dodb.cr/graphs/graphs.ms

.so macros.roff
.TITLE Document Oriented DataBase (DODB)
.AUTHOR Philippe P.
.ABSTRACT1
DODB is a database-as-library, enabling a very simple way to store applications' data: storing serialized
.I documents
(basically any data type) in plain files.
To speed-up searches, attributes of these documents can be used as indexes which leads to create a few symbolic links
.I symlinks ) (
on the disk.

This document briefly presents DODB and its main differences with other database engines.
An experiment is described and analysed to understand the performance that can be expected from this approach.
.ABSTRACT2
.SINGLE_COLUMN
.SECTION Introduction to DODB
A database consists in managing data, enabling queries (preferably fast) to retrieve, to modify, to add and to delete a piece of information.
Anything else is
.UL accessory .

Universities all around the world teach about Structured Query Language (SQL) and relational databases.
.
.de PRIMARY_KEY
.I \\$1 \\$2 \\$3
..
.de FOREIGN_KEY
.I \\$1 \\$2 \\$3
..

.UL "Relational databases"
are built around the idea to put data into
.I tables ,
with typed columns so the database can optimize operations and storage.
A database is a list of tables with relations between them.
For example, let's imagine a database of a movie theater.
The database will have a
.I table
for the list of movies they have
.PRIMARY_KEY idmovie , (
title, duration, synopsis),
a table for the scheduling
.PRIMARY_KEY idschedule , (
.FOREIGN_KEY idmovie ,
.FOREIGN_KEY idroom ,
time slot),
a table for the rooms
.PRIMARY_KEY idroom , (
name), etc.
Tables have relations, for example the table "scheduling" has a column
.I idmovie
which points to entries in the "movie" table.

.UL "The SQL language"
enables arbitrary operations on databases: add, search, modify and delete entries.
Furthermore, SQL also enables to manage administrative operations of the databases themselves: creating databases and tables, managing users with fine-grained authorizations, etc.
SQL is used between the application and the database, to perform operations and to provide results when due.
SQL is also used
.UL outside
the application, by admins for managing databases and potentially by some
.I non-developer
users to retrieve some data without a dedicated interface\*[*].
.FOOTNOTE1
One of the first objectives of SQL was to enable a class of
.I non-developer
users to talk directly to the database so they can access the data without bothering the developers.
This has value for many companies and organizations.
.FOOTNOTE2

Many tools were used or even developed over the years specifically to aleviate the inherent complexity and limitations of SQL.
For example, designing databases becomes difficult when the list of tables grows;
Unified Modeling Language (UML) is then used to provide a graphical overview of the relations between tables.
SQL databases may be fast to retrieve data despite complicated operations, but when multiple sequential operations are required they become slow because of all the back-and-forths with the application;
thus, SQL databases can be scripted to automate operations and provide a massive speed up
.I "stored procedures" , (
see
.I "PL/SQL" ).
Writing SQL requests requires a lot of boilerplate since there is no integration in the programming languages, leading to multiple function calls for any operation on the database;
thus, object-relational mapping (ORM) libraries were created to reduce the massive code duplication.
And so on.

For many reasons, SQL is not a silver bullet to
.I solve
the database problem.
The encountered difficulties mentioned above and the original objectives of SQL not being universal\*[*], other database designs were created\*[*].
.FOOTNOTE1
To say the least!
Not everyone needs to let users access the database without going through the application.
For instance, writing a \f[I]blog\f[] for a small event or to share small stories about your life doesn't require manual operations on the database, fortunately.
.FOOTNOTE2
.FOOTNOTE1
A lot of designs won't be mentioned here.
The actual history of databases is often quite unclear since the categories of databases are sometimes vague, underspecified.
As mentioned, SQL is not a silver bullet and a lot of developers shifted towards other solutions, that's the important part.
.FOOTNOTE2
The NoSQL movement started because the stated goals of many actors from the early Web boom were different from SQL.
The need for very fast operations far exceeded what was practical at the moment with SQL.
This led to the use of more basic methods to manage data such as
.I "key-value stores" ,
which simply associate a value with an
.I index
for fast retrieval.
In this case, there is no need for the database to have
.I tables ,
data may be untyped, the entries may even have different attributes.
Since homogeneity is not necessary anymore, databases have fewer (or different) constraints.
Document-oriented databases are a sub-class of key-value stores, where metadata can be extracted from the entries for further optimizations.
And that's exactly what is being done in Document Oriented DataBase (DODB).

