CN110990358B - Decompression method, electronic equipment and computer readable storage medium - Google Patents

Abstract

The embodiment of the invention relates to the field of data processing, and discloses a decompression method, electronic equipment and a computer readable storage medium. In some embodiments of the present application, the decompression method includes: pre-decoding the data to be decompressed to obtain block information of the data to be decompressed, wherein the block information indicates the position of a compressed block in the data to be decompressed; dividing data to be decompressed into N data blocks according to the block information, wherein each data block at least comprises a compression block, and N is a positive integer; each data block is decompressed concurrently. In this embodiment, the speed of decompression is increased.

Description

A decompression method, electronic device and computer-readable storage medium

technical field

The embodiments of the present invention relate to the field of data processing, and in particular to a decompression method, electronic equipment, and a computer-readable storage medium.

Background technique

Due to the high compression rate and high industry maturity of the GZIP-based data compression format, it is widely used in the Internet and big data fields. For example, the massive access logs of daily content delivery network (Content Delivery Network, CDN) are usually compressed into GZIP package storage, and the compression rate can reach 5 to 6 times. The data stream in this format is transmitted to the big data platform through the network for real-time or offline analysis, which can greatly improve network transmission efficiency and reduce network congestion.

However, the inventors have found that at least the following problems exist in the prior art: the decompression speed of the decompression method based on the GZIP data compression format is too slow.

Contents of the invention

The purpose of the embodiments of the present invention is to provide a decompression method, electronic equipment, and a computer-readable storage medium, so as to increase the decompression speed.

In order to solve the above-mentioned technical problems, the embodiment of the present invention provides a decompression method, comprising the following steps: pre-decoding the data to be decompressed, obtaining block information of the data to be decompressed, and the block information indicates the position of the compressed block in the data to be decompressed ; According to the block information, divide the data to be decompressed into N data blocks, each data block includes at least one compressed block, and N is a positive integer; decompress each data block concurrently.

The embodiment of the present invention also provides an electronic device, including: at least one processor; and a memory connected to the at least one processor in communication; wherein, the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by at least one processor. Executed by one processor, so that at least one processor can execute the decompression method mentioned in the above implementation manner.

Embodiments of the present invention also provide a computer-readable storage medium storing a computer program, and implementing the decompression method mentioned in the above embodiment when the computer program is executed by a processor.

Compared with the prior art, the embodiments of the present invention obtain the block information of the data to be decompressed by pre-decoding the data to be decompressed, so as to divide the data to be decompressed into blocks, so as to realize parallel decompression. Since each divided data block is decompressed in parallel, compared with decompressing the entire file, the decompression speed is improved and the total delay time of decompression is reduced.

In addition, the data to be decompressed is pre-decoded to obtain the block information of the data to be decompressed, which specifically includes: pre-decoding the data to be decompressed, determining the position information of the block tail of each compressed block; according to the position information of the block tail of each compressed block, determining block information.

In addition, the data to be decompressed is pre-decoded to determine the position information of the end of each compressed block, which specifically includes: according to the code table of the data to be decompressed, the code table matching is performed on the characters in the data to be decompressed; 256. Use the character position information as the block end position information of the current compressed block. In this implementation, only the end character of the compressed block is locked, and the distance position is not replaced, and the block is then decompressed in parallel to improve the decompression speed.

In addition, according to the block information, the data to be decompressed is divided into N data blocks, specifically including: according to the block information, according to the preset merging rules, the compressed blocks in the data to be decompressed are merged into N data blocks; wherein, after merging In the data blocks of , the first compressed block of the i+1th data block is the same as the last compressed block of the ith data block, 1≤i<N. In this implementation, each data block is redundant with the last compressed block of the previous data block, so as to ensure that the first compressed block in the data block can find a quote character far enough away when decompressing, and complete normal character replacement.

In addition, the block information also indicates the order of the compressed blocks; the merging rule is: according to the order of the compressed blocks, merge the first compressed block to the Mth compressed block into one data block; determine whether 2M is less than N; where M is positive Integer; if it is determined to be yes, merge the Mth compressed block to the 2M compressed block into one data block; let M=2M, return to the step of judging whether 2M is less than N; if it is determined not to, merge the Mth compressed block to the The Nth compressed block is merged into one data block. In this implementation, the compressed blocks are merged in order to ensure the continuity of content between compressed blocks during subsequent parallel decompression.

