so
  • Contents of the /etc/ld.so.conf configuration file:

    $ cat /etc/ld.so.conf
  • Report of the libraries recognized by the dynamic linker without additional configuration, which includes the directories:

    $ ldconfig -v

16.5. Using Both Static and Dynamic Libraries with GCC

Sometimes it is required to link some libraries statically and some dynamically. This situation brings some challenges.

Prerequisites

Introduction

gcc recognizes both dynamic and static libraries. When the -lfoo option is encountered, gcc will first attempt to locate a shared object (a .so file) containing a dynamically linked version of the foo library, and the look for the archive file (.a) containing a static version of the library. Thus, the following situations can result from this search:

  • Only the shared object is found and gcc links against it dynamically
  • Only the archive is found and gcc links against it statically
  • Both the shared object and archive are found; gcc selects by default dynamic linking against the shared object
  • Neither shared object nor archive is found and linking fails

Because of these rules, the best way to select the static or dynamic version of library for linking is having only that version found by gcc. This can be controlled to some extent by using or leaving out directories containing the library versions, when specifying the -Lpath options.

Additionally, because dynamic linking is the default, the only situation where linking must be explicitly specified is when a library with both versions present should be linked statically. There are two possible resolutions:

  • Specifying the static libraries by file path instead of the -l option
  • Using the -Wl option to pass options to the linker

Specifying the static libraries by file

Usually, gcc is instructed to link against a library foo with the -lfoo option. However, it is possible to specify the full path to file libfoo.a containing the library instead:

$ gcc ... path/to/libfoo.a ...

From the file extension .a, gcc will understand that this is a library to link with the program. However, specifying the full path to the library file is a less flexible method.

Using the -Wl option

The gcc option -Wl is a special option for passing options to the underlying linker. Syntax of this option differs from the other gcc options: It is followed by a comma-separated list of linker options, so that the linker options do not get mixed up with space-separated gcc options.

The ld linker used by gcc offers the options -Bstatic and -Bdynamic to specify whether libraries following this option should be linked statically or dynamically, respectively. After passing -Bstatic and a library to the linker, the default dynamic linking behaviour must be restored manually for the following libraries to be linked dynamically with the -Bdynamic option.

To link a program, linking library first statically (libfirst.a) and second dynamically (libsecond.so):

$ gcc ... -Wl,-Bstatic -lfirst -Wl,-Bdynamic -lsecond ...
Note

gcc can be configured to use linkers other than the default ld. The -Wl option applies to the gold linker, too.

Additional Resources

Chapter 17. Creating libraries with GCC

This chapter describes steps for creating libraries and explains the necessary concepts used by the Linux operating system for libraries.

17.1. Library Naming Conventions

A special file name convention is used for libraries: A library known as foo is expected to exist as file libfoo.so or libfoo.a. This convention is automatically understood by the linking input options of GCC, but not by the output options:

  • When linking against the library, the library can be specified only by its name foo with the -l option as -lfoo:

    $ gcc ... -lfoo ...
  • When creating the library, the full file name libfoo.so or libfoo.a must be specified.

Additional Resources

17.2. The soname Mechanism

Dynamically loaded libraries (shared objects) use a mechanism called soname to manage multiple compatible versions of a library.

Prerequisites

Problem Introduction

A dynamically loaded library (shared object) exists as an independent executable file. This makes it possible to update the library without updating the applications that depend on it. However, the following problems arise with this concept:

  • Identification of the actual version of the library
  • Need for multiple versions of the same library present
  • Signalling ABI compatibility of each of the multiple versions

The soname Mechanism

To resolve this, Linux uses a mechanism called soname.

A library foo version X.Y is ABI-compatible with other versions with the same value of X in version number. Minor changes preserving compatibility increase the number Y. Major changes that break compatibility increase the number X.

The actual library foo version X.Y exists as a file libfoo.so.x.y. Inside the library file, a soname is recorded with value libfoo.so.x to signal the compatibility.

When applications are built, the linker looks for the library by searching for the file libfoo.so. A symbolic link with this name must exist, pointing to the actual library file. The linker then reads the soname from the library file and records it into the application executable file. Finally, the linker creates the application such that it declares dependency on the library using the soname, not name or file name.

When the runtime dynamic linker links an application before running, it reads the soname from application’s executable file. This soname is libfoo.so.x. A symbolic link with this name must exist, pointing to the actual library file. This allows loading the library, regardless of the Y component of version, because the soname does not change.

Note

The Y component of the version number is not limited to just a single number. Additionally, some libraries encode version in their name.

Reading soname from a File

To display the soname of a library file somelibrary:

$ objdump -p somelibrary | grep SONAME

Replace somelibrary with the actual file name of the library you wish to examine.

17.3. Creating Dynamic Libraries with GCC

Dynamically linked libraries (shared objects) allow resource conservation through code reuse and increased security by easier updates of the library code. This section describes the steps to build and install a dynamic library from source.

Prerequisites

Steps

  1. Change to the directory with library sources.
  2. Compile each source file to an object file with the Position independent code option -fPIC:

    $ gcc ... -c -fPIC some_file.c ...

    The object files have the same file names as the original source code files, but their extension is .o.

  3. Link the shared library from the object files:

    $ gcc -shared -o libfoo.so.x.y -Wl,-soname,libfoo.so.x some_file.o ...

    The used major version number is X and minor version number Y.

  4. Copy the libfoo.so.x.y file to an appropriate location, where the system’s dynamic linker can find it. On Red Hat Enterprise Linux, the directory for libraries is /usr/lib64:

    # cp libfoo.so.x.y /usr/lib64

    Note that you need root permissions to manipulate files in this directory.

  5. Create the symlink structure for soname mechanism:

    # ln -s libfoo.so.x.y libfoo.so.x
    # ln -s libfoo.so.x libfoo.so

Additional Resources

17.4. Creating Static Libraries with GCC and ar

Creating libraries for static linking is possible through conversion of object files into a special type of archive file.

Note

Red Hat discourages use of static linking for security reasons. Use static linking only when neccessary, especially against libraries provided by Red Hat.

Prerequisites

Steps

  1. Create intermediate object files with GCC.

    $ gcc -c source_file.c ...

    Append more source files as required. The resulting object files share the file name but use the .o file name extension.

  2. Turn the object files into a static library (archive) using the ar tool from the binutils package.

    $ ar rcs libfoo.a source_file.o ...

    File libfoo.a is created.

  3. Use the nm command to inspect the resulting archive:

    $ nm libfoo.a
  4. Copy the static library file to the appropriate directory.
  5. When linking against the library, GCC will automatically recognize from the .a file name extension that the library is an archive for static linking.

    $ gcc ... -lfoo ...

Additional Resources

  • Linux manual page for the ar tool:

    $ man ar

Chapter 18. Managing More Code with Make

The GNU make utility, commonly abbreviated make, is a tool for controlling the generation of executables from source files. make automatically determines which parts of a complex program have changed and need to be recompiled. make uses configuration files called Makefiles to control the way programs are built.

18.1. GNU make and Makefile Overview

To create a usable form (usually executable files) from the source files of a particular project, perform several necessary steps. Record the actions and their sequence to be able to repeat them later.

Red Hat Enterprise Linux contains GNU make, a build system designed for this purpose.

Prerequisites

  • Understanding the concepts of compiling and linking

GNU make

GNU make reads Makefiles which contain the instructions describing the build process. A Makefile contains multiple rules that describe a way to satisfy a certain condition (target) with a specific action (recipe). Rules can hierarchically depend on another rule.

