WO2017157164A1 - 数据聚合方法及装置 - Google Patents

数据聚合方法及装置 Download PDF

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Publication number
WO2017157164A1
WO2017157164A1 PCT/CN2017/075069 CN2017075069W WO2017157164A1 WO 2017157164 A1 WO2017157164 A1 WO 2017157164A1 CN 2017075069 W CN2017075069 W CN 2017075069W WO 2017157164 A1 WO2017157164 A1 WO 2017157164A1
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WO
WIPO (PCT)
Prior art keywords
service data
timestamp
data
aggregation
server
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Application number
PCT/CN2017/075069
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English (en)
French (fr)
Inventor
李巨雷
Original Assignee
阿里巴巴集团控股有限公司
李巨雷
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 阿里巴巴集团控股有限公司, 李巨雷 filed Critical 阿里巴巴集团控股有限公司
Priority to JP2018544936A priority Critical patent/JP2019510305A/ja
Priority to EP17765706.1A priority patent/EP3432158A1/en
Publication of WO2017157164A1 publication Critical patent/WO2017157164A1/zh
Priority to US16/131,872 priority patent/US20190012348A1/en

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/27—Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2455—Query execution
    • G06F16/24552—Database cache management
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2455—Query execution
    • G06F16/24553—Query execution of query operations
    • G06F16/24554—Unary operations; Data partitioning operations
    • G06F16/24556—Aggregation; Duplicate elimination

Definitions

  • the present invention relates to the field of information technology, and in particular, to a data aggregation method and apparatus.
  • the service data of each category received in different time segments is first written into a file, and then all services of a certain category to be aggregated are read from the file.
  • the data is finally sorted by time and sent to the computing server for data aggregation, for example, sent to a MapReduce (Map Reduction) server for data aggregation, as shown in FIG. 5 .
  • MapReduce Map Reduction
  • the delay of data calculation is high, and since the read service data needs to be sorted once according to time, the process of data aggregation is cumbersome, the efficiency of data aggregation operation is low, and the delay of data aggregation operation is high;
  • the service data is read from the file and the aggregation operation is completed, if the service data with the same time or earlier time as the service data of the aggregation operation is received again, the part of the service data received again is discarded.
  • the accuracy of data aggregation statistics is low.
  • the present invention provides a data aggregation method and apparatus, and the main purpose thereof is to solve the problem that a data aggregation operation has a high delay when aggregating a large amount of service data stored in a cloud environment.
  • Technical problem is to solve the problem that a data aggregation operation has a high delay when aggregating a large amount of service data stored in a cloud environment.
  • the present invention also provides a data aggregation method and apparatus, the main purpose of which is to solve the technical problem of low accuracy of data aggregation statistics.
  • the present invention provides the following technical solutions:
  • the present invention provides a data aggregation method, including:
  • the data aggregation server receives the service data sent by the service data server, where the service data carries a timestamp, and the timestamp is used to identify the time when the service server receives the service data;
  • the data is aggregated with the service data of the target timestamp, and the service data corresponding to the target timestamp is saved in the preset storage location.
  • the present invention provides a data aggregation apparatus, including:
  • a receiving unit configured to receive service data sent by the service data server, where the service data carries a timestamp, where the timestamp is used to identify a time when the service server receives the service data;
  • a detecting unit configured to detect whether the timestamp received by the receiving unit is greater than a target timestamp, where the target timestamp is a timestamp carried by the service data sent by the service data server last time;
  • an aggregation unit configured to: if the detecting unit detects that the timestamp is greater than the target timestamp, perform data aggregation on the service data with the timestamp of the target timestamp, where the service data corresponding to the target timestamp is saved in Preset storage location.
  • the technical solution provided by the embodiment of the present invention has at least the following advantages:
  • the data aggregation method and device when the aggregated service data stored in the cloud environment needs to be aggregated, firstly, the data aggregation server receives the service data server. The service data is then detected in real time whether the timestamp carried in the service data is greater than the timestamp carried by the service data server sent by the data aggregation server last time. If it is greater than, the timestamp is received by the data aggregation server last time. Data aggregation is performed on the service data with the same time stamp carried by the service data sent by the service data server.
  • the file for storing the service data needs to be read and written, and the read service data needs to be sorted once according to time, and if it is received again. Compared with the service data with the same or earlier service data as the aggregation operation, this part of the service data will be discarded.
  • the time stamp carried in the service data sent by the service data server can aggregate the service data in different time periods, improve the efficiency of data aggregation, and reduce the delay of the data aggregation operation and improve the data because the detection operation is performed in real time.
  • the real-time performance of the aggregation; at the same time, because the time tolerance period is set in the present invention the service data with the earlier received time can be aggregated, thereby improving the accuracy of the data aggregation statistics.
  • FIG. 1 is a flowchart of a data aggregation method according to an embodiment of the present invention
  • FIG. 2 is a flowchart of another data aggregation method according to an embodiment of the present invention.
  • FIG. 3 is a schematic structural diagram of a data aggregation apparatus according to an embodiment of the present invention.
  • FIG. 4 is a schematic structural diagram of another data aggregation apparatus according to an embodiment of the present invention.
  • FIG. 5 is a schematic diagram showing an existing data aggregation process provided by the present invention.
  • FIG. 6 is a diagram showing service data time in different service data servers according to an embodiment of the present invention. Poke diagram
  • FIG. 7 is a schematic diagram showing local time of different data aggregation servers according to an embodiment of the present invention.
  • FIG. 8 is a schematic diagram showing time stamps of different service data in a data aggregation server according to an embodiment of the present invention.
  • FIG. 9 is a schematic diagram showing a mapping manner between a service data server time and a data aggregation server time according to an embodiment of the present invention.
  • FIG. 1 A data aggregation method provided by an embodiment of the present invention is shown in FIG. 1 , where the method includes:
  • the data aggregation server receives the service data sent by the service data server.
  • the service data carries a timestamp, and the timestamp is used to identify the time when the service server receives the service data. For example, when the service data server receives the service data at 10:01, the timestamp carried in the service data received by the data aggregation server is 10:01.
  • the data aggregation server is configured to perform aggregation processing on the service data sent by the service data server; the service data may be different categories such as QPS (Query Per Second), PV (Page View, page view amount), and the like.
  • the service data which may also be the service data of different applications of the same category, such as the PV data of the application A and the application B, is not limited in the embodiment of the present invention.
  • the target timestamp is a timestamp carried by the service data when the data aggregation server receives the service data sent by the service data server.
  • Different timestamps may be displayed in a chronological order, such as a list, a time axis, etc., which are not limited in the embodiment of the present invention. For example, when different time stamps are displayed in time series in a time axis manner, on the time axis. The time will continue to scroll forward to ensure that the time on the timeline is the current time.
  • whether the timestamp is greater than the target timestamp that is, whether the time of the currently received service data is detected and the time when the service data was last received is changed, thereby determining whether the service data aggregation operation is triggered.
  • the time stamp may be configured in seconds, and the time stamp may be configured in units of minutes, which is not limited in the embodiment of the present invention.
  • timestamps are configured in seconds, different timestamps are distinguished in seconds, such as 10:01:01, 10:01:02, 10:01:03, etc.; when timestamps are configured in minutes, different Timestamps are distinguished by minutes, such as 10:01, 10:02, 10:03, where 10:01:01-10:01:59 is classified as 10:01, and so on.
  • the service data server configures the timestamp in minutes. If the timestamp of the service data sent by the data aggregation server currently received by the service data server is 10:01, the data aggregation server receives the service data sent by the service data server last time. If the timestamp is 10:01, it is determined that the service data aggregation operation is not triggered. If the data aggregation server currently receives the service data, the service data carries a timestamp of 10:02, which is greater than the data aggregation server last received the service data server. The timestamp 10:01 carried by the service data determines that the service data aggregation operation is triggered.
  • the timestamp is greater than the target timestamp, perform data aggregation on the service data with the timestamp of the target timestamp.
  • the service data corresponding to the target timestamp is saved in a preset storage location.
  • the preset storage location may be a preset mapping relationship table, a preset queue, or the like, which is not limited in the embodiment of the present invention.
  • the timestamp is greater than the target timestamp, such as 10:01 is changed to 10:02, or 10:01:01 is changed to 10:01:02, indicating that the current service data is carried.
