4. Servers¶
Each RDFox instance is associated with a distinct server object, which acts as a top-level container for all information stored in the RDFox instance. A server’s job is to keep track of global configuration options of the RDFox instance, manage data stores that contain various data, and manage roles that identify users capable of interacting with the server. A server instance is created whenever RDFox is started via the command line or via the Java API; in either case, there is a way to specify server options such as the number of threads and the maximum amount of memory that the server should use. The list of all options is given in Section 4.3.
The following diagram illustrates the subcomponents of servers, which are used to structure the information loaded in the system. The basic idea behind each concept is described in the following sections.
Each server is associated with a server version number, which is incremented
for every change to the server’s list of data stores. This version number is
visible in the output of the serverinfo shell command and equivalent API
calls (to see the role manager version number, the extended info must be
requested).
4.1. Data Stores¶
A server is divided into data stores. Each data store is identified by a user-defined name that is unique within the server. A data store acts as a container for data that logically belongs together. Many applications will use one data store to store their data; thus, several applications can share one server, while keeping their data separate. Some applications can use more than one data store, but it is key to remember that all queries and rules are evaluated within the context of a single data store. Thus, all information that an application wishes to access in a single query should be loaded into one data store.
RDFox provides many types of data stores, and each type is identified by a
unique type name (e.g., parallel-nn). Data store types differ in their
maximum capacity, and some support concurrent operations where others do not.
Moreover, each data store can be customized via a number of parameters; for
example, a data store can be configured to use the implicit semantics of
owl:sameAs or not. All parameters are specified as a list of key-value
pairs when the data store is created, and they cannot be changed subsequently.
Data store types and parameters are described in detail in
Section 5.
Data stores organize application data using tuple tables. In-memory tuple tables are used to store application data, like RDF data, in memory. A data store can also reference a number of data sources, which provide access to data in formats other than RDF, such as relational databases or CSV files. Moreover, a data store can contain OWL axioms and rules, which jointly provide inference rules that are to be used for reasoning within a data store. Finally, a data store can contain statistics modules, which keep summaries of the data loaded in the data store that are useful for query planning.
Each data store is assigned a data store ID that is with high probability unique across servers. Clients can use this identifier to ensure that they are referring to the same data store in different API calls.
Each data store is associated with a data store version number, which is incremented every time a data store is compacted or a transaction is successfully committed on the data store. The data store version number can be read using any of the provided APIs.
A data store can be online or offline. When online, a data store is loaded into RAM and can process requests. In contrast, when offline, a data store occupies almost no RAM but also cannot process any requests. When a data store is brought online, it is restored from persistence. Thus, only persistent data stores can be brought offline: bringing a data store that is not persisted offline would result in data loss. A data store can be brought offline only if there are no open connections to it.
4.1.1. Tuple Tables¶
A data store can contain several tuple tables, which are containers for actual data. The data of a tuple table is a collection of items called facts, and each fact can be understood as a list of RDF resources (i.e., IRIs, blank nodes, or literals). Facts with just three components are commonly called triples, and facts with four components are called quads. Each tuple table is identified with a name that must be unique within a data store. Moreover, each tuple table has a minimal and maximal arity, which are numbers determining the smallest and the largest numbers of RDF resources in a fact stored in the tuple table. In most cases, the minimal and maximal arity are the same, in which case they are called just arity.
RDFox uses the very general concept of a tuple table to represent many different kinds of data containers.
In-memory tuple tables are used to store application data in memory. The RDF dataset of a data store is represented using the in-memory tuple tables
DefaultTriplesandQuads. Further detail on in-memory tuple tables is given in Section 6.4.Built-in tuple tables contain facts that can be useful in various applications of RDFox, and they are described in more detail in Section 6.5.
Data source tuple tables provide a ‘virtual view’ over data in non-RDF data sources, such as CSV files, relational databases, or an Apache Solr index. Importing external data is explained in detail in Section 7.
Each fact in a tuple table is associated with one or more fact domains. Intuitively, the domain of a fact reflects how a fact was added to a tuple table — that is, whether a fact was explicitly introduced by the user or derived through reasoning, and so on. Fact domains are described in more detail in Section 6.2.
4.1.2. Data Sources¶
To support accessing data in formats other than RDFox, one or more data sources can be registered with a data store. Registering a data source requires specifying a number of parameters that govern how the data is accessed. Each data source is identified by a name that is unique for the data store. The access to the actual data is provided by data source tuple tables, which are created by referencing previously registered data sources. The process of accessing external data sources is described in more detail in Section 7.
4.1.3. OWL Axioms¶
To support reasoning with OWL ontologies, one can import OWL axioms into a data
store. For example, an OWL axiom can be used to state that the :Professor
class is a subclass of the :Person class; if such an axiom is imported into
a data store, RDFox will automatically infer that each instance of
:Professor is also an instance of :Person. RDFox associates a separate
set of axioms with each named graph, and it provides APIs for adding or
removing axioms in either the Functional-Style Syntax (FSS) or the RDF-based
syntax.
4.1.4. Rules¶
To support general reasoning, one can import Datalog rules into a data
store. Rules can intuitively be understood as “if-then” statements expressing
general truths about a domain of interest. For example, :Person[?X] :-
:Professor[?X] . is a rule stating that every professor is a person. If such
a rule is added to a data store, then if the data store also contains triples
:Peter rdf:type :Professor . and :Paul rdf:type :Professor ., RDFox
will automatically derive triples :Peter rdf:type :Person . and :Paul
rdf:type :Person .. RDFox also supports incremental reasoning: if triple
:Paul rdf:type :Professor . is removed from the data store, RDFox will
automatically remove :Paul rdf:type :Person .. RDFox provides ways to add
and remove rules, as well as to retrieve the rules in a data store. The rule
language of RDFox, the provided reasoning support, and examples of use of
reasoning in practical applications are discussed in detail in
Section 10.
4.1.4.1. Rule Domains¶
Each rule in a data store is associated with one or more of the following three rule domains.
The
userrule domain contains the rules that a user added explicitly, either by using the RDFox API or by importing a Datalog document.The
axiomsrule domain contains rules obtained by translating all OWL axioms in the data store (in both axiom domains), thus allowing RDFox to perform OWL reasoning. Only axioms that conform to the OWL 2 RL profile are translated. Rules in this rule domain cannot be manipulated directly by the user; rather, a user can add/remove triples and/or axioms in theuseraxiom domain, and RDFox will automatically adjust the rules in theaxiomsrule domain.The
internalrule domain contains rules that RDFox uses internally. The exact internal rules used depend on the data store configuration, and they are discussed in more detail in Section 10.6.6.
A rule can belong to multiple rule domains. For example, a rule could be added
by the user to the user rule domain, and it could also be obtained from OWL
axioms and thus be added to the axioms rule domain.
4.1.5. Base IRI and Prefixes¶
Each data store keeps track of a string called base IRI, as well as a mapping of strings to strings called prefixes. Both of these two objects can be manipulated using RDFox’s APIs. These objects are used when importing data into or running queries over a data store. For example, when importing a Turtle 1.1 file into a data store, the import process will proceed as if the base IRI and prefixes associated with the target data store are present before the beginning of the file. Analogously, when evaluating a SPARQL 1.1 query over a data store, the query will be processed as if the base IRI and prefixes associated with the target data store are specified before the query text. This allows applications to set up a default base IRI and prefixes that are implicitly used in all RDFox operations. The following prefix definitions are added to new data stores:
Prefix |
Expansion |
|---|---|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
All interfaces provide a method for clearing a data store’s prefixes. For
example, in the shell issuing prefixes clear will clear the prefixes of the
data store associated with the active data store connection.
4.1.6. Commit Procedure (EXPERIMENTAL)¶
Each data store may have an associated SPARQL update, known as a commit
procedure, that is evaluated as part of committing each read/write
transaction on the store. This can be useful for capturing additional explicit
facts as part of each transaction. See Section 11.6 for more
details.
4.2. Roles¶
A server can contain several roles, each representing an actor (or a type of actor) allowed to access a server. Performing actions on a server or its parts requires first authenticating as one of that server’s roles. The access control model of RDFox is described in detail in Section 12.
