CN116662449A - OLAP query optimization method and system based on broadcast sub-query cache - Google Patents
OLAP query optimization method and system based on broadcast sub-query cache Download PDFInfo
- Publication number
- CN116662449A CN116662449A CN202310704298.3A CN202310704298A CN116662449A CN 116662449 A CN116662449 A CN 116662449A CN 202310704298 A CN202310704298 A CN 202310704298A CN 116662449 A CN116662449 A CN 116662449A
- Authority
- CN
- China
- Prior art keywords
- query
- cache
- sub
- result
- olap
- 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
- 238000000034 method Methods 0.000 title claims abstract description 27
- 238000005457 optimization Methods 0.000 title claims abstract description 13
- 238000004364 calculation method Methods 0.000 claims description 8
- 238000007405 data analysis Methods 0.000 abstract description 7
- 230000007547 defect Effects 0.000 abstract description 3
- 238000013461 design Methods 0.000 abstract description 3
- 238000007726 management method Methods 0.000 description 5
- 238000012986 modification Methods 0.000 description 4
- 230000004048 modification Effects 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 3
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
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
- G06F16/27—Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
-
- 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
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2453—Query optimisation
- G06F16/24534—Query rewriting; Transformation
- G06F16/24539—Query rewriting; Transformation using cached or materialised query results
-
- 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
- G06F16/2455—Query execution
- G06F16/24552—Database cache management
-
- 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
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2474—Sequence data queries, e.g. querying versioned data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/54—Interprogram communication
- G06F9/542—Event management; Broadcasting; Multicasting; Notifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Databases & Information Systems (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Computational Linguistics (AREA)
- Software Systems (AREA)
- Computing Systems (AREA)
- Fuzzy Systems (AREA)
- Mathematical Physics (AREA)
- Probability & Statistics with Applications (AREA)
- Multimedia (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
- Two-Way Televisions, Distribution Of Moving Picture Or The Like (AREA)
- Memory System Of A Hierarchy Structure (AREA)
Abstract
The application discloses an OLAP query optimization method and system based on broadcast sub-query caching. In a time sequence type data analysis scene, the existing OLAP system query result caching scheme has very low cache hit rate, and the application provides a new, finer granularity and flexible caching scheme, namely, local results of sub-query operators are cached, so that the design defect of query result caching is avoided, and the method can be directly applied to a distributed big data analysis system; the application can be directly applied in the cluster environment through a cache broadcasting mechanism, so that the cache of sub-queries can be used at all nodes, the overall cache hit rate is improved, and the query performance of the OLAP is accelerated by fully utilizing the distributed cluster capability.
Description
Technical Field
The application relates to the field of cloud network observable data analysis, in particular to a method and a system for accelerating OLAP query performance.
Background
OLAP (Online Analytical Processing ) is an online analytical processing system, and OLAP is mainly used for querying data. With the continuous development of OLAP, OLAP products are layered endlessly, and most of OLAP systems are based on a ROLAP (relational database online analysis processing) system or a single MOLAP (multidimensional database online analysis processing) system.
With the rapid growth of commercial data volume, the conventional stand-alone OLAP database cannot meet the needs of users, and the modern OLAP system basically adopts a distributed architecture. In a distributed OLAP system, distributed optimization of SQL queries is achieved, thus supporting queries and analysis of massive data.
In modern OLAP systems, the most common SQL query optimization is mainly "query result caching (Query Result Cache)", i.e. a result set for caching a query statement, and if the same query is subsequently performed, the result is directly read from the result set cache without re-execution, so that the query performance is greatly improved.
However, with the increasing data query volume of "time sequence type", due to the design constraint of "query result cache (Query Result Cache)", the SQL of each query needs to be completely consistent to hit the cache, but the query of time sequence data is only partially consistent and unchanged for the inner sub-query, and the outer SQL time parameter is inconsistent, so that the cache cannot be effectively hit each time of query, thereby affecting the query throughput performance of the whole system.
Disclosure of Invention
The application aims at overcoming the defects of the prior art, and provides an OLAP query optimization method and system based on broadcast sub-query caching, which are used for accelerating the OLAP query performance. Compared with the existing OLAP system query result caching scheme, the method and the device can be directly applied to a distributed big data analysis system by using a more fine-grained and flexible caching algorithm, and the query performance of the OLAP is accelerated by fully utilizing the distributed cluster capacity.
