umgeher/leveled
0
Fork 0
Code Issues Pull requests Releases Activity Actions

Ongoing work on documents

Browse source
This commit is contained in:
martinsumner 2017-02-01 13:58:09 +00:00
parent f55753cfc3
commit 3330b39b73
7 changed files with 130 additions and 93 deletions
Download patch file Download diff file Expand all files Collapse all files

100
README.md
View file

@ -1,94 +1,38 @@
# LeveledDB
# LevelEd - A Pure Erlang Key-Value store
## Overview
## Introduction
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.
LevelEd is a work-in-progress prototype of a simple Key-Value store based on the concept of a Log_Structured Merge Tree, with the following characteristics:
The specific goals of this implementation are:
- Optimised for workloads with larger values (e.g. > 4KB) and non-trivial proportion of PUT activity (e.g. > 20% by comparison with GET activity).
- Supporting of values split between metadata and body, and allowing for HEAD requests which have lower overheads than GET requests.
- Trade-offs are made to reduce the intensity of disk workloads, accepting the potential for consequent increases in CPU time and median latency.
- Support for tagging of object types and the implementation of alternative store behaviour based on type.
- Support for low-cost clones without locking to provide for scanning queries, specifically where there is a need to scan across keys and metadata (not values).
- Written in Erlang as a message passing system between Actors.
- 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 store has been developed with a focus on being a potential backend to a Riak KV database, rather than as a generic store. The primary aim of developing (yet another) Riak backend is to examine the potential to reduce the broader costs providing sustained throughput in Riak i.e. to provide equivalent throughput on cheaper hardware. It is also anticipated in having a fully-featured pure Erlang backend may assist in evolving new features through the Riak ecosystem which require end-to-end changes.
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).
The store is not expected to be 'faster' than leveldb (the primary fully-featured Riak backend available today), nor offer improvements in all use cases - especially where values are small.
## Implementation
## More Details
The store is written in Erlang using the actor model, the primary actors being:
For more details on the store:
- A Bookie
- An Inker
- A Penciller
- Worker Clerks
- File Clerks
- An [introduction](docs/INTRO.md) to LevelEd covers some context to the factors motivating design trade-offs in the store.
### The Bookie
- The [design overview](docs/DESIGN.md) explains the actor model used and the basic flow of requests through the store.
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.
- [Future work](docs/FUTURE.md) covers new features being implemented at present, and improvements necessary to make the system production ready.
### The Inker
- There is also a ["Why"](WHYWHYWHYWHYWHY.md) section looking at lower level design choices and the rationale that supports them.
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.
## Is this interesting?
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.
At the initiation of the project I accepted that making a positive contribution to this space is hard - given the superior brainpower and experience of those that have contributed to the KV store problem space in general, and the Riak backend space in particular.
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 target at inception was to do something interesting, something that articulates through working software the potential for improvement to exist by re-thinking certain key assumptions and trade-offs.
### The Penciller
[Initial volume tests](VOLUME.md) indicate that it is at least interesting, with substantial improvements in both throughput and tail latency when compared to eleveldb - in scenarios expected to be optimised for leveled. Note, to be clear, this is statement falls well short of making a general claim that it represents a 'better' Riak backend than eleveldb.
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.
More information can be found in the volume testing section.