CN112685246A - Method and device for processing time sequence data - Google Patents
Method and device for processing time sequence data Download PDFInfo
- Publication number
- CN112685246A CN112685246A CN202011539336.7A CN202011539336A CN112685246A CN 112685246 A CN112685246 A CN 112685246A CN 202011539336 A CN202011539336 A CN 202011539336A CN 112685246 A CN112685246 A CN 112685246A
- Authority
- CN
- China
- Prior art keywords
- data
- time series
- time sequence
- time
- series data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Landscapes
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
- Debugging And Monitoring (AREA)
Abstract
The invention discloses a method and a device for processing time sequence data, wherein the method comprises the following steps: determining first timing data for a first compute node; and second timing data for a second compute node that matches the first compute node; determining a transition position in the first time series data and the second time series data if the first time series data and the second time series data satisfy a corresponding morphological relationship; determining first type state data in the first time sequence data and first type state data in the second time sequence data according to the conversion position; determining third time sequence data according to the first type of state data in the first time sequence data and the first type of state data in the second time sequence data; the problem that time sequence data in the prior art only can reflect the load condition of a computing node and cannot reflect the overall load condition of the HA framework is solved, and various analyses and extended applications on the overall load condition of the HA framework are facilitated.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a method and an apparatus for processing time series data.
Background
A dual-computer cluster system, or called a high availability architecture (HA for short), is a solution that can effectively ensure service continuity in the field of databases/servers. The method is characterized in that the architecture generally comprises 2 computing nodes, wherein one computing node bears task load, and the other computing node is used as a standby. If necessary, the 2 nodes can be switched, i.e. the task load is quickly transferred to the spare computing node. Therefore, the safety and the continuity of the system operation are guaranteed.
In this scenario, each compute node in the HA architecture HAs its own timing data. The timing data may generally reflect a two-dimensional relationship of load/time on the compute node, as shown in FIG. 1. However, the time series data in fig. 1 can only reflect the load condition of the computing node itself, but cannot reflect the actual effective load condition of the HA architecture, so that it is not beneficial to perform various kinds of analysis and extended applications on the actual effective load condition of the HA architecture.
Disclosure of Invention
The invention provides a method and a device for processing time series data, which are used for at least solving the technical problems in the prior art.
In a first aspect, the present invention provides a method for processing time series data, including:
determining first timing data for a first compute node; and second timing data for a second compute node that matches the first compute node;
determining a transition position in the first time series data and the second time series data if the first time series data and the second time series data satisfy a corresponding morphological relationship;
determining first type state data in the first time sequence data and first type state data in the second time sequence data according to the conversion position;
and determining third time sequence data according to the first type of state data in the first time sequence data and the first type of state data in the second time sequence data.
Preferably, if the first time-series data and the second time-series data satisfy the corresponding morphological relationship, determining the transition position in the first time-series data and the second time-series data includes:
if the time window is specific, the first time sequence data comprises a first form, and the second time sequence data comprises a second form corresponding to the first form; the position of the first form is determined as the conversion position of the first time series data, and the position of the second form is determined as the conversion position of the second time series data.
Preferably, the determining the third time series data according to the first type of state data in the first time series data and the first type of state data in the second time series data includes:
fitting the first type state data in the first time sequence data and the first type state data in the second time sequence data to determine third time sequence data;
the fitting process comprises a data splicing process, a data splicing process or a data filling process.
Preferably, the first type of status data includes load status data; the fitting the first type of state data in the first time series data and the first type of state data in the second time series data to determine the third time series data includes:
fitting the load state data in the first time sequence data and the load state data in the second time sequence data to determine the load state data of the high-availability architecture;
determining load status data of the high availability architecture as the third timing data.
Preferably, the first type of status data includes idle status data; the fitting the first type of state data in the first time series data and the first type of state data in the second time series data to determine the third time series data includes:
fitting the idle state data in the first time sequence data and the idle state data in the second time sequence data to determine the idle state data of the high-availability architecture;
determining idle state data of the high availability architecture as the third timing data.