Running make without any options makes it look for a Makefile in the current directory and attempt to reach the default target. The actual Makefile file name can be one of Makefile, makefile, and GNUmakefile. The default target is determined from the Makefile contents.

Makefile Details

Makefiles use a relatively simple syntax for defining variables and rules, which consists of a target and a recipe. The target specifies what is the output if a rule is executed. The lines with recipes must start with the TAB character.

Typically, a Makefile contains rules for compiling source files, a rule for linking the resulting object files, and a target that serves as the entry point at the top of the hierarchy.

Consider the following Makefile for building a C program which consists of a single file, hello.c.

all: hello

hello: hello.o
        gcc hello.o -o hello

hello.o: hello.c
        gcc -c hello.c -o hello.o

This specifies that to reach the target all, file hello is required. To get hello, one needs hello.o (linked by gcc), which in turn is created from hello.c (compiled by gcc).

The target all is the default target because it is the first target that does not start with a period (.). Running make without any arguments is then identical to running make all, when the current directory contains this Makefile.

Typical Makefile

A more typical Makefile uses variables for generalization of the steps and adds a target "clean" - remove everything but the source files.

CC=gcc
CFLAGS=-c -Wall
SOURCE=hello.c
OBJ=$(SOURCE:.c=.o)
EXE=hello

all: $(SOURCE) $(EXE)

$(EXE): $(OBJ)
        $(CC) $(OBJ) -o $@

%.o: %.c
        $(CC) $(CFLAGS) $< -o $@

clean:
        rm -rf $(OBJ) $(EXE)

Adding more source files to such Makefile requires only adding them to the line where the SOURCE variable is defined.

Additional resources

18.2. Example: Building a C Program Using a Makefile

Build a sample C program using a Makefile by following the steps in the below example.

Prerequisites

Steps

  1. Create a directory hellomake and change to this directory:

    $ mkdir hellomake
    $ cd hellomake
  2. Create a file hello.c with the following contents:

    #include <stdio.h>
    
    int main(int argc, char *argv[]) {
      printf("Hello, World!\n");
      return 0;
    }
  3. Create a file Makefile with the following contents:

    CC=gcc
    CFLAGS=-c -Wall
    SOURCE=hello.c
    OBJ=$(SOURCE:.c=.o)
    EXE=hello
    
    all: $(SOURCE) $(EXE)
    
    $(EXE): $(OBJ)
            $(CC) $(OBJ) -o $@
    
    %.o: %.c
            $(CC) $(CFLAGS) $< -o $@
    
    clean:
            rm -rf $(OBJ) $(EXE)
    Caution

    The Makefile recipe lines must start with the tab character! When copying the text above from the browser, you may paste spaces instead. Correct this change manually.

  4. Run make:

    $ make
    gcc -c -Wall hello.c -o hello.o
    gcc hello.o -o hello

    This creates an executable file hello.

  5. Run the executable file hello:

    $ ./hello
    Hello, World!
  6. Run the Makefile target clean to remove the created files:

    $ make clean
    rm -rf hello.o hello

Additional Resources

18.3. Documentation Resources for make

For more information about make, see the resources listed below.

Installed Documentation

  • Use the man and info tools to view manual pages and information pages installed on your system:

    $ man make
    $ info make

Online Documentation

Chapter 19. Using the Eclipse IDE for C and C++ Application Development

Some developers prefer using an IDE instead of an array of command line tools. Red Hat makes available the Eclipse IDE with support for development of C and C++ applications.

Using Eclipse to Develop C and C++ Applications

A detailed description of the Eclipse IDE and its use for developing C and C++ applications is out of scope of this document. Please refer to the resources linked below.

Additional Resources

Part V. Debugging Applications

Debugging applications is a very wide topic. This part provides a developer with the most common techniques for debugging in multiple situations.

Chapter 20. Debugging a Running Application

This chapter will introduce the techniques for debugging an application which can be started as many times as needed, on a machine directly accessible to the developer.

20.1. Enabling Debugging with Debugging Information

To debug applications and libraries, debugging information is required. The following sections describe how to obtain this information.

20.1.1. Debugging Information

While debugging any executable code, two kinds of information allow the tools and by extension the programmer to comprehend the binary code:

  • the source code text
  • a description of how the source code text relates to the binary code

This is referred to as debugging information.

Red Hat Enterprise Linux uses the ELF format for executable binaries, shared libraries, or debuginfo files. Within these ELF files, the DWARF format is used to hold the debug information.

DWARF symbols are read by the readelf -w file command.

Caution

STABS is occasionally used with UNIX. STABS is an older, less capable format. Its use is discouraged by Red Hat. GCC and GDB support STABS production and consumption on a best effort basis only. Some other tools such as Valgrind and elfutils do not support STABS at all.

Additional Resources

20.1.2. Enabling Debugging of C and C++ Applications with GCC

Because debugging information is large, it is not included in executable files by default. To enable debugging of your C and C++ applications with it, you must explicitly instruct the compiler to create it.

Enabling Creation of Debugging Information with GCC

To enable creation of debugging information with GCC when compiling and linking code, use the -g option:

$ gcc ... -g ...
  • Optimizations performed by the compiler and linker can result in executable code which is hard to relate to the original source code: Variables may be optimized out, loops unrolled, operations merged into the surrounding ones etc. This affects debugging negatively. For improved debuging experience, consider setting the optimization with the -Og option. However, changing the optimization level changes the executable code and may change the actual behaviour so as to remove some bugs.
  • The -fcompare-debug GCC option tests code compiled by GCC with debug information and without debug information. The test passes if the resulting two binary files are identical. This test ensures that executable code is not affected by any debugging options, which further ensures that there are no hidden bugs in the debug code. Note that using the -fcompare-debug option significantly increases compilation time. See the GCC manual page for details about this option.
Additional Resources

20.1.3. Debuginfo Packages

Debuginfo packages contain debugging information and debug source code for programs and libraries.

Prerequisites
Debuginfo Packages

For applications and libraries installed in packages from the Red Hat Enterprise Linux repositories, you can obtain the debugging information and debug source code as separate debuginfo packages available through another channel. The debuginfo packages contain .debug files, which contain DWARF debuginfo and the source files used for compiling the binary packages. Debuginfo package contents are installed to the /usr/lib/debug directory.

A debuginfo package provides debugging information valid only for a binary package with the same name, version, release and architecture:

  • Binary package: packagename-version-release.architecture.rpm
  • Debuginfo package: packagename-debuginfo-version-release.architecture.rpm

20.1.4. Getting debuginfo Packages for an Application or Library using GDB

The GNU Debugger (GDB) automatically recognizes missing debug information and resolves the package name.

Prerequisites
Procedure
  1. Start GDB attached to the application or library you want to debug. GDB automatically recognizes missing debugging information and suggests a command to run.

    $ gdb -q /bin/ls
    Reading symbols from /usr/bin/ls...Reading symbols from /usr/bin/ls...(no debugging symbols found)...done.
    (no debugging symbols found)...done.
    Missing separate debuginfos, use: debuginfo-install coreutils-8.22-21.el7.x86_64
    (gdb)
  2. Exit GDB without proceeding further: type q and Enter.

    (gdb) q
  3. Run the command suggested by GDB to install the needed debuginfo packages:

    # debuginfo-install coreutils-8.22-21.el7.x86_64

    Installing a debuginfo package for an application or library installs debuginfo packages for all dependencies, too.

