Synnefo » snf-ganeti

=================

Ganeti 2.0 design

=================

This document describes the major changes in Ganeti 2.0 compared to

the 1.2 version.

The 2.0 version will constitute a rewrite of the 'core' architecture,

paving the way for additional features in future 2.x versions.

.. contents::

Objective

=========

Ganeti 1.2 has many scalability issues and restrictions due to its

roots as software for managing small and 'static' clusters.

Version 2.0 will attempt to remedy first the scalability issues and

then the restrictions.

Background

==========

While Ganeti 1.2 is usable, it severely limits the flexibility of the

cluster administration and imposes a very rigid model. It has the

following main scalability issues:

- only one operation at a time on the cluster [#]_

- poor handling of node failures in the cluster

- mixing hypervisors in a cluster not allowed

It also has a number of artificial restrictions, due to historical design:

- fixed number of disks (two) per instance

- fixed number of NICs

.. [#] Replace disks will release the lock, but this is an exception

       and not a recommended way to operate

The 2.0 version is intended to address some of these problems, and

create a more flexible code base for future developments.

Among these problems, the single-operation at a time restriction is

biggest issue with the current version of Ganeti. It is such a big

impediment in operating bigger clusters that many times one is tempted

to remove the lock just to do a simple operation like start instance

while an OS installation is running.

Scalability problems

--------------------

Ganeti 1.2 has a single global lock, which is used for all cluster

operations.  This has been painful at various times, for example:

- It is impossible for two people to efficiently interact with a cluster

  (for example for debugging) at the same time.

- When batch jobs are running it's impossible to do other work (for example

  failovers/fixes) on a cluster.

This poses scalability problems: as clusters grow in node and instance

size it's a lot more likely that operations which one could conceive

should run in parallel (for example because they happen on different

nodes) are actually stalling each other while waiting for the global

lock, without a real reason for that to happen.

One of the main causes of this global lock (beside the higher

difficulty of ensuring data consistency in a more granular lock model)

is the fact that currently there is no long-lived process in Ganeti

that can coordinate multiple operations. Each command tries to acquire

the so called *cmd* lock and when it succeeds, it takes complete

ownership of the cluster configuration and state.

Other scalability problems are due the design of the DRBD device

model, which assumed at its creation a low (one to four) number of

instances per node, which is no longer true with today's hardware.

Artificial restrictions

-----------------------

Ganeti 1.2 (and previous versions) have a fixed two-disks, one-NIC per

instance model. This is a purely artificial restrictions, but it

touches multiple areas (configuration, import/export, command line)

that it's more fitted to a major release than a minor one.

Architecture issues

-------------------

The fact that each command is a separate process that reads the

cluster state, executes the command, and saves the new state is also

an issue on big clusters where the configuration data for the cluster

begins to be non-trivial in size.

Overview

========

In order to solve the scalability problems, a rewrite of the core

design of Ganeti is required. While the cluster operations themselves

won't change (e.g. start instance will do the same things, the way

these operations are scheduled internally will change radically.

The new design will change the cluster architecture to:

.. image:: arch-2.0.png

This differs from the 1.2 architecture by the addition of the master

daemon, which will be the only entity to talk to the node daemons.

Detailed design

===============

The changes for 2.0 can be split into roughly three areas:

- core changes that affect the design of the software

- features (or restriction removals) but which do not have a wide

  impact on the design

- user-level and API-level changes which translate into differences for

  the operation of the cluster

Core changes

------------

The main changes will be switching from a per-process model to a

daemon based model, where the individual gnt-* commands will be

clients that talk to this daemon (see `Master daemon`_). This will

allow us to get rid of the global cluster lock for most operations,

having instead a per-object lock (see `Granular locking`_). Also, the

daemon will be able to queue jobs, and this will allow the individual

clients to submit jobs without waiting for them to finish, and also

see the result of old requests (see `Job Queue`_).

Beside these major changes, another 'core' change but that will not be

as visible to the users will be changing the model of object attribute

storage, and separate that into name spaces (such that an Xen PVM

instance will not have the Xen HVM parameters). This will allow future

flexibility in defining additional parameters. For more details see

`Object parameters`_.

The various changes brought in by the master daemon model and the

read-write RAPI will require changes to the cluster security; we move

away from Twisted and use HTTP(s) for intra- and extra-cluster

communications. For more details, see the security document in the

doc/ directory.