The application aims at realizing the following technical scheme:
according to a first aspect of the present specification, there is provided an OLAP query optimization method based on broadcast sub-query caching, the method including the steps of:
s1, when a management node receives an SQL query request aiming at time series data, splitting the SQL query request into sub-queries and final result convergence queries according to operators, and caching the execution results of the sub-query physical plan to all working nodes of an OLAP cluster by using a broadcasting mechanism;
s2, when the working node executes SQL query for time series data, directly performing cache query of a sub-query physical plan locally, and if the cache is hit, directly performing operator execution of the next step; if the cache is not hit, sub-query operator execution is carried out, the execution result of the sub-query physical plan is locally cached, and the execution result is updated to all working nodes of the OLAP cluster through broadcasting.
Further, the management node receives an SQL query request aiming at time series data, firstly analyzes SQL into a logic plan, optimizes the logic plan into a physical plan, and then splits the physical plan into sub-queries and final result convergence queries according to operators.
Further, the working node performs hash operation on the received sub-query physical plan to obtain an identification ID, and attempts to obtain a cache of the sub-query physical plan from a local cache module by using the identification ID; if the cache is obtained, loading the cache result into an execution flow, and entering an operator computing stage of hash connection; otherwise, the sub-query physical plan is executed locally, an execution result is loaded into an execution flow, an operator calculation stage of hash connection is entered, and meanwhile, the identification ID of the sub-query physical plan and the execution result are combined into a cache structure to be written into a local cache module.
Further, the local cache structure of the working node is expressed as HashMap < hashID, result >, wherein hashID is an MD5 calculated value for sub-query physical planning, and Result is a data structure value in the memory of the working node.
Further, when the query process of the working node does not hit the cache, sub-query physical plans are executed in the working node, and the execution Result is written into a local cache structure HashMap < HashID, result >; and simultaneously acquiring PRC addresses of all working nodes from the management node, and broadcasting HashMap < HashID, result > to all working nodes.
According to a second aspect of the present specification, there is provided an OLAP query optimization system based on broadcast sub-query caching, the system comprising the following modules:
sub-query module: the system comprises an OLAP cluster working node, a cache module and a sub-query module, wherein the OLAP cluster working node is used for extracting a sub-query physical plan, carrying out hash operation on the physical plan to obtain an identification ID, and using the identification ID to try to obtain the cache of the sub-query physical plan from the cache module; if the cache is obtained, loading the cache result into an execution flow, and entering an operator computing stage of hash connection; otherwise, the sub-query physical plan is executed locally, an execution result is loaded into an execution flow, an operator calculation stage of hash is entered, and meanwhile, the identification ID of the sub-query physical plan and the execution result are combined into a cache structure to be written into a cache module;
and a cache module: the sub-query module is deployed at an OLAP cluster working node and provides the capability of cache writing and parallel reading for the sub-query module; for the first write-in cache, a broadcasting module is called to carry out cache broadcasting on all working nodes of the OLAP cluster;
and a broadcasting module: and providing a cache broadcasting service, acquiring PRC addresses of all working nodes from the management node after receiving a cache broadcasting request from the cache module, and broadcasting an execution result of the sub-query physical plan to all the working nodes.
Further, the cache module provides a cache service based on the LRU policy, and the cache structure is expressed as HashMap < HashID, result > where HashID is an MD5 calculated value for the sub-query physical plan, and Result is a data structure value in the memory of the working node.
Further, the system can be deployed on various forms of computing node platforms, including ECS, docker, physical machine environments.
Compared with the prior art, the application has the following advantages:
firstly, aiming at the data query of the time sequence type, a new, finer granularity and flexible caching algorithm is provided, the design defect of query result caching (Query Result Cache) is avoided, the method can be directly applied to a distributed big data analysis system, and the query performance of the OLAP is accelerated by fully utilizing the distributed cluster capability.
Secondly, the method can be directly applied in a cluster environment through a cache broadcasting mechanism, so that the cache of sub-queries is available at all nodes, the overall cache hit rate is improved, and the query performance of the OLAP is accelerated by fully utilizing the distributed cluster capability.
Drawings
FIG. 1 is a flowchart of an OLAP query optimization method based on broadcast sub-query caching according to an embodiment of the present application;
fig. 2 is a block diagram of an OLAP query optimization system based on broadcast sub-query caching according to an embodiment of the present application.
Detailed Description
In order that the above objects, features and advantages of the application will be readily understood, a more particular description of the application will be rendered by reference to the appended drawings.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application, but the present application may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present application is not limited to the specific embodiments disclosed below.
As shown in fig. 1, the method for optimizing OLAP query based on broadcast sub-query caching according to the embodiment of the present application may be specifically implemented in three steps.