  • the timestamp changes in the forward direction, that is, the real-time triggering of all the service data of the previous timestamp is performed in real time, thereby ensuring the real-time performance of the service data aggregation operation and improving the aggregation operation efficiency of the service data.
  • the data aggregation server When the data aggregation method is required to be aggregated in the cloud environment, the data aggregation server first receives the service data sent by the service data server, and then detects the timestamp carried in the service data in real time. Whether it is greater than the data aggregation The last time the server receives the timestamp of the service data sent by the service data server. If the timestamp is greater than, the timestamp is used to aggregate the service data with the same timestamp carried in the service data sent by the data aggregation server. . When the aggregated service data currently stored in the cloud environment is aggregated, the file for storing the service data needs to be read and written, and the read service data needs to be sorted by time.
  • the timestamp of the current service data is greater than the timestamp of the service data sent by the service data server, and the service data of different time periods can be aggregated to improve the efficiency of data aggregation. And because the detection operation is performed in real time, the delay of the data aggregation operation can be reduced, and the real-time performance of the data aggregation can be improved.
  • the embodiment of the present invention provides another data aggregation method, as shown in FIG. 2, the method includes:
  • the data aggregation server receives the service data sent by the service data server.
  • the service data carries a timestamp, and the timestamp is used to identify the time when the service server receives the service data.
  • the target timestamp is a timestamp carried by the service data when the data aggregation server receives the service data sent by the service data server.
  • the target timestamp is a timestamp carried by the service data when the data aggregation server receives the service data sent by the service data server.
  • the timestamp is greater than the target timestamp, that is, whether the time of the currently received service data is detected and the time when the last received service data is changed, thereby determining whether the service data aggregation is triggered. Operation, which ensures the real-time performance of the aggregation of business data.
  • the method further includes: if the timestamp is the same as the target timestamp, buffering the currently received service data into a preset queue corresponding to the target timestamp.
  • the preset queue caches all service data with a timestamp of the target timestamp. Different preset queues respectively cache service data with different timestamps. Because the delay of caching and reading operations on the queue is low, the service data is cached in the queue. To further reduce the delay of data aggregation operations and improve the real-time performance of data aggregation operations.
  • the timestamp is smaller than the target timestamp, the currently received service data is deleted. If the timestamp changes from 10:01 to 09:50, the currently received service data with a timestamp of 09:50 is deleted, thereby ensuring the accuracy of data aggregation.
  • the timestamp is greater than the target timestamp, perform data aggregation on the service data with the timestamp of the target timestamp.
  • the service data corresponding to the target timestamp is saved in a preset storage location.
  • the preset storage location may be a preset mapping relationship table, a preset queue, or the like, which is not limited in the embodiment of the present invention.
  • the method may further include: determining whether the service data with the timestamp is the target timestamp is received within the first preset time period after the current service data is received. It should be noted that, after the service data of a certain timestamp is aggregated, the service data server may receive the service data of the timestamp again due to the delay of the transmission data, etc., at this time, if the service data is not delayed. If the processing is performed, the service data is lost, and the accuracy of the service data aggregation is low. Therefore, before the data aggregation, the time tolerance period, that is, the first preset time period, can be configured to avoid delaying the received service. Loss of data and improve the accuracy of business data aggregation statistics.
  • the first preset time period may be configured according to a configuration unit of a timestamp. For example, if the timestamp is configured in seconds, the first preset time period may be 1 second, 2 seconds, etc., if the timestamp is configured in minutes, the first preset time period may be 1 minute, 2 minutes. Wait.
  • the configuration accuracy of the time tolerance period can be further improved, thereby further improving the accuracy of the service data aggregation.
  • the step 203 may include: if not received, performing data aggregation on the service data with the timestamp of the target timestamp; if received, receiving the data in the first preset time period.
  • the service data and the timestamp perform data aggregation for the service data of the target timestamp.
  • the service data server configures the timestamp in minutes, and then configures the first preset time period as 5 minutes. If the timestamp of the service data sent by the data aggregation server currently received by the service data server is 10:02, the timestamp of the service data sent by the data aggregation server last received by the service data server is 10:01, and the timestamp is 10:01 business Before the data is aggregated, it is determined whether the service data with the timestamp of 10:01 is received within five minutes after the current time starts. If received, the timestamp received within five minutes is 10:01.
  • the service data is aggregated with the previously received service data with a timestamp of 10:01; if it is not received, it indicates that there is no delayed service data, and the service data with the timestamp of 10:01 is aggregated. The accuracy of data aggregation is guaranteed.
  • the method may further include: establishing a queue corresponding to a timestamp of the currently received service data; and caching the service data in the queue. Since different timestamps correspond to different queues, the currently received service data is saved by creating a new queue.
  • the service data of the same type may be the service data of the same service type, for example, whether the service data of the same type is terminated.
  • the service data of the same type may also be the service data of the same application, for example, determining whether Terminate the service data of receiving application A.
  • the step 204 may include: determining whether the same service data type as the service data type is received in the second preset time period after the current service data is received; if not received, And confirming that the service data server terminates receiving the same service data as the service data type. It should be noted that if the service data of the same service data type is not received within a certain period of time after the service data is currently received, it may be determined that the service data server terminates receiving and receiving the currently received service. Business data with the same data type.
  • the second preset time period may be specifically configured according to a configuration unit of the timestamp. For example, if the timestamp is configured in seconds, the second preset time period may be 1 second, 2 seconds, etc., if the timestamp is configured in minutes, the second preset time period may be 1 minute, 2 minutes. Wait.
  • the accuracy of terminating the received service data can be further improved, thereby improving the accuracy of the service data aggregation operation.
  • the service data server terminates receiving service data of the same type as the service data, performing data aggregation on the currently received service data.
  • the service data server when the service data server terminates receiving the service data of the same type as the service data, it indicates that the data aggregation server no longer receives the service data of the type, that is, it cannot be judged by receiving the service data of the type again. Whether the timestamp of the service data of the type is changed. Therefore, in this case, directly performing data aggregation on the currently received service data ensures that when the service data server terminates receiving the service data of the same type as the service data, It is also possible to perform data aggregation on the currently received service data.
  • the step 205 may include: if the service data server terminates receiving the same service data as the service data type, acquiring an aggregation calculation server corresponding to the next adjacent timestamp different from the current timestamp.
  • the local time of the service data server is corresponding to the local time of an aggregation calculation server, which may be as shown in FIG. 9; according to the local time of the aggregation calculation server, the currently received service Data is aggregated.
  • the local time of the data aggregation server may be displayed in a chronological order, such as a list, a time axis, or the like, which is not limited in the embodiment of the present invention.
  • the local time of the data aggregation server is different from the timestamp configured by the service data server due to network condition conditions and differences in time of different servers.
  • the service data of the service data server 1 is processed by the data aggregation server 1. It can be seen that the timestamp of the service data server 1 is 10:01:01, and the local time of the corresponding data aggregation server 1 is 10:03:01. It can be seen that the local time of the data aggregation server is different from the timestamp configured by the corresponding service data server; and in the data aggregation server, the time stamps of different service data of the same monitoring item reaching the data aggregation server may be different at the same time.
  • the timestamps of different monitoring items may also be different. For example, as shown in FIG. 8, for the same service data PV of the monitoring item, the timestamp of the service data in the application A is 10:00:00 and the application B at the same time. The timestamp of the service data is different from 10:01:10. For the different service data PV and RT of the monitoring item, the time stamp of the service data is not the same. .
  • the time when the service data arrives at the data aggregation server is different from the time when the service data arrives at the service data server. For example, as shown in FIG.
  • the time when a certain service data arrives at the service data server is 10 :01
  • the time when the service data reaches the data aggregation server is 10:03. Therefore, when doing real-time data aggregation, the timestamp configured by the service data server is used as the trigger time, and the data aggregation server local time is not used at the same time. As the trigger time, the accuracy of data aggregation can be guaranteed. And by establishing a one-to-one correspondence between each different timestamp configured by the service data server and the local time of the data aggregation server in chronological order, time consistency can be ensured, when the service data server terminates receiving a certain type of service. In the case of data, the local time of the data aggregation server can still ensure the accurate aggregation of business data of this category.