Master daemon

~~~~~~~~~~~~~

In Ganeti 2.0, we will have the following *entities*:

- the master daemon (on the master node)

- the node daemon (on all nodes)

- the command line tools (on the master node)

- the RAPI daemon (on the master node)

The master-daemon related interaction paths are:

- (CLI tools/RAPI daemon) and the master daemon, via the so called *LUXI* API

- the master daemon and the node daemons, via the node RPC

There are also some additional interaction paths for exceptional cases:

- CLI tools might access via SSH the nodes (for ``gnt-cluster copyfile``

  and ``gnt-cluster command``)

- master failover is a special case when a non-master node will SSH

  and do node-RPC calls to the current master

The protocol between the master daemon and the node daemons will be

changed from (Ganeti 1.2) Twisted PB (perspective broker) to HTTP(S),

using a simple PUT/GET of JSON-encoded messages. This is done due to

difficulties in working with the Twisted framework and its protocols

in a multithreaded environment, which we can overcome by using a

simpler stack (see the caveats section).

The protocol between the CLI/RAPI and the master daemon will be a

custom one (called *LUXI*): on a UNIX socket on the master node, with

rights restricted by filesystem permissions, the CLI/RAPI will talk to

the master daemon using JSON-encoded messages.

The operations supported over this internal protocol will be encoded

via a python library that will expose a simple API for its

users. Internally, the protocol will simply encode all objects in JSON

format and decode them on the receiver side.

For more details about the RAPI daemon see `Remote API changes`_, and

for the node daemon see `Node daemon changes`_.

The LUXI protocol

+++++++++++++++++

As described above, the protocol for making requests or queries to the

master daemon will be a UNIX-socket based simple RPC of JSON-encoded

messages.

The choice of UNIX was in order to get rid of the need of

authentication and authorisation inside Ganeti; for 2.0, the

permissions on the Unix socket itself will determine the access

rights.

We will have two main classes of operations over this API:

- cluster query functions

- job related functions

The cluster query functions are usually short-duration, and are the

equivalent of the ``OP_QUERY_*`` opcodes in Ganeti 1.2 (and they are

internally implemented still with these opcodes). The clients are

guaranteed to receive the response in a reasonable time via a timeout.

The job-related functions will be:

- submit job

- query job (which could also be categorized in the query-functions)

- archive job (see the job queue design doc)

- wait for job change, which allows a client to wait without polling

For more details of the actual operation list, see the `Job Queue`_.

Both requests and responses will consist of a JSON-encoded message

followed by the ``ETX`` character (ASCII decimal 3), which is not a

valid character in JSON messages and thus can serve as a message

delimiter. The contents of the messages will be a dictionary with two

fields:

:method:

  the name of the method called

:args:

  the arguments to the method, as a list (no keyword arguments allowed)

Responses will follow the same format, with the two fields being:

:success:

  a boolean denoting the success of the operation

:result:

  the actual result, or error message in case of failure

There are two special value for the result field:

- in the case that the operation failed, and this field is a list of

  length two, the client library will try to interpret is as an exception,

  the first element being the exception type and the second one the

  actual exception arguments; this will allow a simple method of passing

  Ganeti-related exception across the interface

- for the *WaitForChange* call (that waits on the server for a job to

  change status), if the result is equal to ``nochange`` instead of the

  usual result for this call (a list of changes), then the library will

  internally retry the call; this is done in order to differentiate

  internally between master daemon hung and job simply not changed

Users of the API that don't use the provided python library should

take care of the above two cases.

Master daemon implementation

++++++++++++++++++++++++++++

The daemon will be based around a main I/O thread that will wait for

new requests from the clients, and that does the setup/shutdown of the

other thread (pools).

There will two other classes of threads in the daemon:

- job processing threads, part of a thread pool, and which are

  long-lived, started at daemon startup and terminated only at shutdown

  time

- client I/O threads, which are the ones that talk the local protocol

  (LUXI) to the clients, and are short-lived

Master startup/failover

+++++++++++++++++++++++

In Ganeti 1.x there is no protection against failing over the master

to a node with stale configuration. In effect, the responsibility of

correct failovers falls on the admin. This is true both for the new

master and for when an old, offline master startup.

Since in 2.x we are extending the cluster state to cover the job queue

and have a daemon that will execute by itself the job queue, we want

