Importing relational data into a CubicWeb instance
Importing relational data into a CubicWeb instance#
This tutorial explains how to import data from an external source (e.g. a collection of files) into a CubicWeb cube instance.
First, once we know the format of the data we wish to import, we devise a
data model, that is, a CubicWeb (Yams) schema which reflects the way the data
is structured. This schema is implemented in the
In this tutorial, we will describe such a schema for a particular data set,
the Diseasome data (see below).
Once the schema is defined, we create a cube and an instance. The cube is a specification of an application, whereas an instance is the application per se.
Once the schema is defined and the instance is created, the import can be performed, via the following steps:
Build a custom parser for the data to be imported. Thus, one obtains a Python memory representation of the data.
Map the parsed data to the data model defined in
Perform the actual import of the data. This comes down to “populating” the data model with the memory representation obtained at 1, according to the mapping defined at 2.
This tutorial illustrates all the above steps in the context of relational data stored in the RDF format.
More specifically, we describe the import of Diseasome RDF/OWL data.
Building a data model#
The first thing to do when using CubicWeb for creating an application from scratch is to devise a data model, that is, a relational representation of the problem to be modeled or of the structure of the data to be imported.
In such a schema, we define
an entity type (
EntityType objects) for each type of entity to import. Each such type
has several attributes. If the attributes are of known CubicWeb (Yams) types, viz. numbers,
strings or characters, then they are defined as attributes, as e.g.
attribute = Int()
for an attribute named
attribute which is an integer.
Each such type also has a set of relations, which are defined like the attributes, except that they represent, in fact, relations between the entities of the type under discussion and the objects of a type which is specified in the relation definition.
For example, for the Diseasome data, we have two types of entities, genes and diseases.
Thus, we create two classes which inherit from
class Disease(EntityType): # Corresponds to http://www.w3.org/2000/01/rdf-schema#label label = String(maxsize=512, fulltextindexed=True) ... #Corresponds to http://www4.wiwiss.fu-berlin.de/diseasome/resource/diseasome/associatedGene associated_genes = SubjectRelation('Gene', cardinality='**') ... #Corresponds to 'http://www4.wiwiss.fu-berlin.de/diseasome/resource/diseasome/chromosomalLocation' chromosomal_location = SubjectRelation('ExternalUri', cardinality='?*', inlined=True) class Gene(EntityType): ...
In this schema, there are attributes whose values are numbers or strings. Thus, they are
defined by using the CubicWeb / Yams primitive types, e.g.,
label = String(maxsize=12).
These types can have several constraints or attributes, such as
There are also relations, either between the entity types themselves, or between them
and a CubicWeb type,
ExternalUri. The latter defines a class of URI objects in
CubicWeb. For instance, the
chromosomal_location attribute is a relation between
Disease entity and an
ExternalUri entity. The relation is marked by the CubicWeb /
SubjectRelation method. The latter can have several optional keyword arguments, such as
cardinality which specifies the number of subjects and objects related by the relation type
specified. For example, the
'?*' cardinality in the
chromosomal_relation relation type says
that zero or more
Disease entities are related to zero or one
In other words, a
Disease entity is related to at most one
ExternalUri entity via the
chromosomal_location relation type, and that we can have zero or more
Disease entities in the
For a relation between the entity types themselves, the
associated_genes between a
entity and a
Gene entity is defined, so that any number of
Gene entities can be associated
Disease, and there can be any number of
Disease s if a
Of course, before being able to use the CubicWeb / Yams built-in objects, we need to import them:
from yams.buildobjs import EntityType, SubjectRelation, String, Int from cubicweb.schemas.base import ExternalUri
Building a custom data parser#
The data we wish to import is structured in the RDF format, as a text file containing a set of lines. On each line, there are three fields. The first two fields are URIs (“Universal Resource Identifiers”). The third field is either an URI or a string. Each field bares a particular meaning:
the leftmost field is an URI that holds the entity to be imported. Note that the entities defined in the data model (i.e., in
schema.py) should correspond to the entities whose URIs are specified in the import file.
the middle field is an URI that holds a relation whose subject is the entity defined by the leftmost field. Note that this should also correspond to the definitions in the data model.
the rightmost field is either an URI or a string. When this field is an URI, it gives the object of the relation defined by the middle field. When the rightmost field is a string, the middle field is interpreted as an attribute of the subject (introduced by the leftmost field) and the rightmost field is interpreted as the value of the attribute.
