WO2022188453A1 - Online analysis-based image and grid cube data storage management method and system - Google Patents

Online analysis-based image and grid cube data storage management method and system Download PDF

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WO2022188453A1
WO2022188453A1 PCT/CN2021/130351 CN2021130351W WO2022188453A1 WO 2022188453 A1 WO2022188453 A1 WO 2022188453A1 CN 2021130351 W CN2021130351 W CN 2021130351W WO 2022188453 A1 WO2022188453 A1 WO 2022188453A1
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tile
cube
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storage scheme
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余劲松弟
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福州大学
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/06—Digital input from, or digital output to, record carriers, e.g. RAID, emulated record carriers or networked record carriers
    • G06F3/0601—Interfaces specially adapted for storage systems
    • G06F3/0602—Interfaces specially adapted for storage systems specifically adapted to achieve a particular effect
    • G06F3/061—Improving I/O performance
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/06—Digital input from, or digital output to, record carriers, e.g. RAID, emulated record carriers or networked record carriers
    • G06F3/0601—Interfaces specially adapted for storage systems
    • G06F3/0628—Interfaces specially adapted for storage systems making use of a particular technique
    • G06F3/0638—Organizing or formatting or addressing of data
    • G06F3/064—Management of blocks
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management

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  • the invention belongs to the technical field of big data, and in particular relates to a method and system for online analysis of image and grid cube data storage management.
  • NoSQL database MongoDB An important feature of the NoSQL database MongoDB is that it provides GridFS for storing files with a size of more than 16MB. By default, fs.chunks and fs.files are used to store files.
  • the fs.files collection stores file information
  • fs.chunks stores file information. data.
  • GridFS Many researchers have applied GridFS to image and grid data storage, such as storing remote sensing image data files in GridFS, and using its distributed mechanism to improve data access performance. Compared with the segmentation and storage of a single 2D image data, the data volume of images and grid cubes is an order of magnitude larger than that of 2D image data. Its online analysis application also puts forward new requirements for the existing Gridfs storage solution, that is, the data should be stored in cubes. The tiles are divided, and the application requirements are analyzed according to the online instantiation, and the most suitable segmentation storage scheme is supported to access the required cube tiles, so as to prevent the consumption of bandwidth and computing power in the online instant segmentation process.
  • the traditional image storage method of MongoDB is to store the metadata of remote sensing image data in the fs.files of GridFS, and fs.chunks to store the binary files of the image, and then specify the shard key for sharding to support distributed storage. It supports the horizontal expansion of the database, which is suitable for managing large-scale remote sensing image data.
  • it is necessary to retrieve the relevant 2D images first, and then cut and reconstruct the required images after obtaining all the required images. If the relevant images are missing or resolution, etc. If the parameters are inconsistent, intervention is required during the reconstruction process, the uncertainty is large, and the performance is not high.
  • a single cube tile partitioning storage scheme has fixed tile data size, axis sequence, and tile size parameters.
  • the partitioned tiles have a high degree of fit with the cube request index range, that is, the set of target data block indexes is exactly equal to The requested index range is larger.
  • the I/O and data volume of data blocks are relatively small, which can greatly reduce the transmission of invalid data blocks and the consumption of corresponding computing power, and improve the performance of online instant analysis applications.
  • the index range requested by the client during the online analysis of image and grid cube data is unpredictable. Therefore, various partition storage schemes are required, and the most suitable partition storage scheme is found according to the corresponding request, and then the corresponding target is obtained.
  • Data block indexing to complete the online instant analysis application, to greatly reduce the transmission of invalid data blocks and the corresponding computing power consumption, and improve the online instant analysis application performance.
  • the purpose of the present invention is to solve the above problems, and to provide a method and system for storing and managing images and grid cube data that are analyzed online.
  • an online instant analysis image and grid cube data storage management method comprising: storing the image and grid cube in a variety of tile partitioning schemes, each storage scheme corresponds to one Configuration, compare the I/O number and data size of each configuration through matching rules, obtain the tile partition storage scheme with the least I/O under the condition of data volume, and then obtain the target data block index of the corresponding tile partition storage scheme, Complete the target data block access of the corresponding tile partition storage scheme.
  • each storage scheme corresponds to a configuration, including tile size, axis sequence, tile size parameters ;
  • Set the matching rules for the target data block access of the cube in the online analysis application which is used to compare the I/O number and data volume configured by each scheme, and obtain the minimum I/O watts considering the requested data volume and its coefficient.
  • Slice partition storage scheme obtain the cube request index range in the online analysis application, determine the tile partition storage scheme with the least I/O number considering the amount of requested data and its coefficients according to the matching rules, and then obtain the target data block index, Complete the target data block access of the corresponding tile partition storage scheme.
  • the cube is stored in multiple tile partitioning schemes, one cube is stored in multiple GridFS, and each tile partitioning storage scheme corresponds to a configuration, denoted as pi (1 ⁇ i ⁇ n),
  • the n tile partitioning schemes of the cube are stored as ⁇ p1,p2,...,pn ⁇ .
  • the tile partitioning scheme of the cube is stored as a tile partitioning storage scheme based on MongoDB, and the cube is partitioned along a given axis sequence according to a fixed tile size parameter, and the end of each axis is insufficient.
