CN105512297A - Distributed stream-oriented computation based spatial data processing method and system - Google Patents
Distributed stream-oriented computation based spatial data processing method and system Download PDFInfo
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Abstract
The invention provides a distributed stream-oriented computation based spatial data processing method and system. A message queue of Kafka is established and used for real-time monitor, so that when data are stored in the message queue, Kafka can timely draw the monitored data, further, the data are processed in real time, and the real-time performance of data processing is realized. Besides, data are stored in a database and are further backed up, and the processed data can be updated to the database after the data are processed, so that the data in the database are updated. Real-time computation of geospatial big data is realized through distributed stream-oriented computation, and the method and system can support development and application of geospatial data with high real-time performance.
Description
[technical field]
The present invention relates to geography information and computer realm, be specifically related to the disposal route of the distributed geospatial information in streaming calculating of base.
[background technology]
Along with the widely using of the wireless localization apparatus such as development and mobile phone, personal digital assistant and vehicle mounted guidance of the Modern Surveying & Mapping equipment technologies such as mobile high-spectrum remote-sensing, synthetic-aperture radar, 3 D laser scanning, create increasing spatial data, a huge challenge is treated as in real time to these data.
Current, main employing multi-core parallel concurrent calculates and processes massive spatial data, and similar Hadoop cluster also can be adopted to perform process to massive spatial data.But, be limited to the computing power of single computer and Hadoop cluster MapReduce method to the weakness of data processing poor real, massive spatial data process can not be tackled and the high scene of requirement of real-time, such as the real-time monitoring of large-scale mobile object.
Streaming calculates the instant process be mainly used in data, and the large data grows in the space that can obtain along with people moves towards magnanimity, and Distributed Calculation is also introduced in efficient spatial data handling, i.e. distributive type calculation system.Further, increase income Spark community start attempt in internal memory, carry out distributive type calculating, be much little data block by calculative spatial data handling division of tasks, by making it through different processing nodes in internal memory in a streaming manner, finally converge output, this be geographical spatial data streaming calculate established certain basis.
[summary of the invention]
For the problem that can not realize process in real time in prior art to magnanimity geographical spatial data, the present invention aims to provide a kind of spatial data processing method based on distributive type calculating and system.
The spatial data processing method calculated based on distributive type of the present invention comprises the following steps:
Create Kafka timed task, extract geographical spatial data every the rear of the special time period set by Kafka timed task to data source;
Create the message queue being used for flowing into for described geographical spatial data, and according to the Spatial data types preset, message queue is classified, thus specify the label in described message queue;
Make described geographical spatial data flow into described message queue with the form of data stream to preserve, to carry out real-time Treatment Analysis; And by described geographical spatial data stored in database, to realize backup simultaneously; Data dissimilar in described message queue are distinguished according to the label of specifying in message queue;
Geographical spatial data described in real-time listening; If listened to described geographical spatial data when being stored into described message queue by Kafka timed task, extract the current geographical spatial data be stored in described message queue; According to the filtering rule pre-set, extracted geographical spatial data is filtered, and the data be extracted are sent to Spark cluster Computing Platform;
By Spark cluster Computing Platform based on RDD, according to the logic rules obtained in advance, logical process is carried out to extracted data; By the Data Update after logical process in described database.
Preferably, the Spatial data types preset described in comprises: digital line draws the attribute data of data, image data, digital elevation model and atural object.
Preferably, if the described logical process success carried out extracted data, then the data be extracted are removed from described message queue; If described logical process is unsuccessful, then analyze unsuccessful reason, when analysis result be the data that are extracted is the data not meeting specification, the data be extracted are removed from message queue.
The present invention so provide a kind of based on distributive type calculate spatial data handling system, it is characterized in that, comprising:
One or more data center, described data center is connected to corresponding network multi-core computer, communicates between data center via network;
Kafka unit, for creating Kafka timed task, is extracting geographical spatial data every the rear of the special time period set by Kafka timed task to data center;
Data storage cell, for creating the message queue for flowing into for described geographical spatial data, and classifies to message queue according to the Spatial data types preset, thus specifies the label in described message queue; Make described geographical spatial data flow into described message queue with the form of data stream to preserve, to carry out real-time Treatment Analysis; And by described geographical spatial data stored in database, to realize backup simultaneously; Data dissimilar in described message queue are distinguished according to the label of specifying in message queue;
Monitor and data allocation unit, for geographical spatial data described in real-time listening; If listened to described geographical spatial data when being stored into described message queue by Kafka timed task, extract the current geographical spatial data be stored in described message queue; According to the filtering rule pre-set, extracted geographical spatial data is filtered, and the data be extracted are sent to Spark cluster Computing Platform;
Spark cluster Computing Platform, based on RDD, carries out logical process according to the logic rules obtained in advance to extracted data; By the Data Update after logical process in the database of described data storage cell.
Preferably, the Spatial data types preset described in comprises: digital line draws the attribute data of data, image data, digital elevation model and atural object.
