CN106407395A - A processing method and device for data query - Google Patents

Abstract

The invention provides a data query processing method and device. The method includes: according to the data filtering condition and the starting moment of the data query, acquiring N data points from the time series database, N is a preset soft limit threshold; according to the minimum sampling time of each aggregation function of the data query The public multiple, the time corresponding to the Nth data point, and the start time are used to obtain the limit time; when the limit time is before the end time, start with the time corresponding to the Nth data point, and continue to obtain the limit time from the time series database according to the data filter conditions The previous remaining data points: perform aggregation processing on all data points between the start time and the limit time according to each aggregation function to obtain the first data query result. By adopting the technical scheme of the present invention, less computer resources are consumed during data query, the time consumed is shorter, and the efficiency of data query can be effectively improved.

Description

Data query processing method and device

【Technical field】

The invention relates to the technical field of data processing, in particular to a data query processing method and device.

【Background technique】

With the development of information technology, data has become a very important information in information processing. And in order to study the data more accurately, for data with a time stamp, the time stamp is usually stored together with the data in a time-series database.

Traditional time-series databases are used by a small number of users on a single machine, and users monopolize the resources of the entire time-series database. Therefore, if users run some time-consuming and resource-consuming queries, the impact will not be great. However, with the emergence of various cloud services, huge data and corresponding timestamps need to be stored. The cloud-based time series database is clustered and multi-tenant, and multiple users share the time series database cluster. If a user runs some time-consuming and resource-consuming queries, it will affect the entire cluster, thereby affecting other users. In order to effectively control the impact of time-consuming queries on the system, the existing queries on time-series databases must have a limit function, which can control the original results (before aggregation) or final results (after aggregation) of queries. The number is limited. Among them, compared with restricting the original result of the query (before aggregation), the scheme of restricting the final result of the query (after aggregation) can better ensure the correctness of the aggregation result.

However, the existing solutions for restricting the final result of the query (after aggregation) cannot effectively control the impact of the time-consuming query on the system. For example: there is currently 1 year of data in the database, a total of 100 million data points, the query condition is to limit the final result to 1 data point, and sum the original results every 1 year, then the result obtained is The summation of 100 million data points will consume a large amount of computing resources of the system, and will not play a role in controlling time-consuming data queries.

【Content of invention】

The invention provides a data query processing method and device, which are used to effectively control the time-consuming influence of data query.

The present invention provides a data query processing method, the method comprising:

Acquire N data points from the time-series database according to the data filtering conditions and the starting moment of the data query, where N is a preset soft limit threshold;

According to the least common multiple of the sampling time of each aggregation function of the data query, the time corresponding to the Nth data point and the starting time, obtain the limit time;

When the limited time is before the end time, start from the time corresponding to the Nth data point, and continue to acquire the remaining data points before the limited time from the time series database according to the data filter condition;

Aggregating all data points between the start time and the limit time according to each of the aggregation functions to obtain a first data query result.

Further optionally, in the above-mentioned method, according to each of the aggregation functions, all data points before the time limit are aggregated, and after the data query result is obtained, the method further includes:

Feedback the limited time and the corresponding first data query result to the user.

Further optionally, in the above method, after feeding back the restriction time and the corresponding first data query result to the user, further include:

receiving a continuation query instruction message carrying the time limit sent by the user;

The value at the start time is updated to the value at the limit time, so as to continue data query processing.

Further optionally, in the above-mentioned method, the restriction time is obtained according to the least common multiple of the sampling time of each aggregation function of the data query, the time corresponding to the Nth data point, and the starting time, specifically including:

From the multiples of lcm_sample_time, take the smallest one larger than (soft_limit_time-start_time), and then add start_time, which is equal to limit_time;

Wherein, the lcm_sample_time is the least common multiple of the sampling time of each aggregation function of the data query; the soft_limit_time is the moment corresponding to the Nth data point; the start_time is the starting moment; the limit_time is the time limit.

Further optionally, in the method as described above, before acquiring N data points from the time-series database according to the data filter condition and the starting moment of the data query, it also includes:

The data filter condition specified by the user, the sampling time of each aggregation function of the data query, the start time and the end time of the data query are obtained.

Further optionally, in the above-mentioned method, according to the least common multiple of the sampling time of each aggregation function of the data query, the time corresponding to the Nth data point, and the starting time, before obtaining the limit time, the method includes :

Acquiring the timestamp of the Nth data point from the time series database;

The time stamp of the Nth data point is used as the time corresponding to the Nth data point.

Further optionally, in the above method, when the limiting moment is after the end moment, further comprising:

Starting from the time corresponding to the Nth data point, continue to acquire the remaining data points before the end time from the time series database according to the data filter condition;

Aggregating all data points between the start time and the end time according to each of the aggregation functions to obtain a second data query result.

The present invention also provides a data query processing device, the device comprising:

The data point acquisition module is used to obtain N data points from the time series database according to the data filtering condition and the starting moment of the data query, and the N is a preset soft limit threshold;

The time acquisition module is used to obtain the limited time according to the least common multiple of the sampling time of each aggregation function of the data query, the time corresponding to the Nth data point, and the starting time;

The data point acquisition module is further configured to start from the time corresponding to the Nth data point when the limited time is before the end time, and continue to obtain the data before the limited time from the time series database according to the data filter condition. The remaining data points of ;

An aggregation processing module, configured to perform aggregation processing on all data points between the start time and the limit time according to each of the aggregation functions to obtain a first data query result.

Further optionally, the above-mentioned device also includes:

A sending module, configured to feed back the restriction time and the corresponding first data query result to the user.

Further optionally, the above-mentioned device also includes:

A receiving module, configured to receive a continuation query instruction message carrying the time limit sent by the user;

An update module, configured to update the value at the start time to the value at the limit time, so as to continue data query processing.

Further optionally, in the device as described above, the time acquisition module is specifically configured to select the smallest one that is greater than (soft_limit_time-start_time) from the multiples of lcm_sample_time, and then add start_time to be equal to limit_time;

Wherein, the lcm_sample_time is the least common multiple of the sampling time of each aggregation function of the data query; the soft_limit_time is the moment corresponding to the Nth data point; the start_time is the starting moment; the limit_time is the time limit.

Further optionally, the above-mentioned device further includes: a parameter acquisition module, configured to acquire the data filter condition specified by the user, the sampling time of each aggregation function of the data query, the The start time and the end time.

Further optionally, in the device as described above, the time obtaining module is further configured to obtain the time stamp of the Nth data point from the time series database; the time stamp of the Nth data point as the time corresponding to the Nth data point.

Further optionally, in the above-mentioned device, the data point acquisition module is further configured to start at the time corresponding to the Nth data point when the limited time is after the end time, according to the The data filter condition continues to obtain the remaining data points before the end time from the time series database;

The aggregation processing module is further configured to perform aggregation processing on all data points between the start time and the end time according to each of the aggregation functions to obtain a second data query result.

The processing method and device of data query of the present invention realize the paging query of data due to the use of limited time for data query, and the original data points of each page are limited, which is different from the final result of the query in the prior art ( Aggregated) compared to the restricted technical solution, the same computer resource consumes less resources and takes less time to process a page of data, which can effectively improve the efficiency of data query and truly realize the control of data query to the system Time-consuming impact, but also can improve the user experience. Moreover, the data query processing scheme of the present invention can also effectively ensure the accuracy of the data query results; in addition, the data query processing scheme of the present invention also has strong applicability, not only applicable to the original The technical solution for limiting the result (before aggregation) may also be applicable to the technical solution for limiting the final result of the query (after aggregation).

【Description of drawings】

FIG. 1 is a flowchart of an embodiment of a data query processing method in the present invention.

FIG. 2 is a structural diagram of Embodiment 1 of the data query processing device of the present invention.

FIG. 3 is a structural diagram of Embodiment 2 of the data query processing device of the present invention.

【detailed description】

In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

FIG. 1 is a flowchart of an embodiment of a data query processing method in the present invention. As shown in Figure 1, the data query processing method of this embodiment may specifically include the following steps:

100. Acquiring N data points from the time series database according to the data filtering condition and the starting moment of the data query;

N in this embodiment is a preset soft limit threshold, and the value of N is a positive integer. N in this embodiment is a soft limit value preset in the time series database, which means that when data points are taken from the database according to the soft limit value, initially N data points are taken. Specifically, starting from the starting moment of the data query, N data points are sequentially obtained from the time series database according to the data filtering conditions. The preset soft limit threshold N in this embodiment can be set according to actual needs, for example, the most appropriate preset soft limit threshold can be selected by referring to a query result of a period of historical data in a time series database. The data filtering conditions of this embodiment can specifically limit the table to be queried, which fields meet what conditions, etc., for example, query the temperature of all Shanghai areas, or query the annual humidity in Beijing area or the rainfall of each rainfall in the whole year, etc. data. Since the time series database may contain various information of one or more regions, according to the data filter conditions, the data points that meet the data filter conditions can be obtained from the time series database, and then according to the starting time of the data query and N data points again Get N eligible data points from the data points that meet the data filter condition.

101. According to the least common multiple of the sampling time of each aggregation function of the data query, the time corresponding to the Nth data point and the starting time, obtain the limit time;

Since the amount of data in the time-series database is very large, if you directly obtain the original data in the time-series database when querying data, the amount of data obtained is very large, and sometimes these data cannot really reflect the changing trend of the data. Therefore, an aggregate function is also provided in this embodiment to process the data query results. Specifically, the aggregation function is to aggregate the original results of the query. And the aggregation function in a data query can include one, two or more. For example, query the average value every 5 minutes; where 5 minutes is the sampling time range, and the average value is an aggregation function; another example: the aggregation function for a certain data query can include averaging data points within every five minutes, for each The data points of the hour are summed; the sampling time of the corresponding aggregation function is 5 minutes and 60 minutes respectively; the average and the sum are the aggregation functions respectively.

Since the time series database not only stores the value of the data point, but also stores the time stamp corresponding to each data point, and the time stamp indicates the moment of the data point. Therefore, before this step 101, the following steps may also be included:

(a1) Obtain the timestamp of the Nth data point from the time series database;

(a2) The time stamp of the Nth data point is taken as the time corresponding to the Nth data point.

According to the least common multiple of the sampling time of each aggregation function of the data query, the time corresponding to the Nth data point and the starting time,

102. When the restricted time is before the end time, start from the time corresponding to the Nth data point, and continue to obtain the remaining data points before the restricted time from the time series database according to the data filtering conditions;

103. Perform aggregation processing on all data points between the start time and the limit time according to each aggregation function to obtain a first data query result.

In this embodiment, all data points between the start time and the limit time include the N data points obtained in step 101, and the time corresponding to the Nth data point in step 102, from the time series database according to the data filter condition Continue to obtain the remaining data points before the limited time.

In this embodiment, since the acquisition limit time takes into account the least common multiple of the sampling time of each aggregation function of the data query, it can ensure the integrity of the aggregation processing of the original data points during the data query, thereby ensuring the accuracy of the query results sex. For example, when the sampling time of the aggregation function is 5 minutes and 60 minutes respectively, the least common multiple of the sampling time of each aggregation function of the data query can be selected as 60, so that the original data points obtained before the time limit can be guaranteed to be complete Aggregation processing, and there will be no part of the data that cannot participate in the aggregation processing, resulting in errors in data query results.

In this embodiment, taking the time period between the start time and the end time as an example, if the data points between the start time and the end time are directly obtained during data query, the data query process is very time-consuming, and Very consuming of computer resources. In this embodiment, by setting the limit time, the limit time is located after the start time and before the end time, and data query can be performed in pages during data query. For example, this data query only queries the data between the start time and the limit time. All data points are aggregated according to each aggregation function for all data points between the start time and the limit time to obtain the first data query result. The data query process in this embodiment can reduce the time consumption of data query and save the consumption of computer resources. When the user performs data query, the first data query result before the time limit can be queried for the user.

Further optionally, after step 103 of the above-mentioned embodiment "according to each aggregation function, perform aggregation processing on all data points between the start time and the limit time to obtain the first data query result", the data query in this embodiment Processing methods may also specifically include:

The time limit and the corresponding first data query result are fed back to the user.

Feedback of the time limit and the corresponding first data query result to the user in this embodiment may specifically be by sending the time limit and the corresponding first data query result to a device (such as a mobile terminal or a personal computer) used by the user, The device used by the user is used to display the restriction time and the corresponding first data query result, so as to realize the feedback of the restriction time and the corresponding first data query result to the user.

In this embodiment, by feeding back the time limit and the corresponding first data query result to the user, the user is notified that the data query result this time is the data query result before the time limit. If the user needs to continue data query, he can continue to indicate The data query processing means performs data query. Therefore, further optionally, after the step "feedback to the user of the time limit and the corresponding first data query result", the following steps may also be included:

(b1) receiving the continuation inquiry instruction message of the carrying restriction time sent by the user;

(b2) Update the value at the start time to the value at the limit time to continue data query processing.

Specifically, after receiving the time limit and the corresponding first data query result through the device used, the user needs further data query results, and can send a continuation query carrying the time limit to the data query processing device through the device used. instruction message. In this way, after the data query processing device receives the continuation query instruction message, it determines that further data query needs to be performed, and can first update the value of the start time to the value of the limit time, that is, the limit time is used as a new start time, and continues to follow the Steps 100-103 in the above embodiment perform the next round of data query, and so on, until all the data before the end time is queried.

With the technical solution of this embodiment, when the user needs to query again, the limited time can be used as the starting time to continue data query, which is equivalent to dividing the data query into multiple pages, and at the same time, it can prevent the user from performing a data query , it enters endless waiting, and even makes the user mistakenly think that the network is faulty. Using the technical solution of this embodiment, the data query is divided into pages. The data query of each page takes a short time, and the user does not need to wait for a long time, and the user can quickly The query results can be obtained. And in the technical solution of the embodiment of this city, the user can understand the situation of data query on each page, and when it needs to query again, the user can send a continuation query instruction message carrying the limited time through the device used, and then enter the next page The data query, the user participates in the whole process of data query, which greatly enhances the user experience.

Further optionally, in the technical solution of the above-mentioned embodiment, step 101 "obtain the limited time according to the least common multiple of the sampling time of each aggregation function of the data query, the time corresponding to the Nth data point, and the starting time", which can be specifically Including: from the multiples of lcm_sample_time, take the smallest one larger than (soft_limit_time-start_time), and then add start_time, which is equal to limit_time;

Among them, lcm_sample_time is the least common multiple of the sampling time of each aggregation function of data query; soft_limit_time is the time corresponding to the Nth data point; start_time is the starting time; limit_time is the limit time.

Further optionally, in the technical solution of the above embodiment, before step 100 "obtain N data points from the time series database according to the data filtering conditions and the starting time of the data query", it may also include: obtaining user-specified data Filter conditions, sampling time of each aggregate function of data query, start time and end time of data query.

In this embodiment, the parameter information such as the data filtering condition, the sampling time of each aggregation function of the data query, the start time and the end time of the data query are all specified by the user when performing the data query. Specifically, such information may be carried in the data query request sent by the user through the device used. For example, the processing device for data query can receive the data query request sent by the user through the device used to carry the data filter condition, the sampling time of each aggregation function of the data query, the start time and the end time of the data query, and obtain the data from the data query request Obtain the data filter conditions, the sampling time of each aggregate function of the data query, the start time and end time of the data query. Or before the user makes a data query request, he may first send the parameter information of the data query, that is, the data filter condition, the sampling time of each aggregation function of the data query, the start time and the end time of the data query.

Further optionally, in the technical solutions of the above-mentioned embodiments, when the restriction time is before the end time, when the paged data query reaches the last page, the corresponding restriction time is after the end time. At this time, the data query in this embodiment The processing method may also include the following steps:

(c1) Start at the time corresponding to the Nth data point, and continue to obtain the remaining data points before the end time from the time series database according to the data filter condition;

(c2) Perform aggregation processing on all data points between the start time and the end time according to each aggregation function to obtain a second data query result.

Since the limit time is after the end time, the data points obtained during data query can only be obtained up to the end time. At this time, start from the time corresponding to the Nth data point, and continue to obtain the end time from the time series database according to the data filtering conditions For the previous remaining data points, all data points between the start time and the end time are aggregated according to each aggregation function to obtain the second data query result.

Further optionally, before the step 102 of the above-mentioned embodiment "when the restriction time is before the end time, start at the time corresponding to the Nth data point, and continue to obtain the remaining data points before the restriction time from the time series database according to the data filtering conditions" , may also include judging whether the restricted time is before the end time, and if the restricted time is before the end time, perform step 102 . Otherwise, when the limited time is after the end time, execute steps (c1)-(c2).

In the data query processing scheme of this embodiment, since the data query is paginated at a time limit, the original data points of each page are limited, which is different from the prior art that limits the final result (after aggregation) of the query Compared with other technical solutions, the same computer resource consumes less computer resources and takes less time to process one page of data, which can effectively improve the efficiency of data query and truly control the time-consuming impact of data query on the system. And it can also improve the user experience.

In addition, the prior art also provides a technical solution for restricting the original result of the query (before aggregation). For example, it can be specifically: there are currently 10 data points in the time series database, one every 1 second, and the query condition Yes, limit the original result to 5 data points, and sum the original result every 10 seconds (using an aggregation function to sum), then the result becomes the sum of the previous 5 seconds, not 10 seconds Therefore, the existing technical solutions for restricting the original query results (before aggregation) may easily lead to errors in data query results.

In the data query processing method of this embodiment, since the least common multiple of the sampling time of each aggregation function of the data query is used to set the limit time, it can be guaranteed that the original data points of the data query of each page before the last page can be used for each aggregation The function performs a complete aggregation process, which can ensure the accuracy of the data query results compared with the technical solution of restricting the original results (before aggregation) in the prior art.

To sum up, the data query processing method in this embodiment is not only applicable to the technical solution of restricting the original result of the query (before aggregation), but also applicable to the final result of the query (after aggregation). Therefore, the data query processing method of this embodiment has strong applicability, and the data query time is very short and the data query efficiency is very high.

FIG. 2 is a structural diagram of Embodiment 1 of the data query processing device of the present invention. As shown in FIG. 2 , the data query processing device in this embodiment may specifically include: a data point acquisition module 10 , a time acquisition module 11 and an aggregation processing module 12 .

Wherein the data point acquisition module 10 is used to obtain N data points from the time series database according to the initial moment of the data filtering condition and data query, and N is a preset soft limit threshold; the time acquisition module 11 is used to query according to the data The least common multiple of the sampling time of each aggregate function, the time corresponding to the Nth data point obtained by the data point acquisition module 10 and the start time, and the acquisition limit time; The limit time is located before the end time, starting with the time corresponding to the Nth data point, and continuing to obtain the remaining data points before the limit time from the time series database according to the data filter condition; Each aggregation function aggregates all data points between the start time and the limit time to obtain the first data query result.

In the data query processing device of this embodiment, the implementation principle and technical effect of data query processing by using the above-mentioned modules are the same as those of the above-mentioned related method embodiments. For details, please refer to the records of the above-mentioned related method embodiments, and will not repeat them here. .

FIG. 3 is a structural diagram of Embodiment 2 of the data query processing device of the present invention. The data processing query device of this embodiment further introduces the technical solution of the present invention in more detail on the basis of the technical solution of the above embodiment shown in FIG. 2 .

As shown in FIG. 3 , the data processing query device of this embodiment further includes: a sending module 13 . The sending module 13 is used to feed back the limited time of the time acquisition module 11 and the first data query result processed by the corresponding aggregation processing module 12 to the user.

Further optionally, as shown in FIG. 3 , the data processing query device of this embodiment further includes a receiving module 14 and an updating module 15 .

Wherein the receiving module 14 is used to receive the continuation inquiry instruction message of carrying the limited time sent by the user; the update module 15 is used to update the numerical value of the initial moment to the numerical value of the limited time according to the continuation inquiry instruction message received by the receiving module 14, to continue Perform data query processing. That is, continue to execute the functions of the data point acquisition module 10 , the time acquisition module 11 and the aggregation processing module 12 . That is, after the update module 1 updates the value at the start time to the value at the limit time, it can trigger the start of the data point acquisition module 10 to continue data query processing.

Further optionally, in the data processing query device of this embodiment, the time acquisition module 11 is specifically configured to select the smallest one greater than (soft_limit_time-start_time) from the multiples of lcm_sample_time, and then add start_time to equal limit_time;

Among them, lcm_sample_time is the least common multiple of the sampling time of each aggregation function of data query; soft_limit_time is the time corresponding to the Nth data point; start_time is the starting time; limit_time is the limit time.

Further optionally, as shown in FIG. 3 , the data processing query device of this embodiment further includes a parameter acquisition module 16 . The parameter obtaining module 16 is used to obtain the data filter condition specified by the user, the sampling time of each aggregation function of the data query, the start time and the end time of the data query.

Correspondingly, the data point acquisition module 10 is used to acquire N data points from the time series database according to the data filtering conditions acquired by the parameter acquisition module 16 and the start time of the data query, where N is a preset soft limit threshold. The time acquisition module 11 is used to obtain the limit time according to the least common multiple of the sampling time of each aggregation function of the data query acquired by the parameter acquisition module 16, the time corresponding to the Nth data point, and the start time acquired by the parameter acquisition module 16.

Further optionally, in the data processing query device of this embodiment, the time acquisition module 11 is also used to acquire the timestamp of the Nth data point from the time series database; the timestamp of the Nth data point is used as the Nth data point point at the corresponding moment.

Further optionally, in the data processing query device of this embodiment, the data point acquisition module 10 is also used to start from the time corresponding to the Nth data point when the restricted time is after the end time, and select from the time series database according to the data filter condition Continue to obtain the remaining data points before the end time;

The aggregation processing module 12 is also configured to perform aggregation processing on all data points between the start time and the end time according to each aggregation function to obtain the second data query result.

In the data query processing device of this embodiment, the implementation principle and technical effect of data query processing by using the above-mentioned modules are the same as those of the above-mentioned related method embodiments. For details, please refer to the records of the above-mentioned related method embodiments, and will not repeat them here. .

In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation.

The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, each unit may exist separately physically, or two or more units may be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware, or in the form of hardware plus software functional units.

The above-mentioned integrated units implemented in the form of software functional units may be stored in a computer-readable storage medium. The above-mentioned software functional units are stored in a storage medium, and include several instructions to make a computer device (which may be a personal computer, server, or network device, etc.) or a processor (processor) execute the methods described in various embodiments of the present invention. partial steps. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), magnetic disk or optical disk and other various media that can store program codes. .

The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included in the present invention. within the scope of protection.

Claims (14)

1. a kind of processing method of data query is it is characterised in that methods described includes：

Started according to the initial time of data filtering condition data inquiry, obtain N number of data point from time series database, described N is default soft threshold limit；

The least common multiple in the sampling time of each aggregate function according to data query, the n-th data point corresponding moment and Described initial time, obtains and limits the moment；

Before the described restriction moment is located at finish time, with the described n-th data point corresponding moment, according to data mistake Filter condition continues to obtain the described remainder strong point limiting before the moment from described time series database；

According to each described aggregate function, all data points between described initial time to described restriction moment are carried out at polymerization Reason, obtains the first data query result.

2. method according to claim 1 it is characterised in that according to each described aggregate function to described restriction the moment before All data points carry out polymerization process, after obtaining data query result, methods described also includes：

To restriction moment and corresponding described first data query result described in user feedback.

3. method according to claim 2 is it is characterised in that to limiting moment and corresponding described in described user feedback After described first data query result, methods described also includes：

Receive the continuation inquiry instruction message carrying the described restriction moment that described user sends；

The numerical value of described initial time is updated to the described numerical value limiting the moment, to proceed data query process.

4. method according to claim 1 is it is characterised in that according to sampling time of each aggregate function of data query Least common multiple, n-th data point corresponding moment and described initial time, obtain and limit the moment, specifically include：

From the multiple of lcm_sample_time, take one of the minimum bigger than (soft_limit_time-start_time), Then add start_time, equal to limit_time；

Wherein, described lcm_sample_time is the least common multiple in the sampling time of each described aggregate function of data query； Described soft_limit_time is the described n-th data point corresponding moment；Described start_time is described initial time； Described limit_time is the described restriction moment.

5. according to the arbitrary described method of claim 1-4 it is characterised in that according to data filtering condition data inquiry rise Begin the moment, before obtaining N number of data point from time series database, methods described also includes：

Obtain the described data filtering condition that described user specifies, the sampling time of each described aggregate function of data query, number It is investigated that the described initial time ask and described finish time.

6. according to the arbitrary described method of claim 1-4 it is characterised in that the sampling of each aggregate function according to data query The least common multiple of time, n-th data point corresponding moment and described initial time, obtain before limiting the moment, described Method includes：

The timestamp of described n-th data point is obtained from described time series database；

Using the timestamp of described n-th data point as the described n-th data point corresponding moment.

7. according to the arbitrary described method of claim 1-4 it is characterised in that limiting the moment positioned at described finish time when described Afterwards, methods described also includes：

With the described n-th data point corresponding moment, relayed from described time series database according to described data filtering condition The continuous remainder strong point obtaining before described finish time；

According to each described aggregate function, all data points between described initial time to described finish time are carried out at polymerization Reason, obtains the second data query result.

8. a kind of processing meanss of data query are it is characterised in that described device includes：

Data point acquisition module, for being started according to the initial time that data filtering condition data is inquired about, from time series database The N number of data point of middle acquisition, described N is default soft threshold limit；

Moment acquisition module, for the least common multiple in the sampling time of each aggregate function according to data query, n-th data Point corresponding moment and described initial time, obtain and limit the moment；

Described data point acquisition module, is additionally operable to before the described restriction moment is located at finish time, with described n-th data point The corresponding moment, remaining before continuing to obtain the described restriction moment from described time series database according to data filtering condition Remainder strong point；

Polymerization processing module, for all between the extremely described restriction moment to described initial time according to each described aggregate function Data point carries out polymerization process, obtains the first data query result.

9. device according to claim 8 is it is characterised in that described device also includes：

Sending module, for restriction moment and corresponding described first data query result described in user feedback.

10. device according to claim 9 is it is characterised in that described device also includes：

Receiver module, indicates message for receiving the described continuation inquiry limiting the moment that carries that described user sends；

Update module, for the numerical value of described initial time is updated to the described numerical value limiting the moment, to proceed data Query processing.

11. devices according to claim 8 it is characterised in that described moment acquisition module, specifically for from lcm_ In the multiple of sample_time, take one of the minimum bigger than (soft_limit_time-start_time), then add Start_time, equal to limit_time；

Wherein, described lcm_sample_time is the least common multiple in the sampling time of each described aggregate function of data query； Described soft_limit_time is the described n-th data point corresponding moment；Described start_time is described initial time； Described limit_time is the described restriction moment.

12. according to Claim 8-11 arbitrary described devices it is characterised in that also including：

Parameter acquisition module, for obtaining each described polymerization of the described data filtering condition that described user specifies, data query The sampling time of function, the described initial time of data query and described finish time.

13. according to Claim 8-11 arbitrary described devices it is characterised in that described moment acquisition module, be additionally operable to from institute State the timestamp obtaining described n-th data point in time series database；Using the timestamp of described n-th data point as described The N number of data point corresponding moment.

14. according to Claim 8-11 arbitrary described devices it is characterised in that：

Described data point acquisition module, is additionally operable to after the described restriction moment is located at described finish time, with described n-th number In the strong point corresponding moment, continue to obtain described finish time from described time series database according to described data filtering condition Remainder strong point before；

Described polymerization processing module, is additionally operable to according to each described aggregate function between described initial time extremely described finish time All data points carry out polymerization process, obtain the second data query result.