  • each tile is a GridFS of MongoDB;
  • the configuration of the corresponding tile partition storage scheme includes tile size, axis sequence, tile size information, and the parameter record format is ⁇ v , ⁇ A>, ⁇ t> ⁇
  • v represents the tile size of MongoDB's GridFS
  • ⁇ A> represents the tile axis sequence, represented by ⁇ x, y, z>
  • ⁇ t> represents the tile size, represented by ⁇ l
  • w,h> represents the number of cells of the tile on the corresponding axis.
  • the matching rule for accessing the target data block of the cube in the online analysis application is 10min( ⁇ f(pi,r,s) ⁇ ), and pi represents the i-th tile partition storage scheme.
  • r represents the cube request index range in the online instant analysis application
  • s indicates the coefficient of the cube request data volume in the online instant analysis application
  • f(pi,r,s) indicates the online instant analysis for the i-th tile partition storage scheme
  • IOmin( ⁇ f(pi, r, s) ⁇ ) represents the tile partitioning storage scheme with the least I/O considering the amount of requested data and its coefficients.
  • the target data block index is obtained, based on a matching rule, a tile partitioning storage scheme with the least I/O is obtained considering the amount of requested data and its coefficients, and based on the tile partitioning storage scheme, the corresponding tile partitioning storage scheme is further obtained.
  • the present invention also provides an online instant analysis image and grid cube data storage management system, comprising: a cube storage adaptation module mounted on an online instant analysis image and grid cube application service platform to store images and grid cubes in multiple Tile segmentation storage scheme, each tile segmentation storage scheme corresponds to a configuration; the request preprocessing module mounted on the online instant analysis image and grid cube application service platform, the request preprocessing module, according to the online instant analysis of the client
  • the index range of the cube request in the application according to the pre-given coefficient of the requested data volume of the cube in the online analysis application, according to the matching rule, to determine the solution with the least I/O under the condition of considering the requested data volume and its coefficient, and then obtain the target data Block index
  • the tile segmentation data storage access module installed on the online instant analysis image and grid cube application service platform, according to the target data block index, the corresponding GridFS data block is obtained from MongoDB, returned to the client, and the analysis is completed immediately application.
  • the present invention Compared with the prior art, the present invention has the following beneficial effects: the present invention utilizes MongoDB's GridFS to store images and grid cubes in a variety of tile partitioning schemes, and determines the one with the least I/O considering the amount of requested data and its coefficients.
  • the solution greatly reduces the transmission of invalid data blocks and the consumption of corresponding computing power, and improves the performance of online instant analysis of images and grid cubes.
  • FIG. 1 is a schematic diagram of a module structure of the present invention.
  • an embodiment of the present invention provides an online analysis method for storing image and grid cube data.
  • the main processing steps include: storing the image and grid cube in an online format with various tile segmentation schemes. That is to analyze the MongoDB on the image and grid cube application service platform side, each storage scheme corresponds to a configuration, obtain the index range of the cube request in the online analysis application, and compare the I/O number and data size of each configuration through matching rules to obtain consideration.
  • the tile partition storage scheme with the least I/O under the condition of data volume, access the target data block of the corresponding storage partition scheme, and complete the analysis application.
  • Each partitioning scheme is configured with pi and recorded in MongoDB documents, including tile size, axis sequence, and tile size parameters.
  • This embodiment also provides an example of cube partition storage.
  • the cube is divided according to configuration parameters.
  • Each tile is stored in a GridFS, and its fs.files stores the location information of the tile in the cube, and fs.chunks stores the corresponding cube file. data.
  • This embodiment also provides a matching rule for accessing the target data block of the cube in the online analysis application, which is IOmin( ⁇ f(pi,r,s) ⁇ ), where pi represents the configuration of the i-th tile partitioning storage scheme, r indicates the index range of the cube request in the online instant analysis application, s indicates the coefficient of the cube request data volume in the online instant analysis application, and f(pi,r,s) indicates that the i-th tile partition storage scheme is in the online instant analysis application Under the condition of the cube request index range r, obtain the tiles contained in (including the boundary) or intersecting with r, and the total size of the obtained tiles is less than the requested data volume multiplied by the coefficient s(s>1), IOmin( ⁇ f( pi,r,s) ⁇ ) represents the least I/O scheme considering the amount of requested data and its coefficients.
  • This embodiment also provides an example of the coefficient of the cube request data amount matching the rule, and the coefficient is set as a constant greater than 1.
  • the request index range of the cube in the online instant analysis application of the client according to the coefficient of the cube request data volume in the online instant analysis application given in advance, and according to the matching rule, determine the minimum I/O under the condition of considering the requested data volume and its coefficient. On this basis, the corresponding GridFS tiles are further obtained to complete the target data block access.
  • an embodiment of the present invention also provides an online instant analysis image and grid cube data storage system, including: a cube storage adaptation module mounted on an online instant analysis image and grid cube application service platform to store Images and grid cubes are stored in a variety of tile partitioning schemes, each storage scheme corresponds to a configuration; the request preprocessing module installed on the online instant analysis image and grid cube application service platform, the request preprocessing module, according to the client's The index range of the cube request in the online instant analysis application, according to the coefficient of the cube request data volume in the online instant analysis application, according to the matching rule, to determine the solution with the least I/O under the condition of considering the requested data volume and its coefficient, and then obtain Target data block index; the tile segmentation data storage access module mounted on the online instant analysis image and grid cube application service platform, according to the target data block index, the corresponding GridFS data block is obtained from MongoDB and returned to the client.