  4. In case GDB is not able to suggest the debuginfo package, follow the procedure in Section 20.1.5, “Getting debuginfo Packages for an Application or Library Manually”.
Additional Resources

20.1.5. Getting debuginfo Packages for an Application or Library Manually

You can determine manually debuginfo packages for installation by locating the executable file and finding the package which installs it.

Note

Prefer use of GDB to determine the packages for installation. Use this manual procedure only if GDB is not able to suggest the package to install.

Prerequisites
Procedure
  1. Find the executable file of the application or library.

    1. Use the which command to find the application file.

      $ which nautilus
      /usr/bin/nautilus
    2. Use the locate command to find the library file.

      $ locate libz | grep so
      /usr/lib64/libz.so
      /usr/lib64/libz.so.1
      /usr/lib64/libz.so.1.2.7

      If the original reasons for debugging included error messages, pick the result where the library has the same additional numbers in its file name. If in doubt, try following the rest of the procedure with the result where the library file name includes no additional numbers.

      Note

      The locate command is provided by the mlocate package. To install it and enable its use:

      # yum install mlocate
      # updatedb
  2. Using the file path, search for a package which provides that file.

    # yum provides /usr/lib64/libz.so.1.2.7
    Loaded plugins: product-id, search-disabled-repos, subscription-manager
    zlib-1.2.7-17.el7.x86_64 : The compression and decompression library
    Repo        : @anaconda/7.4
    Matched from:
    Filename    : /usr/lib64/libz.so.1.2.7

    The output provides a list of packages in the format name-version.distribution.platform. In this step, only the package name is important, because the version shown in yum output may not be the actual installed version.

    Important

    If this step does not produce any results, it is not possible to determine which package provided the binary file and this procedure fails.

  3. Use the rpm low-level package management tool to find what package version is installed on the system. Use the package name as an argument:

    $ rpm -q zlib
    zlib-1.2.7-17.el7.x86_64

    The output provides details for the installed package in the format name-version.distribution.platform.

  4. Install the debuginfo packages using the debuginfo-install utility. In the command, use the package name and other details you determined during the previous step:

    # debuginfo-install zlib-1.2.7-17.el7.x86_64

    Installing a debuginfo package for an application or library installs debuginfo packages for all dependencies, too.

Additional Resources

20.2. Inspecting Application Internal State with GDB

To find why an application does not work properly, control its execution and examine its internal state with a debugger. This section describes how to use the GNU Debugger (GDB) for this task.

20.2.1. GNU Debugger (GDB)

A debugger is a tool that enables control of code execution and inspection of the state of the code. This capability is used to investigate what is happening in a program and why.

Red Hat Enterprise Linux contains the GNU debugger (GDB) which offers this functionality through a command line user interface.

For a graphical frontend to GDB, install the Eclipse integrated development environment. See Using Eclipse.

GDB Capabilities

A single GDB session can debug:

  • multithreaded and forking programs
  • multiple programs at once
  • programs on remote machines or in containers with the gdbserver utility connected over a TCP/IP network connection
Debugging Requirements

To debug any executable code, GDB requires the respective debugging information:

  • For programs developed by you, you can create the debugging information while building the code.
  • For system programs installed from packages, their respective debuginfo packages must be installed.

20.2.2. Attaching GDB to a Process

In order to examine a process, GDB must be attached to the process.

Prerequisites
Starting a Program with GDB

When the program is not running as a process, start it with GDB:

$ gdb program

Replace program with file name or path to the program.

GDB sets up to start execution of the program. You can set up breakpoints and the gdb environemnt before beginning execution of the process with the run command.

Attaching GDB to an Already Running Process

To attach GDB to a program already running as a process:

  1. Find the process id (pid) with the ps command:

    $ ps -C program -o pid h
     pid

    Replace program with file name or path to the program.

  2. Attach GDB to this process:

    $ gdb -p pid

    Replace pid with an actual process id number from the ps output.

Attaching an Already Running GDB to an Already Running Process

To attach an already running GDB to an already running program:

  1. Use the shell GDB command to run the ps command and find the program’s process id (pid):

    (gdb) shell ps -C program -o pid h
     pid

    Replace program with file name or path to the program.

  2. Use the attach command to attach GDB to the program:

    (gdb) attach pid

    Replace pid by an actual process id number from the ps output.

Note

In some cases, GDB might not be able to find the respective executable file. Use the file command to specify the path:

(gdb) file path/to/program
Additional Resources

20.2.3. Stepping through Program Code with GDB

Once the GDB debugger is attached to a program, you can use a number of commands to control the execution of the program.

Prerequisites
GDB Commands to Step Through the Code
r (run)
Start the execution of the program. If run is executed with any arguments, those arguments are passed on to the executable as if the program has been started normally. Users normally issue this command after setting breakpoints.
start
Start the execution of the program, and stop at the beginning of the program’s main function. If start is executed with any arguments, those arguments are passed on to the executable as if the program has been started normally.
c (continue)

Continue the execution of the program from the current state. The execution of the program will continue until one of the following becomes true:

  • A breakpoint is reached
  • A specified condition is satisfied
  • A signal is received by the program
  • An error occurs
  • The program terminates
n (next)

Continue the execution of the program from the current state, until next line of code in the current source file is reached. The execution of the program will continue until one of the following becomes true:

  • A breakpoint is reached
  • A specified condition is satisfied
  • A signal is received by the program
  • An error occurs
  • The program terminates
s (step)
The step command also halts execution at each sequential line of code in the current source file. However, if the execution is currently stopped at a source line containing a function call, GDB stops the execution after entering the function call (rather than executing it).
until location
Continue the execution until the code location specified by the location option is reached.
fini (finish)

Resume the execution of the program and halt when execution returns from a function. The execution of the program will continue until one of the following becomes true:

  • A breakpoint is reached
  • A specified condition is satisfied
  • A signal is received by the program
  • An error occurs
  • The program terminates
q (quit)
Terminate the execution and exit GDB.
Additional Resources

20.2.4. Showing Program Internal Values with GDB

Displaying thevalues of a program’s internal variables is important for understanding of what the program is doing. GDB offers multiple commands that you can use to inspect the internal variables. This section describes the most useful of these commands.

Prerequisites
  • Understanding the GDB debugger
GDB Commands to Display the Internal State of a Program
p (print)

Display the value of the argument given. Usually, the argument is the name of a variable of any complexity, from a simple single value to a structure. An argument can also be an expression valid in the current language, including the use of program variables and library functions, or functions defined in the program being tested.

It is possible to extend GDB with pretty-printer Python or Guile scripts for customized display of data structures (such as classes, structs) using the print command.

bt (backtrace)

Display the chain of function calls used to reach the current execution point, or the chain of functions used up until execution was terminated. This is useful for investigating serious bugs (such as segmentation faults) with elusive causes.

Adding the full option to the backtrace command displays local variables, too.

It is possible to extend GDB with frame filter Python scripts for customized display of data displayed using the bt and info frame commands. The term frame refers to the data associated with a single function call.

info

The info command is a generic command to provide information about various items. It takes an option specifying the item to describe.

  • The info args command displays options of the function call that is the currently selected frame.
  • The info locals command displays local variables in the currently selected frame.

For a list of the possible items, run the command help info in a GDB session:

(gdb) help info
l (list)
Show the line in the source code where the program stopped. This command is available only when the program execution is stopped. While not strictly a command to show internal state, list helps the user understand what changes to the internal state will happen in the next step of the program’s execution.
Additional Resources

20.2.5. Using GDB Breakpoints to Stop Execution at Defined Code Locations

In many cases, it is advantageous to let the program execute until a certain line of code is reached.

Prerequisites
  • Understanding GDB
Using Breakpoints in GDB

Breakpoints are markers that tell GDB to stop the execution of a program. Breakpoints are most commonly associated with source code lines: Placing a breakpoint requires specifying the source file and line number.

  • To place a breakpoint:

    • Specify the name of the source code file and the line in that file:

      (gdb) br file:line
    • When file is not present, name of the source file at the current point of execution is used:

      (gdb) br line
    • Alternatively, use a function name to put the breakpoint on its start:

      (gdb) br function_name
  • A program might encounter an error after a certain number of iterations of a task. To specify an additional condition to halt execution:

    (gdb) br file:line if condition

    Replace condition with a condition in the C or C++ language. The meaning of file and line is the same as above.

  • To inspect the status of all breakpoints and watchpoints:

    (gdb) info br
  • To remove a breakpoint by using its number as displayed in the output of info br:

    (gdb) delete number
  • To remove a breakpoint at a given location:

    (gdb) clear file:line
Additional Resources

20.2.6. Using GDB Watchpoints to Stop Execution on Data Access and Changes

In many cases, it is advantageous to let the program execute until certain data changes or is accessed. This section lists the most common

Prerequisites
  • Understanding GDB
Using Watchpoints in GDB

Watchpoints are markers which tell GDB to stop the execution of program. Watchpoints are associated with data: Placing a watchpoint requires specifying an expression describing a variable, multiple variables, or a memory address.

  • To place a watchpoint for data change (write):

    (gdb) watch expression

    Replace expression with an expression that describes what you want to watch. For variables, expression is equal to the name of the variable.

  • To place a watchpoint for data access (read):

    (gdb) rwatch expression
  • To place a watchpoint for any data access (both read and write):

    (gdb) awatch expression
  • To inspect the status of all watchpoints and breakpoints:

    (gdb) info br
  • To remove a watchpoint:

    (gdb) delete num

    Replace the num option with the number reported by the info br command.

Additional Resources

20.2.7. Debugging Forking or Threaded Programs with GDB

Some programs use forking or threads to achieve parallel code execution. Debugging multiple simultaneous execution paths requires special considerations.

Prerequisites
  • Understanding the GDB debugger
  • Understanding the concepts of process forking and threads
Debugging Forked Programs with GDB

Forking is a situation when a program (parent) creates an independent copy of itself (child). Use the following settings and commands to affect what GDB does when a fork occurs:

  • The follow-fork-mode setting controls whether GDB follows the parent or the child after the fork.

    set follow-fork-mode parent
    After a fork, debug the parent process. This is the default.
    set follow-fork-mode child
    After a fork, debug the child process.
    show follow-fork-mode
    Display the current setting of follow-fork-mode.
  • The set detach-on-fork setting controls whether the GDB keeps control of the other (not followed) process or leaves it to run.

    set detach-on-fork on
    The process which is not followed (depending on the value of follow-fork-mode) is detached and runs independently. This is the default.
    set detach-on-fork off
    GDB keeps control of both processes. The process which is followed (depending on the value of follow-fork-mode) is debugged as usual, while the other is suspended.
    show detach-on-fork
    Display the current setting of detach-on-fork.
Debugging Threaded Programs with GDB

GDB has the ability to debug individual threads, and to manipulate and examine them independently. To make GDB stop only the thread that is examined, use the commands set non-stop on and set target-async on. You can add these commands to the .gdbinit file. After that functionality is turned on, GDB is ready to conduct thread debugging.

GDB uses a concept of current thread. By default, commands apply to the current thread only.

info threads
Display a list of threads with their id and gid numbers, indicating the current thread.
thread id
Set the thread with the specified id as the current thread.
thread apply ids command
Apply the command command to all threads listed by ids. The ids option is a space-separated list of thread ids. A special value all applies the command to all threads.
break location thread id if condition
Set a breakpoint at a certain location with a certain condition only for the thread number id.
watch expression thread id
Set a watchpoint defined by expression only for the thread number id.
command&
Execute command command and return immediately to the gdb prompt (gdb), continuing any code execution in the background.
interrupt
Halt execution in the background.
Additional Resources

20.3. Recording Application Interactions

The executable code of applications interacts with the code of the operating system and shared libraries. Recording an activity log of these interactions can provide enough insight into the application’s behavior without debugging the actual application code. Alternatively, analyzing an application’s interactions can help pinpoint the conditions in which a bug manifests.

20.3.1. Tools Useful for Recording Application Interactions

Red Hat Enterprise Linux offers multiple tools for analyzing an application’s interactions should be analyzed.

strace

The strace tool primarily enables logging of system calls (kernel functions) used by an application.

  • The strace output is detailed and explains the calls well, because strace interprets parameters and results with knowledge of the underlying kernel code. Numbers are turned into the respective constant names, bitwise combined flags expanded to flag list, pointers to character arrays dereferenced to provide the actual string, and more. Support for more recent kernel features may be lacking.
  • You can filter the traced calls to reduce the amount of captured data.
  • The use of [command]`strace does not require any particular setup except for setting up the log filter.
  • Tracing the application code with strace results in significant slowdown of the application’s execution. As a result, [command]`strace is not suitable for many production deployments. As an alternative, consider using ltrace or SystemTap.
  • The version of strace available in Red Hat Developer Toolset can also perform system call tampering. This capability is useful for debugging.
ltrace

The [command]`ltrace tool enables logging of an application’s user space calls into shared objects (dynamic libraries).

  • ltrace enables tracing calls to any library.
  • You can filter the traced calls to reduce the amount of captured data.
  • The use of ltrace does not require any particular setup except for setting up the log filter.
  • ltrace is lightweight and fast, offering an alternative to strace: it is possible to trace the respective interfaces in libraries such as glibc with ltrace instead of tracing kernel functions with strace.
  • Because ltrace does not handle a known set of calls like strace, it does not attempt to explain the values passed to library functions. The ltrace output contains only raw numbers and pointers. The interpretation of ltrace output requires consulting the actual interface declarations of the libraries present in the output.
SystemTap

SystemTap is an instrumentation platform for probing running processes and kernel activity on the Linux system. SystemTap uses its own scripting language for programming custom event handlers.

  • Compared to using strace and ltrace, scripting the logging means more work in the initial setup phase. However, the scripting capabilities extend SystemTap’s usefulness beyond just producing logs.
  • SystemTap works by creating and inserting a kernel module. The use of SystemTap is efficient and does not create a significant slowdown of the system or application execution on its own.
  • SystemTap comes with a set of usage examples.
GDB

The GNU Debugger is primarily meant for debugging, not logging. However, some of its features make it useful even in the scenario where an application’s interaction is the primary activity of interest.

  • With GDB, it is possible to conveniently combine the capture of an interaction event with immediate debugging of the subsequent execution path.
  • GDB is best suited for analyzing response to infrequent or singular events, after the initial identification of problematic situation by other tools. Using GDB in any scenario with frequent events becomes inefficient or even impossible.
Additional Resources

20.3.2. Monitoring an Application’s System Calls with strace

The strace tool enables monitoring the system (kernel) calls performed by an application.

