Note: Descriptions are shown in the official language in which they were submitted.
METHOD, DEVICE AND COMPUTER EQUIPMENT FOR IMPROVING USER
PORTRAIT INTERFACE PERFORMANCE
Technical Field
[0001] The present disclosure relates to the technical field of user
portrait, particularly
to a method, apparatus, computer device, and storage medium for improving the
performance of a user poi __ lt ait interface.
Background
[0002] With the development and progress of the society, building user
portraits is
getting more important, user portraits can be used to truly reflects the
user's behavior
trajectory, habit characteristics and service needs and so on in a demonstrate
multi-
dimensional view of data, and enable various fields to improve the capability
of
services, and provide necessary technical support for data mining in the data
analysis.
[0003] Based on user portraits, the requirement of the performance of a
user portrait
interface by an operating party also appeared, currently, Elasticsearc as a
search engine,
or offline tasks tool such as spark as data process tool is applied to achieve
the
requirement of the performance of the user portrait interface.
[0004] However, as the requirement of the performance of the user
portrait interface by
clients is increased, the response to user's requests within a time limit is
required. And
Elasticsearc as the search engine has its own limits, the user portrait
interface is often
times out when large data is aggregated, therefore, the search engine are not
able to
meet the requirement by the operating party and have negative user
experiences.
Invention Content
[0005] Based on this, it is necessary to address the above technical
problems and
provide a method, apparatus, computer device and storage medium for improving
the
performance of a user portrait interface.
1
Date Regue/Date Received 2023-02-21
[0006] On one hand, a method for improving the performance of a user
portrait
interface, comprises:
[0007] Preprocessing label data according to a set task instance;
wherein the label data
includes poi ____ halt label data;
[0008] Obtaining portrait label information selected by a user, and
generating a
conditional query statement according to the portrait label information;
[0009] Distributing the conditional query statement randomly to any
machine of query
nodes;
[0010] Obtaining aggregated data from the machine of query nodes
according to the
conditional query statement, wherein the aggregated data is obtained on a
basis of
portrait label data from a plurality of sharding query nodes machines; and
[0011] Returning the aggregated data to a client teitninal for
displaying to the user.
[0012] In an embodiment, wherein preprocessing label data according to a
set task
instance, comprises:
[0013] Grouping by coding the label data with a pre-set rule according
to the set task
instance, to obtain a plurality of coded data groups;
[0014] According to the plurality of the coded data groups, generating a
plurality of
files with set formats corresponding to the label data;
[0015] Inserting each file with the set format into a corresponding
local engine table;
and
2
Date Regue/Date Received 2023-02-21
[0016] Obtaining the portrait label data from the local engine table and
importing a
corresponding correlation engine table; wherein the correlation engine table
is on the
machine of sharding query nodes.
[0017] In an embodiment, wherein the pre-set rule also comprises:
[0018] Coding the label data according to a size order of the label data
from minimum
to maximum for obtaining the coded data; and
[0019] Grouping the coded data in a manner of a modulo sharding to
obtain the plurality
of coded data groups.
[0020] In an embodiment, wherein, inserting each file with the set
format into the
corresponding local engine table, comprises:
[0021] Inserting each file with the set format into a corresponding
blank local engine
table; and
[0022] Inserting each file with the set format of the corresponding
blank local engine
table into the corresponding local engine table in a manner of a materialized
view.
[0023] In an embodiment, wherein, distributing the conditional query
statement
randomly to any machine of query nodes, comprises:
[0024] Distributing the conditional query statement randomly in a manner
of a random
distribution of Virtual Internet Protocol address (VIP) to any machine of
query nodes.
[0025] In an embodiment, wherein obtaining aggregated data from the
machine of query
nodes according to the conditional query statement, wherein the aggregated
data is
obtained on a basis of portrait label data from a plurality of sharding query
nodes
3
Date Regue/Date Received 2023-02-21
machines, comprises:
[0026] According to both the conditional query statement and each
portrait label data
from each machine of sharding query nodes, obtaining aggregated data of the
portrait
label data from each machine of sharding query nodes by calculating bitmap
functions.
[0027] Wherein the aggregated data is obtained on a basis of the
aggregated data of
portrait label data from each machine of sharding query nodes.
[0028] In an embodiment, wherein method further comprises:
[0029] Associating each corresponding correlation engine table with a
distributed table;
wherein the distributed table is on the machine of sharding query nodes.