  • a cube storage adaptation module mounted on an online instant analysis image and grid cube application service platform to store Images
  • the online instant analysis image and grid cube data storage system involved in the present invention utilizes MongoDB's GridFS to store images and grid cubes in a variety of tile partitioning schemes, and determines the amount of requested data and its coefficients under the conditions.
  • the solution with the least I/O greatly reduces the transmission of invalid data blocks and the corresponding computing power consumption, and improves the performance of the online instant analysis application of images and grid cubes.

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Abstract

The present invention relates to an online analysis-based image and grid cube data storage management method and system. The method comprises: storing an image and grid cube in various tile division schemes, each storage scheme corresponding to one configuration; comparing the I/O amount and the data size of each configuration by means of a matching rule; and obtaining a tile division storage scheme having the minimum I/O under the condition that the data size is considered, and accessing a target data block of the corresponding tile division storage scheme, so as to improve online analysis performance of the cube. By means of the MongoDB-based cube data storage method provided by the present invention, access efficiency of the target data block of the cube under an online analysis application condition is improved, transmission of invalid data blocks and consumption of corresponding computing power are reduced, and online analysis application performance is improved.

Description

在线即分析影像与格网立方体数据存储管理方法及系统Online instant analysis image and grid cube data storage management method and system 技术领域technical field
本发明属于大数据技术领域，具体涉及一种在线即分析影像与格网立方体数据存储管理方法及系统。The invention belongs to the technical field of big data, and in particular relates to a method and system for online analysis of image and grid cube data storage management.
背景技术Background technique
当今已进入大数据时代，特别是对地观测应用不断发展，对多维格网的在线即分析应用提出了新的需求，多维格网数据量随维度增加数据量呈几何增长，在线即分析应用往往仅需要部分数据，传统影像与格网的全量数据访问方式，浪费带宽，无法满足在线即分析应用的性能需求。随着NoSQL数据库的不断发展和成熟，可以较好地解决海量存储和在线即分析方面的应用需求。NoSQL数据库MongoDB的一个重要的特色在于提供了GridFS用于存储大小超过16MB的文件，默认使用fs.chunks和fs.files来存储文件，其中fs.files集合存放文件的信息，fs.chunks存放文件的数据。GridFS已有不少学者已将GridFS应用于影像与格网数据存储，如将遥感影像数据文件存储到GridFS中，并利用其分布式机制以提升数据访问性能。相比较单个二维影像数据的分割存储，影像与格网立方体的数据量比二维影像数据多一个数量级，其在线即分析应用亦对现有Gridfs存储方案提出新的需求，即数据应以立方体瓦片进行分割，按在线即分析应用需求，支持选取最适宜的分割存储方案，访问所需立方体瓦片，以防止在线即分过程中的带宽及算力的消耗。Today has entered the era of big data, especially the continuous development of earth observation applications, which puts forward new requirements for the online analysis application of multi-dimensional grids. The data volume of multi-dimensional grids increases geometrically with the increase of dimensions. Only part of the data is needed, and the full data access method of traditional images and grids wastes bandwidth and cannot meet the performance requirements of online analysis applications. With the continuous development and maturity of NoSQL database, it can better solve the application requirements of mass storage and online analysis. An important feature of the NoSQL database MongoDB is that it provides GridFS for storing files with a size of more than 16MB. By default, fs.chunks and fs.files are used to store files. The fs.files collection stores file information, and fs.chunks stores file information. data. GridFS Many scholars have applied GridFS to image and grid data storage, such as storing remote sensing image data files in GridFS, and using its distributed mechanism to improve data access performance. Compared with the segmentation and storage of a single 2D image data, the data volume of images and grid cubes is an order of magnitude larger than that of 2D image data. Its online analysis application also puts forward new requirements for the existing Gridfs storage solution, that is, the data should be stored in cubes. The tiles are divided, and the application requirements are analyzed according to the online instantiation, and the most suitable segmentation storage scheme is supported to access the required cube tiles, so as to prevent the consumption of bandwidth and computing power in the online instant segmentation process.
传统MongoDB的影像存储方法是将遥感影像数据的元数据放到文件存储到GridFS的fs.files中，fs.chunks存放影像的二进制文件，再指定片键进行分片，以支持分布式存储，并支持对数据库进行横向扩展，适合管理大规模遥感影像数据，在进行立方体在线即分析时需要先检索相关二维影像，在获取所需影像齐全后进行切割重构，如果相关影像缺失或分辨率等参数不一致，还需在重构过程中进行干预，不确定性大，且性能不高。The traditional image storage method of MongoDB is to store the metadata of remote sensing image data in the fs.files of GridFS, and fs.chunks to store the binary files of the image, and then specify the shard key for sharding to support distributed storage. It supports the horizontal expansion of the database, which is suitable for managing large-scale remote sensing image data. When performing cube online analysis, it is necessary to retrieve the relevant 2D images first, and then cut and reconstruct the required images after obtaining all the required images. If the relevant images are missing or resolution, etc. If the parameters are inconsistent, intervention is required during the reconstruction process, the uncertainty is large, and the performance is not high.