Preferably, if the described logical process success carried out extracted data, then the data be extracted remove by monitoring and data allocation unit from described message queue; If described logical process is unsuccessful, then analyze unsuccessful reason, when analysis result be the data that are extracted is the data not meeting specification, to monitor and the data be extracted remove by data allocation unit from message queue.
Visible, the invention provides a kind of spatial data processing method based on streaming calculating and system, by creating the message queue of Kafka, the message queue of Kafka is utilized to carry out real-time listening, with have in the message queue made data stored in time, Kafka can pull these data listened in real time, and then processes in real time data, thus achieves the real-time of data processing.In addition, not merely by data stored in database, also backup is achieved to data, after data are processed, by the Data Update after process in database, thus the Data Update in database can also be made.Present invention achieves streaming in a distributed manner and calculate the real-time operation carrying out the large data of geographical space, various real-time geographical spatial data Application and Development can be supported.
[accompanying drawing explanation]
Below in conjunction with the drawings and specific embodiments, the present invention is further detailed explanation.
Fig. 1 is the method frame figure based on the spatial data handling of distributive type calculating described in the embodiment of the present invention.
Fig. 2 is the method flow diagram based on the spatial data handling of distributive type calculating described in the embodiment of the present invention.
[embodiment]
As the preferred embodiments of the disclosure, provide a kind of spatial data processing method based on distributive type calculating and system.
See Fig. 1 and Fig. 2, the spatial data processing method calculated based on distributive type of the present invention comprises the following steps:
Create Kafka timed task, extract geographical spatial data every the rear of the special time period set by Kafka timed task to data source.Kafka is that a kind of distributed message of high-throughput is issued and ordering system, can realize the transmission of flow data.
Create the message queue being used for flowing into for described geographical spatial data, and according to the Spatial data types preset, message queue is classified, thus specify the label in described message queue; According to the complexity and diversity of geographical spatial data, geographical spatial data is classified, mainly comprise: digital line draws the attribute data etc. of data, image data, digital elevation model and atural object, as the described Spatial data types preset.
Make described geographical spatial data flow into described message queue with the form of data stream to preserve, to carry out real-time Treatment Analysis; And by described geographical spatial data stored in database, to realize backup simultaneously; Data dissimilar in described message queue are distinguished according to the label of specifying in message queue;
Geographical spatial data described in real-time listening; If listened to described geographical spatial data when being stored into described message queue by Kafka timed task, extract the current geographical spatial data be stored in described message queue; According to the filtering rule pre-set, extracted geographical spatial data is filtered, and the data be extracted are sent to Spark cluster Computing Platform;
By Spark cluster Computing Platform based on RDD, according to the logic rules obtained in advance, logical process is carried out to extracted data; By the Data Update after logical process in described database.Spark cluster is emerging high amount of traffic formula Processing Cluster framework, and its core is based on abstract RDD, makes it possible to tackle various large data processing scene in an essentially uniform manner.RDD full name is ResilientDistributedDatasets, is a kind of fault-tolerant and parallel data structure, stored in internal memory and the subregion of control data, and can provide abundant operation to realize the logical process to data.In the present invention, if the described logical process success carried out extracted data, then the data be extracted are removed from described message queue; If described logical process is unsuccessful, then analyze unsuccessful reason, when analysis result be the data that are extracted is the data not meeting specification, the data be extracted are removed from message queue.
And then, the present invention so provide a kind of based on distributive type calculate spatial data handling system, it is characterized in that, comprising:
One or more data center, described data center is connected to corresponding network multi-core computer, communicates between data center via network;
Kafka unit, for creating Kafka timed task, is extracting geographical spatial data every the rear of the special time period set by Kafka timed task to data center;
Data storage cell, for creating the message queue for flowing into for described geographical spatial data, and classifies to message queue according to the Spatial data types preset, thus specifies the label in described message queue; Make described geographical spatial data flow into described message queue with the form of data stream to preserve, to carry out real-time Treatment Analysis; And by described geographical spatial data stored in database, to realize backup simultaneously; Data dissimilar in described message queue are distinguished according to the label of specifying in message queue;
Monitor and data allocation unit, for geographical spatial data described in real-time listening; If listened to described geographical spatial data when being stored into described message queue by Kafka timed task, extract the current geographical spatial data be stored in described message queue; According to the filtering rule pre-set, extracted geographical spatial data is filtered, and the data be extracted are sent to Spark cluster Computing Platform;
Spark cluster Computing Platform, based on RDD, carries out logical process according to the logic rules obtained in advance to extracted data; By the Data Update after logical process in the database of described data storage cell.
Visible, the invention provides a kind of spatial data processing method based on streaming calculating and system, by creating the message queue of Kafka, the message queue of Kafka is utilized to carry out real-time listening, with have in the message queue made data stored in time, Kafka can pull these data listened in real time, and then processes in real time data, thus achieves the real-time of data processing.In addition, not merely by data stored in database, also backup is achieved to data, after data are processed, by the Data Update after process in database, thus the Data Update in database can also be made.
