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Competency Mastery Concurrency ADR #657
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| .. _openedx-learning-adr-0004: | ||
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| 4. How should learner competency mastery be recorded concurrently and at scale? | ||
| ================================================================================ | ||
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| Status | ||
| ------ | ||
| Proposed. | ||
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| Context | ||
| ------- | ||
| When a learner is graded on a subsection (or any other learning instrument associated to a competency | ||
| with a competency criteria, like a course or rubric criterion), the platform must evaluate whether that grade | ||
| demonstrates any attached competencies and record the learner's mastery. Mastery is recorded at | ||
| three levels: the criterion (leaf), the criteria group, and the competency. Per | ||
| :ref:`openedx-learning-adr-0002` and :ref:`openedx-learning-adr-0005`, all three levels are | ||
| *materialized* (stored), not recomputed on read, so that dashboards and other read surfaces stay | ||
| fast. A single grade change therefore writes the changed leaf's status and then re-evaluates and | ||
| re-writes the derived rows from that leaf up to the competency root. The re-evaluation | ||
| is needed for multiple reasons, including notifications, and badge and certificate issuing. Per | ||
| :ref:`openedx-learning-adr-0005`, each level is stored as an ACTIVE row updated in place, holding | ||
| the current status for a learner and node, plus an append-only HISTORY row per genuine status | ||
| advance. | ||
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| **Monotonicity: competency statuses only ever move forward.** Per | ||
| :ref:`openedx-learning-adr-0005`, every node, at every level, advances through a small status | ||
| lattice (``AttemptedNotDemonstrated`` to ``PartiallyAttempted`` to ``Demonstrated``) and is never | ||
| lowered later. This holds for leaf nodes, group nodes, and top-level competency masteries. | ||
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| Two forces shape how recording should happen: | ||
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| - **Same-learner correctness.** A grade change writes the changed leaf and then re-derives the | ||
| group and competency rows above it. Leaf rows are always correct, since each leaf is a pure | ||
| function of its own grade. The derived rows are the hazard: We want to avoid a case where two evaluations for the same learner | ||
| that overlap can each read a stale snapshot of the sibling leaf statuses and each write a derived | ||
| roll-up computed from an incomplete picture (a *write-skew*). | ||
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| - **Throughput.** Grading is bursty and spans a very large number of learners, so the recording | ||
| path must keep up under peak load. | ||
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| Decision | ||
| -------- | ||
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| **1. Every write is a monotone merge, never a blind overwrite.** A node's status is written as | ||
| ``status := max(stored status, newly computed status)`` (a single ``GREATEST``-style ``UPDATE``, | ||
| atomic at the row for the duration of that one statement, with no application-level lock). Because | ||
| the merge takes the higher of the two values, it is commutative, idempotent, and insensitive to | ||
| order. This is why out-of-order delivery and re-delivery are harmless without sequence tracking. | ||
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There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. try to write in plainer english and cut right to the point. here is my understanding of points 1-3. let me know if you disagree.
i know it can be very challenging to distill text like this--it took me 20 minutes write this comment :P but the practice of doing it will force you to refine your ideas and it will help dave and I review more quickly. |
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| **2. When a child advances, its parent is recomputed in the same transaction, under a brief row lock on that parent.** | ||
| The merge in mechanism 1 makes a single-row write safe, but a *conjunctive* | ||
| parent (for example "demonstrated only when all children are demonstrated") is computed by reading | ||
| several child rows first, so two overlapping evaluations for one learner could each read a stale | ||
| sibling and compute a parent that is too low. To prevent that, recomputing a parent takes a | ||
| row-level lock on the parent row (a ``SELECT ... FOR UPDATE``) before reading its children: two | ||
| updates that touch the same parent for the same learner take turns, and the second reads the first's | ||
| committed children and computes from the complete picture. This correctness argument assumes | ||
| ``READ COMMITTED`` isolation (the Open edX platform default on MySQL; higher isolation levels are not | ||
| supported on the platform): under it the lock's own read and the sibling reads that follow it always | ||
| return the latest committed rows, rather than a snapshot fixed at an earlier read in the same | ||
| transaction, which is what a higher level such as ``REPEATABLE READ`` would do. Locks are taken child-before-parent up | ||
| the path to the root, a consistent order, so concurrent updates cannot deadlock. This is an ordinary | ||
| single-row lock. | ||