In addition, the decompression process to the kth data block includes: if it is determined that k=1, decompress from the first compressed block, and decompress to the last predetermined symbol of the last compressed block; if it is determined that 1<k<N, Decompress from the last predetermined symbol of the first compressed block, and decompress to the last predetermined symbol of the last compressed block; if k=N is determined, start to decompress from the last predetermined symbol of the first compressed block until decompression Finish the last compressed block. In this implementation, the integrity of the content of each data block after decompression is guaranteed, which provides a basis for the seamless integration of the Hadoop platform and the streaming computing cluster (Spark computing platform).

In addition, the distributed computing platform divides the data to be decompressed into N data blocks according to the block information, and decompresses each data block concurrently.

In addition, the distributed computing platform communicates with the Spark computing platform, and the distributed computing platform transmits the decompressed data of each data block to the Spark computing platform.

Description of drawings

One or more embodiments are exemplified by the pictures in the corresponding drawings, and these exemplifications do not constitute a limitation to the embodiments. Elements with the same reference numerals in the drawings represent similar elements. Unless otherwise stated, the drawings in the drawings are not limited to scale.

Fig. 1 is the flowchart of the decompression method according to the first embodiment of the present invention;

Fig. 2 is the flowchart of the decompression method according to the second embodiment of the present invention;

3 is a schematic structural diagram of a decompression device according to a third embodiment of the present invention;

Fig. 4 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present invention.

Detailed ways

In order to make the purpose, technical solutions and advantages of the embodiments of the present invention more clear, various implementation modes of the present invention will be described in detail below in conjunction with the accompanying drawings. However, those of ordinary skill in the art can understand that, in each implementation manner of the present invention, many technical details are provided for readers to better understand the present application. However, even without these technical details and various changes and modifications based on the following implementation modes, the technical solution claimed in this application can also be realized.

The first embodiment of the present invention relates to a decompression method, which is applied to an electronic device, such as a server or a terminal. As shown in Figure 1, the decompression method includes the following steps:

Step 101: Perform pre-decoding on the data to be decompressed to obtain block information of the data to be decompressed.

Specifically, the block information indicates the location of the compressed block in the data to be decompressed.

It should be noted that the block information may be the position information of the block header of the compressed block, or the position information of the block tail of the compressed block, or the position information of the block header and the block tail of the compressed block, which is not limited here .

In one embodiment, the block information is the location information of the block trailer of the compressed block. Specifically, the electronic device pre-decodes the data to be decompressed, and determines the position information of the block tail of each compressed block; and determines the block information according to the position information of the block tail of each compressed block.

In one embodiment, the location information of the end of the block may be the addressing location of the end character of the compressed block. Specifically, the code value of the end character of the compressed block is 256, and the electronic device performs code table matching on the characters in the data to be decompressed according to the code table of the data to be decompressed; if the character is matched with a code value of 256, the position of the character Information, as the position information of the block end of the current compressed block.

Assuming that the data to be decompressed is a compressed file (hereinafter referred to as a deflate file) compressed by a lossless data compression algorithm (deflate algorithm), the process for the electronic device to determine block information is as follows: first, the electronic device unpacks the deflate file, that is, remove The header file in the deflate file retains the compressed stream of the deflate file, wherein the header file may include description information of the deflate file, and the description information may indicate that the deflate file is a dynamically compressed file or a statically compressed file. Then, the encoding tree of the deflate file is read and the initial pre-decoding of the compressed stream is done. Regardless of whether the value obtained during the pre-decoding process is greater than or less than 256, discard it and continue parsing backwards; if the value obtained is found to be 256, it means that the compression block has ended, and all the information of the block is cached at this time; follow the same rules Continue processing the next chunk until all chunks have been parsed. Among them, the pre-decoding process is usually to decode the coding tree through the Huffman algorithm to obtain the coding table, and then use the lz77 algorithm to contact each character of the deflate file according to the coding table. This process only judges whether the read character is 256, and does not replace the distance position for repeated characters, so the content in the cached block is still data that has not been fully decompressed. After this step, it is possible to obtain: the compressed block that has not been fully decompressed and the addressing address of the end character of the compressed block. Optionally, during the pre-decoding process, each compressed block is numbered, so that the ID number of each compressed block can be obtained.