[0030] Wherein the aggregated data is obtained on the distributed table
on a basis of the
aggregated data of portrait label data from each corresponding correlation
engine table.
[0031] In another aspect, an apparatus for improving the performance of
a user portrait
interface is provided, wherein the apparatus comprises:
[0032] A data processing module configured to preprocess label data
according to a set
task instance; wherein the label data includes portrait label data;
[0033] An obtaining module configured to obtain portrait label
information selected by
a user, and generating a conditional query statement according to the portrait
label
information;
[0034] A random distribution module configured to distribute the
conditional query
statement randomly to any machine of query nodes;
4
Date Regue/Date Received 2023-02-21
[0035] An aggregating module configured to obtain aggregated data from
the machine
of query nodes according to the conditional query statement, wherein the
aggregated
data is obtained on a basis of portrait label data from a plurality of
sharding query nodes
machines; and
[0036] A data display module configured to return the aggregated data to
a client
teiiiiinal for displaying to the user.
[0037] On the other hand, a computer device, including a memory, a
processor and a
computer program stored in the memory and run on the processor configured to
achieve
following steps when the processor executes the computer program:
[0038] Preprocessing label data according to a set task instance;
wherein the label data
includes poi ____ Li ait label data;
[0039] Obtaining portrait label information selected by a user, and
generating a
conditional query statement according to the portrait label infoimation;
[0040] Distributing the conditional query statement randomly to any
machine of query
nodes;
[0041] Obtaining aggregated data from the machine of query nodes
according to the
conditional query statement, wherein the aggregated data is obtained on a
basis of
portrait label data from a plurality of sharding query nodes machines; and
[0042] Returning the aggregated data to a client teiiiiinal for
displaying to the user.
[0043] In another aspect, a computer readable storage medium stored with
a computer
program configured to achieve following steps when the processor executes the
computer program:
Date Regue/Date Received 2023-02-21
[0044] Preprocessing label data according to a set task instance;
wherein the label data
includes portrait label data;
[0045] Obtaining portrait label infounation selected by a user, and
generating a
conditional query statement according to the portrait label information;
[0046] Distributing the conditional query statement randomly to any
machine of query
nodes;
[0047] Obtaining aggregated data from the machine of query nodes
according to the
conditional query statement, wherein the aggregated data is obtained on a
basis of
portrait label data from a plurality of sharding query nodes machines; and
[0048] Returning the aggregated data to a client terminal for displaying
to the user.
[0049] The above-mentioned method, apparatus, computer device, and
storage medium
for improving the performance of a user portrait interface. Firstly,
preprocessing label
data according to a set task instance; wherein the label data includes
portrait label data;
Then, obtaining portrait label information selected by a user, generating a
conditional
query statement according to the portrait label information; distributing the
conditional
query statement randomly to any machine of query nodes; Finally, obtaining
aggregated
data from the machine of query nodes according to the conditional query
statement,
wherein the aggregated data is obtained on a basis of poitiait label data from
a plurality
of sharding query nodes machines; and returning the aggregated data to a
client terminal
for displaying. Each machine of sharding query nodes needs the portrait label
data that
meet the conditional query statement only, and to perform the data
aggregation, then to
return the aggregation results to the client teiminal. The above method can
fully use the
capability of parallel computing in a distributed system and improve the
computing
speed to further improve the performance of a user portrait interface.
6
Date Regue/Date Received 2023-02-21
Drawing Description
[0050] Figure 1 is an application environment diagram of a method for
improving the
performance of a user portrait interface in an embodiment;
[0051] Figure 2 is a flowchart of the method for improving the
performance of a user
portrait interface in an embodiment;
[0052] Figure 3 is a process diagram of Step 202 of a method for
improving the
performance of a user portrait interface in an embodiment;
[0053] Figure 4 is a structural diagram of an apparatus for improving
the performance
of a user portrait interface in an embodiment;
[0054] Figure 5 is an internal structural diagram of a computer device
in an embodiment.
Specific embodiment methods
[0055] In order to make clearer application purposes, technical solutions, and
advantages,
the present disclosure is further explained in detail with a particular
embodiment thereof,
and with reference to the drawings. It shall be appreciated that these
descriptions are only
intended to be illustrative, but not to limit the scope of the disclosure
thereto.
[0056] The present application provides a method for improving the performance
of a
user portrait interface and applies the method in an application environment
as
shown in Figure 1. In Figure 1, wherein, a client terminal 102 communicates
with
a server 104 through network. The server 104 preprocesses label data according
to
a set task instance; wherein the label data includes portrait label data;
Then,
obtains portrait label information selected by a user, generates a conditional
query
statement according to the portrait label information; distributes randomly
the
7
Date Regue/Date Received 2023-02-21
conditional query statement to any machine of query nodes; the server 104
obtains
aggregated data from the machine of query nodes according to the conditional
query statement, wherein the aggregated data is obtained on a basis of
portrait
label data from a plurality of sharding query nodes machines; and returns the
aggregated data to the client terminal 102 for displaying. Wherein, the client
terminal 102 can be but not limited to various personal computer, laptop,
smart
phone, tablet computer, portable wearable device or sub-server, server 201 can
be
an independent server or a server cluster composed of a plurality of servers
or
cloud computing platform to achieve.
[0057] In an embodiment, as shown in Figure 2, a method for improving the
performance
of a user portrait interface is provided, as an example of the client terminal
in Figure 1,
comprising following steps:
[0058] Step 202, preprocessing label data according to a set task instance;
wherein
the label data includes portrait label data.
[0059] Wherein the task instance is set by ClickHouse system (a column-
oriented
SQL database management system (DBMS) for online analytical processing
(OLAP)), the task instance refers to a Spark (computing engine) task; the
label
data refers to data described characteristics of an operating entity. The
characteristics of the operating entity is described by a plurality of labels
for
reflecting the operating entity in multi-dimensions; wherein the label data
includes
portrait label data and other label data, wherein the portrait label data is
portrait
field data.
[0060] Specifically, according to the set Spark task, preprocessing the label
data,
wherein the label data preprocess is a process for storing the label data and
storing
the label data by category in various engine tables in ClickHouse system, it
is
convenient to call and obtain in a later stage.
8
Date Regue/Date Received 2023-02-21
[0061] Step 204, obtaining portrait label information selected by a user, and
generating a conditional query statement according to the portrait label
info rmati on.
[0062] Specifically, the portrait label information selected by the user is
information
of portrait interface, the structured query statement is generated according
to the
information of portrait interface selected by the suer, then the structured
query
statement is transferred to the conditional query statement, wherein the
conditional
query statement is a statement for querying data in the column-oriented
database
management system directly.
[0063] Step 206, distributing the conditional query statement randomly to any
machine of query nodes.
[0064] Specifically, distributing the conditional query statement randomly to
any
machine of query nodes. Wherein the machines of query nodes are a cluster, a
plurality of query nodes machines are set in the cluster, that is, a group of
mutually
independent computers interconnected through a high-speed network, which form
a group and are managed in a single system mode. The configuration of the
cluster
is used for improving availability and scalability. It is uncertain to which
machine
of query nodes is randomly distributed the conditional query statement.
[0065] Step 208, obtaining aggregated data from the machine of query nodes
according to the conditional query statement, wherein the aggregated data is
obtained on a basis of portrait label data from a plurality of sharding query
nodes
machines.
[0066] Specifically, obtaining aggregated data from the machine of query nodes
according to the conditional query statement. When the conditional query
9
Date Regue/Date Received 2023-02-21
statement has been randomly distributed any machine of query nodes, the
machine
of query nodes distributes the conditional query statement to a plurality of
machines of sharding query nodes, each machine of sharding query nodes
performs
a computing process based on the portrait label data stored on its own machine
only and return the result to the machine of query nodes. The machine of query
nodes receives results from various machines of sharding query nodes and
aggregates the results to obtain the aggregated data. Therefore, the
aggregated data
is obtained on a basis of portrait label data from a plurality of sharding
query nodes
machines.
[0067] Step 210, returning the aggregated data to a client terminal for
displaying to
the user.
[0068] Specifically, returning the aggregated data to a client terminal for
displaying
to the user, it is convenient to check out the results of the aggregated data
responding to the information of portrait label information selected by the
user.