Preferably, the method further comprises the following steps:
and taking the third time sequence data as sample data, and performing data training on a time sequence data analysis model corresponding to a high-availability architecture by using the sample data.
Preferably, the method further comprises the following steps:
and performing data analysis on the third time sequence data by using a time sequence data analysis model corresponding to the high-availability architecture to determine a corresponding analysis result.
In a second aspect, the present invention provides a device for processing time series data, including:
the time sequence data determining module is used for determining first time sequence data of the first computing node; and second timing data for a second compute node that matches the first compute node;
a conversion position determining module, configured to determine a conversion position in the first time series data and the second time series data when the first time series data and the second time series data satisfy a corresponding morphological relationship;
the state data determining module is used for determining first-class state data in the first time sequence data and first-class state data in the second time sequence data according to the conversion position;
and the time sequence data processing module is used for determining third time sequence data according to the first type of state data in the first time sequence data and the first type of state data in the second time sequence data.
In a third aspect, the present invention provides a computer-readable storage medium storing a computer program for executing the method for processing time-series data according to the present invention.
In a fourth aspect, the present invention provides an electronic device comprising:
a processor;
a memory for storing the processor-executable instructions;
the processor is used for reading the executable instruction from the memory and executing the instruction to realize the processing method of the time sequence data.
Compared with the prior art, the time sequence data processing method and the time sequence data processing device provided by the invention have the advantages that the conversion position is determined according to the fact that the first time sequence data and the second time sequence data meet the corresponding morphological relation; determining first type state data in the first time sequence data and first type state data in the second time sequence data according to the conversion position; further determining third timing data; the third time sequence data can completely show the data form of the first type state in the HA framework, the problem that the time sequence data in the prior art only can reflect the load condition of the computing node and cannot reflect the actual and effective load condition of the HA framework is solved, and various analysis and extended application can be performed on the actual and effective load condition of the HA framework more favorably.
Drawings
FIG. 1 is a schematic diagram of timing data of a HA architecture compute node in the prior art;
fig. 2 is a flowchart illustrating a method for processing time series data according to an embodiment of the present invention;
fig. 3 is a schematic diagram illustrating a conversion position of time series data in a time series data processing method according to an embodiment of the invention;
fig. 4 is a schematic diagram of load status data in a method for processing time series data according to an embodiment of the present invention;
FIGS. 5A-B are schematic diagrams illustrating time series data fitting in a method for processing time series data according to an embodiment of the present invention;
fig. 6 is a flowchart illustrating another method for processing time series data according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of idle state data in another method for processing time series data according to an embodiment of the present invention;
fig. 8 is a schematic structural diagram of a device for processing time series data according to an embodiment of the present invention.
Detailed Description
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The HA architecture typically includes 2 compute nodes, which may be referred to herein as a first compute node and a second compute node. During operation, one compute node is burdened with the task load while another compute node is acting as a backup. If necessary, the 2 nodes can be switched, i.e. the task load is quickly transferred to the spare computing node. Therefore, the safety and the continuity of the system operation are guaranteed.
Thus, each compute node in the HA architecture HAs its own timing data. The time series data is shown in fig. 1, in which the vertical axis represents the load state of the computing node and the horizontal axis represents time. At the position of the dashed box in fig. 1, a handover occurs for 2 nodes of the HA architecture. The load of the first computing node is reduced, and the load is converted into standby; the second computing node, on the other hand, has an increased load, and switches from standby to carrying the load. That is to say, the time sequence data can reflect the two-dimensional relationship of load/time on the computing node, intuitively reflect the operation condition of the HA framework, and have extremely high value and diversified purposes.