Note however that some attributes (i.e. relations whose objects are strings)
have their objects defined as strings followed by
^^ and by another URI;
we ignore this part.
Let us show some examples:
of line holding an attribute definition:
<http://www4.wiwiss.fu-berlin.de/diseasome/resource/genes/CYP17A1> <http://www.w3.org/2000/01/rdf-schema#label> "CYP17A1" .The line contains the definition of the
labelattribute of an entity of type
gene. The value of
of line holding a relation definition:
<http://www4.wiwiss.fu-berlin.de/diseasome/resource/diseases/1> <http://www4.wiwiss.fu-berlin.de/diseasome/resource/diseasome/associatedGene> <http://www4.wiwiss.fu-berlin.de/diseasome/resource/genes/HADH2> .The line contains the definition of the
associatedGenerelation between a
diseasesubject entity identified by
geneobject entity defined by
Thus, for parsing the data, we can (:note: see the
define a couple of regular expressions for parsing the two kinds of lines,
RE_ATTSfor parsing the attribute definitions, and
RE_RELSfor parsing the relation definitions.
define a function that iterates through the lines of the file and retrieves (
yields) a (subject, relation, object) tuple for each line. We called it
diseasome_parsermodule. The function needs the file name and the types for which information should be retrieved.
Alternatively, instead of hand-making the parser, one could use the RDF parser provided
Once we get to have the (subject, relation, object) triples, we need to map them into the data model.
Mapping the data to the schema#
In the case of diseasome data, we can just define two dictionaries for mapping
the names of the relations as extracted by the parser, to the names of the relations
as defined in the
schema.py data model. In the
they are called
Given that the relation and attribute names are given in CamelCase in the original data,
mappings are necessary if we follow the PEP08 when naming the attributes in the data model.
For example, the RDF relation
chromosomalLocation is mapped into the schema relation
Once these mappings have been defined, we just iterate over the (subject, relation, object)
tuples provided by the parser and we extract the entities, with their attributes and relations.
For each entity, we thus have a dictionary with two keys,
The value associated to the
attributes key is a dictionary containing (attribute: value)
pairs, where “value” is a string, plus the
cwuri key / attribute holding the URI of
the entity itself.
The value associated to the
relations key is a dictionary containing (relation: value)
pairs, where “value” is an URI.
This is implemented in the
entities_from_rdf interface function of the module
diseasome_parser. This function provides an iterator on the dictionaries containing
relations keys for all entities.
However, this is a simple case. In real life, things can get much more complicated, and the mapping can be far from trivial, especially when several data sources (which can follow different formatting and even structuring conventions) must be mapped into the same data model.
Importing the data#
The data import code should be placed in a Python module. Let us call it
diseasome_import.py. Then, this module should be called via
cubicweb-ctl, as follows:
cubicweb-ctl shell diseasome_import.py -- <other arguments e.g. data file>
In the import module, we should use a store for doing the import. A store is an object which provides three kinds of methods for importing data:
a method for importing the entities, along with the values of their attributes.
a method for importing the relations between the entities.
a method for committing the imports to the database.
In CubicWeb, we have four stores:
ObjectStorebase class for the stores in CubicWeb. It only provides a skeleton for all other stores and provides the means for creating the memory structures (dictionaries) that hold the entities and the relations between them.
RQLObjectStore: store which uses the RQL language for performing database insertions and updates. It relies on all the CubicWeb hooks machinery, especially for dealing with security issues (database access permissions).
NoHookRQLObjectStore: store which uses the RQL language for performing database insertions and updates, but for which all hooks are deactivated. This implies that certain checks with respect to the CubicWeb / Yams schema (data model) are not performed. However, all SQL queries obtained from the RQL ones are executed in a sequential manner, one query per inserted entity.
