Methods, devices, electronic equipment and storage media for monitoring streaming data

Abstract

This application discloses a method, apparatus, electronic device, and storage medium for monitoring streaming data, relating to the field of computer technology, particularly to artificial intelligence fields such as big data and cloud computing. The specific implementation scheme is as follows: Obtaining field data of the measurement fields under the target monitoring dimension corresponding to the target monitoring indicator; wherein, the field data is extracted from the streaming data of the target business system; determining the target time window to which the field data belongs based on the timestamp in the field data; updating the calculation results within the target time window based on the field values ​​of the measurement fields in the field data to obtain the indicator values ​​of the target monitoring indicator within the target time window; pushing the indicator values ​​of the target monitoring indicator within the target time window and the boundary information of the target time window to a time-series database; wherein, the time-series database is used to monitor the target business system.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to the fields of big data, cloud computing and other artificial intelligence, specifically to a method, device, electronic device and storage medium for monitoring streaming data. Background Technology

[0002] In some business systems, large amounts of critical data (such as user behavior logs, advertising interaction information, IoT sensor reports, and transaction records) are continuously generated in streaming form. This data is typically processed by a streaming engine and ultimately flows into downstream systems, providing timely and high-quality data support for core business scenarios such as AI model training, real-time recommendations, risk control decisions, and operational monitoring. To ensure the stability and timeliness of streaming data, appropriate monitoring can be implemented. Summary of the Invention

[0003] This application provides a method, apparatus, electronic device, and storage medium for monitoring streaming data. The specific solution is as follows: According to one aspect of this application, a method for monitoring streaming data is provided, comprising: Obtain the field data of the measurement fields under the target monitoring dimension corresponding to the target monitoring metric; where the field data is extracted from the streaming data of the target business system; Determine the target time window to which the field data belongs based on the timestamp in the field data; Based on the field values ​​of the measurement fields in the field data, update the calculation results within the target time window to obtain the indicator values ​​of the target monitoring indicators within the target time window; The indicator values ​​of the target monitoring indicators within the target time window and the boundary information of the target time window are pushed to the time series database; the time series database is used to monitor the target business system.

[0004] According to another aspect of this application, a monitoring device for streaming data is provided, comprising: The acquisition module is used to acquire field data of the measurement fields under the target monitoring dimension corresponding to the target monitoring metric; the field data is extracted from the streaming data of the target business system. The determination module is used to determine the target time window to which the field data belongs based on the timestamp in the field data; The calculation module is used to update the calculation results within the target time window based on the field values ​​of the measurement fields in the field data, so as to obtain the indicator values ​​of the target monitoring indicators within the target time window. The push module is used to push the indicator values ​​of the target monitoring indicators within the target time window and the boundary information of the target time window to the time series database; the time series database is used to monitor the target business system.

[0005] According to another aspect of this application, an electronic device is provided, comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the method described in the above embodiments.

[0006] According to another aspect of this application, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause the computer to perform the method described in the above embodiments.

[0007] According to another aspect of this application, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the method described in the above embodiments.

[0008] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent from the following description. Attached Figure Description

[0009] The accompanying drawings are provided for a better understanding of this solution and do not constitute a limitation of this application. Wherein: Figure 1 A flowchart illustrating a method for monitoring streaming data according to an embodiment of this application; Figure 2 A flowchart illustrating a method for monitoring streaming data according to another embodiment of this application; Figure 3 A flowchart illustrating a method for monitoring streaming data according to another embodiment of this application; Figure 4 This application provides a schematic diagram of a time window push mechanism. Figure 5 This is a schematic diagram of a streaming data monitoring process provided in an embodiment of this application; Figure 6 A schematic diagram of the structure of a streaming data monitoring device provided in an embodiment of this application; Figure 7 This is a block diagram of an electronic device used to implement the streaming data monitoring method of the embodiments of this application. Detailed Implementation

[0010] The following description, in conjunction with the accompanying drawings, illustrates exemplary embodiments of this application, including various details to aid understanding. These should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this application. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0011] It should be noted that the acquisition, storage, use, and processing of data in this application comply with the relevant provisions of national laws and regulations and do not violate public order and good morals.

[0012] The following description, with reference to the accompanying drawings, outlines a method, apparatus, electronic device, and storage medium for monitoring streaming data according to embodiments of this application.

