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README.md

LSM B-Tree Storage

This erlang-based storage engine implements a structure somewhat like LSM-trees (Log-Structured Merge Trees). The notes below describe how this storage engine work; I have not done extensive studies as how it differs from other storage mechanisms, but a brief brows through available online resources on LSM-trees and Fractal Trees indicates that this storage engine is quite different in several respects.

The storage engine may eventually provide an alternative backend for Basho's Riak. Of the existing backends, lsm_btree is closest to LevelDB in operational characteristics, but it is implemented in just ~1000 lines of Erlang.

Here's the bullet list:

  • Very fast writes and deletes,
  • Reasonably fast reads (N records are stored in log2(N) B-trees),
  • Operations-friendly "append-only" storage (allows you to backup live system, and crash-recovery is very simple)
  • The cost of evicting stale key/values is amortized into insertion, so you don't need to schedule merge to happen at off-peak hours.
  • Supports range queries (and thus eventually Riak 2i.)
  • Doesn't need a boat load of RAM
  • All in 1000 lines of pure Erlang code

Once we're a bit more stable, we'll provide a Riak backend.

How a LSM-BTree Works

If there are N records, there are in log2(N) levels (each being a plain B-tree in a file named "A-level.data"). The file A-0.data has 1 record, A-1.data has 2 records, A-2.data has 4 records, and so on: A-n.data has 2n records.

In "stable state", each level file is either full (there) or empty (not there); so if there are e.g. 20 records stored, then there are only data in filed A-2.data (4 records) and A-4.data (16 records).

OK, I've told you a lie. In practice, it is not practical to create a new file for each insert (injection at level #0), so we allows you to define the "top level" to be a number higher that #0; currently defaulting to #5 (32 records). That means that you take the amortization "hit" for ever 32 inserts.

Lookup

Lookup is quite simple: starting at A-0.data, the sought for Key is searched in the B-tree there. If nothing is found, search continues to the next data file. So if there are N levels, then N disk-based B-tree lookups are performed. Each lookup is "guarded" by a bloom filter to improve the likelihood that disk-based searches are only done when likely to succeed.

Insertion

Insertion works by a mechanism known as B-tree injection. Insertion always starts by constructing a fresh B-tree with 1 element in it, and "injecting" that B-tree into level #0. So you always inject a B-tree of the same size as the size of the level you're injecting it into.

  • If the level being injected into empty (there is no A-level.data file), then the injected B-tree becomes the contents for that level (we just rename the file).
  • Otherwise,
    • The injected tree file is renamed to B-level.data;
    • The files A-level.data and B-level.data are merged into a new temporary B-tree (of roughly double size), X-level.data.
    • The outcome of the merge is then injected into the next level.

While merging, lookups at level n first consults the B-n.data file, then the A-n.data file. At a given level, there can only be one merge operation active.

Overwrite and Delete

Overwrite is done by simply doing a new insertion. Since search always starts from the top (level #0 ... level#n), newer values will be at a lower level, and thus be found before older values. When merging, values stored in the injected tree (that come from a lower-numbered level) have priority over the contained tree.

Deletes are the same: they are also done by inserting a tombstone (a special value outside the domain of values). When a tombstone is merged at the currently highest numbered level it will be discarded. So tombstones have to bubble "down" to the highest numbered level before it can be truly evicted.

Merge Logic

The really clever thing about this storage mechanism is that merging is guaranteed to be able to "keep up" with insertion. Bitcask for instance has a similar merging phase, but it is separated from insertion. This means that there can suddenly be a lot of catching up to do. The flip side is that you can then decide to do all merging at off-peak hours, but it is yet another thing that need to be configured.

With LSM B-Trees; back-pressure is provided by the injection mechanism, which only returns when an injection is complete. Thus, every 2nd insert needs to wait for level #0 to finish the required merging; which - assuming merging has linear I/O complexity - is enough to guarantee that the merge mechanism can keep up at higher-numbered levels.

A further trouble is that merging does in fact not have completely linear I/O complexity, because reading from a small file that was recently written is faster that reading from a file that was written a long time ago (because of OS-level caching); thus doing a merge at level #N+1 is sometimes more than twice as slow as doing a merge at level #N. Because of this, sustained insert pressure may produce a situation where the system blocks while merging, though it does require an extremely high level of inserts. We're considering ways to alleviate this.

Merging can be going on concurrently at each level (in preparation for an injection to the next level), which lets you utilize available multi-core capacity to merge.

Deploying the lsm_btree for testing with Riak/KV

You can deploy lsm_btree into a Riak devrel cluster using the enable-lsm_btree script. Clone the riak repo, change your working directory to it, and then execute the enable-lsm_btree script. It adds lsm_btree as a dependency, runs make all devrel, and then modifies the configuration settings of the resulting dev nodes to use the lsm_btree storage backend.

  1. git clone git://github.com/basho/riak.git
  2. cd riak/deps
  3. git clone git://github.com/basho/lsm_btree.git
  4. cd ..
  5. ./deps/lsm_btree/enable-lsm_btree # which does make all devrel
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