CN115065366A - Compression method, device and equipment of time sequence data and storage medium - Google Patents
Compression method, device and equipment of time sequence data and storage medium Download PDFInfo
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Abstract
The invention discloses a compression method, a compression device, compression equipment and a storage medium of time sequence data. The method comprises the following steps: acquiring a service model set to be compressed, and determining a compression step length; traversing the service model set to be compressed based on a temporary service model and the compression step length, updating the temporary service model in the traversing process, and recording parent time data, wherein the temporary service model is used for caching temporary time sequence data in the compression process, and the parent time data is used for indicating original time data corresponding to original index data meeting preset characteristic requirements in a unit compression range corresponding to the compression step length; and determining a parent time data set and a service model compression result set according to the traversal result. According to the technical scheme of the embodiment of the invention, a large amount of time sequence data is orderly compressed, so that the compression efficiency of the time sequence data is ensured, and parent time data is recorded for a user to look up original time data.
Description
Technical Field
The present invention relates to the field of database technologies, and in particular, to a method, an apparatus, a device, and a storage medium for compressing time series data.
Background
The time-series data refers to time-series data. The time-series data is a data sequence recorded in time series following a uniform index. The time series data can be the number of epochs or the number of epochs. The statistical characteristics and the development rules of the time series in the sample and the time series, the prediction outside the sample and the like can be found by analyzing the time series.
The time-series line graph is a picture of front end visualization of data in a line graph mode according to time sequence in a specified time period. In the time-series line graph, each line segment is not generally a connecting line between two points, but is a line segment describing the trend of a plurality of data points in the time period.
However, if the time-series line graph includes a large number of data points, when the browser loads the time-series line graph, all data points need to be loaded first, which causes a problem that it takes a long time to load the time-series line graph, and may even cause an overflow of the browser memory.
Disclosure of Invention
The invention provides a compression method, a compression device, compression equipment and a storage medium of time sequence data, and solves the problems that a browser processes a large amount of time sequence data and time consumption is long in drawing a time sequence line graph.
In a first aspect, an embodiment of the present invention provides a method for compressing time series data, including:
acquiring a service model set to be compressed, and determining a compression step length, wherein the service model to be compressed in the service model set to be compressed comprises original time sequence data, and the original time sequence data comprises a corresponding relation between original time data and original index data;
traversing the service model set to be compressed based on a temporary service model and the compression step length, updating the temporary service model in the traversing process, and recording parent time data, wherein the temporary service model is used for caching temporary time sequence data in the compression process, and the parent time data is used for indicating original time data corresponding to original index data meeting preset characteristic requirements in a unit compression range corresponding to the compression step length;
and determining a parent time data set and a service model compression result set according to the traversal result.
In a second aspect, an embodiment of the present invention provides an apparatus for compressing time series data, including:
the device comprises a compression step length determining module, a compression step length determining module and a compression step length determining module, wherein the compression step length determining module is used for acquiring a service model set to be compressed and determining the compression step length, the service model to be compressed in the service model set to be compressed comprises original time sequence data, and the original time sequence data comprises the corresponding relation between original time data and original index data;
the traversal module is used for traversing the service model set to be compressed based on a temporary service model and the compression step length, updating the temporary service model in the traversal process, and recording parent time data, wherein the temporary service model is used for caching temporary time sequence data in the compression process, and the parent time data is used for indicating original time data corresponding to original index data meeting preset characteristic requirements in a unit compression range corresponding to the compression step length;
and the compression result and parent time determining module is used for determining a parent time data set and a service model compression result set according to the traversal result.
In a third aspect, an embodiment of the present invention provides an electronic device, including:
at least one processor;
and a memory communicatively coupled to the at least one processor;
wherein the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform the method of compressing time series data of the first aspect.
In a fourth aspect, the present invention provides a computer-readable storage medium, where computer instructions are stored, and the computer instructions are configured to enable a processor to implement the compression method for time-series data of the first aspect when executed.
The compression scheme of the time sequence data provided by the embodiment of the invention obtains the service model set to be compressed, determines the compression step length, wherein, the business model to be compressed in the business model set to be compressed comprises original time sequence data, the original time sequence data comprises the corresponding relation between original time data and original index data, the service model set to be compressed is traversed based on a temporary service model and the compression step length, updating the temporary business model in the traversal process, recording the parent time data, wherein, the temporary service model is used for caching temporary time sequence data in the compression process, the parent class time data is used for indicating the original time data corresponding to the original index data meeting the preset characteristic requirement in the unit compression range corresponding to the compression step length, and determining a parent time data set and a service model compression result set according to the traversal result. By adopting the technical scheme, the compression step length is determined according to the service model set to be compressed, then the service model set to be compressed is traversed, the compressed data is cached in the temporary service model, the parent time data is recorded, and the parent time data and the service model compression result can be determined after traversal is completed.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present invention, nor do they necessarily limit the scope of the invention. Other features of the present invention will become apparent from the following description.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flowchart of a compression method for time series data according to an embodiment of the present invention;
FIG. 2 is a time-series line graph of the same index and the same performance value of different objects according to an embodiment of the present invention;
FIG. 3 is a time-series line graph of different performance values of the same target and the same index according to an embodiment of the present invention;
FIG. 4 is a flowchart of a method for compressing time series data according to a second embodiment of the present invention;
FIG. 5 is a flowchart of traversing a set of business models to be compressed according to a second embodiment of the present invention;
FIG. 6 is a schematic structural diagram of a device for compressing time series data according to a third embodiment of the present invention;
fig. 7 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious 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.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. In the description of the present invention, "a plurality" means two or more unless otherwise specified. "and/or" describes the association relationship of the associated object, indicating that there may be three relationships, for example, a and/or B, which may indicate: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example one
Fig. 1 is a flowchart of a method for compressing time series data according to an embodiment of the present invention, where the method is applicable to a case of compressing time series data, and the method may be executed by a compression apparatus for time series data, where the compression apparatus for time series data may be implemented in a form of hardware and/or software, and the compression apparatus for time series data may be configured in an electronic device, and the electronic device may be formed by two or more physical entities or may be formed by one physical entity.
