Big data integration method and system based on Elasticsearch and Redis

Abstract

The invention relates to the technical field of big data, in particular to a big data integration method and system based on Elasticsearch and Redis, and the method comprises the following steps: collecting multi-source data; preprocessing the dynamic data; distributed data storage and indexing; synchronizing intelligent hotspot data; performing cache optimization data retrieval; analyzing and visualizing the depth data; elastic stretching and load balancing are realized; security and privacy protection are realized; the big data integration method and system based on the Elasticsearch and the Redis have the beneficial effects that by combining the advantages of the Elasticsearch and the Redis and through the processes of data acquisition, preprocessing, data storage, hot data synchronization and the like, the efficiency problem of data storage, retrieval and analysis in a big data environment is solved. The core of the method is to realize rapid indexing, efficient storage, instant retrieval and deep analysis of data so as to meet high requirements of modern enterprises on data processing speed and quality.

Description

Technical Field

[0001] The present invention relates to the technical field of big data, and in particular to a big data integration method and system based on Elasticsearch and Redis. Background Art

[0002] With the rapid development of Internet technology, the amount of data is growing exponentially. Traditional data storage and retrieval methods can no longer meet the needs of the current big data environment. As an efficient search engine, Elasticsearch can quickly process and retrieve large amounts of data. As an in-memory database, Redis is widely used in cache and message queues due to its high-speed read and write capabilities. However, how to effectively integrate these two technologies to achieve better big data integration effects is an important issue facing the current technology field. Summary of the invention

[0003] The purpose of the present invention is to provide a big data integration method and system based on Elasticsearch and Redis to solve the problems raised in the above background technology.

[0004] To achieve the above object, the present invention provides the following technical solution: a big data integration method based on Elasticsearch and Redis, the method comprising the following steps:

[0005] Multi-source data collection;

[0006] Dynamic data preprocessing;

[0007] Distributed data storage and indexing;

[0008] Smart hotspot data synchronization;

[0009] Cache-optimized data retrieval;

[0010] In-depth data analysis and visualization;

[0011] Elastic scaling and load balancing;

[0012] Security and privacy protection.

[0013] Preferably, the specific operations of multi-source data collection include: collecting data in real time or in batches from structured and unstructured data sources, the data sources including but not limited to online transaction records, social media activities, machine-generated logs, and third-party APIs;

[0014] Dynamic data preprocessing specifically includes: developing a dynamic data preprocessing mechanism that automatically adjusts the cleaning, formatting, and conversion strategies based on data type and quality to ensure data consistency and accuracy.

[0015] Preferably, the specific operations of distributed data storage and indexing include: utilizing the distributed characteristics of Elasticsearch to implement a scalable data storage and indexing solution, supporting PB-level data storage requirements, and maintaining millisecond-level search response time;

[0016] The specific operations of intelligent hotspot data synchronization include: proposing an intelligent hotspot data identification and synchronization strategy, dynamically synchronizing hotspot data from Elasticsearch to Redis according to access patterns and data importance, so as to achieve fast data access.

[0017] Preferably, the specific operations of cache-optimized data retrieval include: designing a cache-optimized data retrieval algorithm, the algorithm first checks the Redis cache to achieve instant data delivery, and if the cache misses, efficiently retrieves from Elasticsearch, and intelligently updates the cache to optimize future requests;

[0018] The specific operations of deep data analysis and visualization include: integrating advanced data analysis engines, not only conducting in-depth analysis of the data stored in Elasticsearch, but also presenting the analysis results through visualization tools to help users understand the meaning behind the data and support data-driven decision making.

[0019] Preferably, the specific operations of elastic scaling and load balancing include: an elastic scaling and load balancing mechanism that dynamically adjusts resource allocation according to system load to ensure high availability and stability of the system;

[0020] Specific operations of security and privacy protection include: security measures are integrated in every link of data collection, storage and processing, including data encryption, access control and privacy protection to meet strict data security and compliance requirements.

