CN113297240A - PostgreSQL database synchronization method and system - Google Patents
PostgreSQL database synchronization method and system Download PDFInfo
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- CN113297240A CN113297240A CN202110609729.9A CN202110609729A CN113297240A CN 113297240 A CN113297240 A CN 113297240A CN 202110609729 A CN202110609729 A CN 202110609729A CN 113297240 A CN113297240 A CN 113297240A
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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
- G06F16/23—Updating
- G06F16/2365—Ensuring data consistency and integrity
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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
- G06F16/258—Data format conversion from or to a database
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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
- 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
- G06F9/44—Arrangements for executing specific programs
- G06F9/445—Program loading or initiating
- G06F9/44521—Dynamic linking or loading; Link editing at or after load time, e.g. Java class loading
- G06F9/44526—Plug-ins; Add-ons
Abstract
The invention discloses a synchronization method and a system for a PostgreSQL database, wherein the method comprises the following steps: acquiring a WAL log of a PostgreSQL database; configuring and registering a Kafka Connector to connect the PostgreSQL database and the Kafka cluster; monitoring the WAL log, and storing WAL log data obtained by monitoring into Kafka partition; and consuming topic data in the Kafka cluster, encapsulating the obtained topic data into Json format data, and synchronizing the encapsulated Json format data into an ElasticSearch database. The method and the system adopt a partition algorithm, partition keys can be flexibly defined through configuration parameters, and databases with the same characteristics can be synchronized into the same partition for ordered consumption and storage of signed data.
Description
Technical Field
The invention relates to the field of big data, in particular to a synchronization method and a system for a PostgreSQL database.
Background
In the conventional data maintenance in the ElasticSearch library of a search engine, when the PostgreSQL data of a main database is changed through an application layer service code, the relative interface synchronization data of the ElasticSearch is called into the ElasticSearch at the same time, so that the relative synchronization of the PostgreSQL main data and the ElasticSearch data is realized. The data synchronization process is complex to implement, data maintenance in the ElasticSearch library of the search engine depends on business logic of an application layer, the logic of the business layer is generally complex, and a data synchronization script of the ElasticSearch library needs to be injected into each relevant business code; the maintainability is poor, with the development of business, the business code is increasingly complicated, and the data synchronization work of the Ealstic search library is increasingly difficult.
Disclosure of Invention
One of the purposes of the invention is to provide a postgreSQL database synchronization method and system, which synchronize the data in the postgreSQL database into an ElasticSearch database through format conversion by establishing a Kafka message queue of a plurality of threads, so that rapid data synchronization can be realized.
One purpose of the invention is to provide a synchronization method and system for a postgreSQL database, which realize distributed deployment through KafkaConnector and can reduce data congestion.
Another object of the present invention is to provide a method and a system for synchronizing PostgreSQL databases, where the method and the system use a partition algorithm, flexibly define partition keys through configuration parameters, and synchronize databases with the same characteristics into the same partition for ordered consumption and storage of signed data.
Another object of the present invention is to provide a postgreSQL database synchronization method and system, in which a debezium plugin is configured in a Kafka cluster, so as to implement real-time capture of a specific data table in the postgreSQL database and reduce data synchronization delay.
Another purpose of the present invention is to provide a PostgreSQL database synchronization method and system, where the method and system set up a plurality of threads to consume topic data in a Kafka message queue, and when each thread executes a segment of sleep operation after pulling the topic data, it can ensure that each consuming end can normally consume the topic data.
To achieve at least one of the above objects, the present invention further provides a PostgreSQL database synchronization method, including the steps of:
acquiring a WAL log of a PostgreSQL database;
configuring and registering a Kafka Connector to connect the PostgreSQL database and the Kafka cluster;
monitoring the WAL log, and storing WAL log data obtained by monitoring into Kafka partition;
and consuming topic data in the Kafka cluster, encapsulating the obtained topic data into Json format data, and synchronizing the encapsulated Json format data into an ElasticSearch database.
According to one preferred embodiment of the present invention, Kafka topoic is created after Kafka connectors are configured and registered, partitions in Kafka clusters are designated, and debezium plug-ins are set in the Kafka libs library for real-time monitoring of postgreSQL database.