  • the specific application scenario may be as follows, but is not limited thereto.
  • the method includes: as shown in FIG. 9, the data aggregation server receives the service data sent by the service data server at the time axis SB, and obtains the service data carrying.
  • the timestamp is 11:02, which is greater than the timestamp 11:01 carried by the service data sent by the service data server at the time of the SA at the time axis.
  • the data collection is performed on the service data with the timestamp of 11:01. Further, it is determined that the service data of the same service data type as the time stamp of 11:02 is not received within 5 minutes from the current time, and it can be determined that the service data server terminates receiving the service data with the timestamp of 11:02.
  • Service data of the same type when the local time of the data aggregation server reaches 11:07 (corresponding to 11:03 of the service data server), data aggregation is performed on the service data with the timestamp of 11:02, so that the timestamp is guaranteed. Data aggregation for 11:02 business data.
  • the data aggregation server when the mass service data stored in the cloud environment needs to be aggregated, the data aggregation server first receives the service data sent by the service data server, and then detects the time carried in the service data in real time. Whether the stamp is greater than the timestamp carried by the service data sent by the data aggregation server last time, and if the time stamp is greater than, the timestamp and the timestamp carried by the data aggregation server last time received by the service data server The same business data is used for data aggregation.
  • the file for storing the service data needs to be read and written, and the read service data needs to be sorted once according to time, and if it is received again.
  • the service data of different time periods can be aggregated, the efficiency of data aggregation is improved, and the detection operation is performed in real time, which can reduce the delay of the data aggregation operation.
  • Improve the real-time nature of data aggregation The time tolerance period is set in the example, and the service data with the earlier received time can be aggregated, thereby improving the accuracy of the data aggregation statistics.
  • the embodiment of the present invention provides a data aggregation device.
  • the device may include: a receiving unit 31, a detecting unit 32, and an aggregating unit 33.
  • the receiving unit 31 is configured to receive service data sent by the service data server, where the service data carries a timestamp, where the timestamp is used to identify a time when the service server receives the service data;
  • the detecting unit 32 is configured to detect whether the timestamp received by the receiving unit 31 is greater than a target timestamp, where the target timestamp is a timestamp carried by the service data sent by the service data server last time;
  • the aggregating unit 33 is configured to: if the detecting unit 32 detects that the timestamp is greater than the target timestamp, perform data aggregation on the service data with the timestamp of the target timestamp, and the service data corresponding to the target timestamp Save in the preset storage location.
  • the device embodiment corresponds to the foregoing method embodiment.
  • the device embodiment does not describe the details in the foregoing method embodiments one by one, but it should be clear that the device in this embodiment can Corresponding to implementing all of the foregoing method embodiments.
  • the data aggregation server When the data aggregation device needs to aggregate the massive service data stored in the cloud environment, the data aggregation server first receives the service data sent by the service data server, and then detects the timestamp carried in the service data in real time. Whether it is greater than the timestamp carried in the service data sent by the service data server by the data aggregation server. If the timestamp is greater than, the timestamp is the same as the timestamp carried by the data aggregation server last received by the service data server.
  • Business data for data aggregation When the aggregated service data currently stored in the cloud environment is aggregated, the file for storing the service data needs to be read and written, and the read service data needs to be sorted by time.
  • the timestamp of the current service data is greater than the timestamp of the service data sent by the service data server, and the service data of different time periods can be aggregated to improve the efficiency of data aggregation.
  • the detection operation is performed in real time, the delay of the data aggregation operation can be reduced, and the data aggregation can be improved.
  • the real-time nature of the combination is
  • an embodiment of the present invention provides a data aggregation apparatus.
  • the apparatus may include: a receiving unit 41, a detecting unit 42, and an aggregating unit 43.
  • the receiving unit 41 is configured to receive service data sent by the service data server, where the service data carries a timestamp, where the timestamp is used to identify a time when the service server receives the service data.
  • the detecting unit 42 is configured to detect whether the timestamp received by the receiving unit 41 is greater than a target timestamp, where the target timestamp is a timestamp carried by the service data sent by the service data server last time;
  • the aggregating unit 43 is configured to: if the detecting unit 42 detects that the timestamp is greater than the target timestamp, perform data aggregation on the service data with the timestamp of the target timestamp, and the service corresponding to the target timestamp The data is saved in a preset storage location.
  • the device further includes: a determining unit 44.
  • the determining unit 44 is configured to determine whether the service data with the timestamp is the target timestamp is received in the first preset time period after the current service data is received;
  • the aggregating unit 43 is configured to: if the service data with the timestamp is the target timestamp is not received within the first preset time period after the service data is currently received, the timestamp is the target time Poke the business data for data aggregation; or
  • the service data to be received in the first preset time period Data aggregation is performed with the service data whose time stamp is the target time stamp.
  • the determining unit 44 is further configured to determine whether the service data server terminates receiving service data of the same type as the currently received service data;
  • the aggregating unit 43 is further configured to perform data aggregation on the currently received service data if the service data server terminates receiving service data of the same type as the service data.
  • the device further includes: a confirmation unit 45.
  • the determining unit 44 is specifically configured to determine that after the current service data is received, Whether the same service data type as the service data type is received in the second preset time period;
  • the confirming unit 45 is configured to, if not received, confirm that the service data server terminates receiving service data of the same type as the service data.
  • the aggregating unit 43 may specifically include: an obtaining module 4301 and an aggregation module 4302.
  • the obtaining module 4301 is configured to: if the service data server terminates receiving the service data of the same type as the service data, obtain the local time of the aggregation calculation server corresponding to the next adjacent timestamp different from the current timestamp, Each different timestamp configured by the service data server corresponds to a local time of an aggregate computing server;
  • the aggregation module 4302 is configured to perform data aggregation on the currently received service data according to the local time of the aggregation calculation server.
  • the device further includes: a buffer unit 46 and a deleting unit 47.
  • the cache unit 46 is configured to cache the currently received service data into a preset queue corresponding to the target timestamp, where the timestamp is the same as the target timestamp, in the preset queue Cache all service data with a timestamp of the target timestamp;
  • the deleting unit 47 is configured to delete the currently received service data if the timestamp is less than the target timestamp.
  • the apparatus further includes: an establishing unit 48.
  • the establishing unit 48 is configured to establish a queue corresponding to a timestamp of the currently received service data
  • the cache unit 46 is further configured to cache the service data in the queue.
  • the device embodiment corresponds to the foregoing method embodiment.
  • the device embodiment does not describe the details in the foregoing method embodiments one by one, but it should be clear that the device in this embodiment can Corresponding to implementing all of the foregoing method embodiments.
  • the data aggregation server When the data aggregation device of the embodiment of the present invention needs to aggregate the mass service data stored in the cloud environment, the data aggregation server first receives the service data sent by the service data server, and then detects the time carried in the service data in real time. Whether the stamp is greater than the timestamp carried by the service data sent by the data aggregation server last received by the data aggregation server, if If the value is greater than, the timestamp is aggregated with the service data that is received by the data aggregation server and received by the service data server.
  • the file for storing the service data needs to be read and written, and the read service data needs to be sorted once according to time, and if it is received again.
  • the service data of different time periods can be aggregated, the efficiency of data aggregation is improved, and the detection operation is performed in real time, which can reduce the delay of the data aggregation operation.
  • the real-time performance of the data aggregation is improved.
  • the time tolerance period is set in the embodiment of the present invention, and the service data with the earlier received time can be aggregated, thereby improving the accuracy of the data aggregation statistics.
  • the data aggregation device includes a processor and a memory, and the receiving unit, the detecting unit, the aggregating unit, the determining unit, the confirming unit, the buffer unit, the deleting unit, the establishing unit, and the like are all stored in the memory as program units, and are executed by the processor.
  • the above described program elements in the memory implement the corresponding functions.
  • the processor contains a kernel, and the kernel removes the corresponding program unit from the memory.
  • the kernel can be set to one or more. By adjusting the kernel parameters, there is a problem that the display is stuck when the selected graphic is displayed in the chart.
  • the memory may include non-persistent memory, random access memory (RAM), and/or non-volatile memory in a computer readable medium, such as read only memory (ROM) or flash memory (flash RAM), the memory including at least one Memory chip.