As shown in fig. 1, a method for compressing time series data according to an embodiment of the present invention includes the following steps:
s101, acquiring a service model set to be compressed, and determining a compression step length.
The service model to be compressed in the service model set to be compressed comprises original time sequence data, and the original time sequence data comprises the corresponding relation between original time data and original index data.
In this embodiment, the compression step size of the service model set to be compressed may be determined based on the data amount in the service model set to be compressed, according to the actual situation, such as the ratio between the actual time series data amount and the expected time series data amount. The service model may be understood as a data packet for storing time series data, where the time series data may include time data, an object, index data, and the like, and the compression step may be understood as a range unit for compressing the time series data, where the compression step is 3, and the range unit for compressing the time series data is 3 time series data, that is, compression is performed once every 3 time series data. The browser may draw a time-series line graph based on the loaded time-series data, the time-series line graph may include a time-series line graph of the same performance value of the same index for different objects, a time-series line graph of the same performance value of the same index for the same object, and the like.
For example, fig. 2 is a time-series line graph of the same performance value of the same index for different objects, as shown in fig. 2, beijing, shanghai and guangzhou are three different objects, the abscissa is the date, i.e. the time data, and the ordinate is the temperature value of the index, in fig. 2, the daily average temperature change of each city is compared, the daily average temperature is the performance value of the temperature, i.e. the index data, during a period of time, and in fig. 2, the time-series line graph of the same performance value (daily average temperature) of the same index for different objects (cities) is shown.
For example, fig. 3 is a time-series broken line diagram of different performance values of the same index for the same object, as shown in fig. 3, the object is a database, the abscissa is time data, the ordinate is an index, fig. 3 compares the change of the number of active sessions and the total number of sessions of the database in a period of time, the number of active sessions and the total number of sessions are performance values of the number of sessions, i.e., index data, and fig. 3 shows a time-series broken line of different performance values (the number of active sessions and the total number of sessions) of the same index for the same object (database).
S102, traversing the service model set to be compressed based on the temporary service model and the compression step length, updating the temporary service model in the traversing process, and recording the parent time data.
The temporary business model is used for caching temporary time sequence data in the compression process, and the parent class time data is used for indicating original time data corresponding to original index data meeting the preset characteristic requirements in a unit compression range corresponding to the compression step length.
In this embodiment, first, in the process of traversing the service model set to be compressed, a temporary service model is required to cache, the original time series data of the service model set to be compressed in the compression process is the temporary time series data, then, the service model set to be compressed is traversed, the temporary service model is updated according to a preset characteristic requirement in a unit compression range corresponding to a compression step length, and parent time data is recorded, where the preset characteristic requirement may be that a middle value or a maximum value of index data in the service model set to be compressed is taken, and the unit compression range may be understood as a range in which the time series data compression is performed once, for example, a unit compression range corresponding to a compression step length of 3 indicates that the unit compression range is 3 time series data, that is, each 3 time series data are compressed once.
S103, determining a parent time data set and a service model compression result set according to the traversal result.
In this embodiment, in the traversal process, the temporary time series data and the parent time data that meet the preset feature requirements may be stored in the service model compression result set and the parent time data set, respectively, and after the traversal is completed, the compression result data, that is, the parent time data set and the service model compression result set, may be determined.
The compression method of time sequence data provided by the embodiment of the invention obtains the service model set to be compressed, determines the compression step length, wherein, the business model to be compressed in the business model set to be compressed comprises original time sequence data, the original time sequence data comprises the corresponding relation between original time data and original index data, the service model set to be compressed is traversed based on a temporary service model and the compression step length, updating the temporary business model in the traversal process, recording the parent time data, wherein, the temporary service model is used for caching temporary time sequence data in the compression process, the parent class time data is used for indicating the original time data corresponding to the original index data meeting the preset characteristic requirement in the unit compression range corresponding to the compression step length, and determining a parent time data set and a service model compression result set according to the traversal result. According to the technical scheme of the embodiment of the invention, firstly, the compression step length is determined according to the service model set to be compressed, then, the service model set to be compressed is traversed, the compressed data is cached in the temporary service model, the parent time data is recorded, and the parent time data and the service model compression result can be determined after traversal is completed.
Example two
Fig. 4 is a flowchart of a time series data compression method provided in the second embodiment of the present invention, and the technical solution of the second embodiment of the present invention is further optimized based on the foregoing optional technical solutions, and a specific compression manner for compressing time series data is given.
