An article explains data sharing, model calibration and real-time update in digital twin systems
In the cutting-edge digital twin model system, the data lake sharing service is like the central nervous system, playing an irreplaceable key role, and driving the precise calibration and continuous update iteration of the model with strong power. Relying on the carefully planned and rigorously designed data sharing process, once there are any subtle changes in the structured data in the data lake and the complex and diverse unstructured data such as geological reservoirs, the system will automatically and sensitively capture these changes like a well-trained intelligent guard, and initiate a comprehensive and accurate update and calibration operation for the digital twin model at the first time. In this way, the oil and gas reservoir model can reflect the dynamic changes of the underground reservoir in real time, the wellbore model accurately simulates the flow characteristics of the fluid in the wellbore, and the pipeline network model clearly shows the operating status of the oil and gas transportation pipeline network, ensuring that they are always highly consistent with the actual production conditions, thereby laying a solid foundation for the efficient and stable operation of the entire digital twin system, giving it a high degree of reliability and practical value.
1. Data sharing, model calibration and implementation update construction content
Oracle data synchronization
In the challenging field of big data extraction, the commonly used tool Sqoop has certain applications, but its shortcomings are also obvious. When faced with TB-level massive data extraction tasks, its processing efficiency is difficult to meet the needs of rapid response. For example, in a database migration project of a large enterprise, involving the extraction of several TB of data, when Sqoop is used for operation, the whole process is time-consuming and lengthy, seriously affecting the progress of the project. When performing incremental data extraction, Sqoop needs to modify the source database table structure. This operation not only requires professional technicians to perform delicate operations, the process is complicated, and it will also have a great impact on the performance of the source database, which may cause the database to have response delays and unstable services during operation.
In sharp contrast, for Oracle data sources, Golden Gate is a more outstanding data extraction tool. It can efficiently complete data capture, conversion and delivery in a very short time of seconds. The log-based structured data replication strategy it adopts is like installing a precise tracker in the data information flow, which can capture the changed data from the online log in near real time and store these valuable data in Trail format files. The outstanding advantage of Golden Gate is that it captures the changed data by deeply analyzing the log files, and this process only takes up very few system resources. Even if the amount of data stored in Oracle is extremely large, such as the core database of some large financial institutions, the amount of stored data reaches the PB level, and the system load is already heavy, it will hardly have any negative impact on the operating efficiency of Oracle. In actual applications, when a multinational bank uses Golden Gate for data extraction, during the daily business period when the database is running at high load, the data extraction work is stable, and it does not cause any impact on the bank's core business system, which effectively guarantees the efficiency and stability of data extraction.
MySQL data synchronization
MySQL InnoDB has an independent and complete log mechanism, and its master-slave synchronization function mainly relies on binlog to achieve. There are three different working modes of binlog, each with its own characteristics in the data recording and synchronization process:
Row mode: In this mode, the log will be like a high-precision recorder, recording in detail the specific modification form of each row of data. When the data is transmitted to the slave, the same data will be accurately modified one by one according to these meticulous records to ensure the consistency and accuracy of the data between the master and slave servers. This mode performs well in scenarios with extremely high data consistency requirements, such as e-commerce order data processing and financial transaction record storage, and can effectively avoid business problems caused by data inconsistency.
Statement mode: In this mode, every SQL statement that modifies the data will be fully recorded in the master's bin-log. When the slave replicates the data, the SQL process will parse these records and execute the same SQL statement as the master, thereby achieving synchronous update of the data. This mode has certain advantages in some scenarios with relatively low requirements for data consistency but high requirements for data synchronization efficiency, such as website content updates and general business data statistics, and can quickly complete data synchronization.
Mixed mode: MySQL will intelligently and flexibly choose between Statement mode and Row mode according to the characteristics of each specific SQL statement to determine the most appropriate log record form. For example, for some simple query and modification operations, Statement mode may be selected to improve recording efficiency; while for operations involving complex data updates or that may affect data consistency, Row mode will be selected. This intelligent selection mechanism cleverly takes into account the accuracy and efficiency of data recording, and can play a good performance in most practical application scenarios.