[0069] The
above-mentioned method for improving the performance of a user portrait
interface. Firstly, preprocessing label data according to a set task instance;
wherein the
label data includes portrait label data; Then, obtaining portrait label
information
selected by a user, generating a conditional query statement according to the
portrait
label information; distributing the conditional query statement randomly to
any
machine of query nodes; Finally, obtaining aggregated data from the machine of
query
nodes according to the conditional query statement, wherein the aggregated
data is
obtained on a basis of portrait label data from a plurality of sharding query
nodes
machines; and returning the aggregated data to a client terminal for
displaying. Each
machine of sharding query nodes needs the portrait label data that meet the
conditional
query statement only, and to perform the data aggregation, then to return the
aggregation results to the client terminal. The above method can fully use the
capability
of parallel computing in a distributed system and improve the computing speed
to
Date Regue/Date Received 2023-02-21
further improve the performance of a user portrait interface.
[0070] In an embodiment, as shown in Figure 3, wherein preprocessing label
data
according to a set task instance, further comprises the following steps:
[0071] Step 2022, grouping by coding the label data with a pre-set rule
according to
the set task instance, to obtain a plurality of coded data groups.
[0072] Wherein the pre-set rule refers to coding the label data according to a
size
order of the label data from minimum to maximum for obtaining the coded data.
[0073] Specifically, in a manner of a modulo sharding, the rule is to divide
the coded
data into N parts, wherein the N is the number of query nodes plus 1, in other
words, the number of the machines of query nodes plus one, so that the coded
data
are uniformly distributed on each machine of sharding query nodes. The coded
data is obtained by a remainder for a specific value for determining the coded
data
should be arranged in which coded data group, the coded data with the same
remainder are arranged in a group, and the plurality of coded data groups is
obtained. For example, the label data is sorted from minimum to maximum to be
1-m, the number of the machines of sharding query nodes plus one is N, that
is,
the number of the coded data group is N groups, prior to the coded data
grouped
into each coded data group, 1-m of the coded data should have a remainder
after
division by value K, the coded data having the same reminder is grouped
together,
then the plurality of coded data groups are obtained.
[0074] Step 2024, according to the plurality of the coded data groups,
generating a
plurality of files with set formats corresponding to the label data.
[0075] Specifically, according to the plurality of the coded data groups,
generating
a plurality of files with set formats corresponding to the label data, each
coded
11
Date Regue/Date Received 2023-02-21
data group generates a file with a set format, wherein the set format is HDFS
(Hadoop Distributed File System) file.
[0076] Step 2026, inserting each file with the set format into a corresponding
local
engine table.
[0077] Specifically, as a plurality of files with the set formats are
generated, each
file with the set format is inserted into the corresponding local engine
table,
wherein the local engine table is a built-in engine table in ClickHouse.
[0078] Step 2028, obtaining the portrait label data from the local engine
table, and
importing a corresponding correlation engine table; wherein the correlation
engine
table is on the machine of sharding query nodes.
[0079] Specifically, obtaining the portrait label data from the local engine
table,
wherein the files with the set formats stored in each local engine table
includes
both portrait label data and other label data. The portrait label data is
classified as
hot data, which refers to online data accessed by the computing nodes
frequently,
the hot data is often used to be computed nearby since the access of the hot
data
in a great need and required a highly efficiency. Other label data is
classified as
cold data, wherein the cold data refers to offline data accessed rarely, and
the cold
data is stored centrally, has a lower access frequency and a lower efficiency,
therefore the cold data can be configured centrally. The hot data is the
portrait
label data stored in each local engine table in the present application, and
the
portrait label data obtained from each local engine table is imported into the
corresponding correlation engine table. Wherein the correlation engine table
is on
the machine of sharding query nodes, each correlation engine table in each
machine of sharding query nodes stores the corresponding portrait label
information, it is convenient to obtain in a later stage, and reduce loading
time for
data.
12
Date Regue/Date Received 2023-02-21
[0080] The above-mentioned manner of sharding for storing, which divides the
label
data into the plurality of coded data groups according to a preset rule, then
respectively store the plurality of coded data groups into various machines of
sharding query nodes, to reduce the access pressure of each machine of
sharding
query nodes and to improve the performance of the entire database system.
[0081] In an embodiment, wherein, inserting each file with the set format into
the
corresponding local engine table, further comprises the following steps:
[0082] Inserting each file with the set format into a corresponding blank
local engine
table; and
[0083] Inserting each file with the set format of the corresponding blank
local engine
table into the corresponding local engine table in a manner of a materialized
view.
[0084] Wherein the blank local engine table is a built-in engine table in
ClickHouse.
[0085] Specifically, inserting each file with the set founat into a
corresponding
blank local engine table; and inserting each file with the set format of the
corresponding blank local engine table into the corresponding local engine
table
in a manner of a materialized view. The materialized view is a database object
including a query result, which is a local copy of the remote data, or which
is used
to generating a summary table based on a data table summation. The storage of
the
materialized view is based on data of a remote table, also named as snapshots.
This
method enable each file with the set format in the bland local engine table to
be
inserted into the corresponding local engine table, to increase the storing
speed.
[0086] In an embodiment, wherein, distributing the conditional query statement
randomly to any machine of query nodes, further comprises the following steps:
13
Date Regue/Date Received 2023-02-21
[0087] Distributing the conditional query statement randomly in a manner of a
random distribution of Virtual Internet Protocol address (VIP) to any machine
of
query nodes.
[0088] Wherein the distribution of VIP refers to the tasks assigned to
multiple
operating units in a balance manner. As we know, the general IP address is
bound
with a physical network interface card while the VIP is not bound with a
physical
network interface. When a computer in an outer network access to an internal
network of a company via a domain, a DNS (The Domain Name System) server in
the internal network resolves the domain name to a VIP. When the computer in
the outer network obtains the VIP and sends the data packet to the VIP. But
the
VIP is not connected with any physical devices in the internal network, which
is
implemented via ARP (Address Resolution Protocol). In other words, the MAC
address (Media Access Control) mapped in the VIP is controllable. The VIP can
be dynamically mapped in different MAC addresses in the internal network,
which
means to be mapped in different devices for load balancing.
[0089] Specifically, by applying the step of distributing the conditional
query
statement randomly in a manner of a random distribution of Virtual Internet
Protocol address (VIP) to any machine of query nodes, each machine of query
nodes can be balanced, to balance the workload of each machine of query nodes.
[0090] In an embodiment, wherein obtaining aggregated data from the machine of
query nodes according to the conditional query statement, wherein the
aggregated
data is obtained on a basis of portrait label data from a plurality of
sharding query
nodes machines, further comprises the following steps:
[0091] According to both the conditional query statement and each portrait
label
data from each machine of sharding query nodes, obtaining aggregated data of
the
14
Date Regue/Date Received 2023-02-21
portrait label data from each machine of sharding query nodes by calculating
bitmap functions.
[0092] Wherein the aggregated data is obtained on a basis of the aggregated
data of
portrait label data from each machine of sharding query nodes.
[0093] Specifically, according to both the conditional query statement and
each
portrait label data from each machine of sharding query nodes, obtaining
aggregated data of the portrait label data from each machine of sharding query
nodes by calculating bitmap functions. Each machine of sharding query nodes
performs aggregation computing based on the portrait label data stored on its
own
machine only via bitmap functions, to obtain the aggregated data of portrait
label
data, and the aggregated data is obtained on a basis of the aggregated data of
portrait label data from each machine of sharding query nodes to improve the
entire query speed.
[0094] In an embodiment, wherein method further comprises the following steps:
[0095] Associating each corresponding correlation engine table with a
distributed
table; wherein the distributed table is on the machine of sharding query
nodes.
[0096] Wherein the aggregated data is obtained on the distributed table on a
basis of
the aggregated data of portrait label data from each corresponding correlation
engine table.
[0097] Wherein the distributed table is a built-in engine table in ClickHouse.
[0098] Specifically, associating each corresponding correlation engine table
with a
distributed table; wherein the distributed table is on the machine of sharding
query
nodes.
Date Regue/Date Received 2023-02-21
[0099] When the aggregation operation is executed by the machine of query
nodes,
the aggregated data of portrait label data associated with each correlation
engine
table is aggregated on the distributed table, to obtain aggregated data and
achieve
the aggregation operation.
[0100] What
should be understood is although the steps of the process diagram of
Figures 2-3 are shown in sequence as indicated by the arrows, these steps are
not
necessarily executed in the order indicated by the arrows. Unless explicitly
provided
instruction in this article, there is no strict order in which these steps can
be performed,
and they can be performed in any other orders. In addition, at least partial
steps of
Figures 2-3 can include more sub steps or multiple stages, these sub steps or
stages are
not necessarily completed at the same time but can be executed in different
time, the
execution order of these sub steps or stages is also not necessarily in
sequence order but
can be performed alternately with the other steps or sub steps of other steps
or at least
one part of the other stages.
[0101] In an embodiment, as shown in Figure 4, an apparatus for improving the
performance of a user portrait interface is provided, wherein the apparatus
comprises: a data processing module 302, an obtaining module 304, a random
distribution module 306, an aggregating module 308 and a data display module
310.
[0102] A data processing module 302 is configured to preprocess label data
according to a set task instance; wherein the label data includes portrait
label data;
[0103] An obtaining module 304 is configured to obtain portrait label
information
selected by a user, and generating a conditional query statement according to
the
portrait label infoimation;
16
Date Regue/Date Received 2023-02-21
[0104] A random distribution module 306 is configured to distribute the
conditional
query statement randomly to any machine of query nodes;
[0105] An aggregating module 308 is configured to obtain aggregated data from
the
machine of query nodes according to the conditional query statement, wherein
the
aggregated data is obtained on a basis of portrait label data from a plurality
of
sharding query nodes machines; and
[0106] A data display module 310 is configured to return the aggregated data
to a
client terminal for displaying to the user.
[0107] In an embodiment, the data processing module 302 is further configured
to:
[0108] Group by coding the label data with a pre-set rule according to
the set task
instance, to obtain a plurality of coded data groups;
[0109] According to the plurality of the coded data groups, generate a
plurality of files
with set formats corresponding to the label data;
[0110] Insert each file with the set format into a corresponding local
engine table; and
[0111] Obtain the portrait label data from the local engine table, and
import a
corresponding correlation engine table; wherein the correlation engine table
is on the
machine of sharding query nodes.
[0112] In an embodiment, wherein the pre-set rule of the apparatus also
comprises:
[0113] Coding the label data according to a size order of the label data
from minimum
to maximum for obtaining the coded data; and
17
Date Regue/Date Received 2023-02-21
[0114] Grouping the coded data in a manner of a modulo sharding to
obtain the plurality
of coded data groups.
[0115] In an embodiment, the data processing module 302 is further
configured to:
[0116] Insert each file with the set format into a corresponding blank
local engine table;
and
[0117] Insert each file with the set format of the corresponding blank
local engine table
into the corresponding local engine table in a manner of a materialized view.
[0118] In an embodiment, the random distribution module 306 is further
configured to:
[0119] distribute the conditional query statement randomly in a manner
of a random
distribution of Virtual Internet Protocol address (VIP) to any machine of
query nodes.
[0120] In an embodiment, the aggregating module 308 is further
configured to:
[0121] According to both the conditional query statement and each
portrait label data
from each machine of sharding query nodes, obtain aggregated data of the
portrait label
data from each machine of sharding query nodes by calculating bitmap
functions.
[0122] Wherein the aggregated data is obtained on a basis of the
aggregated data of
portrait label data from each machine of sharding query nodes.
[0123] In an embodiment, the apparatus is further configured to:
[0124] Associate each corresponding correlation engine table with a
distributed table;
wherein the distributed table is on the machine of sharding query nodes.
18
Date Regue/Date Received 2023-02-21
[0125] Wherein the aggregated data is obtained on the distributed table
on a basis of the
aggregated data of portrait label data from each corresponding correlation
engine table.
[0126] For the specific limitation of an apparatus for improving the
performance of
a user portrait interface can refer to the above-mentioned method for
improving
the performance of a user portrait interface, which will not be repeated here.
Each
module of the above apparatus for improving the performance of a user portrait
interface can be achieved fully or partly by software, hardware, and their
combinations. The above modules can be embedded in the processor or
independent of the processor in computer device and can store in the memory of
computer device in form of software, so that the processor can call and
execute
the operations corresponding to the above modules.
[0127] In an embodiment, a computer device is provided, the computer
device can be a
server whose internal structure diagram is shown in Figure 5. The computer
device
includes a processor, a memory, a network interface, and a database connected
through
a system bus. The processor of the computer device is configured to provide
calculation
and control capabilities. The memory of the computer device includes non-
volatile
storage medium and internal memory. The memory of non-volatile storage medium
has
an operation system, computer programs and database. The internal memory
provides
an environment for the operation system and computer program running in a non-
volatile storage medium. The network interface of the computer device is used
to
communicate with an external terminal through a network connection. The
computer
program is executed by the processor to implement a method for improving the
performance of a user portrait interface.