Optionally, the obtaining a service model set to be compressed and determining a compression step size include: acquiring a service model set to be compressed, and determining an actual time sequence data point number, an expected time sequence data point number and a preset object; and determining the compression step length according to the number of the actual time sequence data points and the number of the expected time sequence data points. The method has the advantages that the time sequence data are orderly compressed according to different objects in the time sequence data, and the trend of the time sequence line graph generated according to the compression result is not influenced.
Optionally, traversing the set of service models to be compressed based on the temporary service model and the compression step length, updating the temporary service model in the traversal process, and recording parent time data, including: the method comprises the steps of traversing a to-be-compressed service model set in a plurality of continuous unit compression ranges based on a temporary service model and a compression step length for each preset object, updating the temporary service model in the traversing process, and recording parent time data, wherein the unit compression range is determined according to the compression step length, and the total range of the unit compression ranges corresponds to the to-be-compressed service model set.
Optionally, after determining whether the temporary counting step corresponding to the current preset object is smaller than the compression step, the method further includes: if not, adding the temporary business model corresponding to the current preset object into the business model compression result set; and adding the parent time data of the temporary service model corresponding to the current preset object into the parent time data set. The method has the advantages that in the process of traversing the service model set to be compressed, the compression result is generated step by step, and the efficiency and the orderliness of compressing time sequence data are ensured.
As shown in fig. 4, a method for compressing time series data according to the second embodiment of the present invention specifically includes the following steps:
s201, acquiring a service model set to be compressed, and determining an actual time sequence data point number, an expected time sequence data point number and a preset object.
Specifically, if the time-series line graphs are used for drawing time-series line graphs of different objects with the same index and the same expression value, the actual time-series data point number can be understood as the number of the service models to be compressed in the service model set to be compressed, the expected time-series data point number can be understood as the number of the expected time-series data points, namely the number of the compressed service models, and the specific numerical value can be determined according to the actual conditions, such as the loading capacity and the memory capacity of a browser.
For example, if the time-series line graph is used for drawing time-series line graphs of different objects with the same index and the same expression value, the obtained content recorded in the service model set to be compressed is (date, city name, temperature), the actual time-series data point number is 360, and the expected time-series data point number may be set to 120, where the first 9 service models to be compressed in the service model set to be compressed specifically are: (4/1, beijing, 20), (4/1, shanghai, 18), (4/1, guangzhou, 25), (4/2, beijing, 20.5), (4/2, shanghai, 17.5) (4/2, guangzhou, 24), (4/3, beijing, 19), (4/3, shanghai, 17) and (4/3, guangzhou, 25.5), the preset objects of the business model to be compressed are beijing, shanghai and guangzhou.
Optionally, if the time-series line graph is used for drawing time-series line graphs of the same object with the same index and different expression values, the number of actual time-series data points can be understood as the product of the number of the service models to be compressed in the service model set to be compressed and the number of lines, in the time-series line graphs of the same object with the same index and different expression values, one preset object can correspond to a plurality of lines, and the number of the lines corresponds to the number of original indexes of the service models to be compressed in the service model set to be compressed.
For example, if the time sequence line graph is used for drawing time sequence line graphs of the same object with different performance values of the same index, the obtained record content of the service model set to be compressed is (time, number of active sessions of the database, total session number of the database), if the number of lines is 2, and the number of service models to be compressed in the service model set to be compressed is 60, the actual number of time sequence data points is 120, and the expected number of time sequence data points may be set to 40, where the first 3 service models to be compressed in the service model set to be compressed are specifically: (13:00, 13, 15), (13:01, 12, 16) and (13:02, 10, 13), the preset object of the business model to be compressed is a database.
S202, determining the compression step length according to the number of the actual time sequence data points and the number of the expected time sequence data points.
Specifically, the compression step length may be determined by rounding up the quotient of the actual number of time series data points divided by the expected number of time series data points.
Illustratively, if the actual time series data point number is 67 and the expected time series data point number is 22, the compression step size is 4, i.e., 67/22 is rounded up by the quotient 3.04.
S203, traversing the service model set to be compressed in continuous multiple unit compression ranges according to each preset object based on the temporary service model and the compression step length, updating the temporary service model in the traversing process, and recording parent time data.
The unit compression range is determined according to the compression step length, and the total range of the unit compression ranges corresponds to the service model set to be compressed.
Specifically, by using the temporary service model, in the service model set to be compressed, for the service model to be compressed of each preset object, the range is compressed by taking the compression step length as a unit, the service model set to be compressed is traversed, and parent time data is recorded.
Optionally, before traversing the service model set to be compressed based on the temporary service model and the compression step, the method further includes: initializing a temporary counting step corresponding to each preset object, wherein the temporary counting step is used for counting the cache steps; initializing a temporary service model corresponding to each preset object; and initializing the parent class time data of the temporary service model corresponding to each preset object.
Specifically, before traversing the service model set to be compressed, the count step, the temporary service model, and the parent time data of the temporary service model corresponding to each preset object may be initialized, where the initialization may be performed by setting the count step, the temporary time data and temporary index data in the temporary service model, and the parent time data of the temporary service model to zero.
Optionally, fig. 5 is a flowchart of traversing a service model set to be compressed, as shown in fig. 5, step S203 may include:
s2031, determining a current business model to be compressed and a current preset object to be traversed in a current unit compression range, judging whether a temporary counting step length corresponding to the current preset object is smaller than a compression step length, if so, executing S2032, and if not, executing S2039.