to have more resilience for the master role.

The following algorithm will happen whenever a node is ready to

transition to the master role, either at startup time or at node

failover:

#. read the configuration file and parse the node list

   contained within

#. query all the nodes and make sure we obtain an agreement via

   a quorum of at least half plus one nodes for the following:

    - we have the latest configuration and job list (as

      determined by the serial number on the configuration and

      highest job ID on the job queue)

    - there is not even a single node having a newer

      configuration file

    - if we are not failing over (but just starting), the

      quorum agrees that we are the designated master

    - if any of the above is false, we prevent the current operation

      (i.e. we don't become the master)

#. at this point, the node transitions to the master role

#. for all the in-progress jobs, mark them as failed, with

   reason unknown or something similar (master failed, etc.)

Since due to exceptional conditions we could have a situation in which

no node can become the master due to inconsistent data, we will have

an override switch for the master daemon startup that will assume the

current node has the right data and will replicate all the

configuration files to the other nodes.

**Note**: the above algorithm is by no means an election algorithm; it

is a *confirmation* of the master role currently held by a node.

Logging

+++++++

The logging system will be switched completely to the standard python

logging module; currently it's logging-based, but exposes a different

API, which is just overhead. As such, the code will be switched over

to standard logging calls, and only the setup will be custom.

With this change, we will remove the separate debug/info/error logs,

and instead have always one logfile per daemon model:

- master-daemon.log for the master daemon

- node-daemon.log for the node daemon (this is the same as in 1.2)

- rapi-daemon.log for the RAPI daemon logs

- rapi-access.log, an additional log file for the RAPI that will be

  in the standard HTTP log format for possible parsing by other tools

Since the `watcher`_ will only submit jobs to the master for startup

of the instances, its log file will contain less information than

before, mainly that it will start the instance, but not the results.

Node daemon changes

+++++++++++++++++++

The only change to the node daemon is that, since we need better

concurrency, we don't process the inter-node RPC calls in the node

daemon itself, but we fork and process each request in a separate

child.

Since we don't have many calls, and we only fork (not exec), the

overhead should be minimal.

Caveats

+++++++

A discussed alternative is to keep the current individual processes

touching the cluster configuration model. The reasons we have not

chosen this approach is:

- the speed of reading and unserializing the cluster state

  today is not small enough that we can ignore it; the addition of

  the job queue will make the startup cost even higher. While this

  runtime cost is low, it can be on the order of a few seconds on

  bigger clusters, which for very quick commands is comparable to

  the actual duration of the computation itself

- individual commands would make it harder to implement a

  fire-and-forget job request, along the lines "start this

  instance but do not wait for it to finish"; it would require a

  model of backgrounding the operation and other things that are

  much better served by a daemon-based model

Another area of discussion is moving away from Twisted in this new

implementation. While Twisted has its advantages, there are also many

disadvantages to using it:

- first and foremost, it's not a library, but a framework; thus, if

  you use twisted, all the code needs to be 'twiste-ized' and written

  in an asynchronous manner, using deferreds; while this method works,

  it's not a common way to code and it requires that the entire process

  workflow is based around a single *reactor* (Twisted name for a main

  loop)

- the more advanced granular locking that we want to implement would

  require, if written in the async-manner, deep integration with the

  Twisted stack, to such an extend that business-logic is inseparable

  from the protocol coding; we felt that this is an unreasonable request,

  and that a good protocol library should allow complete separation of

  low-level protocol calls and business logic; by comparison, the threaded

  approach combined with HTTPs protocol required (for the first iteration)

  absolutely no changes from the 1.2 code, and later changes for optimizing

  the inter-node RPC calls required just syntactic changes (e.g.

  ``rpc.call_...`` to ``self.rpc.call_...``)

Another issue is with the Twisted API stability - during the Ganeti

1.x lifetime, we had to to implement many times workarounds to changes

in the Twisted version, so that for example 1.2 is able to use both

Twisted 2.x and 8.x.

In the end, since we already had an HTTP server library for the RAPI,

we just reused that for inter-node communication.

Granular locking