The specific use of the time series data can be, for example, visual presentation (i.e., as in fig. 1) for direct viewing by a worker. The time sequence data analysis model can be used as a training sample of the time sequence data analysis model, and the trained time sequence data analysis model can predict the future load state of the HA framework or perform data analysis of other layers. Of course, the time series data analysis model can also be used as input data of the time series data analysis model which completes training, so that the time series data analysis model outputs an analysis result.
However, the time series data in fig. 1 can only reflect the load condition of the computing node itself, but cannot reflect the actual effective load condition of the HA architecture, so that it is not beneficial to perform various kinds of analysis and extended applications on the actual effective load condition of the HA architecture. That is, in the visual presentation, the worker cannot visually find out the actual payload status of the HA architecture as a whole. In the training and analyzing process of the time sequence data analysis model, only the high-load section part in the time sequence data is usually concerned; the low-load section part may generate interference, which affects the accuracy of training and analysis of the time series data analysis model.
Therefore, embodiments of the present invention provide a method for processing time series data, so as to solve at least the above technical problems in the prior art. As shown in fig. 2, the method in this embodiment includes the following steps:
As described above, the first compute node and the second compute node are 2 compute nodes in the HA architecture. The corresponding first time series data and second time series data can also be as shown in fig. 1. And need not be described in further detail herein.
It should be noted that, in this embodiment, it is preferable that node information related to node IPs of the first and second compute nodes, virtual IPs of the HA architecture, application names running on the compute nodes, application types, and the like, be acquired from a Configuration Management Database (i.e., a CMDB) in advance before this step. Through the node information, it can be confirmed in advance whether the first computing node and the second computing node are matched, that is, whether the first computing node and the second computing node belong to the same HA architecture. If the two are matched, the method of the embodiment can be further executed.
As can be seen from the foregoing, when the HA architecture is switched between nodes, that is, one computing node is switched from load bearing to standby, and the other computing node is switched from standby to load bearing, the corresponding time series data (i.e., two load curves) of the two nodes inevitably show a specific form change. This change in morphology is shown in fig. 1 in dashed outline, as one rises and the other falls. In the present embodiment, the transition positions in the first time-series data and the second time-series data are determined based on this characteristic. The transition position may be considered as a position where a load in the time-series data is changed drastically, or may be considered as a time range where node switching occurs in the HA architecture.
Specifically, for example, within a specific time window, the first time sequence data includes a first pattern, and the second time sequence data includes a second pattern corresponding to the first pattern; the position of the first modality is determined as the conversion position of the first time series data and the position of the second modality is determined as the conversion position of the second time series data.
The first and second modes are modes of sudden increase or sudden decrease of the timing data due to node switching of the HA architecture. In practical terms, it may be that the first form represents a sudden increase and the second form represents a sudden decrease; of course, the converse may be true where the first form represents a sudden decrease and the second form represents a sudden increase; the principle is the same in both cases, and it can be inferred that node switching occurs in the HA architecture.
It should be noted that, in the process of switching the HA architecture node, the load transfer is completed in a short time. That is, after the load of one computing node decreases, the load of another computing node increases. The entire process takes time, typically within seconds. That is, the time ranges of the occurrence of the first form and the second form representing the load change, which means the HA architecture node switching, need to be within a specific time window, so that the two can be considered to have a relationship. Otherwise, exceeding the time window, the load change cannot be considered to be caused by HA architecture node switching; in other words, the data pattern of the load change cannot be referred to as the first pattern and the second pattern specified in the present embodiment.
It should be further noted that, in the present embodiment, a detection algorithm may be used to analyze the time series data, so as to determine whether the first form and the second form appear in the time series data. For example, it can be determined whether the value change between the previous data point and the next data point in the time series data exceeds a specific first threshold; the first morphology and the second morphology may be considered to be present if a particular first threshold is exceeded. The first and second states may also be considered to occur if the first derivative (i.e., slope) of the load curve corresponding to the calculated time series data reaches a certain value. A sliding window with a specific width can be established along the horizontal axis of the time sequence data, and the mean value of the time sequence data within the range of the sliding window is calculated; if the mean exceeds a certain second threshold as the sliding window slides, the first morphology and the second morphology may be considered to be present.
