WO2023020247A1 - Method and apparatus for precision reduction of time series index data, and computer device - Google Patents

Abstract

Disclosed are a method and apparatus for precision reduction of time series index data, and a computer device. The method comprises: receiving time series index data (110); searching for a corresponding precision reduction algorithm according to the type of the time series index data (120); and performing precision reduction on metadata information of the time series index data according to the precision reduction algorithm (130). The present application solves the problems in the related art of high cost and inconvenient operation during precision reduction of time series data.

Description

Method, device and computer equipment for processing timing index data with reduced accuracy technical field

The present application relates to the field of data processing, in particular to a method, device and computer equipment for processing timing index data with reduced accuracy.

Background technique

In monitoring and other fields, data with time series characteristics is to collect the current value of a certain indicator at intervals. This kind of data is all floating-point numbers or integer values, and often changes little or even unchanged for a period of time. Therefore, some implementations different from general compression algorithms can be used to compress the data volume to a higher compression ratio. The data may be referred to as time series indicator data. Since time-series data is usually time-sensitive, most users only care about the most recent period of data, and do not care much about the data of a long time ago. Compared with the business data, it does not matter even if the monitoring data has a certain loss of precision. Therefore, In order to reduce disk usage for time-series data, in addition to using special compression algorithms, old data will also be considered for precision reduction.

The current time-series databases, such as influxdb, etc., adopt the method of reducing accuracy to allow users to define dml statements (database operation statements) by themselves. In this implementation method, whenever time-series data needs to be processed with precision reduction, the user needs to manually add a dml statement, and the operation and maintenance cost is high, and the operation is not friendly.

At present, no effective solution has been proposed for the problems of high cost and inconvenient operation in processing time-series data with reduced accuracy in related technologies.

Contents of the invention

Embodiments of the present application provide a method, device, and computer equipment for processing time series index data with reduced precision, so as to at least solve the problems of high cost and inconvenient operation in the related art of processing time series index data with reduced precision.

In the first aspect, an embodiment of the present application provides a method for processing timing index data with reduced accuracy, the method comprising:

Receive timing index data;

Searching for a corresponding precision reduction algorithm according to the type of the timing index data;

According to the precision reduction algorithm, the metadata information of the time series indicator data is subjected to precision reduction processing.

In some of these embodiments, before searching for a corresponding precision reduction algorithm according to the type of timing index data, the method further includes:

Configuring a precision reduction algorithm corresponding to the timing index data;

Associating the precision reduction algorithm of the time series index data with the metadata information of the time series index data.

In some of these embodiments, performing precision reduction processing on the metadata information of the timing index data according to the precision reduction algorithm includes:

Detecting the storage duration of the timing index data;

After detecting that the storage duration is longer than the preset duration, the metadata information of the timing index data is subjected to precision reduction processing according to the found precision reduction algorithm.

In some of these embodiments, the method also includes:

A storage strategy for the timing index data is configured, and the storage strategy specifies that the timing index data is stored for a preset time to perform precision reduction processing.

In some of these embodiments, the configuring the precision reduction algorithm corresponding to the timing index data includes:

Configure a corresponding precision reduction algorithm in the data write request of the timing index data; or

When the storage system creates the schema table, configure the precision reduction algorithm corresponding to the time series index data.

In some of these embodiments, the precision reduction algorithm of the timing index data includes:

Max: Take the maximum value of all timing index data within the preset time precision range;

Min: Take the minimum value of all timing index data within the preset time precision range;

Gauge: Take the last value of all timing index data within the preset time precision range;

Sum: Take the sum of all timing index data within the preset time precision range;

Avg: take at least one of the sum of all timing index data within the preset time precision range, and the quantity of all timing index data within the preset time precision range.

In some of these embodiments, each of the time series index data corresponds to at least one of the precision reduction algorithms; and performing precision reduction processing on the metadata information of the time series index data according to the precision reduction algorithm includes:

After detecting that the storage duration is longer than the preset duration, according to the found multiple precision reduction algorithms, the metadata information of the timing index data is respectively subjected to precision reduction processing to obtain multiple sets of reduced precision timing index data.

In the second aspect, the embodiment of the present application provides a timing index data precision reduction processing device, including a receiving module, a search module and a processing module; wherein:

A receiving module, configured to receive timing index data;

The search module is used to find the corresponding precision reduction algorithm according to the type of time series index data;

The processing module is configured to perform precision reduction processing on the metadata information of the time series indicator data according to the precision reduction algorithm.