.UL "Contrary to SQL" ,
DODB has a very narrow scope: to provide a library enabling to store, retrieve, modify and delete data.
In this way, DODB transforms any application in a database manager.
DODB doesn't provide an interactive shell, there is no request language to perform arbitrary operations on the database, no statistical optimizations of the requests based on query frequencies, etc.
Instead, DODB reduces the complexity of the infrastructure, stores data in plain files and enables simple manual scripting with widespread unix tools.
Simplicity is key.

.UL "Contrary to other NoSQL databases" ,
DODB doesn't provide an application but a library, nothing else.
The idea is to help developers to store their data themselves, not depending on
. I yet-another-all-in-one
massive tool.
The library writes (and removes) data on a storage device, has a few retrieval and update mechanisms and that's it\*[*].
.FOOTNOTE1
The lack of features
.I is
the feature.
Even with that motto, the tool still is expected to be convenient for most applications.
.FOOTNOTE2

This document will provide an extensive documentation on how DODB works and how to use it.
Limitations are also clearly stated in a dedicated section.
A few experiments are described to provide an overview of the performance you can expect from this approach.
Finally, a conclusion is drawn based on a real-world usage of this library.
.
.SECTION How DODB works
DODB is a hash table.
The key of the hash is an auto-incremented number, the value is the stored data.
The following section will explain the file-system representation of the data and the very few added mechanisms to speed-up searches.
.SS Storing data
When a value is added, it is serialized\*[*] and written in a dedicated file.
.FOOTNOTE1
Serialization is currently in JSON.
CBOR is a work-in-progress.
Nothing binds DODB to a particular format.
.FOOTNOTE2
The key of the hash is a number, auto-incremented, used as the name of the stored file.
The following example shows the content of the file system after adding three values.
.de TREE1
.QP
.KS
.ft CW
.nf
..
.de TREE2
.ft
.fi
.KE
.QE
..
.TREE1
$ tree storage/
storage
`-- data
     +-- 0000000000
     +-- 0000000001
     `-- 0000000002
.TREE2
In this example, the directory
.I storage/data
contains all three serialized values, with a formated number as their file name.
.SS Indexes
Database entries can be
.I indexed
based on their attributes.
There are currently three main ways to search a value by its attributes: basic indexes, partitions and tags.
.SSS Basic indexes (1 to 1 relation)
Basic indexes represent one-to-one relations, such as an index in SQL.
For example, in a database of
.I cars ,
each car can have a dedicted (unique) name.
This
.I name
attribute can be used to speed-up searches.
On the file-system, this will be translated as this:
.TREE1
storage
+-- data
|    `-- 0000000000
`-- indexes
      `-- by_name
          `-- Ford C-MAX -> ../../data/0000000000
.TREE2
As shown, the file "Ford C-MAX" is a symbolic link to a data file.
The name of the symlink file has been extracted from the value itself, enabling to list all the cars and their names with a simple
.UL ls
in the
.I storage/indexes/by_name/
directory.
.TBD
.SECTION Basic usage of the DODB library
.TBD
.SECTION A few more options
.TBD
.SECTION Limits of DODB
.TBD
.SECTION Experimental scenario
.LP
The following experiment shows the performance of DODB based on quering durations.
Data can be searched via
.I indexes ,
as for SQL databases.
Three possible indexes exist in DODB:
(a) basic indexes, representing 1 to 1 relations, the document's attribute is related to a value and each value of this attribute is unique,
(b) partitions, representing 1 to n relations, the attribute has a value and this value can be shared by other documents,
(c) tags, representing n to n relations, enabling the attribute to have multiple values whose are shared by other documents.