The algorithms for determining the first form and the second form may be used independently or jointly according to requirements, and are not limited herein. In addition, the change of the data throughput of the first computing node and the second computing node in the time window of the first form and the second form can be further determined. In combination with the change in data throughput, it can be further verified whether an HA fabric node switch HAs occurred within the time window.
After the corresponding first form and second form are determined, the position of the first form may be determined as the conversion position of the first time series data, and the position of the second form may be determined as the conversion position of the second time series data. As shown in fig. 3.
The transition location represents a node switch of the HA architecture. For a computing node, a switch location also means that it is switched from carrying load to standby, or from standby to taking up load. Based on the transition location, the time series data of the compute node can therefore be split into two parts to represent the two states (load bearing and standby) of the compute node, respectively.
The first type of state may refer to a load state of the computing node, and may also refer to an idle (i.e., standby) state of the computing node, which is not limited in the present invention. In this embodiment, it may be assumed that the first type state is a load state, that is, the first type state data is load state data; accordingly, the idle state may be referred to as a second type state, i.e., the second type state data is idle state data.
The present embodiment will first focus on the portion of the time-series data that bears the load. So that the first kind of status data in the first time series data, i.e. the load status data before the transition position of the first time series data in fig. 3, can be obtained; and the first kind of state data in the second time series data, i.e. the load state data of the second time series data after the transition position in fig. 3, is obtained. The load state data of the two parts respectively represent the operation state when the respective computing node bears the load.
In this embodiment, the fitting process may be specifically performed on the first type state data in the first time series data and the first type state data in the second time series data to generate the third time series data. The third time series data, which is the time series data obtained after the processing by the method in this embodiment, can completely show the data form of the first type state in the HA architecture, and exclude the corresponding second type state.
In this embodiment, on the premise that the first type of state is assumed to be a load state, the fitting process is specifically to perform fitting process on the load state data in the first time series data and the load state data in the second time series data to determine the load state data actually valid by the HA architecture. The actual valid load status data of the HA architecture is processed as the result of the time series data, i.e. the third time series data, as shown in fig. 4.
It should be noted that, the specific manner of the fitting process may include a data splicing process, or a data padding process.
The data splicing process may be directly splicing the first type state data in the first time series data and the first type state data in the second time series data according to a time sequence. For example, the load status data of the first time series data before the transition position in fig. 3 and the load status data of the second time series data after the transition position are spliced to obtain the load status data actually valid for the HA architecture in fig. 4.
However, as can be seen from fig. 4, the load curve in fig. 4 is not smooth at the splice location (as indicated by the dashed circle), and there is some distortion. It is preferable that the splicing positions thereof are also appropriately processed during the fitting process to make them more realistic.
If the third time series data does not have a periodic characteristic in the application, i.e. the load value is allowed to be shifted with respect to the time axis, a data splicing process may be employed. I.e. deleting part of the data in the dashed circle in fig. 4 and shifting the rest for splicing. As shown in fig. 5A. In a scenario where the third time series data is used as a training sample, the data splicing process may be performed.
If the third time series data has a periodic characteristic in application, that is, the load value is not allowed to be shifted relative to the time axis, a data padding process may be adopted. That is, part of the data in the dotted circle in fig. 4 is deleted, and dummy data is supplemented in the deleted part by the padding algorithm, so that the load curve of the third time-series data is smooth and complete and there is no offset from the time axis. As shown in fig. 5B, the dotted line (in a straight line) is filled with the dummy data. Filling in the dummy data according to the filling algorithm is a conventional technical means in the art, and need not be described herein. In a scenario in which the third time series data is visually presented, the data filling process described above may be adopted.
