A method for Elasticsearch data migration and synchronization

Abstract

The present invention relates to an Elasticsearch data migration synchronization method, belonging to the technical field of data migration, and the method comprises: step 1: establishing a connection with an Elasticsearch cluster; step 2: constructing a corresponding query statement according to a query condition configured by a user, so as to retrieve data in an Elasticsearch index; step 3: transmitting the processed data to a framework of DataX; step 4: dynamically adjusting the number of connections of the Elasticsearch cluster and the concurrency of the query according to the complexity of the query and the size of the data volume; step 5: when an abnormal situation occurs during data reading, the abnormal situation is captured and identified, and a retry operation is performed according to the configured number of retries and the retry interval time; if it still fails after multiple retries, an error message is recorded, and it is decided whether to continue to perform the data reading task according to the specific situation. The present invention can ensure that the data of Elasticsearch is completely migrated to other databases with high efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of data migration, and in particular to an Elasticsearch data migration synchronization method. Background Art

[0002] Elasticsearch data migration and synchronization requirements may arise in the following situations:

[0003] 1. Technology Upgrade

[0004] 1. Elasticsearch version upgrade: As Elasticsearch continues to launch new versions, it brings more features, performance optimization, and security improvements. In order to enjoy these benefits, enterprises may decide to migrate data to upgrade to the new version. For example, the new version may provide more efficient search algorithms, better distributed architecture, or enhanced security features.

[0005] The data format and storage method may change between different versions, so data migration is required to ensure the compatibility and availability of data in the new environment.

[0006] 2. Hardware and infrastructure upgrade: As the business grows, the original hardware equipment may not be able to meet the growing data storage and processing needs. Enterprises may choose to upgrade hardware, such as replacing higher-performance servers, increasing storage capacity, or using faster network equipment. In this case, Elasticsearch data needs to be migrated to a new hardware environment.

[0007] Infrastructure upgrades may also include adopting new cloud service providers, deploying containerized environments, or using more advanced storage technologies. These changes may require data migration to adapt to the new infrastructure.

[0008] 2. Changes in business requirements

[0009] 1. Data volume growth: As the business expands, the amount of data continues to increase, and the original Elasticsearch cluster may not be able to bear such a huge data load. At this time, data migration is needed to expand the cluster scale, increase the number of nodes, or adopt a distributed storage solution to ensure that the system can continue to process and store data efficiently.

[0010] The growth in data volume may also lead to higher requirements for search performance and response time, requiring data migration to optimize index structure, adjust storage strategies, or adopt more efficient query methods.

[0011] 2. Business integration and splitting: During the development process, an enterprise may integrate or split its business, which may affect the data architecture of Elasticsearch. For example, the data sources of multiple departments need to be merged into a unified Elasticsearch cluster, or a business unit needs to be separated and establish its own Elasticsearch environment. In these cases, data migration is required to meet the needs of business integration or splitting.

[0012] Business integration may also involve the unification and standardization of data formats, requiring data cleansing and conversion during the data migration process.

[0013] 3. System Migration and Reconstruction

[0014] 1. Cloud migration: More and more companies are choosing to migrate their businesses to cloud platforms to enjoy the flexibility, scalability, and cost-effectiveness of cloud services. If the original Elasticsearch is deployed in a local environment, data migration is required to migrate it to the Elasticsearch service of the cloud service provider.

[0015] Cloud migration may also involve data security and compliance considerations, and it is necessary to ensure the security and privacy of data during the migration process.

[0016] 2. System Reconstruction: Enterprises may reconstruct existing systems to improve system performance, maintainability, or scalability. If Elasticsearch is part of the system, data migration may be required to adapt to the new system architecture. For example, redesigning the data model, adjusting the index structure, or adopting a new storage engine.

[0017] System reconstruction may also involve updating the technology stack, which requires integrating Elasticsearch with new technology components, which may also require data migration.