The scenario is simple: adding values to a database with indexes (basic, partitions and tags) then query 100 times a value based on the different indexes.
Loop and repeat.

Four instances of DODB are tested:
.BULLET \fIuncached database\f[] shows the achievable performance with a strong memory constraint (nothing can be kept in-memory) ;
.BULLET \fIuncached data but cached index\f[] shows the improvement you can expect by having a cache on indexes ;
.BULLET \fIcached database\f[] shows the most basic use of DODB\*[*] ;
.BULLET \fIRAM only\f[], the database doesn't have a representation on disk (no data is written on it).
The \fIRAM only\f[] instance shows a possible way to use DODB: to keep a consistent API to store data, including in-memory data with a lifetime related to the application's.
.ENDBULLET
.FOOTNOTE1
Having a cached database will probably be the most widespread use of DODB.
When memory isn't scarce, there is no point not using it to achieve better performance.
.FOOTNOTE2

The computer on which this test is performed\*[*] is a AMD PRO A10-8770E R7 (4 cores), 2.8 GHz.When mentioned, the
.I disk
is actually a
.I "temporary file-system (tmpfs)"
to enable maximum efficiency.
.FOOTNOTE1
A very simple $50 PC, buyed online.
Nothing fancy.
.FOOTNOTE2

The library is written in Crystal and so is the benchmark (\f[CW]spec/benchmark-cars.cr\f[]).
Nonetheless, despite a few technicalities, the objective of this document is to provide an insight on the approach used in DODB more than this particular implementation.

The manipulated data type can be found in \f[CW]spec/db-cars.cr\f[].
.SOURCE Ruby ps=9 vs=9p
class Car
	property name     : String        # 1-1 relation
	property color    : String        # 1-n relation
	property keywords : Array(String) # n-n relation
end
.SOURCE
.
.SS Basic indexes (1 to 1 relations)
.LP
An index enables to match a single value based on a small string.
In our example, each \f[CW]car\f[] has an unique \fIname\f[] which is used as an index.

The following graph represents the result of 100 queries of a car based on its name.
The experiment starts with a database containing 1,000 cars and goes up to 250,000 cars.

.so graph_query_index.grap

Since there is only one value to retrieve, the request is quick and time is almost constant.
When the value and the index are kept in memory (see \f[CW]RAM only\f[] and \f[CW]Cached db\f[]), the retrieval is almost instantaneous (about 50 to 120 ns).
In case the value is on the disk, deserialization takes about 15 µs (see \f[CW]Uncached db, cached index\f[]).
The request is a little longer when the index isn't cached (see \f[CW]Uncached db and index\f[]); in this case DODB walks the file-system to find the right symlink to follow, thus slowing the process even more, by up to 20%.

.TS
allbox tab(:);
c | lw(4.0i) | cew(1.4i).
DODB instance:Comment and database usage:T{
compared to RAM only
T}
RAM only:T{
Worst memory footprint (all data must be in memory), best performance.
T}:-
Cached db and index:T{
Performance for retrieving a value is the same as RAM only while
enabling the admin to manually search for data on-disk.
T}:about the same perfs
Uncached db, cached index::300 to 400x slower
Uncached db and index:T{
Best memory footprint, worst performance.
T}:400 to 500x slower
.TE

.B Conclusion :
as expected, retrieving a single value is fast and the size of the database doesn't matter much.
Each deserialization and, more importantly, each disk access is a pain point.
Caching the value enables a massive performance gain, data can be retrieved several hundred times quicker.
.bp
.SS Partitions (1 to n relations)
.LP

.so graph_query_partition.grap

.bp
.SS Tags (n to n relations)
.LP
.so graph_query_tag.grap
.
.SECTION Future work
.TBD
.SECTION Conclusion
.TBD