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When attempting to apply a client’s update to some branch, the server must be able to gracefully handle the case that the update condition is not satisfiable on the latest snapshot (i.e., the graph pattern(s) in the user’s WHERE block). This can happen either because (a) another client updated the model concurrently, or (b) the local client was using stale knowledge, or (c) the client submitted an unsatisfiable update condition. No matter the cause, the sever should attempt to apply the client’s update onto a compatible version of the model that satisfies the update condition, if one exists. If successful, the server should deem the update as causing a merge conflict and create a new branch to be diff'ed against. If unsuccessful, the server can respond with the appropriate 412 Precondition Failed HTTP status.
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Unfortunately, the SPARQL 1.1 Protocol does not standardize any type of response content to a SPARQL Update request. Some triplestores will provide detailed information about how many triples were affected by an update, while others simply indicate whether the request succeeded or failed as an atomic unit by using HTTP status codes. For these reasons, the server must first test the UPDATE_CONDITION using an ASK query before issuing an update operation. The ASK query returns a boolean to indicate whether the given WHERE block matches the specified dataset(s). Fortunately, the server can issue multiple ASK queries in parallel, allowing the triplestore to handle load-balancing the queries against separate graphs and reduce the total time spent waiting for query results to determine which commit will be used to apply the update. Depending on several factors, using the read-only ASK operation to search multiple graphs in parallel to find those which satisfy the UPDATE_CONDITION might even outperform the aforementioned update-until-it-works technique, though this is only speculation.
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If zero graphs match the update condition, the request is deemed unsatisfiable and an appropriate HTTP error code is returned to the client. If multiple graphs match the update condition, the one corresponding to the most recent commit is selected. The result is that merge conflicts are automatically detected by employing the update condition to assess whether an update’s dependencies are satisfied on any given state of a model.
1.2. Effective staging graph
First, see https://openmbee.atlassian.net/wiki/spaces/OPENMBEE/pages/613613569/Tags+and+Snapshots#Staging-graphs for an explanation of staging graphs.
An ‘effective’ staging graph simply refers to a graph that is used to apply an update, whether or not it was previously dedicated to being a staging graph.
Normally, if a staging graph is available for the parent commit, then this graph is selected to be the effective staging graph for the update and the algorithm continues to 1.3 .
However, it is possible that a staging graph for the latest commit is not yet available. This can happen when multiple updates are made to the same branch concurrently and the triplestore has not yet had enough time to COPY the snapshot graph to a dedicated staging graph. In this scenario, Flexo MMS uses the snapshot graph as an effective staging graph. This technique ultimately defers the COPY operation to a later time once any concurrent writes have settled. See https://openmbee.atlassian.net/wiki/spaces/OPENMBEE/pages/616497153/Layer+1+Update+Procedure#Effective-staging-graph-example below for an example of using an ephemeral snapshot as an effective staging graph.
1.3. SPARQL update execution
Having selected an effective staging graph to apply the model update to, Flexo MMS then executes a SPARQL update against the triplestore that performs several operations at a single atomic unit. They are summarized as:
detach the effective staging graph from its current owner
create a new ephemeral commit object in the project’s metadata graph
apply the model update to the effective staging graph
attach the effective staging graph to the new ephemeral commit
1.4. Commit stabilization
Following a series of 1 or more successive writes to a branch, the server must stabilize commit data by creating new staging graphs and dropping old snapshots that are no longer needed. Delaying this action by some predetermined amount of time helps improve the performance of any queries or updates that target or depend on the parent commit before its snapshot is dropped.
Effective staging graph example
In the following diagram, e4a1c represents the latest commit at state #01 of the model. Notice how a snapshot graph and a staging graph materialize this same commit:
| Code Block |
|---|
#01:
*snapshot -╮
├- *staging
╎
(x)---e4a1c |
Upon an update, a new commit 17ccd is created by using the staging graph to evolve the state of the model to #02:
| Code Block |
|---|
#02:
*snapshot -╮
╎ ╭*ephemeral-snapshot-1
╎ ╎
(x)---e4a1c---17ccd |
At this point, 17ccd does not yet have its own staging graph. Since it is brand new, it’s snapshot graph is marked as “ephemeral”, which means that (a) it does not yet have a staging graph and (b) it’s (super*)parent still has a snapshot being used for interim reads and updates. Before a staging graph for this new commit is built, Flexo MMS finishes processing any remaining concurrent updates. In this example, one such update creates a new commit 8f155. The server uses the aforementioned ephemeral snapshot graph as the effective staging graph to apply this update. The new state of the model at #03 looks like:
| Code Block |
|---|
#03:
*snapshot -╮
╎ ╭*ephemeral-snapshot-2
╎ ╎
(x)---e4a1c---17ccd---8f155 |
Once the pipeline of concurrent writes have ceased, Flexo MMS is able to stabilize the latest commit’s snapshot at #04:
| Code Block |
|---|
#04:
*snapshot -╮ *snapshot -╮
╎ ├- *staging
╎ ╎
(x)---e4a1c---17ccd---8f155 |
Finally, the now expired snapshot for the original commit is dropped and Flexo MMS settles on the resting state for this model at #05:
| Code Block |
|---|
#05:
*snapshot -╮
├- *staging
╎
(x)---e4a1c---17ccd---8f155 |