For example, if the time-series line graph is used for drawing the same index and the same expression value of different objects, the content recorded in the service model set to be compressed is (date, city name, temperature), and the first 9 service models to be compressed in the service model set to be compressed specifically are: (4/1, beijing, 20), (4/1, shanghai, 18), (4/1, guangzhou, 25), (4/2, beijing, 20.5), (4/2, shanghai, 17.5) (4/2, guangzhou, 24), (4/3, beijing, 19), (4/3, shanghai, 17) and (4/3, guangzhou, 25.5), the preset objects of the business model to be compressed are beijing, shanghai and guangzhou, the compression step length is 3, the count step length respectively corresponding to 3 preset objects, the temporary time data and temporary index data in the temporary business model and the father class time of the temporary business model are all zero, the traversal is started from the first business model to be compressed, the business model to be compressed is the current business model to be compressed, the preset object corresponding to the current business model to be compressed is the current preset object, and judging whether the temporary counting step length corresponding to the current preset object is smaller than the compression step length, wherein the temporary counting step length corresponding to Beijing is zero and is smaller than the compression step length 3 in the example.
For example, if the time-series line graph is used for drawing the same object, the same index and different performance values, the content recorded in the service model set to be compressed is (time, the number of active sessions in the database, and the total number of sessions in the database), and the first 3 service models to be compressed in the service model set to be compressed specifically are: (13:00, 13, 15), (13:01, 12, 16) and (13:02, 10, 13), the preset object of the service model to be compressed is a database, the compression step is 3, the count step corresponding to the preset object, the temporary time data and temporary index data in the temporary service model and the parent class time of the temporary service model are all zero, traversal is started from the first service model to be compressed, the service model to be compressed is the current service model to be compressed, the preset object corresponding to the current service model to be compressed is the current preset object, whether the temporary count step corresponding to the current preset object is smaller than the compression step is judged, in this case, the temporary count step corresponding to the database is zero and is smaller than the compression step 3.
And S2032, updating the temporary counting step length corresponding to the current preset object.
Wherein, the updating mode is that the step counting is added with 1.
Specifically, if the provisional counting step corresponding to the current preset object is smaller than the compression step, the provisional step count corresponding to the current preset object is incremented by one.
S2033, updating the temporary time data in the temporary service model corresponding to the current preset object to the original time data in the current service model to be compressed.
For example, if the time-series line graph is used for drawing the same index and the same expression value of different objects, the current service model to be compressed is as follows: (4/1, beijing, 20), if the temporary service model corresponding to the current beijing is (0, beijing, 0), and the original time data in the service model to be compressed is 4/1, the temporary time data in the temporary service model corresponding to the current beijing may be updated to 4/1, and the temporary service model corresponding to the current beijing is (4/1, beijing, 0).
For example, if the time-series line graph is used for drawing the same object, the same index and different expression values, the current service model to be compressed is as follows: (13:00, 13, 15), if the temporary service model corresponding to the current database is (0, 0, 0), and the original time data in the current service model to be compressed is 13:00, the temporary time data in the temporary service model corresponding to the current database can be updated to 13:00, and the temporary service model corresponding to the current database is (13:00, 0, 0).
S2034, judging whether the original index data in the current service model to be compressed is missing, if so, executing S2038, and if not, executing S2035.
For example, if the time-series line graph is used for drawing the same index and the same expression value of different objects, the current service model to be compressed is as follows: (4/3, beijing, NULL) or (4/3, beijing,), that is, if the original index data in the current service model to be compressed is NULL, it may be determined that there is a deficiency in the original index data in the current service model to be compressed, S2038 is performed, and if there is no deficiency, S2035 is performed.
For example, if the current service model to be compressed is a time-series line graph for plotting different performance values of the same index of the same object, the current service model to be compressed is one of (13:00, NULL, 15), (13:00, 13, NULL), (13:00, NULL), (13:00, 15), (13:00, 13,) and (13:00, that is, the original index data in the current service model to be compressed is NULL, it may be determined that there is a deficiency in the original index data in the current service model to be compressed, S2038 is performed, and if there is no deficiency, S2035 is performed.
S2035, determining whether the temporary index data in the temporary service model corresponding to the current preset object is smaller than the original index data in the current service model to be compressed, if so, executing S2036, and if not, executing S2038.
For example, if the time-series line graph is used for drawing the same index and the same expression value of different objects, the current service model to be compressed is as follows: (4/1, beijing, 20), if the temporary service model corresponding to the current beijing is (4/1, beijing, 0), and the original index data in the service model to be compressed is 20, it may be determined whether the temporary index data in the temporary service model corresponding to the current beijing is smaller than the original index data in the service model to be compressed, in this case, the temporary index data 0 in the temporary service model corresponding to the beijing is smaller than the original index data 20 in the service model to be compressed.
For example, if the time-series line graph is used for drawing the same object, the same index and different expression values, the current service model to be compressed is as follows: (13:00, 13, 15), the temporary service model corresponding to the current database is (13:00, 0, 0), and the original index data in the current service model to be compressed is 13 and 15, then it can be determined whether the temporary index data in the temporary service model corresponding to the current database is smaller than the original index data in the current service model to be compressed, in this case, the temporary index data 0 in the temporary service model corresponding to the database is smaller than the original index data 13 and 15 in the current service model to be compressed.
