CN117370314A - Distributed database system collaborative optimization and data processing system and method - Google Patents
Distributed database system collaborative optimization and data processing system and method Download PDFInfo
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- CN117370314A CN117370314A CN202311428217.8A CN202311428217A CN117370314A CN 117370314 A CN117370314 A CN 117370314A CN 202311428217 A CN202311428217 A CN 202311428217A CN 117370314 A CN117370314 A CN 117370314A
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- 238000005457 optimization Methods 0.000 title claims abstract description 31
- 238000012545 processing Methods 0.000 title claims abstract description 27
- 238000000034 method Methods 0.000 title claims description 17
- 230000007246 mechanism Effects 0.000 claims abstract description 19
- 238000006243 chemical reaction Methods 0.000 claims abstract description 8
- 238000004140 cleaning Methods 0.000 claims abstract description 6
- 230000005540 biological transmission Effects 0.000 claims abstract description 5
- 238000011084 recovery Methods 0.000 claims abstract description 5
- 230000004044 response Effects 0.000 claims abstract description 5
- 238000013480 data collection Methods 0.000 claims description 9
- 238000013075 data extraction Methods 0.000 claims description 9
- 238000003672 processing method Methods 0.000 claims description 7
- 238000013515 script Methods 0.000 claims description 6
- 238000004422 calculation algorithm Methods 0.000 claims description 4
- 238000004458 analytical method Methods 0.000 claims description 3
- 238000007405 data analysis Methods 0.000 claims description 3
- 238000007418 data mining Methods 0.000 claims description 3
- 238000013500 data storage Methods 0.000 claims description 3
- 239000012634 fragment Substances 0.000 claims description 3
- 238000007726 management method Methods 0.000 claims description 3
- 238000013501 data transformation Methods 0.000 claims 2
- 238000013506 data mapping Methods 0.000 description 6
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 238000010606 normalization Methods 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000013467 fragmentation Methods 0.000 description 1
- 238000006062 fragmentation reaction Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
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- 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
- G06F16/217—Database tuning
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
- G06F11/14—Error detection or correction of the data by redundancy in operation
- G06F11/1402—Saving, restoring, recovering or retrying
- G06F11/1446—Point-in-time backing up or restoration of persistent data
- G06F11/1458—Management of the backup or restore process
- G06F11/1464—Management of the backup or restore process for networked environments
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- 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
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- 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
- G06F16/25—Integrating or interfacing systems involving database management systems
- G06F16/254—Extract, transform and load [ETL] procedures, e.g. ETL data flows in data warehouses
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- 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
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/602—Providing cryptographic facilities or services
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/604—Tools and structures for managing or administering access control systems
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/62—Protecting access to data via a platform, e.g. using keys or access control rules
- G06F21/6218—Protecting access to data via a platform, e.g. using keys or access control rules to a system of files or objects, e.g. local or distributed file system or database
-
- 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
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5083—Techniques for rebalancing the load in a distributed system
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Abstract
The invention relates to the technical field of databases, which comprises the steps of firstly collecting data from a plurality of data sources, performing cleaning, conversion and standardization treatment, and simultaneously introducing performance optimization strategies such as a caching mechanism, load balancing and the like to improve the response speed of a system. And then, the processed data are segmented according to rules, and each segment contains a part of data and is distributed to different database nodes, so that the distributed storage and processing of the data are realized. Meanwhile, the scalability of the system is considered, including horizontal expansion and vertical expansion. And the operation and data transmission among the database nodes are optimized through a collaborative optimization mechanism, and a fault-tolerant mechanism such as backup and recovery strategies are introduced, so that the overall performance, stability and reliability of the system are improved. The invention provides a high-efficiency, safe and stable solution for large-scale data processing and has wide application prospect.
Description
Technical Field
The invention relates to the technical field of databases, in particular to a distributed database system collaborative optimization and data processing system and method.
Background
Conventional single point database systems face performance bottlenecks and scalability limitations when handling large-scale data. To solve this problem, distributed database systems have been developed. The distributed database system dispersedly stores data on a plurality of nodes, and realizes the efficient processing and storage of large-scale data through parallel processing and collaborative optimization.
However, existing distributed database systems still have some drawbacks, including challenges in performance optimization, security assurance, data consistency, and the like. Therefore, the invention provides a novel collaborative optimization method of a distributed database system, which solves the problems in the prior art by the steps of data collection, fragmentation, node allocation, collaborative optimization and the like and introducing a performance optimization strategy and a safety mechanism.
Disclosure of Invention
Accordingly, the present invention is directed to a distributed database system collaborative optimization, a data processing system and a method thereof, which solve the above-mentioned problems.
Based on the above purpose, the invention provides a distributed database system collaborative optimization, a data processing system and a method.