~~~~~~~~~~~~~~~~

We want to make sure that multiple operations can run in parallel on a Ganeti

Cluster. In order for this to happen we need to make sure concurrently run

operations don't step on each other toes and break the cluster.

This design addresses how we are going to deal with locking so that:

- we preserve data coherency

- we prevent deadlocks

- we prevent job starvation

Reaching the maximum possible parallelism is a Non-Goal. We have identified a

set of operations that are currently bottlenecks and need to be parallelised

and have worked on those. In the future it will be possible to address other

needs, thus making the cluster more and more parallel one step at a time.

This section only talks about parallelising Ganeti level operations, aka

Logical Units, and the locking needed for that. Any other synchronization lock

needed internally by the code is outside its scope.

Library details

+++++++++++++++

The proposed library has these features:

- internally managing all the locks, making the implementation transparent

  from their usage

- automatically grabbing multiple locks in the right order (avoid deadlock)

- ability to transparently handle conversion to more granularity

- support asynchronous operation (future goal)

Locking will be valid only on the master node and will not be a

distributed operation. Therefore, in case of master failure, the

operations currently running will be aborted and the locks will be

lost; it remains to the administrator to cleanup (if needed) the

operation result (e.g. make sure an instance is either installed

correctly or removed).

A corollary of this is that a master-failover operation with both

masters alive needs to happen while no operations are running, and

therefore no locks are held.

All the locks will be represented by objects (like

``lockings.SharedLock``), and the individual locks for each object

will be created at initialisation time, from the config file.

The API will have a way to grab one or more than one locks at the same time.

Any attempt to grab a lock while already holding one in the wrong order will be

checked for, and fail.

The Locks

+++++++++

At the first stage we have decided to provide the following locks:

- One "config file" lock

- One lock per node in the cluster

- One lock per instance in the cluster

All the instance locks will need to be taken before the node locks, and the

node locks before the config lock. Locks will need to be acquired at the same

time for multiple instances and nodes, and internal ordering will be dealt

within the locking library, which, for simplicity, will just use alphabetical

order.

Each lock has the following three possible statuses:

- unlocked (anyone can grab the lock)

- shared (anyone can grab/have the lock but only in shared mode)

- exclusive (no one else can grab/have the lock)

Handling conversion to more granularity

+++++++++++++++++++++++++++++++++++++++

In order to convert to a more granular approach transparently each time we

split a lock into more we'll create a "metalock", which will depend on those

sub-locks and live for the time necessary for all the code to convert (or

forever, in some conditions). When a metalock exists all converted code must

acquire it in shared mode, so it can run concurrently, but still be exclusive

with old code, which acquires it exclusively.

In the beginning the only such lock will be what replaces the current "command"

lock, and will acquire all the locks in the system, before proceeding. This

lock will be called the "Big Ganeti Lock" because holding that one will avoid

any other concurrent Ganeti operations.

We might also want to devise more metalocks (eg. all nodes, all nodes+config)

in order to make it easier for some parts of the code to acquire what it needs

without specifying it explicitly.

In the future things like the node locks could become metalocks, should we

decide to split them into an even more fine grained approach, but this will

probably be only after the first 2.0 version has been released.

Adding/Removing locks

+++++++++++++++++++++

When a new instance or a new node is created an associated lock must be added

to the list. The relevant code will need to inform the locking library of such

a change.

This needs to be compatible with every other lock in the system, especially

metalocks that guarantee to grab sets of resources without specifying them

explicitly. The implementation of this will be handled in the locking library

itself.

When instances or nodes disappear from the cluster the relevant locks

must be removed. This is easier than adding new elements, as the code

which removes them must own them exclusively already, and thus deals

with metalocks exactly as normal code acquiring those locks. Any

operation queuing on a removed lock will fail after its removal.

Asynchronous operations

+++++++++++++++++++++++

For the first version the locking library will only export synchronous

operations, which will block till the needed lock are held, and only fail if

the request is impossible or somehow erroneous.

In the future we may want to implement different types of asynchronous

operations such as:

- try to acquire this lock set and fail if not possible

- try to acquire one of these lock sets and return the first one you were

  able to get (or after a timeout) (select/poll like)

These operations can be used to prioritize operations based on available locks,

rather than making them just blindly queue for acquiring them. The inherent

risk, though, is that any code using the first operation, or setting a timeout

for the second one, is susceptible to starvation and thus may never be able to

get the required locks and complete certain tasks. Considering this

providing/using these operations should not be among our first priorities.

Locking granularity

+++++++++++++++++++

For the first version of this code we'll convert each Logical Unit to

acquire/release the locks it needs, so locking will be at the Logical Unit

level.  In the future we may want to split logical units in independent

"tasklets" with their own locking requirements. A different design doc (or mini

design doc) will cover the move from Logical Units to tasklets.

Code examples

+++++++++++++

In general when acquiring locks we should use a code path equivalent to::

  lock.acquire()

  try:

    ...

    # other code

  finally:

    lock.release()

This makes sure we release all locks, and avoid possible deadlocks. Of

course extra care must be used not to leave, if possible locked

structures in an unusable state. Note that with Python 2.5 a simpler

syntax will be possible, but we want to keep compatibility with Python

2.4 so the new constructs should not be used.

In order to avoid this extra indentation and code changes everywhere in the

Logical Units code, we decided to allow LUs to declare locks, and then execute

their code with their locks acquired. In the new world LUs are called like

this::

  # user passed names are expanded to the internal lock/resource name,

  # then known needed locks are declared

  lu.ExpandNames()

  ... some locking/adding of locks may happen ...

  # late declaration of locks for one level: this is useful because sometimes

  # we can't know which resource we need before locking the previous level

  lu.DeclareLocks() # for each level (cluster, instance, node)

  ... more locking/adding of locks can happen ...

  # these functions are called with the proper locks held

  lu.CheckPrereq()

  lu.Exec()

  ... locks declared for removal are removed, all acquired locks released ...

The Processor and the LogicalUnit class will contain exact documentation on how

locks are supposed to be declared.

Caveats

+++++++

This library will provide an easy upgrade path to bring all the code to

granular locking without breaking everything, and it will also guarantee

against a lot of common errors. Code switching from the old "lock everything"

lock to the new system, though, needs to be carefully scrutinised to be sure it

is really acquiring all the necessary locks, and none has been overlooked or

forgotten.

The code can contain other locks outside of this library, to synchronise other

threaded code (eg for the job queue) but in general these should be leaf locks

or carefully structured non-leaf ones, to avoid deadlock race conditions.

Job Queue

~~~~~~~~~

Granular locking is not enough to speed up operations, we also need a

queue to store these and to be able to process as many as possible in

parallel.

A Ganeti job will consist of multiple ``OpCodes`` which are the basic

element of operation in Ganeti 1.2 (and will remain as such). Most

command-level commands are equivalent to one OpCode, or in some cases

to a sequence of opcodes, all of the same type (e.g. evacuating a node

will generate N opcodes of type replace disks).

Job execution—“Life of a Ganeti job”

++++++++++++++++++++++++++++++++++++

#. Job gets submitted by the client. A new job identifier is generated and

   assigned to the job. The job is then automatically replicated [#replic]_

   to all nodes in the cluster. The identifier is returned to the client.

#. A pool of worker threads waits for new jobs. If all are busy, the job has

   to wait and the first worker finishing its work will grab it. Otherwise any

   of the waiting threads will pick up the new job.

#. Client waits for job status updates by calling a waiting RPC function.

   Log message may be shown to the user. Until the job is started, it can also

   be canceled.

#. As soon as the job is finished, its final result and status can be retrieved

   from the server.

#. If the client archives the job, it gets moved to a history directory.

   There will be a method to archive all jobs older than a a given age.

.. [#replic] We need replication in order to maintain the consistency across

   all nodes in the system; the master node only differs in the fact that

   now it is running the master daemon, but it if fails and we do a master

   failover, the jobs are still visible on the new master (though marked as

   failed).

Failures to replicate a job to other nodes will be only flagged as

errors in the master daemon log if more than half of the nodes failed,

otherwise we ignore the failure, and rely on the fact that the next

update (for still running jobs) will retry the update. For finished

jobs, it is less of a problem.

Future improvements will look into checking the consistency of the job

list and jobs themselves at master daemon startup.

Job storage

+++++++++++

Jobs are stored in the filesystem as individual files, serialized

using JSON (standard serialization mechanism in Ganeti).

The choice of storing each job in its own file was made because:

- a file can be atomically replaced

- a file can easily be replicated to other nodes

- checking consistency across nodes can be implemented very easily, since

  all job files should be (at a given moment in time) identical

The other possible choices that were discussed and discounted were:

- single big file with all job data: not feasible due to difficult updates

- in-process databases: hard to replicate the entire database to the

  other nodes, and replicating individual operations does not mean wee keep

  consistency

Queue structure

+++++++++++++++

All file operations have to be done atomically by writing to a temporary file

and subsequent renaming. Except for log messages, every change in a job is

stored and replicated to other nodes.

::

  /var/lib/ganeti/queue/

    job-1 (JSON encoded job description and status)

    […]

    job-37

    job-38

    job-39

    lock (Queue managing process opens this file in exclusive mode)

    serial (Last job ID used)

    version (Queue format version)

Locking

+++++++

Locking in the job queue is a complicated topic. It is called from more than

one thread and must be thread-safe. For simplicity, a single lock is used for

the whole job queue.

A more detailed description can be found in doc/locking.txt.

Internal RPC

++++++++++++

RPC calls available between Ganeti master and node daemons:

jobqueue_update(file_name, content)

  Writes a file in the job queue directory.

jobqueue_purge()

  Cleans the job queue directory completely, including archived job.

jobqueue_rename(old, new)

  Renames a file in the job queue directory.

Client RPC

++++++++++

RPC between Ganeti clients and the Ganeti master daemon supports the following

operations:

SubmitJob(ops)

  Submits a list of opcodes and returns the job identifier. The identifier is

  guaranteed to be unique during the lifetime of a cluster.

WaitForJobChange(job_id, fields, […], timeout)

  This function waits until a job changes or a timeout expires. The condition

  for when a job changed is defined by the fields passed and the last log

  message received.

QueryJobs(job_ids, fields)

  Returns field values for the job identifiers passed.

CancelJob(job_id)

  Cancels the job specified by identifier. This operation may fail if the job

  is already running, canceled or finished.

ArchiveJob(job_id)

  Moves a job into the …/archive/ directory. This operation will fail if the

  job has not been canceled or finished.

Job and opcode status

+++++++++++++++++++++

Each job and each opcode has, at any time, one of the following states:

Queued

  The job/opcode was submitted, but did not yet start.

Waiting

  The job/opcode is waiting for a lock to proceed.

Running

  The job/opcode is running.

Canceled

  The job/opcode was canceled before it started.

Success

  The job/opcode ran and finished successfully.

Error

  The job/opcode was aborted with an error.

If the master is aborted while a job is running, the job will be set to the

Error status once the master started again.

History

+++++++

Archived jobs are kept in a separate directory,

``/var/lib/ganeti/queue/archive/``.  This is done in order to speed up

the queue handling: by default, the jobs in the archive are not

touched by any functions. Only the current (unarchived) jobs are

parsed, loaded, and verified (if implemented) by the master daemon.

Ganeti updates

++++++++++++++

The queue has to be completely empty for Ganeti updates with changes

in the job queue structure. In order to allow this, there will be a

way to prevent new jobs entering the queue.

Object parameters