Claims (6)
1., based on the spatial data processing method that distributive type calculates, it is characterized in that, comprise the following steps:
Create Kafka timed task, extract geographical spatial data every the rear of the special time period set by Kafka timed task to data source;
Create the message queue being used for flowing into for described geographical spatial data, and according to the Spatial data types preset, message queue is classified, thus specify the label in described message queue;
Make described geographical spatial data flow into described message queue with the form of data stream to preserve, to carry out real-time Treatment Analysis; And by described geographical spatial data stored in database, to realize backup simultaneously; Data dissimilar in described message queue are distinguished according to the label of specifying in message queue;
Geographical spatial data described in real-time listening; If listened to described geographical spatial data when being stored into described message queue by Kafka timed task, extract the current geographical spatial data be stored in described message queue; According to the filtering rule pre-set, extracted geographical spatial data is filtered, and the data be extracted are sent to Spark cluster Computing Platform;
By Spark cluster Computing Platform based on RDD, according to the logic rules obtained in advance, logical process is carried out to extracted data; By the Data Update after logical process in described database.
2. the spatial data processing method calculated based on distributive type according to claim 1, is characterized in that, described in the Spatial data types that presets comprise: digital line draws the attribute data of data, image data, digital elevation model and atural object.
3. the spatial data processing method calculated based on distributive type according to claim 1, is characterized in that, if the described logical process success carried out extracted data, then the data be extracted is removed from described message queue; If described logical process is unsuccessful, then analyze unsuccessful reason, when analysis result be the data that are extracted is the data not meeting specification, the data be extracted are removed from message queue.
4., based on the spatial data handling system that distributive type calculates, it is characterized in that, comprising:
One or more data center, described data center is connected to corresponding network multi-core computer, communicates between data center via network;
Kafka unit, for creating Kafka timed task, is extracting geographical spatial data every the rear of the special time period set by Kafka timed task to data center;
Data storage cell, for creating the message queue for flowing into for described geographical spatial data, and classifies to message queue according to the Spatial data types preset, thus specifies the label in described message queue; Make described geographical spatial data flow into described message queue with the form of data stream to preserve, to carry out real-time Treatment Analysis; And by described geographical spatial data stored in database, to realize backup simultaneously; Data dissimilar in described message queue are distinguished according to the label of specifying in message queue;
Monitor and data allocation unit, for geographical spatial data described in real-time listening; If listened to described geographical spatial data when being stored into described message queue by Kafka timed task, extract the current geographical spatial data be stored in described message queue; According to the filtering rule pre-set, extracted geographical spatial data is filtered, and the data be extracted are sent to Spark cluster Computing Platform;
Spark cluster Computing Platform, based on RDD, carries out logical process according to the logic rules obtained in advance to extracted data; By the Data Update after logical process in the database of described data storage cell.
5. the spatial data handling system calculated based on distributive type according to claim 4, is characterized in that, described in the Spatial data types that presets comprise: digital line draws the attribute data of data, image data, digital elevation model and atural object.
6. the spatial data handling system calculated based on distributive type according to claim 4, it is characterized in that, if the described logical process success carried out extracted data, then the data be extracted remove by monitoring and data allocation unit from described message queue; If described logical process is unsuccessful, then analyze unsuccessful reason, when analysis result be the data that are extracted is the data not meeting specification, to monitor and the data be extracted remove by data allocation unit from message queue.
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| CN105956135A (en) * | 2016-05-12 | 2016-09-21 | 南京唯实科技有限公司 | Storm-based real-time data computing platform |
| CN106648904A (en) * | 2017-01-09 | 2017-05-10 | 大连理工大学 | Self-adaptive rate control method for stream data processing |
| CN107317838A (en) * | 2017-05-24 | 2017-11-03 | 重庆邮电大学 | A kind of astronomical metadata archiving method and system based on stream data processing framework |
| CN108710918A (en) * | 2018-05-23 | 2018-10-26 | 北京奇艺世纪科技有限公司 | A kind of fusion method and device of the multi-modal information of live video |
| CN109669931A (en) * | 2018-12-29 | 2019-04-23 | 上海携程商务有限公司 | Historical data exception analysis method, system, equipment and storage medium |
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| CN105956135A (en) * | 2016-05-12 | 2016-09-21 | 南京唯实科技有限公司 | Storm-based real-time data computing platform |
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| CN107317838A (en) * | 2017-05-24 | 2017-11-03 | 重庆邮电大学 | A kind of astronomical metadata archiving method and system based on stream data processing framework |
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| CN110758478A (en) * | 2019-11-27 | 2020-02-07 | 佳讯飞鸿（北京）智能科技研究院有限公司 | Pre-warning system and method for railway signal equipment |
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| CN111143415B (en) * | 2019-12-26 | 2023-12-29 | 政采云有限公司 | Data processing method, device and computer readable storage medium |
| CN111598036A (en) * | 2020-05-22 | 2020-08-28 | 广州地理研究所 | Urban group geographic environment knowledge base construction method and system of distributed architecture |
| CN111931066A (en) * | 2020-09-11 | 2020-11-13 | 四川新网银行股份有限公司 | Real-time recommendation system design method |
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Application publication date: 20160420 |
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