WO2020228452A1 - 非结构化数据处理方法和非结构化数据处理系统 - Google Patents
非结构化数据处理方法和非结构化数据处理系统 Download PDFInfo
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- WO2020228452A1 WO2020228452A1 PCT/CN2020/083704 CN2020083704W WO2020228452A1 WO 2020228452 A1 WO2020228452 A1 WO 2020228452A1 CN 2020083704 W CN2020083704 W CN 2020083704W WO 2020228452 A1 WO2020228452 A1 WO 2020228452A1
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- the present disclosure relates to the field of data processing technology, and in particular to an unstructured data processing method and an unstructured data processing system.
- DFS Distributed File System
- the present disclosure provides an unstructured data processing method, including:
- the target data corresponding to the multiple unstructured data is stored in a target structured data file, and the target structured data file is used in a distributed file system.
- the unstructured data processing method further includes:
- the index information includes file name, file type, and/or file retrieval field information.
- the unstructured data is an image, audio, video, document, custom object, XML or HTML.
- the distributed file system is a hadoop distributed file system.
- the obtaining of unstructured data includes: reading an unstructured data file in a file list, wherein the file list includes multiple unstructured data files; determining the unstructured data that is read Whether the data file exists; if it exists, cache the read unstructured data file into a byte array; if it does not exist, read the next unstructured data file in the file list.
- Performing serialization processing on unstructured data to obtain serialized data includes: establishing a processing thread to serialize the byte array to obtain the serialized data.
- said obtaining unstructured data includes: reading all unstructured data files in the file list, and obtaining the number N of unstructured data files in the file list.
- the serialization processing on the unstructured data to obtain the serialized data includes: establishing N processing threads; for the N unstructured data files in the file list, simultaneously using the N The processing thread performs serialization processing.
- the present disclosure also provides an unstructured data processing method, including:
- the serialized data in the target data is deserialized to obtain unstructured data.
- the present disclosure also provides an unstructured data processing system, including:
- the acquisition module is used to acquire unstructured data
- the serialization processing module is used to serialize the unstructured data to obtain serialized data
- connection module is used to connect the serialized data and the index information of the unstructured data to obtain target data
- the storage module is configured to store a plurality of the target data in a target structured data file, and the target structured data file is used in a distributed file system.
- the unstructured data processing system further includes an upload module; wherein, the upload module is used to upload the target structured data file to the distributed file system.
- the present disclosure also provides an unstructured data processing system, including:
- the reading module is used to read the target structured data file
- An obtaining module configured to obtain at least one target data in the target structured data file
- the deserialization processing module is used to deserialize the serialized data in the target data to obtain unstructured data.
- the unstructured data processing system further includes a distributed processing module; wherein the distributed processing module is used to perform distributed processing on the unstructured data obtained by the deserialization processing module.
- the present disclosure also provides an unstructured data processing system, including a processor, a memory, and a computer program stored on the memory and capable of running on the processor.
- the computer program is executed when the processor is executed. The steps of the above unstructured data processing method.
- the present disclosure also provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps of the aforementioned unstructured data processing method are realized.
- FIG. 1 is a schematic flowchart of an unstructured data processing method according to some embodiments of the disclosure
- FIG. 2 is a schematic diagram of the storage structure of a target structured data file according to some embodiments of the present disclosure
- FIG. 3 is a schematic flowchart of an unstructured data processing method according to some embodiments of the disclosure.
- FIG. 4 is a schematic flowchart of an unstructured data processing method according to some embodiments of the disclosure.
- FIG. 5 is a schematic flowchart of an unstructured data processing method according to some embodiments of the disclosure.
- FIG. 6 is a schematic structural diagram of an unstructured data processing system according to some embodiments of the disclosure.
- FIG. 7 is a schematic structural diagram of an unstructured data processing system according to some embodiments of the disclosure.
- FIG. 8 is a schematic diagram of the overall framework of an unstructured data processing system according to some embodiments of the disclosure.
- FIG. 9 is a schematic structural diagram of an unstructured data processing system according to some embodiments of the disclosure.
- FIG. 10 is a schematic structural diagram of an unstructured data processing system according to some embodiments of the disclosure.
- DFS Distributed File System
- Many nodes form a file system network, which can effectively solve the storage and management of massive data. problem.
- Each node can be distributed in different locations, through the network for communication and data transmission between nodes.
- people use a distributed file system, they don't need to care about which node the data is stored on or from which node the data is obtained from, but only need to manage and store the data in the file system like a local file system.
- the present disclosure provides an unstructured data processing method and an unstructured data processing system, which are used to solve the problem of storing a large amount of small unstructured data in a distributed file system in the related art, causing a waste of storage space, and Issues affecting the efficiency of distributed processing.