技术问题technical problem
单一的立方体瓦片分割存储方案，具有固定的瓦片数据大小、轴序列、瓦片尺寸参数，当分割的瓦片与立方体请求索引范围契合度较高时，即目标数据块索引的集合刚好比请求索引范围大一些，这时候数据块的I/O和数据量都相对少，可以大幅减少无效数据块的传输及相应算力的消耗，提升在线即分析应用性能。然而，影像与格网立方体数据在线即分析应用过程中的客户端请求索引范围是不可预测的，因此需要多种分割存储方案，并根据相应的请求寻找最适合的分割存储方案，再获取相应目标数据块索引，完成在线即分析应用，以大幅减少无效数据块的传输及相应算力的消耗，提升在线即分析应用性能。A single cube tile partitioning storage scheme has fixed tile data size, axis sequence, and tile size parameters. When the partitioned tiles have a high degree of fit with the cube request index range, that is, the set of target data block indexes is exactly equal to The requested index range is larger. At this time, the I/O and data volume of data blocks are relatively small, which can greatly reduce the transmission of invalid data blocks and the consumption of corresponding computing power, and improve the performance of online instant analysis applications. However, the index range requested by the client during the online analysis of image and grid cube data is unpredictable. Therefore, various partition storage schemes are required, and the most suitable partition storage scheme is found according to the corresponding request, and then the corresponding target is obtained. Data block indexing, to complete the online instant analysis application, to greatly reduce the transmission of invalid data blocks and the corresponding computing power consumption, and improve the online instant analysis application performance.
技术解决方案technical solutions
本发明的目的在于解决上述问题，提供一种的在线即分析影像与格网立方体数据存储管理方法及系统。The purpose of the present invention is to solve the above problems, and to provide a method and system for storing and managing images and grid cube data that are analyzed online.
为实现上述目的，本发明的技术方案是：一种在线即分析影像与格网立方体数据存储管理方法，包括：将影像与格网立方体以多种瓦片分割方案存储，每种存储方案对应一个配置，通过匹配规则比较每种配置的I/O数及数据大小，获得顾及数据量条件下的I/O最少的瓦片分割存储方案，再获取相应瓦片分割存储方案的目标数据块索引，完成相应瓦片分割存储方案的目标数据块访问。具体地：将影像与格网立方体以多种瓦片分割方案存储，设置基于MongoDB的瓦片分割存储方案的配置，每种存储方案对应一个配置，包括瓦片大小、轴序列、瓦片尺寸参数；设置在线即分析应用中立方体的目标数据块访问的匹配规则，用于比较每种方案配置的I/O数及数据量，并获得顾及请求数据量及其系数条件下I/O最少的瓦片分割存储方案；获得在线即分析应用中立方体请求索引范围，根据匹配规则，以确定顾及请求数据量及其系数条件下I/O数最少的瓦片分割存储方案，再获取目标数据块索引，完成相应瓦片分割存储方案的目标数据块访问。In order to achieve the above purpose, the technical solution of the present invention is: an online instant analysis image and grid cube data storage management method, comprising: storing the image and grid cube in a variety of tile partitioning schemes, each storage scheme corresponds to one Configuration, compare the I/O number and data size of each configuration through matching rules, obtain the tile partition storage scheme with the least I/O under the condition of data volume, and then obtain the target data block index of the corresponding tile partition storage scheme, Complete the target data block access of the corresponding tile partition storage scheme. Specifically: store images and grid cubes in a variety of tile partitioning schemes, set the configuration of the tile partitioning storage scheme based on MongoDB, each storage scheme corresponds to a configuration, including tile size, axis sequence, tile size parameters ;Set the matching rules for the target data block access of the cube in the online analysis application, which is used to compare the I/O number and data volume configured by each scheme, and obtain the minimum I/O watts considering the requested data volume and its coefficient. Slice partition storage scheme; obtain the cube request index range in the online analysis application, determine the tile partition storage scheme with the least I/O number considering the amount of requested data and its coefficients according to the matching rules, and then obtain the target data block index, Complete the target data block access of the corresponding tile partition storage scheme.
在本发明一实施例中，所述立方体以多种瓦片分割方案存储，一个立方体存在多个GridFS中，每种瓦片分割存储方案对应一个配置，记为pi(1≤i≤n)，立方体的n种瓦片分割方案存储表示为{p1,p2,...,pn}。In an embodiment of the present invention, the cube is stored in multiple tile partitioning schemes, one cube is stored in multiple GridFS, and each tile partitioning storage scheme corresponds to a configuration, denoted as pi (1≤i≤n), The n tile partitioning schemes of the cube are stored as {p1,p2,...,pn}.
