5 .. contents:: :depth: 4
7 This is a design document about the innards of Ganeti's job processing.
8 Readers are advised to study previous design documents on the topic:
10 - :ref:`Original job queue <jqueue-original-design>`
11 - :ref:`Job priorities <jqueue-job-priority-design>`
12 - :doc:`LU-generated jobs <design-lu-generated-jobs>`
15 Current state and shortcomings
16 ==============================
18 Ever since the introduction of the job queue with Ganeti 2.0 there have
19 been situations where we wanted to run several jobs in a specific order.
20 Due to the job queue's current design, such a guarantee can not be
21 given. Jobs are run according to their priority, their ability to
22 acquire all necessary locks and other factors.
24 One way to work around this limitation is to do some kind of job
25 grouping in the client code. Once all jobs of a group have finished, the
26 next group is submitted and waited for. There are different kinds of
27 clients for Ganeti, some of which don't share code (e.g. Python clients
28 vs. htools). This design proposes a solution which would be implemented
29 as part of the job queue in the master daemon.
35 With the implementation of :ref:`job priorities
36 <jqueue-job-priority-design>` the processing code was re-architectured
37 and became a lot more versatile. It now returns jobs to the queue in
38 case the locks for an opcode can't be acquired, allowing other
39 jobs/opcodes to be run in the meantime.
41 The proposal is to add a new, optional property to opcodes to define
42 dependencies on other jobs. Job X could define opcodes with a dependency
43 on the success of job Y and would only be run once job Y is finished. If
44 there's a dependency on success and job Y failed, job X would fail as
45 well. Since such dependencies would use job IDs, the jobs still need to
46 be submitted in the right order.
50 # Update description below if finalized job status change
51 constants.JOBS_FINALIZED == frozenset([
52 constants.JOB_STATUS_CANCELED,
53 constants.JOB_STATUS_SUCCESS,
54 constants.JOB_STATUS_ERROR,
57 The new attribute's value would be a list of two-valued tuples. Each
58 tuple contains a job ID and a list of requested status for the job
59 depended upon. Only final status are accepted
60 (:pyeval:`utils.CommaJoin(constants.JOBS_FINALIZED)`). An empty list is
61 equivalent to specifying all final status (except
62 :pyeval:`constants.JOB_STATUS_CANCELED`, which is treated specially).
63 An opcode runs only once all its dependency requirements have been
66 Any job referring to a cancelled job is also cancelled unless it
67 explicitely lists :pyeval:`constants.JOB_STATUS_CANCELED` as a requested
70 In case a referenced job can not be found in the normal queue or the
71 archive, referring jobs fail as the status of the referenced job can't
74 With this change, clients can submit all wanted jobs in the right order
75 and proceed to wait for changes on all these jobs (see
76 ``cli.JobExecutor``). The master daemon will take care of executing them
77 in the right order, while still presenting the client with a simple
80 Clients using the ``SubmitManyJobs`` interface can use relative job IDs
81 (negative integers) to refer to jobs in the same submission.
83 .. highlight:: javascript
85 Example data structures::
91 { "OP_ID": "OP_INSTANCE_REPLACE_DISKS", ..., },
92 { "OP_ID": "OP_INSTANCE_FAILOVER", ..., },
96 # Second job, runs in parallel with first job
100 { "OP_ID": "OP_INSTANCE_MIGRATE", ..., },
104 # Third job, depending on success of previous jobs
108 { "OP_ID": "OP_NODE_SET_PARAMS",
118 Implementation details
119 ----------------------
121 Status while waiting for dependencies
122 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
124 Jobs waiting for dependencies are certainly not in the queue anymore and
125 therefore need to change their status from "queued". While waiting for
126 opcode locks the job is in the "waiting" status (the constant is named
127 ``JOB_STATUS_WAITLOCK``, but the actual value is ``waiting``). There the
128 following possibilities:
130 #. Introduce a new status, e.g. "waitdeps".
134 - Clients know for sure a job is waiting for dependencies, not locks
138 - Code and tests would have to be updated/extended for the new status
139 - List of possible state transitions certainly wouldn't get simpler
140 - Breaks backwards compatibility, older clients might get confused
142 #. Use existing "waiting" status.
146 - No client changes necessary, less code churn (note that there are
147 clients which don't live in Ganeti core)
148 - Clients don't need to know the difference between waiting for a job
149 and waiting for a lock; it doesn't make a difference
150 - Fewer state transitions (see commit ``5fd6b69479c0``, which removed
151 many state transitions and disk writes)
155 - Not immediately visible what a job is waiting for, but it's the
156 same issue with locks; this is the reason why the lock monitor
157 (``gnt-debug locks``) was introduced; job dependencies can be shown
158 as "locks" in the monitor
160 Based on these arguments, the proposal is to do the following:
162 - Rename ``JOB_STATUS_WAITLOCK`` constant to ``JOB_STATUS_WAITING`` to
163 reflect its actual meanting: the job is waiting for something
164 - While waiting for dependencies and locks, jobs are in the "waiting"
166 - Export dependency information in lock monitor; example output::
168 Name Mode Owner Pending
169 job/27491 - - success:job/34709,job/21459
170 job/21459 - - success,error:job/14513
173 Cost of deserialization
174 ~~~~~~~~~~~~~~~~~~~~~~~
176 To determine the status of a dependency job the job queue must have
177 access to its data structure. Other queue operations already do this,
178 e.g. archiving, watching a job's progress and querying jobs.
180 Initially (Ganeti 2.0/2.1) the job queue shared the job objects
181 in memory and protected them using locks. Ganeti 2.2 (see :doc:`design
182 document <design-2.2>`) changed the queue to read and deserialize jobs
183 from disk. This significantly reduced locking and code complexity.
184 Nowadays inotify is used to wait for changes on job files when watching
187 Reading from disk and deserializing certainly has some cost associated
188 with it, but it's a significantly simpler architecture than
189 synchronizing in memory with locks. At the stage where dependencies are
190 evaluated the queue lock is held in shared mode, so different workers
191 can read at the same time (deliberately ignoring CPython's interpreter
194 It is expected that the majority of executed jobs won't use
195 dependencies and therefore won't be affected.
198 Other discussed solutions
199 =========================
204 At a first look it might seem to be better to put dependencies on
205 previous jobs at a job level. However, it turns out that having the
206 option of defining only a single opcode in a job as having such a
207 dependency can be useful as well. The code complexity in the job queue
208 is equivalent if not simpler.
210 Since opcodes are guaranteed to run in order, clients can just define
211 the dependency on the first opcode.
213 Another reason for the choice of an opcode-level attribute is that the
214 current LUXI interface for submitting jobs is a bit restricted and would
215 need to be changed to allow the addition of job-level attributes,
216 potentially requiring changes in all LUXI clients and/or breaking
217 backwards compatibility.
223 There's at least one implementation of a batched job executor twisted
224 into the ``burnin`` tool's code. While certainly possible, a client-side
225 solution should be avoided due to the different clients already in use.
226 For one, the :doc:`remote API <rapi>` client shouldn't import
227 non-standard modules. htools are written in Haskell and can't use Python
228 modules. A batched job executor contains quite some logic. Even if
229 cleanly abstracted in a (Python) library, sharing code between different
230 clients is difficult if not impossible.
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