(1) A new caching scheme is designed, and local results of sub-query operators are cached instead of SQL query results.
The caching scheme is specifically implemented in a working node Worker of the OLAP cluster. The Worker node is responsible for the execution of the physical plan of SQL. The traditional SQL query result caching (Query Result Cache) is to cache the SQL query result at the Proxy layer Proxy, and the cache hit rate is very low in a time sequence type data analysis scene; the local result caching is carried out on the operator layer of the SQL physical plan, the local result caching is finer and more flexible, the cache hit rate can be greatly improved in a time sequence type data analysis scene, and the query performance of the OLAP is fully utilized by the aid of the distributed cluster capability.
(2) When the management node Master receives an SQL query request aiming at time series data, the SQL is analyzed into a logical plan, the logical plan is optimized into a physical plan, the physical plan is split into sub-queries according to operators, and the final results are gathered into queries. And the execution result of the sub-query physical plan is cached in each workbench node of the distributed system by using a broadcasting mechanism.
The operator splitting is mainly aimed at the splitting of physical plan, and sub-queries are generally optimized to be hash-connected HashJoin. The method comprises the steps that a Worker node performs hash operation on received sub-query physical plan to obtain an identification ID, and the identification ID is used for attempting to obtain a cache of the sub-query physical plan from a local cache module of the Worker node; if the cache is obtained, loading the cache result into an execution flow Pipeline, and entering an operator calculation stage of HashJoin; if the cache is not obtained, the sub-query physical plan is executed locally, an execution Result is loaded into a Pipeline and enters an operator calculation stage of HashJoin, and meanwhile, the identification ID of the sub-query physical plan and the execution Result are combined into a cache structure HashMap < HashID, and Result > is written into a local cache module of a workbench node.
In the cache structure HashMap < HashID, result > is the MD5 calculated value for sub-query physical plan, and Result is the data structure value in the memory of the workbench node.
(3) When the Worker node executes SQL query for time series data, directly carrying out local sub-query physical plan cache query, and if the cache is hit, directly carrying out next operator execution; if the cache is not hit, sub-query operator execution is carried out, the execution result of sub-query physical plan is locally cached, and the execution result is updated to all the workbench nodes of the OLAP cluster through broadcasting.
When the query process does not hit the cache, sub-query physical plan is executed at the workbench node, and the Result after the execution is written into a local cache structure HashMap < HashID, result >; meanwhile, all the Worker node PRC addresses are acquired from the Master node, and HashMap < HashID, result > is broadcasted to all the Worker nodes. By the method, the sub-query cache is available at all nodes, the overall cache hit rate is improved, and the query performance of the OLAP is accelerated by fully utilizing the distributed cluster capability.
As shown in fig. 2, an OLAP query optimization system based on broadcast sub-query caching according to an embodiment of the present application includes the following modules:
sub-query module: the method comprises the steps of disposing at a workbench node, extracting physical plan of sub-query, carrying out hash operation on the physical plan, obtaining an identification ID, and using the identification ID to try to obtain the cache of the sub-query physical plan from a cache module; if the cache is obtained, loading the cache result into a Pipeline, and entering an operator calculation stage of HashJoin; if the cache is not obtained, the sub-query physical plan is executed locally, an execution Result is loaded into a Pipeline and enters an operator calculation stage of HashJoin, and meanwhile, the identification ID of the sub-query physical plan and the execution Result are combined into a cache structure HashMap < HashID, and Result > is written into a cache module.
And a cache module: the system is deployed at a workbench node, provides cache service based on an LRU strategy, and provides cache writing and parallel reading capabilities for a sub-query module. The core is a hash table HashTable, and the control is performed by using a read-write lock, and the cache structure is HashMap < HashID, result >. And for the first write-in cache, a broadcasting module is called to broadcast the cache of all the workbench nodes of the OLAP cluster.
And a broadcasting module: and after receiving the cache broadcasting request from the cache module, the cache broadcasting service is provided, all the PRC addresses of the workbench nodes are obtained from the Master node, and the HashMap < HashID, result > is broadcasted to all the workbench nodes. By the method, the sub-query cache is available at all nodes, the overall cache hit rate is improved, and the query performance of the OLAP is accelerated by fully utilizing the distributed cluster capability.
The embodiment of the application realizes a prototype system on the ECS platform based on the Arian cloud and tests the effect of the method. However, according to different hardware devices used by the computing node, the embodiment of the application can be popularized and deployed to platforms such as a physical machine and a Docker.