A distributed database system collaborative optimization, data processing system and method, comprising the following steps:
a. and (3) data collection: collecting data from a plurality of data sources, cleaning, converting and standardizing the data, and introducing performance optimization strategies such as a caching mechanism and load balancing to improve the response speed of the system;
b. data slicing: slicing the cleaned, converted and standardized data according to a certain rule, wherein each slice contains a part of data;
c. node allocation: distributing each fragment to different database nodes, realizing distributed storage and processing of data, and considering the expandability of the system, including horizontal expansion and vertical expansion;
d. collaborative optimization: through a collaboration mechanism, operation and data transmission among database nodes are optimized, and a fault-tolerant mechanism such as backup and recovery strategies are introduced to improve the overall performance, stability and reliability of the system.
Further, in the data collection step, the data sources include, but are not limited to, business systems, sensors, social media, log files.
Further, the data cleaning step includes removing duplicate data, filling up missing values, denoising operations, while emphasizing the security and privacy protection of the data.
Further, the normalization process step includes converting the data to a uniform standard format for subsequent processing and analysis, while describing the use of a consistency protocol or algorithm to ensure that the data in the distributed system remains consistent.
Further, a data processing method of the distributed database system comprises the following steps:
a. and (3) data query: the data to be processed is obtained from the distributed database system through a query language or an application program interface, and can be queried by using an SQL statement or the application program interface to obtain the data to be processed;
b. and (3) data extraction: extracting the acquired data, extracting required data fields, and extracting the data through a regular expression and a pattern matching method;
c. data conversion: converting the extracted data fields to meet service requirements and subsequent data mining and analysis tasks, and converting the data through an ETL tool or a custom script;
d. and (3) data storage: the converted data is stored in a distributed database system for subsequent querying and use while emphasizing the implementation of data backup, version control, data archive management policies.
Further, in the data query step, the data to be processed can be obtained by querying through an SQL sentence or an application program interface.
In the data extraction step, data extraction can be performed by a regular expression and a pattern matching method.
Further, in the data conversion step, data conversion may be performed through an ETL tool or a custom script.
The invention has the beneficial effects that:
1. by introducing performance optimization strategies such as a caching mechanism and load balancing, the invention can obviously improve the query response speed of the distributed database system, realize the horizontal expansion of the system and better adapt to the ever-increasing data volume.
2. In the data collection and processing process, the invention introduces security mechanisms such as data encryption, access control, identity verification and the like so as to ensure the security and privacy protection of the data. This is of great importance for processing data containing sensitive information.
3. The invention ensures that the data in the distributed system is kept consistent by adopting a consistency protocol or algorithm. Meanwhile, a fault-tolerant mechanism, such as a backup and recovery strategy, is introduced, so that the fault tolerance and reliability of the system are improved, and the stability of the system in the face of node faults or network problems is ensured.
Drawings
In order to more clearly illustrate the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only of the invention and that other drawings can be obtained from them without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a data processing method according to an embodiment of the invention;
FIG. 2 is a flow chart of a data processing system according to an embodiment of the present invention.
Detailed Description
The present invention will be further described in detail with reference to specific embodiments in order to make the objects, technical solutions and advantages of the present invention more apparent.
As shown in fig. 1 to 2, a distributed database system collaborative optimization, data processing system and method include the following steps:
a. and (3) data collection: collecting data from a plurality of data sources, cleaning, converting and standardizing the data, and introducing performance optimization strategies such as a caching mechanism and load balancing to improve the response speed of the system;
b. data slicing: slicing the cleaned, converted and standardized data according to a certain rule, wherein each slice contains a part of data;
c. node allocation: distributing each fragment to different database nodes, realizing distributed storage and processing of data, and considering the expandability of the system, including horizontal expansion and vertical expansion;
d. collaborative optimization: through a collaboration mechanism, operation and data transmission among database nodes are optimized, and a fault-tolerant mechanism such as backup and recovery strategies are introduced to improve the overall performance, stability and reliability of the system.
In particular embodiments, in the data collection step, the data sources include, but are not limited to, business systems, sensors, social media, log files, the data cleansing step includes removing duplicate data, filling in missing values, denoising operations while emphasizing the security and privacy protection of the data, and the normalization processing step includes converting the data into a unified standard format for subsequent processing and analysis while describing the use of a consistency protocol or algorithm to ensure that the data in the distributed system remains consistent.
A distributed database system data processing method, comprising the steps of:
a. and (3) data query: the data to be processed is obtained from the distributed database system through a query language or an application program interface, and can be queried by using an SQL statement or the application program interface to obtain the data to be processed;
b. and (3) data extraction: extracting the acquired data, extracting required data fields, and extracting the data through a regular expression and a pattern matching method;
c. data conversion: converting the extracted data fields to meet service requirements and subsequent data mining and analysis tasks, and converting the data through an ETL tool or a custom script;
d. and (3) data storage: the converted data is stored in a distributed database system for subsequent querying and use while emphasizing the implementation of data backup, version control, data archive management policies.
Specifically, in the data query step, query can be performed through an SQL statement or an application program interface to obtain data to be processed.
