Full Code of krestenkrab/hanoidb for AI

Repository: krestenkrab/hanoidb
Branch: master
Commit: 68333fa51a6f
Files: 35
Total size: 1.0 MB

Directory structure:
gitextract_kab_p6bk/

├── .gitignore
├── .travis.yml
├── DESIGN.md
├── LICENSE
├── Makefile
├── README.md
├── TODO
├── doc/
│   └── design_diagrams.graffle
├── include/
│   ├── hanoidb.hrl
│   └── plain_rpc.hrl
├── rebar.config
├── src/
│   ├── gb_trees_ext.erl
│   ├── hanoidb.app.src
│   ├── hanoidb.erl
│   ├── hanoidb.hrl
│   ├── hanoidb_app.erl
│   ├── hanoidb_bloom.erl
│   ├── hanoidb_dense_bitmap.erl
│   ├── hanoidb_fold_worker.erl
│   ├── hanoidb_level.erl
│   ├── hanoidb_merger.erl
│   ├── hanoidb_nursery.erl
│   ├── hanoidb_reader.erl
│   ├── hanoidb_sparse_bitmap.erl
│   ├── hanoidb_sup.erl
│   ├── hanoidb_util.erl
│   ├── hanoidb_writer.erl
│   ├── plain_rpc.erl
│   └── vbisect.erl
├── test/
│   ├── hanoidb_drv.erl
│   ├── hanoidb_merger_tests.erl
│   ├── hanoidb_tests.erl
│   └── hanoidb_writer_tests.erl
└── tools/
    ├── basho_bench_driver_hanoidb.erl
    └── visualize-hanoi.sh

================================================
FILE CONTENTS
================================================

================================================
FILE: .gitignore
================================================
ebin
deps
*~
.eunit
.project


================================================
FILE: .travis.yml
================================================
language: erlang
otp_release:
   - R16B03
   - R15B03
   - 17.0
   - 18.0




================================================
FILE: DESIGN.md
================================================
# Hanoi's Design

### Basics
If there are N records, there are in log<sub>2</sub>(N)  levels (each being a plain B-tree in a file named "A-*level*.data").  The file `A-0.data` has 1 record, `A-1.data` has 2 records, `A-2.data` has 4 records, and so on: `A-n.data` has 2<sup>n</sup> records.

In "stable state", each level file is either full (there) or empty (not there); so if there are e.g. 20 records stored, then there are only data in filed `A-2.data` (4 records) and `A-4.data` (16 records).

OK, I've told you a lie.  In practice, it is not practical to create a new file for each insert (injection at level #0), so we allows you to define the "top level" to be a number higher that #0; currently defaulting to #5 (32 records).  That means that you take the amortization "hit" for ever 32 inserts.

### Lookup
Lookup is quite simple: starting at `A-0.data`, the sought for Key is searched in the B-tree there.  If nothing is found, search continues to the next data file.  So if there are *N* levels, then *N* disk-based B-tree lookups are performed.  Each lookup is "guarded" by a bloom filter to improve the likelihood that disk-based searches are only done when likely to succeed.

### Insertion
Insertion works by a mechanism known as B-tree injection.  Insertion always starts by constructing a fresh B-tree with 1 element in it, and "injecting" that B-tree into level #0.  So you always inject a B-tree of the same size as the size of the level you're injecting it into.

- If the level being injected into empty (there is no A-*level*.data file), then the injected B-tree becomes the contents for that level (we just rename the file).
- Otherwise,
    - The injected tree file is renamed to B-*level*.data;
        - The files A-*level*.data and B-*level*.data are merged into a new temporary B-tree (of roughly double size), X-*level*.data.
        - The outcome of the merge is then injected into the next level.

While merging, lookups at level *n* first consults the B-*n*.data file, then the A-*n*.data file.  At a given level, there can only be one merge operation active.

### Overwrite and Delete
Overwrite is done by simply doing a new insertion.  Since search always starts from the top (level #0 ... level#*n*), newer values will be at a lower level, and thus be found before older values.  When merging, values stored in the injected tree (that come from a lower-numbered level) have priority over the contained tree.

Deletes are the same: they are also done by inserting a tombstone (a special value outside the domain of values).  When a tombstone is merged at the currently highest numbered level it will be discarded.  So tombstones have to bubble "down" to the highest numbered level before it can be truly evicted.


## Merge Logic
The really clever thing about this storage mechanism is that merging is guaranteed to be able to "keep up" with insertion.   Bitcask for instance has a similar merging phase, but it is separated from insertion.  This means that there can suddenly be a lot of catching up to do.  The flip side is that you can then decide to do all merging at off-peak hours, but it is yet another thing that need to be configured.

