CN105701215A - Hadoop MapReduce-based data connection method and device - Google Patents
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Abstract
The invention provides an Hadoop MapReduce-based data connection method and device. The method comprises the following steps: sequentially connecting default character information and time information behind primary key identifier information of first class of data to obtain first information; sequentially connecting the default character information and multiple 0 with the same character number as the time information of the first class of data behind the primary key identifier information of second class of data to obtain second information; with the first information and the second information as key information of MapReduce, connecting the data in a first data table and a second data table according to an MapReduce model; and with the primary key identifier information in the first information and the second information as partition information, calculating the partition to which each data obtained after connecting the data in the first data table and the second data table, and carrying out sorting in each partition with the key information, wherein the first class of data comprise the primary key identifier information and the time information; and the second class of data comprise the primary key identifier information. According to the method, memory application during connecting operation can be reduced and the data processing efficiency is improved.
Description
Technical field
The present invention relates to technical field of data processing, particularly relate to a kind of data connecting method based on HadoopMapReduce and device。
Background technology
At present, the tissue of big data mainly completes via field of distributed file processing with processing, the technology of main flow is Hadoop Open Source Platform, it provides field of distributed file processing (HadoopDistributedFileSystem, it is called for short HDFS) and distributed computing framework MapReduce, respectively as the framework of the storage of big data and process。The MapReduce treatment technology of Hadoop is accepted extensively by big data industry, processes various mass data every day。For improving treatment effeciency and the ability of Hadoop, a lot of developers constantly make improvements。
In data cube computation (join) method of existing HadoopMapReduce, for example, as shown in Figure 1, it is necessary to vehicle parking data A table and vehicle static information data B table are done join operation。Owing to A table is to stop data, data volume is big, and can dynamically increase, and B table is the static data of vehicle, and data volume is few and fixing。So using dictionary to be buffered in internal memory B table data when doing the calculating of attended operation。When use, read from dictionary。This operation can be subject to the impact of memory size, because the internal memory of the more big use of static data amount is also more big。When can storage allocation store requisite space less than data time, this calculating failure。
In consideration of it, the use of internal memory when the data cube computation (join) how reducing HadoopMapReduce operates, increasing data-handling efficiency becomes to be presently required and solves the technical problem that。
Summary of the invention
For solving above-mentioned technical problem, the present invention provides a kind of data connecting method based on HadoopMapReduce and device, it is possible to the use of internal memory when the data cube computation (join) of minimizing HadoopMapReduce operates, and increases data-handling efficiency。
First aspect, the present invention provides a kind of data connecting method based on HadoopMapReduce, including:
It is sequentially connected with preset characters information and temporal information after major key identification information in primary sources, obtains the first information；
It is sequentially connected with preset characters information and identical with the number of characters of the temporal information in described primary sources multiple 0 after major key identification information in secondary sources, obtains the second information；
Using key word key information as MapReduce of the described first information and described second information, according to MapReduce model, the data of primary sources Yu secondary sources are attached, and using the major key identification information in the described first information and described second information as subregion partition information, calculate and the every data obtained after the data cube computation of primary sources and secondary sources is belonged to which partition, and use key information to be ranked up in each partition；
Wherein, described primary sources include: major key identification information and temporal information；Described secondary sources include: major key identification information。
Alternatively, the major key identification information of described primary sources and the major key identification information of described secondary sources correspond to same information。
Alternatively, described preset characters information is comma。
Alternatively, the number of characters of the temporal information in described primary sources is 6, and the plurality of 0 is 000000。
Alternatively, described primary sources are vehicle parking tables of data, and the major key identification information of described primary sources is vehicle id field, and described primary sources also include: point of interest POI stops id information；
Described secondary sources are vehicle static information data table, and the major key identification information of described secondary sources is vehicle id field, and described secondary sources also include: vehicle registration temporal information。
Second aspect, the present invention provides a kind of data cube computation join device based on HadoopMapReduce, including:
First link block, for being sequentially connected with preset characters information and temporal information after the major key identification information in primary sources, obtains the first information；
Second link block, for being sequentially connected with preset characters information and identical with the number of characters of the temporal information in described primary sources multiple 0 after the major key identification information in secondary sources, obtains the second information；
