# Efficient saving of multi-source buffers

December 16, 2019## Preface

Several years ago I was working on libbls, a library implementing editable buffers that can efficiently hold contents originating from both memory and file sources. Typical clients of this libraries are programs that need to edit arbitrary portions of very large files, like hex editors or wave editors, while minimizing the required memory usage.

One of the more interesting problems I encountered was devising an efficient way to save buffers to their corresponding files in place, while minimizing the extra memory (or disk space) used in the process. This post describes how this problem, which turns out to be NP-hard, can be modeled as a graph, and how we can provide a reasonable solution by using a variation of a standard graph algorithm.

## Introduction

Buffers in libbls are described in terms of segments that contain data from either memory or file sources. In order to conserve memory (and sometimes to even make the editing feasible) the data from file sources is not loaded into main memory. Instead, the buffer keeps information about the contained files and ranges and reads the data in vm-page-sized chunks when needed. So, a buffer could look like:

```
B: |F1:0-9 |M1:10-18|F1:10-19 |M2:10-19|F2:0-6|
```

Where F1 and F2 are file sources, M1 and M2 are memory sources and S:B-E denotes a segment representing bytes from the range [B, E] of source S.

In the simple case, saving a buffer to a file consists of just reading data from the various sources and writing them out to the target file. Things become more complicated when the target file happens to be one of the file sources used in the buffer.

This post aims to illustrate the issues that can arise in such cases and propose an elegant way to resolve them.

## Illustrating the problem

Saving the buffer to a file typically involves reading each segment in order and writing it out to the file. However, if the segment source is the same as the file that we are writing to, and depending on the ordering of segments, we may face a serious problem.

To illustrate the problem, consider a buffer B containing segment [0-9] of file F at range [10-19] of the buffer, and assume that we want to write the buffer back to file F:

S F: |0-9 |.........| S B: |.........|F:0-9 | ^ ^ ^ 0 10 20

Writing the buffer segments in order leads to a situation where we first write out buffer range [0-9] to file F, overwriting the contents of the file at that range. So, when moving on to the next segment, trying to write out the range [10-19] of the buffer, the data we are going to read from the file (and therefore write) is not the original data of [F:0-9], but the data belonging to the previous segment of the buffer we just wrote at positions [0-9].

The solution in this case is to first write out range [10-19] of the buffer to the file, so that the data we use is the original file data, and only then write out any other segments.

The problem can become even more complicated when we have more segments from the target file. A particularly interesting case arises when a segment from the buffer which belongs to the target file happens to overlap with another segment of F which is also present in the buffer:

S T F: |0-9 |.........|20-29 |.........| T S B: |.......|F:20-29 |...........|F:0-9 | ^ ^ ^ ^ 0 8 18 30

In this case segment S in F overlaps with segment T in B. We can simply try to adapt the strategy used in the previous case and first write out the two target file segments. This works but only if we are extra careful. In this case, if we first write segment T then when we try to read the data of segment S we will read wrong data (file range 8-9 will contain data from segment T). If we do it the other way around everything works wonderfully.

Taking the last example one step further, consider what happens if we have cyclic overlaps:

S T F: |0-9 |.........|20-29 |....| T S B: |.......|F:20-29 |......|F:0-9 | ^ ^ ^ ^ ^ 0 8 18 25 30

Now segment S in F overlaps with segment T in B, and segment T in F overlaps with segment S in B. In this case there is no way to order the writes of segments S and T so that we end up with the correct data.

A straightforward but wasteful way of tackling this problem is to save the whole buffer to a temporary file and then move the file to its final location. This works, and in some cases is preferred since it has the benefit of atomicity, but requires extra space for the temporary file. When buffers reach many GiB in size this method may prove to be unfeasible.

A more efficient method is to try to eliminate the cyclic overlaps by removing at least one of the overlaps involved. In the previous case we can replace segment T in B with two segments M and T1 so that no overlap occurs. Segment M will contain a part of T's data but will actually store them in memory (or a temporary file if necessary) and the T1 segment will just be a T minus the range stored in M. This approach still requires some amount of extra memory, but in most cases this amount is much smaller than the size of the whole buffer. Using this scheme we have:

S T1 F: |0-9 |...........|22-29 |....| M T1 S B: |.......|.|F:22-29|......|F:0-9 | ^ ^ ^ ^ ^ 0 8 18 25 30

We have managed to eliminate the first overlap and now we can safely save the buffer by first writing T1 then S and then the remaining segments of B.

## Modeling the problem

The examples presented in the previous section show representative but simple cases of the problems we can run into. In order to be able to handle cases of arbitrary complexity it is useful to create a model of the problem. We can then use this model to come up with algorithms that provide generic solutions.

The model we are going to present uses graphs. In these *overlap graphs*
vertices represent segments from the file we are going to save to, that are
also present in the buffer we are going to save. Edges between vertices denote
segment overlap: an edge from vertex P to vertex Q denotes that segment P in
the target file overlaps with segment Q in the buffer. Every edge carries with
it the size of the overlapping range.

Here are some buffers and their respective overlap graphs (P* denotes a self-loop on P):

```
P Q R
F: |0-9 |....|15-24 |.........|35-42 |
P
B1: |........................|F:0-9 |.......| P
P Q
B2: |.........|F:0-9 |...|F:15-24 |.......| Q --> P
P Q
B3: |....|F:0-9 |......|F:20-29 |..........| P* Q*
R P Q
B4: |F:35-42|F:0-9 |...........|F:20-29 |..| R <-- P* <-- Q
\___________^
```

## Solving the problem

Using the overlap graph we can now answer the question: in what order should the vertices (segments) be written to the file so that the correct data is written?

If the graph has no cycles then we can process the vertices in topological order. Topological order guarantees that each vertex is processed only when its dependencies have already been processed and written to file, and thus that no unwritten file segment in the buffer overlaps with the destination range of that vertex.

As a special case, we can process a vertex that has a self-loop but no other incoming edge. This is achieved by first writing to the file the non-overlapping part of the vertex and then the rest.

In order to handle graphs with cycles (except self-loops) we must find a way to break the cycles. This can be achieved by removing the correct edges. An edge from P to Q can be removed by replacing Q in the buffer by the segments Q1 and M as described previously. M contains an in memory copy of data of the overlapping range. Q1 is the part of Q that remains. This technique eliminates the overlap (because the overlapping part is now stored in memory) and removes the edge. By choosing the edges to remove wisely, we can minimize the amount of extra memory we are going to need.

Once we have an acyclic graph (self-loops allowed) we can process it as described previously.

Let's manually apply this method to an example:

```
P Q R
F: |0-9 |....|15-24 |.........|35-42 |
R P Q
B4: |F:35-42|F:0-9 |...........|F:20-29 |..| R <-- P* <-- Q
\___________^
```

We first have to make the graph acyclic. We have a few choices for which edge to break:

- Q->P, a 3 byte overlap
- P->R, a 7 byte overlap
- R->Q, a 5 byte overlap

To minimize the required extra memory we break the Q->P edge by storing the last three bytes of P in memory as segment M, so we get:

```
P1 Q R
F: |0-6 |.......|15-24 |.........|35-42 |
R P1 M Q
B4: |F:35-42|F:0-6 |..|...........|F:20-29 |..| Q <-- R <-- P1
```

Note that coincidentally we also resolved the self-loop of P. Now that we have an acyclic graph we can process the buffer segments in topological order, thus we first store P1 in range [8-14] of the file, next is R at [0-7] and finally Q at range [30-40] followed by all other portions of the buffer.

## Implementation Details

libbls implements the solution described above when saving a file in place. The high level logic for the algorithm resides in bless_buffer_save. The implementation of the algorithm within that function can be conceptually split into three steps. Some implementation details for each step are given below, along with links to the functions that provide the relevant functionality.

#### Step 1: Creating the overlap graph

Relevant functions:

**buffer_file.c**: create_overlap_graph

**overlap_graph.c**: overlap_graph_add_segment

To create the overlap graph we have to scan the buffer segment collection for
segments that belong to the file we want to save to. Each new segment that is
found must be checked against all previous found file segments for overlap.
This is an *O(n + k*f(k))* algorithm where n is the number of segments in the
segment collection and *k* the number of segments belonging to the file. If we
check for overlap naively then *f(k) = O(k)* and therefore the algorithm is
*O(n + k*k)*. We can use an interval tree so that *f(k) = O(logk)* and the
algorithm becomes *O(n + k*logk)*. As we don't expect *k* to grow too large,
the implementation currently uses the naive approach for simplicity.

#### Step 2: Making the graph acyclic

Relevant functions:

**overlap_graph.c**: overlap_graph_remove_cycles

In the second step the goal is to remove cycles from the graph, while trying to minimize the total weight of the removed edges in order to minimize extra memory usage. However, it turns out that the problem we are trying to solve, minimum feedback arc set, is NP-Hard! Don't despair though; we can get reasonable results with only little extra work.

The undirected counterpart of the minimum feedback arc set problem is the minimum (or maximum) spanning tree (MST) problem, which can be efficiently solved by using various methods. We can use these methods to give a solution (but not the best) for our problem, too. We can do that because MST algorithms remove all undirected cycles and therefore all directed ones, too. The caveat is that they remove more that we need them to; undirected cycles that are not directed.

MST algorithms work by processing edges in non-decreasing or non-increasing order of weight and adding them to the already formed tree (Prim) or trees (Kruskal) if they aren't already connected to them by an undirected path. Because our goal is to avoid directed cycles we can be more lax with the rules of adding an edge. A simple but useful observation is that if the destination node of an edge has no outgoing edges then a directed cycle cannot be formed, regardless of whether there is a pre-existing undirected path between the two nodes. Similarly, if the source node of an edge has no incoming edges a directed cycle cannot be formed either, regardless of the pre-existence of an undirected path between the source and destination nodes.

We can use the previous observations to decrease the number of edges that are incorrectly deleted by the MST algorithms when acting on directed graphs. Although we don't completely eliminate the erroneous deletions, these rules give reasonable results while keeping the complexity the same as in the undirected case.

Of the MST algorithms, Kruskal's algorithm can be used unchanged in directed graphs because it works directly on edges and doesn't care about their direction. It also works on disconnected graphs, since it finds a spanning forest. For these reasons, it was selected as the basis for the algorithm used by libbls.

#### Step 3: Removing edges and saving to file

Relevant functions:

**overlap_graph.c**: overlap_graph_get_removed_edges

**buffer_file.c**: break_edge

**buffer_util.c**: segcol_store_in_memory,
segcol_store_in_file

**overlap_graph.c**: overlap_graph_get_vertices_topo

In the previous step we calculated which edges would need to be removed to create an acyclic graph. In this step we first perform the actual removal of these edges by storing the associated data in memory or a temporary file as described previously.

Removing the edges has the unfortunate side effect of further altering the graph as segments are removed, added or changed in the segment collection. This means that after removing the edges we must re-calculate the overlap graph for the segment collection. There may be some way to avoid re-calculating the whole graph, making only local changes to the existing graph as vertices are altered but we will use the simple (although more costly) way for now.

We then use this new overlap graph, which is guaranteed not to have any cycles, to get the vertices in topological order and save them to the target file.