CN107562533B - Data loading processing method and device - Google Patents
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Abstract
The embodiment of the application discloses a data loading processing method and device. The method comprises the following steps: acquiring a plurality of user data; calculating the hash value of each user data; and according to the hash value, dividing the user data with the same hash value into the same computing node for data loading processing. By utilizing the embodiment of the application, the associated data of the large-scale user data can be loaded quickly and in real time.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a data loading processing method and apparatus.
Background
In recent years, with the rapid development of big data processing technology, a large amount of user data needs to be processed in real time in many business scenarios. In a typical application scenario, for the e-commerce network platform, a situation of user data explosion often occurs in an important promotional activity day, and for this reason, the e-commerce network platform needs to rapidly process the user data explosion to meet the needs of the user in real time. In the process of processing user data, each platform often needs to load associated data of the user log data from a background database according to the user log data, where the associated data may include, for example, historical behavior data of a user, personal information data of the user, and the like. Therefore, how to quickly acquire the associated data of the massive user log data is an urgent problem to be solved in the process of processing the big data.
Due to the large data volume and complex data relationship of the associated data in the database, the associated data of a plurality of user log data are often loaded in parallel through a plurality of computing nodes. After the associated data is loaded to the computing node, when the associated data is needed, the related data can be directly obtained from the computing node. In the prior art, the modes of loading data through a plurality of computing nodes mainly include two modes, one mode is a full-load mode, that is, all the associated data are loaded into all the computing nodes. However, the full load mode is generally used in the case of a small amount of associated data, and once the amount of data is large, the situation of memory overflow of the compute node is often caused, and the application scenario of large data cannot be satisfied. The second method is a least recently used loading method, that is, a part of commonly used associated data is cached on a computing node, although the method can load associated data of a plurality of users, the processing efficiency of the method is limited, if the computing node lacks part of associated data, reloading is needed, and in addition, the storage pressure on the computing node is also quite large. In addition, the two methods cannot realize horizontal extension of the computing nodes, that is, data which can be loaded by each computing node cannot be determined in advance.
Therefore, there is a need in the art for a way to load user-associated data quickly and in real time.
Disclosure of Invention
An object of the embodiments of the present application is to provide a data loading processing method and apparatus, which can load associated data of large-scale user data quickly and in real time.
The data loading processing method and device provided by the embodiment of the application are specifically realized as follows:
a data load processing method, the method comprising:
acquiring a plurality of user data;
calculating the hash value of each user data;
and according to the hash value, dividing the user data with the same hash value into the same computing node for data loading processing.
A data record processing apparatus, the apparatus comprising:
a user data acquisition unit for acquiring a plurality of user data;
a hash value calculation unit for calculating a hash value of each user data;
and the data dividing unit is used for dividing the user data with the same hash value into the same computing node according to the hash value to carry out data loading processing.
A data logging processing apparatus comprising a processor and a memory for storing processor-executable instructions, the instructions when executed by the processor effecting:
acquiring a plurality of user data;
calculating the hash value of each user data;
and according to the hash value, dividing the user data with the same hash value into the same computing node for data loading processing.
The data loading processing method and device provided by the application can calculate the hash value of each user data in the process of loading the associated data of a large amount of user data, and divide the user data with the same hash value into the same calculation node for loading processing. On one hand, the user data is divided according to the hash value of the user data, and the hash value has randomness, so that massive user data can be distributed to each computing node in a balanced manner, the function of each computing node is exerted, and the loading processing of related data is completed in real time and efficiently. On the other hand, the computing node can also acquire the associated data corresponding to the hash value from a background database and load the associated data into the computing node in advance, so that the associated data of the user data can be directly read from the corresponding computing node when needed, and the data processing efficiency is improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments described in the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without any creative effort.
FIG. 1 is a flow diagram of a method according to an embodiment of a data load processing method provided herein;
fig. 2 is a flowchart of a method of an embodiment of a user data dividing method provided in the present application;
FIG. 3 is a method flow diagram of one embodiment of an associated data acquisition method provided herein;
FIG. 4 is a schematic diagram of an application scenario provided herein;
FIG. 5 is a block diagram of an embodiment of a data loading processing apparatus provided in the present application;
fig. 6 is a schematic block diagram of another embodiment of a data load processing apparatus provided in the present application.