  • RAM random access memory
  • ROM read only memory
  • flash RAM flash memory
  • the present application also provides a computer program product, when executed on a data processing device, adapted to perform program code initialization with the following method steps: the data aggregation server receives service data sent by the service data server, and the service data carries a timestamp, where the timestamp is used to identify the time when the service server receives the service data, and whether the timestamp is greater than a target timestamp, where the target timestamp is received by the data aggregation server last time. a timestamp carried in the service data sent by the service data server; if the timestamp is greater than the target timestamp, the data is aggregated with the service data of the target timestamp The service data corresponding to the target timestamp is saved in a preset storage location.
  • embodiments of the present application can be provided as a method, system, or computer program product.
  • the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment in combination of software and hardware.
  • the application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) including computer usable program code.
  • the computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture comprising the instruction device.
  • the apparatus implements the functions specified in one or more blocks of a flow or a flow and/or block diagram of the flowchart.
  • These computer program instructions can also be loaded onto a computer or other programmable data processing device such that a series of operational steps are performed on a computer or other programmable device to produce computer-implemented processing for execution on a computer or other programmable device.
  • the instructions provide steps for implementing the functions specified in one or more of the flow or in a block or blocks of a flow diagram.
  • a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
  • processors CPUs
  • input/output interfaces network interfaces
  • memory volatile and non-volatile memory
  • the memory may include non-persistent memory, random access memory (RAM), and/or non-volatile memory in a computer readable medium, such as read only memory (ROM) or flash memory.
  • RAM random access memory
  • ROM read only memory
  • Memory is an example of a computer readable medium.
  • Computer readable media includes both permanent and non-persistent, removable and non-removable media.
  • Information storage can be implemented by any method or technology.
  • the information can be computer readable instructions, data structures, modules of programs, or other data.
  • Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read only memory. (ROM), electrically erasable programmable read only memory (EEPROM), flash memory or other memory technology, compact disk read only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, Magnetic tape cartridges, magnetic tape storage or other magnetic storage devices or any other non-transportable media can be used to store information that can be accessed by a computing device.
  • computer readable media does not include temporary storage of computer readable media, such as modulated data signals and carrier waves.

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Abstract

一种数据聚合方法及装置，涉及信息技术领域，可以解决的一个技术问题是数据聚合统计精确度较低；另一个技术问题是数据聚合操作延迟较高，从而可以提高数据聚合的效率，保证数据聚合的实时性。所述方法为：当需要对云环境中存储的海量业务数据进行聚合时，首先数据聚合服务器接收业务数据服务器发送的业务数据，然后实时检测业务数据中携带的时间戳是否大于所述数据聚合服务器上一次接收到业务数据服务器发送的业务数据携带的时间戳，若大于，则将时间戳与数据聚合服务器上一次接收到业务数据服务器发送的业务数据携带的时间戳相同的业务数据进行数据聚合。所述方法及装置主要用于对云环境中存储的海量业务数据进行聚合。

Description

数据聚合方法及装置 技术领域
本发明涉及信息技术领域，尤其涉及一种数据聚合方法及装置。
背景技术
随着互联网的不断发展，在云环境下进行业务数据的存储与统计也越来越普遍。例如，对QPS(Query Per Second，每秒查询率)、PV(Page View，页面浏览量)等不同类别的业务数据进行存储与统计。由于在云环境下存储的数据量很大，因此在对云环境中存储的海量业务数据进行统计时，需要对海量的业务数据进行聚合。
目前，在对云环境中存储的海量业务数据进行聚合时，首先将不同时间段接收到的各个类别的业务数据写入到文件中，然后从文件中读取需要聚合的某一类别的全部业务数据，最后将读取的业务数据按照时间进行排序后发送给计算服务器进行数据聚合，例如发送给MapReduce(映射归约)服务器进行数据聚合，具体可以如图5所示。然而，由于在进行数据聚合时，需要对文件进行读写操作，而为了解决进行文件读写操作过程中存在的文件锁冲突的问题，会在数据聚合时进行一次保存有待聚合数据的文件复制过程，从而造成数据计算的延迟较高，并且由于还需要将读取的业务数据按照时间进行一次排序，从而造成数据聚合的过程较为繁琐，数据聚合操作的效率较低，数据聚合操作延迟较高；同时由于业务数据在从文件中读取并完成聚合操作后，如果再次接收到与进行聚合操作的业务数据时间相同或者时间更早的业务数据，再次接收到的这部分业务数据将被丢弃，从而造成数据聚合统计的精确度较低。
发明内容
有鉴于此，本发明提供一种数据聚合方法及装置，主要目的是解决在对云环境中存储的海量业务数据进行聚合时存在数据聚合操作延迟较高 的技术问题。
与此同时，本发明还提供一种数据聚合方法及装置，主要目的是解决数据聚合统计精确度较低的技术问题。
为达到上述目的，本发明提供如下技术方案：
一方面，本发明提供一种数据聚合方法，包括：
数据聚合服务器接收业务数据服务器发送的业务数据，所述业务数据携带有时间戳，所述时间戳用于标识所述业务服务器接收到所述业务数据的时间；
检测所述时间戳是否大于目标时间戳，所述目标时间戳为所述数据聚合服务器上一次接收到所述业务数据服务器发送的业务数据携带的时间戳；
若所述时间戳大于所述目标时间戳，则将时间戳为所述目标时间戳的业务数据进行数据聚合，所述目标时间戳对应的业务数据保存在预置存储位置。
另一方面，本发明提供一种数据聚合装置，包括：
接收单元，用于接收业务数据服务器发送的业务数据，所述业务数据携带有时间戳，所述时间戳用于标识所述业务服务器接收到所述业务数据的时间；
检测单元，用于检测所述接收单元接收的所述时间戳是否大于目标时间戳，所述目标时间戳为上一次接收到所述业务数据服务器发送的业务数据携带的时间戳；
聚合单元，用于若所述检测单元检测所述时间戳大于所述目标时间戳，则将时间戳为所述目标时间戳的业务数据进行数据聚合，所述目标时间戳对应的业务数据保存在预置存储位置。
借由上述技术方案，本发明实施例提供的技术方案至少具有下列优点：
本发明提供的一种数据聚合方法及装置，当需要对云环境中存储的海量业务数据进行聚合时，首先数据聚合服务器接收业务数据服务器发送的 业务数据，然后实时检测业务数据中携带的时间戳是否大于所述数据聚合服务器上一次接收到业务数据服务器发送的业务数据携带的时间戳，若大于，则将时间戳与数据聚合服务器上一次接收到业务数据服务器发送的业务数据携带的时间戳相同的业务数据进行数据聚合。与目前在对云环境中存储的海量业务数据进行聚合时，都需要对用于存储业务数据的文件进行读写操作，并且还需要将读取的业务数据按照时间进行一次排序，同时如果再次接收到与进行聚合操作的业务数据时间相同或者时间更早的业务数据，这部分业务数据将被丢弃相比，本发明由于只需要通过检测当前业务数据的时间戳是否大于数据聚合服务器上一次接收到业务数据服务器发送的业务数据携带的时间戳，即可实现对不同时间段的业务数据进行聚合，提高数据聚合的效率，并且由于检测操作是实时进行的，可以降低数据聚合操作的延迟，提高数据聚合的实时性；同时由于本发明中设置了时间容忍期，可以对当前接收到的时间较早的业务数据进行聚合操作，进而提高了数据聚合统计的精确度。
上述说明仅是本发明技术方案的概述，为了能够更清楚了解本发明的技术手段，而可依照说明书的内容予以实施，并且为了让本发明的上述和其它目的、特征和优点能够更明显易懂，以下特举本发明的具体实施方式。
附图说明
通过阅读下文优选实施方式的详细描述，各种其他的优点和益处对于本领域普通技术人员将变得清楚明了。附图仅用于示出优选实施方式的目的，而并不认为是对本发明的限制。而且在整个附图中，用相同的参考符号表示相同的部件。在附图中：
图1示出了本发明实施例提供的一种数据聚合方法的流程图；
图2示出了本发明实施例提供的另一种数据聚合方法的流程图；
图3示出了本发明实施例提供的一种数据聚合装置的结构示意图；
图4示出了本发明实施例提供的另一种数据聚合装置的结构示意图；
图5示出了本发明提供的现有数据聚合流程示意图；
图6示出了本发明实施例提供的不同业务数据服务器中业务数据时间 戳示意图；
图7示出了本发明实施例提供的不同数据聚合服务器本地时间示意图；
图8示出了本发明实施例提供的数据聚合服务器中不同业务数据的时间戳示意图；
图9示出了本发明实施例提供的业务数据服务器时间与数据聚合服务器时间的映射方式示意图。
具体实施方式
下面将参照附图更详细地描述本公开的示例性实施例。虽然附图中显示了本公开的示例性实施例，然而应当理解，可以以各种形式实现本公开而不应被这里阐述的实施例所限制。相反，提供这些实施例是为了能够更透彻地理解本公开，并且能够将本公开的范围完整的传达给本领域的技术人员。
本发明实施例提供的一种数据聚合方法，如图1所示，所述方法包括：
101、数据聚合服务器接收业务数据服务器发送的业务数据。
其中，所述业务数据携带有时间戳，所述时间戳用于标识所述业务服务器接收到所述业务数据的时间。例如，业务数据服务器接收到业务数据的时间为10:01，则数据聚合服务器接收到的业务数据中携带的时间戳为10:01。所述数据聚合服务器用于对业务数据服务器发送的业务数据进行聚合处理；所述业务数据可以为QPS(Query Per Second，每秒查询率)、PV(Page View，页面浏览量)等不同类别的业务数据，也可以为同一类别不同应用的业务数据，如应用A和应用B的PV数据，本发明实施例不做限定。
102、检测所述时间戳是否大于目标时间戳。
其中，所述目标时间戳为所述数据聚合服务器上一次接收到所述业务数据服务器发送的业务数据时该业务数据携带的时间戳。不同时间戳可以按照时间顺序以列表、时间轴等方式进行展示，本发明实施例不做限定，例如，当不同时间戳按照时间顺序以时间轴等方式进行展示时，时间轴上 的时间会不断向前滚动，以保证时间轴上的时间为当前时间。对于本发明实施例，通过检测所述时间戳是否大于目标时间戳，即检测当前接收到的业务数据的时间与上一次接收到业务数据的时间是否发生变化，从而确定是否触发业务数据聚合操作，从而保证了对业务数据进行聚合操作的实时性。
对于本发明实施例，可以以秒为单位配置时间戳、以分钟为单位配置时间戳等，本发明实施例不做限定。当以秒为单位配置时间戳时，不同时间戳以秒来进行区分，如10:01:01、10:01:02、10:01:03等；当以分钟为单位配置时间戳时，不同时间戳以分钟来进行区分，如10:01、10:02、10:03，其中，10:01:01-10:01:59都归为10:01，以此类推。
例如，业务数据服务器以分钟为单位配置时间戳，若数据聚合服务器当前接收业务数据服务器发送的业务数据携带的时间戳为10:01，数据聚合服务器上一次接收业务数据服务器发送的业务数据携带的时间戳同为10:01，则确定不触发业务数据聚合操作；若数据聚合服务器当前接收业务数据服务器发送的业务数据携带的时间戳为10:02，大于数据聚合服务器上一次接收业务数据服务器发送的业务数据携带的时间戳10:01，则确定触发业务数据聚合操作。
103、若所述时间戳大于所述目标时间戳，则将时间戳为所述目标时间戳的业务数据进行数据聚合。
其中，所述目标时间戳对应的业务数据保存在预置存储位置。所述预置存储位置可以为预置映射关系表、预置队列等，本发明实施例不做限定。对于本发明实施例，若所述时间戳大于所述目标时间戳，如由10:01变为了10:02、或者由10:01:01变为了10:01:02，说明当前业务数据携带的时间戳向前发生了变化，即实时触发对上一时间戳的全部业务数据进行聚合操作，从而保证了业务数据聚合操作的实时性，提高了业务数据的聚合操作效率。
本发明实施例提供的一种数据聚合方法，当需要对云环境中存储的海量业务数据进行聚合时，首先数据聚合服务器接收业务数据服务器发送的业务数据，然后实时检测业务数据中携带的时间戳是否大于所述数据聚合 服务器上一次接收到业务数据服务器发送的业务数据携带的时间戳，若大于，则将时间戳与数据聚合服务器上一次接收到业务数据服务器发送的业务数据携带的时间戳相同的业务数据进行数据聚合。与目前在对云环境中存储的海量业务数据进行聚合时，都需要对用于存储业务数据的文件进行读写操作，并且还需要将读取的业务数据按照时间进行一次排序相比，本发明由于只需要通过检测当前业务数据的时间戳是否大于数据聚合服务器上一次接收到业务数据服务器发送的业务数据携带的时间戳，即可实现对不同时间段的业务数据进行聚合，提高数据聚合的效率，并且由于检测操作是实时进行的，可以降低数据聚合操作的延迟，同时可以提高数据聚合的实时性。
进一步地，本发明实施例提供另一种数据聚合方法，如图2所示，所述方法包括：
201、数据聚合服务器接收业务数据服务器发送的业务数据。
其中，所述业务数据携带有时间戳，所述时间戳用于标识所述业务服务器接收到所述业务数据的时间。所述数据聚合服务器和所述业务数据的相应描述可以参照图1所述实施例的对应描述，在此不再赘述。
202、检测所述时间戳是否大于目标时间戳。
其中，所述目标时间戳为所述数据聚合服务器上一次接收到所述业务数据服务器发送的业务数据时该业务数据携带的时间戳。所述时间戳的配置方式的具体描述可以参照图1所述实施例的对应描述，在此不再赘述。
对于本发明实施例，通过检测所述时间戳是否大于目标时间戳，即检测当前接收到的业务数据的时间与上一次接收到业务数据的时间是否向前发生变化，从而确定是否触发业务数据聚合操作，从而保证了对业务数据进行聚合操作的实时性。
进一步地，步骤202之后还可以包括：若所述时间戳与所述目标时间戳相同，则将当前接收到的所述业务数据缓存到与所述目标时间戳对应的预置队列中。其中，所述预置队列中缓存有时间戳为所述目标时间戳的全部业务数据。不同预置队列分别缓存有时间戳不同的业务数据，由于对队列进行缓存和读取操作的延迟较低，因此，将业务数据缓存到队列中，可 以进一步降低数据聚合操作的延迟，提高数据聚合操作的实时性。若所述时间戳小于所述目标时间戳，则将当前接收到的所述业务数据删除。如时间戳由10:01变为了09:50，则将当前接收到的时间戳为09:50的业务数据删除，从而保证数据聚合的准确性。
203、若所述时间戳大于所述目标时间戳，则将时间戳为所述目标时间戳的业务数据进行数据聚合。
其中，所述目标时间戳对应的业务数据保存在预置存储位置。所述预置存储位置可以为预置映射关系表、预置队列等，本发明实施例不做限定。
对于本发明实施例，步骤203之前还可以包括：判断当前接收到所述业务数据之后的第一预置时间段内，是否接收到时间戳为所述目标时间戳的业务数据。需要说明的是，当某一个时间戳的业务数据进行聚合后，由于传输数据延迟等原因，业务数据服务器有可能再次接收到该时间戳的业务数据，此时，如果不对延迟接收到的业务数据进行处理，会造成业务数据的丢失，进而使得业务数据聚合的准确性较低，因此，在进行数据聚合之前，通过配置时间容忍期，即第一预置时间段，可以避免延迟接收到的业务数据的丢失，提高业务数据聚合统计的准确性。
其中，所述第一预置时间段可以根据时间戳的配置单位进行配置。例如，若时间戳的配置单位为秒，则第一预置时间段可以为1秒、2秒等，若时间戳的配置单位为分钟，则第一预置时间段可以为1分钟、2分钟等。通过根据时间戳的配置单位配置第一预置时间段，可以进一步提高时间容忍期的配置准确性，进而进一步提高业务数据聚合的准确性。
此时，步骤203具体可以包括：若未接收到，则将时间戳为所述目标时间戳的业务数据进行数据聚合；若接收到，则将在所述第一预置时间段内接收到的所述业务数据和时间戳为所述目标时间戳的业务数据进行数据聚合。
例如，业务数据服务器以分钟为单位配置时间戳，则此时将第一预置时间段配置为5分钟。若数据聚合服务器当前接收业务数据服务器发送的业务数据携带的时间戳为10:02，数据聚合服务器上一次接收业务数据服务器发送的业务数据携带的时间戳为10:01，则在对时间戳为10:01的业务 数据进行聚合前，判断从当前时间开始后的五分钟内是否接收到携带的时间戳为10:01的业务数据，若接收到，则将在五分钟内接收到的时间戳为10:01的业务数据和之前接收到的时间戳为10:01的业务数据进行数据聚合；若未接收到，说明此时不存在延迟的业务数据，将时间戳为10:01的业务数据进行数据聚合，从而保证了数据聚合的准确性。
进一步地，步骤203之后还可以包括：建立与当前接收到的业务数据的时间戳对应的队列；将所述业务数据缓存在所述队列中。由于不同时间戳对应不同的队列，因此通过新建队列对当前接收到的业务数据进行保存。
204、判断所述业务数据服务器是否终止接收与所述当前接收到的业务数据类型相同的业务数据。
其中，类型相同的业务数据具体可以为业务数据类型相同的业务数据，例如，判断是否终止接收类别为QPS的业务数据；类型相同的业务数据具体还可以为相同应用的业务数据，例如，判断是否终止接收应用A的业务数据。
对于本发明实施例，步骤204具体可以包括：判断在当前接收到所述业务数据之后的第二预置时间段内，是否接收到与所述业务数据类型相同的业务数据；若未接收到，则确认所述业务数据服务器终止接收与所述业务数据类型相同的业务数据。需要说明的是，若在当前接收到所述业务数据之后的一定时间段内，未接收到与所述业务数据类型相同的业务数据，可以确定业务数据服务器终止接收与所述当前接收到的业务数据类型相同的业务数据。
其中，第二预置时间段具体可以根据时间戳的配置单位进行配置。例如，若时间戳的配置单位为秒，则第二预置时间段可以为1秒、2秒等，若时间戳的配置单位为分钟，则第二预置时间段可以为1分钟、2分钟等。通过根据时间戳的配置单位配置第二预置时间段，可以进一步提高终止接收业务数据的判断精度，进而提高业务数据聚合操作的准确性。
205、若所述业务数据服务器终止接收与所述业务数据类型相同的业务数据，则对当前接收到的业务数据进行数据聚合。
对于本发明实施例，当业务数据服务器终止接收与所述业务数据类型相同的业务数据时，说明数据聚合服务器不再接收该类型业务数据，即此时无法通过再次接收到该类型业务数据来判断该类型业务数据的时间戳是否发生变化，因此在该种情况下，直接对当前接收到的业务数据进行数据聚合，可以保证当业务数据服务器终止接收与所述业务数据类型相同的业务数据时，也可以完成对当前接收到的业务数据进行数据聚合。
对于本发明实施例，步骤205具体可以包括：若所述业务数据服务器终止接收与所述业务数据类型相同的业务数据，则获取与当前时间戳不同的下一个相邻时间戳对应的聚合计算服务器的本地时间，所述业务数据服务器配置的每一个不同的时间戳分别对应一个聚合计算服务器的本地时间，具体可以如图9所示；根据所述聚合计算服务器的本地时间对当前接收到的业务数据进行数据聚合。其中，数据聚合服务器的本地时间可以按照时间顺序以列表、时间轴等方式进行展示，本发明实施例不做限定。
需要说明的是，由于网络状况条件、不同服务器的时间存在差异等问题，会使得数据聚合服务器的本地时间与业务数据服务器配置的时间戳并不相同，例如，如图6和图7所示，业务数据服务器1的业务数据由数据聚合服务器1处理，可以看出业务数据服务器1配置的时间戳为10:01:01，而此时对应的数据聚合服务器1的本地时间为10:03:01，可见数据聚合服务器的本地时间与对应的业务数据服务器配置的时间戳并不相同；并且在数据聚合服务器中，在同一时刻同一个监控项的不同业务数据到达数据聚合服务器的时间戳也可能不同，不同监控项的时间戳也可能不同，例如，如图8所示，对于监控项相同的业务数据PV，在同一时刻应用A中该业务数据的时间戳10:00:00与应用B中该业务数据的时间戳10:01:10不同；对于监控项不同的业务数据PV与RT，业务数据的时间戳也不相同。另外，由于业务数据采集手段的原因，业务数据到达数据聚合服务器的时间与该业务数据到达业务数据服务器的时间不同，例如，如图9所示，某一个业务数据到达业务数据服务器的时间为10:01，该业务数据到达数据聚合服务器的时间为10:03，因此，在做实时数据聚合时，通过采用业务数据服务器配置的时间戳作为触发时间，而不同时采用数据聚合服务器本地时间 作为触发时间，可以保证数据聚合的准确。并且通过按照时间顺序建立业务数据服务器配置的每一个不同的时间戳与数据聚合服务器的本地时间之间的一一对应关系，可以保证时间的一致性，当业务数据服务器终止接收某一类别的业务数据时，通过数据聚合服务器的本地时间依然可以保证准确进行该类别业务数据的聚合操作。
对于本发明实施例，具体的应用场景可以如下所示，但不限于此，包括：如图9所示，数据聚合服务器在时间轴SB点接收业务数据服务器发送的业务数据,获取该业务数据携带的时间戳为11:02，检测到大于上一次在时间轴SA点接收业务数据服务器发送的业务数据携带的时间戳11:01,此时对时间戳为11:01的业务数据进行数据集合。进一步地，判断在从当前时间开始的5分钟内，未接收到与时间戳为11:02的业务数据类型相同的业务数据，可以确定业务数据服务器终止接收与时间戳为11:02的业务数据类型相同的业务数据，此时当数据聚合服务器的本地时间到达11:07时(对应业务数据服务器的11:03)，对时间戳为11:02的业务数据进行数据聚合，从而保证对时间戳为11:02的业务数据进行数据聚合。