The foregoing is merely a preferred embodiment of the present application, and the present application has been disclosed in the above description of the preferred embodiment, but is not limited thereto. Any person skilled in the art can make many possible variations and modifications to the technical solution of the present application or modifications to equivalent embodiments using the methods and technical contents disclosed above, without departing from the scope of the technical solution of the present application. Therefore, any simple modification, equivalent variation and modification of the above embodiments according to the technical substance of the present application still fall within the scope of the technical solution of the present application.
Claims (8)
1. An OLAP query optimization method based on broadcast sub-query caching, comprising the steps of:
s1, when a management node receives an SQL query request aiming at time series data, splitting the SQL query request into sub-queries and final result convergence queries according to operators, and caching the execution results of the sub-query physical plan to all working nodes of an OLAP cluster by using a broadcasting mechanism;
s2, when the working node executes SQL query for time series data, directly performing cache query of a sub-query physical plan locally, and if the cache is hit, directly performing operator execution of the next step; if the cache is not hit, sub-query operator execution is carried out, the execution result of the sub-query physical plan is locally cached, and the execution result is updated to all working nodes of the OLAP cluster through broadcasting.
2. The method of claim 1, wherein the management node receives the SQL query request for the time series data, first parses the SQL into a logical plan, optimizes the logical plan into a physical plan, and then splits the physical plan into sub-queries and final result aggregate queries by operators.
3. The method of claim 1, wherein the working node hashes the received sub-query physical plan to obtain an identification ID, and uses the identification ID to attempt to obtain a cache of the sub-query physical plan from a local cache module; if the cache is obtained, loading the cache result into an execution flow, and entering an operator computing stage of hash connection; otherwise, the sub-query physical plan is executed locally, an execution result is loaded into an execution flow, an operator calculation stage of hash connection is entered, and meanwhile, the identification ID of the sub-query physical plan and the execution result are combined into a cache structure to be written into a local cache module.
4. The method of claim 1, wherein the local cache structure of the working node is expressed as HashMap < hashd, result >, wherein hashd is an MD5 calculated value for the sub-query physical plan, and Result is a data structure value in the working node memory.
5. The method of claim 4, wherein the sub-query physical plan is executed at the working node when the query process of the working node does not hit the cache, and the execution Result is written into a local cache structure HashMap < hashd, result >; and simultaneously acquiring PRC addresses of all working nodes from the management node, and broadcasting HashMap < HashID, result > to all working nodes.
6. An OLAP query optimization system based on broadcast sub-query caching, comprising:
sub-query module: the system comprises an OLAP cluster working node, a cache module and a sub-query module, wherein the OLAP cluster working node is used for extracting a sub-query physical plan, carrying out hash operation on the physical plan to obtain an identification ID, and using the identification ID to try to obtain the cache of the sub-query physical plan from the cache module; if the cache is obtained, loading the cache result into an execution flow, and entering an operator computing stage of hash connection; otherwise, the sub-query physical plan is executed locally, an execution result is loaded into an execution flow, an operator calculation stage of hash is entered, and meanwhile, the identification ID of the sub-query physical plan and the execution result are combined into a cache structure to be written into a cache module;
and a cache module: the sub-query module is deployed at an OLAP cluster working node and provides the capability of cache writing and parallel reading for the sub-query module; for the first write-in cache, a broadcasting module is called to carry out cache broadcasting on all working nodes of the OLAP cluster;
and a broadcasting module: and providing a cache broadcasting service, acquiring PRC addresses of all working nodes from the management node after receiving a cache broadcasting request from the cache module, and broadcasting an execution result of the sub-query physical plan to all the working nodes.
7. The system of claim 6, wherein the caching module provides a cache service based on an LRU policy, and the cache structure is expressed as HashMap < hashd, result >, where hashd is an MD5 calculated value for a sub-query physical plan, and Result is a data structure value in a working node memory.