[0021] A big data integration system based on Elasticsearch and Redis is applied to a big data integration method based on Elasticsearch and Redis. The system comprises: a multi-source data acquisition module, which acquires data in real time or in batches from structured and unstructured data sources, wherein the data sources include but are not limited to online transaction records, social media activities, machine-generated logs, and third-party APIs, thereby achieving comprehensive data integration.

[0022] Preferably, it also includes a dynamic data preprocessing module, which automatically adjusts the cleaning, formatting and conversion strategies according to the data type and quality to ensure the consistency and accuracy of the data, thereby improving the efficiency and accuracy of subsequent data processing.

[0023] Preferably, a distributed data storage and indexing module is also included, which utilizes the distributed characteristics of Elasticsearch to implement a scalable data storage and indexing solution, supports PB-level data storage requirements, and maintains millisecond-level search response time, thereby improving data retrieval efficiency.

[0024] Preferably, it also includes an intelligent hotspot data synchronization module and a cache-optimized data retrieval module, which dynamically synchronize hotspot data from Elasticsearch to Redis according to access patterns and data importance. The cache-optimized data retrieval module first checks the Redis cache to achieve instant data delivery. If the cache misses, it efficiently retrieves from Elasticsearch and intelligently updates the cache to optimize future requests, thereby achieving fast access to data and efficient retrieval.

[0025] Preferably, it also includes a deep data analysis and visualization module, an elastic scaling and load balancing module, and a security and privacy protection module;

[0026] The deep data analysis and visualization module conducts in-depth analysis of the data stored in Elasticsearch and presents the analysis results through visualization tools; the elastic scaling and load balancing module dynamically adjusts resource allocation according to the system load to ensure high availability and stability of the system; the security and privacy protection module integrates security measures in each link of data collection, storage and processing, including data encryption, access control and privacy protection, to meet strict data security and compliance requirements.

[0027] Compared with the prior art, the present invention has the following beneficial effects:

[0028] The big data integration method and system based on Elasticsearch and Redis proposed in the present invention solves the efficiency problems of data storage, retrieval and analysis in a big data environment by combining the advantages of Elasticsearch and Redis through data collection, preprocessing, data storage and hot data synchronization. The core of this method is to achieve fast indexing, efficient storage, instant retrieval and in-depth analysis of data to meet the high requirements of modern enterprises for data processing speed and quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 is a flow chart of the method of the present invention;

[0030] Figure 2 This is a system architecture diagram of the present invention;

[0031] Figure 3 This is a diagram of the high-availability Redis service architecture of the present invention;

[0032] Figure 4The data retrieval flow chart of the present invention. DETAILED DESCRIPTION

[0033] In order to make the purpose and technical solution of the present invention clearly and completely described, and the advantages more clearly understood, the embodiments of the present invention are further described in detail with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are part of the embodiments of the present invention, rather than all of the embodiments, and are only used to explain the embodiments of the present invention, and are not used to limit the embodiments of the present invention. All other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0034] For example, see Figure 1 The present invention provides a technical solution: a big data integration method based on Elasticsearch and Redis, the method comprising the following steps:

[0035] 1. Multi-source data collection:

[0036] Develop a data collection framework that can interface with multiple data sources, including but not limited to databases, file systems, APIs, and network logs. Implement data extraction modules to support periodic and real-time data collection strategies to accommodate different data update frequencies and timeliness requirements.

[0037] 2. Dynamic data preprocessing:

[0038] Design data cleaning algorithms to identify and remove duplicate, invalid or incomplete data records. Implement data conversion tools to convert data in different formats into a standard format suitable for Elasticsearch indexing. Introduce data formatting processes to ensure that data has a consistent structure and type before storage.

[0039] 3. Distributed data storage and indexing:

[0040] Configure the Elasticsearch cluster to support high-concurrency data writing and query operations.

[0041] Design a reasonable indexing strategy, including the number of shards and replicas, to optimize data storage efficiency and query performance. Implement an automated data indexing process to ensure that newly collected data can be quickly indexed and retrieved.

