CN103617268A - Method and device for processing big data - Google Patents
Method and device for processing big data Download PDFInfo
- Publication number
- CN103617268A CN103617268A CN201310646544.0A CN201310646544A CN103617268A CN 103617268 A CN103617268 A CN 103617268A CN 201310646544 A CN201310646544 A CN 201310646544A CN 103617268 A CN103617268 A CN 103617268A
- Authority
- CN
- China
- Prior art keywords
- hadoop
- task
- query
- external environment
- data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2453—Query optimisation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/22—Indexing; Data structures therefor; Storage structures
- G06F16/2219—Large Object storage; Management thereof
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
- G06F16/282—Hierarchical databases, e.g. IMS, LDAP data stores or Lotus Notes
Abstract
The invention discloses a method and device for processing big data. The method includes the steps that in the external environment of a hadoop distributed system infrastructure, a preset division strategy is used for dividing a hadoop total query task; hadoop internal query is conducted respectively in a hadoop distributed storage system according to the divided hadoop query task; all internal query results are stored to the hadoop external environment according to a preset storage strategy; statistics are made on storage information, and the storage information is displayed in a hadoop external query mode. The invention further discloses the device corresponding to the method. According to the method and device for processing the big data, the total query task is divided in the hadoop external environment, after the division query task is executed, the internal query results are correspondingly stored in a hierarchical mode according to the task division, paging browse of hadoop big data access is achieved, and data statistics are made on the premise that system performance is not affected.
Description
Technical field
The application relates to large data processing field, espespecially a kind of method and device of the large data processing of realization based on distributed system architecture hadoop.
Background technology
The development of digital living, has produced the mass data that volume increases sharply.The distributed system architecture for large data acquisition (hadoop) of current many industries is processed, to realize the integration of data and to share.Although hadoop has very strong extensibility, because also making the application of hadoop, its high performance design is restricted.The online discharge record inquiry of take is example, to being stored in the large data of hadoop while carrying out data query, because only supporting the stream-oriented file of distributed file system (HDFS), reads hadoop, for Query Result, can only carry out from start to end order reads, this can realize Query Result had to larger gap across page browsing with when the external environment condition of hadoop is carried out data query.When hadoop makes number statistics to surfing flow record in addition, due to the stream-oriented file restriction of HDFS, Statistical Speed is very slow and increased the burden of system.In addition, cross high-frequency inquiry, also increased system overhead.
From above analyzing examples, at present by hadoop internal environment, in hadoop system architecture, when large data are inquired about, HDFS under hadoop internal environment only supports streaming mode file reading, be application program in HDFS, the mode of file reading is for from beginning order to ending at every turn, this makes the efficiency step-down in the time need to reading inquiry file medium content.In addition, if carry out data query statistics, speed is very slow and increased system burden.If enquiry frequency is too high, can increase system overhead again.In a word, the disposal route of the HDFS of hadoop to data query and demonstration, has affected the use of user to data access at present, and the method simultaneously adopting has at present affected the serviceability of hadoop.
Summary of the invention
In order to address the above problem, the invention provides the method for the large data processing of a kind of hadoop, can realize large data query result is shown across page browsing, and improve query statistic efficiency not affecting under large data framework system performance.
In order to reach object of the present invention, the application provides a kind of method that realizes large data processing, comprising:
In distributed system architecture hadoop external environment condition, utilize the fractionation strategy setting in advance to split the total query task of hadoop;
According to the hadoop query task splitting, in the distributed memory system of hadoop, carry out respectively the inner inquiry of hadoop;
Each inner Query Result is stored in to hadoop external environment condition according to the storage policy setting in advance;
By hadoop external inquiry mode, storage information is added up and shown.
Further, split strategy and comprise: distribute according to the time and/or, inquiring user flow type of service is inquired about fractionation.
The method of adding up further, comprises:
Every one page that each is split to the inside Query Result of task carries out query statistic, and the query statistic information of the inner Query Result of the every one page of each fractionation task is merged, and obtains the statistical information of corresponding fractionation task;
The statistical information of corresponding fractionation task is merged, obtain the statistics of total query task.
Further, according to storage policy, being stored in hadoop external environment condition comprises:
Inner Query Result is divided into according to storage policy: normality data and abnormal data;
According to normality data and abnormal data, carry out classification storage,
When inner Query Result is normality data, when normality data volume is greater than the memory size of hadoop external environment condition, according to memory size, first, the query page that ever accessed is crossed is kept in internal memory, partly deposits inner Query Result in internal memory; Residue normality data deposit in the hard disk of hadoop external environment condition; When normality data volume is less than the memory size of hadoop external environment condition, all inner Query Results are stored in internal memory;
When inner Query Result is abnormal data, inner Query Result is all stored in the hard disk of external environment condition.
Further, the method also comprises: according to enquiry frequency, set the deletion cycle to delete the normality data in inner Query Result.
On the other hand, the invention provides a kind of device of realizing large data processing, be arranged at hadoop external environment condition, comprising: control module, task split cells, classification storage unit, statistics display unit; Wherein,
Control module, for when receiving query task, control task split cells carries out task fractionation; Controlling classification storage unit stores inner Query Result; Controlling statistics display unit adds up and shows the inside Query Result of storage;
Task split cells, splits the total query task of hadoop for the fractionation strategy setting in advance, and mails to hadoop inside and carries out data query;
Classification storage unit, receives the inner Query Result of hadoop for arranging, and according to storage policy, is stored in hadoop external environment condition;
Statistics display unit, for being added up and show classification storage information by query statement.
Further, fractionation strategy comprise: distribute according to the time and/or, inquiring user flow type of service is inquired about fractionation.
Further, add up and comprise:
Every one page that each is split to the inside Query Result of task carries out query statistic, and the every one page query statistic of each fractionation task information is merged, and obtains the statistical information of corresponding fractionation task;
The statistical information of corresponding fractionation task is merged, obtain the statistics of total query task.
Further, according to storage policy, being stored in hadoop external environment condition comprises:
Inner Query Result is divided into according to storage policy: normality data and abnormal data;
According to normality data and abnormal data, carry out classification storage,
When inner Query Result is normality data, when normality data volume is greater than the memory size of hadoop external environment condition, according to memory size, first, the query page that ever accessed is crossed is kept in internal memory, partly deposits inner Query Result in internal memory; Residue normality data deposit in the hard disk of hadoop external environment condition; When normality data volume is less than the memory size of hadoop external environment condition, all inner Query Results are stored in internal memory;
When inner Query Result is abnormal data, inner Query Result is all stored in the hard disk of external environment condition.
Further, this device also comprises delete cells, for according to enquiry frequency, is set to delete the normality data in inner Query Result the deletion cycle.
The present invention proposes a kind of method that realizes large data processing, comprising: in distributed system architecture hadoop external environment condition, utilize the fractionation strategy setting in advance to split the total query task of hadoop; According to the hadoop query task splitting, in the distributed memory system of hadoop, carry out respectively the inner inquiry of hadoop; Each inner Query Result is stored in to hadoop external environment condition according to the storage policy setting in advance; By hadoop external inquiry mode, storage information is added up and shown.By the inventive method, in hadoop external environment condition, total query task is split, after executing fractionation query task, inner Query Result is split and to carry out corresponding classification storage according to task, realize the Tabbed browsing of the large data access of hadoop and under system performance, carry out data statistics not affecting.
Accompanying drawing explanation
Accompanying drawing is used to provide the further understanding to present techniques scheme, and forms a part for instructions, is used from the application's embodiment mono-technical scheme of explaining the application, does not form the restriction to present techniques scheme.
Fig. 1 is the process flow diagram that the present invention realizes the method for large data processing;
Fig. 2 is the structured flowchart that the present invention realizes the device of large data processing.
Embodiment
For making the application's object, technical scheme and advantage clearer, hereinafter in connection with accompanying drawing, the application's embodiment is elaborated.It should be noted that, in the situation that not conflicting, the embodiment in the application and the feature in embodiment be combination in any mutually.
Fig. 1 is the process flow diagram that the present invention realizes the method for large data processing, and as shown in Figure 1, the large data processing of take based on hadoop is example, comprising:
In this step, split strategy and comprise: distribute according to the time and/or, inquiring user flow type of service is inquired about fractionation.
The total query task of the hadoop of take is example as inquiring about customer traffic, when carrying out query task fractionation, suppose to inquire about fractionation by inquiring user flow type of service, by the existing statistics that customer flow is used, distinguish: suppose to divide into: flow intelligent, the flow golden mean of the Confucian school and flow are reticent.Can inquire about fractionation according to the inquiring user flow type of service of above-mentioned differentiation, the flow usage ratio of supposing above three kinds of clients is 9:3:1, three kinds of clients are when carrying out customer traffic inquiry, and its data on flows and flow usage ratio are proportional.Therefore, in the data of hadoop storage, flow intelligent's data are maximum, and the amount of user data of the flow golden mean of the Confucian school is placed in the middle, and the amount of user data of flow silence is minimum.When data volume is many, the query task after fractionation is more.Suppose that the thread resources that the task of each fractionation takies is identical, in order to keep the efficient of data query, the query task after the many shared fractionation of user of data volume is many; And the few user data of data volume, splitting of task is relatively few.If distributed and carried out task fractionation by inquiring user flow type of service binding time, the number of tasks splitting according to different types of client is with identical with customer flow type of service fractionation number.Distribute by the time of using in conjunction with flow, the time period of using in flow set, owing to producing more data on flows, therefore carry out query task while splitting, the time period that data on flows is concentrated, the corresponding number of tasks splitting is more; And flow is used the unconcentrated time period, the number of tasks that query task splits is less.Suppose the query task of flow intelligent in total query task to be split as 9, and 8 days beginning of the month and the 8 days the end of month are flow use time of concentration, the middle of the month, flow was used less, while carrying out task fractionation, the data query at 8 days beginning of the month and the 8 days the end of month can respectively be split as 3 query tasks, and 14 days middle of the month (supposing one month 30 days) although the time is longer, because data volume is little, also only split into 3 query tasks.When the present invention splits Strategy Design; can carry out with reference to the statistics of prior art; after query task statistics of the present invention; the data that also can obtain according to self are adjusted accordingly; it splits strategy, and not one deck is constant; be more than for clearer explanation the present invention for example, the protection content being not meant to limit the present invention.By query task is split as to the query task number being directly proportional to ratio data relation according to query strategy, for each query task distributes corresponding thread resources, realize reasonable that total query task splits, effectively improve the work efficiency of query task.
It should be noted that, the inside inquiry here refers to the inquiry of carrying out in the distributed memory system of hadoop.
In this step, according to storage policy, be stored in hadoop external environment condition and comprise:
Inner Query Result is divided into according to storage policy: normality data and abnormal data;
According to normality data and abnormal data, carry out classification storage,
When inner Query Result is normality data, when normality data volume is greater than the memory size of hadoop external environment condition, according to memory size, first, the query page that ever accessed is crossed is kept in internal memory, partly deposits inner Query Result in internal memory; Residue normality data deposit in the hard disk of hadoop external environment condition; When normality data volume is less than the memory size of hadoop external environment condition, all inner Query Results are stored in internal memory.It should be noted that, be kept at the duty of the data based internal memory in internal memory, generally with 80% left and right of hardware memory size, make the max cap. of storage normality data.
When inner Query Result is abnormal data, inner Query Result is all stored in the hard disk of external environment condition.
It should be noted that, normality data are the user of regular complaint in business system, and in system, have had the data of inquiry overwriting; Abnormal data are the user's that often do not complain in business system data.And the query page that ever accessed is crossed, refer to the carrying out of recording in the inquiry log of system the content of inquiry, this part can obtain by system journal of the prior art.This part is the content that prior art exists, and does not repeat them here.
Because the external environment condition of storage, so its inner Query Result, by identical with the query display of prior art hadoop external environment condition, embodies with the form of multipage.
Every one page that each is split to the inside Query Result of task carries out query statistic, and the query statistic information of the inner Query Result of the every one page of each fractionation task is merged, and obtains the statistical information of corresponding fractionation task;
The statistical information of corresponding fractionation task is merged, obtain the statistics of total query task.
The inventive method also comprises: according to enquiry frequency, set the deletion cycle to delete the normality data in inner Query Result.
It should be noted that, the normality data in the inside Query Result in internal memory and hard disk can be deleted with certain deletion cycle, and general data in EMS memory be take 7 days as a deletion cycle.And data in hard disk be take 15 days as the deletion cycle.Certainly according to actual conditions, enquiry frequency is higher, illustrate that to need the cycle of query statistic information updating shorter, so its deletion cycle may be shorter.
Fig. 2 is the large data processing equipment structured flowchart of a kind of hadoop of the present invention, as shown in Figure 2, the device of the large data processing of a kind of distributed system architecture hadoop, be arranged at hadoop external environment condition, comprise: control module, task split cells, classification storage unit, statistics display unit; Wherein,
Control module, for when receiving query task, control task split cells carries out task fractionation; Controlling classification storage unit stores inner Query Result; Controlling statistics display unit adds up and shows the inside Query Result of storage;
Task split cells, splits the total query task of hadoop for the fractionation strategy setting in advance, and mails to hadoop inside and carries out data query.Fractionation strategy comprises: distribute according to the time and/or, inquiring user flow type of service is inquired about fractionation.
Classification storage unit, receives the inner Query Result of hadoop for arranging, and according to storage policy, is stored in hadoop external environment condition.
Further, according to storage policy, being stored in hadoop external environment condition comprises:
Inner Query Result is divided into according to storage policy: normality data and abnormal data;
According to normality data and abnormal data, carry out classification storage,
When inner Query Result is normality data, when normality data volume is greater than the memory size of hadoop external environment condition, according to memory size, first, the query page that ever accessed is crossed is kept in internal memory, partly deposits inner Query Result in internal memory; Residue normality data deposit in the hard disk of hadoop external environment condition; When normality data volume is less than the memory size of hadoop external environment condition, all inner Query Results are stored in internal memory;
When inner Query Result is abnormal data, inner Query Result is all stored in the hard disk of external environment condition.
Statistics display unit, for being added up and show classification storage information by query statement.
Further, statistics comprises: every one page that each is split to the inside Query Result of task carries out query statistic, and the every one page query statistic of each fractionation task information is merged, and obtains the statistical information of corresponding fractionation task;
The statistical information of corresponding fractionation task is merged, obtain the statistics of total query task.
Apparatus of the present invention also comprise delete cells, for according to enquiry frequency, are set to delete the normality data in inner Query Result the deletion cycle.
Although the disclosed embodiment of the application as above, the embodiment that described content only adopts for ease of understanding the application, not in order to limit the application.Those of skill in the art under any the application; do not departing under the prerequisite of the disclosed spirit and scope of the application; can in the form of implementing and details, carry out any modification and variation; but the application's scope of patent protection, still must be as the criterion with the scope that appending claims was defined.
Claims (10)
1. a method that realizes large data processing, is characterized in that, comprising:
In distributed system architecture hadoop external environment condition, utilize the fractionation strategy setting in advance to split the total query task of hadoop;
According to the hadoop query task splitting, in the distributed memory system of hadoop, carry out respectively the inner inquiry of hadoop;
Each inner Query Result is stored in to hadoop external environment condition according to the storage policy setting in advance;
By hadoop external inquiry mode, storage information is added up and shown.
2. method according to claim 1, is characterized in that, described fractionation strategy comprises: distribute according to the time and/or, inquiring user flow type of service is inquired about fractionation.
3. method according to claim 1, is characterized in that, described in the method for adding up comprise:
Every one page that each is split to the inside Query Result of task carries out query statistic, and the query statistic information of the inner Query Result of the every one page of each fractionation task is merged, and obtains the statistical information of corresponding fractionation task;
The statistical information of corresponding fractionation task is merged, obtain the statistics of total query task.
4. method according to claim 1, is characterized in that, describedly according to storage policy, is stored in hadoop external environment condition and comprises:
Inner Query Result is divided into according to storage policy: normality data and abnormal data;
According to normality data and abnormal data, carry out classification storage,
When inner Query Result is normality data, when normality data volume is greater than the memory size of hadoop external environment condition, according to memory size, first, the query page that ever accessed is crossed is kept in internal memory, partly deposits inner Query Result in internal memory; Residue normality data deposit in the hard disk of hadoop external environment condition; When normality data volume is less than the memory size of hadoop external environment condition, all inner Query Results are stored in internal memory;
When inner Query Result is abnormal data, inner Query Result is all stored in the hard disk of external environment condition.
5. method according to claim 4, is characterized in that, the method also comprises: according to enquiry frequency, set the deletion cycle to delete the normality data in inner Query Result.
6. a device of realizing large data processing, is characterized in that, is arranged at hadoop external environment condition, comprising: control module, task split cells, classification storage unit, statistics display unit; Wherein,
Control module, for when receiving query task, control task split cells carries out task fractionation; Controlling classification storage unit stores inner Query Result; Controlling statistics display unit adds up and shows the inside Query Result of storage;
Task split cells, splits the total query task of hadoop for the fractionation strategy setting in advance, and mails to hadoop inside and carries out data query;
Classification storage unit, receives the inner Query Result of hadoop for arranging, and according to storage policy, is stored in hadoop external environment condition;
Statistics display unit, for being added up and show classification storage information by query statement.
7. device according to claim 6, is characterized in that, described fractionation strategy comprises: distribute according to the time and/or, inquiring user flow type of service is inquired about fractionation.
8. device according to claim 6, is characterized in that, described in add up and comprise:
Every one page that each is split to the inside Query Result of task carries out query statistic, and the every one page query statistic of each fractionation task information is merged, and obtains the statistical information of corresponding fractionation task;
The statistical information of corresponding fractionation task is merged, obtain the statistics of total query task.
9. device according to claim 6, is characterized in that, describedly according to storage policy, is stored in hadoop external environment condition and comprises:
Inner Query Result is divided into according to storage policy: normality data and abnormal data;
According to normality data and abnormal data, carry out classification storage,
When inner Query Result is normality data, when normality data volume is greater than the memory size of hadoop external environment condition, according to memory size, first, the query page that ever accessed is crossed is kept in internal memory, partly deposits inner Query Result in internal memory; Residue normality data deposit in the hard disk of hadoop external environment condition; When normality data volume is less than the memory size of hadoop external environment condition, all inner Query Results are stored in internal memory;
When inner Query Result is abnormal data, inner Query Result is all stored in the hard disk of external environment condition.
10. device according to claim 9, is characterized in that, this device also comprises delete cells, for according to enquiry frequency, is set to delete the normality data in inner Query Result the deletion cycle.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310646544.0A CN103617268B (en) | 2013-12-04 | 2013-12-04 | Method and device for processing big data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310646544.0A CN103617268B (en) | 2013-12-04 | 2013-12-04 | Method and device for processing big data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN103617268A true CN103617268A (en) | 2014-03-05 |
| CN103617268B CN103617268B (en) | 2017-01-18 |
Family
ID=50167971
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201310646544.0A Active CN103617268B (en) | 2013-12-04 | 2013-12-04 | Method and device for processing big data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103617268B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105677650A (en) * | 2014-11-18 | 2016-06-15 | 中国移动通信集团公司 | Cache data storage method and device |
| WO2019000962A1 (en) * | 2017-06-26 | 2019-01-03 | 平安科技（深圳）有限公司 | Revenue calculation method and device, and computer readable storage medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1588358A (en) * | 2004-08-26 | 2005-03-02 | 陈红 | Treating method and system for MDX multidimensional data search statement |
| CN102779183A (en) * | 2012-07-02 | 2012-11-14 | 华为技术有限公司 | Data inquiry method, equipment and system |
| CN102799622A (en) * | 2012-06-19 | 2012-11-28 | 北京大学 | Distributed structured query language (SQL) query method based on MapReduce expansion framework |
| CN103235835A (en) * | 2013-05-22 | 2013-08-07 | 曙光信息产业(北京)有限公司 | Inquiry implementation method for database cluster and device |
-
2013
- 2013-12-04 CN CN201310646544.0A patent/CN103617268B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1588358A (en) * | 2004-08-26 | 2005-03-02 | 陈红 | Treating method and system for MDX multidimensional data search statement |
| CN102799622A (en) * | 2012-06-19 | 2012-11-28 | 北京大学 | Distributed structured query language (SQL) query method based on MapReduce expansion framework |
| CN102779183A (en) * | 2012-07-02 | 2012-11-14 | 华为技术有限公司 | Data inquiry method, equipment and system |
| CN103235835A (en) * | 2013-05-22 | 2013-08-07 | 曙光信息产业(北京)有限公司 | Inquiry implementation method for database cluster and device |
Non-Patent Citations (3)
| Title |
|---|
| 程莹 等: "基于Hadoop及关系型数据库的海量数据分析研究", 《电信科学》 * |
| 苏翔宇 等: "Hadoop整合Cassandra处理海量数据", 《电脑知识与技术》 * |
| 覃雄派 等: "大数据分析——RDBMS与MapReduce的竞争与共生", 《软件学报》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105677650A (en) * | 2014-11-18 | 2016-06-15 | 中国移动通信集团公司 | Cache data storage method and device |
| CN105677650B (en) * | 2014-11-18 | 2019-06-25 | 中国移动通信集团公司 | A kind of data cached storage method and device |
| WO2019000962A1 (en) * | 2017-06-26 | 2019-01-03 | 平安科技（深圳）有限公司 | Revenue calculation method and device, and computer readable storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN103617268B (en) | 2017-01-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10229141B2 (en) | Parallel load in a column-store database | |
| US8943107B2 (en) | Generating and using temporal metadata partitions | |
| KR101994021B1 (en) | File manipulation method and apparatus | |
| CN109063196B (en) | Data processing method and device, electronic equipment and computer readable storage medium | |
| CN103646073A (en) | Condition query optimizing method based on HBase table | |
| CN103885887B (en) | User data storage method, read method and system | |
| CN103167026A (en) | Processing method, system and device for cloud storage environmental data | |
| US20150112934A1 (en) | Parallel scanners for log based replication | |
| CN109190025B (en) | Information monitoring method, device, system and computer readable storage medium | |
| CN105933736A (en) | Log processing method and device | |
| CN102148870A (en) | Cloud storage system and implementation method thereof | |
| CN109766206A (en) | A kind of log collection method and system | |
| CN104572505A (en) | System and method for ensuring eventual consistency of mass data caches | |
| CN104717247A (en) | Method and system for dynamically scheduling storage resources in cloud storage system | |
| DE202023101653U1 (en) | Cross-organization and cross-cloud automated data pipelines | |
| CN104021088A (en) | Log storage method and device | |
| CN103617268A (en) | Method and device for processing big data | |
| CN103645873A (en) | Method for achieving high-efficiency data caching in trend curve system | |
| CN107329832B (en) | Data receiving method and device | |
| CN103327112A (en) | Desktop synchronization method based on cloud storage and device for realizing desktop synchronization | |
| CN102780620B (en) | A kind of network processes device and message processing method | |
| CN106354724A (en) | File archiving method and file reading method and device | |
| CN112035207A (en) | Page menu dynamic loading method and device based on database | |
| KR101530441B1 (en) | Method and apparatus for processing data based on column | |
| CN110019497B (en) | Data reading method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant |