Skip to main content

System V Message Queues in Debian 6.x/Knoppix 6.x

A message queue is a System V IPC object that allows different or unrelated processes to exchange messages. Any process can create a message queue, modify it and leave it for other processes to modify later on. Any process can immediately remove a message queue regardless of whether another process is using it. Message queues exist until they are removed or system shutdown.

The data strucuture of the message queue object is:
struct msqid_ds
{
  struct ipc_perm msg_perm;
  msgqnum_t msg_qnum;    /* no of messages on queue */
  msglen_t msg_qbytes;   /* bytes max on a queue */
  pid_t msg_lspid;       /* PID of last msgsnd(2) call */
  pid_t msg_lrpid;       /* PID of last msgrcv(2) call */
  time_t msg_stime;      /* last msgsnd(2) time */
  time_t msg_rtime;      /* last msgrcv(2) time */
  time_t msg_ctime;      /* last change time */
};

Both commands list all the message queue objects in use on the system:
cat /proc/sysvipc/msg;
ipcs -q; ipcs -q -t; ipcs -q -p; ipcs -q -c; ipcs -q -l; ipcs -q -u;

Create message queue objects on the system:
ipcmk -Q -p <permission bits>;

Remove message queue objects from the system:
ipcrm -Q <msg key>;
ipcrm -q <msg id>;

Do you have a suggestion about how to improve this blog? Let's talk about it. Contact me at David.Brenner.Jr@Gmail.com or 720-584-5229.

Comments

Popular posts from this blog

The meaning of time in reinforcement learning

Reinforcement learning (RL) is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning is concerned with how software agents ought to take actions in an environment in order to maximize the notion of cumulative reward through the process of trial and error. In reinforcement learning an agent starts at an empty state then analyzes the available datasets according to a policy of positive states and negative states. Rather than being explicitly taught as in supervised learning the correct set of actions for performing a task, reinforcement learning uses rewards as signals for positive states and punishments as signals for negative states. The agent obtains the best path to a desirable reward as a cumulation of positive states and negative states. As compared to unsupervised learning, reinforcement learning is different in terms of goals. While the goal in unsupervised learning is to find similarities and differences...

Threat hunting polymorphic malware in Linux with Python

You can investigate suspicious activity that could be polymorphic malware by collecting relevant machine data from your endpoint. You can use the machine data to create your own analysis. Before you start your investigation you will need to determine normal activity on your endpoint. Normal activity is the scope of functionality of the software on your endpoint during periods of low activity and high activity. You will need some kind of software that periodically collects specific machine data from your endpoint like my software developed in Python that's available for free download at https://github.com/davidbrennerjr/server-stats-collector Ingest one or more of the following machine data from Category #1. Ingest one or more of the following machine data from Category #2. And ingest one or more of the following machine data from Category #3. Category #1 General system-wide error messages from /var/log/syslog Auditing logs of application rule...

Application behavior monitoring in Linux with Python

You can monitor application behaviors by collecting relevant machine data from your endpoint. You can use the machine data to investigate suspicious activity and create your own analysis. Before you start your investigation you will need to determine normal activity on your endpoint. Normal activity is the scope of functionality of the software on your endpoint during periods of low activity and high activity. You will need some kind of software that periodically collects specific machine data from your endpoint like my software developed in Python that's available for free download at https://github.com/davidbrennerjr/server-stats-collector Ingest one or more of the following machine data from Category #1. Ingest one or more of the following machine data from Category #2. Category #1 General system-wide error messages from /var/log/syslog Auditing logs of application rulesets Auditing logs of security contexts Auditing logs of ...