With LSM B-Trees; back-pressure is provided by the injection mechanism, which only returns when an injection is complete.  Thus, every 2nd insert needs to wait for level #0 to finish the required merging; which - assuming merging has linear I/O complexity - is enough to guarantee that the merge mechanism can keep up at higher-numbered levels.

A further trouble is that merging does in fact not have completely linear I/O complexity, because reading from a small file that was recently written is faster that reading from a file that was written a long time ago (because of OS-level caching); thus doing a merge at level #*N+1*  is sometimes more than twice as slow as doing a merge at level #*N*.  Because of this, sustained insert pressure may produce a situation where the system blocks while merging, though it does require an extremely high level of inserts.  We're considering ways to alleviate this.

Merging can be going on concurrently at each level (in preparation for an injection to the next level), which lets you utilize available multi-core capacity to merge.


```
ABC are data files at a given level
  A oldest
  C newest
  X is being merged into from [A+B]

  270     76 [AB X|ABCX|AB X|ABCX|ABCX|ABCX|ABCX|ABCX|A   |    |    |    |    |    |    |    |    |    |
  271     76 [ABCX|ABCX|AB X|ABCX|ABCX|ABCX|ABCX|ABCX|A   |    |    |    |    |    |    |    |    |    |
  272     77 [A   |AB X|ABCX|ABCX|ABCX|ABCX|ABCX|ABCX|A   |    |    |    |    |    |    |    |    |    |
  273     77 [AB X|AB X|ABCX|ABCX|ABCX|ABCX|ABCX|ABCX|A   |    |    |    |    |    |    |    |    |    |
  274     77 [ABCX|AB X|ABCX|ABCX|ABCX|ABCX|ABCX|ABCX|A   |    |    |    |    |    |    |    |    |    |
  275     78 [A   |ABCX|ABCX|ABCX|ABCX|ABCX|ABCX|ABCX|A   |    |    |    |    |    |    |    |    |    |
  276     78 [AB X|ABCX|ABCX|ABCX|ABCX|ABCX|ABCX|ABCX|A   |    |    |    |    |    |    |    |    |    |
  277     79 [ABCX|ABCX|ABCX|ABCX|ABCX|ABCX|ABCX|ABCX|A   |    |    |    |    |    |    |    |    |    |
  278     79 [ABCX|ABCX|ABCX|ABCX|ABCX|ABCX|ABCX|  C |AB  |    |    |    |    |    |    |    |    |    |
  279     79 [ABCX|ABCX|ABCX|ABCX|ABCX|ABCX|ABCX|  C |AB X|    |    |    |    |    |    |    |    |    |
  280     79 [ABCX|ABCX|ABCX|ABCX|ABCX|ABCX|ABCX|A   |AB X|    |    |    |    |    |    |    |    |    |
  281     79 [ABCX|ABCX|ABCX|ABCX|ABCX|ABCX|  C |AB  |AB X|    |    |    |    |    |    |    |    |    |
  282     80 [ABCX|ABCX|ABCX| BC |AB  |AB  |AB X|AB X|AB X|    |    |    |    |    |    |    |    |    |
  283     80 [ABCX|ABCX|ABCX|  C |AB X|AB  |AB X|AB X|AB X|    |    |    |    |    |    |    |    |    |
  284     80 [A   |AB X|AB X|AB X|AB X|AB X|AB X|AB X|AB X|    |    |    |    |    |    |    |    |    |
  285     80 [AB X|AB X|AB X|AB X|AB X|AB X|AB X|AB X|AB X|    |    |    |    |    |    |    |    |    |
  286     80 [ABCX|AB X|AB X|AB X|AB X|AB X|AB X|AB X|AB X|    |    |    |    |    |    |    |    |    |
  287     80 [A   |ABCX|AB X|AB X|AB X|AB X|AB X|AB X|AB X|    |    |    |    |    |    |    |    |    |
```


When merge finishes, X is moved to the next level [becomes first open slot, in order of A,B,C], and the files merged (AB in this case) are deleted. If there is a C, then that becomes A of the next size.
When X is closed and clean, it is actually intermittently renamed M so that if there is a crash after a merge finishes, and before it is accepted at the next level then the merge work is not lost, i.e. an M file is also clean/closed properly. Thus, if there are M's that means that the incremental merge was not fast enough.

ABC files have 2^level KVs in it, regardless of the size of those KVs. XM files have 2^(level+1) approximately ... since tombstone merges might reduce the numbers or repeat PUTs of cause.

### File Descriptors
Hanoi needs a lot of file descriptors, currently   6*⌈log<sub>2</sub>(N)-TOP_LEVEL⌉, with a nursery of size 2<sup>TOP_LEVEL</sup>, and N Key/Value pairs in the store.   Thus, storing 1.000.000 KV's need 72 file descriptors, storing 1.000.000.000 records needs 132 file descriptors, 1.000.000.000.000 records needs 192.