[0018] Since the amount of Elasticsearch data is extremely large, traditional data migration solutions cannot perform real-time incremental synchronization migration of Elasticsearch data. Migration is subject to errors and omissions, and the migration speed is very slow. Summary of the invention

[0019] In order to solve the above-mentioned prior art problem, how to completely and efficiently migrate and export Elasticsearch data, the present invention provides an Elasticsearch data migration and synchronization method.

[0020] An Elasticsearch data migration and synchronization method includes the following steps:

[0021] Step 1: Establish a connection with the Elasticsearch cluster;

[0022] Step 2: Construct the corresponding query statement according to the query conditions configured by the user to retrieve data in the Elasticsearch index;

[0023] Step 3: After reading the data from the source data engine, it is processed and matched with the fields and structures required by the target data source and transferred to the DataX framework for further transmission to the target data source; the ElasticsearchReader and DataX framework interact through predefined interfaces and data formats;

[0024] Step 4: Dynamically adjust the number of connections and query concurrency of the Elasticsearch cluster according to the complexity of the query and the amount of data;

[0025] Step 5: When an abnormal situation occurs during the data reading process, the abnormal situation is captured and identified, and the retry operation is performed according to the configured number of retries and retry interval; if it still fails after multiple retries, the error information is recorded, and a decision is made whether to continue the data reading task based on the specific business situation and the importance of the data reading task.

[0026] Furthermore, the step 1 specifically includes:

[0027] Step 101: Read the Java client or RESTful API of Elasticsearch;

[0028] Step 102: Establish a communication connection with each node in the cluster according to the configured Elasticsearch cluster address, port number and other related parameters.

[0029] Furthermore, the step 2 specifically includes:

[0030] Step 201: Read the query conditions configured by the user, and check whether the configuration complies with the Elasticsearch query syntax and JSON syntax format;

[0031] Step 202: construct a corresponding query statement according to the query conditions configured by the user;

[0032] Step 203: Convert the query statement into JSON format and send it to the Elasticsearch cluster through the RESTful API to perform data retrieval.

[0033] Furthermore, in step 202, a corresponding query statement is constructed according to the query condition configured by the user, specifically:

[0034] Step 221: construct an initial query statement according to the query conditions configured by the user;

[0035] Step 2022: clarify the business target data according to the query conditions and determine the data characteristics;

[0036] Step 223: Analyze the structure of the business target data and obtain field associations;

[0037] Step 2024: Run the initial query statement in the test environment, check the accuracy of the result, and fine-tune through feedback until the complete business target data is obtained to obtain the final query statement.

[0038] Furthermore, in step 3, the ElasticsearchReader and the DataX framework interact through a predefined interface and data format, specifically including:

[0039] Data preparation stage: Obtain the record data set to be transmitted from the data source and organize it to conform to the pre-defined specific record format requirements;

[0040] Batch division stage: divide the sorted record data into multiple batches according to the set batch size parameters;

[0041] Transmission preparation stage: pre-process the record data of each batch, including adding batch identification and data verification;

[0042] Data transmission stage: each batch of data is transmitted to the corresponding receiving end through the network connection in turn;

[0043] Transmission confirmation phase: After the receiving end successfully receives and processes a batch of data, the receiving end returns a confirmation message; the sending end receives the confirmation message, indicating that the batch transmission is successful, and continues to transmit the next batch of data; if the sending end does not receive the confirmation message within the predetermined time, it sends the batch of data again according to the preset retry strategy until the transmission is successful or the retry limit is reached, and performs corresponding error handling.

[0044] Furthermore, the step 4 is specifically as follows:

[0045] Step 401: monitor the Elasticsearch cluster, collect query-related performance indicators, parse the query statements, count the number of fields involved, the number of filter conditions, and the number of aggregation operations, and regularly obtain the total number of documents in the Elasticsearch index and the data size of each shard;

[0046] Step 402: determine the query complexity, and set and adjust the number of connections according to the query complexity;

[0047] Step 403: After adjusting the number of connections, continuously monitor the performance indicators of the Elasticsearch cluster. If the performance does not improve or deteriorates after the adjustment, roll back the adjustment operation or further optimize the adjustment strategy.