在本发明一实施例中，所述立方体的瓦片分割方案存储为基于MongoDB的瓦片分割存储方案，将立方体沿给定的轴序列，按固定瓦片尺寸参数进行分割，每轴末端不足部分用固定值null填充获得的固定大小的瓦片，每一个瓦片为MongoDB的一个GridFS；相应瓦片分割存储方案的配置包括瓦片大小、轴序列、 瓦片尺寸信息，参数记录格式为{v,<A>,<t>}，v表示MongoDB的GridFS瓦片大小，<A>表示瓦片轴序列，用<x,y,z>表示，<t>表示瓦片尺寸，用<l,w,h>表示瓦片在相应轴上的格元数。In an embodiment of the present invention, the tile partitioning scheme of the cube is stored as a tile partitioning storage scheme based on MongoDB, and the cube is partitioned along a given axis sequence according to a fixed tile size parameter, and the end of each axis is insufficient. Fill the obtained fixed-size tiles with a fixed value of null, each tile is a GridFS of MongoDB; the configuration of the corresponding tile partition storage scheme includes tile size, axis sequence, tile size information, and the parameter record format is {v ,<A>,<t>}, v represents the tile size of MongoDB's GridFS, <A> represents the tile axis sequence, represented by <x, y, z>, <t> represents the tile size, represented by <l, w,h> represents the number of cells of the tile on the corresponding axis.
在本发明一实施例中，所述在线即分析应用中立方体的目标数据块访问的匹配规则为IOmin({f(pi,r,s)})，pi表示第i种瓦片分割存储方案的配置，r表示在线即分析应用中立方体请求索引范围，s表示在线即分析应用中立方体请求数据量的系数，f(pi,r,s)表示针对第i种瓦片分割存储方案在在线即分析应用中立方体请求索引范围r条件下，获取包含于或与r相交的瓦片，且所获得瓦片大小总量小于请求数据量乘以系数s(s>1)，IOmin({f(pi,r,s)})表示顾及请求数据量及其系数条件下I/O最少的瓦片分割存储方案。In an embodiment of the present invention, the matching rule for accessing the target data block of the cube in the online analysis application is 10min({f(pi,r,s)}), and pi represents the i-th tile partition storage scheme. Configuration, r represents the cube request index range in the online instant analysis application, s indicates the coefficient of the cube request data volume in the online instant analysis application, f(pi,r,s) indicates the online instant analysis for the i-th tile partition storage scheme Under the condition of the index range r of the cube request in the application, obtain the tiles contained in or intersecting with r, and the total size of the obtained tiles is less than the requested data volume multiplied by the coefficient s(s>1), IOmin({f(pi, r, s)}) represents the tile partitioning storage scheme with the least I/O considering the amount of requested data and its coefficients.
在本发明一实施例中，目标数据块索引获取，基于匹配规则获得顾及请求数据量及其系数条件下I/O最少的瓦片分割存储方案，在该瓦片分割存储方案基础上进一步获取相应的GridFS瓦片，完成目标数据块访问。In an embodiment of the present invention, the target data block index is obtained, based on a matching rule, a tile partitioning storage scheme with the least I/O is obtained considering the amount of requested data and its coefficients, and based on the tile partitioning storage scheme, the corresponding tile partitioning storage scheme is further obtained. GridFS tiles, complete target data block access.
本发明还提供了一种在线即分析影像与格网立方体数据存储管理系统，包括：搭载于在线即分析影像与格网立方体应用服务平台上的立方体存储适配模块将影像与格网立方体以多种瓦片分割存储方案，每种瓦片分割存储方案对应一个配置；搭载于在线即分析影像与格网立方体应用服务平台上的请求预处理模块，该请求预处理模块，按照客户端的在线即分析应用中立方体请求索引范围，根据预先给定的在线即分析应用中立方体请求数据量的系数，根据匹配规则，以确定顾及请求数据量及其系数条件下I/O最少的方案，再获取目标数据块索引；搭载于在线即分析影像与格网立方体应用服务平台上的瓦片分割数据存储访问模块，按照获得目标数据块索引，从MongoDB获得相应的GridFS数据块，返回给客户端，完成即分析应用。The present invention also provides an online instant analysis image and grid cube data storage management system, comprising: a cube storage adaptation module mounted on an online instant analysis image and grid cube application service platform to store images and grid cubes in multiple Tile segmentation storage scheme, each tile segmentation storage scheme corresponds to a configuration; the request preprocessing module mounted on the online instant analysis image and grid cube application service platform, the request preprocessing module, according to the online instant analysis of the client The index range of the cube request in the application, according to the pre-given coefficient of the requested data volume of the cube in the online analysis application, according to the matching rule, to determine the solution with the least I/O under the condition of considering the requested data volume and its coefficient, and then obtain the target data Block index; the tile segmentation data storage access module installed on the online instant analysis image and grid cube application service platform, according to the target data block index, the corresponding GridFS data block is obtained from MongoDB, returned to the client, and the analysis is completed immediately application.
有益效果beneficial effect
相较于现有技术，本发明具有以下有益效果：本发明利用MongoDB的GridFS将影像与格网立方体以多种瓦片分割方案存储，确定顾及请求数据量及其系数条件下I/O最少的方案，大幅减少无效数据块的传输及相应算力的消耗，提升影像与格网立方体在线即分析应用性能。Compared with the prior art, the present invention has the following beneficial effects: the present invention utilizes MongoDB's GridFS to store images and grid cubes in a variety of tile partitioning schemes, and determines the one with the least I/O considering the amount of requested data and its coefficients. The solution greatly reduces the transmission of invalid data blocks and the consumption of corresponding computing power, and improves the performance of online instant analysis of images and grid cubes.