[0013] Figure 1 This is a flowchart illustrating a method for monitoring streaming data provided in an embodiment of this application.

[0014] The streaming data monitoring method of this application embodiment can be executed by the streaming data monitoring device of this application embodiment, which can be configured in an electronic device.

[0015] Among them, electronic devices can be any device with computing capabilities, such as personal computers, mobile terminals, servers, etc. Mobile terminals can be hardware devices with various operating systems, touch screens and / or displays, such as in-vehicle devices, mobile phones, tablets, personal digital assistants, wearable devices, etc.

[0016] like Figure 1 As shown, the monitoring method for this streaming data includes: Step 101: Obtain the field data of the measurement field under the target monitoring dimension corresponding to the target monitoring metric.

[0017] In this application, the field data can be extracted from streaming data of the target business system. For example, field data of the measurement field under the same target monitoring dimension under the target monitoring metric can be extracted from streaming data and sent to the same task management instance for processing, so that the same task management instance can continuously obtain field data of the measurement field under the same target monitoring dimension under the target monitoring metric.

[0018] For example, the field data may include, but is not limited to, a measure field, the field value of the measure field, the data identifier of the field data, and a timestamp. The timestamp can be used to indicate the time when the streaming data from which the field data was extracted was generated.

[0019] For example, if a user clicks on an ad in a certain ad slot at time t1, generating a data record, then the timestamp obtained from time t1 is the timestamp in that data record.

[0020] For example, the metric field can refer to the field required to calculate the target monitoring metric.

[0021] Taking the target business system as an example of a commercial advertising system, the target monitoring metric could be the advertising revenue per hour, the measurement field could be the cost generated by a single user's action on an advertisement, and the target monitoring dimension could be a specific advertisement placement.

[0022] For example, in an e-commerce system, the measurement field could be the order amount, while in a financial system, the measurement field could be the transaction amount.

[0023] It should be noted that the target business system can be a commercial advertising system, e-commerce system, financial system, etc., which are just examples. The target business system can also be other business systems, and this application does not limit it.

[0024] Step 102: Determine the target time window to which the field data belongs based on the timestamp in the field data.

[0025] In this application, the target time window can be the current time window, a historical time window, or a new time window; there is no limitation on this. Here, the new time window can be understood as the next time window after the current time window.

[0026] For example, based on the boundary information of each time window, it can be determined which time window the timestamp falls within, and that time window is the target time window.

[0027] The boundary information of a time window can include the left and right boundaries of the time window.

[0028] Step 103: Update the calculation results within the target time window based on the field values ​​in the field data to obtain the indicator values ​​of the target monitoring indicators within the target time window.

[0029] In this application, the calculation results within the target time window can be updated using the field values ​​of the measurement fields in the field data, based on the calculation method of the target monitoring indicators, to obtain the indicator values ​​of the target monitoring indicators within the target time window. Here, the indicator values ​​of the target monitoring indicators within the target time window can be understood as data that can be pushed to the time-series database.

[0030] For example, the target monitoring indicator can be calculated by performing calculations on the field values ​​of the measurement fields in the field data belonging to the same time window to obtain the indicator values ​​of the target monitoring indicator within the target time window.

[0031] For example, the calculation method can be accumulation (or aggregation), counting, averaging, taking the maximum or minimum value, etc.

[0032] Taking an advertising system as an example, you can obtain the hourly advertising revenue by accumulating the costs generated by user interactions with ads within each hour, or by counting the number of ad impressions within each hour, or by averaging the average revenue generated per user interaction.

[0033] For example, the calculation can be performed by combining multiple metric fields. For instance, in an advertising system, the click-through rate (CTR) is obtained based on the ratio of clicks to impressions, and the conversion rate is obtained based on the ratio of conversions to clicks.

[0034] For example, the calculation method associated with the target monitoring indicator is accumulation. The field values ​​of the measurement field in the field data that are continuously received and belong to the same time window can be accumulated into the current calculation result of the time window until a new time window is opened. The calculation result obtained is then used as the indicator value of the target monitoring indicator within the time window.

[0035] Step 104: Push the indicator values ​​of the target monitoring indicators within the target time window and the boundary information of the target time window to the time series database.

[0036] In this application, the boundary information of the target time window may include the left boundary of the target time window, the right boundary of the target time window, etc., and the time series database may refer to a time series database.