The third time series data obtained in this embodiment may completely show the data form of the first type state in the HA architecture. On the premise that the first-class state is assumed as the load state, the third time series data is equivalent to the actual and effective load state data of the HA architecture, and the load condition of the HA architecture can be completely shown. Therefore, the problem that time sequence data in the prior art can only reflect the load condition of the computing node and cannot reflect the actual and effective load condition of the HA framework is solved, and the problem that various analyses and extended applications can be carried out on the actual and effective load condition of the HA framework is facilitated.
According to the technical scheme, the beneficial effects of the embodiment are as follows: determining a conversion position according to the first time sequence data and the second time sequence data meeting the corresponding morphological relation; determining first type state data in the first time sequence data and first type state data in the second time sequence data according to the conversion position; further determining third timing data; the third time sequence data can completely show the data form of the first type state in the HA framework, the problem that the time sequence data in the prior art only can reflect the load condition of the computing node and cannot reflect the actual and effective load condition of the HA framework is solved, and various analysis and extended application can be performed on the actual and effective load condition of the HA framework more favorably.
Fig. 2 shows only a basic embodiment of the method of the present invention, and based on this, certain optimization and expansion can be performed, and other preferred embodiments of the method can also be obtained.
Fig. 6 shows another embodiment of the method for processing time series data according to the present invention. On the basis of the foregoing embodiments, the present embodiment specifically describes the method on the premise that the first type state is assumed to be the idle state. The method specifically comprises the following steps:
601, determining first time sequence data of a first computing node; and second timing data for a second compute node that matches the first compute node.
The above steps 601 to 602 are the same as those in the previous embodiments, and are not described herein.
Referring to fig. 3, in this embodiment, idle state data of the first time series data after the transition position in fig. 3 and idle state data of the second time series data before the transition position are obtained.
And step 604, fitting the idle state data in the first time sequence data and the idle state data in the second time sequence data to determine the idle state data of the high-availability architecture.
In this embodiment, the idle state data of the HA architecture obtained after the fitting process is shown in fig. 7. The specific fitting processing manner can refer to the specific description in step 204.
The third time sequence data in this embodiment is equivalent to the idle state data of the HA architecture, and can completely show the performance of the spare part of the HA architecture.
As can be seen from the two embodiments shown in fig. 2 and fig. 6, the method of the present invention processes the time series data to obtain the actual effective load status data and idle status data of the HA architecture. Unlike the prior art in which the computation nodes are used as the dimension of the time sequence data, the invention instead uses the running state (load or idle) as the dimension of the time sequence data. The method can provide time sequence data with different dimensions, so that deeper value of the time sequence data can be conveniently mined, and more diversified application and analysis of the time sequence data can be realized.
It should be noted that, after the third time series data is obtained, the following applications may be performed on the third time series data in the present invention, including the following:
the third time series data as shown in fig. 4, 5A, 5B, and 7 may be visually presented for direct viewing by the staff. So that the working personnel can intuitively know the actual effective load condition of the HA framework and the performance state of the standby part.
And taking the third time sequence data as sample data, and performing data training on the time sequence data analysis model corresponding to the HA architecture by using the sample data. The time series data analysis model may include an analysis model for load state data and an analysis model for idle state data. The actual and effective load state data of the HA framework can be used as a training sample of an analysis model of the load state data; the idle state data of the HA architecture may be used as training samples of an analytical model of the idle state data. Therefore, unnecessary data are not interfered in the training process, and the accuracy of the time sequence data analysis model training can be improved.
And performing data analysis on the third time sequence data by using a time sequence data analysis model corresponding to the HA architecture to determine a corresponding analysis result. After the time series data analysis model is trained, the new third time series data can be directly used as the input data of the model for the model to predict the load state or perform data analysis of other layers.