SQLGenObjectStore: store which uses the SQL language directly. It inserts entities either sequentially, by executing an SQL query for each entity, or directly by using one PostGRES
COPY FROMquery for a set of similarly structured entities.
For really massive imports (millions or billions of entities), there
is a cube
dataio which contains another store, called
MassiveObjectStore. This store is similar to
except that anything related to CubicWeb is bypassed. That is, even the
CubicWeb EID entity identifiers are not handled. This store is the fastest,
but has a slightly different API from the other four stores mentioned above.
Moreover, it has an important limitation, in that it doesn’t insert inlined 1
relations in the database.
An inlined relation is a relation defined in the schema with the keyword argument
inlined=True. Such a relation is inserted in the database as an attribute of the entity whose subject it is.
In the following section we will see how to import data by using the stores
Using the stores in
ObjectStore is seldom used in real life for importing data, since it is
only the base store for the other stores and it doesn’t perform an actual
import of the data. Nevertheless, the other three stores, which import data,
are based on
ObjectStore and provide the same API.
All three stores
SQLGenObjectStore provide exactly the same API for importing data, that is
entities and relations, in an SQL database.
Before using a store, one must import the
dataimport module and then initialize
the store, with the current
session as a parameter:
import cubicweb.dataimport as cwdi ... store = cwdi.RQLObjectStore(session)
Each such store provides three methods for data import:
create_entity(Etype, **attributes), which allows us to add an entity of the Yams type
Etypeto the database. This entity’s attributes are specified in the
attributesdictionary. The method returns the entity created in the database. For example, we add two entities, a person, of
Persontype, and a location, of
person = store.create_entity('Person', name='Toto', age='18', height='190') location = store.create_entity('Location', town='Paris', arrondissement='13')
relate(subject_eid, r_type, object_eid), which allows us to add a relation of the Yams type
r_typeto the database. The relation’s subject is an entity whose EID is
subject_eid; its object is another entity, whose EID is
object_eid. For example 2:
store.relate(person.eid(), 'lives_in', location.eid(), **kwargs)
kwargsis only used by the
relatemethod and is here to allow us to specify the type of the subject of the relation, when the relation is defined as inlined in the schema.
eidmethod of an entity defined via
the entity identifier as assigned by CubicWeb when creating the entity. This only works for entities defined via the stores in the CubicWeb’s
The keyword argument that is understood by
subjtypeand holds the type of the subject entity. For the example considered here, this comes to having 3:
store.relate(person.eid(), 'lives_in', location.eid(), subjtype=person.cw_etype)
subjtypeis not specified, then the store tries to infer the type of the subject. However, this doesn’t always work, e.g. when there are several possible subject types for a given relation type.
cw_etypeattribute of an entity defined via
the type of the entity just created. This only works for entities defined via the stores in the CubicWeb’s
dataimportmodule. In the example considered here,
All the other stores but
flush(), which allows us to perform the actual commit into the database, along with some cleanup operations. Ideally, this method should be called as often as possible, that is after each insertion in the database, so that database sessions are kept as atomic as possible. In practice, we usually call this method twice: first, after all the entities have been created, second, after all relations have been created.
Note however that before each commit the database insertions have to be consistent with the schema. Thus, if, for instance, an entity has an attribute defined through a relation (viz. a
SubjectRelation) with a
"+"object cardinality, we have to create the entity under discussion, the object entity of the relation under discussion, and the relation itself, before committing the additions to the database.
flushmethod is simply called as:
MassiveObjectStore in the
This store, available in the
dataio cube, allows us to
fully dispense with the CubicWeb import mechanisms and hence
to interact directly with the database server, via SQL queries.
Moreover, these queries rely on PostGreSQL’s
COPY FROM instruction
to create several entities in a single query. This brings tremendous
performance improvements with respect to the RQL-based data insertion
However, the API of this store is slightly different from the API of
the stores in CubicWeb’s
Before using the store, one has to import the
dataimport module, then initialize the store by giving it the
from cubicweb_dataio import dataimport as mcwdi ... store = mcwdi.MassiveObjectStore(session)
MassiveObjectStore provides six methods for inserting data
into the database:
init_rtype_table(SubjEtype, r_type, ObjEtype), which specifies the creation of the tables associated to the relation types in the database. Each such table has three column, the type of the subject entity, the type of the relation (that is, the name of the attribute in the subject entity which is defined via the relation), and the type of the object entity. For example:
store.init_rtype_table('Person', 'lives_in', 'Location')
Please note that these tables can be created before the entities, since they only specify their types, not their unique identifiers.
create_entity(Etype, **attributes), which allows us to add new entities, whose attributes are given in the
attributesdictionary. Please note however that, by default, this method does not return the created entity. The method is called, for example, as in:
store.create_entity('Person', name='Toto', age='18', height='190', uri='http://link/to/person/toto_18_190') store.create_entity('Location', town='Paris', arrondissement='13', uri='http://link/to/location/paris_13')
In order to be able to link these entities via the relations when needed, we must provide ourselves a means for uniquely identifying the entities. In general, this is done via URIs, stored in attributes like
cwuri. The name of the attribute is irrelevant as long as its value is unique for each entity.
relate_by_iid(subject_iid, r_type, object_iid)allows us to actually relate the entities uniquely identified by
object_iidvia a relation of type
r_type. For example:
store.relate_by_iid('http://link/to/person/toto_18_190', 'lives_in', 'http://link/to/location/paris_13')
Please note that this method does not work for inlined relations!
convert_relations(SubjEtype, r_type, ObjEtype, subj_iid_attribute, obj_iid_attribute)allows us to actually insert the relations in the database. At one call of this method, one inserts all the relations of type
rtypebetween entities of given types.
object_iid_attributeare the names of the attributes which store the unique identifiers of the entities, as assigned by the user. These names can be identical, as long as their values are unique. For example, for inserting all relations of type
Locationentities, we write:
store.convert_relations('Person', 'lives_in', 'Location', 'uri', 'uri')
flush()performs the actual commit in the database. It only needs to be called after
relate_by_iidcalls. Please note that
relate_by_iiddoes not perform insertions into the database, hence calling
flush()for it would have no effect.
cleanup()performs database cleanups, by removing temporary tables. It should only be called at the end of the import.
Application to the Diseasome data#
We define an import function,
diseasome_import, which does basically four things:
creates and initializes the store to be used, via a line such as:
store = cwdi.SQLGenObjectStore(session)
cwdiis the imported
calls the diseasome parser, that is, the
entities_from_rdffunction in the
diseasome_parsermodule and iterates on its result, in a line such as:
for entity, relations in parser.entities_from_rdf(filename, ('gene', 'disease')):
parseris the imported
filenameis the name of the file containing the data (with its path), e.g.
creates the entities to be inserted in the database; for Diseasome, there are two kinds of entities:
entities defined in the data model, viz.
Diseasein our case.
entities which are built in CubicWeb / Yams, viz.
ExternalUriwhich define URIs.
As we are working with RDF data, each entity is defined through a series of URIs. Hence, each “relational attribute” 4 of an entity is defined via an URI, that is, in CubicWeb terms, via an
ExternalUrientity. The entities are created, in the loop presented above, as such:
ent = store.create_entity(etype, **entity)
etypeis the appropriate entity type, either
- By “relational attribute” we denote an attribute (of an entity) which
is defined through a relation, e.g. the
Diseaseentities, which is defined through a relation between a
ExternalUrientities are as many as URIs in the data file. For them, we define a unique attribute,
uri, which holds the URI under discussion:
extu = store.create_entity('ExternalUri', uri="http://path/of/the/uri")
creates the relations between the entities. We have relations between:
entities defined in the schema, e.g. between
Geneentities, such as the
associated_genesrelation defined for
entities defined in the schema and
ExternalUrientities, such as
The way relations are added to the database depends on the store:
for the stores in the CubicWeb
dataimportmodule, we only use
store.relate, in another loop, on the relations (that is, a loop inside the preceding one, mentioned at step 2):
for rtype, rels in relations.iteritems(): ... store.relate(ent.eid(), rtype, extu.eid(), **kwargs)
kwargsis a dictionary designed to accommodate the need for specifying the type of the subject entity of the relation, when the relation is inlined and
SQLGenObjectStoreis used. For example:
... store.relate(ent.eid(), 'chromosomal_location', extu.eid(), subjtype='Disease')
dataimportmodule, the relations are created in three steps:
first, a table is created for each relation type, as in:
... store.init_rtype_table(ent.cw_etype, rtype, extu.cw_etype)
which comes down to lines such as:
store.init_rtype_table('Disease', 'associated_genes', 'Gene') store.init_rtype_table('Gene', 'gene_id', 'ExternalUri')
second, the URI of each entity will be used as its identifier, in the
relate_by_iidmethod, such as:
disease_uri = 'http://www4.wiwiss.fu-berlin.de/diseasome/resource/diseases/3' gene_uri = '<http://www4.wiwiss.fu-berlin.de/diseasome/resource/genes/HSD3B2' store.relate_by_iid(disease_uri, 'associated_genes', gene_uri)
third, the relations for each relation type will be added to the database, via the
convert_relationsmethod, such as in:
store.convert_relations('Disease', 'associated_genes', 'Gene', 'cwuri', 'cwuri')
store.convert_relations('Gene', 'hgnc_id', 'ExternalUri', 'cwuri', 'uri')
uriare the attributes which store the URIs of the entities defined in the data model, and of the
flushes all relations and entities:
which performs the actual commit of the inserted entities and relations in the database.
MassiveObjectStore is used, then a cleanup of temporary SQL tables should be performed
at the end of the import:
In order to time the import script, we just decorate the import function with the
from logilab.common.decorators import timed ... @timed def diseasome_import(session, filename): ...
After running the import function as shown in the “Importing the data” section, we obtain two time measurements:
diseasome_import clock: ... / time: ...
Here, the meanings of these measurements are 5:
clockis the time spent by CubicWeb, on the server side (i.e. hooks and data pre- / post-processing on SQL queries),
timeis the sum between
clockand the time spent in PostGreSQL.
The meanings of the
timemeasurements, when using the
@timeddecorators, were taken from a blog post on massive data import in CubicWeb.
The import function is put in an import module, named
diseasome_import here. The module is called
directly from the CubicWeb shell, as follows:
cubicweb-ctl shell diseasome_instance diseasome_import.py \ -- -df diseasome_import_file.nt -st StoreName
The module accepts two arguments:
the data file, introduced by
-df [--datafile], and
the store, introduced by
The timings (in seconds) for different stores are given in the following table, for
Disease entities and 3919
Gene entities with the import module
CubicWeb time (clock)
PostGreSQL time (time - clock)
In this tutorial we have seen how to import data in a CubicWeb application instance. We have first seen how to create a schema, then how to create a parser of the data and a mapping of the data to the schema. Finally, we have seen four ways of importing data into CubicWeb.
Three of those are integrated into CubicWeb, namely the
SQLGenObjectStore stores, which have a common API:
RQLObjectStoreis by far the slowest, especially its time spent on the CubicWeb side, and so it should be used only for small amounts of “sensitive” data (i.e. where security is a concern).
NoHookRQLObjectStoreslashes by almost four the time spent on the CubicWeb side, but is also quite slow; on the PostGres side it is as slow as the previous store. It should be used for data where security is not a concern, but consistency (with the data model) is.
SQLGenObjectStoreslashes by three the time spent on the CubicWeb side and by five the time spent on the PostGreSQL side. It should be used for relatively great amounts of data, where security and data consistency are not a concern. Compared to the previous store, it has the disadvantage that, for inlined relations, we must specify their subjects’ types.
For really huge amounts of data there is a fourth store,
dataio cube. It provides a blazing performance with respect to all other stores:
it is almost 25 times faster than
RQLObjectStore and almost three times faster than
SQLGenObjectStore. However, it has a few usage caveats that should be taken into account:
it cannot insert relations defined as inlined in the schema,
no security or consistency check is performed on the data,
its API is slightly different from the other stores.
Hence, this store should be used when security and data consistency are not a concern, and there are no inlined relations in the schema.