[0048] Furthermore, while dynamically adjusting the number of connections of the Elasticsearch cluster and the concurrency of queries in step 4, memory management is also included, specifically: real-time monitoring and optimization of memory usage, timely release of unused objects and resources, and prevention of memory leaks.

[0049] The beneficial effects of the present invention are reflected in that the present invention can ensure the complete migration of Elasticsearch data to other databases, such as Mysql, MongoDB, Elasticsearch, Redis, etc., with fast migration speed and high efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 The present invention provides a flow chart of the method. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on 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.

[0052] Example 1: Reference Figure 1 As shown, an Elasticsearch data migration and synchronization method according to an embodiment of the present invention includes the following steps:

[0053] Step 1: Establish a connection to the Elasticsearch cluster, specifically:

[0054] Step 101: Read the Java client or RESTFUL API of Elasticsearch;

[0055] Step 102: Establish a communication connection with each node in the cluster according to the configured Elasticsearch cluster address, port number and other related parameters.

[0056] Step 2: Construct the corresponding query statement according to the query conditions configured by the user to retrieve data in the Elasticsearch index. Specifically:

[0057] Step 201: Read the query conditions configured by the user, and check whether the configuration complies with the Elasticsearch query syntax and JSON syntax format;

[0058] Step 202: construct a corresponding query statement according to the query conditions configured by the user;

[0059] Step 221: construct an initial query statement according to the query conditions configured by the user; clarify the business target data according to the query conditions and determine the data characteristics; analyze the structure of the business target data and obtain field associations; configure the query and column field formats in the documents and examples, run the initial query statement in the test environment, check the accuracy of the results, and fine-tune through feedback until the complete business target data is obtained to obtain the final query statement;

[0060] Step 203: Convert the query statement into JSON format and send it to the Elasticsearch cluster through the RESTful API to perform data retrieval.

[0061] Step 3: After the data from the source data engine is read out, it is processed and matched with the fields and structures required by the target data source and transferred to the DataX framework for further transmission to the target data source;

[0062] The ElasticsearchReader and DataX framework interact through predefined interfaces and data formats. Specifically:

[0063] 1) Data preparation stage: First, obtain the set of record data to be transmitted from the data source (such as database, file, etc.). Organize this data to make it conform to the pre-defined specific record format requirements. For example, the specific record format may stipulate that each record contains fields such as "timestamp, data category, specific value", so make sure that each record is organized according to this specification.

[0064] 2) Batch division stage: According to the set batch size parameter (assuming the batch size is `batchSize`), the sorted record data is divided into multiple batches. You can loop through the record collection, and when `batchSize` records are accumulated, a batch is divided and processed in turn.

[0065] 3) Transmission preparation stage: For each batch of data, necessary preprocessing is performed, such as adding batch identification, performing simple data verification, etc., to ensure the integrity and accuracy of the batch data so as to meet the processing expectations of the recipient.

[0066] 4) Data transmission stage: Call `RecordSender.sendToWriter()` method to transmit each batch of data to the corresponding receiving end (i.e. writing end) through network connection or other means. During the transmission process, there may be corresponding protocols to ensure reliable data transmission, such as using TCP protocol to ensure that data is not lost or misordered, and monitoring the transmission status in real time. If there is an abnormal situation such as transmission failure, appropriate retry or error recording operation will be performed.

[0067] 5) Transmission confirmation phase: After the receiving end successfully receives and processes a batch of data, wait for the receiving end to return confirmation information. If confirmation information is received, it indicates that the batch transmission is successful, and the next batch of data will continue to be transmitted; if confirmation information is not received within a certain period of time, the batch of data will be sent again according to the preset retry strategy until the transmission is successful or the retry limit is reached, and the corresponding error processing is performed.

[0068] Step 4: Dynamically adjust the number of connections and query concurrency of the Elasticsearch cluster based on the complexity of the query and the amount of data. Specifically:

[0069] Step 401: Use Elasticsearch's own monitoring tool in combination with Prometheus and Grafana to monitor the Elasticsearch cluster to continuously collect query-related performance indicators. Parse the query statement, count the query complexity evaluation indicators such as the number of fields involved, the number of filter conditions, and the number of aggregation operations, and regularly obtain the total number of documents in the Elasticsearch index and the data size of each shard.

[0070] Step 402: When the query complexity is low (for example, a simple single-field equal value query, and the data scan ratio is less than 10%) and the data volume is small (the total index data volume is less than 1 million documents), the initial number of connections can be set to a smaller threshold, such as 5-10 connections. As the query concurrency increases, if the query response time remains at a low level (for example, the average response time is less than 100 milliseconds) and the cluster resource utilization does not exceed 50% (comprehensive indicators such as CPU, memory, disk I / O, etc.), the number of connections can be gradually increased by 2-3 connections each time until a moderate upper limit of the number of connections is reached, such as 20-30 connections.

[0071] Step 403: After adjusting the number of connections, continuously monitor cluster performance indicators to verify the effectiveness of the adjustment. If the performance does not improve or deteriorates after the adjustment, roll back the adjustment operation in time or further optimize the adjustment strategy.

[0072] Step 5: When an abnormal situation occurs during the data reading process, the abnormal situation is captured and identified, and a retry operation is performed according to the configured number of retries and retry interval time; if it still fails after multiple retries, the error information is recorded, and a decision is made based on the specific situation whether to continue the data reading task.

[0073] In this embodiment, while dynamically adjusting the number of connections of the Elasticsearch cluster and the concurrency of the query in step 4, it also includes memory management, specifically: real-time monitoring and optimization of memory usage, timely release of unused objects and resources, and prevention of memory leaks.

[0074] In this embodiment, step 5 uses the exception handling structure provided by the programming language (such as the `trycatch` block in Java) to wrap the data reading operation. In this way, any exception that occurs during the reading process can be captured. In the `catch` block, the specific exception situation is determined by judging the type of the captured exception object. Different exception types represent different root causes of the problem, for example: `FileNotFoundException`, indicating that the specified data source file cannot be found, it may be that the file path is wrong or the file does not exist. `SQLException` may involve database issues.

[0075] Get the two key parameters of the number of retries (assuming it is represented by the variable `retryCount`) and the retry interval (represented by the variable `retryInterval`) from the configuration file or the pre-set configuration class / object. These configuration parameters can be reasonably set according to the actual business scenario, the importance of data reading, and the stability of the data source. Use a loop structure (such as a `for` loop or a `while` loop) to perform the retry operation. If the data is read successfully during the retry process, that is, no exception is thrown again, then jump out of the retry loop and continue the subsequent data processing process, such as parsing and transmitting the read data.

[0076] If the data reading operation still fails after `retryCount` retries, the error information needs to be recorded. The detailed information of the exception (including exception type, exception message, occurrence time, etc.) can be written to a log file (using a log framework such as `Log4j`, `Slf4j`, etc.) for subsequent troubleshooting and analysis.

[0077] In the description of the embodiments of the present invention, it should be understood that the terms "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "center", "top", "bottom", "top", "bottom", "inside", "outside", "inner side", "outer side" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. Among them, "inside" refers to an internal or enclosed area or space. "Periphery" refers to the area surrounding a specific component or a specific area.

[0078] In the description of the embodiments of the present invention, the terms "first", "second", "third", and "fourth" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first", "second", "third", and "fourth" may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, "multiple" means two or more.

[0079] In the description of the embodiments of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "install", "connect", "connect", and "assemble" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a direct connection, or an indirect connection through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0080] In the description of the embodiments of the present invention, specific features, structures, materials or characteristics may be combined in a suitable manner in any one or more embodiments or examples.

[0081] In the description of the embodiments of the present invention, the term "and / or" herein is merely a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " herein generally indicates that the associated objects before and after are in an "or" relationship.

[0082] 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. An Elasticsearch data migration and synchronization method, characterized in that: The following steps are involved: Step 1: Establish a connection with the Elasticsearch cluster; Step 2: Construct the corresponding query statement according to the query conditions configured by the user to retrieve data in the Elasticsearch index; Step 3: After reading the data from the source data engine, it is processed and matched with the fields and structures required by the target data source and transferred to the DataX framework for further transmission to the target data source; the ElasticsearchReader and DataX framework interact through predefined interfaces and data formats; Step 4: Dynamically adjust the number of connections and query concurrency of the Elasticsearch cluster according to the complexity of the query and the amount of data; Step 5: When an abnormal situation occurs during the data reading process, the abnormal situation is captured and identified, and the retry operation is performed according to the configured number of retries and retry interval; if it still fails after multiple retries, the error information is recorded, and a decision is made whether to continue the data reading task based on the specific business situation and the importance of the data reading task.

2. According to the Elasticsearch data migration and synchronization method of claim 1, it is characterized in that: The step 1 specifically includes: Step 101: Read the Java client or RESTful API of Elasticsearch; Step 102: Establish a communication connection with each node in the cluster according to the configured Elasticsearch cluster address, port number and other related parameters.

3. The Elasticsearch data migration and synchronization method according to claim 1, characterized in that: The step 2 specifically includes: Step 201: Read the query conditions configured by the user, and check whether the configuration complies with the Elasticsearch query syntax and JSON syntax format; Step 202: construct a corresponding query statement according to the query conditions configured by the user; Step 203: Convert the query statement into JSON format and send it to the Elasticsearch cluster through the RESTful API to perform data retrieval.

4. The Elasticsearch data migration and synchronization method according to claim 3, characterized in that: In step 202, a corresponding query statement is constructed according to the query condition configured by the user, specifically: Step 221: construct an initial query statement according to the query conditions configured by the user; Step 2022: clarify the business target data according to the query conditions and determine the data characteristics; Step 223: Analyze the structure of the business target data and obtain field associations; Step 2024: Run the initial query statement in the test environment, check the accuracy of the result, and fine-tune through feedback until the complete business target data is obtained to obtain the final query statement.

5. The Elasticsearch data migration and synchronization method according to claim 1, characterized in that: In step 3, the ElasticsearchReader and the DataX framework interact through predefined interfaces and data formats, specifically including: Data preparation stage: Obtain the record data set to be transmitted from the data source and organize it to conform to the pre-defined specific record format requirements; Batch division stage: divide the sorted record data into multiple batches according to the set batch size parameters; Transmission preparation stage: pre-process the record data of each batch, including adding batch identification and data verification; Data transmission stage: each batch of data is transmitted to the corresponding receiving end through the network connection in turn; Transmission confirmation phase: After the receiving end successfully receives and processes a batch of data, the receiving end returns a confirmation message; the sending end receives the confirmation message, indicating that the batch transmission is successful, and continues to transmit the next batch of data; if the sending end does not receive the confirmation message within the predetermined time, it sends the batch of data again according to the preset retry strategy until the transmission is successful or the retry limit is reached, and performs corresponding error handling.

6. The Elasticsearch data migration and synchronization method according to claim 1, characterized in that: The step 4 is specifically as follows: Step 401: monitor the Elasticsearch cluster, collect query-related performance indicators, parse the query statements, count the number of fields involved, the number of filter conditions, and the number of aggregation operations, and regularly obtain the total number of documents in the Elasticsearch index and the data size of each shard; Step 402: determine the query complexity, and set and adjust the number of connections according to the query complexity; Step 403: After adjusting the number of connections, continuously monitor the performance indicators of the Elasticsearch cluster. If the performance does not improve or deteriorates after the adjustment, roll back the adjustment operation or further optimize the adjustment strategy.

7. The Elasticsearch data migration and synchronization method according to claim 1, characterized in that: In step 4, the number of connections of the Elasticsearch cluster and the concurrency of the query are dynamically adjusted, and memory management is also included, specifically: real-time monitoring and optimization of memory usage, timely release of unused objects and resources, and prevention of memory leaks.

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