4.3. Server Parameters¶
When a server is instantiated, it can be given a number of parameters that
govern various aspects of the server’s operation. All parameters are specified
as key-value pairs. When an RDFox instance is created from the command line, the
server parameters are passed as arguments to the RDFox executable as described
in Section 18. If an RDFox instance is started from Java, the
server parameters can be specified as arguments to the
tech.oxfordsemantic.jrdfox.client.ConnectionFactory.startLocalServer()
method; please refer to the Javadoc for more information. In all cases,
when the server-directory parameter is set, RDFox will load additional
parameters from a file named parameters within the server directory if it
exists. See Section 4.3.1 for details on the format of this
file.
The following table describes all available server parameters.
Option |
Value |
Description |
|---|---|---|
|
|
If the value is |
|
a string containing a space- separated list of URI schemes |
Specifies a space-separated list of
schemes that are allowed to be used
in the SPARQL 1.1 |
|
|
If the value is |
|
a string |
Specifies the directory into which
API logs will be written. Default is
directory |
|
|
Limits the amount of each input that
is recorded during import operations as
a part of an API log to the specified
number of bytes. The value |
|
A duration of |
Specifies the interval between successive
polls of the file system for each
component configured to use
|
|
a string |
Specifies the license content verbatim. This parameter is not set by default. See Section 2.4.1.3 for the precedence of license-related options. |
|
a string |
Specifies the path to the license key
file to use. The default value is
|
|
an integer |
Specifies the initial value for the maximum of memory (in MB) that the RDFox instance should use. The default is 0.9 times the installed memory. |
|
a hostname and UDP port number separated separated by a plus symbol |
Specifies a hostname and UDP port number
to which other instances should send
notifications. This setting is ignored
unless |
|
an integer |
Specifies the initial number of threads that the system will use for tasks such as reasoning and importation. The default is the number of logical processors available on the machine. |
|
|
If the value is |
|
an integer |
The physical sector size of the disk
containing the server directory in bytes.
This parameter is used when the
|
|
a string |
The name of an encryption algorithm
supported by the available OpenSSL
crypto library. If an OpenSSL executable
is available, a list of the supported
algorithms can be obtained with command
|
|
a string |
A base64-encoded encryption key. If this
parameter is set, the server will use it
to encrypt and decrypt all persisted
data using the algorithm specified by
the |
|
a string |
Specifies the directory to which RDFox should restrict any file system access where the path is specified as part of an API call or shell command. The purpose of this feature is to prevent an attacker from probing the host’s file system using RDFox. The default value is the working directory of the RDFox process. Sandboxing of file access can be disabled by setting this option to the empty string. |
|
a string |
Specifies the directory to be used for persistence of data and as the default location for API logs. When this parameter is not specified, RDFox will be unable to use persistence. See also Section 4.3.1. |
4.3.1. The Server Parameters File¶
When an RDFox server is configured to use a server directory, it will inspect
the directory for a file named server.params and, if the file is found,
attempt to load server parameters from it. Parameter values specified
explicitly by the user (for example via the command line arguments when using
the RDFox executable) take precedence over values from the parameters file.
The parameters file must be encoded in UTF-8. Lines with # as the first
non-whitespace character are ignored, as are empty lines. Each (parameter name,
parameter value) pair must appear on a single line with optional leading
whitespace followed by the parameter name, more whitespace, the value and
optional trailing whitespace. Values that contain whitespace must be enclosed in
double quotes ("). Double quotes within values must be escaped as \",
newlines as \n, and backslashes as \\.
The following text block shows an example parameters file:
# Use 'file' persistence
persistence file
# Enable loading of file: and https: URLs only
allowed-schemes-on-load "file https"
# Restrict importing, exporting and reading of shell scripts to the ``/data`` directory
sandbox-directory /data
4.3.2. Specifying Durations¶
Several parameters of the server and other RDFox components control waiting
times between actions; in other words, durations. All such duration parameters
are specified as follows. To specify a finite waiting time, a parameter value
must contain a non-negative integer followed by an optional space and an
optional unit specifier. The accepted unit specifiers are s (the default)
and ms. To specify an infinite waiting time the value can be set to the
string unlimited although this is not accepted for all duration parameter
names.