[0037] In this application, the indicator values ​​of the target monitoring indicators within the target time window and the boundary information of the target time window can be directly pushed to the time series database. Alternatively, the indicator values ​​of the target monitoring indicators within the target time window and the boundary information of the target time window can be pushed to the time series database in response to the push timing that satisfies the association of the target monitoring indicators. There are no limitations on this. For example, the push timing can be when a new time window is opened, or within a certain period of time after the new time window is opened, or at other times. There are no limitations on this.

[0038] For example, the indicator values ​​of the target monitoring indicators within the target time window, the left boundary of the target time window, etc., can be asynchronously pushed to the time series database via the HTTP protocol.

[0039] For example, a time-series database can be used to monitor a target business system. For example, the monitoring system can read the target monitoring metric values ​​and boundary information of each time window from the time-series database, determine the chronological order of the target monitoring metric values ​​based on the boundary information of each time window, generate an indicator curve for the target monitoring metric based on this chronological order, and display the indicator curve.

[0040] In this embodiment, the target time window to which each piece of streaming data belongs is accurately determined based on the timestamp in the field data, ensuring that each piece of streaming data is assigned to the correct calculation window. The calculation results within the window are dynamically updated based on the field value of the metric field, thereby generating accurate and consistent target monitoring indicator values. The indicator values ​​and their corresponding time window boundary information are written into the time series database. This not only ensures the orderliness and traceability of the indicator data in the time dimension, but also significantly improves the accuracy, timeliness, and controllability of the streaming monitoring system, providing a reliable data foundation for real-time business insights, anomaly alarms, and historical backtracking analysis.

[0041] Figure 2 This is a flowchart illustrating a method for monitoring streaming data according to another embodiment of this application.

[0042] like Figure 2 As shown, the monitoring method for this streaming data includes: Step 201: Obtain the field data of the measurement field under the target monitoring dimension corresponding to the target monitoring indicator.

[0043] In this application, step 201 can be implemented in any of the embodiments of this application, so it will not be described in detail here.

[0044] In some embodiments, the field data of the measurement field can be obtained in the following way: the indicator configuration information and message queue identifier of the target monitoring indicator can be obtained, the streaming data can be read from the target message queue corresponding to the message queue identifier according to the message queue identifier, and the streaming data can be processed according to the indicator configuration information to obtain the field data.

[0045] For example, the indicator configuration information may include, but is not limited to, the name of the target monitoring indicator, the name of the metric field, the name of the dimension field, and the name of the timestamp field. For example, the name of the dimension field can be the target monitoring dimension.

[0046] As can be seen, the indicator configuration information may include, but is not limited to, the target monitoring indicator, filtering logic, measurement fields, target monitoring dimensions, and the name of the timestamp field.

[0047] For example, the filtering logic may include field extraction and traffic targeting logic. For instance, the field extraction and traffic targeting logic in the metric configuration information is as follows: "ETL_SQL": "INSERT INTO \"TXT\" (fake_col)SELECT \"18\" AS \"field1\", \"2\" AS \"field2\", \"3\" AS \"field3\", \"29\"AS \"time_field\" FROM \"TXT\" where \"field2\" in (\"3\")"".

[0048] The SELECT clause contains the field extraction logic. "18" AS "field1" means mapping the 18th value in the text to field field1, "2" AS "field2" means mapping the 2nd value in the text to field field2, "3" AS "field3" means mapping the 3rd value in the text to field field3, and "29" AS "time_field" means mapping the 29th value in the text to field time_field. The WHERE clause contains the traffic targeting logic; specifically, "WHERE "field2" IN ("3")" is the filtering condition, which selects the traffic where field field2 is 3.

[0049] Optionally, if word filtering is involved, users can create a word list in advance on a specified distributed file system path and fill in the word list name, fields to be filtered and methods in the indicator configuration information. That is, the filtering logic can also include word list filtering logic.

[0050] For example, the filtering methods in the vocabulary filtering logic can include being in the vocabulary (e.g., represented by "in"), not being in the vocabulary (e.g., represented by "notin"), etc.

[0051] For example, if the field in the word filtering logic is "field1" and the filtering method is "in", then when using the word filtering logic for filtering, the data with the field "field1" will be retained. If the filtering method is "notin", then when using the word filtering logic for filtering, all other data except the data with the field "field1" will be retained.