Prerequisites
Steps
  1. Identify the system calls you wish to monitor.
  2. If the program you want to monitor is not running, start strace and specify the program:

    $ strace -fvttTyy -s 256 -e trace=call program

    Replace call with the system calls to be displayed. You can use the -e trace=call option multiple times. If left out, strace will display all system call types. See the strace(1) manual page for more information.

    If the program is already running, find its process id (pid) and attach strace to it:

    $ ps -C program
    (...)
    $ strace -fvttTyy -s 256 -e trace=call -ppid

    If you do not wish to trace any forked processes or threads, leave out the -f option.

  3. strace displays the system calls made by the application and their details.

    In most cases, an application and its libraries make a large number of calls and strace output appears immediately, if no filter for system calls is set.

  4. strace exits when the program exits.

    To terminate the monitoring before the traced program exits, press ctrl+C.

    • If strace started the program, the program terminates together with strace.
    • If you attached strace to an already running program, the program terminates together with strace.
  5. Analyze the list of system calls done by the application.

    • Problems with resource access or availability are present in the log as calls returning errors.
    • Values passed to the system calls and patterns of call sequences provide insight into the causes of the application’s behaviour.
    • If the application crashes, the important information is probably at the end of log.
    • The output contains a lot of unnecessary information. However, you can construct a more precise filter and repeat the procedure.
Note

It is advantageous to both see the output and save it to a file. Use the tee command to achieve this:

$ strace ... | tee your_log_file.log
Additional Resources

20.3.3. Monitoring Application’s Library Function Calls with ltrace

The ltrace tool enables monitoring of the calls done by an application to functions available in libraries (shared objects).

Prerequisites
Steps
  1. Identify the libraries and functions of interest, if possible.
  2. If the program you want to monitor is not running, start ltrace and specify program:

    $ ltrace -f -l library -e function program

    Use the options -e and -l to filter the output:

    • Supply the function names to be displayed as function. The -e function option can be used multiple times. If left out, ltrace will display calls to all functions.
    • Instead of specifying functions, you can specify whole libraries with the -l library option. This option behaves similarly to the -e function option.

    See the ltrace(1)_ manual page for more information.

    If the program is already running, find its process id (pid) and attach ltrace to it:

    $ ps -C program
    (...)
    $ ltrace ... -ppid

    If you do not wish to trace any forked processes or threads, leave out the -f option.

  3. ltrace displays the library calls made by the application.

    In most cases, an application will make a large number of calls and ltrace output appears immediately, if no filter is set.

  4. ltrace exits when the program exits.

    To terminate the monitoring before the traced program exits, press ctrl+C.

    • If ltrace started the program, the program terminates together with ltrace.
    • If you attached ltrace to an already running program, the program terminates together with ltrace.
  5. Analyze the list of library calls done by the application.

    • If the application crashes, the important information is probably at the end of log.
    • The output contains a lot of unnecessary information. However, you can construct a more precise filter and repeat the procedure.
Note

It is advantageous to both see the output and save it to a file. Use the tee command to achieve this:

$ ltrace ... | tee your_log_file.log
Additional Resources
  • The strace(1) manual page.
  • Red Hat Developer Toolset User Guide — Chapter ltrace

20.3.4. Monitoring Application’s System Calls with SystemTap

The SystemTap tool enables registering custom event handlers for kernel events. In comparison with strace, it is harder to use but more efficient and enables more complicated processing logic.

Prerequisites
Steps
  1. Create a file my_script.stp with the contents:

    probe begin
    {
      printf("waiting for syscalls of process %d \n", target())
    }
    
    probe syscall.*
    {
      if (pid() == target())
        printf("%s(%s)\n", name, argstr)
    }
    
    probe process.end
    {
      if (pid() == target())
        exit()
    }
  2. Find the process ID (pid) of the process you wish to monitor:

    $ ps -aux
  3. Run SystemTap with the script:

    # stap my_script.stp -x pid

    The value of pid is the process id.

    The script is compiled to a kernel module which is then loaded. This introduces a slight delay between entering the command and getting the output.

  4. When the process performs a system call, the call name and its parameters are printed to the terminal.
  5. The script exits when the process terminates, or when you press Ctrl+C.
Additional Resources

20.3.5. Using GDB to Intercept Application System Calls

GDB enables stopping the execution in various kinds of situations arising during the execution of a program. To stop the execution when the program performs a system call, use a GDB catchpoint.

Prerequisites

Stopping Program Execution on System Calls with GDB

  1. Set the catchpoint:

    (gdb) catch syscall syscall-name

    The command catch syscall sets a special type of breakpoint that halts execution when a system call is performed by the program.

    The syscall-name option specifies the name of the call. You can specify multiple catchpoints for various system calls. Leaving out the syscall-name option causes GDB to stop on any system call.

  2. If the program has not started execution, start it:

    (gdb) r

    If the program execution is only halted, resume it:

    (gdb) c
  3. GDB halts execution after any specified system call is performed by the program.
Additional Resources

20.3.6. Using GDB to Intercept Handling of Signals by Applications

GDB enables stopping the execution in various kinds of situations arising during the execution of a program. To stop the execution when the program receives a signal from the operating system, use a GDB catchpoint.

Prerequisites
Stopping Program Execution on Receiving a Signal with GDB
  1. Set the catchpoint:

    (gdb) catch signal signal-type

    The command catch signal sets a special type of a breakpoint that halts execution when a signal is received by the program. The signal-type option specifies the type of the signal. Use the special value 'all' to catch all signals.

  2. If the program has not started execution, start it:

    (gdb) r

    If the program execution is only halted, resume it:

    (gdb) c
  3. GDB halts execution after the program receives any specified signal.
Additional Resources

Chapter 21. Debugging a Crashed Application

Sometimes, it is not possible to debug an application directly. In these situations, you can collect information about the application at the moment of its termination and analyze it afterwards.

21.1. Core Dumps

This section decribes what a core dump is and how to use it.

Prerequisites

  • Understanding debugging information

Description

A core dump is a copy of a part of the application’s memory at the moment the application stopped working, stored in the ELF format. It contains all the application’s internal variables and stack, which enables inspection of the application’s final state. When augmented with the respective executable file and debugging information, it is possible to analyze a core dump file with a debugger in a way similar to analyzing a running program.

The Linux operating system kernel can record core dumps automatically, if this functionality is enabled. Alternatively, you can send a signal to any running application to generate a core dump regardless of its actual state.

Warning

Some limits might affect the ability to generate a core dump.

21.2. Recording Application Crashes with Core Dumps

To record application crashes, set up core dump saving and add information about the system.

Steps

  1. Enable core dumps. Edit the file /etc/systemd/systemd.conf and change the line containing DefaultLimitCORE to the following:

    DefaultLimitCORE=infinity
  2. Reboot the system:

    # shutdown -r now
  3. Remove the limits for core dump sizes:

    # ulimit -c unlimited

    To reverse this change, run the command with value 0 instead of unlimited.

  4. When an application crashes, a core dump is generated. The default location for core dumps is the application’s working directory at the time of crash.
  5. Create an SOS report to provide additional information about the system:

    # sosreport

    This creates a tar archive containing information about your system, such as copies of configuration files.

  6. Transfer the core dump and the SOS report to the computer where the debugging will take place. Transfer the executable file, too, if it is known.

    Important

    When the executable file is not known, subsequent analysis of the core file identifies it.

  7. Optional: Remove the core dump and SOS report after transferring them, to free up disk space.

Additional Resources

21.3. Inspecting Application Crash States with Core Dumps

Prerequisites

  • Core dump file and sosreport
  • GDB and elfutils installed on the system

Steps

  1. To identify the executable file where the crash occurred, run the eu-unstrip command with the core dump file:

    $ eu-unstrip -n --core=./core.9814
    0x400000+0x207000 2818b2009547f780a5639c904cded443e564973e@0x400284 /usr/bin/sleep /usr/lib/debug/bin/sleep.debug [exe]
    0x7fff26fff000+0x1000 1e2a683b7d877576970e4275d41a6aaec280795e@0x7fff26fff340 . - linux-vdso.so.1
    0x35e7e00000+0x3b6000 374add1ead31ccb449779bc7ee7877de3377e5ad@0x35e7e00280 /usr/lib64/libc-2.14.90.so /usr/lib/debug/lib64/libc-2.14.90.so.debug libc.so.6
    0x35e7a00000+0x224000 3ed9e61c2b7e707ce244816335776afa2ad0307d@0x35e7a001d8 /usr/lib64/ld-2.14.90.so /usr/lib/debug/lib64/ld-2.14.90.so.debug ld-linux-x86-64.so.2

    The output contains details for each module on a line, spearated by spaces. The information is listed in this order:

    1. The memory address where the module was mapped
    2. The build-id of the module and where in the memory it was found.
    3. The module’s executable file name, displayed as - when unknown, or as . when the module has not been loaded loaded from a file
    4. The source of debugging information, displayed as a file name when available, as . when contained in the executable file itself, or as - when not present at all
    5. The shared library name (soname), or [exe] for the main module

    In this example, the important details are the file name /usr/bin/sleep and the build-id 2818b2009547f780a5639c904cded443e564973e on the line containing the text [exe]. With this information, you can identify the executable file required for analyzing the core dump.

  2. Get the executable file that crashed.

    • If possible, copy it from the system where the crash occurred. Use the file name extracted from the core file.
    • Alternatively, use an identical executable file on your system. Each executable file built on Red Hat Enterprise Linux contains a note with an unique build-id value. Determine the build-id of the relevant locally available executable files:

      $ eu-readelf -n executable_file

      Use this information to match the executable file on the remote system with your local copy. The build-id of the local file and build-id listed in the core dump must match.

    • Finally, if the application is installed from a RPM package, you can get the executable file from the package. Use the sosreport output to find the exact version of the package required.
  3. Get the shared libraries used by the executable file. Use the same steps as for the executable file.
  4. If the application is distributed as a package, load the executable file in GDB, to display hints for missing debuginfo packages. For more details, see Section 20.1.4, “Getting debuginfo Packages for an Application or Library using GDB”.
  5. To examine the core file in detail, load the executable file and core dump file with GDB:

    $ gdb -e executable_file -c core_file

    Further messages about missing files and debugging information help you identify what is missing for the debugging session. Return to the previous step if needed.

    If the application’s debugging information is available as a file instead of as a package, load this file in GDB with the symbol-file command:

    (gdb) symbol-file program.debug

    Replace program.debug with the actual file name.

    Note

    It might not be necessary to install the debugging information for all executable files contained in the core dump. Most of these executable files are libraries used by the application code. These libraries might not directly contribute to the problem you are analyzing, and you do not need to include debugging information for them.

  6. Use the GDB commands to inspect the state of the application at the moment it crashed. See Section 20.2, “Inspecting Application Internal State with GDB”.

    Note

    When analyzing a core file, GDB is not attached to a running process. Commands for controlling execution have no effect.

Additional Resources

21.4. Dumping Process Memory with gcore

The workflow of core dump debugging enables the analysis of the program’s state offline. In some cases, it is advantageous to use this workflow with a program that is still running, such as when it is hard to access the environment with the process. You can use the gcore command to dump memory of any process while it is still running.

Prerequisites

Steps

To dump a process memory using gcore:

  1. Find out the process id (pid). Use tools such as ps, pgrep, and top:

    $ ps -C some-program
  2. Dump the memory of this process:

    $ gcore -o filename pid

    This creates a file filename is created and dumps the process memory in it. While the memory is being dumped, the execution of the process is halted.

  3. After the core dump is finished, the process resumes normal execution.
  4. Create an SOS report to provide additional information about the system:

    # sosreport

    This creates a tar archive containing information about your system, such as copies of configuration files.

  5. Transfer the program’s executable file, core dump, and the SOS report to the computer where the debugging will take place.
  6. Optional: Remove the core dump and SOS report after transferring them, to free up disk space.

Additional resources

21.5. Dumping Protected Process Memory with GDB

You can mark the memory of processes as not to be dumped. This can save resources and ensure additional security when the process memory contains sensitive data: in banking or accounting applications or on whole virtual machines. Both kernel core dumps (kdump) and manual core dumps (gcore, GDB) do not dump memory marked this way.

In some cases, it is necessary to dump the whole contents of the process memory regardless of these protections. This procedure shows how to do this using the GDB debugger.

Prerequisites

Steps

  1. Set GDB to ignore the settings in the /proc/PID/coredump_filter file:

    (gdb) set use-coredump-filter off
  2. Set GDB to ignore the memory page flag VM_DONTDUMP:

    (gdb) set dump-excluded-mappings on
  3. Dump the memory:

    (gdb) gcore core-file

    Replace core-file with name of file where you want to dump the memory.

Additional Resources

Part VI. Monitoring Performance

Developers profile programs to focus attention on the areas of the program that have the largest impact on performance. The types of data collected include what section of the program consumes the most processor time, and where memory is allocated. Profiling collects data from the actual program execution. Thus, the quality of the data collect is influenced by the actual tasks being performed by the program. The tasks performed during profiling should be representative of actual use; this ensures that problems arising from realistic use of the program are addressed during development.

Red Hat Enterprise Linux includes a number of different tools (Valgrind, OProfile, perf, and SystemTap) to collect profiling data. Each tool is suitable for performing specific types of profile runs, as described in the following sections.

Chapter 22. Valgrind

Valgrind is an instrumentation framework for building dynamic analysis tools that can be used to profile applications in detail. The default installation alrready provides five standard tools. Valgrind tools are generally used to investigate memory management and threading problems. The Valgrind suite also includes tools that allow the building of new profiling tools as required.

Valgrind provides instrumentation for user-space binaries to check for errors, such as the use of uninitialized memory, improper allocation/freeing of memory, and improper arguments for systemcalls. Its profiling tools can be used by normal users on most binaries; however, compared to other profilers, Valgrind profile runs are significantly slower. To profile a binary, Valgrind rewrites its executable and instruments the rewritten binary. Valgrind's tools are most useful for looking for memory-related issues in user-space programs; it is not suitable for debugging time-specific issues or kernel-space instrumentation and debugging.

Valgrind reports are most useful and accurate whhen debuginfo packages are installed for the programs or libraries under investigation. See Section 20.1, “Enabling Debugging with Debugging Information”.

22.1. Valgrind Tools

The Valgrind suite is composed of the following tools:

memcheck
This tool detects memory management problems in programs by checking all reads from and writes to memory and intercepting all system calls to malloc, new, free, and delete. memcheck is perhaps the most used Valgrind tool, as memory management problems can be difficult to detect using other means. Such problems often remain undetected for long periods, eventually causing crashes that are difficult to diagnose.
cachegrind
cachegrind is a cache profiler that accurately pinpoints sources of cache misses in code by performing a detailed simulation of the I1, D1 and L2 caches in the CPU. It shows the number of cache misses, memory references, and instructions accruing to each line of source code; cachegrind also provides per-function, per-module, and whole-program summaries, and can even show counts for each individual machine instructions.
callgrind
Like cachegrind, callgrind can model cache behavior. However, the main purpose of callgrind is to record callgraphs data for the executed code.
massif
massif is a heap profiler; it measures how much heap memory a program uses, providing information on heap blocks, heap administration overheads, and stack sizes. Heap profilers are useful in finding ways to reduce heap memory usage. On systems that use virtual memory, programs with optimized heap memory usage are less likely to run out of memory, and may be faster as they require less paging.
helgrind

In programs that use the POSIX pthreads threading primitives, helgrind detects synchronization errors. Such errors are:

  • Misuses of the POSIX pthreads API
  • Potential deadlocks arising from lock ordering problems
  • Data races (that is, accessing memory without adequate locking)

Valgrind also allows you to develop your own profiling tools. In line with this, Valgrind includes the lackey tool, which is a sample that can be used as a template for generating your own tools.

22.2. Using Valgrind

The valgrind package and its dependencies install all the necessary tools for performing a Valgrind profile run. To profile a program with Valgrind, use:

~]$ valgrind --tool=toolname program

See Section 22.1, “Valgrind Tools” for a list of arguments for toolname. In addition to the suite of Valgrind tools, none is also a valid argument for toolname; this argument allows you to run a program under Valgrind without performing any profiling. This is useful for debugging or benchmarking Valgrind itself.

You can also instruct Valgrind to send all of its information to a specific file. To do so, use the option --log-file=filename. For example, to check the memory usage of the executable file hello and send profile information to output, use:

~]$ valgrind --tool=memcheck --log-file=output hello

See Section 22.3, “Additional information” for more information on Valgrind, along with other available documentation on the Valgrind suite of tools.

22.3. Additional information

For more extensive information on Valgrind, see man valgrind. Red Hat Enterprise Linux also provides a comprehensive Valgrind Documentation book available as PDF and HTML in:

  • /usr/share/doc/valgrind-version/valgrind_manual.pdf
  • /usr/share/doc/valgrind-version/html/index.html

Chapter 23. OProfile

OProfile is a low overhead, system-wide performance monitoring tool provided by the oprofile package. It uses the performance monitoring hardware on the processor to retrieve information about the kernel and executables on the system, such as when memory is referenced, the number of second-level cache requests, and the number of hardware interrupts received. OProfile is also able to profile applications that run in a Java Virtual Machine (JVM).

The following is a selection of the tools provided by OProfile. Note that the legacy opcontrol tool and the new operf tool are mutually exclusive.

ophelp
Displays available events for the system’s processor along with a brief description of each.
operf
Intended to replace opcontrol. The operf tool uses the Linux Performance Events subsystem, allowing you to target your profiling more precisely, as a single process or system-wide, and allowing OProfile to co-exist better with other tools using the performance monitoring hardware on your system. Unlike opcontrol, no initial setup is required, and it can be used without the root privileges unless the --system-wide option is in use.
opimport
Converts sample database files from a foreign binary format to the native format for the system. Only use this option when analyzing a sample database from a different architecture.
opannotate
Creates an annotated source for an executable if the application was compiled with debugging symbols.
opreport
Retrieves profile data.
opcontrol
This tool is used to start and stop the OProfile daemon (oprofiled) and configure a profile session.
oprofiled
Runs as a daemon to periodically write sample data to disk.

Legacy mode (opcontrol, oprofiled, and post-processing tools) remains available, but it is no longer the recommended profiling method. For a detailed description of the legacy mode, see the Configuring OProfile Using Legacy Mode chapter in the System Administrator’s Guide.

23.1. Using OProfile

operf is the recommended tool for collecting profiling data. The tool does not require any initial configuration, and all options are passed to it on the command line. Unlike the legacy opcontrol tool, operf can run without root privileges. See the Using operf chapter in the System Administrator’s Guide for detailed instructions on how to use the operf tool.

Example 23.1. Using operf to Profile a Java Program

In the following example, the operf tool is used to collect profiling data from a Java (JIT) program, and the opreport tool is then used to output per-symbol data.

  1. Install the demonstration Java program used in this example. It is a part of the java-1.8.0-openjdk-demo package, which is included in the Optional channel. See Enabling Supplementary and Optional Repositories for instructions on how to use the Optional channel. When the Optional channel is enabled, install the package:

    ~]# yum install java-1.8.0-openjdk-demo
  2. Install the oprofile-jit package for OProfile to be able to collect profiling data from Java programs:

    ~]# yum install oprofile-jit
  3. Create a directory for OProfile data:

    ~]$ mkdir ~/oprofile_data
  4. Change into the directory with the demonstration program:

    ~]$ cd /usr/lib/jvm/java-1.8.0-openjdk/demo/applets/MoleculeViewer/
  5. Start the profiling:

    ~]$ operf -d ~/oprofile_data appletviewer \
    -J"-agentpath:/usr/lib64/oprofile/libjvmti_oprofile.so" example2.html
  6. Change into the home directory and analyze the collected data:

    ~]$ cd
    ~]$ opreport --symbols --threshold 0.5

    A sample output may look like the following:

    $ opreport --symbols --threshold 0.5
    Using /home/rkratky/oprofile_data/samples/ for samples directory.
    
    WARNING! Some of the events were throttled. Throttling occurs when
    the initial sample rate is too high, causing an excessive number of
    interrupts.  Decrease the sampling frequency. Check the directory
    /home/rkratky/oprofile_data/samples/current/stats/throttled
    for the throttled event names.
    
    warning: /dm_crypt could not be found.
    warning: /e1000e could not be found.
    warning: /kvm could not be found.
    CPU: Intel Ivy Bridge microarchitecture, speed 3600 MHz (estimated)
    Counted CPU_CLK_UNHALTED events (Clock cycles when not halted) with a unit mask of 0x00 (No unit mask) count 100000
    samples  %        image name               symbol name
    14270    57.1257  libjvm.so                /usr/lib/jvm/java-1.8.0-openjdk-1.8.0.51-1.b16.el7_1.x86_64/jre/lib/amd64/server/libjvm.so
    3537     14.1593  23719.jo                 Interpreter
    690       2.7622  libc-2.17.so             fgetc
    581       2.3259  libX11.so.6.3.0          /usr/lib64/libX11.so.6.3.0
    364       1.4572  libpthread-2.17.so       pthread_getspecific
    130       0.5204  libfreetype.so.6.10.0    /usr/lib64/libfreetype.so.6.10.0
    128       0.5124  libc-2.17.so             __memset_sse2

23.2. OProfile Documentation

For more extensive information on OProfile, see the oprofile(1) manual page. Red Hat Enterprise Linux also provides two comprehensive guides to OProfile in file:///usr/share/doc/oprofile-version/:

OProfile Manual
A comprehensive manual with detailed instructions on the setup and use of OProfile is found at file:///usr/share/doc/oprofile-version/oprofile.html
OProfile Internals
Documentation on the internal workings of OProfile, useful for programmers interested in contributing to the OProfile upstream, can be found at file:///usr/share/doc/oprofile-version/internals.html

Chapter 24. SystemTap

SystemTap is a useful instrumentation platform for probing running processes and kernel activity on the Linux system. To execute a probe:

  1. Write SystemTap scripts that specify which system events (for example, virtual file system reads, packet transmissions) should trigger specified actions (for example, print, parse, or otherwise manipulate data).
  2. SystemTap translates the script into a C program, which it compiles into a kernel module.
  3. SystemTap loads the kernel module to perform the actual probe.

