justanotherdot / marble

garbage-collecting on-disk object store, supporting higher level KV stores and databases.

marble

Garbage-collecting disk-based object-store. See examples/kv.rs for a minimal key-value store built on top of this.

Supports 4 methods:

• read: designed for low-latency, concurrent reads of objects
• write_batch: designed for bulky, high-throughput writes of large batches of objects, ideal for compacting write-ahead logs into a set of updates to specific objects. Does not block calls to read except for brief moments where metadata is being updated.
• maintenance: compacts backing storage files that have become fragmented. Blocks concurrent calls to write_batch but does not block readers any more than write_batch does. Returns the number of successfully rewritten objects.
• file_statistics: returns statistics about live and total objects in the backing storage files.

Defragmentation is always generational, and will group rewritten objects together. Written objects can be further sharded based on a configured partition_function which allows you to shard objects by ObjectId and the size of the object raw bytes.

Marble solves a pretty basic problem in database storage: storing arbitrary bytes on-disk, getting them back, and defragmenting files.

You can think of it as a KV where keys are non-zero u64's, and values are arbitrary blobs of raw bytes.

Writes are meant to be performed in bulk by some background process. Each call to Marble::write_batch creates at least one new file that stores the objects being written. Multiple calls to fsync occur for each call to write_batch. It is blocking. Object metadata is added to the backing wait-free pagetable incrementally, not atomically, so if you rely on batch atomicity, you should serve the batch's objects directly from a cache of your own until write_batch returns. However, upon crash, batches are recovered atomically.

Reads can continue mostly unblocked while batch writes and maintenance are being handled.

You are responsible for:

• calling Marble::maintenance at appropriate intervals to defragment storage files.
• choosing appropriate configuration tunables for your desired space and write amplification.
• ensuring the Config.partition_function is set to a function that appropriately shards your objects based on their ObjectId and/or size. Ideally, objects that have expected death times will be colocated in a shard so that work spent copying live objects is minimized.
• allocating and managing free ObjectId's.

If you want to create an industrial database on top of Marble, you will probably also want to add:

• logging and a write cache for accumulating updates that occasionally get flushed to Marble via write_batch. Remember, each call to write_batch creates at least one new file and fsyncs multiple times, so you should batch calls appropriately. Once the log or write cache has reached an appropriate size, you can have a background thread write a corresponding batch of objects to its storage, and once write_batch returns, the corresponding log segments and write cache can be deleted, as the objects will be available via Marble::read.
• an appropriate read cache. Marble::read always reads directly from disk.
• for maximum SSD friendliness, your own log should be configurable to be written to a separate storage device, to avoid comingling writes that have vastly different expected death times.
• dictionary-based compression for efficiently compressing objects that may be smaller than 64k.

Ideas for getting great garbage collection performance:

• give certain kinds of objects a certain ObjectId range. for example, tree index nodes can be above 1<<63, and tree leaf nodes can be below that point. The Config.partition_function can return the shard 0 for leaf nodes, and 1 for index nodes, and they will always be written to separate files.
• WiscKey-style sharding of large items from other items, based on the size of the object. Assign a shard ID based on which power of 2 the object size is.
• Basically any sharding strategy that tends to group items together that exhibit some amount of locality in terms of expected mutations or overall lifespan.

In short, you get to focus on a bunch of the fun parts of building your own database, without so much effort spent on boring file garbage collection.