In a third aspect, the embodiment of the present application provides a computer device, including a memory, a processor, and a computer program stored on the memory and operable on the processor. When the processor executes the computer program, Realize the method for processing timing index data with reduced precision as described in the first aspect above.

In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the method for processing timing index data with reduced accuracy as described in the first aspect above is implemented.

Compared with related technologies, the timing index data reduction processing method provided by the embodiment of the present application configures the accuracy reduction algorithm corresponding to each timing index data; Corresponding storage of metadata information; acquiring the storage policy of the time series index data; the storage policy specifies that the time series index data is stored for a preset time, and the time series index data is subjected to precision reduction processing; according to the precision reduction algorithm With the storage strategy, the metadata information of the time-series index data is subjected to precision-reducing processing, which solves the problems of high cost and inconvenient operation in the related art of performing precision-reduction processing on time-series data.

The details of one or more embodiments of the application are set forth in the accompanying drawings and the description below, so as to make other features, objects, and advantages of the application more comprehensible.

Description of drawings

The drawings described here are used to provide a further understanding of the application and constitute a part of the application. The schematic embodiments and descriptions of the application are used to explain the application and do not constitute an improper limitation to the application. In the attached picture:

FIG. 1 is a flow chart of a method for processing timing index data to reduce precision provided in an embodiment of the present application;

FIG. 2 is a structural block diagram of a timing index data storage device provided in an embodiment of the present application;

Fig. 3 is an internal structural diagram of a computer device provided by an embodiment of the present application.

Detailed ways

In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be described and illustrated below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present application, and are not intended to limit the present application. Based on the embodiments provided in the present application, all other embodiments obtained by persons of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

Obviously, the accompanying drawings in the following description are only some examples or embodiments of the present application, and those skilled in the art can also apply the present application to other similar scenarios. In addition, it can also be understood that although such development efforts may be complex and lengthy, for those of ordinary skill in the art relevant to the content disclosed in this application, the technology disclosed in this application Some design, manufacturing or production changes based on the content are just conventional technical means, and should not be understood as insufficient content disclosed in this application.

Reference in this application to an "embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the present application. The occurrences of this phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is understood explicitly and implicitly by those of ordinary skill in the art that the embodiments described in this application can be combined with other embodiments without conflict.

Unless otherwise defined, the technical terms or scientific terms involved in the application shall have the usual meanings understood by those with ordinary skill in the technical field to which the application belongs. Words such as "a", "an", "an" and "the" involved in this application do not indicate a limitation on quantity, and may indicate singular or plural numbers. The terms "comprising", "comprising", "having" and any variations thereof involved in this application are intended to cover non-exclusive inclusion; for example, a process, method, system, product or process that includes a series of steps or modules (units). The apparatus is not limited to the listed steps or units, but may further include steps or units not listed, or may further include other steps or units inherent to the process, method, product or apparatus. The "plurality" involved in this application refers to two or more than two. "And/or" describes the association relationship of associated objects, indicating that there may be three types of relationships. For example, "A and/or B" may indicate: A exists alone, A and B exist simultaneously, and B exists independently. The character "/" generally indicates that the contextual objects are an "or" relationship. The terms "first", "second", "third" and the like involved in this application are only used to distinguish similar objects, and do not represent a specific ordering of objects.

This embodiment provides a method for processing timing index data with reduced accuracy. Fig. 1 is a flowchart of a time series data storage method according to an embodiment of the present application. As shown in Fig. 1, the process includes the following steps:

Step 110, receiving timing index data;

Step 120, according to the type of the timing index data, search for a corresponding precision reduction algorithm;

Step 130: Perform precision reduction processing on the metadata information of the timing index data according to the precision reduction algorithm.

Time-series index data can be understood as time-series data reflecting a certain index, which is also referred to as time-series data in this application. In the field of monitoring, monitoring data is time-sensitive. In order to reduce the disk occupation of the time-series data obtained by monitoring, in addition to using the compression algorithm, the time-series data will also be processed to reduce precision. For example, a certain indicator reports an integer value (4 bytes) of data every 1 minute, so 1440 bytes of time series data will be generated in 24 hours. After 1 hour of time-series data, use precision reduction processing to reduce the precision of the data from 1 minute to 10 minutes at the expense of a certain decrease in data precision, and only need to store 144 bytes of data. 10 minutes may be the preset accuracy range, and of course, in other embodiments, the preset accuracy range may also be 20 minutes or 30 minutes. The current time-series databases, such as influxdb, adopt the method of reducing precision: let users define dml statements by themselves, calculate the result of reducing precision from the original data, and then store it in another table to achieve the purpose of reducing precision. For example, the following statement will generate a data that reduces the precision of the original data to 10 minutes, and the precision reduction type is summation: Select sum(api_counter) as api_counter_10min from origin_table into downsampling_table_10min. In this implementation, whenever time series data needs to be processed with reduced accuracy, the user needs to manually add a dml statement, which has high operation and maintenance costs and is not user-friendly.

This application configures the corresponding precision reduction algorithm for each time series data, stores the time series index data after receiving it, and searches for the corresponding precision reduction algorithm according to the type of the stored time series index data when it needs to perform precision reduction processing. The metadata information of the indicator data is processed with precision reduction.

Before finding the corresponding precision reduction algorithm according to the type of time series index data, it is necessary to configure different precision reduction algorithms according to different types of time series data. And after the configuration is completed, it will be stored in association with the timing index data. The storage method is not limited, for example, it may be a mapping table. For example, the cpu usage rate can generally be considered to take the maximum value and/or average value within a certain period of time as the result of the reduced precision. When storing time series index data, the metadata information of the time series index data and the precision reduction algorithm configured for it are stored correspondingly. The precision reduction algorithm may only include the type of precision reduction, such as the maximum value, the average value or the last value of a period of time, and the above period of time may be a unified time set by the storage system. Preferably, the precision reduction algorithm includes a preset time precision range and a precision reduction type. For example, the precision reduction algorithm may be Max: take the maximum value of all timing index data within the preset time precision range. The preset time precision range may be set according to the data characteristics of the time series index data, and the preset time precision ranges set for different types of time series index data may be different.

Specifically, according to the precision reduction algorithm, performing precision reduction processing on the metadata information of the time series index data may be that the user issues a control instruction, triggering the metadata information of at least one time series index data to be processed according to the corresponding precision reduction processing algorithm. Reduce precision processing. The control command may carry corresponding identification information, and the timing indicator data may also carry corresponding identification information. The control instruction can include multiple identification information in one instruction to control the precision reduction processing of multiple time series index data at the same time, without requiring the user to manually add a dml statement whenever the time series data needs to be processed with precision reduction.

In some of these embodiments, the accuracy reduction algorithm for timing index data includes:

Max: Take the maximum value of all timing index data within the preset time precision range;

Min: Take the minimum value of all timing index data within the preset time precision range;

Gauge: Take the last value of all timing index data within the preset time precision range;

Sum: Take the sum of all timing index data within the preset time precision range;

Avg: take at least one of the sum of all timing index data within the preset time precision range, and the quantity of all timing index data within the preset time precision range.

It can be understood that the types of precision reduction algorithms mentioned above are only examples, and those skilled in the art can also set other types of precision reduction algorithms. In addition, the preset time precision ranges in each type of precision reduction algorithm mentioned above may be different.

In order to illustrate the solution of the present application more clearly, the following examples are used for illustration. For example, to count the number of calls to each API, it is necessary to add all values within a certain period of time as the result of reduced precision. The details are shown in the following table, which shows the usage rate of a certain machine's cpu at each time point. The preset time accuracy is 5 minutes, and the last value within this period is taken.

The following table shows the number of times an API is called at each time point. The preset time precision is 5 minutes, and the sum of all values during this period is taken.

Through the above steps, the present application correspondingly stores the metadata of the time series index data and the precision reduction algorithm configured for it when storing the time series index data. For example, a corresponding precision reduction algorithm may be configured in each data write request of the timing index data; or, when the storage system creates a schema table, specify a precision reduction algorithm for each timing index data. Specifically, when sending a write request at the writing end of the storage system, add a reduced precision type (ie, a reduced precision algorithm) to each timing index data. When the storage system writes data, the precision type of the indicator is written into the storage system as the metadata information of the indicator.

The writing protocol of the traditional time series data storage system is: Insert into table set api_counter=? , cpu_usage=X, time=X. Traditional storage system data storage format:

time	00:01	...
Api_counter	16	...
cpu_usage	16	...

The storage system writing protocol of this application: Insert into table set api_counter＝X with sum, cpu_usage＝X with gauge, time＝X.