It should be noted that there may be other configuration management databases and monitoring systems in the HA architecture. The configuration management database and monitoring system also HAs the function of inferring HA architecture node switching. After the conversion position in the time sequence data is determined based on the method, namely the node switching of the HA framework is deduced, the conversion position and the deduction result of the configuration management database and the monitoring system can be mutually verified, so that whether the node switching of the HA framework occurs or not can be judged more accurately. The verification result can be applied to the subsequent steps of the method of the present invention, and any other necessary applications can be made, which are not limited in the present invention.
Fig. 8 shows an embodiment of a device for processing time series data according to the present invention. The apparatus of this embodiment is a physical apparatus for performing the methods described in FIGS. 2-7. The technical solution is essentially the same as that in the above embodiment, and the corresponding description in the above embodiment is also applicable to this embodiment. The device in the embodiment comprises:
a time series data determination module 801, configured to determine first time series data of a first computing node; and second timing data for a second compute node that matches the first compute node.
A conversion position determining module 802, configured to determine a conversion position in the first time series data and the second time series data when the first time series data and the second time series data satisfy a corresponding morphological relationship.
The state data determining module 803 is configured to determine, according to the transition position, first type state data in the first time series data and first type state data in the second time series data.
The time sequence data processing module 804 is configured to determine third time sequence data according to the first type of state data in the first time sequence data and the first type of state data in the second time sequence data.
In addition, on the basis of the embodiment shown in fig. 8, it is preferable that:
the conversion location determination module 802 includes:
the shape determining unit 821 is configured to determine a first shape included in the first time series data and a second shape corresponding to the first shape included in the second time series data within a specific time window.
A position determining unit 822, configured to determine a position of the first form as a conversion position of the first time series data, and determine a position of the second form as a conversion position of the second time series data.
The time-series data processing module 804 includes:
the first fitting unit 841 is configured to perform fitting processing on the load status data in the first time series data and the load status data in the second time series data to determine load status data of the high-availability architecture.
The second fitting unit 842 is configured to perform fitting processing on the idle state data in the first time series data and the idle state data in the second time series data to determine the idle state data of the high-availability architecture.
A third timing data determination unit 843, configured to determine the load status data of the high-availability architecture as third timing data; or, determining the idle state data of the high availability architecture as the third timing data.
The fitting process includes a data stitching process, a data splicing process, or a data padding process.
In addition to the above-described methods and apparatus, embodiments of the invention may also be a computer program product comprising computer program instructions which, when executed by a processor, cause the processor to perform the steps in the methods according to various embodiments of the invention described in the "exemplary methods" section above of this specification.
The computer program product may write program code for carrying out operations for embodiments of the present invention in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server.
Furthermore, embodiments of the present invention may also be a computer-readable storage medium having stored thereon computer program instructions which, when executed by a processor, cause the processor to perform steps in methods according to various embodiments of the present invention described in the "exemplary methods" section above of this specification.
The computer-readable storage medium may take any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may include, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The basic principles of the present invention have been described above with reference to specific embodiments, but it should be noted that the advantages, effects, etc. mentioned in the present invention are only examples and are not limiting, and the advantages, effects, etc. must not be considered to be possessed by various embodiments of the present invention. Furthermore, the foregoing disclosure of specific details is for the purpose of illustration and description and is not intended to be limiting, since the invention is not limited to the specific details described above.
The block diagrams of devices, apparatuses, systems involved in the present invention are only given as illustrative examples and are not intended to require or imply that the connections, arrangements, configurations, etc. must be made in the manner shown in the block diagrams. These devices, apparatuses, devices, systems may be connected, arranged, configured in any manner, as will be appreciated by those skilled in the art. Words such as "including," "comprising," "having," and the like are open-ended words that mean "including, but not limited to," and are used interchangeably therewith. The words "or" and "as used herein mean, and are used interchangeably with, the word" and/or, "unless the context clearly dictates otherwise. The word "such as" is used herein to mean, and is used interchangeably with, the phrase "such as but not limited to".
It should also be noted that in the apparatus, devices and methods of the present invention, the components or steps may be broken down and/or re-combined. These decompositions and/or recombinations are to be regarded as equivalents of the present invention.