S2036, updating the temporary index data in the temporary service model corresponding to the current preset object to the original index data in the current service model to be compressed.
For example, if the time-series line graph is used for drawing the same index and the same expression value of different objects, the current service model to be compressed is as follows: (4/1, beijing, 20), if the temporary business model corresponding to the current beijing is (4/1, beijing, 0), the temporary index data in the temporary business model corresponding to the current beijing may be updated to 20, and the temporary business model corresponding to the current beijing is (4/1, beijing, 20).
For example, if the time-series line graph is used for drawing the same object, the same index and different expression values, the current service model to be compressed is as follows: (13:00, 13, 15), if the temporary business model corresponding to the current database is (13:00, 0, 0), the temporary index data in the temporary business model corresponding to the current database may be updated to 13 and 15, and the temporary business model corresponding to the current database is (13:00, 13, 15).
S2037, updating the parent time data of the temporary service model corresponding to the current preset object to the original time data in the current service model to be compressed.
For example, if the time-series line graph is used for drawing the same index and the same expression value of different objects, the current service model to be compressed is as follows: (4/1, beijing, 20), in this example, after the parent class time data 0 of the temporary service model corresponding to the current beijing is updated to the original time data 4/1 in the current service model to be compressed, the process continues to execute S2038.
Exemplarily, if the time sequence line graph is drawn for the same object and the same index and different expression values, the current service model to be compressed is: (13:00, 13, 15), in this example, after the parent time data 0 of the temporary service model corresponding to the current database is updated to the original time data 13:00 in the current service model to be compressed, the process continues to execute S2038.
S2038, judging whether the end of the service model set to be compressed is traversed, if so, executing S2039, and if not, executing S2031.
For example, if the time-series line graph is used for drawing the same index and the same expression value of different objects, the current service model to be compressed is as follows: (4/1, beijing, 20), at this time, the temporary counting step corresponding to beijing is 1 and is smaller than the compression step 3, if it is determined that the current service model to be compressed is the last service model to be compressed of the service model set to be compressed, the service model set to be compressed is not determined any more, and S2039 is directly executed. If the current service model to be compressed is not the last service model to be compressed of the service model set to be compressed, S2031 is executed.
For example, if the time-series line graph is used for drawing the same object, the same index and different expression values, the current service model to be compressed is as follows: (13:00, 13, 15), at this time, the temporary counting step size is 1 and is smaller than the compression step size 3, if the current service model to be compressed is the last service model to be compressed of the service model set to be compressed, the service model set to be compressed is not judged any more, and S2039 is directly executed. If the current service model to be compressed is not the last service model to be compressed of the service model set to be compressed, S2031 is executed.
S2039, adding the temporary business model corresponding to the current preset object into the business model compression result set.
For example, if the time-series line graph is used for drawing the time-series line graphs of different objects with the same index and the same expression value, after the loop determination of S2031 to S2038, the temporary service model corresponding to the current beijing is (4/3, beijing, 20.5), the temporary counting step length corresponding to the current beijing is added to 3, and is no longer smaller than the compression step length 3, and the temporary service model corresponding to the current beijing (4/3, beijing, 20.5) is added to the service model compression result set.
Optionally, because the service models to be compressed in the service model set to be compressed exist in a certain order, for example, the service models to be compressed corresponding to the three objects of beijing, shanghai, and guangzhou appear alternately and cyclically, if the temporary counting step length corresponding to the first object, beijing, shanghai, and guangzhou, is incremented to 3, that is, the service models to be compressed corresponding to the three objects of beijing, shanghai, and guangzhou have all completed model compression within a unit compression range, the temporary service models corresponding to the three objects of beijing, shanghai, and guangzhou can be simultaneously added to the service model compression result set when the temporary counting step length corresponding to beijing is incremented to 3 or has traversed to the end of the service model set to be compressed.
For example, if the time-series line graph is used for drawing time-series line graphs of the same object and different performance values of the same index, after the loop determination of the above S2031 to S2038, the temporary service model corresponding to the current database is (13:02, 13, 16), the temporary count step corresponding to the current database is added to 3, and is no longer smaller than the compression step 3, and the temporary service model corresponding to the current database (13:02, 13, 16) is added to the service model compression result set.
S2040, adding the parent time data of the temporary service model corresponding to the current preset object to a parent time data set.
For example, if the time-series line graph is used for drawing time-series line graphs of different objects with the same index and the same expression value, and if the parent time data of the temporary business model corresponding to the current beijing is 4/2 after the loop determination of S2031 to S2038, the parent time data 4/2 of the temporary business model corresponding to the current beijing is added to the parent time data set.
Optionally, because the service models to be compressed in the service model set to be compressed exist in a certain order, for example, the service models to be compressed corresponding to the three objects of beijing, shanghai and guangzhou appear alternately and cyclically, if the temporary counting step length corresponding to the beijing of the first object is accumulated to 3, the temporary counting step lengths of the service models to be compressed corresponding to the shanghai and the guangzhou are also accumulated to 3, that is, the service models to be compressed corresponding to the three objects of beijing, shanghai and guangzhou all complete model compression within a unit compression range, when the temporary counting step length corresponding to the beijing is accumulated to 3 or the end of the service model set to be compressed has been traversed, the parent class time data of the temporary service models corresponding to the three objects of beijing, shanghai and guangzhou can be simultaneously added to the parent class time data set.