[0128] The skilled in the art can understand that the structure shown in
Figure 5 is only
partial structural diagram related this application solution and not
constitute limitation
to the computer device applied on the current application solution, the
specific
computer device can include more or less components than what is shown in the
figure,
19
Date Regue/Date Received 2023-02-21
or combinations of some components or different components to what is shown in
the
figure.
[0129] Ti an
embodiment, a computer device is provided, including a memory, a
processor and a computer program stored in the memory and ran on the processor
configured to achieve the following steps when the processor executes the
computer
program:
[0130] Preprocessing label data according to a set task instance; wherein the
label
data includes portrait label data;
[0131] Obtaining portrait label information selected by a user, and generating
a
conditional query statement according to the portrait label information;
[0132] Distributing the conditional query statement randomly to any machine of
query nodes;
[0133] Obtaining aggregated data from the machine of query nodes according to
the
conditional query statement, wherein the aggregated data is obtained on a
basis of
portrait label data from a plurality of sharding query nodes machines; and
[0134] Returning the aggregated data to a client terminal for displaying to
the user.
[0135] In an embodiment, the processor is configured to achieve the following
steps
when the processor executes the computer program of preprocessing label data
according to a set task instance:
[0136] Grouping by coding the label data with a pre-set rule according to the
set task
instance, to obtain a plurality of coded data groups.
Date Regue/Date Received 2023-02-21
[0137] According to the plurality of the coded data groups, generating a
plurality of
files with set formats corresponding to the label data.
[0138] Inserting each file with the set format into a corresponding local
engine table.
[0139] Obtaining the portrait label data from the local engine table and
importing a
corresponding correlation engine table; wherein the correlation engine table
is on
the machine of sharding query nodes.
[0140] In an embodiment, the processor is further configured to achieve the
following steps when the processor executes the computer program:
[0141] Coding the label data according to a size order of the label data from
minimum to maximum for obtaining the coded data; and
[0142] Grouping the coded data in a manner of a modulo sharding to obtain the
plurality of coded data groups.
[0143] In an embodiment, the processor is further configured to achieve the
following steps when the processor executes the computer program of inserting
each file with the set format into the corresponding local engine table:
[0144] Inserting each file with the set format into a corresponding blank
local engine
table; and
[0145] Inserting each file with the set format of the corresponding blank
local engine
table into the corresponding local engine table in a manner of a materialized
view.
[0146] In an embodiment, the processor is further configured to achieve the
following steps when the processor executes the computer program of
distributing
21
Date Regue/Date Received 2023-02-21
the conditional query statement randomly to any machine of query nodes:
[0147] Distributing the conditional query statement randomly in a manner of a
random distribution of Virtual Internet Protocol address (VIP) to any machine
of
query nodes.
[0148] In an
embodiment, the processor is further configured to achieve the
following steps when the processor executes the computer program of obtaining
aggregated data from the machine of query nodes according to the conditional
query statement, wherein the aggregated data is obtained on a basis of
portrait
label data from a plurality of sharding query nodes machines, further
comprises
the following steps:
[0149] According to both the conditional query statement and each portrait
label
data from each machine of sharding query nodes, obtaining aggregated data of
the
portrait label data from each machine of sharding query nodes by calculating
bitmap functions.
[0150] Wherein the aggregated data is obtained on a basis of the aggregated
data of
portrait label data from each machine of sharding query nodes.
[0151] In an embodiment, the processor is further configured to achieve the
following steps when the processor executes the computer program:
[0152] Associating each corresponding correlation engine table with a
distributed
table; wherein the distributed table is on the machine of sharding query
nodes.
[0153] Wherein the aggregated data is obtained on the distributed table on a
basis of
the aggregated data of portrait label data from each corresponding correlation
engine table.
22
Date Regue/Date Received 2023-02-21
[0154] In an embodiment, a computer readable storage medium is provided, the
medium stored with computer program and the processor performs the following
steps when executing the computer program:
[0155] Preprocessing label data according to a set task instance; wherein the
label
data includes portrait label data;
[0156] Obtaining portrait label information selected by a user, and generating
a
conditional query statement according to the portrait label information;
[0157] Distributing the conditional query statement randomly to any machine of
query nodes;
[0158] Obtaining aggregated data from the machine of query nodes according to
the
conditional query statement, wherein the aggregated data is obtained on a
basis of
portrait label data from a plurality of sharding query nodes machines; and
[0159] Returning the aggregated data to a client terminal for displaying to
the user.