The previous description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects without departing from the scope of the invention. Thus, the present invention is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
The foregoing description has been presented for purposes of illustration and description. Furthermore, the description is not intended to limit embodiments of the invention to the form disclosed herein. While a number of example aspects and embodiments have been discussed above, those of skill in the art will recognize certain variations, modifications, alterations, additions and sub-combinations thereof.
Claims (10)
1. A method for processing time series data comprises the following steps:
determining first timing data for a first compute node; and second timing data for a second compute node that matches the first compute node;
determining a transition position in the first time series data and the second time series data if the first time series data and the second time series data satisfy a corresponding morphological relationship;
determining first type state data in the first time sequence data and first type state data in the second time sequence data according to the conversion position;
and determining third time sequence data according to the first type of state data in the first time sequence data and the first type of state data in the second time sequence data.
2. The method of claim 1, wherein determining the transition location in the first time series data and the second time series data comprises, if the first time series data and the second time series data satisfy the respective morphological relationship:
if the time window is specific, the first time sequence data comprises a first form, and the second time sequence data comprises a second form corresponding to the first form; the position of the first form is determined as the conversion position of the first time series data, and the position of the second form is determined as the conversion position of the second time series data.
3. The method of claim 1, wherein determining third time series data according to the first type of status data in the first time series data and the first type of status data in the second time series data comprises:
fitting the first type state data in the first time sequence data and the first type state data in the second time sequence data to determine third time sequence data;
the fitting process comprises a data splicing process, a data splicing process or a data filling process.
4. The method of claim 3, wherein the first type of status data comprises load status data; the fitting the first type of state data in the first time series data and the first type of state data in the second time series data to determine the third time series data includes:
fitting the load state data in the first time sequence data and the load state data in the second time sequence data to determine the load state data of the high-availability architecture;
determining load status data of the high availability architecture as the third timing data.
5. The method of claim 3, wherein the first type of status data comprises idle status data; the fitting the first type of state data in the first time series data and the first type of state data in the second time series data to determine the third time series data includes:
fitting the idle state data in the first time sequence data and the idle state data in the second time sequence data to determine the idle state data of the high-availability architecture;
determining idle state data of the high availability architecture as the third timing data.
6. The method of any of claims 1 to 5, further comprising:
and taking the third time sequence data as sample data, and performing data training on a time sequence data analysis model corresponding to a high-availability architecture by using the sample data.
7. The method of any of claims 1 to 5, further comprising:
and performing data analysis on the third time sequence data by using a time sequence data analysis model corresponding to the high-availability architecture to determine a corresponding analysis result.
8. An apparatus for processing time series data, comprising:
the time sequence data determining module is used for determining first time sequence data of the first computing node; and second timing data for a second compute node that matches the first compute node;
a conversion position determining module, configured to determine a conversion position in the first time series data and the second time series data when the first time series data and the second time series data satisfy a corresponding morphological relationship;
the state data determining module is used for determining first-class state data in the first time sequence data and first-class state data in the second time sequence data according to the conversion position;
and the time sequence data processing module is used for determining third time sequence data according to the first type of state data in the first time sequence data and the first type of state data in the second time sequence data.
9. A computer-readable storage medium storing a computer program for executing the method of processing time-series data according to any one of claims 1 to 7.
10. An electronic device, comprising:
a processor;
a memory for storing the processor-executable instructions;
the processor is used for reading the executable instructions from the memory and executing the instructions to realize the time series data processing method of any one of the claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011539336.7A CN112685246B (en) | 2020-12-23 | 2020-12-23 | Time sequence data processing method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011539336.7A CN112685246B (en) | 2020-12-23 | 2020-12-23 | Time sequence data processing method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112685246A true CN112685246A (en) | 2021-04-20 |
| CN112685246B CN112685246B (en) | 2023-04-28 |
Family
ID=75451179
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011539336.7A Active CN112685246B (en) | 2020-12-23 | 2020-12-23 | Time sequence data processing method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112685246B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2016123972A1 (en) * | 2015-02-02 | 2016-08-11 | 中兴通讯股份有限公司 | Load balancing method and load balancing apparatus |
| CN109408254A (en) * | 2018-09-30 | 2019-03-01 | 上海与德科技有限公司 | A kind of information processing method, system and server |
| CN109936473A (en) * | 2017-12-19 | 2019-06-25 | 华耀(中国)科技有限公司 | Distributed computing system and its operation method based on deep learning prediction |
| CN110149237A (en) * | 2019-06-13 | 2019-08-20 | 东北大学 | A kind of Hadoop platform calculate node load predicting method |
| US20200034745A1 (en) * | 2015-10-19 | 2020-01-30 | Nutanix, Inc. | Time series analysis and forecasting using a distributed tournament selection process |
-
2020
- 2020-12-23 CN CN202011539336.7A patent/CN112685246B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2016123972A1 (en) * | 2015-02-02 | 2016-08-11 | 中兴通讯股份有限公司 | Load balancing method and load balancing apparatus |
| US20200034745A1 (en) * | 2015-10-19 | 2020-01-30 | Nutanix, Inc. | Time series analysis and forecasting using a distributed tournament selection process |
| CN109936473A (en) * | 2017-12-19 | 2019-06-25 | 华耀(中国)科技有限公司 | Distributed computing system and its operation method based on deep learning prediction |
| CN109408254A (en) * | 2018-09-30 | 2019-03-01 | 上海与德科技有限公司 | A kind of information processing method, system and server |
| CN110149237A (en) * | 2019-06-13 | 2019-08-20 | 东北大学 | A kind of Hadoop platform calculate node load predicting method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112685246B (en) | 2023-04-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10318366B2 (en) | System and method for relationship based root cause recommendation | |
| US8719791B1 (en) | Display of aggregated stack traces in a source code viewer | |
| Soualhia et al. | Infrastructure fault detection and prediction in edge cloud environments | |
| CN107924360B (en) | Diagnostic framework in a computing system | |
| CN109543891B (en) | Method and apparatus for establishing capacity prediction model, and computer-readable storage medium | |
| CN106940671B (en) | Method, device and system for monitoring running of task threads in cluster | |
| CN112202617B (en) | Resource management system monitoring method, device, computer equipment and storage medium | |
| CN110647318B (en) | Method, device, equipment and medium for creating instance of stateful application | |
| CN115335821B (en) | Offloading statistics collection | |
| CN108664603A (en) | A kind of method and device of abnormal polymerization value that repairing time series data | |
| US9823905B2 (en) | Event based code generation | |
| WO2014187157A1 (en) | Methods and systems for determining user online time | |
| CN116974874A (en) | Database testing method and device, electronic equipment and readable storage medium | |
| CN113835918A (en) | Server fault analysis method and device | |
| CN117931583B (en) | Equipment cluster running state prediction method, electronic equipment and storage medium | |
| US8732323B2 (en) | Recording medium storing transaction model generation support program, transaction model generation support computer, and transaction model generation support method | |
| CN113641544B (en) | Method, apparatus, device, medium and product for detecting application state | |
| CN112685246A (en) | Method and device for processing time sequence data | |
| CN112766470A (en) | Feature data processing method, instruction sequence generation method, device and equipment | |
| CN111159009A (en) | Pressure testing method and device for log service system | |
| CN113141407B (en) | Page resource loading method and device and electronic equipment | |
| CN112685390B (en) | Database instance management method and device and computing equipment | |
| CN111679924B (en) | Reliability simulation method and device for componentized software system and electronic equipment | |
| CN113495841A (en) | Compatibility detection method, device, equipment, storage medium and program product | |
| CN111309716A (en) | Maintenance method and device applied to PAS case base and computer equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |