HanoiDB implements an ordered k/v pairs storage engine in Erlang. The primary index is a log-structured merge tree (LSM-BTree) implemented using "doubling sizes" persistent ordered sets of kvp.
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bloom (from basho) uses a NIF implementation which may be faster and use less memory; test before use. The “old” bloom, a.k.a. “scalable bloom” is pure erlang, which also has nice properties. Note! Switching to ebloom changes the file format so the default is still to use ‘scalable bloom’. |
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doc | ||
include | ||
src | ||
test | ||
tools | ||
.gitignore | ||
DESIGN.md | ||
LICENSE | ||
Makefile | ||
README.md | ||
rebar.config | ||
TODO |
HanoiDB Indexed Key/Value Storage
HanoiDB implements an indexed, key/value storage engine. The primary index is a log-structured merge tree (LSM-BTree) implemented using "doubling sizes" persistent ordered sets of key/value pairs, similar is some regards to LevelDB. HanoiDB includes a visualizer which when used to watch a living database resembles the "Towers of Hanoi" puzzle game, which inspired the name of this database.
Features
- Insert, Delete and Read all have worst case O(log2(N)) latency.
- Incremental space reclaimation: The cost of evicting stale key/values
is amortized into insertion
- you don't need a separate eviction thread to keep memory use low
- you don't need to schedule merges to happen at off-peak hours
- Operations-friendly "append-only" storage
- allows you to backup live system
- crash-recovery is very fast and the logic is straight forward
- all data subject to CRC32 checksums
- data can be compressed on disk to save space
- Efficient range queries
- Riak secondary indexing
- Fast key and bucket listing
- Uses bloom filters to avoid unnecessary lookups on disk
- Time-based expiry of data
- configure the database to expire data older than n seconds
- specify a lifetime in seconds for any particular key/value pair
- Efficient resource utilization
- doesn't store all keys in memory
- uses a modest number of file descriptors proportional to the number of levels
- I/O is generally balanced between random and sequential
- low CPU overhead
- ~2000 lines of pure Erlang code in src/*.erl
HanoiDB is developed by Trifork, a Riak expert solutions provider, and Basho
Technologies, makers of Riak. HanoiDB can be used in Riak via the
riak_kv_tower_backend
repository.
Configuration options
Put these values in your app.config
in the hanoidb
section
{hanoidb, [
{data_root, "./data/hanoidb"},
%% Enable/disable on-disk compression.
%%
{compress, none | gzip},
%% Expire (automatically delete) entries after N seconds.
%% When this value is 0 (zero), entries never expire.
%%
{expiry_secs, 0},
%% Sync strategy `none' only syncs every time the
%% nursery runs full, which is currently hard coded
%% to be evert 256 inserts or deletes.
%%
%% Sync strategy `sync' will sync the nursery log
%% for every insert or delete operation.
%%
{sync_strategy, none | sync | {seconds, N}},
%% The page size is a minimum page size, when a page fills
%% up to beyond this size, it is written to disk.
%% Compression applies to such units of page size.
%%
{page_size, 8192},
%% Read/write buffer sizes apply to merge processes.
%% A merge process has two read buffers and a write
%% buffer, and there is a merge process *per level* in
%% the database.
%%
{write_buffer_size, 524288}, % 512kB
{read_buffer_size, 524288}, % 512kB
%% The merge strategy is one of `fast' or `predictable'.
%% Both have same log2(N) worst case, but `fast' is
%% sometimes faster; yielding latency fluctuations.
%%
{merge_strategy, fast | predictable}
]},
Contributors
- Kresten Krab Thorup @krestenkrab
- Greg Burd @gburd
- Jesper Louis Andersen @jlouis
- Steve Vinoski @vinoski
- Erik Søe Sørensen, @eriksoe
- Yamamoto Takashi @yamt
- Joseph Wayne Norton @norton