[0160] In an embodiment, the computer program of preprocessing label data
according to a set task instance is executed by the processor to achieve the
following steps:
[0161] Grouping by coding the label data with a pre-set rule according to the
set task
instance, to obtain a plurality of coded data groups.
[0162] According to the plurality of the coded data groups, generating a
plurality of
files with set formats corresponding to the label data.
23
Date Regue/Date Received 2023-02-21
[0163] Inserting each file with the set format into a corresponding local
engine table.
[0164] Obtaining the portrait label data from the local engine table, and
importing a
corresponding correlation engine table; wherein the correlation engine table
is on
the machine of sharding query nodes.
[0165] In an embodiment, the computer program is executed by the processor to
achieve the following steps:
[0166] Coding the label data according to a size order of the label data from
minimum to maximum for obtaining the coded data; and
[0167] Grouping the coded data in a manner of a modulo sharding to obtain the
plurality of coded data groups.
[0168] In an embodimentõ the computer program of inserting each file with the
set
format into the corresponding local engine table is executed by the processor
to
achieve the following steps:
[0169] Inserting each file with the set format into a corresponding blank
local engine
table; and
[0170] Inserting each file with the set format of the corresponding blank
local engine
table into the corresponding local engine table in a manner of a materialized
view.
[0171] In an embodiment, the computer program of distributing the conditional
query statement randomly to any machine of query nodes, is executed by the
processor to achieve the following steps:
[0172] Distributing the conditional query statement randomly in a manner of a
24
Date Regue/Date Received 2023-02-21
random distribution of Virtual Internet Protocol address (VIP) to any machine
of
query nodes.
[0173] In an embodiment, the computer program of obtaining aggregated data
from
the machine of query nodes according to the conditional query statement,
wherein
the aggregated data is obtained on a basis of portrait label data from a
plurality of
sharding query nodes machines, is executed by the processor to achieve the
following step:
[0174] According to both the conditional query statement and each portrait
label
data from each machine of sharding query nodes, obtaining aggregated data of
the
portrait label data from each machine of sharding query nodes by calculating
bitmap functions.
[0175] Wherein the aggregated data is obtained on a basis of the aggregated
data of
portrait label data from each machine of sharding query nodes.
[0176] In an embodiment, the computer program is executed by the processor to
achieve the following steps:
[0177] Associating each corresponding correlation engine table with a
distributed
table; wherein the distributed table is on the machine of sharding query
nodes.
[0178] Wherein the aggregated data is obtained on the distributed table on a
basis of
the aggregated data of portrait label data from each corresponding correlation
engine table.
[0179] The skilled in the art can understand that all or partial of procedures
from the
above-mentioned methods can be perfoimed by computer program instructions
through
related hardware, the mentioned computer program can be stored in a non-
volatile
Date Recue/Date Received 2023-02-21
material computer readable storage medium, this computer can include various
embodiment procedures from the abovementioned methods when execution. Any
reference to the memory, the storage, the database, or the other media used in
each
embodiment provided in current application can include non-volatile and/or
volatile
memory. Non-volatile memory can include read-only memory (ROM), programable
ROM (PROM), electrically programmable ROM (EPRPMD), electrically erasable
programmable ROM (EEPROM) or flash memory. Volatile memory can include
random access memory (RAM) or external cache memory. As an instruction but not
limited to, RAM is available in many forms such as static RAM (SRAM), dynamic
RAM (DRAMD), synchronous DRAM (SDRAM), dual data rate SDRAM
(DDRSDRAM), enhanced SRAM (ESDRAM), synchronal link (Synchlink) DRAM
(SLDRAM), memory bus (Rambus), direct RAM (RDRAM), direct memory bus
dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.
[0180] The technical features of the above-mentioned embodiments can be
randomly
combined, for concisely statement, not all possible combinations of technical
features
in the abovementioned embodiments are described. However, if there are no
conflicts
in the combinations of these technical features, it shall be within the scope
of the present
description.
[0181] The above-mentioned embodiments are only several embodiments in this
disclosure and the description is more specific and detailed but cannot be
understood
as the limitation of the scope of the invention patent. Evidently those
ordinary skilled
in the art can make various modifications and variations to the disclosure
without
departing from the spirit and scope of the disclosure. Therefore, the appended
claims
are intended to be construed as encompassing the described embodiment and all
the
modifications and variations coming into the scope of the disclosure.
26
Date Regue/Date Received 2023-02-21