[0042] 4. Smart hotspot data synchronization:

[0043] Develop an intelligent cache synchronization mechanism to dynamically synchronize hot data to Redis based on data access patterns and popularity. Implement a data invalidation strategy to ensure that cached data in Redis remains consistent with data in Elasticsearch. Configure a Redis cluster (such as Figure 2 ) to support high availability and fast reading and writing of data.

[0044] 5. Cache-optimized data retrieval:

[0045] Implement an efficient data retrieval engine that first queries the Redis cache to provide a fast response (the retrieval flow chart is as follows Figure 3 ). Design a fallback mechanism in case of cache misses to ensure that the process of retrieving data from Elasticsearch is seamless and efficient. Develop an intelligent preloading algorithm to predict and preload the data that may be needed into the cache based on user behavior and access patterns.

[0046] 6. In-depth data analysis and visualization:

[0047] Integrate data analysis tools to provide the ability to perform complex queries and analyses on data stored in Elasticsearch. Implement data visualization components to intuitively display analysis results in the form of charts, dashboards, etc. Develop a report generation module that supports custom report content and format to meet the needs of different users.

[0048] 7. System monitoring and maintenance:

[0049] Implement a system monitoring module to monitor data flow, system performance, and anomalies in real time. Design automated maintenance tools, including index optimization, cache cleanup, and system upgrades. Introduce a fault recovery mechanism to ensure that the system can quickly resume normal operation when encountering a fault.

[0050] 8. Security and Privacy Protection:

[0051] Implement encryption and security measures at every stage of data collection, transmission, storage and processing. Implement fine-grained access control to ensure that only authorized users can access sensitive data. Comply with data privacy regulations to ensure the protection of personal and sensitive information.

[0052] Embodiment 2, refer to the attached Figures 2 to 4, based on the first embodiment, a big data integration system based on Elasticsearch and Redis is proposed, which is applied to the big data integration method based on Elasticsearch and Redis. The system includes: a multi-source data acquisition module, which collects data in real time or batches from structured and unstructured data sources, including but not limited to online transaction records, social media activities, machine-generated logs, and third-party APIs, so as to achieve comprehensive integration of data.

[0053] It also includes a dynamic data preprocessing module, which automatically adjusts the cleaning, formatting and conversion strategies according to the data type and quality to ensure the consistency and accuracy of the data, thereby improving the efficiency and accuracy of subsequent data processing.

[0054] It also includes a distributed data storage and indexing module, which uses the distributed characteristics of Elasticsearch to implement a scalable data storage and indexing solution, supports PB-level data storage requirements, and maintains millisecond-level search response time, thereby improving data retrieval efficiency.

[0055] It also includes an intelligent hotspot data synchronization module and a cache-optimized data retrieval module, which dynamically synchronize hotspot data from Elasticsearch to Redis based on access patterns and data importance. The cache-optimized data retrieval module first checks the Redis cache for instant data delivery. If the cache misses, it efficiently retrieves from Elasticsearch and intelligently updates the cache to optimize future requests, thereby achieving fast access and efficient retrieval of data.

[0056] It also includes deep data analysis and visualization modules, elastic scaling and load balancing modules, and security and privacy protection modules;

[0057] The deep data analysis and visualization module conducts in-depth analysis of the data stored in Elasticsearch and presents the analysis results through visualization tools; the elastic scaling and load balancing module dynamically adjusts resource allocation according to the system load to ensure high availability and stability of the system; the security and privacy protection module integrates security measures in each link of data collection, storage and processing, including data encryption, access control and privacy protection, to meet strict data security and compliance requirements.

[0058] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A big data integration method based on Elasticsearch and Redis, characterized by: The method comprises the following steps: Multi-source data collection; Dynamic data preprocessing; Distributed data storage and indexing; Smart hotspot data synchronization; Cache-optimized data retrieval; In-depth data analysis and visualization; Elastic scaling and load balancing; Security and privacy protection.

2. A big data integration method based on Elasticsearch and Redis according to claim 1, characterized in that: The specific operations of multi-source data collection include: real-time or batch data collection from structured and unstructured data sources, including but not limited to online transaction records, social media activities, machine-generated logs, and third-party APIs; Dynamic data preprocessing specifically includes: developing a dynamic data preprocessing mechanism that automatically adjusts the cleaning, formatting, and conversion strategies based on data type and quality to ensure data consistency and accuracy.

3. The big data integration method based on Elasticsearch and Redis according to claim 1, characterized in that: The specific operations of distributed data storage and indexing include: using the distributed characteristics of Elasticsearch to implement scalable data storage and indexing solutions, supporting PB-level data storage requirements, and maintaining millisecond-level search response times; The specific operations of intelligent hotspot data synchronization include: proposing an intelligent hotspot data identification and synchronization strategy, dynamically synchronizing hotspot data from Elasticsearch to Redis according to access patterns and data importance, so as to achieve fast data access.

4. The big data integration method based on Elasticsearch and Redis according to claim 1, characterized in that: The specific operations of cache-optimized data retrieval include: designing a cache-optimized data retrieval algorithm that first checks the Redis cache for immediate data delivery, and if the cache misses, efficiently retrieves from Elasticsearch, and intelligently updates the cache to optimize future requests; The specific operations of deep data analysis and visualization include: integrating advanced data analysis engines, not only conducting in-depth analysis of the data stored in Elasticsearch, but also presenting the analysis results through visualization tools to help users understand the meaning behind the data and support data-driven decision making.

5. The big data integration method based on Elasticsearch and Redis according to claim 1, characterized in that: The specific operations of elastic scaling and load balancing include: an elastic scaling and load balancing mechanism that dynamically adjusts resource allocation according to system load to ensure high availability and stability of the system; Specific operations of security and privacy protection include: security measures are integrated in every link of data collection, storage and processing, including data encryption, access control and privacy protection to meet strict data security and compliance requirements.

6. A big data integration system based on Elasticsearch and Redis, applied to the big data integration method based on Elasticsearch and Redis according to any one of claims 1 to 5, characterized in that: The system includes: a multi-source data collection module that collects data in real time or in batches from structured and unstructured data sources, including but not limited to online transaction records, social media activities, machine-generated logs, and third-party APIs, thereby achieving comprehensive data integration.

7. The big data integration system based on Elasticsearch and Redis according to claim 6, characterized in that: It also includes a dynamic data preprocessing module, which automatically adjusts the cleaning, formatting and conversion strategies according to the data type and quality to ensure the consistency and accuracy of the data, thereby improving the efficiency and accuracy of subsequent data processing.

8. The big data integration system based on Elasticsearch and Redis according to claim 6, characterized in that: It also includes a distributed data storage and indexing module, which uses the distributed characteristics of Elasticsearch to implement a scalable data storage and indexing solution, supports PB-level data storage requirements, and maintains millisecond-level search response time, thereby improving data retrieval efficiency.

9. The big data integration system based on Elasticsearch and Redis according to claim 6, characterized in that: It also includes an intelligent hotspot data synchronization module and a cache-optimized data retrieval module, which dynamically synchronize hotspot data from Elasticsearch to Redis based on access patterns and data importance. The cache-optimized data retrieval module first checks the Redis cache for instant data delivery. If the cache misses, it efficiently retrieves from Elasticsearch and intelligently updates the cache to optimize future requests, thereby achieving fast access and efficient retrieval of data.

10. The big data integration system based on Elasticsearch and Redis according to claim 6, characterized in that: It also includes deep data analysis and visualization modules, elastic scaling and load balancing modules, and security and privacy protection modules; The deep data analysis and visualization module conducts in-depth analysis on the data stored in Elasticsearch and presents the analysis results through visualization tools; The elastic scaling and load balancing module dynamically adjusts resource allocation according to the system load to ensure high availability and stability of the system. The security and privacy protection module integrates security measures in every link of data collection, storage and processing, including data encryption, access control and privacy protection to meet strict data security and compliance requirements.

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