- FIG. 1 is a schematic flowchart of an unstructured data processing method according to some embodiments of the present disclosure.
- the unstructured data processing method includes:
- Step 11 Obtain unstructured data
- Unstructured data is data with irregular or incomplete data structure. There is no predefined data model and it is not convenient to use the two-dimensional logical table of the database to represent the data.
- the unstructured data may be images, audios, videos, documents (such as word files, PDF documents, etc.), custom objects, XML (extensible markup language) or HTML (hypertext markup language), etc.
- the unstructured data can be obtained from a file, or can be obtained from a message or the like.
- the file can be a file stored locally or a file stored in a distributed file system.
- Step 12 Perform serialization processing on the unstructured data to obtain serialized data
- Serialization is a mechanism for processing object streams.
- the so-called object stream is to stream the content of objects.
- the streamed objects can be read and written, and the streamed objects can be transmitted between networks.
- multiple methods can be used to serialize unstructured data.
- the Base64 encoding method is used to serialize unstructured data.
- Base64 is a kind of binary representation based on 64 printable characters.
- Data method may also be used, for example, a Base62x encoding method.
- Step 13 Connect the serialized data and the index information of the unstructured data to obtain target data
- the index information may include file name, file type, and/or file retrieval field information.
- symbols such as separators can be used to separate the serialized data and index information, so that index information and serialized data can be distinguished subsequently.
- Step 14 Store a plurality of the target data in a target structured data file, and the target structured data file is used in a distributed file system.
- the target data when multiple target data corresponding to multiple unstructured data are merged and stored in the target structured data file, the target data can be stored in a specified order, for example, according to the sequence of serialization processing, etc.
- the target data stored in the target structured data file can be seen in Figure 2, where the file index information can be a single column or multiple columns, and can include file name, file type and/or file retrieval field information.
- the storage structure is simple, which can effectively save the required storage space, and when performing distributed processing, only large structured data files need to be scheduled Batch or stream processing is performed on the multiple small unstructured data, which improves the efficiency of distributed processing.
- the method may further include: uploading the target structured data file to the distributed file system for subsequent follow-up Distributed processing.
- the performing serialization processing on the unstructured data to obtain serialized data includes: establishing a processing thread to target multiple unstructured data to be processed Each of the unstructured data is serialized by sequentially using the processing thread.
- one processing thread is used to sequentially serialize each unstructured data among the multiple unstructured data to be processed, which occupies less processing resources.
- FIG. 3 is a schematic flowchart of an unstructured data processing method according to some embodiments of the present disclosure.
- the unstructured data processing method includes:
- Step 31 Read one unstructured data file in the file list, where the file list includes multiple unstructured data files;
- each unstructured data file in the file list can be read sequentially according to the file name.
- Step 32 Determine whether the read file exists, if yes, go to step 33, otherwise, return to step 31 to read the next unstructured data file in the file list;
- Step 33 Buffer the read unstructured data file into a byte (Byte) array.
- Step 34 Establish a processing thread to serialize the byte array to obtain serialized data
- Step 35 Connect the serialized data of the unstructured data file with the index information of the unstructured data file to obtain target data, and output the target data to the target structured data file.
- Step 36 Determine whether there are unprocessed unstructured data files in the file list, if yes, return to step 31, read the next unstructured data file in the file list; otherwise, go to step 37;
- Step 37 Upload the target structured data file to the distributed file system.
- one processing thread is used to sequentially serialize each unstructured data file, which occupies less processing resources.
- the performing serialization processing on the unstructured data to obtain the serialized data includes: establishing N processing threads for multiple unstructured data to be processed The N of the unstructured data are serialized using the N processing threads at the same time, where N is a positive integer greater than 1, and N is less than or equal to the number of the unstructured data to be processed. For example, if there are 100 unstructured data to be processed, 100 processing threads can be established, and the 100 unstructured data can be serialized at the same time. Of course, it is also possible to establish 50 processing threads to process the 100 unstructured data in two batches.
- FIG. 4 is a schematic flowchart of an unstructured data processing method according to some embodiments of the present disclosure.
- the unstructured data processing method includes:
- Step 41 Read all unstructured data files in the file list, and obtain the number N of unstructured data files in the file list;
- Step 42 Establish N processing threads
- Step 43 For the N unstructured data files in the file list, the N processing threads are simultaneously used for serialization processing.
- Step 44 Connect the serialized data of the unstructured data file with the index information of the unstructured data file to obtain target data, and output the target data to the target structured data file.
- Step 45 Upload the target structured data file to the distributed file system.
