~bzr-pqm/bzr/bzr.dev

.. This document describes _how_ to do use case analyses and what we want

.. to get out of them; for the specific cases see the files referenced by

.. performance-roadmap.txt 

Analysing a specific use case

=============================

The analysis of a use case needs to provide as outputs:

 * The functional requirements that the use case has to satisfy.

 * The file level operations and access patterns that will give the best

   performance.

 * A low friction API which will allow the use case to be implemented.

 * The release of bzr (and thus the supported features) for which the analysis

   was performed. The feature set of bzr defines the access patterns and data

   required to implement any use case. So when we add features, their design

   changes the requirements for the parts of the system they alter, so we need

   to re-analyse use cases when bzr's feature set changes. If future plans are

   considered in the analysis with the intention of avoiding rework, these

   should also be mentioned.

Performing the analysis

=======================

The analysis needs to be able to define the characteristics of the

involved disk storage and APIs. That means we need to examine the data

required for the operation, in what order it is required, on both the

read and write sides, and how that needs to be presented to be

consistent with our layering.

As a quick example: 'annotation of a file requires the file id looked up

from the tree, the basis revision id from the tree, and then the text of

that fileid-revisionid pair along with the creating revision id

allocated to each line, and the dotted revision number of each of those

revision ids.' All three of our key domain objects are involved here,

but we haven't defined any characteristics of the api or disk facilities

yet. We could then do that by saying something like 'the file-id lookup

should degrade gracefully as trees become huge. The tree basis id should

be constant time. Retrieval of the annotated text should be roughly

constant for any text of the same size regardless of the number of

revisions contributing to its content. Mapping of the revision ids to

dotted revnos could be done as the text is retrieved, but its completely

fine to post-process the annotated text to obtain dotted-revnos.'

What factors should be considered?

==================================

Obviously, those that will make for an extremely fast system :). There

are many possible factors, but the ones I think are most interesting to

design with are:

- baseline overhead:

   - The time to get bzr ready to begin the use case.

- scaling: how does performance change when any of the follow aspects

  of the system are ratcheted massively up or down:

   - number of files/dirs/symlinks/subtrees in a tree (both working and 

     revision trees)

   - size of any particular file

   - number of elements within a single directory

   - length of symlinks

   - number of changes to any file over time

     (subordinately also the number of merges of the file)

   - number of commits in the ancestry of a branch

     (subordinately also the number of merges)

   - number of revisions in a repository

   - number of fileids in a repository

   - number of ghosts in a given graph (revision or per-file)

   - number of branches in a repository

   - number of concurrent readers for a tree/branch/repository

   - number of concurrent writers for objects that support that.

   - latency to perform file operations (e.g. slow disks, network file systems,

     our VFS layer and FTP/SFTP/etc)

   - bandwidth to the disk storage

   - latency to perform semantic operations (hpss specific)

   - bandwidth when performing semantic operations.

- locality of reference: If an operation requires data that is located

  within a small region at any point, we often get better performance 

  than with an implementation of the same operation that requires the

  same amount of data but with a lower locality of reference. Its 

  fairly tricky to add locality of reference after the fact, so I think

  its worth considering up front.

Using these factors, to the annotate example we can add that its

reasonable to do two 'semantic' round trips to the local tree, one to

the branch object, and two to the repository. In file-operation level

measurements, in an ideal world there would be no more than one round

trip for each semantic operation. What there must not be is one round

trip per revision involved in the revisionid->dotted number mapping, nor

per each revision id attributed to a line in the text. 

Not all the items mentioned above are created equal. The analysis should

include the parameters considered and the common case values for each - the

optimisation should be around the common cases not around the exceptions.

For instance, we have a smart server now; file level operations are relatively

low latency and we should use that as the common case. At this point we intend

to preserve the performance of the dumb protocol networking, but focus on

improving network performance via the smart server and thus escape the

file-level operation latency considerations.

Many performance problems only become visible when changing the scaling knobs

upwards to large trees. On small trees its our baseline performance that drives

incremental improvements; on large trees its the amount of processing per item

that drives performance. A significant goal therefore is to keep the amount of

data to be processed under control. Ideally we can scale in a sublinear fashion

for all operations, but we MUST NOT scale even linearly for operations that

invoke a latency multiplier. For example, reading a file on disk requires

finding the inode for the file, then the block with the data and returning the

contents. Due to directory grouping logic we pay a massive price to read files

if we do not group the reads of files within the same directory.