Connect order module, for using key word key information as MapReduce of the described first information and described second information, according to MapReduce model, the data of primary sources Yu secondary sources are attached, and using the major key identification information in the described first information and described second information as subregion partition information, calculate and the every data obtained after the data cube computation of primary sources and secondary sources is belonged to which partition, and use key information to be ranked up in each partition；
Wherein, described primary sources include: major key identification information and temporal information；Described secondary sources include: major key identification information。
Alternatively, the major key identification information of described primary sources and the major key identification information of described secondary sources correspond to same information。
Alternatively, described preset characters information is comma。
Alternatively, the number of characters of the temporal information in described primary sources is 6, and the plurality of 0 is 000000。
Alternatively, described primary sources are vehicle parking tables of data, and the major key identification information of described primary sources is vehicle id field, and described primary sources also include: point of interest POI stops id information；
Described secondary sources are vehicle static information data table, and the major key identification information of described secondary sources is vehicle id field, and described secondary sources also include: vehicle registration temporal information。
As shown from the above technical solution, the data connecting method based on HadoopMapReduce of the present invention and device, it is possible to reduce the use of internal memory when the data cube computation (join) of HadoopMapReduce operates, increase data-handling efficiency。
Accompanying drawing explanation
Fig. 1 is a kind of schematic diagram of connection result of data connecting method connecting data and using existing HadoopMapReduce；
The schematic flow sheet of a kind of data connecting method based on HadoopMapReduce that Fig. 2 provides for one embodiment of the invention；
Fig. 3 uses described in embodiment illustrated in fig. 2 based on the data connecting method of the HadoopMapReduce schematic diagram to the A table shown in Fig. 1 and the connection procedure of the data of B table；
The structural representation of a kind of data connection device based on HadoopMapReduce that Fig. 4 provides for one embodiment of the invention；
The Hadoop data storage pattern sectional drawing that Fig. 5 provides for the embodiment of the present invention。
Detailed description of the invention
For making the purpose of the embodiment of the present invention, technical scheme and advantage clearly, below in conjunction with the accompanying drawing in the embodiment of the present invention, technical scheme in the embodiment of the present invention is carried out clear, complete description, obviously, described embodiment is only a part of embodiment of the present invention, rather than whole embodiments。Based on embodiments of the invention, the every other embodiment that those of ordinary skill in the art obtain under not making creative work premise, broadly fall into the scope of protection of the invention。
Fig. 2 illustrates the schematic flow sheet of the data cube computation based on HadoopMapReduce (join) method that one embodiment of the invention provides, as in figure 2 it is shown, the data connecting method based on HadoopMapReduce of the present embodiment is as described below。
201, it is sequentially connected with preset characters information and temporal information after the major key identification information in primary sources, obtains the first information。
202, it is sequentially connected with preset characters information and identical with the number of characters of the temporal information in described primary sources multiple 0 after the major key identification information in secondary sources, obtains the second information。
203, using key word key information as MapReduce of the described first information and described second information, according to MapReduce model, the data of primary sources Yu secondary sources are attached, and using the major key identification information in the described first information and described second information as subregion partition information, calculate and the every data obtained after the data cube computation of primary sources and secondary sources is belonged to which partition, and use key information to be ranked up in each partition。
Wherein, described primary sources include: major key identification information and temporal information；Described secondary sources include: major key identification information。
In a particular application, the major key identification information of primary sources described in the present embodiment and the major key identification information of described secondary sources correspond to same information。
In a particular application, major key identification information described in the present embodiment can know (identity for primary key, it is called for short id) field, described preset characters information can be preset characters field, described temporal information can be time field, described subregion partition information can be subregion partition field, and described key word key information can be key word key field。
In a particular application, described in the present embodiment, preset characters information can be preferably comma。
In a particular application, the number of characters of the temporal information in primary sources described in the present embodiment can be preferably 6, and correspondingly, the plurality of 0 is 000000。
It will be appreciated that the partition in the present embodiment is the meaning in " district ", it is simply that data want multidomain treat-ment, a district is exactly a logical space。Partition field determines which district is data assign to processes, and all data are assigned in different partition according to partition information by the present embodiment, are ranked up according to key information in each partition。
It should be noted that Hadoop data usually store in the form of text, as it is shown in figure 5, one record of a behavior, centre separates with comma, so primary sources and secondary sources in the present embodiment are also store in the form of text。
Data cube computation based on HadoopMapReduce (join) method of the present embodiment, it is possible to reduce the use of internal memory during the data cube computation operation of HadoopMapReduce, increase data-handling efficiency
