Billy

Billy (previously BagDB) is a super simple datastore. It can't quite be called a database, because it avoids implementing some of the most complex parts of an actual database. It's intended to be used in very particular circumstances.

It is named after the bookcase, because it's very much like a bookcase, with N shelves of various heights.

Cheats

A 'proper' database is very complex, and has to solve several difficult problems:

• Maintain an index. This serves several purposes:
  • It decouples the key, which is the 'external' identifier, from the actual internal storage representation. This gives the database freedom to store the data 'wherever' it fits best. Having this decoupling means that when elements are deleted, other pieces of data can be moved to overwrite the freed element, and the database can be made more compact.
  • The index allows for the external caller to 'forget' the existence of a piece of data, and later on, when it discovers that it needs the data represented by key, it can query the database for it, and the database can look it up from disk and hand it back to the process.

What if?

But what if we don't need to maintain an index? There are two obvious cavats here:

• Without an index, we cannot move the data around after it's written. Compaction will not be possible. This can lead to fragmentation; where inserts/deletes fragments the buffer space, caused by differently-sized data-chunks, eventually deteriorating performance due to many small gaps spread out across the entire storage space.
• Without an index, the external caller can no longer 'forget' about the data, and later query for a specific key. This means that the usability is limited to datasets which can be / will be backed by an in-memory reference map.

What are the upsides?

• Without an index, we don't need to maintain a complex index-implementation, but can be very low on resource consumption. No extra allocated memory for index maintenance, no background threads for compaction work.

In practice

For the proposal by @karalabe about implementing a disk-backed transaction pool for geth, we have very special circumstances:

• The disk-backed storage is indeed backed by an in-memory structure of metadata.
• The payloads will roughly equally heavy on write, delete and read operations.
• The data is somewhat transient, meaning that it's expected that the mean-storage time for a piece of data is measured in minutes rather than weeks.

Implementation

The bagdb uses has the following API:

• Put(data []byte) uint64. This operation stores the given data, and returns a 'direct' reference to where the data is stored. By 'direct', it means that there is no indirection involved, the returned key is a direct reference to the shelf and slot where the data can later be found.
  • The billy uses a set of shelves. Each shelf has a dynamic number of slots, where each slot within a shelf is a fixed size. This design is meant to alleviate the fragmentation problem: if a piece of data is 168 bytes, and our shelf sizes are 100, 200, 400, 800, 1600 .... , then billy will choose the shelf with 200 byte size. The 168 bytes of data will be placed into shelf 1, at the first free slot.
• Delete(key uint64). The delete operation will simply look up the shelf, and tell the shelf that the identified slot now is free for re-use. It will be overwritten during a later Put operation.
• Get(key uint64). Again, this is a very trivial operation: find the shelf, load the identified slot, and return.

Compaction

Saying that we can't do compaction is not strictly true: there are two things that can be (and are) done to minimize the disk usage.

• Truncate-on-delete
  • Truncate-on-delete is what it sounds like: when we delete items at the end of the file, we truncate the file. This has a slight performance hit: a normal delete never touches the disk, but only appends to the in-memory gap slice. In order to increase the chance for an opportunity to delete, the gaps are kept sorted, and we always prefer writing to lower gaps, leaving the higher gaps for later.
• Compact-on-open
  • Compact-on-open uses the fact that before the external calles is notified about the data content, we have the freedom to reorder the data, and uses this period overwrite any gaps and truncate the underlying file.

Data format

The identifer for accessing an item, a uint64 is composed as follows:

The items themselves are stored with size as a 32-bit big-endian encoded integer, followed by the item itself. The 'slack-space' after size is not cleared, so might contain old data.

uint32: size | <data>