In the usual MySQL layout architecture, the solution of "2 master master databases (vip) + 1 slave slave database + 1 backup disaster recovery database" is generally adopted. Among them, the 2 master master databases achieve load balancing and high availability through virtual IP (vip), ensuring that a large number of data requests can be stably processed in high-concurrency business scenarios. The slave slave database synchronizes the master database data in real time, which can be used to share the pressure of read operations and improve the overall performance of the system. The disaster recovery database is mainly used in remote disaster recovery scenarios. Due to the particularity of its application scenarios, the data update frequency is relatively low, and the real-time requirements are relatively low. In addition, during the deployment process, many factors such as remote network delay and data transmission security need to be considered, which causes many inconveniences.
Real-time streaming synchronization of unstructured data
With the help of a distributed data collection system, data scattered in various servers can be comprehensively collected and accurately transmitted to a designated storage location, such as centralized storage such as HDFS and HBase.
The core workflow of the data collection system is to first collect data from the data source (Source), where the data source can be various types of devices, such as server log files, sensor real-time data output, etc. Then the data is safely and reliably delivered to the designated destination (Sink). In order to ensure the absolute safety and successful delivery of data during transmission, the data will be temporarily cached in the Channel before it actually reaches the destination. Only when the data successfully reaches the destination (Sink) will the data collection system delete its own cached data to ensure the integrity and consistency of the data. This mechanism of caching first and then confirming deletion is like setting up multiple security checkpoints on the highway of data transmission to ensure that every piece of data can reach the destination accurately.
The core component of the data acquisition system is Agent, which is essentially a Java process running on the data collection node (that is, the server node). The system supports the collaborative working mode of multi-level Agents, which can follow one after another to form a coherent data processing link. For example, the Sink of the previous Agent can smoothly write data to the Source of the next Agent, thereby realizing efficient and coherent data processing. This multi-level serial working mode is like a relay race. Each Agent can maximize its efficiency in its own stage to ensure the rapid flow of data. At the same time, Agent also has powerful expansion capabilities and supports fan-in and fan-out functions. Fan-in means that the Source can accept input data from multiple different sources at the same time. For example, in a smart city project, a data collection node can simultaneously receive data from multiple devices such as traffic cameras, environmental sensors, and smart meters; while fan-out means that the Sink can output data to multiple different destinations. For example, in a data analysis scenario, data can be simultaneously output to a data warehouse for long-term storage and to a real-time analysis platform for immediate processing, greatly improving the flexibility and efficiency of data processing.
The system has a strong data storage adaptation capability and can store data generated by applications in a variety of different types of centralized storage, such as HDFS, HBase, etc. In the actual data collection process, when the data collection speed exceeds the data writing speed, that is, when the data collection peak occurs, the system will automatically make intelligent adjustments between the data producer and the data receiver to ensure that the data can be transmitted smoothly and efficiently between the two. Its data transmission process has high reliability, strong fault tolerance, good upgradeability, convenient manageability, and flexible customizability. It can not only efficiently and quickly store the log information generated by multiple website servers into HDFS/HBase, but also quickly transfer the data obtained from multiple servers to the Hadoop ecosystem for subsequent processing. In addition, the application scenarios of this system are extremely wide, not only limited to processing log information, but also able to access and collect various event data generated by large-scale social network nodes, such as user interaction records on social media platforms, player behavior data in online games, etc. It supports a variety of different types of access resource data and access data types, and provides comprehensive support for complex data transmission scenarios such as multi-path traffic, multi-channel access traffic, multi-channel access traffic, and context routing. The entire system has good horizontal expansion capabilities and can easily cope with the increasing data processing pressure as business needs grow. For example, in a certain Internet giant company, with the rapid development of business, the amount of data has increased exponentially. By horizontally expanding the data collection system, it successfully met the growing data processing needs and ensured the stable operation of various businesses.