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Analysing a specific use case
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=============================
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The analysis of a use case needs to provide as outputs:
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* The functional requirements that the use case has to satisfy.
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* The file level operations and access patterns that will give the best
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* A low friction API which will allow the use case to be implemented.
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* The release of bzr (and thus the supported features) for which the analysis
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was performed. The feature set of bzr defines the access patterns and data
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required to implement any use case. So when we add features, their design
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changes the requirements for the parts of the system they alter, so we need
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to re-analyse use cases when bzr's feature set changes. If future plans are
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considered in the analysis with the intention of avoiding rework, these
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should also be mentioned.
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Performing the analysis
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=======================
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The analysis needs to be able to define the characteristics of the
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involved disk storage and APIs. That means we need to examine the data
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required for the operation, in what order it is required, on both the
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read and write sides, and how that needs to be presented to be
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consistent with our layering.
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As a quick example: 'annotation of a file requires the file id looked up
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from the tree, the basis revision id from the tree, and then the text of
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that fileid-revisionid pair along with the creating revision id
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allocated to each line, and the dotted revision number of each of those
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revision ids.' All three of our key domain objects are involved here,
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but we haven't defined any characteristics of the api or disk facilities
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yet. We could then do that by saying something like 'the file-id lookup
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should degrade gracefully as trees become huge. The tree basis id should
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be constant time. Retrieval of the annotated text should be roughly
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constant for any text of the same size regardless of the number of
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revisions contributing to its content. Mapping of the revision ids to
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dotted revnos could be done as the text is retrieved, but its completely
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fine to post-process the annotated text to obtain dotted-revnos.'
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What factors should be considered?
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==================================
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Obviously, those that will make for an extremely fast system :). There
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are many possible factors, but the ones I think are most interesting to
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- The time to get bzr ready to begin the use case.
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- scaling: how does performance change when any of the follow aspects
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of the system are ratched massively up or down:
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- number of files/dirs/symlinks/subtrees in a tree (both working and
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- size of any particular file
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- number of elements within a single directory
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- number of changes to any file over time
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(subordinately also the number of merges of the file)
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- number of commits in the ancestry of a branch
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(subordinately also the number of merges)
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- number of revisions in a repository
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- number of fileids in a repository
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- number of ghosts in a given graph (revision or per-file)
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- number of branches in a repository
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- number of concurrent readers for a tree/branch/repository
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- number of concurrent writers for objects that support that.
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- latency to perform file operations (e.g. slow disks, network file systems,
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our VFS layer and FTP/SFTP/etc)
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- bandwidth to the disk storage
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- latency to perform semantic operations (hpss specific)
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- bandwidth when performing semantic operations.
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- locality of reference: If an operation requires data that is located
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within a small region at any point, we often get better performance
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than with an implementation of the same operation that requires the
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same amount of data but with a lower locality of reference. Its
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fairly tricky to add locality of reference after the fact, so I think
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its worth considering up front.
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Using these factors, to the annotate example we can add that its
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reasonable to do two 'semantic' round trips to the local tree, one to
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the branch object, and two to the repository. In file-operation level
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measurements, in an ideal world there would be no more than one round
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trip for each semantic operation. What there must not be is one round
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trip per revision involved in the revisionid->dotted number mapping, nor
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per each revision id attributed to a line in the text.
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Not all the items mentioned above are created equal. The analysis should
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include the parameters considered and the common case values for each - the
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optimisation should be around the common cases not around the exceptions.
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For instance, we have a smart server now; file level operations are relatively
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low latency and we should use that as the common case. At this point we intend
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to preserve the performance of the dumb protocol networking, but focus on
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improving network performance via the smart server and thus escape the
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file-level operation latency considerations.
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Many performance problems only become visible when changing the scaling knobs
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upwards to large trees. On small trees its our baseline performance that drives
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incremental improvements; on large trees its the amount of processing per item
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that drives performance. A significant goal therefore is to keep the amouont of
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data to be processed under control. Ideally we can scale in a sublinear fashion
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for all operations, but we MUST NOT scale even linearly for operations that
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invoke a latency multiplier. For example, reading a file on disk requires
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finding the inode for the file, then the block with the data and returning the
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contents. Due to directory grouping logic we pay a massive price to read files
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if we do not group the reads of files within the same directory.
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doc/developers/performance-use-case-analysis.txt