Detailed Description
In order to make those skilled in the art better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The data loading processing method described in the present application is described in detail below with reference to the accompanying drawings. Fig. 1 is a schematic method flow diagram of an embodiment of a data loading processing method provided in the present application. Although the present application provides method steps as shown in the following examples or figures, more or fewer steps may be included in the method based on conventional or non-inventive efforts. In the case of steps where no necessary causal relationship exists logically, the order of execution of the steps is not limited to that provided by the embodiments of the present application. The method can be executed sequentially or in parallel in the data loading process in practice according to the method shown in the embodiment or the figure (for example, a parallel processor or a multi-thread processing environment).
Specifically, as shown in fig. 1, an embodiment of a data loading processing method provided by the present application may include:
s11: a plurality of user data is acquired.
In this embodiment, the user data may include data such as user logs generated on various platforms, and the user logs include various user operation data such as user search data, user transaction data, user browsing data, and the like. In an actual application scenario, when the user data is processed, more data associated with the user data often needs to be acquired. For example, in a typical scenario, a user a clicks on a shopping platform to browse a certain commodity, and for this purpose, the shopping platform needs to acquire more historical operation behaviors of the user a on such a commodity so as to recommend a suitable commodity to the user a. The historical operating behavior of user a is often stored in a background database of the shopping platform, for example, the data of all users may be stored in the background database in the form of a dimension table. It can be seen that for large platforms, the data in the background database is often in the order of tens of millions or even hundreds of millions, and even the data of a single user is not small in amount. If the relevant data of each user is acquired from the background database one by one in the period of user data explosion, the data acquisition efficiency is low, and the real-time association of mass data cannot be met.
S12: a hash value of each user data is calculated.
In this embodiment, the hash value of each user data may be calculated separately. When the plurality of user data are processed, the plurality of user data may be processed in parallel through a plurality of computing nodes, where the computing nodes may be a single server or a server cluster formed by a plurality of servers, and the application is not limited herein. The computing node in this embodiment is configured to load associated data of the user data, where the associated data is data such as user history data and user profile associated with the user data. After the associated data of the user data is loaded to the computing node, the associated data can be directly read from the computing node when needed.
Since the user data is loaded by a plurality of computing nodes, this causes problems in allocating a large amount of user data to each computing node for processing with relative balance. Due to the real-time performance and concurrency of the plurality of user data, the user data cannot be sorted in time and then distributed to each computing node. Therefore, user data distributed by each computing node is unbalanced easily, user data of part of the computing nodes is large, loading pressure of part of the computing nodes is large, processing efficiency is low, and large data requests cannot be responded in real time on the whole.
In this embodiment, the hash calculation may be performed on the user data to obtain a hash value of each user data. In this embodiment, in the process of calculating the hash value of the user data, hash calculation may be performed on all data or part of data of the user data. Generally, the user data has a fixed data format, and typically the user data is a character string composed of a plurality of fields, each field contains a kind of user information, for example, the user data may have a data format of { user ID + data type + operation time + associated merchandise + … }. In an embodiment of the present application, when performing hash calculation on the user data, unique identification information in the user data may be calculated, and specifically, the calculating a hash value of each user data includes:
SS 1: acquiring unique identification information of a user from the user data;
SS 2: and calculating the hash value of the unique identification information, and taking the hash value of the unique identification information as the hash value of the user data.
In this embodiment, the unique identification information may include information capable of uniquely identifying user data, such as ID information, telephone information, ID card information, and address information of the user, and the unique identification information may also include any combination of the above information. The user data can be personalized by taking the hash value of the user unique identification information as the hash value of the user data, and the calculated hash value has higher randomness, so that the user data can be uniformly distributed to each computing node for processing by the hash value.
In one embodiment of the application, the hash value of the unique identification information may comprise one of:
a hash value of the unique identification information; a partial sequence intercepted from the unique identifying information hash value.
In this embodiment, the calculation manner of the hash value may include a hash algorithm, and the hash value may be calculated by using a hash algorithm, such as message digest 2(MD2), message digest 4(MD4), message digest 5(MD5), secure hash algorithm-1 (SHA-1), and the like. The hashing algorithm may convert data of any length into binary data of a fixed length, such as 128-bit or 256-bit binary code. In this embodiment, the hash value of the unique identification information may be used as the hash value of the unique identification information. In other embodiments, a partial sequence intercepted from the unique identification information hash value may also be taken as the hash value of the unique identification information. In one example, the first 16-bit binary sequence intercepted from the binary hash value may be taken as the hash value of the unique identification information. Of course, the sequence intercepted from any part of the hash value may also be used as the hash value of the unique identification information, and the application is not limited herein.
