Difference between revisions of "KDE PIM/Akonadi Next"
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'''This is WIP!
'''This is WIP!
== Comments/Thoughts/Questions ==
== Comments/Thoughts/Questions ==
Revision as of 20:23, 1 December 2014
This is WIP!
- Incremental changes can be recorded by message buffer => resource can replay incremental changes
- Each modification message is associated with a specific revision
- Syncronizer can do conflict resolution while writing to the store
- The revision that this change applies to is still available => 3-way merge is possible
- Changes are only serialized per store by the syncronizer, not globally. (which is an advantage)
- Copying of domain objects may defeat mmapped buffers if individual properties get copied (all data is read from disk). We have to make sure only the pointer gets copied.
- Tags/Relations now need a target resource on creation. This is a change from the current situation where you can just create a tag, and every resource can synchronize it. It requires a bit more work but results also in a more pretictable system, which we'll also need if we want to support different tag storage locations (shared tag set in a shared folder).
- How is lazy loading triggered? Through a synchronize command to the syncronizer?
- Yes, a sync command. So sync commands need to be able to specify a context (which a resource may ignore, of course)
- Resource-to-resource moves (e.g. expiring mail from an imap folder to a local folder)
- Should the "source" resource become a client to the other resource, and drive the process? This would allow greatest atomicity. It does imply being able to issue cross-resource move commands.
- If we use the mmapped buffers to avoid having a property level query API, lazy loading of properties becomes difficult. Currently we specify how much of each item we need using the parts, allowing the lazy loading to fetch missing parts. If we no longer specify what we want we cannot lazy load that part and at the point where the buffer is accessed it's already too late.
- How do we record changes?
- The message adaptor could do that.
- How do we deal with unknown properties on the application side if the storage format doesn't support it yet?
- N adapters have to be updated for a new property in the domain object
- => We could have a base class that stores any unhandled property in a generic key-value part of the message that is implemented by every store.
- How well can we free up memory with mmapped buffers?
- 1000 objects are loaded with minimal access (subject)
- A bunch of attachments are opened => we stream each file to tmp disk and open it.
- Can we unload the no-longer required attachments?
- => I suppose we'd have to munmap the buffer and mmap it again, replacing the pointer.
- This may mean we require a shared wrapper for the pointer so we can replace the pointer everywhere it's used.
- Filtering: If the client-side filtering is implemented as filter, can we still move imap messages to a local folder (since we'd have to move the message to another store, and remove it from the source).
- Datastreaming: It seems with the current message model it is not possible for the resource to stream large payload's directly to disk.
- As a possible solution a resource could decide to store large payloads externally, stream directly to disk, and then pass a message containing the file location to the system.
- How do we deal with mass insertions? => n inserted entities should not result in n update notifications with n updates.
- I suppose simple event compression should largely solve this issue.
- Undo framework: Application sessions keep data around for long enough that we can rollback to earlier states of the store.
- Note that this would need to work accross stores.
- iTip handling in form of a filter? i.e. a kolab resource could install a filter that automatically dispatches iTip invitations based on the events stored.
- The synchronization can either:
- Generate a full diff directly on top of the db. The diffing process can work against a single revision, and could even stop writing other changes to disk while the process is ongoing (but doesn't have to due to the revision). It then generates a necessary changeset for the store.
- If the source supports incremental changes the changeset can directly be generated from that information.
The changeset is then simply inserted in the regular modification queue and processed like all other modifications. The synchronizer already know that it doesn't have to replay this changeset to the source, since replay no longer goes via the store.
- Socket activated processes (for the resource shell): http://0pointer.de/blog/projects/socket-activation.html
- Memory mapped: http://symas.com/mdb/