Specifically, in the data extraction step, data extraction can be performed by a regular expression and a pattern matching method, and in the data conversion step, data conversion can be performed by an ETL tool or a custom script
In order to more clearly describe the specific embodiments of the invention, some examples and code segments are provided below to demonstrate how the above-described steps can be implemented.
Data collection embodiment:
example code (Python):
python
Copy code
def collect_data(data_sources):
cleaned_data＝[]
for source in data_sources:
raw_data＝fetch_raw_data(source)
cleaned_data+＝clean_data(raw_data)
return cleaned_data
this code demonstrates a Python function, receives as input a plurality of data sources, and obtains raw data from each data source, which is then cleaned.
Data slicing implementation:
example code (Python):
python
Copy code
def shard_data(cleaned_data,num_shards):
shard_size＝len(cleaned_data)//num_shards
shards＝[cleaned_data[i:i+shard_size]for iin range(0,len(cleaned_data),shard_size)]
return shards
the code segments the data after the cleaning process according to the specified rule, and stores the segmented data in a list.
Node assignment implementation:
example code (Python):
python
Copy code
def allocate_to_nodes(shards,database_nodes):
node_data_mapping＝{}
for i,shard in enumerate(shards):
node = database_nodes [ i% len (database_nodes) ] # cycle is allocated to different nodes
if node not in node_data_mapping:
node_data_mapping[node]＝[]
node_data_mapping[node].extend(shard)
return node_data_mapping
The code distributes the fragmented data to different database nodes, and realizes the distributed storage and processing of the data.
Collaborative optimization implementation:
example code (Python):
python
Copy code
def optimize_nodes(node_data_mapping):
# implementing collaborative optimization strategies, e.g. optimizing operations and data transfer
# introduces fault-tolerant mechanisms, e.g. backup and restore policies
optimized_data= { } # optimized data
return optimized_data
In the code, the operation and data transmission among different database nodes are optimized through a cooperative mechanism, and meanwhile, a fault-tolerant mechanism is introduced to improve the overall performance, stability and reliability of the system.
The present invention is intended to embrace all such alternatives, modifications and variances which fall within the broad scope of the appended claims. Therefore, any omission, modification, equivalent replacement, improvement, etc. of the present invention should be included in the scope of the present invention.
Claims (8)
1. A distributed database system co-optimization, data processing system, comprising the steps of:
a. and (3) data collection: collecting data from a plurality of data sources, cleaning, converting and standardizing the data, and introducing performance optimization strategies such as a caching mechanism and load balancing to improve the response speed of the system;
b. data slicing: slicing the cleaned, converted and standardized data according to a certain rule, wherein each slice contains a part of data;
c. node allocation: distributing each fragment to different database nodes, realizing distributed storage and processing of data, and considering the expandability of the system, including horizontal expansion and vertical expansion;
d. collaborative optimization: through a collaboration mechanism, operation and data transmission among database nodes are optimized, and a fault-tolerant mechanism such as backup and recovery strategies are introduced to improve the overall performance, stability and reliability of the system.
2. A distributed database system co-optimization, data processing system according to claim 1, wherein in the data collection step, the data sources include, but are not limited to, business systems, sensors, social media, log files.
3. A distributed database system collaborative optimization, data processing system according to claim 2, wherein the data cleansing step includes removing duplicate data, filling missing values, denoising operations while emphasizing data security and privacy protection.
4. A distributed database system co-optimization, data processing system as in claim 3 wherein said standardized processing step includes converting the data into a unified standard format for subsequent processing and analysis, while describing the use of a consistency protocol or algorithm to ensure that the data in the distributed system remains consistent.
5. A distributed database system co-optimization, data processing method according to any of claims 1-4, comprising the steps of:
a. and (3) data query: the data to be processed is obtained from the distributed database system through a query language or an application program interface, and can be queried by using an SQL statement or the application program interface to obtain the data to be processed;
b. and (3) data extraction: extracting the acquired data, extracting required data fields, and extracting the data through a regular expression and a pattern matching method;
c. data conversion: converting the extracted data fields to meet service requirements and subsequent data mining and analysis tasks, and converting the data through an ETL tool or a custom script;
d. and (3) data storage: the converted data is stored in a distributed database system for subsequent querying and use while emphasizing the implementation of data backup, version control, data archive management policies.
6. The collaborative optimization and data processing method of a distributed database system according to claim 5, wherein in the data query step, the query can be performed through an SQL statement or an application program interface to obtain the data to be processed.
7. The collaborative optimization and data processing method of a distributed database system according to claim 6, wherein in the data extraction step, data extraction can be performed by a regular expression and pattern matching method.
8. The collaborative optimization and data processing method of a distributed database system according to claim 7, wherein in the data transformation step, data transformation can be performed by ETL tools or custom scripts.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117688105A (en) * | 2024-02-04 | 2024-03-12 | 成都威世通智能科技有限公司 | High-reliability general artificial intelligence core software algorithm system |
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2023
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117688105A (en) * | 2024-02-04 | 2024-03-12 | 成都威世通智能科技有限公司 | High-reliability general artificial intelligence core software algorithm system |
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