ceph-mgr plugin author guide ============================ Creating a plugin ----------------- In pybind/mgr/, create a python module. Within your module, create a class that inherits from ``MgrModule``. The most important methods to override are: * a ``serve`` member function for server-type modules. This function should block forever. * a ``notify`` member function if your module needs to take action when new cluster data is available. * a ``handle_command`` member function if your module exposes CLI commands. Installing a plugin ------------------- Once your module is present in the location set by the ``mgr module path`` configuration setting, you can enable it via the ``ceph mgr module enable`` command:: ceph mgr module enable mymodule Note that the MgrModule interface is not stable, so any modules maintained outside of the Ceph tree are liable to break when run against any newer or older versions of Ceph. Logging ------- ``MgrModule`` instances have a ``log`` property which is a logger instance that sends log messages into the Ceph logging layer where they will be recorded in the mgr daemon's log file. Use it the same way you would any other python logger. The python log levels debug, info, warn, err are mapped into the Ceph severities 20, 4, 1 and 0 respectively. Exposing commands ----------------- Set the ``COMMANDS`` class attribute of your plugin to a list of dicts like this:: COMMANDS = [ { "cmd": "foobar name=myarg,type=CephString", "desc": "Do something awesome", "perm": "rw" } ] The ``cmd`` part of each entry is parsed in the same way as internal Ceph mon and admin socket commands (see mon/MonCommands.h in the Ceph source for examples) Config settings --------------- Modules have access to a simple key/value store (keys and values are byte strings) for storing configuration. Don't use this for storing large amounts of data. Config values are stored using the mon's config-key commands. Hints for using these: * Reads are fast: ceph-mgr keeps a local in-memory copy * Don't set things by hand with "ceph config-key", the mgr doesn't update at runtime (only set things from within modules). * Writes block until the value is persisted, but reads from another thread will see the new value immediately. Any config settings you want to expose to users from your module will need corresponding hooks in ``COMMANDS`` to expose a setter. Accessing cluster data ---------------------- Modules have access to the in-memory copies of the Ceph cluster's state that the mgr maintains. Accessor functions as exposed as members of MgrModule. Calls that access the cluster or daemon state are generally going from Python into native C++ routines. There is some overhead to this, but much less than for example calling into a REST API or calling into an SQL database. There are no consistency rules about access to cluster structures or daemon metadata. For example, an OSD might exist in OSDMap but have no metadata, or vice versa. On a healthy cluster these will be very rare transient states, but plugins should be written to cope with the possibility. Note that these accessors must not be called in the modules ``__init__`` function. This will result in a circular locking exception. .. py:currentmodule:: mgr_module .. automethod:: MgrModule.get .. automethod:: MgrModule.get_server .. automethod:: MgrModule.list_servers .. automethod:: MgrModule.get_metadata .. automethod:: MgrModule.get_counter What if the mons are down? -------------------------- The manager daemon gets much of its state (such as the cluster maps) from the monitor. If the monitor cluster is inaccessible, whichever manager was active will continue to run, with the latest state it saw still in memory. However, if you are creating a module that shows the cluster state to the user then you may well not want to mislead them by showing them that out of date state. To check if the manager daemon currently has a connection to the monitor cluster, use this function: .. automethod:: MgrModule.have_mon_connection Sending commands ---------------- A non-blocking facility is provided for sending monitor commands to the cluster. .. automethod:: MgrModule.send_command Implementing standby mode ------------------------- For some modules, it is useful to run on standby manager daemons as well as on the active daemon. For example, an HTTP server can usefully serve HTTP redirect responses from the standby managers so that the user can point his browser at any of the manager daemons without having to worry about which one is active. Standby manager daemons look for a subclass of ``StandbyModule`` in each module. If the class is not found then the module is not used at all on standby daemons. If the class is found, then its ``serve`` method is called. Implementations of ``StandbyModule`` must inherit from ``mgr_module.MgrStandbyModule``. The interface of ``MgrStandbyModule`` is much restricted compared to ``MgrModule`` -- none of the Ceph cluster state is available to the module. ``serve`` and ``shutdown`` methods are used in the same way as a normal module class. The ``get_active_uri`` method enables the standby module to discover the address of its active peer in order to make redirects. See the ``MgrStandbyModule`` definition in the Ceph source code for the full list of methods. For an example of how to use this interface, look at the source code of the ``dashboard`` module. Logging ------- Use your module's ``log`` attribute as your logger. This is a logger configured to output via the ceph logging framework, to the local ceph-mgr log files. Python log severities are mapped to ceph severities as follows: * DEBUG is 20 * INFO is 4 * WARN is 1 * ERR is 0 Shutting down cleanly --------------------- If a module implements the ``serve()`` method, it should also implement the ``shutdown()`` method to shutdown cleanly: misbehaving modules may otherwise prevent clean shutdown of ceph-mgr. Is something missing? --------------------- The ceph-mgr python interface is not set in stone. If you have a need that is not satisfied by the current interface, please bring it up on the ceph-devel mailing list. While it is desired to avoid bloating the interface, it is not generally very hard to expose existing data to the Python code when there is a good reason.