================================================
FILE: LICENSE
================================================

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================================================
FILE: Makefile
================================================
REBAR=		rebar
DIALYZER=	dialyzer


.PHONY: plt analyze all deps compile get-deps clean

all: get-deps compile

deps: get-deps compile

get-deps:
	@$(REBAR) get-deps

compile:
	@$(REBAR) compile

clean:
	@$(REBAR) clean

test: eunit

eunit: compile clean-test-btrees
	@$(REBAR) eunit skip_deps=true

eunit_console:
	erl -pa .eunit deps/*/ebin

clean-test-btrees:
	rm -fr .eunit/Btree_* .eunit/simple

plt: compile
	$(DIALYZER) --build_plt --output_plt .hanoi.plt \
		-pa deps/snappy/ebin \
	-pa deps/snappy/ebin \
	-pa deps/lz4/ebin \
	-pa deps/ebloom/ebin \
		-pa deps/plain_fsm/ebin \
		deps/plain_fsm/ebin \
		--apps erts kernel stdlib ebloom lz4 snappy

analyze: compile
	$(DIALYZER) --plt .hanoi.plt \
	-pa deps/snappy/ebin \
	-pa deps/lz4/ebin \
	-pa deps/ebloom/ebin \
	-pa deps/plain_fsm/ebin \
	ebin

analyze-nospec: compile
	$(DIALYZER) --plt .hanoi.plt \
	-pa deps/plain_fsm/ebin \
        --no_spec \
	ebin

repl:
	erl -pz deps/*/ebin -pa ebin


================================================
FILE: README.md
================================================
# HanoiDB Indexed Key/Value Storage

[![Build Status](https://travis-ci.org/krestenkrab/hanoidb.svg?branch=master)](https://travis-ci.org/krestenkrab/hanoidb)

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](http://code.google.com/p/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*(log<sub>2</sub>(*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

```erlang
 {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},

          %% "Level0" files has 2^N KVs in it, defaulting to 1024.
          %% If the database is to contain very small KVs, this is
          %% likely too small, and will result in many unnecessary
          %% file operations.  (Subsequent levels double in size).
          {top_level, 10}  % 1024 Key/Values
         ]},
```


### 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


================================================
FILE: TODO
================================================
* Phase 1: Minimum viable product (in order of priority)
  * lager; check for uses of lager:error/2
  * configurable TOP_LEVEL size
  * test new snappy compression support
  * status and statistics
    * for each level {#merges, {merge-time-min, max, average}}
  * add @doc strings and and -spec's
  * check to make sure every error returns with a reason {error, Reason}


* Phase 2: Production Ready
  * dual-nursery
  * cache for read-path
    * {cache, bytes(), name} share max(bytes) cache named 'name' via etc
  * snapshot entire database (fresh directory w/ hard links to all files)
  * persist merge progress (to speed up re-opening a HanoiDB)
  * support for future file format changes
    * Define a standard struct which is the metadata added at the end of the
      file, e.g. [btree-nodes] [meta-data] [offset of meta-data]. This is written
      in hanoi_writer:flush_nodes, and read in hanoi_reader:open2.

* Phase 3: Wish List
  * add truncate/1 - quickly truncates a database to 0 items
  * count/1 - return number of items currently in tree
  * adaptive nursery sizing
  * backpressure on fold operations
    - The "sync_fold" creates a snapshot (hard link to btree files), which
      provides consistent behavior but may use a lot of disk space if there is
      a lot of insertion going on.
    - The "async_fold" folds a limited number, and remembers the last key
      serviced, then picks up from there again. So you could see intermittent
      puts in a subsequent batch of results.
  * add block-level encryption support


## NOTES:

1: make the "first level" have more thatn 2^5 entries (controlled by the constant TOP_LEVEL in hanoi.hrl); this means a new set of files is opened/closed/merged for every 32 insert/updates/deletes. Setting this higher will just make the nursery correspondingly larger, which should be absolutely fine.

2: Right now, the streaming btree writer emits a btree page based on number of elements. This could be changed to be based on the size of the node (say, some block-size boudary) and then add padding at the end so that each node read becomes a clean block transfer. Right now, we're probably taking way to many reads.

3: Also, there is no caching of read nodes. So every time a btree node is visited it is also read from disk and term_to_binary'ed. But we need a caching system for that to work well (https://github.com/cliffmoon/cherly is difficult to build), it needs to be rebar-ified.

4: Also, the format for btree nodes could probably be optimized. Right now it's just binary_to_term of a key/value list as far as I remember. Perhaps we dont have to deserialize the entire thing.

5: It might also be good to employ a scheduler (github.com/esl/jobs<http://github.com/esl/jobs>) for issuing merges; because I think that it can be a problem for the OS if there are too many merges going on at the same time.


================================================
FILE: doc/design_diagrams.graffle
================================================
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