For example, Fig. 1 is connection (join) operating result (C table) of one connection join data (A table and B table) and the data connecting method using existing HadoopMapReduce, can use method described in the present embodiment that the data of A table and B table are attached operation, namely primary sources described in the present embodiment are vehicle parking tables of data (i.e. A table), the major key identification information of described primary sources is vehicle id field, described primary sources also include: point of interest (PointofInterest is called for short POI) stops id field；Secondary sources described in the present embodiment are vehicle static information data table (i.e. B table), and the major key identification information of described secondary sources is vehicle id field, and described secondary sources also include: vehicle registration temporal information。
Use method described in the present embodiment that the data of A table and B table are attached (join), including:
It is sequentially connected with ", " and " time " field after a1, " vehicle id " field in A table, obtains " vehicle id, time " field；
Connect after a2, " vehicle id " field in B table ", 000000 ", obtain " vehicle id, 000000 " field。
A3, by key word key field as MapReduce of described " vehicle id; time " field and " vehicle id; 000000 " field, according to MapReduce model, the data of primary sources Yu secondary sources are attached, and by " vehicle id " field in " vehicle id; time " field and " vehicle id; 000000 " field as subregion partition field, calculate and the every data obtained after the data cube computation of primary sources and secondary sources is belonged to which partition, and use key field to be ranked up in each partition。
Use method described in the present embodiment to connection (join) process of A table and the data of B table as shown in Figure 3, shown in F table in Fig. 3, before the static data of each car comes the dynamic data of vehicle naturally, so when needing the static data of associated vehicle, static data feature just can be read in by program up front, thus being no longer necessary to a static data all read in internal memory dictionary。
It will be appreciated that Hadoop process is mass data, the simply sample data provided in Fig. 1 and Fig. 3, real data volume can be very big, it is often desirable that the data of same car process in same district, so using vehicle id as the foundation of subregion。
It will be appreciated that primary sources and secondary sources in the present embodiment are also store in the form of text, in order to that sees is directly perceived, ability form is deposited and represented in the present embodiment is illustrated。
Data cube computation based on HadoopMapReduce (join) method of the present embodiment, it is possible to reduce the use of internal memory during the data cube computation operation of HadoopMapReduce, increase data-handling efficiency。
Fig. 4 illustrates the structural representation of a kind of data connection device based on HadoopMapReduce that one embodiment of the invention provides, as shown in Figure 4, data cube computation based on HadoopMapReduce (join) device of the present embodiment, including: first link block the 41, second link block 42 and connection order module 43；
First link block 41, for being sequentially connected with preset characters information and temporal information after the major key identification information in primary sources, obtains the first information；
Second link block 42, for being sequentially connected with preset characters information and identical with the number of characters of the temporal information in described primary sources multiple 0 after the major key identification information in secondary sources, obtains the second information；
Connect order module 43, for using key word key information as MapReduce of the described first information and described second information, according to MapReduce model, the data of primary sources Yu secondary sources are attached, and using the major key identification information in the described first information and described second information as subregion partition information, calculate and the every data obtained after the data cube computation of primary sources and secondary sources is belonged to which partition, and use key information to be ranked up in each partition；
Wherein, described primary sources include: major key identification information and temporal information；Described secondary sources include: major key identification information。
In a particular application, the major key identification information of primary sources described in the present embodiment and the major key identification information of described secondary sources correspond to same information。
In a particular application, major key identification information described in the present embodiment can know id field for primary key, described preset characters information can be preset characters field, described temporal information can be time field, described subregion partition information can be subregion partition field, and described key word key information can be key word key field。
In a particular application, described in the present embodiment, preset characters information can be preferably comma。
In a particular application, the number of characters of the temporal information in primary sources described in the present embodiment can be preferably 6, and correspondingly, the plurality of 0 is 000000。
For example, in one specifically application, primary sources described in the present embodiment can be vehicle parking tables of data, and the major key identification information of described primary sources is vehicle id field, and described primary sources also include: point of interest POI stops id information；Described secondary sources can be vehicle static information data table, and the major key identification information of described secondary sources is vehicle id field, and described secondary sources also include: vehicle registration temporal information。
Can use device described in the present embodiment that the data of the A table in Fig. 1 and B table are attached (join) operation, its process is as shown in Figure 3, shown in F table in Fig. 3, before the static data of each car comes the dynamic data of vehicle naturally, so when needing the static data of associated vehicle, static data feature just can be read in by program up front, thus being no longer necessary to a static data all read in internal memory dictionary。
It should be noted that, Hadoop data usually store in the form of text, as shown in Figure 5, one behavior one record, centre separates with comma, so primary sources and secondary sources in the present embodiment are also store in the form of text, in order to that sees is directly perceived, ability form is deposited and represented in the present embodiment is illustrated。
The data connection device based on HadoopMapReduce of the present embodiment, it is possible to reduce the use of internal memory during the data cube computation operation of HadoopMapReduce, increase data-handling efficiency。
The data connection device based on HadoopMapReduce of the present embodiment, it is possible to for performing the technical scheme of embodiment of the method shown in earlier figures 2, it is similar with technique effect that it realizes principle, repeats no more herein。
One of ordinary skill in the art will appreciate that: all or part of step realizing above-mentioned each embodiment of the method can be completed by the hardware that programmed instruction is relevant。Aforesaid program can be stored in a computer read/write memory medium。This program upon execution, performs to include the step of above-mentioned each embodiment of the method；And aforesaid storage medium includes: the various media that can store program code such as ROM, RAM, magnetic disc or CDs。
Last it is noted that various embodiments above is only in order to illustrate technical scheme, it is not intended to limit；Although the present invention being described in detail with reference to foregoing embodiments, it will be understood by those within the art that: the technical scheme described in foregoing embodiments still can be modified by it, or wherein some or all of technical characteristic is carried out equivalent replacement；And these amendments or replacement, do not make the essence of appropriate technical solution depart from the scope of various embodiments of the present invention technical scheme。
Claims (10)
1. the data connecting method based on HadoopMapReduce, it is characterised in that including:
It is sequentially connected with preset characters information and temporal information after major key identification information in primary sources, obtains the first information；
It is sequentially connected with preset characters information and identical with the number of characters of the temporal information in described primary sources multiple 0 after major key identification information in secondary sources, obtains the second information；
Using key word key information as MapReduce of the described first information and described second information, according to MapReduce model, the data of primary sources Yu secondary sources are attached, and using the major key identification information in the described first information and described second information as subregion partition information, calculate and the every data obtained after the data cube computation of primary sources and secondary sources is belonged to which partition, and use key information to be ranked up in each partition；
Wherein, described primary sources include: major key identification information and temporal information；Described secondary sources include: major key identification information。
2. method according to claim 1, it is characterised in that the major key identification information of described primary sources and the major key identification information of described secondary sources correspond to same information。
3. method according to claim 2, it is characterised in that described preset characters information is comma。
4. method according to claim 2, it is characterised in that the number of characters of the temporal information in described primary sources is 6, the plurality of 0 is 000000。
5. method according to claim 2, it is characterised in that described primary sources are vehicle parking tables of data, the major key identification information of described primary sources is vehicle id field, and described primary sources also include: point of interest POI stops id information；
Described secondary sources are vehicle static information data table, and the major key identification information of described secondary sources is vehicle id field, and described secondary sources also include: vehicle registration temporal information。
6. the data connection device based on HadoopMapReduce, it is characterised in that including:
First link block, for being sequentially connected with preset characters information and temporal information after the major key identification information in primary sources, obtains the first information；
Second link block, for being sequentially connected with preset characters information and identical with the number of characters of the temporal information in described primary sources multiple 0 after the major key identification information in secondary sources, obtains the second information；
Connect order module, for using key word key information as MapReduce of the described first information and described second information, according to MapReduce model, the data of primary sources Yu secondary sources are attached, and using the major key identification information in the described first information and described second information as subregion partition information, calculate and the every data obtained after the data cube computation of primary sources and secondary sources is belonged to which partition, and use key information to be ranked up in each partition；
Wherein, described primary sources include: major key identification information and temporal information；Described secondary sources include: major key identification information。
7. device according to claim 6, it is characterised in that the major key identification information of described primary sources and the major key identification information of described secondary sources correspond to same information。
8. device according to claim 7, it is characterised in that described preset characters information is comma。
9. device according to claim 7, it is characterised in that the number of characters of the temporal information in described primary sources is 6, the plurality of 0 is 000000。
10. device according to claim 7, it is characterised in that described primary sources are vehicle parking tables of data, the major key identification information of described primary sources is vehicle id field, and described primary sources also include: point of interest POI stops id information；
Described secondary sources are vehicle static information data table, and the major key identification information of described secondary sources is vehicle id field, and described secondary sources also include: vehicle registration temporal information。
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| CN106874322A (en) * | 2016-06-27 | 2017-06-20 | 阿里巴巴集团控股有限公司 | A kind of data table correlation method and device |
| CN107577531A (en) * | 2016-07-05 | 2018-01-12 | 阿里巴巴集团控股有限公司 | Load-balancing method and device |
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