It is worth mentioning that the separation of the compressed block and the addressing position of the end character of the compressed block provide the basis for subsequent parallel decompression.

It is worth mentioning that the numbering of each compression block provides a basis for subsequent merging of compression blocks.

Step 102: Divide the data to be decompressed into N data blocks according to the block information.

Specifically, each data block includes at least one compressed block, and N is a positive integer.

In one embodiment, the electronic device can treat each compressed block as a data block.

In one embodiment, the distributed computing platform may divide the data to be decompressed into N data blocks according to the block information, and decompress each data block concurrently.

Step 103: Decompress each data block concurrently.

Specifically, the electronic device can decompress each data block in parallel through the distributed computing platform. According to the decompressed data of each data block, the final decompressed file of the data to be decompressed is determined.

In one embodiment, the distributed computing platform communicates with the Spark computing platform, and the distributed computing platform transmits the decompressed data of each data block to the Spark computing platform.

It should be noted that the above is only for illustration and does not limit the technical solution of the present invention.

Compared with the prior art, the decompression method provided in this embodiment obtains the block information of the data to be decompressed by pre-decoding the data to be decompressed, so as to divide the data to be decompressed into blocks, so as to realize parallel decompression. Since each divided data block is decompressed in parallel, compared with decompressing the entire file, the decompression speed is improved and the total delay time of decompression is reduced.

The second embodiment of the present invention relates to a decompression method, and this embodiment is an illustration of step 102 and step 103 of the first embodiment.

Specifically, as shown in FIG. 2 , in this embodiment, step 201 to step 203 are included, wherein step 201 is substantially the same as step 101 in the first embodiment, and will not be repeated here. Here are the main differences:

Step 201: Perform pre-decoding on the data to be decompressed to obtain block information of the data to be decompressed.

Step 202: Merge the compressed blocks in the data to be decompressed into N data blocks according to the block information and according to the preset merging rules.

Specifically, in the merged data blocks, the first compressed block of the i+1th data block is the same as the last compressed block of the ith data block, 1≤i<N.

It is worth mentioning that merging multiple compression blocks into one compression block can avoid too many parallel decompression tasks in the electronic device and occupy the resources of the electronic device.

In one embodiment, the block information also indicates the order of the compressed blocks. For example, the block information also includes the number of the compressed block, that is, the ID sequence number. The merging rule is: according to the order of the compressed blocks, the first compressed block to the Mth compressed blocks are merged into one data block; judge whether 2M is less than N; wherein, M is a positive integer; if yes, merge the Mth compressed block to the 2M compressed block into one data block; let M=2M, return Execute the step of judging whether 2M is smaller than N; if it is determined not to be, merge the Mth compressed block to the Nth compressed block into one data block.

It should be noted that those skilled in the art can understand that in practical applications, M can be determined according to the data size of each compressed block and the parallel processing capability of the distributed computing platform, which is not limited here.

It is worth mentioning that the merging is performed according to the order of the ID numbers of each compressed block, which ensures the continuity of the content between the compressed blocks and the compressed blocks during subsequent parallel decompression.

It is worth mentioning that each data block is redundant with the last compressed block of the previous data block to ensure that the first compressed block in the data block can find a quote character far enough away when decompressing to complete normal character replacement.

Assume that the data to be decompressed is a deflate file (GZIP file), and the distributed computing platform is a hadoop platform. The Hadoop platform merges a predetermined number of compressed blocks into Hadoop partitions (data blocks) of appropriate size in sequence according to the ID numbers of each compressed block. Wherein, the predetermined number can be set as required. Since the size of the compressed block of the GZIP file itself is about tens of K, and the partition size of Hadoop is usually 64M, starting a processing task for every tens of K of data is not the optimal solution for big data systems. Therefore, after identifying and caching all block information, the Hadoop platform should merge the compressed blocks accordingly. Considering that the amount of data will increase several times after decompression, in this embodiment, 100 consecutive compressed blocks are synthesized into a large set as a hadoop partition. Each hadoop partition provides the number of compressed blocks contained in the partition, ID sequence number, compressed block content, etc., as part of the Hadoop partition information.