8. The system of claim 6, wherein the system is capable of being deployed on various forms of computing node platforms, including ECS, docker, physical machine environments.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310704298.3A CN116662449B (en) | 2023-06-14 | 2023-06-14 | OLAP query optimization method and system based on broadcast sub-query cache |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310704298.3A CN116662449B (en) | 2023-06-14 | 2023-06-14 | OLAP query optimization method and system based on broadcast sub-query cache |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116662449A true CN116662449A (en) | 2023-08-29 |
| CN116662449B CN116662449B (en) | 2024-06-04 |
Family
ID=87713596
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310704298.3A Active CN116662449B (en) | 2023-06-14 | 2023-06-14 | OLAP query optimization method and system based on broadcast sub-query cache |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116662449B (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110072006A1 (en) * | 2009-09-18 | 2011-03-24 | Microsoft Corporation | Management of data and computation in data centers |
| CN103324724A (en) * | 2013-06-26 | 2013-09-25 | 华为技术有限公司 | Method and device for processing data |
| CN114329155A (en) * | 2021-12-30 | 2022-04-12 | 北京诺司时空科技有限公司 | Multi-mode storage caching system containing time sequence database and query method |
| US11308106B1 (en) * | 2018-05-21 | 2022-04-19 | Amazon Technologies, Inc. | Caching results for sub-queries to different data store locations |
| CN115221186A (en) * | 2022-06-09 | 2022-10-21 | 网易（杭州）网络有限公司 | Data query method, system and device and electronic equipment |
| CN115587114A (en) * | 2022-09-23 | 2023-01-10 | 阿里云计算有限公司 | System and query method |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105426467B (en) * | 2015-11-16 | 2018-11-20 | 北京京东尚科信息技术有限公司 | A kind of SQL query method and system for Presto |
| JP7524962B2 (en) * | 2020-10-15 | 2024-07-30 | 日本電信電話株式会社 | Data processing device, data processing method and data processing program |
| CN114328598B (en) * | 2021-11-29 | 2024-06-11 | 上海沄熹科技有限公司 | Cache optimization method and system for pipeline based on ClickHouse database |
-
2023
- 2023-06-14 CN CN202310704298.3A patent/CN116662449B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110072006A1 (en) * | 2009-09-18 | 2011-03-24 | Microsoft Corporation | Management of data and computation in data centers |
| CN103324724A (en) * | 2013-06-26 | 2013-09-25 | 华为技术有限公司 | Method and device for processing data |
| US11308106B1 (en) * | 2018-05-21 | 2022-04-19 | Amazon Technologies, Inc. | Caching results for sub-queries to different data store locations |
| CN114329155A (en) * | 2021-12-30 | 2022-04-12 | 北京诺司时空科技有限公司 | Multi-mode storage caching system containing time sequence database and query method |
| CN115221186A (en) * | 2022-06-09 | 2022-10-21 | 网易（杭州）网络有限公司 | Data query method, system and device and electronic equipment |
| CN115587114A (en) * | 2022-09-23 | 2023-01-10 | 阿里云计算有限公司 | System and query method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116662449B (en) | 2024-06-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11416486B2 (en) | System and method for optimizing large database management systems with multiple optimizers | |
| US10572484B2 (en) | Duplicate reduction or elimination with hash join operations | |
| US9990398B2 (en) | Inferring dimensional metadata from content of a query | |
| US6950823B2 (en) | Transparent edge-of-network data cache | |
| US10642831B2 (en) | Static data caching for queries with a clause that requires multiple iterations to execute | |
| US8037040B2 (en) | Generating continuous query notifications | |
| US20160350347A1 (en) | Techniques for evaluating query predicates during in-memory table scans | |
| US20030088579A1 (en) | Collecting statistics in a database system | |
| CN111177148B (en) | Method for automatically building and dividing tables of hydropower database | |
| WO1998038587A1 (en) | Method of using a cache to determine the visibility to a remote database client of a plurality of database transactions | |
| US11567934B2 (en) | Consistent client-side caching for fine grained invalidations | |
| Peng et al. | Adaptive distributed RDF graph fragmentation and allocation based on query workload | |
| CN109299101B (en) | Data retrieval method, device, server and storage medium | |
| US10528590B2 (en) | Optimizing a query with extrema function using in-memory data summaries on the storage server | |
| US7949655B2 (en) | Joins of relations in an object relational database system | |
| KR20200092095A (en) | Transaction control method to synchronize DML statements in relational database to NoSQL database | |
| CN116662449B (en) | OLAP query optimization method and system based on broadcast sub-query cache | |
| Yao et al. | Relational Database Query Optimization Strategy Based on Industrial Internet Situation Awareness System | |
| CN110471914B (en) | Dimension association method and system in real-time data processing | |
| WO2020192225A1 (en) | Remote sensing data indexing method for spark, system and electronic device | |
| CN117290352A (en) | Index establishing method, electronic equipment and computer storage equipment | |
| CN118410068B (en) | Database SQL query optimization method, terminal and storage medium | |
| Chen | Efficient partitioner for distributed OLTP DBMS | |
| Basu et al. | Centralized versus distributed index schemes in OODBMS-a performance analysis | |
| Barlos et al. | A load balanced multicomputer relational database system for highly skewed data |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |