CN113282618A - Optimization scheme and system for retrieval of active clusters of Elasticissearch - Google Patents
Optimization scheme and system for retrieval of active clusters of Elasticissearch Download PDFInfo
- Publication number
- CN113282618A CN113282618A CN202110675617.3A CN202110675617A CN113282618A CN 113282618 A CN113282618 A CN 113282618A CN 202110675617 A CN202110675617 A CN 202110675617A CN 113282618 A CN113282618 A CN 113282618A
- Authority
- CN
- China
- Prior art keywords
- index
- data
- active cluster
- cold
- active
- 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.)
- Pending
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
- G06F16/21—Design, administration or maintenance of databases
- G06F16/215—Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
-
- 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
- G06F16/2228—Indexing structures
-
- 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
- G06F16/27—Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Databases & Information Systems (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Computational Linguistics (AREA)
- Quality & Reliability (AREA)
- Software Systems (AREA)
- Computing Systems (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The invention provides an optimization scheme for searching an Elasticissearch active cluster, which comprises the following steps: step S1, developing a timing operation, defining a maximum ID variable, reading the newly added data of the payment order table every minute, and storing the data to the elastic search active cluster; step S2, after the data synchronization is completed, the rolling index is adopted to store the synchronized elastic search active cluster; step S3, mapping key words on records by adopting an inverted index in the active cluster of the elastic search, wherein each key word corresponds to a series of record files; step S4, an index is composed of a plurality of fragments, and the fragments are distributed on a plurality of service nodes of the elastic search active cluster; step S5, moving the elastic search active cluster indexes exceeding the preset time to the newly designed cold index; step S6, the cold index only reserves the payment order number field as the type of the keyword; step S7, only one slice is set for the slice configuration of the cold index; the invention can effectively improve the resource utilization rate of the Elasticissearch active cluster.
Description
Technical Field
The invention relates to the technical field of computer payment systems, in particular to an elastic search active cluster retrieval optimization scheme and system.
Background
The Elasticissearch is a distributed, highly-extended and highly-real-time search and data analysis engine which can conveniently enable a large amount of data to have the capabilities of searching, analyzing and exploring. This also makes ES widely used in log collection, data analysis, data search. The ES is different from the forward index design of the traditional relational database, and adopts reverse index, namely, key words are mapped to records, each key word corresponds to a series of record files, and the key words appear in the records. ES is a time-over-space scheme, which generates several times of storage files, so ES consumes more storage space and generates higher server cost. This requires rational index design, slice design, and field design of the ES, and misuse of the ES can result in quickly filling up resources with limited storage capacity, resulting in functional failure.
Disclosure of Invention
In order to overcome the problems, the invention aims to provide an Elasticissearch active cluster retrieval optimization scheme capable of effectively improving the resource utilization rate of the Elasticissearch active cluster.
The invention is realized by adopting the following scheme: an Elasticissearch active cluster retrieval optimization scheme, comprising the steps of:
step S1, developing a timing operation, defining a maximum ID variable, reading the newly added data of the payment order table every minute, storing the data to the elastic search active cluster, and realizing data synchronization;
step S2, after the data synchronization is completed, the rolling index is adopted to store the synchronized elastic search active cluster;
step S3, mapping key words on records by adopting an inverted index in the active cluster of the elastic search, wherein each key word corresponds to a series of record files;
step S4, an index is composed of a plurality of fragments, and the fragments are distributed on a plurality of service nodes of the elastic search active cluster;
step S5, moving the active cluster indexes of the elasticsearch exceeding the preset time, moving the active cluster indexes into a newly designed cold index, and deleting the active cluster data of the elasticsearch exceeding the preset time from the hot index;
step S6, the cold index only keeps the payment order number field as the type of the keyword, and the other fields are all set as index false;
and step S7, only one slice is set for the slice configuration of the cold index, so that the search requirement is reduced.
Further, the step S1 is further specifically: developing a timing operation, defining a maximum ID variable, wherein maxID is 0, reading new data with payment order table ID > maxID every minute, storing the latest ID to maxID, and storing the read latest data to the elasticsearch active cluster.
Further, the step S2 is further specifically: after data synchronization is completed, the elastic search active cluster is stored by adopting a rolling index, so that the condition that the storage capacity of a single index is overlarge is avoided.
Further, the step S3 is further specifically: the elastic search active cluster adopts inverted index, namely, key words are mapped to records, each key word corresponds to a series of record files, and the key words appear in the records.
Further, the step S4 is further specifically: an index is composed of a plurality of fragments, and the fragments are distributed on a plurality of service nodes of the elastic search active cluster, so that the distributed search capability is provided.
Further, the step S5 is further specifically: separating historical payment order data to provide more storage resources for hot data, developing another timing operation, executing once a month, moving the active cluster indexes of the elastic search exceeding the preset time, moving the active cluster indexes into a newly designed cold index, and deleting the active cluster indexes from the hot index; the division threshold of the cold and hot data is divided by a time dimension, and the data exceeding a preset time is called cold data.
Further, the step S6 is further specifically: the index of the cold data only keeps the order number field as a keyword type, and the rest fields are all set as index false, so that the cold data only provides the search function of the order number, and other fields only store and do not provide the search function, thereby maximally saving the storage space of the cold data.
The invention also comprises an Elasticissearch active cluster retrieval optimization system, which comprises a data synchronization module, a rolling index module, an index field module, an index fragment configuration module, a cold data separation module, a cold index design module and a cold index distribution configuration module; the data synchronization module is used for developing a timing operation, defining a maximum ID variable, reading newly added data of a payment order table every minute, and storing the data to an elastic search active cluster to realize data synchronization; the rolling index module is used for synchronizing the data to the elastic search active cluster and storing the data by adopting a rolling index after the data synchronization is finished; the index field module, namely the elastic search active cluster, adopts inverted indexes to map key words on records, and each key word corresponds to a series of record files; the index fragment configuration module is used for configuring the index fragment, namely, one index is composed of a plurality of fragments, and the fragments are distributed on a plurality of service nodes of the elastic search active cluster; the cold data separation module moves the active cluster indexes of the elasticsearch exceeding the preset time into the newly designed cold index by developing another timing operation, and deletes the active cluster data of the elasticsearch exceeding the preset time from the hot index; the cold index design module only keeps the payment order number field as the type of the keyword, and the other fields are all set as index false; the cold index allocation configuration module is used for configuring the cold index into one fragment, so that the search requirement is reduced.
Further, the data synchronization module is further specifically: developing a timing operation, defining a maximum ID variable, wherein maxID is 0, reading new data with payment order table ID > maxID every minute, storing the latest ID to maxID, and storing the read latest data to the elasticsearch active cluster.
Further, the scroll index module is further specifically configured to: after data synchronization is completed, the elastic search active cluster is stored by adopting a rolling index, so that the condition that the storage capacity of a single index is overlarge is avoided.
Further, the index field module is further specifically: the elastic search active cluster adopts inverted index, namely, key words are mapped to records, each key word corresponds to a series of record files, and the key words appear in the records.
Further, the index shard configuration module is further specifically: an index is composed of a plurality of fragments, and the fragments are distributed on a plurality of service nodes of the elastic search active cluster, so that the distributed search capability is provided.
Further, the cold data separation module is further specifically: separating historical payment order data to provide more storage resources for hot data, developing another timing operation, executing once a month, moving the active cluster indexes of the elastic search exceeding the preset time, moving the active cluster indexes into a newly designed cold index, and deleting the active cluster indexes from the hot index; the division threshold of the cold and hot data is divided by a time dimension, and the data exceeding a preset time is called cold data.
Further, the cold index design module is further specifically: the index of the cold data only keeps the order number field as a keyword type, and the rest fields are all set as index false, so that the cold data only provides the search function of the order number, and other fields only store and do not provide the search function, thereby maximally saving the storage space of the cold data.
The invention has the beneficial effects that: the invention can reasonably plan the index design, the fragment design and the field design of the Elasticissearch active cluster, avoid occupying the resources too quickly under the limited storage capacity caused by abusing the Elasticissearch active cluster, waste the server resources and effectively improve the resource utilization rate of the Elasticissearch active cluster.
Drawings
FIG. 1 is a schematic flow diagram of the process of the present invention.
Fig. 2 is a schematic block diagram of the system of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
Referring to fig. 1, an elastic search active cluster retrieval optimization scheme of the present invention includes the following steps:
step S1, developing a timing operation, defining a maximum ID variable, reading the newly added data of the payment order table every minute, storing the data to the elastic search active cluster, and realizing data synchronization;
step S2, after the data synchronization is completed, the rolling index is adopted to store the synchronized elastic search active cluster;
step S3, mapping key words on records by adopting an inverted index in the active cluster of the elastic search, wherein each key word corresponds to a series of record files;
step S4, an index is composed of a plurality of fragments, and the fragments are distributed on a plurality of service nodes of the elastic search active cluster;
step S5, moving the active cluster indexes of the elasticsearch exceeding the preset time, moving the active cluster indexes into a newly designed cold index, and deleting the active cluster data of the elasticsearch exceeding the preset time from the hot index;
step S6, the cold index only keeps the payment order number field as the type of the keyword, and the other fields are all set as index false;
and step S7, only one slice is set for the slice configuration of the cold index, so that the search requirement is reduced.
The invention is further illustrated by the following example:
hereinafter, an exemplary optimization scheme for jumping cluster retrieval of an elastic search of a payment order system is described, where the elastic search is hereinafter referred to as ES in short.
Step 1, data synchronization
Developing a timing operation, defining a maximum ID variable, wherein maximum is 0, reading newly added data of the order table ID > maximum every minute, and storing the latest ID to the maximum. The latest data read is stored to the ES.
Step 2, rolling index design
After the data synchronization operation in step 1 is completed, if the data synchronized to the ES are all stored in one index, the search efficiency is affected if the single index is too large, so in order to prevent this problem, a rolling index is used. The rolling index is to cut the index by using a certain condition, and the case is divided by using the time month, so the generated index names are order _ index _202101, order _ index _202102, order _ index _202103 and order _ index _202104.
Step 3, designing index field
The ES is different from the forward index design of the traditional relational database, and adopts reverse index, namely, key words are mapped to records, each key word corresponds to a series of record files, and the key words appear in the records. ES is a time-over-space scheme, which generates several times of storage files, so ES consumes more storage space and generates higher server cost. And reasonably setting an index structure field, and not abusing word segmentation, such as an order number and a request IP field, only needing complete matching and not needing word segmentation query, so that the index structure field is only set to be a field keyword type and not to be a field text type, word segmentation index is carried out on the field to be a text type ES, and word segmentation is not carried out on the field to be the text type ES. In addition, fields which do not need to be queried, such as remarks and the like, can be set as index: false, so that extra index files cannot be generated only by storing and not indexing, and the storage space is saved.
Step 4, index fragment configuration
An index is composed of a plurality of shards that are distributed across a plurality of service nodes of an ES cluster to provide the capability for distributed searching. For example, an ES cluster is composed of 3 server nodes, the number of indexed shards is set to 3, and 3 shards are uniformly distributed on the 3 server nodes, so as to provide maximum distributed search capability. In addition, in order to ensure high availability of the query and prevent single point failure, we need to set copies of the fragments, namely 1, so that each fragment generates one copy, and although more copies contribute to providing higher retrieval capability under concurrent search, twice the stored data is generated correspondingly.
Step 5, cold data separation
With the increase of service in time, the limited storage capacity of the server will be occupied continuously, and the frequent expansion of the storage capacity of the server will result in higher operation and maintenance costs. There is a need to segregate historical order data to provide more storage resources for hot data. Separating cold data is not deleting discarded cold data, but cold data still needs to be searched, except that the search frequency and search requirements are not so high. And developing a timing operation, executing once every month, moving the ES indexes exceeding the preset time, moving the ES indexes into the newly designed cold indexes, and deleting the ES indexes from the hot indexes. The segmentation threshold of the cold and hot data is divided by a time dimension, the data exceeding the preset time are all regarded as the cold data, and the judgment basis is determined by a specific service scene. The preset time may be between one and two years.
Step 6, designing cold index
Since the index is a cold index, the index is redesigned, only the order number field is reserved as a keyword type, and the rest fields are all set as index: false, so that the cold data only provides a search function of the order number, and other fields only store and do not provide the search function, thereby maximally saving the storage space of the cold data.
Step 7, cold index slicing configuration
Since the index is a cold index, the slice configuration of the index is optimized, the search frequency of cold data is low, the requirement of fast search can be reduced, only 1 slice is arranged, and although the advantage of distributed search is not provided, the capacity of 2/3 is saved. Cold data also need not provide high availability, eliminating copies of data, further reducing storage capacity.
In summary, based on the pain points, the invention discloses an elastic search active cluster retrieval optimization scheme and system: 1. data synchronization, which synchronizes data to ES by using timing operation; 2. the design of rolling index, in order to prevent the single index from being too big, utilize time to carry on the rolling index; 3. the index field is designed, the index structure field is reasonably set, and word segmentation is not abused, so that the storage space is saved; 4. index fragment configuration, namely setting the number of index fragments according to the number of nodes of a cluster, and additionally setting 1 copy to ensure high availability of data; 5. separating cold data at regular time; 6. designing a cold index, wherein the cold index only supports a unique key query field; 7. the cold index is configured in a slicing mode, only 1 main slice and 0 copy are reserved, and therefore cold data storage space is saved.
Referring to fig. 2, the present invention further includes an elastic search active cluster retrieval optimization system, which includes a data synchronization module, a rolling index module, an index field module, an index fragment configuration module, a cold data separation module, a cold index design module, and a cold index allocation configuration module; the data synchronization module is used for developing a timing operation, defining a maximum ID variable, reading newly added data of a payment order table every minute, and storing the data to an elastic search active cluster to realize data synchronization; the rolling index module is used for synchronizing the data to the elastic search active cluster and storing the data by adopting a rolling index after the data synchronization is finished; the index field module, namely the elastic search active cluster, adopts inverted indexes to map key words on records, and each key word corresponds to a series of record files; the index fragment configuration module is used for configuring the index fragment, namely, one index is composed of a plurality of fragments, and the fragments are distributed on a plurality of service nodes of the elastic search active cluster; the cold data separation module moves the active cluster indexes of the elasticsearch exceeding the preset time into the newly designed cold index by developing another timing operation, and deletes the active cluster data of the elasticsearch exceeding the preset time from the hot index; the cold index design module only keeps the payment order number field as the type of the keyword, and the other fields are all set as index false; the cold index allocation configuration module is used for configuring the cold index into one fragment, so that the search requirement is reduced.
The data synchronization module is further specifically: developing a timing operation, defining a maximum ID variable, wherein maxID is 0, reading new data with payment order table ID > maxID every minute, storing the latest ID to maxID, and storing the read latest data to the elasticsearch active cluster.
The rolling index module is further specifically: after data synchronization is completed, the elastic search active cluster is stored by adopting a rolling index, so that the condition that the storage capacity of a single index is overlarge is avoided.
The index field module is further specifically: the elastic search active cluster adopts inverted index, namely, key words are mapped to records, each key word corresponds to a series of record files, and the key words appear in the records.
The index fragment configuration module is further specifically configured to: an index is composed of a plurality of fragments, and the fragments are distributed on a plurality of service nodes of the elastic search active cluster, so that the distributed search capability is provided.
The cold data separation module is further embodied as follows: separating historical payment order data to provide more storage resources for hot data, developing another timing operation, executing once a month, moving the active cluster indexes of the elastic search exceeding the preset time, moving the active cluster indexes into a newly designed cold index, and deleting the active cluster indexes from the hot index; the division threshold of the cold and hot data is divided by a time dimension, and the data exceeding a preset time is called cold data.
The cold index design module is further embodied as follows: the index of the cold data only keeps the order number field as a keyword type, and the rest fields are all set as index false, so that the cold data only provides the search function of the order number, and other fields only store and do not provide the search function, thereby maximally saving the storage space of the cold data.
The above description is only a preferred embodiment of the present invention, and all equivalent changes and modifications made in accordance with the claims of the present invention should be covered by the present invention.
Claims (14)
1. An optimization scheme for searching for active clusters of Elasticissearch, which is characterized by comprising the following steps:
step S1, developing a timing operation, defining a maximum ID variable, reading the newly added data of the payment order table every minute, storing the data to the elastic search active cluster, and realizing data synchronization;
step S2, after the data synchronization is completed, the rolling index is adopted to store the synchronized elastic search active cluster;
step S3, mapping key words on records by adopting an inverted index in the active cluster of the elastic search, wherein each key word corresponds to a series of record files;
step S4, an index is composed of a plurality of fragments, and the fragments are distributed on a plurality of service nodes of the elastic search active cluster;
step S5, moving the active cluster indexes of the elasticsearch exceeding the preset time, moving the active cluster indexes into a newly designed cold index, and deleting the active cluster data of the elasticsearch exceeding the preset time from the hot index;
step S6, the cold index only keeps the payment order number field as the type of the keyword, and the other fields are all set as index false;
and step S7, only one slice is set for the slice configuration of the cold index, so that the search requirement is reduced.
2. The optimization scheme for the retrieval of the active clusters of the Elasticsearch of claim 1, wherein: the step S1 further includes: developing a timing operation, defining a maximum ID variable, wherein maxID is 0, reading new data with payment order table ID > maxID every minute, storing the latest ID to maxID, and storing the read latest data to the elasticsearch active cluster.
3. The optimization scheme for the retrieval of the active clusters of the Elasticsearch of claim 1, wherein: the step S2 further includes: after data synchronization is completed, the elastic search active cluster is stored by adopting a rolling index, so that the condition that the storage capacity of a single index is overlarge is avoided.
4. The optimization scheme for the retrieval of the active clusters of the Elasticsearch of claim 1, wherein: the step S3 further includes: the elastic search active cluster adopts inverted index, namely, key words are mapped to records, each key word corresponds to a series of record files, and the key words appear in the records.
5. The optimization scheme for the retrieval of the active clusters of the Elasticsearch of claim 1, wherein: the step S4 further includes: an index is composed of a plurality of fragments, and the fragments are distributed on a plurality of service nodes of the elastic search active cluster, so that the distributed search capability is provided.
6. The optimization scheme for the retrieval of the active clusters of the Elasticsearch of claim 1, wherein: the step S5 further includes: separating historical payment order data to provide more storage resources for hot data, developing another timing operation, executing once a month, moving the active cluster indexes of the elastic search exceeding the preset time, moving the active cluster indexes into a newly designed cold index, and deleting the active cluster indexes from the hot index; the division threshold of the cold and hot data is divided by a time dimension, and the data exceeding a preset time is called cold data.
7. The optimization scheme for the retrieval of the active clusters of the Elasticsearch of claim 1, wherein: the step S6 further includes: the index of the cold data only keeps the order number field as a keyword type, and the rest fields are all set as index false, so that the cold data only provides the search function of the order number, and other fields only store and do not provide the search function, thereby maximally saving the storage space of the cold data.
8. An Elasticissearch active cluster retrieval optimization system is characterized in that: the system comprises a data synchronization module, a rolling index module, an index field module, an index fragment configuration module, a cold data separation module, a cold index design module and a cold index distribution configuration module; the data synchronization module is used for developing a timing operation, defining a maximum ID variable, reading newly added data of a payment order table every minute, and storing the data to an elastic search active cluster to realize data synchronization; the rolling index module is used for synchronizing the data to the elastic search active cluster and storing the data by adopting a rolling index after the data synchronization is finished; the index field module, namely the elastic search active cluster, adopts inverted indexes to map key words on records, and each key word corresponds to a series of record files; the index fragment configuration module is used for configuring the index fragment, namely, one index is composed of a plurality of fragments, and the fragments are distributed on a plurality of service nodes of the elastic search active cluster; the cold data separation module moves the active cluster indexes of the elasticsearch exceeding the preset time into the newly designed cold index, and deletes the active cluster indexes of the elasticsearch exceeding the preset time from the hot index; the cold index design module only keeps the payment order number field as the type of the keyword, and the other fields are all set as index false; the cold index allocation configuration module is used for configuring the cold index into one fragment, so that the search requirement is reduced.
9. The system of claim 8, wherein the optimization system for active cluster retrieval of Elasticsearch comprises: the data synchronization module is further specifically: developing a timing operation, defining a maximum ID variable, wherein maxID is 0, reading new data with payment order table ID > maxID every minute, storing the latest ID to maxID, and storing the read latest data to the elasticsearch active cluster.
10. The system of claim 8, wherein the optimization system for active cluster retrieval of Elasticsearch comprises: the rolling index module is further specifically: after data synchronization is completed, the elastic search active cluster is stored by adopting a rolling index, so that the condition that the storage capacity of a single index is overlarge is avoided.
11. The system of claim 8, wherein the optimization system for active cluster retrieval of Elasticsearch comprises: the index field module is further specifically: the elastic search active cluster adopts inverted index, namely, key words are mapped to records, each key word corresponds to a series of record files, and the key words appear in the records.
12. The system of claim 8, wherein the optimization system for active cluster retrieval of Elasticsearch comprises: the index fragment configuration module is further specifically configured to: an index is composed of a plurality of fragments, and the fragments are distributed on a plurality of service nodes of the elastic search active cluster, so that the distributed search capability is provided.
13. The system of claim 8, wherein the optimization system for active cluster retrieval of Elasticsearch comprises: the cold data separation module is further embodied as follows: separating historical payment order data to provide more storage resources for hot data, developing another timing operation, executing once a month, moving the active cluster indexes of the elastic search exceeding the preset time, moving the active cluster indexes into a newly designed cold index, and deleting the active cluster indexes from the hot index; the division threshold of the cold and hot data is divided by a time dimension, and the data exceeding a preset time is called cold data.
14. The system of claim 8, wherein the optimization system for active cluster retrieval of Elasticsearch comprises: the cold index design module is further embodied as follows: the index of the cold data only keeps the order number field as a keyword type, and the rest fields are all set as index false, so that the cold data only provides the search function of the order number, and other fields only store and do not provide the search function, thereby maximally saving the storage space of the cold data.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110675617.3A CN113282618A (en) | 2021-06-18 | 2021-06-18 | Optimization scheme and system for retrieval of active clusters of Elasticissearch |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110675617.3A CN113282618A (en) | 2021-06-18 | 2021-06-18 | Optimization scheme and system for retrieval of active clusters of Elasticissearch |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113282618A true CN113282618A (en) | 2021-08-20 |
Family
ID=77284860
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110675617.3A Pending CN113282618A (en) | 2021-06-18 | 2021-06-18 | Optimization scheme and system for retrieval of active clusters of Elasticissearch |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113282618A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116089545A (en) * | 2023-04-07 | 2023-05-09 | 云筑信息科技(成都)有限公司 | Method for collecting storage medium change data into data warehouse |
| CN116541364A (en) * | 2023-07-06 | 2023-08-04 | 中电科新型智慧城市研究院有限公司 | Log data storage method and device, terminal equipment and storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106503079A (en) * | 2016-10-10 | 2017-03-15 | 语联网（武汉）信息技术有限公司 | A kind of blog management method and system |
| US20170169044A1 (en) * | 2015-12-15 | 2017-06-15 | Le Holdings (Beijing) Co., Ltd. | Property retrieval apparatus, method and system |
| CN111460023A (en) * | 2020-04-29 | 2020-07-28 | 上海东普信息科技有限公司 | Service data processing method, device, equipment and storage medium based on elastic search |
| CN111552664A (en) * | 2020-03-24 | 2020-08-18 | 福建天泉教育科技有限公司 | Method and storage medium for intelligently scheduling cold and hot of storage system |
| CN112181987A (en) * | 2020-10-12 | 2021-01-05 | 嘉联支付有限公司 | Non-time sequence data processing method |
-
2021
- 2021-06-18 CN CN202110675617.3A patent/CN113282618A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170169044A1 (en) * | 2015-12-15 | 2017-06-15 | Le Holdings (Beijing) Co., Ltd. | Property retrieval apparatus, method and system |
| CN106503079A (en) * | 2016-10-10 | 2017-03-15 | 语联网（武汉）信息技术有限公司 | A kind of blog management method and system |
| CN111552664A (en) * | 2020-03-24 | 2020-08-18 | 福建天泉教育科技有限公司 | Method and storage medium for intelligently scheduling cold and hot of storage system |
| CN111460023A (en) * | 2020-04-29 | 2020-07-28 | 上海东普信息科技有限公司 | Service data processing method, device, equipment and storage medium based on elastic search |
| CN112181987A (en) * | 2020-10-12 | 2021-01-05 | 嘉联支付有限公司 | Non-time sequence data processing method |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116089545A (en) * | 2023-04-07 | 2023-05-09 | 云筑信息科技(成都)有限公司 | Method for collecting storage medium change data into data warehouse |
| CN116089545B (en) * | 2023-04-07 | 2023-08-22 | 云筑信息科技(成都)有限公司 | Method for collecting storage medium change data into data warehouse |
| CN116541364A (en) * | 2023-07-06 | 2023-08-04 | 中电科新型智慧城市研究院有限公司 | Log data storage method and device, terminal equipment and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Luo et al. | LSM-based storage techniques: a survey | |
| CN107423422B (en) | Spatial data distributed storage and search method and system based on grid | |
| CN103020204B (en) | A kind of method and its system carrying out multi-dimensional interval query to distributed sequence list | |
| CN100345143C (en) | Method and apparatus for data processing | |
| CN113282618A (en) | Optimization scheme and system for retrieval of active clusters of Elasticissearch | |
| CN102169507A (en) | Distributed real-time search engine | |
| CN104239377A (en) | Platform-crossing data retrieval method and device | |
| CN103678694A (en) | Method and system for establishing reverse index file of video resources | |
| CN105117502A (en) | Search method based on big data | |
| CN112650736B (en) | Data migration method and device | |
| CN105740295A (en) | Processing method and device for distributed data | |
| CN103198153A (en) | Metadata clustering management method and module applied to distributed file system | |
| CN116541427B (en) | Data query method, device, equipment and storage medium | |
| Challa et al. | DD-Rtree: A dynamic distributed data structure for efficient data distribution among cluster nodes for spatial data mining algorithms | |
| WO2023143095A1 (en) | Method and system for data query | |
| CN115114294A (en) | Self-adaption method and device of database storage mode and computer equipment | |
| Singh et al. | SWST: A disk based index for sliding window spatio-temporal data | |
| CN115114296A (en) | Index structure layout method based on template B + Tree | |
| CN115114374A (en) | Transaction execution method and device, computing equipment and storage medium | |
| CN113849499A (en) | Data query method and device, storage medium and electronic device | |
| CN104657513A (en) | File operation and rapid retrieval method in embedded system | |
| CN113672583A (en) | Big data multi-data source analysis method and system based on storage and calculation separation | |
| CN116578609A (en) | Distributed searching method and device based on inverted index | |
| Koutroumanis et al. | Scalable Spatio-temporal Indexing and Querying over a Document-oriented NoSQL Store. | |
| CN114090538A (en) | Data backtracking method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210820 |
|
| RJ01 | Rejection of invention patent application after publication |