Note : Les descriptions sont présentées dans la langue officielle dans laquelle elles ont été soumises.
CA 03176758 2022-09-23
METHOD AND APPARATUS FOR INTRODUCING DATA TO A GRAPH DATABASE
BACKGROUND OF THE INVENTION
Technical Field
[0001] The present invention relates to the technical field of data
processing, and more
particularly to a method and an apparatus for introducing data to a graph
database.
Description of Related Art
[0002] Spark is a data processing technique based on clusters and memory. It
is able to process
massive data when having plural machines assembled together, and can be
integrated with
graph computing frameworks to compute data. Spark can not only be integrated
in
different ways but also pre-process data (through aggregation, filtration, and
conversion)
and introduce the pre-processed data into graph databases.
[0003] In the prior art, there are several ways to introduce data into graph
databases, including
compiling create languages, loading CSV languages, and using a batch inserter,
batch
import, and Neo4j -import. Except for create languages, all of these ways have
one thing
in common that they require the file of interest to be converted into the csv
format, which
is a trouble in real-world production- and development-related environments.
For
example, in a production-related environment, data are usually confidential
and thus it is
not feasible to most companies to export data from the production-related
environment
into a csv file, and this approach does not support real-time insertion.
Furthermore, this
is particularly impractical for massive data. As to the latter three, they are
incapable of
real-time introduction. To be specific, introduction of data requires
deactivation of a neo4j
(a type of graph databases) server, and thus is inherently impossible to be
real-time.
[0004] Hence, how to introduce data into a graph database with increased speed
is crucial to
construction of graphs, and is a pressing issue to address in the art.
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SUMMARY OF THE INVENTION
[0005] For addressing the issues of the prior art, embodiments of the present
invention provide
a method and an apparatus for introducing data to a graph database, which
overcome the
problems of the prior art such as the necessity of converting data into the
csv format
before the data can be introduced into a graph database and the incapability
of adjusting
the speed of data introduction.
[0006] To solve the foregoing one or more technical problems, the present
invention adopts the
following technical schemes.
[0007] In one aspect, the present invention provides a method for introducing
data to a graph
database. The method comprising the following steps:
[0008] registering a user-defined spark udf to a graph database program, so
that the graph
database is connected to a spark resource through the spark udf;
[0009] creating node attribute indexes in the graph database;
[0010] using the spark resource to make enquiry to a hive database to acquire
enquiry-generated
data;
[0011] re-partitioning the enquiry-generated data and registering them as a
temporary data table;
and
[0012] introducing the temporary data table to the graph database using the
spark udf and the
node attribute indexes.
[0013] Further, before the step of registering a user-defined spark udf to a
graph database
program, the method further comprises:
[0014] setting up a driver in the spark udf for connecting the graph database
and writing it in a
static method.
[0015] Further, before the step of registering a user-defined spark udf to a
graph database
program the method further comprises:
[0016] defining parameters for exporting and importing the spark udf.
[0017] Further, after the step of introducing the temporary data table to the
graph database the
method further comprises:
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[0018] turning off the driver of the graph database and the spark resource.
[0019] Further, the step of using the spark resource to make enquiry to a hive
database comprises:
[0020] using a reduce operator of the spark resource to perform corresponding
computation on
the enquiry-generated data.
[0021] In another aspect, the present invention provides an apparatus for
introducing data to a
graph database. The apparatus comprises:
[0022] a connecting module, for registering a user-defined spark udf to a
graph database program,
so that the graph database is connected to a spark resource through the spark
udf;
[0023] a creating module, for creating node attribute indexes in the graph
database;
[0024] an enquiring module, for using the spark resource to make enquiry to a
hive database to
acquire enquiry-generated data;
[0025] a partitioning module, for re-partitioning the enquiry-generated data
and registering them
as a temporary data table; and
[0026] an introducing module, for introducing the temporary data table to the
graph database
using the spark udf and the node attribute indexes.
[0027] Further, the apparatus further comprises:
[0028] a driver connecting module, for setting up a driver in the spark udf
for connecting the
graph database and writing it in a static method.
[0029] Further, the apparatus further comprises:
[0030] a configuring module, for defining parameters for exporting and
importing the spark udf.