Of course, in other embodiments, the hash value of the entire user data may also be calculated, and all or a partial sequence of the hash values may be taken as the hash value of the user data. Generally, the user data starts with a user ID value, and therefore, the user data can also be personalized by using a partial sequence starting with the hash value of the entire user data as the hash value of the user data, thereby improving the randomness of the hash value.
S13: and according to the hash value, dividing the user data with the same hash value into the same computing node for data loading processing.
In this embodiment, the user data with the same hash value may be divided into the same computing node to perform data loading processing. In a specific example, a platform is provided with a plurality of computing nodes for loading the data associated with the user data. In this example, after the hash value of each user data is obtained through calculation, the user data may be divided into corresponding calculation nodes according to the value size of the hash value to perform data loading processing. For example, if the first 16-bit binary sequence of the hash value of the user ID value corresponding to the user data is used as the hash value of the user data, the numerical range of the hash value is 0000-FFFF, and at this time, the user data with the same hash value may be divided into the same computing node for data loading processing, for example, the user data with hash values of 0000, 0101, and 1C01 may be divided into computing node 1 for data recording processing, the user data with hash values of 0010, B111, and 0EF1 may be divided into computing node 2 for data loading processing, and so on. Therefore, the user data can be randomly grouped, and massive user data can be distributed to each computing node in a balanced manner to be subjected to data loading processing.
As shown in fig. 2, the dividing the user data with the same hash value into the same computing node for data loading processing may include:
s21: obtaining a plurality of association data associated with the plurality of user data;
s22: and respectively acquiring the hash value of each associated data, and loading the associated data of which the hash value is matched with the user data hash value processed by the computing node into the computing node.
In this embodiment, the plurality of associated data may be stored in a background database of each platform, for example, the associated data may be stored in a data format of a data dimension table, the data dimension table may be formed by associating a plurality of fact tables, and the fact table may include, for example, a user profile, a transaction dimension table, a time dimension table, a region dimension table, and the like. Therefore, according to the unique identification information such as the user ID, the user telephone and the like in the user data, various information such as the city where the user is located, the historical operation behavior of the user and the like can be obtained from the associated data. In this embodiment, the hash values of the associated data may also be obtained separately, for example, the hash values of the user IDs corresponding to the associated data may be calculated in the same manner. After obtaining the hash value of each associated data, associated data whose hash value matches the user data hash value processed by the computing node may be loaded into the computing node. Therefore, the associated data of the user data can be loaded into each computing node in advance with higher processing efficiency, and when the associated data of the user data is needed, the associated data can be directly read from the specified computing node without searching and loading the computing node from the background database in real time.
Specifically, the matching of the user data hash value and the associated data hash value may include at least one of:
the user data hash value is the same as the associated data hash value;
and the associated data hash value and the user data hash value meet the corresponding relation of a preset algorithm.
Wherein the association data hash value being the same as the user data hash value may include the association data hash value being the same as the user data hash value. When it is determined that the associated data hash value is the same as the user data hash value, the associated data hash value may be considered to match the user data hash value. Or, the associated data hash value and the user data hash value satisfy a predetermined algorithm corresponding relationship, for example, a binary code of the associated data hash value may be subjected to a weighting operation to obtain the associated data hash value after the weighting operation, and when it is determined that the associated data hash value after the weighting operation is the same as the user data hash value, it may be considered that the associated data hash value matches the user data hash value. Of course, the matching method of the user data hash value and the associated data hash value is not limited to the above example, and other modifications are possible for those skilled in the art based on the teachings of the present application, but all that can be achieved is covered by the protection scope of the present application as long as the achieved function and effect are the same or similar to the present application.
In this embodiment, as shown in fig. 3, after dividing the user data with the same hash value into the same computing node for data loading processing, the method may further include:
s31: when the associated data of the user data is needed, calculating the hash value of the user data;
s32: determining a computing node for processing the user data corresponding to the hash value according to the hash value;
s33: and acquiring the associated data of the user data from the computing node.