For example, if the time-series line graph is used for drawing the time-series line graphs of the same object with different performance values of the same index, and if the parent time data of the temporary service model corresponding to the current database is (13:00, 13:01) after the loop judgment of the above S2031-S2038, the parent time data (13:00, 13:01) of the temporary service model corresponding to the current database is added to the parent time data set.
Further, after the parent class time data of the temporary service model corresponding to the current preset object is added to the parent class time data set, the method further includes: and setting the temporary counting step length corresponding to the current preset object, the index data in the temporary service model corresponding to the current preset object and the parent class time data of the temporary service model corresponding to the current preset object to zero.
Specifically, after S2040 is executed, the temporary count step corresponding to the current preset object, the temporary time data and the temporary index data in the temporary service model, and the parent time data of the temporary service model need to be set to zero.
For example, if the time-series line graph is used to draw time-series line graphs of different objects with the same index and the same expression value, and the preset object is beijing, shanghai, and guangzhou, after step S2040 is executed, the parent class time data of the temporary service model corresponding to beijing, shanghai, and guangzhou is already added to the parent class time data set, the temporary counting step lengths corresponding to the beijing, shanghai, and guangzhou objects, the temporary time data and temporary index data in the temporary service model, and the parent class time data of the temporary service model may be set to zero.
And S204, determining a parent time data set and a service model compression result set according to the traversal result.
The method for compressing time series data comprises the steps of firstly determining data such as compression step length according to a service model set to be compressed, then initializing a temporary service model and parent time data, traversing the service model set to be compressed according to preset conditions, storing the time series data meeting the preset conditions into the temporary service model, recording the parent time data, then storing the time series data and the parent time data in the temporary service model into a service model compression result set and a parent time data set respectively, and finally determining the parent time data and a service model compression result after traversing is completed.
On the basis of the above embodiment, after determining the parent class time data set and determining the service model compression result set according to the traversal result, the method includes: providing the business model compression result set to a browser so that the browser can visually display the trend of time series data in the business model compression result set; and providing the parent time data set for a browser so that a user can look up original time data corresponding to the index data in the service model compression result set.
Specifically, after the compression is completed, the service model compression result set is provided to the browser, so that the browser loads the service model compression result set and draws a corresponding time series data line graph. Because the time data in the service model compression result set is the nth time data of the service model set to be compressed in the unit compression range, wherein n is the compression step value, the parent time data set can be provided for a browser, and a user can consult the original time data corresponding to the index data in the time sequence data line graph.
EXAMPLE III
Fig. 6 is a schematic structural diagram of a time series data compression apparatus according to a third embodiment of the present invention. As shown in fig. 6, the apparatus includes: a compression step determining module 301, a traversal module 302, and a compression result and parent class time determining module 303, wherein:
the device comprises a compression step length determining module, a compression step length determining module and a compression step length determining module, wherein the compression step length determining module is used for acquiring a service model set to be compressed and determining the compression step length, the service model to be compressed in the service model set to be compressed comprises original time sequence data, and the original time sequence data comprises the corresponding relation between original time data and original index data;
the traversal module is used for traversing the service model set to be compressed based on a temporary service model and the compression step length, updating the temporary service model in the traversal process, and recording parent time data, wherein the temporary service model is used for caching temporary time sequence data in the compression process, and the parent time data is used for indicating original time data corresponding to original index data meeting preset characteristic requirements in a unit compression range corresponding to the compression step length;
and the compression result and parent time determining module is used for determining a parent time data set and a service model compression result set according to the traversal result.
According to the compression device of the time sequence data, the compression step length is determined according to the service model set to be compressed, then the service model set to be compressed is traversed, the compressed data are cached into the temporary service model, the parent time data are recorded, and the parent time data and the service model compression result can be determined after traversal is completed.
Optionally, the compression step determining module 301 includes:
the data point and preset object determining unit is used for acquiring a service model set to be compressed and determining an actual time sequence data point, an expected time sequence data point and a preset object;
and the compression step length determining unit is used for determining the compression step length according to the actual time sequence data point number and the expected time sequence data point number.
Optionally, the apparatus further comprises:
a temporary counting step initialization module, configured to initialize a temporary counting step corresponding to each preset object before traversing the service model set to be compressed based on the temporary service model and the compression step, where the temporary counting step is used for counting cache steps;
the temporary business model initialization module is used for initializing the temporary business model corresponding to each preset object;
and the parent time data initialization module is used for initializing the parent time data of the temporary service model corresponding to each preset object.
Optionally, traversing the set of service models to be compressed based on the temporary service model and the compression step, updating the temporary service model in the traversal process, and recording parent time data, including: and traversing the service model set to be compressed in a plurality of continuous unit compression ranges based on a temporary service model and the compression step length for each preset object, updating the temporary service model in the traversal process, and recording parent time data, wherein the unit compression ranges are determined according to the compression step length, and the total range of the unit compression ranges corresponds to the service model set to be compressed.
Optionally, the traversing module 302 includes:
the temporary counting step length judging unit is used for determining a current to-be-compressed service model to be traversed in a current unit compression range and a current preset object and judging whether the temporary counting step length corresponding to the current preset object is smaller than the compression step length or not;
a temporary counting step updating unit, configured to update the temporary counting step corresponding to the current preset object when the result of the judgment by the temporary counting step judging unit is yes, where the updating mode is to add 1 to the step count;
the temporary time data updating unit is used for updating the temporary time data in the temporary service model corresponding to the current preset object into original time data in the current service model to be compressed;
the temporary index data judgment unit is used for judging whether the temporary index data in the temporary service model corresponding to the current preset object is smaller than the original index data in the current service model to be compressed;
a temporary index data updating unit, configured to update temporary index data in a temporary service model corresponding to the current preset object to original index data in the current service model to be compressed when a determination result of the temporary index data determining unit is yes;
a parent time data updating unit, configured to update parent time data of the temporary service model corresponding to the current preset object to original time data in the current service model to be compressed;
a tail traversal judging unit, configured to judge whether to traverse to the tail of the service model set to be compressed;
and the execution skipping first unit is used for informing the temporary counting step length judging unit to repeatedly execute the operation of determining the current to-be-compressed service model to be traversed and the current preset object in the current unit compression range when the judgment result of the tail traversal judging unit is negative.
Optionally, the traversing module 302 further includes:
a temporary service model adding unit, configured to add the temporary service model corresponding to the current preset object to a service model compression result set if the judgment result of the temporary counting step length judging unit is negative;
and the parent time data adding unit is used for adding the parent time data of the temporary service model corresponding to the current preset object to a parent time data set.
Optionally, the traversing module 302 further includes:
and the zero setting unit is used for setting the temporary counting step length corresponding to the current preset object, the index data in the temporary service model corresponding to the current preset object and the parent time data of the temporary service model corresponding to the current preset object to zero.
Optionally, the traversing module 302 further includes:
and the data missing judging unit is used for judging whether the original index data in the current business model to be compressed is missing or not before judging whether the temporary index data in the temporary business model corresponding to the current preset object is smaller than the original index data in the current business model to be compressed or not.
And the execution skipping second unit is used for informing the tail traversal judging unit to execute the operation of judging whether the operation is traversed to the tail of the service model set to be compressed when the judgment result of the data missing judging unit is yes.
Optionally, the apparatus further comprises:
the compression result set providing module is used for providing the business model compression result set to a browser so that the browser can visually display the trend of the time sequence data in the business model compression result set;
and the parent time data set providing module is used for providing the parent time data set to a browser so that a user can look up original time data corresponding to the index data in the service model compression result set.
The compression device for time sequence data provided by the embodiment of the invention can execute the compression method for time sequence data provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.
Example four
FIG. 7 illustrates a schematic diagram of an electronic device 40 that may be used to implement an embodiment of the present invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 7, the electronic device 40 includes at least one processor 41, and a memory communicatively connected to the at least one processor 41, such as a Read Only Memory (ROM)42, a Random Access Memory (RAM)43, and the like, wherein the memory stores a computer program executable by the at least one processor, and the processor 41 may perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM)42 or the computer program loaded from the storage unit 48 into the Random Access Memory (RAM) 43. In the RAM 43, various programs and data necessary for the operation of the electronic apparatus 40 can also be stored. The processor 41, the ROM 42, and the RAM 43 are connected to each other via a bus 44. An input/output (I/O) interface 45 is also connected to the bus 44.
A number of components in the electronic device 40 are connected to the I/O interface 45, including: an input unit 46 such as a keyboard, a mouse, etc.; an output unit 47 such as various types of displays, speakers, and the like; a storage unit 48 such as a magnetic disk, an optical disk, or the like; and a communication unit 49 such as a network card, modem, wireless communication transceiver, etc. The communication unit 49 allows the electronic device 40 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
In some embodiments, the method of compression of time series data may be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 48. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 40 via the ROM 42 and/or the communication unit 49. When the computer program is loaded into the RAM 43 and executed by the processor 41, one or more steps of the method of compression of time series data described above may be performed. Alternatively, in other embodiments, processor 41 may be configured to perform the compression method of the timing data by any other suitable means (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. A computer program can execute entirely on a machine, partly on a machine, as a stand-alone software package partly on a machine and partly on a remote machine or entirely on a remote machine or server.
The computer device provided by the above can be used to execute the compression method of time series data provided by any of the above embodiments, and has corresponding functions and advantages.
EXAMPLE five
In the context of the present invention, a computer-readable storage medium may be a tangible medium, the computer-executable instructions when executed by a computer processor for performing a method of compression of time-series data, the method comprising:
acquiring a service model set to be compressed, and determining a compression step length, wherein the service model to be compressed in the service model set to be compressed comprises original time sequence data, and the original time sequence data comprises a corresponding relation between original time data and original index data;
traversing the service model set to be compressed based on a temporary service model and the compression step length, updating the temporary service model in the traversing process, and recording parent time data, wherein the temporary service model is used for caching temporary time sequence data in the compression process, and the parent time data is used for indicating original time data corresponding to original index data meeting preset characteristic requirements in a unit compression range corresponding to the compression step length;
and determining a parent time data set and a service model compression result set according to the traversal result.
In the context of the present invention, a computer readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, 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 computer device provided by the above can be used to execute the compression method of time series data provided by any of the above embodiments, and has corresponding functions and advantages.