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| **3. Entry point: edx-platform subsection grade change.** edx-platform | ||
| computes subsection grades in an async celery task (`recalculate_subsection_grade_v3`) triggered by a score-change signal, not on the | ||
| request thread. After that task writes the subsection grade, it calls a public openedx-core function | ||
| within the same transaction; this function does the monotone merge and the upward roll-up. This should be generalized as needed to other places that trigger a competency status update. | ||
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| **4. The ACTIVE writes, the HISTORY appends, and the roll-ups all commit atomically with the | ||
| subsection grade.** The leaf, group, and competency ACTIVE writes from mechanisms 1 and 2, and the | ||
| HISTORY row appended for each genuine advance, run inside the same transaction that mechanism 3 | ||
| opened for the subsection-grade write, so they commit as a single unit with it. If any step fails, that transaction rolls back and the task retries, leaving | ||
| behind neither a partial roll-up nor an ACTIVE status whose advance went unrecorded. A unique | ||
| constraint on the advance (learner, node, and status; :ref:`openedx-learning-adr-0002`) makes the | ||
| append idempotent, so a retried task or a redelivered grade event collapses to a no-op rather than | ||
| writing a duplicate row. | ||
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| **5. Only an advance is appended to HISTORY.** The monotone merge in mechanism 1 often leaves a status | ||
| where it was, because the newly computed status equals or is lower than the stored one. Those writes | ||
| append nothing: a redelivered grade event, a downward grade correction, and a recompute that confirms | ||
| the current status all leave HISTORY untouched. So the recorder writes at most one HISTORY row per | ||
| learner, node, and step up the lattice, which is what bounds HISTORY to the same order of magnitude as | ||
| ACTIVE rather than to grading volume (:ref:`openedx-learning-adr-0005`). | ||
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| Rejected Alternatives | ||
| --------------------- | ||
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| 1. Prevent concurrent writes with a coarser lock, either deployment-wide or per-learner. | ||
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| - Pros: | ||
| - Correctness comes from a single lock rather than from the monotone-merge argument, so it is | ||
| simpler to reason about. | ||
| - A per-learner lock (for example a database advisory lock keyed on a hash of the user id) | ||
| still lets different learners record in parallel, and gives the same per-learner | ||
| serialization the chosen design relies on. | ||
| - Cons: | ||
| - A single deployment-wide lock serializes recording across every learner, giving up the | ||
| throughput the design needs under bursty grading. | ||
| - A per-learner lock still serializes a single learner's independent competencies against each | ||
| other even when they never contend. | ||
| - Either lock adds lock-lifecycle machinery (acquisition, release, and handling a holder that | ||
| dies) across a very large key space. | ||
| - The chosen design needs no such lock: the monotone merge (mechanism 1) makes each single-row | ||
| write safe, and the brief per-parent row lock (mechanism 2) serializes only writers that | ||
| actually contend for the same parent row of the same learner, so different learners, and | ||
| different competencies of one learner, still record in parallel. | ||
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| 2. Recompute derived levels on read instead of materializing them. | ||
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| - Pros: | ||
| - Eliminates the derived group and competency status rows and the roll-up writes entirely, | ||
| leaving nothing to keep consistent on write. | ||
| - Cons: | ||
| - Moves the full bottom-up tree evaluation onto the hot read path, the opposite of what | ||
| dashboards and other read surfaces need (a direct indexed lookup). | ||
| - Settled against in :ref:`openedx-learning-adr-0002`. | ||
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| 3. Send an event to openedx-core and update competency statuses in a separate celery task. | ||
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| - Pros: | ||
| - Decouples the mastery update from the grade write, so grade recording does not depend on | ||
| competency code being installed or fast. | ||
| - Cons: | ||
| - Without a shared transaction, a failure or a lost event leaves the grade and its mastery rows | ||
| permanently out of sync (data drift), with no way to roll them back together. | ||
| - Recording the ACTIVE writes in the same transaction as the grade (mechanism 3) instead makes | ||
| the grade and its mastery consequences commit or fail as a unit. | ||
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| 4. Append the leaf HISTORY row outside the grade transaction, as a retrying task dispatched with | ||
| ``transaction.on_commit``. | ||
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| This would be mandatory if the HISTORY table were ever | ||
| routed to a separate database alias, since a write on another connection cannot be atomic | ||
| with the primary transaction. Since we decided that every status table lives in the main database | ||
| (:ref:`openedx-learning-adr-0005`), this is unnecessary. | ||
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