本发明实施例提供的另一种数据聚合方法，当需要对云环境中存储的海量业务数据进行聚合时，首先数据聚合服务器接收业务数据服务器发送的业务数据，然后实时检测业务数据中携带的时间戳是否大于所述数据聚合服务器上一次接收到业务数据服务器发送的业务数据携带的时间戳，若大于，则将时间戳与数据聚合服务器上一次接收到业务数据服务器发送的业务数据携带的时间戳相同的业务数据进行数据聚合。与目前在对云环境中存储的海量业务数据进行聚合时，都需要对用于存储业务数据的文件进行读写操作，并且还需要将读取的业务数据按照时间进行一次排序，同时如果再次接收到与进行聚合操作的业务数据时间相同或者时间更早的业务数据，这部分业务数据将被丢弃相比，本发明实施例由于只需要通过检测当前业务数据的时间戳是否大于数据聚合服务器上一次接收到业务数据服务器发送的业务数据携带的时间戳，即可实现对不同时间段的业务数据进行聚合，提高数据聚合的效率，并且由于检测操作是实时进行的，可以降低数据聚合操作的延迟，提高数据聚合的实时性；同时由于本发明实 施例中设置了时间容忍期，可以对当前接收到的时间较早的业务数据进行聚合操作，进而提高了数据聚合统计的精确度。
进一步地，作为图1所示方法的具体实现，本发明实施例提供一种数据聚合装置，如图3所示，所述装置可以包括：接收单元31、检测单元32、聚合单元33。
接收单元31，用于接收业务数据服务器发送的业务数据，所述业务数据携带有时间戳，所述时间戳用于标识所述业务服务器接收到所述业务数据的时间；
检测单元32，用于检测所述接收单元31接收的所述时间戳是否大于目标时间戳，所述目标时间戳为上一次接收到所述业务数据服务器发送的业务数据携带的时间戳；
聚合单元33，用于若所述检测单元32检测所述时间戳大于所述目标时间戳，则将时间戳为所述目标时间戳的业务数据进行数据聚合，所述目标时间戳对应的业务数据保存在预置存储位置。
需要说明的是，该装置实施例与前述方法实施例对应，为便于阅读，本装置实施例不再对前述方法实施例中的细节内容进行逐一赘述，但应当明确，本实施例中的装置能够对应实现前述方法实施例中的全部内容。
本发明实施例提供的一种数据聚合装置，当需要对云环境中存储的海量业务数据进行聚合时，首先数据聚合服务器接收业务数据服务器发送的业务数据，然后实时检测业务数据中携带的时间戳是否大于所述数据聚合服务器上一次接收到业务数据服务器发送的业务数据携带的时间戳，若大于，则将时间戳与数据聚合服务器上一次接收到业务数据服务器发送的业务数据携带的时间戳相同的业务数据进行数据聚合。与目前在对云环境中存储的海量业务数据进行聚合时，都需要对用于存储业务数据的文件进行读写操作，并且还需要将读取的业务数据按照时间进行一次排序相比，本发明由于只需要通过检测当前业务数据的时间戳是否大于数据聚合服务器上一次接收到业务数据服务器发送的业务数据携带的时间戳，即可实现对不同时间段的业务数据进行聚合，提高数据聚合的效率，并且由于检测操作是实时进行的，可以降低数据聚合操作的延迟，同时可以提高数据聚 合的实时性。
进一步地，作为图2所示方法的具体实现，本发明实施例提供一种数据聚合装置，如图4所示，所述装置可以包括：接收单元41、检测单元42、聚合单元43。
接收单元41，用于接收业务数据服务器发送的业务数据，所述业务数据携带有时间戳，所述时间戳用于标识所述业务服务器接收到所述业务数据的时间；
检测单元42，用于检测所述接收单元41接收的所述时间戳是否大于目标时间戳，所述目标时间戳为上一次接收到所述业务数据服务器发送的业务数据携带的时间戳；
聚合单元43，用于若所述检测单元42检测所述时间戳是否大于所述目标时间戳，则将时间戳为所述目标时间戳的业务数据进行数据聚合，所述目标时间戳对应的业务数据保存在预置存储位置。
进一步地，所述装置还包括：判断单元44。
所述判断单元44，用于判断当前接收到所述业务数据之后的第一预置时间段内，是否接收到时间戳为所述目标时间戳的业务数据；
所述聚合单元43，具体用于若当前接收到所述业务数据之后的第一预置时间段内未接收到时间戳为所述目标时间戳的业务数据，则将时间戳为所述目标时间戳的业务数据进行数据聚合；或者
若当前接收到所述业务数据之后的第一预置时间段内接收到时间戳为所述目标时间戳的业务数据，则将在所述第一预置时间段内接收到的所述业务数据和时间戳为所述目标时间戳的业务数据进行数据聚合。
进一步地，所述判断单元44，还用于判断所述业务数据服务器是否终止接收与所述当前接收到的业务数据类型相同的业务数据；
所述聚合单元43，还用于若所述业务数据服务器终止接收与所述业务数据类型相同的业务数据，则对当前接收到的业务数据进行数据聚合。
进一步地，所述装置还包括：确认单元45。
所述判断单元44，具体用于判断在当前接收到所述业务数据之后的第 二预置时间段内，是否接收到与所述业务数据类型相同的业务数据；
所述确认单元45，用于若未接收到，则确认所述业务数据服务器终止接收与所述业务数据类型相同的业务数据。
进一步地，所述聚合单元43具体可以包括：获取模块4301、聚合模块4302。
获取模块4301，用于若所述业务数据服务器终止接收与所述业务数据类型相同的业务数据，则获取与当前时间戳不同的下一个相邻时间戳对应的聚合计算服务器的本地时间，所述业务数据服务器配置的每一个不同的时间戳分别对应一个聚合计算服务器的本地时间；
聚合模块4302，用于根据所述聚合计算服务器的本地时间对当前接收到的业务数据进行数据聚合。
进一步地，所述装置还包括：缓存单元46、删除单元47。
缓存单元46，用于若所述时间戳与所述目标时间戳相同，则将当前接收到的所述业务数据缓存到与所述目标时间戳对应的预置队列中，所述预置队列中缓存有时间戳为所述目标时间戳的全部业务数据；
删除单元47，用于若所述时间戳小于所述目标时间戳，则将当前接收到的所述业务数据删除。
进一步地，所述装置还包括：建立单元48。
所述建立单元48，用于建立与当前接收到的业务数据的时间戳对应的队列；
所述缓存单元46，还用于将所述业务数据缓存在所述队列中。
需要说明的是，该装置实施例与前述方法实施例对应，为便于阅读，本装置实施例不再对前述方法实施例中的细节内容进行逐一赘述，但应当明确，本实施例中的装置能够对应实现前述方法实施例中的全部内容。
本发明实施例提供的另一种数据聚合装置，当需要对云环境中存储的海量业务数据进行聚合时，首先数据聚合服务器接收业务数据服务器发送的业务数据，然后实时检测业务数据中携带的时间戳是否大于所述数据聚合服务器上一次接收到业务数据服务器发送的业务数据携带的时间戳，若 大于，则将时间戳与数据聚合服务器上一次接收到业务数据服务器发送的业务数据携带的时间戳相同的业务数据进行数据聚合。与目前在对云环境中存储的海量业务数据进行聚合时，都需要对用于存储业务数据的文件进行读写操作，并且还需要将读取的业务数据按照时间进行一次排序，同时如果再次接收到与进行聚合操作的业务数据时间相同或者时间更早的业务数据，这部分业务数据将被丢弃相比，本发明实施例由于只需要通过检测当前业务数据的时间戳是否大于数据聚合服务器上一次接收到业务数据服务器发送的业务数据携带的时间戳，即可实现对不同时间段的业务数据进行聚合，提高数据聚合的效率，并且由于检测操作是实时进行的，可以降低数据聚合操作的延迟，提高数据聚合的实时性；同时由于本发明实施例中设置了时间容忍期，可以对当前接收到的时间较早的业务数据进行聚合操作，进而提高了数据聚合统计的精确度。
所述数据聚合装置包括处理器和存储器，上述接收单元、检测单元、聚合单元、判断单元、确认单元、缓存单元、删除单元、建立单元等均作为程序单元存储在存储器中，由处理器执行存储在存储器中的上述程序单元来实现相应的功能。
处理器中包含内核，由内核去存储器中调取相应的程序单元。内核可以设置一个或以上，通过调整内核参数来解决在对图表中被选中的图形进行显示时存在显示卡顿的问题。
存储器可能包括计算机可读介质中的非永久性存储器，随机存取存储器(RAM)和/或非易失性内存等形式，如只读存储器(ROM)或闪存(flash RAM)，存储器包括至少一个存储芯片。
本申请还提供了一种计算机程序产品，当在数据处理设备上执行时，适于执行初始化有如下方法步骤的程序代码：数据聚合服务器接收业务数据服务器发送的业务数据，所述业务数据携带有时间戳，所述时间戳用于标识所述业务服务器接收到所述业务数据的时间；检测所述时间戳是否大于目标时间戳，所述目标时间戳为所述数据聚合服务器上一次接收到所述业务数据服务器发送的业务数据携带的时间戳；若所述时间戳大于所述目标时间戳不相同，则将时间戳为所述目标时间戳的业务数据进行数据聚 合，所述目标时间戳对应的业务数据保存在预置存储位置。
本领域内的技术人员应明白，本申请的实施例可提供为方法、系统、或计算机程序产品。因此，本申请可采用完全硬件实施例、完全软件实施例、或结合软件和硬件方面的实施例的形式。而且，本申请可采用在一个或多个其中包含有计算机可用程序代码的计算机可用存储介质(包括但不限于磁盘存储器、CD-ROM、光学存储器等)上实施的计算机程序产品的形式。