The storage system data storage format of this application:

time	Type=time	00:01	...
Api_counter	Type=sum	16	...
cpu_usage	Type = gauge	16	...

It can be seen from the storage formats of the above two storage system data that this application adds a precision reduction processing algorithm corresponding to the timing index data when storing data. In this way, the operation and maintenance personnel can realize the reduced precision storage of time series data without creating a separate task for each new indicator. At the same time, different time series index data can choose different precision reduction algorithms according to their respective data characteristics, and calculate reasonable data precision reduction results. When the data needs to be processed with precision reduction, the user does not need to add dml statements for each data. By viewing the precision reduction algorithm corresponding to the corresponding time series index data, the effect of automatic precision reduction processing can be realized, which solves the problem of time series data in related technologies. The problem of high cost and inconvenient operation exists in the process of reducing precision.

In some of these embodiments, according to the precision reduction algorithm, the metadata information of the time series indicator data is subjected to precision reduction processing, including:

Detecting the storage duration of the timing index data;

After detecting that the storage duration is longer than the preset duration, the metadata information of the timing index data is subjected to precision reduction processing according to the found precision reduction algorithm.

In this embodiment, the storage policy of the timing index is configured at the same time, so that after the timing index data is detected to be stored for a preset time, the accuracy reduction processing is automatically performed on the timing index data according to the precision reduction algorithm. Specifically, after it is detected that the storage time of the time series data written in the storage system reaches the preset time, the storage operation of the time series data is re-initiated, and the precision reduction algorithm corresponding to the time series data is used, which can also be called the precision reduction type. The metadata information of time series data is reduced to the appropriate result. The above preset time can be 10 minutes, 20 minutes or one day, etc., and can be set according to the type of time series data or the actual situation of the user, and is not specifically limited in this embodiment.

In some other embodiments, the time series index data is reduced in accuracy by the accuracy reduction algorithm, and the original accuracy data can be replaced with the reduced accuracy data according to the user's needs, so as to reduce the disk usage. It is also possible to keep multiple copies of the original precision and reduced precision data at the same time, so as to achieve the purpose of using the reduced precision data to speed up the query and using the original precision time series data to retain the details.

In some of these embodiments, each of the timing index data corresponds to at least one of the precision reduction algorithms.

Considering the needs of users, users may need to analyze time series data from multiple dimensions. For example, at 1, 2, 3, 4, and 5 moments, the usage rates of the cpu are 10, 20, 30, 40, and 50 respectively. If they are combined into one value (reduced precision processing), if you want to see the peak value, you need to take the maximum value of 50. , if you want to see the average value, you need to average 30. In actual scenarios, if users want to observe whether the CPU has spikes, they need to look at the maximum value. If they just want to observe the whole situation, it is not appropriate to use the maximum value, and the average value is more appropriate. Therefore, in this embodiment, at least one precision reduction algorithm is correspondingly configured for each time series index data, so as to facilitate the user to accurately analyze the data from multiple dimensions, and then judge the corresponding index situation.

When only one precision reduction algorithm is configured for time series index data, the corresponding storage statement is:

Insert into table set api_counter=? with sum, cpu_usage=? with gauge, time=?

In this embodiment, the storage statement can be written as Insert into table set api_counter=? with sum, cpu_usage=? with[avg,max],time=? .

The query statement of the original solution is select cpu_usage from table, and the query statement of the solution in this embodiment can be written as:

select cpu_usage_avg from table;

select cpu_usage_max from table.

In this embodiment, after it is detected that the storage duration of the timing index data reaches a preset duration, multiple sets of reduced-precision timing index data are respectively calculated according to multiple precision reduction algorithms corresponding to the timing index data.

In some of these embodiments, the configuration of the precision reduction algorithm corresponding to each timing index data includes:

Configure a corresponding precision reduction algorithm in each data write request of the timing index data; or,

When the storage system creates the schema table, a precision reduction algorithm for each time series index data is specified.

In this embodiment, two storage modes are set according to the type of the storage system. If the storage system has a schema (the concept of a table), you can specify the type of precision reduction for each time series data when creating the table. If the storage system does not have the concept of schema, you can include the precision reduction type of each indicator when sending the write request.

By way of example, assume that the storage system only stores one cpu usage data, and then the user wants to reduce the accuracy according to the average value and the maximum value.

For storage systems with schema, metadata information will be recorded, column=cpu, downsampling=[avg,max]; in this way, cpu=? can be written when writing data. . After the storage system receives it, it is enough to directly save the value of the metadata. When the accuracy is reduced later, it is found from the metadata that avg and max are required to reduce the accuracy, and the accuracy is reduced for the two types of accuracy reduction. deal with.

For a storage system without a schema, there is no metadata information, so you need to add metadata when writing data, that is, cpu=? with[avg,max], when the storage system receives it, it not only needs to store cpu=? , also need to save cpu downsampling=[avg,max]. When performing precision reduction processing later, read downsampling directly from the data to perform precision reduction.

For example, we need to record the following information:

cpu 00:01 = 10;

cpu 00:02 = 20;

cpu 00:03 = 30.

In the storage system with shcmea, the records are:

metadata: column=cpu,downsampling=[avg,max];

Actual data: 00:01=10, 00:02=20, 00:03=30.

In the storage system without schema, it is recorded that:

metadata: None;

Actual data: 00:01=10[avg,max], 00:02=20[avg,max], 00:03=30[avg,max].

Because no matter how many time points of data are written in, the type of precision reduction is certain, so the actual storage can be compressed into:

metadata: none;

Actual data: [avg,max], 00:01=10, 00:02=20, 00:03=30.

It can be considered that metadata is a very small table in which data is written only when creating a schema table under normal circumstances, and the actual data is a large table in which new data is written in all the time.

The storage systems without a schema and those with a schema are basically similar in actual storage, because even a system without a schema can dynamically write metadata to the metadata table when writing data. The storage system of the schema can finally store the data in the following format:

Metadata: column=? ,[downsampling];

Actual data, column=? ,[value=timestamp, value=timestamp...].

This embodiment also provides a time series indicator data precision reduction processing device, which is used to implement the above embodiments and preferred implementation modes, and what has already been described will not be repeated. As used below, the terms "module", "unit", "subunit" and the like may be a combination of software and/or hardware that realize a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementations in hardware, or a combination of software and hardware are also possible and contemplated.

FIG. 3 is a structural block diagram of a timing index data storage device according to an embodiment of the present application. As shown in FIG. 3 , the device includes a receiving module 210, a search module 220, and a processing module 230; wherein:

A receiving module 210, configured to receive timing index data;

A search module 220, configured to search for a corresponding precision reduction algorithm according to the type of timing index data;

The processing module 230 is configured to perform precision reduction processing on the timing index data according to the precision reduction algorithm.

This application adds a precision reduction algorithm corresponding to the timing index data when storing data. In this way, the operation and maintenance personnel can realize the reduced-precision storage of time series data without creating a separate task for each new indicator. At the same time, different time series index data can choose different precision reduction algorithms according to their respective data characteristics, and calculate reasonable data precision reduction results. When the data needs to be processed with precision reduction, the user does not need to add dml statements for each data. By viewing the precision reduction algorithm corresponding to the corresponding time series index data, the effect of automatic precision reduction processing can be realized, which solves the problem of time series data in related technologies. The problem of high cost and inconvenient operation exists in the process of reducing precision.

In some of these embodiments, it also includes a first configuration module, configured to configure a precision reduction algorithm corresponding to the timing index data; and associate the precision reduction algorithm of the timing index data with the metadata information of the timing index data.

In some of these embodiments, the processing module 230 is also used for:

Detecting the storage duration of the timing index data;

After detecting that the storage duration is longer than the preset duration, the metadata information of the timing index data is subjected to precision reduction processing according to the found precision reduction algorithm.

In some of these embodiments, a second configuration module is also included, configured to: configure a storage policy for the timing index data, where the storage policy specifies that the timing index data is stored for a preset time to perform precision reduction processing.

In some of these embodiments, the first configuration module is also used for:

Configure a corresponding precision reduction algorithm in the data write request of the timing index data; or,

When the storage system creates the schema table, configure the precision reduction algorithm corresponding to the time series index data.

In some of these embodiments, the precision reduction algorithm of the timing index data includes:

Max: Take the maximum value of all timing index data within the preset time precision range;

Min: Take the minimum value of all timing index data within the preset time precision range;

Gauge: Take the last value of all timing index data within the preset time precision range;

Sum: Take the sum of all timing index data within the preset time precision range;

Avg: take at least one of the sum of all timing index data within the preset time precision range, and the quantity of all timing index data within the preset time precision range.

In some of these embodiments, each of the timing index data corresponds to at least one of the precision reduction algorithms; the processing module 230 is further configured to: after detecting that the storage duration is longer than the preset duration, according to the searched multiple precision reduction algorithms , respectively performing precision reduction processing on the metadata information of the time series index data to obtain multiple sets of time series index data with reduced precision.

It should be noted that each of the above-mentioned modules may be a function module or a program module, and may be realized by software or by hardware. For the modules implemented by hardware, the above modules may be located in the same processor; or the above modules may be located in different processors in any combination.

In addition, the method for processing time-series indicator data to reduce precision in the embodiment of the present application described in conjunction with FIG. 1 may be implemented by a computer device. FIG. 3 is a schematic diagram of a hardware structure of a computer device according to an embodiment of the present application.

The computer device may comprise a processor 31 and a memory 32 storing computer program instructions.

Specifically, the processor 31 may include a central processing unit (CPU), or an Application Specific Integrated Circuit (ASIC for short), or may be configured to implement one or more integrated circuits in the embodiments of the present application.

Wherein, the memory 32 may include a mass memory for data or instructions. For example without limitation, memory 32 may include hard disk drive (Hard Disk Drive, referred to as HDD), floppy disk drive, solid state drive (Solid State Drive, referred to as SSD), flash memory, optical disc, magneto-optical disc, magnetic tape or general serial Bus (Universal Serial Bus, referred to as USB) driver or a combination of two or more of the above. Memory 32 may comprise removable or non-removable (or fixed) media, where appropriate. Memory 32 may be internal or external to the data processing arrangement, where appropriate. In a particular embodiment, memory 32 is a non-volatile (Non-Volatile) memory. In a specific embodiment, the memory 32 includes a read-only memory (Read-Only Memory, referred to as ROM) and a random access memory (Random Access Memory, referred to as RAM). Where appropriate, the ROM can be a mask-programmed ROM, a programmable ROM (Programmable Read-Only Memory, referred to as PROM), an erasable PROM (Erasable Programmable Read-Only Memory, referred to as EPROM), an electronically programmable Erase PROM (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), electrically rewritable ROM (Electrically Alterable Read-Only Memory, referred to as EAROM) or flash memory (FLASH) or a combination of two or more of these. Where appropriate, the RAM can be a Static Random-Access Memory (SRAM for short) or a Dynamic Random-Access Memory (DRAM for short), where the DRAM can be a fast page Mode Dynamic Random Access Memory (Fast Page Mode Dynamic Random Access Memory, referred to as FPMDRAM), Extended Data Output Dynamic Random Access Memory (Extended Date Out Dynamic Random Access Memory, referred to as EDODRAM), Synchronous Dynamic Random Access Memory (Synchronous Dynamic Random-Access Memory, referred to as SDRAM), etc.

The memory 32 can be used to store or cache various data files required for processing and/or communication, as well as possible computer program instructions executed by the processor 31 .

The processor 31 reads and executes the computer program instructions stored in the memory 32 to implement any one of the timing index data precision reduction processing methods in the above embodiments.

In some of these embodiments, the computer device may further include a communication interface 33 and a bus 30 . Wherein, as shown in FIG. 3 , the processor 31 , the memory 32 , and the communication interface 33 are connected through the bus 30 and complete mutual communication.

The communication interface 33 is used to realize the communication between various modules, devices, units and/or devices in the embodiment of the present application. The communication interface 33 can also implement data communication with other components such as external devices, image/data acquisition equipment, databases, external storage, and image/data processing workstations.

Bus 30 includes hardware, software, or both, and couples the components of the computer device to each other. The bus 30 includes but is not limited to at least one of the following: a data bus (Data Bus), an address bus (Address Bus), a control bus (Control Bus), an expansion bus (Expansion Bus), and a local bus (Local Bus). For example without limitation, the bus 30 may include an Accelerated Graphics Port (AGP for short) or other graphics bus, an Enhanced Industry Standard Architecture (Extended Industry Standard Architecture, EISA for short) bus, a Front Side Bus (Front Side Bus) , FSB for short), Hyper Transport (HT) interconnection, Industry Standard Architecture (ISA) bus, InfiniBand interconnection, Low Pin Count, Referred to as LPC) bus, memory bus, Micro Channel Architecture (Micro Channel Architecture, referred to as MCA) bus, peripheral component interconnect (Peripheral Component Interconnect, referred to as PCI) bus, PCI-Express (PCI-X) bus, serial Advanced Technology Attachment (Serial Advanced Technology Attachment, referred to as SATA) bus, Video Electronics Standards Association Local Bus (referred to as VLB) bus or other suitable bus or a combination of two or more of these. Where appropriate, bus 30 may comprise one or more buses. Although the embodiments of this application describe and illustrate a particular bus, this application contemplates any suitable bus or interconnect.

The computer device may execute the method for processing timing index data with reduced precision in the embodiment of the present application based on the acquired program instructions, thereby implementing the method for processing timing index data with reduced precision described in conjunction with FIG. 1 .

In addition, in combination with the timing index data precision reduction processing method in the foregoing embodiments, the embodiments of the present application may provide a computer-readable storage medium for implementation. Computer program instructions are stored on the computer-readable storage medium; when the computer program instructions are executed by a processor, any method for processing timing index data with reduced accuracy in the above-mentioned embodiments is implemented.

The technical features of the above-mentioned embodiments can be combined arbitrarily. To make the description concise, all possible combinations of the technical features in the above-mentioned embodiments are not described. However, as long as there is no contradiction in the combination of these technical features, should be considered as within the scope of this specification.

The above-mentioned embodiments only represent several implementation modes of the present application, and the description thereof is relatively specific and detailed, but it should not be construed as limiting the scope of the patent for the invention. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the scope of protection of the patent application should be based on the appended claims.

Claims (10)

1. A method for processing time series index data with reduced accuracy, characterized in that the method comprises:

   Receive timing index data;

   Searching for a corresponding precision reduction algorithm according to the type of the timing index data;

   According to the precision reduction algorithm, the metadata information of the time series indicator data is subjected to precision reduction processing.
2. The method according to claim 1, wherein, before searching for a corresponding precision reduction algorithm according to the type of timing index data, the method further comprises:

   Configuring a precision reduction algorithm corresponding to the timing index data;

   Associating the precision reduction algorithm of the time series index data with the metadata information of the time series index data.
3. The method according to claim 1, wherein, according to the precision reduction algorithm, performing precision reduction processing on the metadata information of the timing index data includes:

   Detecting the storage duration of the timing index data;

   After detecting that the storage duration is longer than the preset duration, the metadata information of the timing index data is subjected to precision reduction processing according to the found precision reduction algorithm.
4. The method according to claim 3, further comprising:

   A storage strategy for the timing index data is configured, and the storage strategy specifies that the timing index data is stored for a preset time to perform precision reduction processing.
5. The method according to claim 2, wherein the configuring the precision reduction algorithm corresponding to the timing index data includes:

   Configure a corresponding precision reduction algorithm in the data write request of the timing index data; or

   When the storage system creates the schema table, configure the precision reduction algorithm corresponding to the time series index data.
6. The method according to claim 1, wherein the precision reduction algorithm of the timing index data comprises:

   Max: Take the maximum value of all timing index data within the preset time precision range;

   Min: Take the minimum value of all timing index data within the preset time precision range;

   Gauge: Take the last value of all timing index data within the preset time precision range;

   Sum: Take the sum of all timing index data within the preset time precision range;

   Avg: take at least one of the sum of all timing index data within the preset time precision range, and the quantity of all timing index data within the preset time precision range.
7. The method according to claim 6, characterized in that each of the timing index data corresponds to at least one of the precision reduction algorithms; and the precision reduction is performed on the metadata information of the timing index data according to the precision reduction algorithm processing, including:

   After detecting that the storage duration is longer than the preset duration, according to the found multiple precision reduction algorithms, the metadata information of the timing index data is respectively subjected to precision reduction processing to obtain multiple sets of reduced precision timing index data.
8. A timing index data reduction precision processing device, including a receiving module, a search module and a processing module; wherein:

   A receiving module, configured to receive timing index data;

   The search module is used to find the corresponding precision reduction algorithm according to the type of time series index data;

   The processing module is configured to perform precision reduction processing on the metadata information of the time series indicator data according to the precision reduction algorithm.
9. A computer device, comprising a memory, a processor, and a computer program stored on the memory and operable on the processor, characterized in that, when the processor executes the computer program, the computer program according to claims 1 to 1 is implemented. The processing method for reducing precision of time series index data described in any one of 7.
10. A computer-readable storage medium, on which a computer program is stored, wherein, when the program is executed by a processor, the method for processing time-series index data with reduced precision according to any one of claims 1 to 7 is implemented.

Images