附图说明Description of drawings
图1为本发明的模块结构示意图。FIG. 1 is a schematic diagram of a module structure of the present invention.
本发明的实施方式Embodiments of the present invention
下面结合附图，对本发明的技术方案进行具体说明。The technical solutions of the present invention will be described in detail below with reference to the accompanying drawings.
如图1所示，本发明一种本发明实施例提供一种在线即分析影像与格网立方体数据存储方法，主要处理步骤包括：将影像与格网立方体以多种瓦片分割方案存储于在线即分析影像与格网立方体应用服务平台端的MongoDB，每种存储方案对应一个配置，获得在线即分析应用中立方体请求索引范围，通过匹配规则比较每种配置的I/O数及数据大小，获得顾及数据量条件下的I/O最少的瓦片分割存储方案，访问相应存储分割方案的目标数据块，完成即分析应用。As shown in FIG. 1 , an embodiment of the present invention provides an online analysis method for storing image and grid cube data. The main processing steps include: storing the image and grid cube in an online format with various tile segmentation schemes. That is to analyze the MongoDB on the image and grid cube application service platform side, each storage scheme corresponds to a configuration, obtain the index range of the cube request in the online analysis application, and compare the I/O number and data size of each configuration through matching rules to obtain consideration. The tile partition storage scheme with the least I/O under the condition of data volume, access the target data block of the corresponding storage partition scheme, and complete the analysis application.
将影像与格网立方体以多种瓦片分割方案存储于GridFS，每个分割方案配置pi以MongoDB文档记录，包含瓦片大小、轴序列、瓦片尺寸参数。Store images and grid cubes in GridFS with multiple tile partitioning schemes. Each partitioning scheme is configured with pi and recorded in MongoDB documents, including tile size, axis sequence, and tile size parameters.
本实施例还给出了立方体分割存储实例，立方体按照配置参数进行分割，每个瓦片存放在一个GridFS中，其fs.files存放瓦片在立方体中的位置信息，fs.chunks存放相应立方体文件数据。This embodiment also provides an example of cube partition storage. The cube is divided according to configuration parameters. Each tile is stored in a GridFS, and its fs.files stores the location information of the tile in the cube, and fs.chunks stores the corresponding cube file. data.
本实施例还给出了在线即分析应用中立方体的目标数据块访问的匹配规则，为IOmin({f(pi,r,s)})，pi表示第i种瓦片分割存储方案的配置，r表示在线即分析应用中立方体请求索引范围，s表示在线即分析应用中立方体请求数据量的系数，f(pi,r,s)表示针对第i种瓦片分割存储方案在在线即分析应用中立方体请求索引范围r条件下，获取包含于(含边界)或与r相交的瓦片，且所获得瓦片大小总量小于请求数据量乘以系数s(s>1)，IOmin({f(pi,r,s)})表示顾及请求数据量及其系数条件下I/O最少的方案。This embodiment also provides a matching rule for accessing the target data block of the cube in the online analysis application, which is IOmin({f(pi,r,s)}), where pi represents the configuration of the i-th tile partitioning storage scheme, r indicates the index range of the cube request in the online instant analysis application, s indicates the coefficient of the cube request data volume in the online instant analysis application, and f(pi,r,s) indicates that the i-th tile partition storage scheme is in the online instant analysis application Under the condition of the cube request index range r, obtain the tiles contained in (including the boundary) or intersecting with r, and the total size of the obtained tiles is less than the requested data volume multiplied by the coefficient s(s>1), IOmin({f( pi,r,s)}) represents the least I/O scheme considering the amount of requested data and its coefficients.
本实施例还给出了匹配规则的立方体请求数据量的系数实例，将该系数设为大于1的常数。This embodiment also provides an example of the coefficient of the cube request data amount matching the rule, and the coefficient is set as a constant greater than 1.
按照客户端的在线即分析应用中立方体请求索引范围，根据预先给定的在线即分析应用中立方体请求数据量的系数，根据匹配规则，以确定顾及请求数据量及其系数条件下I/O最少的方案，在此基础上进一步获取相应的GridFS瓦片，完成目标数据块访问。According to the request index range of the cube in the online instant analysis application of the client, according to the coefficient of the cube request data volume in the online instant analysis application given in advance, and according to the matching rule, determine the minimum I/O under the condition of considering the requested data volume and its coefficient. On this basis, the corresponding GridFS tiles are further obtained to complete the target data block access.
如图1所示，本发明实施例还提供了一种在线即分析影像与格网立方体数据存储系统，包括：搭载于在线即分析影像与格网立方体应用服务平台上的立方体存储适配模块将影像与格网立方体以多种瓦片分割方案存储，每种存储方案对应一个配置；搭载于在线即分析影像与格网立方体应用服务平台上的请求预处理模块，请求预处理模块，按照客户端的在线即分析应用中立方体请求索引范围，根据预先给定的在线即分析应用中立方体请求数据量的系数根据匹配规则，以确定顾及请求数据量及其系数条件下I/O最少的方案，再获取目标数据块索引；搭载于在线即分析影像与格网立方体应用服务平台上的瓦片分割数据存储访问模块，按照获得目标数据块索引，从MongoDB获得相应的GridFS数据块，返回给客户端。As shown in FIG. 1 , an embodiment of the present invention also provides an online instant analysis image and grid cube data storage system, including: a cube storage adaptation module mounted on an online instant analysis image and grid cube application service platform to store Images and grid cubes are stored in a variety of tile partitioning schemes, each storage scheme corresponds to a configuration; the request preprocessing module installed on the online instant analysis image and grid cube application service platform, the request preprocessing module, according to the client's The index range of the cube request in the online instant analysis application, according to the coefficient of the cube request data volume in the online instant analysis application, according to the matching rule, to determine the solution with the least I/O under the condition of considering the requested data volume and its coefficient, and then obtain Target data block index; the tile segmentation data storage access module mounted on the online instant analysis image and grid cube application service platform, according to the target data block index, the corresponding GridFS data block is obtained from MongoDB and returned to the client.
综上所述，本发明所涉及的在线即分析影像与格网立方体数据存储系统利用MongoDB的GridFS将影像与格网立方体以多种瓦片分割方案存储，确定顾及请求数据量及其系数条件下I/O最少的方案，大幅减少无效数据块的传输及相应算力的消耗，提升影像与格网立方体在线即分析应用性能。To sum up, the online instant analysis image and grid cube data storage system involved in the present invention utilizes MongoDB's GridFS to store images and grid cubes in a variety of tile partitioning schemes, and determines the amount of requested data and its coefficients under the conditions. The solution with the least I/O greatly reduces the transmission of invalid data blocks and the corresponding computing power consumption, and improves the performance of the online instant analysis application of images and grid cubes.
以上所述仅为本发明的优选实施例而已，并不用于限制本发明，对本领域的技术人员来说，本发明可以有各种更改和变化。凡在本发明的精神和原则之内，所做的任何修改、等同替换、改进等，均应包含在本发明的范围之内。The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included within the scope of the present invention.

Claims (7)

  1. 一种在线即分析影像与格网立方体数据存储管理方法，其特征在于，包括：将影像与格网立方体以多种瓦片分割方案存储，每种存储方案对应一个配置，通过匹配规则比较每种配置的I/O数及数据大小，获得顾及数据量条件下的I/O最少的瓦片分割存储方案，再获取相应瓦片分割存储方案的目标数据块索引，完成相应瓦片分割存储方案的目标数据块访问。A method for online instant analysis of image and grid cube data storage and management, characterized by comprising: storing images and grid cubes in a variety of tile partitioning schemes, each storage scheme corresponds to a configuration, and comparing each type of The configured I/O number and data size, obtain the tile partition storage scheme with the least I/O under the condition of data volume, and then obtain the target data block index of the corresponding tile partition storage scheme, and complete the corresponding tile partition storage scheme. Target data block access.
  2. 根据权利要求1所述的在线即分析影像与格网立方体数据存储管理方法，其特征在于，具体包括：将影像与格网立方体以多种瓦片分割方案存储，设置基于MongoDB的瓦片分割存储方案的配置，每种存储方案对应一个配置，包括瓦片大小、轴序列、瓦片尺寸参数；设置在线即分析应用中立方体的目标数据块访问的匹配规则，用于比较每种方案配置的I/O数及数据量，并获得顾及请求数据量及其系数条件下I/O最少的瓦片分割存储方案；获得在线即分析应用中立方体请求索引范围，根据匹配规则，以确定顾及请求数据量及其系数条件下I/O数最少的瓦片分割存储方案，再获取目标数据块索引，完成相应瓦片分割存储方案的目标数据块访问。The online instant analysis image and grid cube data storage management method according to claim 1, characterized in that it specifically comprises: storing the image and the grid cube in a variety of tile partitioning schemes, and setting a MongoDB-based tile partition storage The configuration of the scheme, each storage scheme corresponds to a configuration, including tile size, axis sequence, and tile size parameters; set the matching rules for accessing the target data block of the cube in the online analysis application, which is used to compare the I of each scheme configuration. The number of /O and the amount of data, and obtain the tile partition storage scheme with the least I/O under the condition of considering the amount of requested data and its coefficient; obtain the index range of the cube request in the online analysis application, and determine the amount of requested data according to the matching rules. The tile partition storage scheme with the least number of I/Os under the condition of its coefficients, and then obtain the target data block index to complete the target data block access of the corresponding tile partition storage scheme.
  3. 根据权利要求1或2所述的在线即分析影像与格网立方体数据存储管理方法，其特征在于，所述立方体以多种瓦片分割方案存储，一个立方体存在多个GridFS中，每种瓦片分割存储方案对应一个配置，记为p i(1≤i≤n)，立方体的n种瓦片分割方案存储表示为{p 1,p 2,...,p n}。 The online instant analysis image and grid cube data storage management method according to claim 1 or 2, wherein the cube is stored in multiple tile partitioning schemes, one cube exists in multiple GridFS, and each tile is stored in multiple GridFS. The partitioning storage scheme corresponds to a configuration, denoted as p i (1≤i≤n), and the n-tile partitioning scheme storage of the cube is represented as {p 1 ,p 2 ,...,p n }.
  4. 根据权利要求1或2所述的在线即分析影像与格网立方体数据存储管理方法，其特征在于，所述立方体的瓦片分割方案存储为基于MongoDB的瓦片分割存储方案，将立方体沿给定的轴序列，按固定瓦片尺寸参数进行分割，每轴末端不足部分用固定值null填充获得的固定大小的瓦片，每一个瓦片为MongoDB的一个GridFS；相应瓦片分割存储方案的配置包括瓦片大小、轴序列、瓦片尺寸信息，参数记录格式为{v,<A>,<t>}，v表示MongoDB的GridFS瓦片大小，<A>表示瓦片轴序列，用<x,y,z>表示，<t>表示瓦片尺寸，用<l,w,h>表示瓦片在相应轴上的格元数。The online instant analysis image and grid cube data storage management method according to claim 1 or 2, wherein the tile partitioning scheme of the cube is stored as a MongoDB-based tile partitioning storage scheme, and the cube is stored along a given The axis sequence is divided according to the fixed tile size parameter. The insufficient part at the end of each axis is filled with a fixed value of null to obtain fixed-size tiles. Each tile is a GridFS of MongoDB; the configuration of the corresponding tile segmentation storage scheme includes: Tile size, axis sequence, tile size information, the parameter record format is {v,<A>,<t>}, v represents MongoDB's GridFS tile size, <A> represents tile axis sequence, use <x, y, z> means, <t> means the tile size, and <l, w, h> means the number of cells of the tile on the corresponding axis.
  5. 根据权利要求2所述的在线即分析影像与格网立方体数据存储管理方法，其特征在于，所述在线即分析应用中立方体的目标数据块访问的匹配规则为IO min({f(p i,r,s)})，p i表示第i种瓦片分割存储方案的配置，r表示在线即分析应用中立方体请求索引范围，s表示在线即分析应用中立方体请求数据量的系数，f(p i,r,s)表示针对第i种瓦片分割存储方案在在线即分析应用中立方体请求索引范围r条件下，获取包含于或与r相交的瓦片，且所获得瓦片大小总量小于请求数据量乘以系数s(s>1)，IO min({f(p i,r,s)})表示顾及请求数据量及其系数条件下I/O最少的瓦片分割存储方案。 The online instant analysis image and grid cube data storage management method according to claim 2, wherein the matching rule for accessing the cube's target data block in the online instant analysis application is 10 min ({f( pi , r,s)}), pi represents the configuration of the i -th tile partition storage scheme, r represents the cube request index range in the online analysis application, s represents the coefficient of the cube request data volume in the online analysis application, f(p i , r, s) indicates that for the i-th tile partitioning storage scheme, under the condition of the cube request index range r in the online analysis application, the tiles that are included in or intersected with r are obtained, and the total size of the obtained tiles is less than The requested data volume is multiplied by the coefficient s(s>1), and IO min ({f(pi ,r, s )}) represents the tile partitioning storage scheme with the least I/O considering the requested data volume and its coefficient.
  6. 根据权利要求5所述的在线即分析影像与格网立方体数据存储管理方法，其特征在于，目标数据块索引获取，基于匹配规则获得顾及请求数据量及其系数条件下I/O最少的瓦片分割存储方案，在该瓦片分割存储方案基础上进一步获取相应的GridFS瓦片，完成目标数据块访问。The online instant analysis image and grid cube data storage management method according to claim 5, wherein the target data block index is obtained, and the tile with the least I/O is obtained based on the matching rule considering the requested data amount and its coefficient. The partitioned storage scheme, based on the tile partitioned storage scheme, further obtains the corresponding GridFS tiles to complete the target data block access.
  7. 一种在线即分析影像与格网立方体数据存储管理系统，其特征在于，包括：搭载于在线即分析影像与格网立方体应用服务平台上的立方体存储适配模块将影像与格网立方体以多种瓦片分割存储方案，每种瓦片分割存储方案对应一个配置；搭载于在线即分析影像与格网立方体应用服务平台上的请求预处理模块，该请求预处理模块，按照客户端的在线即分析应用中立方体请求索引范围，根据预先给定的在线即分析应用中立方体请求数据量的系数，根据匹配规则，以确定顾及请求数据量及其系数条件下I/O最少的方案，再获取目标数据块索引；搭载于在线即分析影像与格网立方体应用服务平台上的瓦片分割数据存储访问模块，按照获得目标数据块索引，从MongoDB获得相应的GridFS数据块，返回给客户端，完成即分析应用。An online instant analysis image and grid cube data storage management system, characterized by comprising: a cube storage adaptation module mounted on an online instant analysis image and grid cube application service platform to store the image and grid cube in various Tile segmentation storage scheme, each tile segmentation storage scheme corresponds to a configuration; the request preprocessing module mounted on the online instant analysis image and grid cube application service platform, the request preprocessing module, according to the online instant analysis application of the client Neutral cube request index range, according to the pre-given coefficient of the requested data volume in the online instant analysis application, according to the matching rule, to determine the solution with the least I/O considering the requested data volume and its coefficient, and then obtain the target data block Index; the tile segmentation data storage access module mounted on the online instant analysis image and grid cube application service platform, obtains the corresponding GridFS data block from MongoDB according to the target data block index, returns it to the client, and completes the instant analysis application .
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