As can be seen from the above, after determining the hash value of the user data processed by the computing node, all the associated data of the user data can be loaded into the corresponding computing node according to the hash value. In this way, when the hash value of a certain user data is obtained by calculation and the associated data of the user data corresponding to the hash value is known to be loaded to the computing node, the required associated data can be directly read from the computing node. Specifically, in this embodiment, when the associated data of the user data is needed, the hash value of the user data may be calculated. After obtaining the hash value, a computing node corresponding to the hash value may be determined and processed according to the hash value. Upon determining the computing node, data associated with the user data may be obtained from the computing node.
The method of the embodiment of the present application is described below by a specific application scenario. During the period that a certain online shopping platform holds a great-preference shopping festival, the online shopping platform generates large-scale user data relative to the non-shopping festival due to the sharp increase of the operation times of browsing, collecting, purchasing, paying and the like of a user before and on the day of the shopping festival. After the online shopping platform acquires the user data, the online shopping platform needs to process the user data. For example, in order to predict the preference of the user, historical behavior data of the user needs to be acquired from a background database of the online shopping platform according to behavior data of the user such as browsing, collection and the like, so as to perform data learning, predict commodities that the user may prefer, and recommend the predicted commodities to the user. In this regard, the online shopping platform needs to set some computing nodes to load the associated data of the user data from the background database. As shown in FIG. 4, compute node B0-Bn is considered the compute node that handles data loads. By using the above embodiment of the present application, the hash value of each user data can be calculated, and the user data contains the unique identification information of the user, such as the user ID, the user phone, and the like. Because the data volume of the user data is extremely large, the online shopping platform can also set a plurality of computing node users to read the user data and calculate the hash value of the user data. As shown in FIG. 4, A0-Am may be set as a compute node that computes the user data. u0, u1, …, u2005, … may characterize unique identification information in the user data for uniquely identifying a user.
In this scenario, a hash value of a user ID corresponding to the user data may be calculated, and the first 16-bit binary sequence of the hash value may be intercepted as the hash value of the user data. As shown in fig. 4, in the user data processed by the computing node a0, the first 16-bit binary sequence of the hash value of the user u0 is 0000, the first 16-bit binary sequence of the hash value of u8 is 0010, and the first 16-bit binary sequence of the hash value of u120 is 0100. Since the range of the 16-bit binary sequence of the hash value processed by the computing node B0 is preset to 0000 + 0111, and the first 16-bit binary sequences of the hash values of the users u0, u8 and u120 all fall within the range of the sequence processed by the computing node B0, the user data of the users u0, u8 and u120 can be divided into the computing node B0 for data loading processing. During the data loading process, the computing node B0 may obtain the associated data corresponding to u0, u8, and u120 from the database. As shown in fig. 4, the database counts the users corresponding to the hash values, so that the computing node B0 can narrow the search range and quickly obtain the associated data of the relevant users from the database. Similarly, the processing method for the user data of other users is the same as the above processing method, and is not described herein again.
The computing node B0 may load all data of u0, u8 and u120 directly from the database into the node during the data loading process. Subsequently, the compute node B0 may notify the compute node A0-Am of the user information that the completion data has been loaded. At this time, while compute node A1 is processing user data to user u8, the associated data for user u8 may be read directly from compute node B0.
According to the data loading processing method, the hash value of each user data can be calculated in the process of loading the associated data of a large amount of user data, and the user data with the same hash value is divided into the same calculation node for loading processing. On one hand, the user data is divided according to the hash value of the user data, and the hash value has randomness, so that massive user data can be distributed to each computing node in a balanced manner, the function of each computing node is exerted, and the loading processing of related data is completed in real time and efficiently. On the other hand, the computing node can also acquire the associated data corresponding to the hash value from a background database and load the associated data into the computing node in advance, so that the associated data of the user data can be directly read from the corresponding computing node when needed, and the data processing efficiency is improved.
In another aspect of the present application, a data loading processing apparatus is further provided, and fig. 5 is a schematic block structure diagram of an embodiment of the data loading processing apparatus provided in the present application, and as shown in fig. 5, the apparatus 50 may include:
a user data acquisition unit 51 for acquiring a plurality of user data;
a hash value calculation unit 52 for calculating a hash value of each user data;
and the data dividing unit 53 is configured to divide the user data with the same hash value into the same computing node according to the hash value to perform data loading processing.
The data loading processing device provided by the application can calculate the hash value of each user data in the process of loading the associated data of a large amount of user data, and divide the user data with the same hash value into the same calculation node for loading processing. On one hand, the user data is divided according to the hash value of the user data, and the hash value has randomness, so that massive user data can be distributed to each computing node in a balanced manner, the function of each computing node is exerted, and the loading processing of related data is completed in real time and efficiently. On the other hand, the computing node can also acquire the associated data corresponding to the hash value from a background database and load the associated data into the computing node in advance, so that the associated data of the user data can be directly read from the corresponding computing node when needed, and the data processing efficiency is improved.
Optionally, in an embodiment of the present application, the dividing the user data with the same hash value into the same computing node for data loading processing may include:
obtaining a plurality of association data associated with the plurality of user data;
and respectively acquiring the hash value of each associated data, and loading the associated data of which the hash value is matched with the user data hash value processed by the computing node into the computing node.
Optionally, in an embodiment of the present application, the matching of the user data hash value and the associated data hash value may include at least one of:
the user data hash value is the same as the associated data hash value;
and the associated data hash value and the user data hash value meet the corresponding relation of a preset algorithm.
Optionally, in an embodiment of the present application, the calculating the hash value of each user data may include:
acquiring unique identification information of a user from the user data;
and calculating the hash value of the unique identification information, and taking the hash value of the unique identification information as the hash value of the user data.
Optionally, in an embodiment of the present application, the hash value of the unique identification information may include one of:
a hash value of the unique identification information; a partial sequence intercepted from the unique identifying information hash value.
Optionally, in an embodiment of the present application, the apparatus may further include:
a hash value determining unit, configured to calculate a hash value of user data when associated data of the user data is needed;
a node determining unit, configured to determine, according to the hash value, a computing node that processes user data corresponding to the hash value;
and the associated data acquisition unit is used for acquiring the associated data of the user data from the computing node.
Fig. 6 is a schematic block diagram of another embodiment of a data record processing apparatus provided in the present application, where as shown in fig. 6, the apparatus includes a processor and a memory for storing processor-executable instructions, and when the processor executes the instructions, the processor may implement:
acquiring a plurality of user data;
calculating the hash value of each user data;
and according to the hash value, dividing the user data with the same hash value into the same computing node for data loading processing.
Optionally, in an embodiment of the application, when the processor divides the user data with the same hash value into the same computing node for data loading processing in the implementation step, the method may include:
obtaining a plurality of association data associated with the plurality of user data;
and respectively acquiring the hash value of each associated data, and loading the associated data of which the hash value is matched with the user data hash value processed by the computing node into the computing node.
Optionally, in an embodiment of the present application, when the processor executes the instructions, the processor may further implement:
when the associated data of the user data is needed, calculating the hash value of the user data;
determining a computing node for processing the user data corresponding to the hash value according to the hash value;
and acquiring the associated data of the user data from the computing node.
In the 90 s of the 20 th century, improvements in a technology could clearly distinguish between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain the corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical modules. For example, a Programmable Logic Device (PLD), such as a Field Programmable Gate Array (FPGA), is an integrated circuit whose Logic functions are determined by programming the Device by a user. A digital system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Furthermore, nowadays, instead of manually making an Integrated Circuit chip, such Programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific Programming Language, which is called Hardware Description Language (HDL), and HDL is not only one but many, such as abel (advanced Boolean Expression Language), ahdl (alternate Hardware Description Language), traffic, pl (core universal Programming Language), HDCal (jhdware Description Language), lang, Lola, HDL, laspam, hardward Description Language (vhr Description Language), vhal (Hardware Description Language), and vhigh-Language, which are currently used in most common. It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.
The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, and an embedded microcontroller, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic for the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may thus be considered a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.
The units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above clients or servers are described separately with their functions divided into various units. Of course, the functionality of the units may be implemented in one or more software and/or hardware when implementing the present application.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (15)
1. A data load processing method, the method comprising:
acquiring a plurality of user data; wherein the user data comprises information for uniquely identifying a user;
calculating the hash value of each user data;
and according to the hash value, dividing the user data with the same hash value into the same computing node for data loading processing, wherein the computing node is used for pre-loading associated data of which the hash value is matched with the user data hash value processed by the computing node, and the associated data is stored in a background database.
2. The method of claim 1, wherein the partitioning of user data having the same hash value into the same compute node for data loading processing comprises:
obtaining a plurality of association data associated with the plurality of user data;
and respectively acquiring the hash value of each associated data, and loading the associated data of which the hash value is matched with the user data hash value processed by the computing node into the computing node.
3. The method of claim 2, the user data hash value matching the association data hash value comprising at least one of:
the user data hash value is the same as the associated data hash value;
and the associated data hash value and the user data hash value meet the corresponding relation of a preset algorithm.
4. A method according to any of claims 1-3, said calculating a hash value for each user data comprising:
acquiring unique identification information of a user from the user data;
and calculating the hash value of the unique identification information, and taking the hash value of the unique identification information as the hash value of the user data.
5. The method of claim 4, the hash value of the unique identification information comprising one of:
a hash value of the unique identification information; a partial sequence intercepted from the unique identifying information hash value.
6. The method of claim 1, after partitioning user data having the same hash value into the same compute node for data loading processing, the method further comprising:
when the associated data of the user data is needed, calculating the hash value of the user data;
determining a computing node for processing the user data corresponding to the hash value according to the hash value;
and acquiring the associated data of the user data from the computing node.
7. A data record processing apparatus, the apparatus comprising:
a user data acquisition unit for acquiring a plurality of user data; wherein the user data comprises information for uniquely identifying a user;
a hash value calculation unit for calculating a hash value of each user data;
and the data dividing unit is used for dividing the user data with the same hash value into the same computing node for data loading processing according to the hash value, wherein the computing node is used for pre-loading associated data of which the hash value is matched with the user data hash value processed by the computing node, and the associated data is stored in a background database.
8. The apparatus of claim 7, wherein said partitioning user data having the same hash value into the same compute node for data loading processing comprises:
obtaining a plurality of association data associated with the plurality of user data;
and respectively acquiring the hash value of each associated data, and loading the associated data of which the hash value is matched with the user data hash value processed by the computing node into the computing node.
9. The apparatus of claim 8, the user data hash value matching the association data hash value comprising at least one of:
the user data hash value is the same as the associated data hash value;
and the associated data hash value and the user data hash value meet the corresponding relation of a preset algorithm.
10. The apparatus of any of claims 7-9, the calculating the hash value for each user data comprising:
acquiring unique identification information of a user from the user data;
and calculating the hash value of the unique identification information, and taking the hash value of the unique identification information as the hash value of the user data.
11. The apparatus of claim 10, the hash value of the unique identification information comprising one of:
a hash value of the unique identification information; a partial sequence intercepted from the unique identifying information hash value.
12. The apparatus of claim 7, further comprising:
a hash value determining unit, configured to calculate a hash value of user data when associated data of the user data is needed;
a node determining unit, configured to determine, according to the hash value, a computing node that processes user data corresponding to the hash value;
and the associated data acquisition unit is used for acquiring the associated data of the user data from the computing node.
13. A data logging processing apparatus comprising a processor and a memory for storing processor-executable instructions, the instructions when executed by the processor effecting:
acquiring a plurality of user data; wherein the user data comprises information for uniquely identifying a user;
calculating the hash value of each user data;
and according to the hash value, dividing the user data with the same hash value into the same computing node for data loading processing, wherein the computing node is used for pre-loading associated data of which the hash value is matched with the user data hash value processed by the computing node, and the associated data is stored in a background database.
14. The apparatus of claim 13, the processor when implementing the step of partitioning user data having the same hash value into the same compute node for data loading processing comprises:
obtaining a plurality of association data associated with the plurality of user data;
and respectively acquiring the hash value of each associated data, and loading the associated data of which the hash value is matched with the user data hash value processed by the computing node into the computing node.
15. The apparatus of claim 13, the processor when executing the instructions further implements:
when the associated data of the user data is needed, calculating the hash value of the user data;
determining a computing node for processing the user data corresponding to the hash value according to the hash value;
and acquiring the associated data of the user data from the computing node.
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| CN108304503A (en) * | 2018-01-18 | 2018-07-20 | 阿里巴巴集团控股有限公司 | A kind of processing method of data, device and equipment |
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