- multiple processing threads are used to simultaneously serialize multiple unstructured data files, which can effectively improve processing efficiency.
- the distributed file system may be a hadoop distributed file system (HDFS).
- HDFS Hadoop distributed file system
- it can also be other types of distributed file systems, such as FastDFS, GFS (Google File System), or TFS.
- FIG. 5 is a schematic flowchart of an unstructured data processing method according to some embodiments of the present disclosure.
- the unstructured data processing method includes:
- Step 51 Read the target structured data file, the target structured data file is obtained by using the unstructured data processing method in any of the above embodiments;
- Step 52 Obtain at least one target data in the target structured data file
- part of the target data in the target structured data file may be processed, or all target data may be processed.
- Step 53 Deserialize the serialized data in the target data to obtain unstructured data.
- one processing thread when deserializing multiple serialized data, one processing thread may be used to sequentially deserialize each serialized data, or multiple processing threads may be used to simultaneously perform deserialization on multiple serialized data.
- the serialized data is deserialized.
- the unstructured data processing method of the embodiment of the present disclosure may further include: performing distributed processing, such as batch or streaming processing, on the unstructured data obtained by the deserialization process.
- distributed processing such as batch or streaming processing
- Mapreduce for example, Mapreduce, Spark, etc.
- Spark can be used to process structured data files in batch or streaming mode.
- the structured data file is read out, and the serialized data in the file is deserialized, and then multiple unstructured data in the structured data file can be processed
- processing efficiency can be effectively improved.
- FIG. 6 some embodiments of the present disclosure also provide an unstructured data processing system 60, including:
- the obtaining module 61 is used to obtain unstructured data
- the serialization processing module 62 is configured to perform serialization processing on the unstructured data to obtain serialized data
- connection module 63 is configured to connect the serialized data and the index information of the unstructured data to obtain target data
- the storage module 64 is configured to store a plurality of the target data in a target structured data file, and the target structured data file is used in a distributed file system.
- the storage structure is simple, which can effectively save the required storage space, and when performing distributed processing, only large structured data files need to be scheduled Batch or stream processing is performed on the multiple small unstructured data, which improves the efficiency of distributed processing.
- the unstructured data processing system further includes:
- the upload module is used to upload the target structured data file to the distributed file system.
- the index information includes file name, file type, and/or file retrieval field information.
- the unstructured data is an image, audio, video, document, custom object, XML or HTML.
- the distributed file system is a hadoop distributed file system.
- an unstructured data processing system 70 including:
- the reading module 71 is configured to read a target structured data file, which is obtained by using the unstructured data processing method in the foregoing embodiment;
- the obtaining module 72 is configured to obtain at least one target data in the target structured data file
- the deserialization processing module 73 is configured to deserialize the serialized data in the target data to obtain unstructured data.
- the unstructured data processing system of the embodiment of the present disclosure may further include: a distributed processing module, configured to perform distributed processing on the unstructured data obtained by the deserialization processing module, such as batch or stream ⁇ Type processing.
- a distributed processing module configured to perform distributed processing on the unstructured data obtained by the deserialization processing module, such as batch or stream ⁇ Type processing.
- the structured data file is read out, and the serialized data in the file is deserialized, so that multiple small unstructured data in the structured data file can be processed.
- Data is processed in batches or streaming, because only large structured data files need to be scheduled, which can effectively improve processing efficiency.
- FIG. 8 is a schematic diagram of the overall framework of an unstructured data processing system according to some embodiments of the present disclosure.
- the serialization processing module can be used to serialize multiple images first to obtain Target structured data files, and upload the target structured data files to a distributed file system (the Hadoop file storage system in Figure 8).
- a distributed file system the Hadoop file storage system in Figure 8.
- use the hadoop distributed computing framework to deserialize the target structured data file (as shown in Maper in Figure 8 for deserialization), and then perform other operations on the unstructured data obtained by deserialization Distributed processing, such as shuffle unstructured data, and then input the reorganized data into the Reducer for processing.
- FIG. 9 is a schematic structural diagram of an unstructured data processing system according to some embodiments of the present disclosure.
- the unstructured data processing system 90 includes a processor 91 and a memory 92.
- the unstructured data processing system 90 further includes: a computer program stored in the memory 92 and capable of running on the processor 91, and when the computer program is executed by the processor 91, the following steps are implemented:
- the target data corresponding to the multiple unstructured data is stored in a target structured data file, and the target structured data file is used in a distributed file system.
- the following steps may be implemented: uploading the target structured data file to the distributed file system.
- the index information includes file name, file type, and/or file retrieval field information.
- the unstructured data is an image, audio, video, document, custom object, XML or HTML.
- the distributed file system is a hadoop distributed file system.
- FIG. 10 is a schematic structural diagram of an unstructured data processing system according to some embodiments of the present disclosure.
- the unstructured data processing system 100 includes a processor 101 and a memory 102.
- the unstructured data processing system 100 further includes: a computer program stored in the memory 102 and capable of running on the processor 101, and when the computer program is executed by the processor 101, the following steps are implemented:
- the serialized data in the target data is deserialized to obtain unstructured data.
- the embodiments of the present disclosure also provide a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, each process of the above-mentioned unstructured data processing method embodiment is realized, and To achieve the same technical effect, in order to avoid repetition, I will not repeat them here.
- the computer-readable storage medium such as read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), magnetic disk or optical disk, etc.
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Claims (14)
- 一种非结构化数据处理方法,包括:获取非结构化数据;对所述非结构化数据进行序列化处理,得到序列化数据;将所述序列化数据与所述非结构化数据的索引信息进行连接,得到目标数据;将多个所述非结构化数据对应的目标数据存储至目标结构化数据文件中,所述目标结构化数据文件用于分布式文件系统。
- 如权利要求1所述的非结构化数据处理方法,还包括:将所述目标结构化数据文件上传至所述分布式文件系统。
- 如权利要求1所述的非结构化数据处理方法,其中,所述索引信息包括文件名、文件类型和/或文件检索字段信息。
- 如权利要求1所述的非结构化数据处理方法,其中,所述非结构化数据为图像、音频、视频、文档、自定义对象、XML或HTML。
- 如权利要求1或2所述的非结构化数据处理方法,其中,所述分布式文件系统为hadoop分布式文件系统。
- 如权利要求1所述的非结构化数据处理方法,其中,所述获取非结构化数据,包括:读取文件列表中的一个非结构化数据文件,其中,所述文件列表中包括多个非结构化数据文件;判断读取的非结构化数据文件是否存在;若存在,将读取的非结构化数据文件缓存至一个字节数组中;若不存在,读取所述文件列表中的下一个非结构化数据文件;所述对所述非结构化数据进行序列化处理,得到序列化数据,包括:建立一个处理线程,对所述字节数组进行序列化处理,得到所述序列化数据。
- 如权利要求1所述的非结构化数据处理方法,其中,所述获取非结构化数据,包括:读取文件列表中的所有非结构化数据文件,获取所述文件列表中的非结构化数据文件的个数N;所述对所述非结构化数据进行序列化处理,得到序列化数据,包括:建立N个处理线程;针对所述文件列表中的N个所述非结构化数据文件,同时 采用所述N个处理线程进行序列化处理 。
- 一种非结构化数据处理方法,包括:读取目标结构化数据文件,所述目标结构化数据文件采用如权利要求1-5任一项所述的非结构化数据处理方法得到;获取所述目标结构化数据文件中的至少一个目标数据;对所述目标数据中的序列化数据进行反序列化处理,得到非结构化数据。
- 一种非结构化数据处理系统,包括:获取模块,用于获取非结构化数据;序列化处理模块,用于对所述非结构化数据进行序列化处理,得到序列化数据;连接模块,用于将所述序列化数据与所述非结构化数据的索引信息进行连接,得到目标数据;存储模块,用于将多个所述目标数据存储至目标结构化数据文件中,所述目标结构化数据文件用于分布式文件系统。
- 如权利要求9所述的非结构化数据处理系统,还包括:上传模块;其中,所述上传模块用于将所述目标结构化数据文件上传至所述分布式文件系统。
- 一种非结构化数据处理系统,包括:读取模块,用于读取目标结构化数据文件,所述目标结构化数据文件采用如权利要求1-7任一项所述的非结构化数据处理方法得到;获取模块,用于获取所述目标结构化数据文件中的至少一个目标数据;反序列化处理模块,用于对所述目标数据中的序列化数据进行反序列化处理,得到非结构化数据。
- 如权利要求11所述的非结构化数据处理系统,还包括:分布式处理模块;其中,所述分布式处理模块用于对所述反序列化处理模块得到的非结构化数据进行分布式处理。
- 一种非结构化数据处理系统,包括处理器、存储器及存储在所述存储器上并可在所述处理器上运行的计算机程序,所述计算机程序被所述处理器执行时实现如权利要求1至8中任一项所述的非结构化数据处理方法的步 骤。
- 一种计算机可读存储介质,所述计算机可读存储介质上存储计算机程序,所述计算机程序被处理器执行时实现如权利要求1至8中任一项所述的非结构化数据处理方法的步骤。
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