According to another preferred embodiment of the present invention, the synchronization method further comprises the steps of:
configuring algorithm parameters to establish a kafka partition algorithm, and setting the number of kafka partitions;
defining partition keys according to a partition algorithm, and calculating a partition value of each partition;
and storing topic data with the same characteristics in the same partition according to the partition value.
According to another preferred embodiment of the present invention, the partition value calculation method comprises:
acquiring a specified field in configuration parameters, and calculating a machine code of the specified field;
and performing modular calculation according to the machine code of the specified field.
According to another preferred embodiment of the present invention, the synchronization method comprises the steps of:
setting thread configuration parameters, and generating a plurality of threads according to the thread configuration parameters;
and starting a plurality of threads according to the thread configuration parameters, and independently monitoring data under the topic appointed in the Kafka cluster by each started thread.
According to another preferred embodiment of the present invention, the method for thread to listen to topic data further comprises the following steps: and after each thread acquires the monitored data under the topic, controlling the thread to sleep for the designated time according to the thread configuration parameters.
According to another preferred embodiment of the present invention, after thread monitoring is started, if the WAL log data is found to be changed in the course of monitoring the WAL log, an application function in the maycourr partitioner class is called to calculate a partition key, the partition value of the changed WAL log data is calculated according to the partition algorithm, and the updated WAL log data is stored in the designated Kafka partition according to the calculated partition value.
According to another preferred embodiment of the present invention, the process of encapsulating topoic data into data in Json format further comprises the following steps:
acquiring change data under topic, judging whether an enterprise acquiring the change data has sign-in setting or not, and if not, not processing the acquired change data;
judging whether the changed data has a field deleting instruction; if yes, calling an ElasticSearch interface to delete the changed data;
and if the changed data is judged to be the updated data, converting the updated data into the entity object of the data in the Json format, and performing word segmentation operation on the field needing fuzzy query in the updated data.
To achieve at least one of the above objects, the present invention further provides a PostgreSQL database synchronization system, which adopts and executes the above PostgreSQL database synchronization method.
The invention further provides a computer-readable storage medium, which stores and applies the PostgreSQL database synchronization system.
Drawings
Fig. 1 is a schematic flow chart showing a synchronization method of a PostgreSQL database according to the present invention.
FIG. 2 is a flow chart of the partitioning algorithm of the present invention.
Detailed Description
The following description is presented to disclose the invention so as to enable any person skilled in the art to practice the invention. The preferred embodiments in the following description are given by way of example only, and other obvious variations will occur to those skilled in the art. The basic principles of the invention, as defined in the following description, may be applied to other embodiments, variations, modifications, equivalents, and other technical solutions without departing from the spirit and scope of the invention.
It is understood that the terms "a" and "an" should be interpreted as meaning that a number of one element or element is one in one embodiment, while a number of other elements is one in another embodiment, and the terms "a" and "an" should not be interpreted as limiting the number.
Referring to fig. 1, the present invention provides a synchronization method and system for PostgreSQL database, wherein the method mainly includes the following steps: firstly, a log function of a PostgreSQL database needs to be started, an account and an authority of the PostgreSQL database are configured, and Kafka connectors are further configured and registered, it should be noted that Kafka connectors are a data transmission tool capable of collecting data from the database to Kafka topic, low-delay transmission from the database to a Kafka cluster can be achieved, and distributed environment deployment is achieved through different interfaces by the Kafka connectors, so that delay of database synchronization is reduced.
In the process of registering Kafka Connector, Kafka topic needs to be created for establishing a communication link between the PostgreSQL database and the Kafka cluster, in a preferred embodiment of the present invention, 3 Kafka partitions need to be specified in the process of creating Kafka topic, and addresses of the 3 Kafka partitions are respectively set, for example: three partitions, partition1, partition2, partition3, may be set by instructions and the addresses corresponding to the 3 partitions may be set to: 192.168.95.158:2181, 192.168.95.159:2181, 192.168.95.160:2181. It should be noted that each topic may specify multiple partitions (partitions), and the message has a unique ordered ID in each partition of the topic, where the ID is offset, and in the Kafka Connector registration process, the debezium plugin is further stored in the Kafka libs library to monitor the data change of the PostgreSQL database in real time and capture the changed data in real time. It should be noted that the debezium plugin can capture the data list specified in the PostgreSQL database through the configuration parameters. Therefore, the technical effect of directional grabbing monitoring can be achieved.