It should be noted that, in the embodiment of the compression apparatus for time series data, the units and modules included in the compression apparatus are only divided according to functional logic, but are not limited to the above division as long as the corresponding functions can be implemented; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (11)
1. A method for compressing time series data, comprising:
acquiring a service model set to be compressed, and determining a compression step length, wherein the service model to be compressed in the service model set to be compressed comprises original time sequence data, and the original time sequence data comprises a corresponding relation between original time data and original index data;
traversing the service model set to be compressed based on a temporary service model and the compression step length, updating the temporary service model in the traversing process, and recording parent time data, wherein the temporary service model is used for caching temporary time sequence data in the compression process, and the parent time data is used for indicating original time data corresponding to original index data meeting preset characteristic requirements in a unit compression range corresponding to the compression step length;
and determining a parent time data set and a service model compression result set according to the traversal result.
2. The method of claim 1, wherein the obtaining the set of service models to be compressed and determining the compression step size comprise:
acquiring a service model set to be compressed, and determining an actual time sequence data point number, an expected time sequence data point number and a preset object;
and determining the compression step length according to the number of the actual time sequence data points and the number of the expected time sequence data points.
3. The method according to claim 2, further comprising, before traversing the set of business models to be compressed based on the temporary business model and the compression step size:
initializing a temporary counting step corresponding to each preset object, wherein the temporary counting step is used for counting the cache steps;
initializing a temporary service model corresponding to each preset object;
and initializing the parent class time data of the temporary service model corresponding to each preset object.
4. The method according to claim 3, wherein traversing the set of business models to be compressed based on the temporary business model and the compression step size, updating the temporary business model during traversal, and recording parent time data comprises:
traversing the service model set to be compressed in a plurality of continuous unit compression ranges based on a temporary service model and the compression step length for each preset object, updating the temporary service model in the traversal process, and recording parent time data, wherein the unit compression ranges are determined according to the compression step length, and the total range of the unit compression ranges corresponds to the service model set to be compressed;
for each preset object, traversing the set of service models to be compressed in the current unit compression range based on the temporary service model and the compression step length, updating the temporary service model in the traversal process, and recording parent time data, including:
determining a current business model to be compressed and a current preset object to be traversed in a current unit compression range, and judging whether a temporary counting step length corresponding to the current preset object is smaller than the compression step length;
if so, updating a temporary counting step length corresponding to the current preset object, wherein the updating mode is that the step length counting is increased by 1;
updating the temporary time data in the temporary service model corresponding to the current preset object into original time data in the current service model to be compressed;
judging whether the temporary index data in the temporary service model corresponding to the current preset object is smaller than the original index data in the current service model to be compressed;
if so, updating the temporary index data in the temporary service model corresponding to the current preset object to the original index data in the current service model to be compressed;
updating the parent time data of the temporary service model corresponding to the current preset object into original time data in the current service model to be compressed;
judging whether the service model set to be compressed is traversed to the end of the service model set to be compressed;
if not, repeatedly executing the operation of determining the current to-be-compressed service model to be traversed in the current unit compression range and the current preset object.
5. The method according to claim 4, wherein after said determining whether the temporary counting step corresponding to the current preset object is smaller than the compressing step, further comprising:
if not, adding the temporary business model corresponding to the current preset object into a business model compression result set;
and adding the parent time data of the temporary business model corresponding to the current preset object into a parent time data set.
6. The method according to claim 5, wherein after the adding the parent time data of the temporary business model corresponding to the current preset object to the parent time data set, the method further comprises:
and setting the temporary counting step length corresponding to the current preset object, the index data in the temporary service model corresponding to the current preset object and the parent class time data of the temporary service model corresponding to the current preset object to zero.
7. The method according to claim 4, before the determining whether the temporary index data in the temporary service model corresponding to the current preset object is smaller than the original index data in the service model to be compressed, further comprising:
judging whether the original index data in the current business model to be compressed is missing;
and if so, executing the operation of judging whether the operation is traversed to the tail of the service model set to be compressed.
8. The method of claim 1, further comprising, after determining the parent temporal data set and determining the business model compression result set according to the traversal result:
providing the business model compression result set to a browser so that the browser can visually display the trend of time series data in the business model compression result set;
and providing the parent time data set for a browser so that a user can look up original time data corresponding to the index data in the service model compression result set.
9. An apparatus for compressing time series data, comprising:
the device comprises a compression step length determining module, a compression step length determining module and a compression step length determining module, wherein the compression step length determining module is used for acquiring a service model set to be compressed and determining the compression step length, the service model to be compressed in the service model set to be compressed comprises original time sequence data, and the original time sequence data comprises the corresponding relation between original time data and original index data;
the traversal module is used for traversing the service model set to be compressed based on a temporary service model and the compression step length, updating the temporary service model in the traversal process, and recording parent time data, wherein the temporary service model is used for caching temporary time sequence data in the compression process, and the parent time data is used for indicating original time data corresponding to original index data meeting preset characteristic requirements in a unit compression range corresponding to the compression step length;
and the compression result and parent time determining module is used for determining a parent time data set and a service model compression result set according to the traversal result.
10. An electronic device, characterized in that the electronic device comprises:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of compressing time series data of any one of claims 1 to 8.
11. A computer-readable storage medium, having stored thereon computer instructions for causing a processor to execute a method of compressing time series data according to any one of claims 1 to 8.
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