Among them, when merging compressed blocks into hadoop partitions, they can be merged in sequence according to the ID numbers of each compressed block, such as the first hadoop partition being #1, #2, #3, #4, and a hadoop partition being #8 , #9, #10, #11, and so on. The purpose of merging according to the order of the ID numbers of compressed blocks is to ensure that the content continuity between blocks can be satisfied during subsequent parallel decompression, and then the decompression of each hadoop block can be successfully completed.

In addition, when merging each compressed block into a hadoop partition, it can also be satisfied that the first compressed block of each hadoop partition is the last compressed block of the previous hadoop partition. At the beginning of each hadoop partition (except the first hadoop partition), redundancy is added to the end block of the previous hadoop partition. If the first hadoop partition is #1, #2, #3, #4, a total of 4 blocks, then the second hadoop partition should contain #4, #5, #6, #7, #8, to ensure block #4 Included; the third hadoop partition should contain #8, #9, #10, #11, #12..., ensure block #8 is included, and so on, the purpose is to ensure that the first block in the partition is decompressed Quoted characters far enough away can be found to perform normal character substitution.

Step 203: Decompress each data block concurrently.

Specifically, the decompression process for the kth data block includes: if it is determined that k=1, decompress from the first compressed block, and decompress to the last predetermined symbol of the last compressed block; if it is determined that 1<k< N, decompress from the last predetermined symbol of the first compressed block, and decompress to the last predetermined symbol of the last compressed block; if k=N is determined, decompress from the last predetermined symbol of the first compressed block, Until the last compressed block is decompressed.

It should be noted that the predetermined symbol may be a newline character, or other specified symbols, such as a colon, which is not limited in this embodiment.

It should be noted that the electronic device can decompress the data in each data block through the LZ77 algorithm, or select an appropriate decompression algorithm to decompress the data in each data block according to the compression algorithm of the data to be decompressed. mode does not restrict the algorithm used by the decompression process.

Assume, the predetermined symbol is a newline character. After the distributed computing platform forms hadoop partitions in the previous step, it obtains the partition information list, and then decompresses the content of each hadoop partition in parallel. The decompression process for each hadoop partition is:

(1) Determine whether the currently decompressed hadoop partition is the first partition; if it is the first partition, start decoding from the first compressed block of the current partition until the last visible line break of the last compressed block of the current partition . The string after the last newline character is considered incomplete, discarded when the current partition is decompressed, and left for the next partition to process. If it is not the first partition, go to the next step;

(2) Determine whether the currently decompressed hadoop partition is the last partition. If it is not the last partition, decoding starts from the last visible newline character of the first compressed block of the current partition and ends at the last visible newline character of the last compressed block. That is, the data before the last newline character of the first compressed block is considered to have been included and processed in the previous partition. If it is the last partition, go to the next step;

(3) Decoding starts from the last visible line break of the first compressed block of the current partition, and decompresses completely until the last compressed block.

The purpose of the above steps is to solve the problem that the decompressed content in the Hadoop partition cannot be directly used for subsequent RDD construction and concurrent calculation of big data. Due to the GZIP compression feature, the compressed blocks allocated in each partition, especially the first block and the tail block, cannot guarantee that the first character of the first block is exactly the first character of a text record, nor can it guarantee that the last character of the tail block A character is exactly the end-of-line character of a text record. In this case, directly handing over the partition content to the subsequent distributed computing may cause abnormal situations such as system error reporting. Through the above steps, splitting based on the last newline character of the first block and the last block of each hadoop partition can ensure that the content of the partition is read completely, and then ensure that the Spark computing platform can construct a memory-based Spark RDD, that is, the minimum The computing unit finally realizes the parallel decompression of GZIP files, which makes the seamless integration of Hadoop platform and streaming computing cluster (Spark computing platform).

The inventor found that the core consumption of existing GZIP-based file collection and big data calculation and analysis systems lies in the process of decompressing GZIP. Due to the unique encapsulation header of GZIP files, and the storage of compressed blocks is not based on whole byte storage, but the continuous storage of binary stream (bit stream), since the stream does not specifically provide the start, end and summary information of each compressed block, it cannot Obtain the list information of all blocks in the stream at one time. These characteristics make GZIP not naturally support hadoop partitions, nor support parallel reading and decompression. Naturally, it does not support Hadoop's partitioning and block features, which means that no matter how large the scale of the big data platform is, no matter how many physical machines and CPUs there are, it will not be able to take advantage of it. For a GZIP file, in the decompression stage, the default parallel multi-task decompression and calculation cannot be achieved, and only a single-core single-process decompression can be performed. This bottleneck greatly limits the computing power and usage efficiency of the big data platform. However, this embodiment provides a decompression method, obtains the block information of the compressed block through pre-decoding, and adapts the decoding process of the Hadoop platform to make adaptive improvements, so that GZIP files can be decoded in parallel using the Hadoop platform, and the decoding speed is improved.