本申请是参照根据本申请实施例的数据聚合方法、装置、和计算机程序产品的流程图和/或方框图来描述的。应理解可由计算机程序指令实现流程图和/或方框图中的每一流程和/或方框、以及流程图和/或方框图中的流程和/或方框的结合。可提供这些计算机程序指令到通用计算机、专用计算机、嵌入式处理机或其他可编程数据处理设备的处理器以产生一个机器，使得通过计算机或其他可编程数据处理设备的处理器执行的指令产生用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的装置。
这些计算机程序指令也可存储在能引导计算机或其他可编程数据处理设备以特定方式工作的计算机可读存储器中，使得存储在该计算机可读存储器中的指令产生包括指令装置的制造品，该指令装置实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能。
这些计算机程序指令也可装载到计算机或其他可编程数据处理设备上，使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理，从而在计算机或其他可编程设备上执行的指令提供用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的步骤。
在一个典型的配置中，计算设备包括一个或多个处理器(CPU)、输入/输出接口、网络接口和内存。
存储器可能包括计算机可读介质中的非永久性存储器，随机存取存储器(RAM)和/或非易失性内存等形式，如只读存储器(ROM)或闪存(flash RAM)。存储器是计算机可读介质的示例。
计算机可读介质包括永久性和非永久性、可移动和非可移动媒体可以由任何方法或技术来实现信息存储。信息可以是计算机可读指令、数据结构、程序的模块或其他数据。计算机的存储介质的例子包括，但不限于相变内存(PRAM)、静态随机存取存储器(SRAM)、动态随机存取存储器(DRAM)、其他类型的随机存取存储器(RAM)、只读存储器(ROM)、电可擦除可编程只读存储器(EEPROM)、快闪记忆体或其他内存技术、只读光盘只读存储器(CD-ROM)、数字多功能光盘(DVD)或其他光学存储、磁盒式磁带，磁带磁磁盘存储或其他磁性存储设备或任何其他非传输介质，可用于存储可以被计算设备访问的信息。按照本文中的界定，计算机可读介质不包括暂存电脑可读媒体(transitory media)，如调制的数据信号和载波。
以上仅为本申请的实施例而已，并不用于限制本申请。对于本领域技术人员来说，本申请可以有各种更改和变化。凡在本申请的精神和原理之内所作的任何修改、等同替换、改进等，均应包含在本申请的权利要求范围之内。

Claims (14)

  1. 一种数据聚合方法，其特征在于，包括：
    数据聚合服务器接收业务数据服务器发送的业务数据，所述业务数据携带有时间戳，所述时间戳用于标识所述业务服务器接收到所述业务数据的时间；
    检测所述时间戳是否大于目标时间戳，所述目标时间戳为所述数据聚合服务器上一次接收到所述业务数据服务器发送的业务数据携带的时间戳；
    若所述时间戳大于所述目标时间戳，则将时间戳为所述目标时间戳的业务数据进行数据聚合，所述目标时间戳对应的业务数据保存在预置存储位置。
  2. 根据权利要求1所述的数据聚合方法，其特征在于，所述将时间戳为所述目标时间戳的业务数据进行数据聚合之前，所述方法还包括：
    判断当前接收到所述业务数据之后的第一预置时间段内，是否接收到时间戳为所述目标时间戳的业务数据；
    所述将时间戳为所述目标时间戳的业务数据进行数据聚合包括：
    若未接收到，则将时间戳为所述目标时间戳的业务数据进行数据聚合；
    若接收到，则将在所述第一预置时间段内接收到的所述业务数据和时间戳为所述目标时间戳的业务数据进行数据聚合。
  3. 根据权利要求1所述的数据聚合方法，其特征在于，所述若所述时间戳大于所述目标时间戳，则将时间戳为所述目标时间戳的业务数据进行数据聚合之后，所述方法还包括：
    判断所述业务数据服务器是否终止接收与所述当前接收到的业务数据类型相同的业务数据；
    若所述业务数据服务器终止接收与所述业务数据类型相同的业务数 据，则对当前接收到的业务数据进行数据聚合。
  4. 根据权利要求3所述的数据聚合方法，其特征在于，所述判断所述业务数据服务器是否终止接收与所述当前接收到的业务数据类型相同的业务数据包括：
    判断在当前接收到所述业务数据之后的第二预置时间段内，是否接收到与所述业务数据类型相同的业务数据；
    若未接收到，则确认所述业务数据服务器终止接收与所述业务数据类型相同的业务数据。
  5. 根据权利要求3或4所述的数据聚合方法，其特征在于，若所述业务数据服务器终止接收与所述业务数据类型相同的业务数据，所述对当前接收到的业务数据进行数据聚合包括：
    若所述业务数据服务器终止接收与所述业务数据类型相同的业务数据，则获取与当前时间戳不同的下一个相邻时间戳对应的聚合计算服务器的本地时间，所述业务数据服务器配置的每一个不同的时间戳分别对应一个聚合计算服务器的本地时间；
    根据所述聚合计算服务器的本地时间对当前接收到的业务数据进行数据聚合。
  6. 根据权利要求1所述的数据聚合方法，其特征在于，所述检测所述时间戳是否大于目标时间戳之后，所述方法还包括：
    若所述时间戳与所述目标时间戳相同，则将当前接收到的所述业务数据缓存到与所述目标时间戳对应的预置队列中，所述预置队列中缓存有时间戳为所述目标时间戳的全部业务数据；
    若所述时间戳小于所述目标时间戳，则将当前接收到的所述业务数据删除。
  7. 根据权利要求1所述的数据聚合方法，其特征在于，所述若所述时间戳大于目标时间戳，则将时间戳为所述目标时间戳的业务数据进行数据聚合之后，所述方法还包括：
    建立与当前接收到的业务数据的时间戳对应的队列；
    将所述业务数据缓存在所述队列中。
  8. 一种数据聚合装置，其特征在于，包括：
    接收单元，用于接收业务数据服务器发送的业务数据，所述业务数据携带有时间戳，所述时间戳用于标识所述业务服务器接收到所述业务数据的时间；
    检测单元，用于检测所述接收单元接收的所述时间戳是否大于目标时间戳，所述目标时间戳为上一次接收到所述业务数据服务器发送的业务数据携带的时间戳；
    聚合单元，用于若所述检测单元检测所述时间戳大于所述目标时间戳，则将时间戳为所述目标时间戳的业务数据进行数据聚合，所述目标时间戳对应的业务数据保存在预置存储位置。
  9. 根据权利要求8所述的数据聚合装置，其特征在于，所述装置还包括：判断单元；
    所述判断单元，用于判断当前接收到所述业务数据之后的第一预置时间段内，是否接收到时间戳为所述目标时间戳的业务数据；
    所述聚合单元，具体用于若当前接收到所述业务数据之后的第一预置时间段内未接收到时间戳为所述目标时间戳的业务数据，则将时间戳为所述目标时间戳的业务数据进行数据聚合；或者
    若当前接收到所述业务数据之后的第一预置时间段内接收到时间戳为所述目标时间戳的业务数据，则将在所述第一预置时间段内接收到的所述业务数据和时间戳为所述目标时间戳的业务数据进行数据聚合。
  10. 根据权利要求9所述的数据聚合装置，其特征在于，
    所述判断单元，还用于判断所述业务数据服务器是否终止接收与所述当前接收到的业务数据类型相同的业务数据；
    所述聚合单元，还用于若所述业务数据服务器终止接收与所述业务数据类型相同的业务数据，则对当前接收到的业务数据进行数据聚合。
  11. 根据权利要求10所述的数据聚合装置，其特征在于，所述装置还包括：确认单元；
    所述判断单元，具体用于判断在当前接收到所述业务数据之后的第二预置时间段内，是否接收到与所述业务数据类型相同的业务数据；
    所述确认单元，用于若未接收到，则确认所述业务数据服务器终止接收与所述业务数据类型相同的业务数据。
  12. 根据权利要求10或11所述的数据聚合装置，其特征在于，所述聚合单元包括：
    获取模块，用于若所述业务数据服务器终止接收与所述业务数据类型相同的业务数据，则获取与当前时间戳不同的下一个相邻时间戳对应的聚合计算服务器的本地时间，所述业务数据服务器配置的每一个不同的时间戳分别对应一个聚合计算服务器的本地时间；
    聚合模块，用于根据所述聚合计算服务器的本地时间对当前接收到的业务数据进行数据聚合。
  13. 根据权利要求8所述的数据聚合装置，其特征在于，所述装置还包括：
    缓存单元，用于若所述时间戳与所述目标时间戳相同，则将当前接收到的所述业务数据缓存到与所述目标时间戳对应的预置队列中，所述预置队列中缓存有时间戳为所述目标时间戳的全部业务数据；
    删除单元，用于若所述时间戳小于所述目标时间戳，则将当前接收到的所述业务数据删除。
  14. 根据权利要求13所述的数据聚合装置，其特征在于，所述装置还包括：建立单元；
    所述建立单元，用于建立与当前接收到的业务数据的时间戳对应的队列；
    所述缓存单元，还用于将所述业务数据缓存在所述队列中。
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