~~~~~~~~~~~~~~~~~

Across all cluster configuration data, we have multiple classes of

parameters:

A. cluster-wide parameters (e.g. name of the cluster, the master);

   these are the ones that we have today, and are unchanged from the

   current model

#. node parameters

#. instance specific parameters, e.g. the name of disks (LV), that

   cannot be shared with other instances

#. instance parameters, that are or can be the same for many

   instances, but are not hypervisor related; e.g. the number of VCPUs,

   or the size of memory

#. instance parameters that are hypervisor specific (e.g. kernel_path

   or PAE mode)

The following definitions for instance parameters will be used below:

:hypervisor parameter:

  a hypervisor parameter (or hypervisor specific parameter) is defined

  as a parameter that is interpreted by the hypervisor support code in

  Ganeti and usually is specific to a particular hypervisor (like the

  kernel path for `PVM`_ which makes no sense for `HVM`_).

:backend parameter:

  a backend parameter is defined as an instance parameter that can be

  shared among a list of instances, and is either generic enough not

  to be tied to a given hypervisor or cannot influence at all the

  hypervisor behaviour.

  For example: memory, vcpus, auto_balance

  All these parameters will be encoded into constants.py with the prefix "BE\_"

  and the whole list of parameters will exist in the set "BES_PARAMETERS"

:proper parameter:

  a parameter whose value is unique to the instance (e.g. the name of a LV,

  or the MAC of a NIC)

As a general rule, for all kind of parameters, “None” (or in

JSON-speak, “nil”) will no longer be a valid value for a parameter. As

such, only non-default parameters will be saved as part of objects in

the serialization step, reducing the size of the serialized format.

Cluster parameters

++++++++++++++++++

Cluster parameters remain as today, attributes at the top level of the

Cluster object. In addition, two new attributes at this level will

hold defaults for the instances:

- hvparams, a dictionary indexed by hypervisor type, holding default

  values for hypervisor parameters that are not defined/overridden by

  the instances of this hypervisor type

- beparams, a dictionary holding (for 2.0) a single element 'default',

  which holds the default value for backend parameters

Node parameters

+++++++++++++++

Node-related parameters are very few, and we will continue using the

same model for these as previously (attributes on the Node object).

Instance parameters

+++++++++++++++++++

As described before, the instance parameters are split in three:

instance proper parameters, unique to each instance, instance

hypervisor parameters and instance backend parameters.

The “hvparams” and “beparams” are kept in two dictionaries at instance

level. Only non-default parameters are stored (but once customized, a

parameter will be kept, even with the same value as the default one,

until reset).

The names for hypervisor parameters in the instance.hvparams subtree

should be choosen as generic as possible, especially if specific

parameters could conceivably be useful for more than one hypervisor,

e.g. ``instance.hvparams.vnc_console_port`` instead of using both

``instance.hvparams.hvm_vnc_console_port`` and

``instance.hvparams.kvm_vnc_console_port``.

There are some special cases related to disks and NICs (for example):

a disk has both Ganeti-related parameters (e.g. the name of the LV)

and hypervisor-related parameters (how the disk is presented to/named

in the instance). The former parameters remain as proper-instance

parameters, while the latter value are migrated to the hvparams

structure. In 2.0, we will have only globally-per-instance such

hypervisor parameters, and not per-disk ones (e.g. all NICs will be

exported as of the same type).

Starting from the 1.2 list of instance parameters, here is how they

will be mapped to the three classes of parameters:

- name (P)

- primary_node (P)

- os (P)

- hypervisor (P)

- status (P)

- memory (BE)

- vcpus (BE)

- nics (P)

- disks (P)

- disk_template (P)

- network_port (P)

- kernel_path (HV)

- initrd_path (HV)

- hvm_boot_order (HV)

- hvm_acpi (HV)

- hvm_pae (HV)

- hvm_cdrom_image_path (HV)

- hvm_nic_type (HV)

- hvm_disk_type (HV)

- vnc_bind_address (HV)

- serial_no (P)

Parameter validation

++++++++++++++++++++

To support the new cluster parameter design, additional features will

be required from the hypervisor support implementations in Ganeti.

The hypervisor support  implementation API will be extended with the

following features:

:PARAMETERS: class-level attribute holding the list of valid parameters

  for this hypervisor

:CheckParamSyntax(hvparams): checks that the given parameters are

  valid (as in the names are valid) for this hypervisor; usually just

  comparing ``hvparams.keys()`` and ``cls.PARAMETERS``; this is a class

  method that can be called from within master code (i.e. cmdlib) and

  should be safe to do so

:ValidateParameters(hvparams): verifies the values of the provided

  parameters against this hypervisor; this is a method that will be

  called on the target node, from backend.py code, and as such can

  make node-specific checks (e.g. kernel_path checking)

Default value application

+++++++++++++++++++++++++

The application of defaults to an instance is done in the Cluster

object, via two new methods as follows:

- ``Cluster.FillHV(instance)``, returns 'filled' hvparams dict, based on

  instance's hvparams and cluster's ``hvparams[instance.hypervisor]``

- ``Cluster.FillBE(instance, be_type="default")``, which returns the

  beparams dict, based on the instance and cluster beparams

The FillHV/BE transformations will be used, for example, in the RpcRunner

when sending an instance for activation/stop, and the sent instance

hvparams/beparams will have the final value (noded code doesn't know

about defaults).

LU code will need to self-call the transformation, if needed.

Opcode changes

++++++++++++++

The parameter changes will have impact on the OpCodes, especially on

the following ones:

- ``OpCreateInstance``, where the new hv and be parameters will be sent as

  dictionaries; note that all hv and be parameters are now optional, as

  the values can be instead taken from the cluster

- ``OpQueryInstances``, where we have to be able to query these new

  parameters; the syntax for names will be ``hvparam/$NAME`` and

  ``beparam/$NAME`` for querying an individual parameter out of one

  dictionary, and ``hvparams``, respectively ``beparams``, for the whole

  dictionaries

- ``OpModifyInstance``, where the the modified parameters are sent as

  dictionaries

Additionally, we will need new OpCodes to modify the cluster-level

defaults for the be/hv sets of parameters.

Caveats

+++++++

One problem that might appear is that our classification is not

complete or not good enough, and we'll need to change this model. As

the last resort, we will need to rollback and keep 1.2 style.