SystemTap scripts are useful for monitoring system operation and diagnosing system issues with minimal intrusion into the normal operation of the system. You can quickly instrument running system test hypotheses without having to recompile and re-install instrumented code. To compile a SystemTap script that probes kernel-space, SystemTap uses information from three different kernel information packages:

  • kernel-variant-devel-version
  • kernel-variant-debuginfo-version
  • kernel-debuginfo-common-arch-version

These kernel information packages must match the kernel to be probed. In addition, to compile SystemTap scripts for multiple kernels, the kernel information packages of each kernel must also be installed.

24.1. Additional Information

For more detailed information about SystemTap, see the following Red Hat documentation:

Chapter 25. Performance Counters for Linux (PCL) Tools and perf

Performance Counters for Linux (PCL) is a new kernel-based subsystem that provides a framework for collecting and analyzing performance data. These events will vary based on the performance monitoring hardware and the software configuration of the system. Red Hat Enterprise Linux 6 includes this kernel subsystem to collect data and the user-space tool perf to analyze the collected performance data. The PCL subsystem can be used to measure hardware events, including retired instructions and processor clock cycles. It can also measure software events, including major page faults and context switches. For example, PCL counters can compute the Instructions Per Clock (IPC) from a process’s counts of instructions retired and processor clock cycles. A low IPC ratio indicates the code makes poor use of the CPU. Other hardware events can also be used to diagnose poor CPU performance.

Performance counters can also be configured to record samples. The relative frequency of samples can be used to identify which regions of code have the greatest impact on performance.

25.1. Perf Tool Commands

Useful perf commands include the following:

perf stat
This perf command provides overall statistics for common performance events, including instructions executed and clock cycles consumed. Options allow selection of events other than the default measurement events.
perf record
This perf command records performance data into a file which can be later analyzed using perf report.
perf report
This perf command reads the performance data from a file and analyzes the recorded data.
perf list
This perf command lists the events available on a particular machine. These events will vary based on the performance monitoring hardware and the software configuration of the system.

Use perf help to obtain a complete list of perf commands. To retrieve man page information on each perf command, use perf help command.

25.2. Using Perf

Using the basic PCL infrastructure for collecting statistics or samples of program execution is relatively straightforward. This section provides simple examples of overall statistics and sampling.

To collect statistics on make and its children, use the following command:

# perf stat -- make all

The perf command collects a number of different hardware and software counters. It then prints the following information:

Performance counter stats for 'make all':

  244011.782059  task-clock-msecs         #      0.925 CPUs
          53328  context-switches         #      0.000 M/sec
            515  CPU-migrations           #      0.000 M/sec
        1843121  page-faults              #      0.008 M/sec
   789702529782  cycles                   #   3236.330 M/sec
  1050912611378  instructions             #      1.331 IPC
   275538938708  branches                 #   1129.203 M/sec
     2888756216  branch-misses            #      1.048 %
     4343060367  cache-references         #     17.799 M/sec
      428257037  cache-misses             #      1.755 M/sec

  263.779192511  seconds time elapsed

The perf tool can also record samples. For example, to record data on the make command and its children, use:

# perf record -- make all

This prints out the file in which the samples are stored, along with the number of samples collected:

[ perf record: Woken up 42 times to write data ]
[ perf record: Captured and wrote 9.753 MB perf.data (~426109 samples) ]

As of Red Hat Enterprise Linux 6.4, a new functionality to the {} group syntax has been added that allows the creation of event groups based on the way they are specified on the command line.

The current --group or -g options remain the same; if it is specified for record, stat, or top command, all the specified events become members of a single group with the first event as a group leader.

The new {} group syntax allows the creation of a group like:

# perf record -e '{cycles, faults}' ls

The above results in a single event group containing cycles and faults events, with the cycles event as the group leader.

All groups are created with regards to threads and CPUs. As such, recording an event group within two threads on a server with four CPUs will create eight separate groups.

It is possible to use a standard event modifier for a group. This spans over all events in the group and updates each event modifier settings.

# perf record -r '{faults:k,cache-references}:p'

The above command results in the :kp modifier being used for faults, and the :p modifier being used for the cache-references event.

Performance Counters for Linux (PCL) Tools conflict with OProfile

Both OProfile and Performance Counters for Linux (PCL) use the same hardware Performance Monitoring Unit (PMU). If OProfile is currently running while attempting to use the PCL perf command, an error message like the following occurs when starting OProfile:

Error: open_counter returned with 16 (Device or resource busy). /usr/bin/dmesg may provide additional information.

Fatal: Not all events could be opened.

To use the perf command, first shut down OProfile:

# opcontrol --deinit

You can then analyze perf.data to determine the relative frequency of samples. The report output includes the command, object, and function for the samples. Use perf report to output an analysis of perf.data. For example, the following command produces a report of the executable that consumes the most time:

# perf report --sort=comm

The resulting output:

# Samples: 1083783860000
#
# Overhead          Command
# ........  ...............
#
    48.19%         xsltproc
    44.48%        pdfxmltex
     6.01%             make
     0.95%             perl
     0.17%       kernel-doc
     0.05%          xmllint
     0.05%              cc1
     0.03%               cp
     0.01%            xmlto
     0.01%               sh
     0.01%          docproc
     0.01%               ld
     0.01%              gcc
     0.00%               rm
     0.00%              sed
     0.00%   git-diff-files
     0.00%             bash
     0.00%   git-diff-index

The column on the left shows the relative frequency of the samples. This output shows that make spends most of this time in xsltproc and the pdfxmltex. To reduce the time for the make to complete, focus on xsltproc and pdfxmltex. To list the functions executed by xsltproc, run:

# perf report -n --comm=xsltproc

This generates:

comm: xsltproc
# Samples: 472520675377
#
# Overhead  Samples                    Shared Object  Symbol
# ........ ..........  .............................  ......
#
    45.54%215179861044  libxml2.so.2.7.6               [.] xmlXPathCmpNodesExt
    11.63%54959620202  libxml2.so.2.7.6               [.] xmlXPathNodeSetAdd__internal_alias
     8.60%40634845107  libxml2.so.2.7.6               [.] xmlXPathCompOpEval
     4.63%21864091080  libxml2.so.2.7.6               [.] xmlXPathReleaseObject
     2.73%12919672281  libxml2.so.2.7.6               [.] xmlXPathNodeSetSort__internal_alias
     2.60%12271959697  libxml2.so.2.7.6               [.] valuePop
     2.41%11379910918  libxml2.so.2.7.6               [.] xmlXPathIsNaN__internal_alias
     2.19%10340901937  libxml2.so.2.7.6               [.] valuePush__internal_alias

Appendix A. Revision History

Revision 7-5.1, Tue Apr 10 2018, Vladimír Slávik
Build for the 7.5 GA release.
Revision 7-5, Tue Jan 9 2018, Vladimír Slávik
Published preview of new book version for 7.5 Beta
Revision 7-4.1, Tue Aug 22 2017, Vladimír Slávik
Update for new releases of linked products.
Revision 7-4, Wed Jul 26 2017, Vladimír Slávik
Build for 7.4 GA release. New chapter about setting up a workstation for development.
Revision 1-12, Fri May 26 2017, Vladimír Slávik
Update to remove outdated information.
Revision 7-3.9, Mon May 15 2017, Robert Krátký
Build for 7.4 Beta release.

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