It is worth mentioning that since the data input to the Hadoop platform is uncompressed data to be compressed, compared with the method of inputting the decompressed data into the Hadoop platform for partitioning, the decompression efficiency during real-time calculation on the Hadoop platform is improved. Preprocessing and caching enables parallel decompression to reduce the total delay time of decompression. The larger the compressed file size, the more efficient the boost will be. In addition, the storage space of the decompressed intermediate files in Hadoop is reduced by more than 10 times. Through this implementation mode, the Hadoop platform is seamlessly integrated with the streaming computing framework, such as the Spark computing platform, and developers of big data business applications only need to focus on the large-scale distributed computing development of the business, and will not be affected by the technology brought by GZIP. Restrictions and care about purely technical issues such as how to improve decompression efficiency.

It should be noted that the above is only for illustration and does not limit the technical solution of the present invention.

Compared with the prior art, the decompression method provided in this embodiment obtains the block information of the data to be decompressed by pre-decoding the data to be decompressed, so as to divide the data to be decompressed into blocks, so as to realize parallel decompression. Since each divided data block is decompressed in parallel, compared with decompressing the entire file, the decompression speed is improved and the total delay time of decompression is reduced. In addition, the merging is performed according to the sequence of the ID numbers of each compressed block, which ensures the continuity of content between compressed blocks during subsequent parallel decompression. Each data block is redundant with the last compressed block of the previous data block, so as to ensure that the first compressed block in the data block can find a quote character far enough away to complete normal character replacement when decompressing.

The step division of the above various methods is only for the sake of clarity of description. During implementation, it can be combined into one step or some steps can be split and decomposed into multiple steps. As long as they include the same logical relationship, they are all within the scope of protection of this patent. ; Adding insignificant modifications or introducing insignificant designs to the algorithm or process, but not changing the core design of the algorithm and process are all within the scope of protection of this patent.

The third embodiment of the present invention relates to a decompression device, as shown in FIG. 3 , including: a pre-decoding module 301 , a blocking module 302 and a decompression module 303 . The pre-decoding module 301 is configured to pre-decode the data to be decompressed to obtain block information of the data to be decompressed, and the block information indicates the position of the compressed block in the data to be decompressed. The block module 302 is used to divide the data to be decompressed into N data blocks according to the block information, each data block includes at least one compressed block, and N is a positive integer; the decompression module 303 is used to concurrently process each data block to unzip.

It is not difficult to find that this embodiment is a system embodiment corresponding to the first embodiment, and this embodiment can be implemented in cooperation with the first embodiment. The relevant technical details mentioned in the first embodiment are still valid in this embodiment, and will not be repeated here to reduce repetition. Correspondingly, the relevant technical details mentioned in this implementation manner can also be applied in the first implementation manner.

It is worth mentioning that all the modules involved in this embodiment are logical modules. In practical applications, a logical unit can be a physical unit, or a part of a physical unit, or multiple physical units. Combination of units. In addition, in order to highlight the innovative part of the present invention, units that are not closely related to solving the technical problems proposed by the present invention are not introduced in this embodiment, but this does not mean that there are no other units in this embodiment.

The fourth embodiment of the present invention relates to an electronic device, as shown in FIG. 4 , including: at least one processor 401; and a memory 402 communicatively connected to at least one processor 401; An instruction executed by one processor 401, the instruction is executed by at least one processor 401, so that at least one processor 401 can execute the decompression method mentioned in the foregoing implementation manner.

The electronic device includes: one or more processors 401 and a memory 402, one processor 401 is taken as an example in FIG. 4 . The processor 401 and the memory 402 may be connected through a bus or in other ways. In FIG. 4 , connection through a bus is taken as an example. The memory 402, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs and modules. The processor 401 executes various functional applications and data processing of the device by running non-volatile software programs, instructions and modules stored in the memory 402, that is, implements the above decompression method.

The memory 402 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application program required by at least one function; the data storage area may store an option list and the like. In addition, the memory 402 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage devices. In some implementations, the memory 402 may optionally include a memory set remotely relative to the processor 401, and these remote memories may be connected to an external device through a network. Examples of the aforementioned networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

One or more modules are stored in the memory 402, and when executed by one or more processors 401, execute the decompression method in any of the above method implementations.

The above-mentioned products can execute the methods provided in the embodiments of this application, and have the corresponding functional modules and beneficial effects for executing the methods. For technical details not described in detail in this embodiment, please refer to the methods provided in the embodiments of this application.

A fifth embodiment of the present invention relates to a computer-readable storage medium storing a computer program. The above method embodiments are implemented when the computer program is executed by the processor.

That is, those skilled in the art can understand that all or part of the steps in the method of the above-mentioned embodiments can be completed by instructing related hardware through a program, the program is stored in a storage medium, and includes several instructions to make a device ( It may be a single-chip microcomputer, a chip, etc.) or a processor (processor) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk, and other media that can store program codes.

Those of ordinary skill in the art can understand that the above-mentioned embodiments are specific examples for realizing the present invention, and in practical applications, various changes can be made to it in form and details without departing from the spirit and spirit of the present invention. scope.

Claims (9)

1. A decompression method, characterized in that, comprising: Pre-decoding the data to be decompressed to obtain block information of the data to be decompressed, the block information indicating the position of the compressed block in the data to be decompressed; According to the block information, divide the data to be decompressed into N data blocks, each data block includes at least one compressed block, and N is a positive integer; decompressing each of said data blocks concurrently; According to the block information, dividing the data to be decompressed into N data blocks specifically includes: Merging the compressed blocks in the data to be decompressed into N data blocks according to the block information according to a preset merging rule; Wherein, in the merged data blocks, the first compressed block of the i+1th data block is the same as the last compressed block of the ith data block, 1≤i<N. 2. The decompression method according to claim 1, wherein the pre-decoding of the data to be decompressed to obtain the block information of the data to be decompressed specifically includes: Perform pre-decoding on the data to be decompressed, and determine the position information of the block tail of each compressed block; The block information is determined according to the position information of the block tail of each compressed block. 3. The decompression method according to claim 2, wherein the pre-decoding of the data to be decompressed is carried out to determine the position information of the block tail of each compressed block, specifically comprising: performing code table matching on the characters in the data to be decompressed according to the code table of the data to be decompressed; If the code value matched by the character is 256, the position information of the character is used as the position information of the block end of the current compressed block. 4. The decompression method according to claim 1, wherein the block information also indicates the order of the compressed blocks; the merging rule is: According to the sequence of the compressed blocks, merging the first compressed block to the Mth compressed block into one data block; Determine whether 2M is less than N; where M is a positive integer; If yes, merge the M compressed block to the 2M compressed block into one data block; make M=2M, return to the step of judging whether 2M is less than N; If it is determined not to be, merge the Mth compressed block to the Nth compressed block into one data block. 5. decompression method according to claim 1, is characterized in that, the decompression process to the kth data block comprises: If it is determined that k=1, decompress from the first compressed block, and decompress to the last predetermined symbol of the last compressed block; If it is determined that 1<k<N, decompress from the last predetermined symbol of the first compressed block, and decompress to the last predetermined symbol of the last compressed block; If it is determined that k=N, decompress from the last predetermined symbol of the first compressed block until the last compressed block is decompressed. 6. The decompression method according to any one of claims 1 to 5, wherein the data to be decompressed is divided into N data blocks by a distributed computing platform according to the block information, and concurrently Each of said data blocks is decompressed. 7. decompression method according to claim 6, is characterized in that, described distributed computing platform is connected with Spark computing platform communication, and described distributed computing platform transmits the decompressed data of each described data block to described Spark computing platform. 8. An electronic device, comprising: at least one processor; and, memory communicatively coupled to the at least one processor; Wherein, the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor, so that the at least one processor can perform any one of claims 1 to 7. The decompression method described in the item. 9. A computer-readable storage medium storing a computer program, wherein the computer program implements the decompression method according to any one of claims 1 to 7 when executed by a processor.

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