The invention synchronizes the data in the PostgreSQL database to the ElasticSearch database through the Kafka cluster, and in the synchronization process, the format conversion of WAL log data in the PostgreSQL database needs to be carried out, and the format conversion mode comprises the following steps: modify the value of the plug.name parameter in the configuration to: wal2json streaming, so that wal log data format can be converted to json format data and synchronized to the ElasticSearch database.
It should be noted that, in order to save WAL log data obtained from the PostgreSQL database into a designated Kafka cluster, the present invention further provides a Kafka partitioning algorithm, where the partitioning algorithm includes setting a partition number, where the three Kafka partitions can be set by setting a transform. For example: the method includes the following steps of setting a transform, a syntax partitioning, a key, and a list field in a program code as follows: form _ data _ code, reimburse _ data _ code, and corp _ reimburse _ data _ code, calculating a field value of the form number (form _ data _ code) of the field, and regarding the field value as a partition value, wherein the calculation method of the field value includes: and acquiring the machine code of the field in the computer, performing modular calculation on the machine code, and taking the result of the modular calculation as the field value of the field (form _ data _ code) and the calculation result as the partition value of the field. And storing the data of the partition value in the same partition, thereby completing the ordered storage and consumption of the data of the same type. It should be noted that, in the present invention, other fields with multiple same types in the WAL log data may also be embedded into the code that needs to be partitioned into values, and the WAL log data with different partition values may be stored in a classified manner.
For better explaining the present invention, the present invention provides a flow diagram of the partitioning algorithm shown in fig. 2, first, WAL log data needs to be read, and configuration information in the WAL log data is acquired; judging whether topic where the WAL log data is located is the topic in the configuration, if so, further judging whether a partition key field exists; and if so, further carrying out partition value calculation on the fields configured by the configuration items, returning the partition value calculation result, and storing the WAL log data in a specified Kafka partition according to the partition value. When the monitoring of WAL log data of a PostgreSQL database is started, judging whether the monitored data is changed or not, and if not, not processing; if a change occurs, the application function in the maycurPartitioner class is called to compute the partition key and the partition value for the specified field is computed, thereby storing the updated WAL log data in the specified Kafka partition.
In order to reduce data congestion, the invention further executes multithreading consumption of the topic data at a consumption end, and the consumption method of the multithreading topic data comprises the following steps: setting thread configuration parameters, and setting the thread configuration parameters into a plurality of threads, wherein the thread configuration parameters are as follows: and afka, consumer, thread, num, sign, for, executing the starting operation of the corresponding thread according to the parameter configuration of the thread. Configuring thread monitoring parameters, wherein the thread monitoring parameters are kafka. topic. sign.for, monitoring data under the appointed topic can be realized by modifying the thread monitoring configuration parameters, further feeding monitoring results back to a kafka cluster, and uploading the monitoring results through the kafka cluster to obtain a data consumption state. It should be noted that the present invention sets a timing sleep command, configures kafka duration parameters, and controls the thread to sleep for a period of time when each thread pulls data under topic each time, thereby ensuring normal data consumption of the consuming end.
In the process of performing data conversion on wal log data and converting the log data into a data format which is matched with an ElasticSearch database, the following operations are also performed: acquiring changed data under topic, judging whether a receiving enterprise has sign-in setting, if not, directly skipping, and not processing the changed data under topic;
judging whether a deleting instruction exists in the field according to the 'OP' field of the data under the changed topic, and calling an ElasticSearch interface according to the document number to delete the changed data if the deleting instruction exists;
judging whether the changed data is an updating operation, if so, packaging the updated data into an entity object (SignForTask) which is adapted to a Json format of an ElasticSearch database, and performing word segmentation operation on fields which need fuzzy query in configuration parameters of the changed data.
It should be noted that the above method of the present invention provides a synchronization method from a PostgreSQL database to an ElasticSearch database, where the data synchronization method is based on the synchronization operation of a Kafka cluster, and relates to real-time monitoring and partition storage and service processing of the PostgreSQL database, and it is not necessary to inject complex service codes into the ElasticSearch database, and it is possible to implement ordered and efficient data synchronization and service processing.
In particular, according to the embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication section, and/or installed from a removable medium. The computer program, when executed by a Central Processing Unit (CPU), performs the above-described functions defined in the method of the present application. It should be noted that the computer readable medium mentioned above in the present application may be a computer readable signal medium or a computer readable storage medium or any combination of the two. The computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wire segments, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless section, wire section, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
It will be understood by those skilled in the art that the embodiments of the present invention described above and illustrated in the drawings are given by way of example only and not by way of limitation, the objects of the invention having been fully and effectively achieved, the functional and structural principles of the present invention having been shown and described in the embodiments, and that various changes or modifications may be made in the embodiments of the present invention without departing from such principles.
Claims (10)
1. A PostgreSQL database synchronization method is characterized by comprising the following steps:
acquiring a WAL log of a PostgreSQL database;
configuring and registering a Kafka Connector to connect the PostgreSQL database and the Kafka cluster;
monitoring the WAL log, and storing WAL log data obtained by monitoring into Kafka partition;
and consuming topic data in the Kafka cluster, encapsulating the obtained topic data into Json format data, and synchronizing the encapsulated Json format data into an ElasticSearch database.
2. The postgreSQL database synchronization method of claim 1, wherein Kafka topoic is created after configuring and registering Kafka Connector, and partitions in Kafka cluster are designated, and debezium plug-ins are set in the Kafka libs library for real-time monitoring of the postgreSQL database.
3. The PostgreSQL database synchronization method according to claim 1, further comprising the steps of:
configuring algorithm parameters to establish a kafka partition algorithm, and setting the number of kafka partitions;
defining partition keys according to a partition algorithm, and calculating a partition value of each partition;
and storing topic data with the same characteristics in the same partition according to the partition value.
4. The PostgreSQL database synchronization method according to claim 3, wherein the partition values are calculated by:
acquiring a specified field in configuration parameters, and calculating a machine code of the specified field;
and performing modular calculation according to the machine code of the specified field.
5. The PostgreSQL database synchronization method according to claim 1, characterized in that the synchronization method comprises the following steps:
setting thread configuration parameters, and generating a plurality of threads according to the thread configuration parameters;
and starting a plurality of threads according to the thread configuration parameters, and independently monitoring data under the topic appointed in the Kafka cluster by each started thread.
6. The PostgreSQL database synchronization method according to claim 5, wherein the method for the thread to listen to topic data further comprises the following steps: and after each thread acquires the monitored data under the topic, controlling the thread to sleep for the designated time according to the thread configuration parameters.
7. The postgreSQL database synchronization method according to claim 5, wherein after thread snooping is started, if WAL log data change is found in the process of snooping the WAL log, an application function in a maycourr partitioner class is called to calculate a partition key, a changed WAL log data partition value is calculated according to the partition algorithm, and the updated WAL log data is stored to a specified Kafka partition according to the calculated partition value.
8. The PostgreSQL database synchronization method according to claim 1, wherein the process of encapsulating topic data into Json format data further comprises the following steps:
acquiring change data under topic, judging whether an enterprise acquiring the change data has sign-in setting or not, and if not, not processing the acquired change data;
judging whether the changed data has a field deleting instruction; if yes, calling an ElasticSearch interface to delete the changed data;
and if the changed data is judged to be the updated data, converting the updated data into the entity object of the data in the Json format, and performing word segmentation operation on the field needing fuzzy query in the updated data.
9. A PostgreSQL database synchronization system that employs and executes a PostgreSQL database synchronization method as claimed in any of the preceding claims 1-8.
10. A computer-readable storage medium storing and using a PostgreSQL database synchronization system according to claim 9.
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