[0031] Further, the apparatus further comprises:
[0032] a deactivating module, for turning off the driver of the graph database
and the spark
resource
[0033] Further, the step of using the spark resource to make enquiry to a hive
database comprises:
[0034] using a reduce operator of the spark resource to perform corresponding
computation on
the enquiry-generated data.
[0035] The technical schemes of the embodiments of the present invention
provide the following
beneficial effects:
[0036] 1. The method and apparatus for introducing data to a graph database of
the embodiments
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of the present invention use the combination of spark and graph databases to
realize real-
time introduction of data while eliminating the need of exporting data into
the csv format;
[0037] 2. The method and apparatus for introducing data to a graph database of
the embodiments
of the present invention use the spark technology to achieve easy optimization
of spark
performance and adjustment of speed of data introduction; and
[0038] 3. The method and apparatus for introducing data to a graph database of
the embodiments
of the present invention use the feature of spark about concurrency to
accelerate
introduction of data without data loss.
BRIEF DESCRIPTION OF THE DRAWINGS
[0039] To better illustrate the technical schemes as disclosed in the
embodiments of the present
invention, accompanying drawings referred in the description of the
embodiments below
are introduced briefly. It is apparent that the accompanying drawings as
recited in the
following description merely provide a part of possible embodiments of the
present
invention, and people of ordinary skill in the art would be able to obtain
more drawings
according to those provided herein without paying creative efforts, wherein:
[0040] FIG. 1 is a flowchart of a method for introducing data to a graph
database according to
one exemplificative embodiment; and
[0041] FIG. 2 is a structural diagram of an apparatus for introducing data to
a graph database
according to one exemplificative embodiment.
DETAILED DESCRIPTION OF THE INVENTION
[0042] To make the foregoing objectives, features, and advantages of the
present invention
clearer and more understandable, the following description will be directed to
some
embodiments as depicted in the accompanying drawings to detail the technical
schemes
disclosed in these embodiments. It is, however, to be understood that the
embodiments
referred herein are only a part of all possible embodiments and thus not
exhaustive. Based
on the embodiments of the present invention, all the other embodiments can be
conceived
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without creative labor by people of ordinary skill in the art, and all these
and other
embodiments shall be encompassed in the scope of the present invention.
[0043] FIG. 1 is a flowchart of a method for introducing data to a graph
database according to
one exemplificative embodiment. As shown, the method comprises the following
steps.
[0044] The step Si involves registering a user-defined spark udf to a graph
database program, so
that the graph database is connected to a spark resource through the spark
udf.
[0045] Specifically, in the embodiment of the present invention, with the
spark udf defined by
the user, the graph database and the spark resource can be combined (i.e.,
having
connection between the graph database and the spark resource), so that the
data can be
introduced into the graph database in a real-time manner without having to
exporting the
data into the csv format. To compile the user-defined spark udf, development
can be
implemented using the Java language or other computer-programming languages.
Additionally, the compiled, user-defined spark udf must be registered in the
graph
database program first because in Java-based methods, only registered user-
defined udf
can be used. It is to be noted that, in the embodiment of the present
invention, the use of
the spark udf facilitates optimization of spark performance by, for example,
deciding how
many portions are the data enquired from hive re-partitioned, how to set spark
concurrency, how many computing nodes (executors) to be assigned to a spark
task, how
much memory space to be assigned to each executor, how many cores to be set
for one
executor, and how much memory space to be assigned to the driver.
[0046] It is to be noted that the Spark udf is such set that when the
functions provided by spark
are unable to satisfy user needs, the user-defined function can be used to
realize its own
business logic. Following is an example:
[0047] public class
CreateACCTOBANKCARD2
implements
UDF5<String,String,Integer,String,String,String>,
Serializable {
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@Override
public String call(final String pay acct no, final String ttl amt, final
Integer
ttl times, final String latest_pay time, final String rcvr user) throws
Exception {
[0048] //This udf has five input parameters and one output parameter
//Its own function works here
return "1";
1
}
[0049] S2 is about creating node attribute indexes in the graph database.
[0050] Specifically, for introduction of massive data, in order to prevent
duplication in terms of
node and relationship and to ensure fast search, in the embodiment of the
present
invention, node attribute indexes is created in the graph database to provide
every node
in the graph database with an attribute index. Without the attribute indexes,
data insertion
can significantly slow down.
[0051] For example:
[0052] private static Driver driver = null;
static {
driver = OperateNeo4j.connectNeo4j(Constants.url, Constants.neo4jUser,
Constants.neo4jPassword);
}
Session session = driver.session();
//The identity card number is taken as the index
session.run("create index on :IDNTY NMBR(Idnty Nmbr)");
//The account number is taken as the index
session.run("create index on :ACCT NMBR(Acct No)").
[0053] S3 involves using the spark resource to make enquiry to a hive database
to acquire
enquiry-generated data.
[0054] Specifically, the embodiment of the present invention is about
introducing the data of the
hive table into the graph database. To introduce the data of the hive table
into the graph
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database, the hive database can be first enquired using the spark resource
(specifically,
by using the compiled spark sql language to find out the data from the hive
database), so
as to acquire the enquiry-generated data.
[0055] S4 involves re-partitioning the enquiry-generated data and registering
them as a
temporary data table.
[0056] Specifically, in the embodiment of the present invention, the data got
from the hive
database using the spark sql are repartitioned. When doing computation for a
resilient
distributed dataset (RDD), spark initiates a task for every partition, so the
number of the
partitions of the RDD determines the total number of the tasks. In this way,
with
optimization of spark performance, the total number of the tasks can be set by
setting
the number of the partitions of the RDD. By setting the number of the required
computing nodes (executors) and the number of cores in every computing nide,
these
tasks can be executed concurrently at the same time, so as to accelerate
introduction of
data into the graph database. It is to be noted that an RDD, or a resilient
distributed
dataset, is the basic data abstract in Spark. It represents an immutable,
partitionable set
in which elements can be computed concurrently. An RDD has the features of a
data
flow model, including automatic failover, location-aware scheduling, and
scalability. An
RDD allows users to explicitly cache data in the memory during multiple
enquires, so
that the subsequent enquires can reuse these data. This significantly improves
speed of
enquires. In addition, since Spark features concurrent computing, each task
can execute
a part of the whole data without data loss.
[0057] It is to be noted that, in the embodiment of the present invention, the
reason of setting
the partitions is that, without the partitions, the number of partitions of
the enquiry
results would be the same as the number of partitions it the hive table, so
the
concurrency level, and in turn the number of tasks executed concurrently,
could not be
enhanced. With the re-partitioning step, the number of tasks executed
concurrently can
be increased, thereby accelerating execution.
[0058] S5 involves introducing the temporary data table to the graph database
using the spark
udf and the node attribute indexes.
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[0059] Specifically, by having the foregoing user-defined spark udf, together
with the node
attribute indexes created in the graph database, the present invention can
have the
temporary data table introduced into the graph database. In the embodiment of
the present
invention, since the hive database is connected through Spark, data can be
introduced into
the graph database directly, without having to exporting the data into a csv
file, and real-
time insertion can be achieved.
[0060] As a preferred implementation, in the embodiment of the present
invention, before the
step of registering a user-defined spark udf to a graph database program, the
method
further comprises:
[0061] setting up a driver in the spark udf for connecting the graph database
and writing it in a
static method.
[0062] Specifically, in the embodiment of the present invention, the driver in
the user-defined
spark udf for connecting the graph database (e.g., neo4j) must be written in
the static
method. This is to reduce the times of connection between the spark udf and
the graph
database connecting, thereby reducing resource consumption.
[0063] As a preferred implementation, in the embodiment of the present
invention, before the
step of registering a user-defined spark udf to a graph database program the
method
further comprises:
[0064] defining parameters for exporting and importing the spark udf.
[0065] Specifically, in the embodiment of the present invention, the input and
output parameters
of the spark udf must be defined. In other words, it is necessary to well
define the number
and the types of the parameters, the type of the output parameters, and that
the main return
value cannot be null.
[0066] As a preferred implementation, in the embodiment of the present
invention, after the step
of introducing the temporary data table to the graph database the method
further
comprises:
[0067] turning off the driver of the graph database and the spark resource.
