snapshot: turn _refinedgroups into a coroutine We are now almost ready to start adding refining logic.

snapshot: also use None as a stop value for `_refinegroup` This is yet another small step toward turning `_refinegroups` into a co-routine.

snapshot: add refining logic at the findeltainfo level Once we found a delta, we want to have the candidates logic challenge it, searching for a better candidate. The logic at the lower level is still missing. We'll introduce it later. Adding small changes in individual commits make it simpler to explain the code change. This is another small step toward turning `_refinegroups` into a co-routine.

snapshot: use None as a stop value when looking for a good delta Having clear stop value should help keep clear logic around the co-routine. The alternative of using a StopIteration exception give a messier result. This is one small step toward turning `_refinegroups` into a co-routine.

snapshot: introduce an intermediate `_refinedgroups` generator This method will be used to improve the search for a good snapshot base. To keep things simpler, we introduce the necessary function before doing any delta base logic change. The next handful of commits will focus on refactoring the code to let that new logic land as clearly as possible. # General Idea Right now, the search for a good delta base stop whenever we found a good one. However, when using sparse-revlog, we should probably try a bit harder. We do significant effort to increase delta re-use by jumping on "unrelated" delta chains that provide better results. Moving to another chain for a better result is good, but we have no guarantee we jump at a reasonable point in that new chain. When we consider over the chains related to the parents, we start from the higher-level snapshots. This is a way to consider the snapshot closer to the current revision that has the best chance to produce a small delta. We do benefit from this walk order when jumping to a better "unrelated" stack. To counter-balance this, we'll introduce a way to "refine" the result. After a good delta have been found, we'll keep searching for a better delta, using the current best one as a starting point. # Target Setup The `finddeltainfo` method is responsible for the general search for a good delta. It requests candidates base from `_candidategroups` and decides which one are usable. The `_candidategroups` generator act as a top-level filter, it does not care about how we pick candidates, it just does basic filtering, excluding revisions that have been tested already or that are an obvious misfit. The `_rawgroups` generator is the one with the actual ancestors walking logic, It does not care about what would do a good delta and what was already tested, it just issues the initial candidates. We introduce a new `_refinedgroup` function to bridge the gap between `_candidategroups` and `_rawgroups`. It delegates the initial iteration logic and then performing relevant refining of the valid base once found. (This logic is yet to be added to function) All these logics are fairly independent and easier to understand when standing alone, not mixed with each other. It also makes it easy to test and try different approaches for one of those four layers without affecting the other ones. # Technical details To communicate `finddeltainfo` choice of "current best delta base" to the `_refinegroup` logic, we plan to use python co-routine feature. The `_candidategroups` and `_refinegroup` generators will become co-routine. This will allow `_refinegroup` to detect when a good delta have been found and triggers various refining steps. For now, `_candidategroups` will just pass the value down the stack. After poking at various option, the co-routine appears the best to keep each layers focus on its duty, without the need to spread implementation details across layers.

snapshot: consider unrelated snapshots at a similar level first This new step is inserted before considering using a level-N snapshot as a base for a level-N+1 snapshot. We first check if existing level-N+1 snapshots using the same base would be a suitable base for a level-N+2 snapshot. This increases snapshot reuse and limits the risk of snapshot explosion in very branchy repositories. Using a "deeper" snapshot as the base also results in a smaller snapshot since it builds a level-N+2 intermediate snapshot instead of an N+1 one. This logic is similar for the one we added in a previous commit. In that previous commit is only applied to level-0 "siblings". We can see this effect in the test repository. Snapshots moved from lower levels to higher levels.

snapshot: consider all snapshots in the parents' chains There are no reasons to only consider full snapshot as a possible base for an intermediate snapshot. Now that the basic principles have been set, we can start adding more levels of snapshots. We now consider all snapshots in the parent's chains (full or intermediate). This creates a chain of intermediate snapshots, each smaller than the previous one. # Effect On The Test Repository In the test repository, we can see a decrease in the revlog size and slightly shorter delta chain. However, that approach creates snapshots more frequently, increasing the risk of ending into problematic cases in very branchy repositories (not triggered by the test repository). The next changesets will remove that risk by adding logic that increases deltas reuse.

snapshot: search for unrelated but reusable full-snapshot # New Strategy Step: Reusing Snapshot Outside Of Parents' Chain. If no suitable bases were found in the parent's chains, see if we could reuse a full snapshot not directly related to the current revision. Such search can be expensive, so we only search for snapshots appended to the revlog *after* the bases used by the parents of the current revision (the one we just tested). We assume the parent's bases were created because the previous snapshots were unsuitable, so there are low odds they would be useful now. This search gives a chance to reuse a delta chain unrelated to the current revision. Without this re-use, topological branches would keep reopening new full chains. Creating more and more snapshots as the repository grow. In repositories with many topological branches, the lack of delta reuse can create too many snapshots reducing overall compression to nothing. This results in a very large repository and other usability issues. For now, we still focus on creating level-1 snapshots. However, this principle will play a large part in how we avoid snapshot explosion once we have more snapshot levels. # Effects On The Test Repository In the test repository we created, we can see the beneficial effect of such reuse. We need very few level-0 snapshots and the overall revlog size has decreased. The `hg debugrevlog` call, show a "lvl-2" snapshot. It comes from the existing delta logic using the `prev` revision (revlog's tip) as the base. In this specific case, it turns out the tip was a level-1 snapshot. This is a coincidence that can be ignored. Finding and testing against all these unrelated snapshots can have a performance impact at write time. We currently focus on building good deltas chain we build. Performance concern will be dealt with later in another series.

snapshot: try intermediate snapshot against parents' base # Regarding The Series Started By This Changeset This is the first changesets of a group adjusting delta chain strategy to build a useful chain of intermediate snapshots. The series will introduce a full strategy to produce chains of multiple snapshots on top of which a "usual" delta chain will be built. That strategy will have multiple steps to maximize snapshot reuse, avoiding pathological cases and improving overall compression in very branchy repositories. An important property of sparse-revlog using such snapshot-chain is that they can use very short delta chain without problematic impact on the resulting compression. Shorter delta chains are important to achieve good performance. To make each step clear, we'll introduce them one by one. See the end of this series for full details. # Regarding This Changeset Before this change, if we cannot store the current revision as a delta against a "simple" candidate (p1, p2, prev), we created a new level-0 snapshot (also called full snapshot). As the first step, we introduce a simple strategy: try an intermediate level-1 snapshot against the chain base of the "current revision" parents. The "current revision" is the one we are currently trying to store in the revlog, triggering this search for a good delta base. The first item in the chain is always a level-0 snapshot. # Effect On The Test Repository We can already see the effect on the test-repository. Most of the snapshots have shifted from level 0 to level 1. The overall size has slightly decreased. (However, keep in mind that this repository only emulates real data) # Regarding Statistic The current series focuses on improving the chain built. Improving the performance of this logic will be done as a second step. Sparse-revlog is still experimental and disabled by default. We'll provide more statistic about resulting size and delta chain at the end of this series.

sparse-revlog: add a test checking revlog deltas for a churning file The test repository contains 5000 revisions and is therefore slow to build: five minutes with CHG, over fifteen minutes without. It is too slow to build during the test. Bundling all content produce a sizeable result, 20BM, too large to be committed. Instead, we commit a script to build the expected bundle and the test checks if the bundle is available. Any run of the script will produce the same repository content, using resulting in the same hashes. Using smaller repositories was tried, however, it misses most of the cases we are planning to improve. Having them in a 5000 repository is already nice, we usually see these case in repositories in the order of magnitude of one million revisions. This test will be very useful to check various changes strategy for building delta to store in a sparse-revlog. In this series we will focus our attention on the following metrics: The ones that will impact the final storage performance (size, space): * size of the revlog data file (".hg/store/data/*.d") * chain length info The ones that describe the deltas patterns: * number of snapshot revision (and their level) * size taken by snapshot revision (and their level)