LeveledDB

Overview

LeveledDB is an experimental Key/Value store based on the Log-Structured Merge Tree concept, written in Erlang. It is not currently suitable for production systems, but is intended to provide a proof of concept of the potential benefits of different design trade-offs in LSM Trees.

The specific goals of this implementation are:

• Be simple and straight-forward to understand and extend
• Support objects which have keys, secondary indexes, a value and potentially some metadata which provides a useful subset of the information in the value
• Support a HEAD request which has a lower cost than a GET request, so that requests requiring access only to metadata can gain efficiency by saving the full cost of returning the entire value
• Tries to reduce write amplification when compared with LevelDB, to reduce disk contention but also make rsync style backup strategies more efficient

The system context for the store at conception is as a Riak backend store with a complete set of backend capabilities, but one intended to be use with relatively frequent iterators, and values of non-trivial size (e.g. > 4KB).

Implementation

The store is written in Erlang using the actor model, the primary actors being:

• A Bookie
• An Inker
• A Penciller
• Worker Clerks
• File Clerks

The Bookie

The Bookie provides the public interface of the store, liaising with the Inker and the Penciller to resolve requests to put new objects, and fetch those objects. The Bookie keeps a copy of key changes and object metadata associated with recent modifications, but otherwise has no direct access to state within the store. The Bookie can replicate the Penciller and the Inker to provide clones of the store. These clones can be used for querying across the store at a given snapshot.

The Inker

The Inker is responsible for keeping the Journal of all changes which have been made to the store, with new writes being append to the end of the latest journal file. The Journal is an ordered log of activity by sequence number.

Changes to the store should be acknowledged if and only if they have been persisted to the Journal. The Inker can find a value in the Journal through a manifest which provides a map between sequence numbers and Journal files. The Inker can only efficiently find a value in the store if the sequence number is known, and so the sequence number is always part of the metadata maintained by the Penciller in the Ledger.

The Inker can also scan the Journal from a particular sequence number, for example to recover the Penciller's lost in-memory state following a shutdown.

The Penciller

The Penciller is responsible for maintaining a Ledger of Keys, Index entries and Metadata (including the sequence number) that represent a near-real-time view of the contents of the store. The Ledger is a merge tree ordered into Levels of exponentially increasing size, with each level being ordered across files and within files by Key. Get requests are handled by checking each level in turn - from the top (Level 0), to the basement (up to Level 8). The first match for a given key is the returned answer.

Changes ripple down the levels in batches and require frequent rewriting of files, in particular at higher levels. As the Ledger does not contain the full object values, this write amplification associated with the flow down the levels is limited to the size of the key and metadata.

The Penciller keeps an in-memory view of new changes that have yet to be persisted in the Ledger, and at startup can request the Inker to replay any missing changes by scanning the Journal.

Worker Clerks

Both the Inker and the Penciller must undertake compaction work. The Inker must garbage collect replaced or deleted objects form the Journal. The Penciller must merge files down the tree to free-up capacity for new writes at the top of the Ledger.

Both the Penciller and the Inker each make use of their own dedicated clerk for completing this work. The Clerk will add all new files necessary to represent the new view of that part of the store, and then update the Inker/Penciller with the new manifest that represents that view. Once the update has been acknowledged, any removed files can be marked as delete_pending, and they will poll the Inker (if a Journal file) or Penciller (if a Ledger file) for it to confirm that no users of the system still depend on the old snapshot of the store to be maintained.

File Clerks

Every file within the store has is owned by its own dedicated process (modelled as a finite state machine). Files are never created or accessed by the Inker or the Penciller, interactions with the files are managed through messages sent to the File Clerk processes which own the files.

The File Clerks themselves are ignorant to their context within the store. For example a file in the Ledger does not know what level of the Tree it resides in. The state of the store is represented by the Manifest which maintains a picture of the store, and contains the process IDs of the file clerks which represent the files.

Cloning of the store does not require any file-system level activity - a clone simply needs to know the manifest so that it can independently make requests of the File Clerk processes, and register itself with the Inker/Penciller so that those files are not deleted whilst the clone is active.

The Journal files use a constant database format almost exactly replicating the CDB format originally designed by DJ Bernstein. The Ledger files use a bespoke format with is based on Google's SST format, with the primary difference being that the bloom filters used to protect against unnecessary lookups are based on the Riak Segment IDs of the key, and use single-hash rice-encoded sets rather using the traditional bloom filter size-optimisation model of extending the number of hashes used to reduce the false-positive rate.

File clerks spend a short initial portion of their life in a writable state. Once they have left a writing state, they will for the remainder of their life-cycle, be in an immutable read-only state.

Paths

The PUT path for new objects and object changes depends on the Bookie interacting with the Inker to ensure that the change has been persisted with the Journal, the Ledger is updated in batches after the PUT has been completed.

The HEAD path needs the Bookie to look in his cache of recent Ledger changes, and if the change is not present consult with the Penciller.

The GET path follows the HEAD path, but once the sequence number has been determined through the response from the Ledger the object itself is fetched from the journal via the Inker.

All other queries (folds over indexes, keys and objects) are managed by cloning either the Penciller, or the Penciller and the Inker.

Trade-Offs

Further information of specific design trade-off decisions is provided:

• What is a log-structured merge tree?
• Memory management
• Backup and Recovery
• The Penciller memory
• File formats
• Stalling, pausing and back-pressure
• Riak Anti-Entropy
• Riak and HEAD requests
• Riak and alternative queries

Naming Things is Hard

The naming of actors within the model is very loosely based on the slang associated with an on-course Bookmaker.

Learning

The project was started in part as a learning exercise. This is my first Erlang project, and has been used to try and familiarise myself with Erlang concepts. However, there are undoubtedly many lessons still to be learned about how to write good Erlang OTP applications.