[0068] Specifically, after the temporary data table is introduced into the
graph database, the
driver of the graph database and the spark resource need to be turn off to
save resources.
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[0069] As a preferred implementation, in the embodiment of the present
invention, the step of
using the spark resource to make enquiry to a hive database comprises:
[0070] using a reduce operator of the spark resource to perform corresponding
computation on
the enquiry-generated data.
[0071] Specifically, an action operator of spark is requisite for triggering
execution of spark
because only action operators can execute computation. As to selection of the
operator,
in the embodiment of the present invention, among action operators, the reduce
operator
is used rather than other operators such as collect and show. This is because
the other
operators like collect and show can have negative impact on performance, and
the show
operator is unable to compute all data. In other words, in the embodiment of
the present
invention, the reduce operator is used to trigger execution of spark with
accelerated data
while eliminating the risk of data loss.
[0072] FIG. 2 is a structural diagram of an apparatus for introducing data to
a graph database
according to one exemplificative embodiment. As shown, the apparatus
comprises:
[0073] a connecting module, for registering a user-defined spark udf to a
graph database program,
so that the graph database is connected to a spark resource through the spark
udf;
[0074] a creating module, for creating node attribute indexes in the graph
database;
[0075] an enquiring module, for using the spark resource to make enquiry to a
hive database to
acquire enquiry-generated data;
[0076] a partitioning module, for re-partitioning the enquiry-generated data
and registering them
as a temporary data table; and
[0077] an introducing module, for introducing the temporary data table to the
graph database
using the spark udf and the node attribute indexes.
[0078] As a preferred implementation, in the embodiment of the present
invention, the apparatus
further comprises:
[0079] a driver connecting module, for setting up a driver in the spark udf
for connecting the
graph database and writing it in a static method.
[0080] As a preferred implementation, in the embodiment of the present
invention, the apparatus
further comprises:
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[0081] a configuring module, for defining parameters for exporting and
importing the spark udf.
[0082] As a preferred implementation, in the embodiment of the present
invention, the apparatus
further comprises:
[0083] a deactivating module, for turning off the driver of the graph database
and the resource.
[0084] As a preferred implementation, in the embodiment of the present
invention, the step of
using the spark resource to make enquiry to a hive database comprises:
[0085] using a reduce operator of the spark resource to perform corresponding
computation on
the enquiry-generated data.
[0086] To sum up, the technical schemes of the embodiments of the present
invention provide
the following beneficial effects:
[0087] 1. The method and apparatus for introducing data to a graph database of
the embodiments
of the present invention use the combination of spark and graph databases to
realize real-
time introduction of data while eliminating the need of exporting data into
the csv format;
[0088] 2. The method and apparatus for introducing data to a graph database of
the embodiments
of the present invention use the spark technology to achieve easy optimization
of spark
performance and adjustment of speed of data introduction; and
[0089] 3. The method and apparatus for introducing data to a graph database of
the embodiments
of the present invention use the feature of spark about concurrency to
accelerate
introduction of data without data loss.
[0090] It is to be noted that work division among the foregoing functional
modules for the order-
based phoning system of the present embodiment to implement delivery is merely
exemplary. In practical implementations, the work division may be made among
different
functional modules. In other words, the internal architecture of the order-
based phoning
system may be reconfigured with different functional modules to perform all or
a part of
the functions as described previously. In addition, since the order-based
phoning system
of the present embodiment and the disclosed order-based phoning method in the
previous
embodiment stem from the same conception, the details of its implementation
can be
learned from the description made to the method of the previous embodiment,
and no
repetition is made herein.
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[0091] As will be appreciated by people of ordinary skill in the art,
implementation of all or a
part of the steps of the method of the present invention as described
previously may be
realized by having a program instruct related hardware components. The program
may
be stored in a computer-readable storage medium, and the program is about
performing
the individual steps of the methods described in the foregoing embodiments.
The storage
medium may be a ROM/RAM, a hard drive, an optical disk, or the like.
[0092] The preferred embodiments of the present invention described previously
are not intended
to limit the present invention. Any modification, equivalent replacement, and
improvement made under the spirit and principle of the present invention shall
be
included in the scope of the present invention.
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