[0052] For example, the configuration information for each monitoring metric can be pre-stored. Alternatively, the configuration information for the monitoring metrics can be stored in Redis.

[0053] For example, the metric configuration identifier and the corresponding metric configuration set can be stored in the form of key-value pairs. For example, a metric configuration set can include the metric configuration information of one monitoring metric, or it can include the metric configuration information of multiple monitoring metrics, without limitation.

[0054] For example, the target monitoring metric configuration information and message queue identifier can be obtained in the following way: the configuration file can be parsed to obtain the message queue identifier, the metric configuration identifier and the service connection information corresponding to the metric configuration identifier, and based on the service connection information, a connection network can be established with the storage service, a configuration retrieval request can be sent to the storage service to obtain the metric configuration set corresponding to the metric configuration identifier, and then the metric configuration information of the target monitoring metric can be obtained from the metric configuration set.

[0055] For example, service connection information may include storage address, port, etc. For instance, if the metric configuration set is stored in Redis, then the service connection information may include the Redis address and port.

[0056] For example, obtaining the indicator configuration information of a target monitoring indicator from the indicator configuration set may include obtaining the indicator configuration information of one target monitoring indicator, or obtaining the indicator configuration information of multiple target monitoring indicators, etc. This application does not limit this.

[0057] Since different message queues store different types of streaming data, streaming data can be read from the corresponding target message queue based on the obtained message queue identifier.

[0058] For example, streaming data can be collected from a target message queue using the built-in message queue connector. Since the built-in message queue connector can directly subscribe to the data source without intermediate storage media, end-to-end processing efficiency can be improved.

[0059] Optionally, if the target monitoring metric configuration information includes a word list filtering logic, parsing the configuration file can also obtain the word list storage location information, and the specific content of the word list can be obtained based on the storage location information.

[0060] Optionally, parsing the configuration file can also obtain the window length of the time window. For example, if the window length is 60 minutes, the time window can be from 0:00 to 1:00, the time window from 1:00 to 2:00, the time window from 2:00 to 3:00, and so on.

[0061] In this embodiment, message queue identifiers, indicator configuration identifiers, and corresponding service connection information are obtained by parsing the configuration file. A network connection with external storage services is dynamically established, and the indicator configuration set under the specified indicator configuration identifier is pulled as needed. Then, the configuration information of the target monitoring indicator is accurately extracted from it. This achieves decoupling of configuration and calculation logic and dynamic loading at runtime, so that the monitoring system can respond to changes in business rules without restarting. This can significantly improve the flexibility, maintainability, and multi-tenant support capabilities of the streaming monitoring architecture, while reducing the deployment complexity and operation and maintenance risks caused by hard-coded configuration.

[0062] For example, the indicator configuration information may include target monitoring indicators, filtering logic, measurement fields, and target monitoring dimensions. The streaming data can be processed according to the indicator configuration information to obtain field data in the following ways: The streaming data can be filtered according to the filtering logic to obtain target data. Field values, target monitoring dimensions associated with the field values, and timestamps can be extracted from the target data. The target monitoring indicators and target monitoring dimensions can be concatenated to obtain data identifiers. Field data can be obtained based on field values, timestamps, and data identifiers.

[0063] For example, if the filtering logic includes field extraction and traffic targeting logic, the streaming data can be filtered using this logic to obtain the target data; if the filtering logic includes field extraction and traffic targeting logic and word filtering logic, the streaming data can be filtered first using field extraction and traffic targeting logic to obtain the data obtained from the first filtering, and then the word filtering logic can be used to filter the data obtained from the first filtering to obtain the target data.

[0064] For example, if the field in the word filtering logic is "field1" and the filtering method is "in", then when using the word filtering logic for filtering, the data with the field "field1" in the first filtering result will be retained to obtain the target data. If the filtering method is "notin", then when using the word filtering logic for filtering, the data other than the data with the field "field1" in the first filtering result will be retained to obtain the target data.

[0065] For example, the field value, timestamp, data identifier, etc. of the measurement field can be used as field data, and the data identifier serves as a unique identifier for the field data.

[0066] To differentiate data from different dimensions, for example, for the same target monitoring metric, if there are multiple target monitoring dimensions, for each target monitoring dimension, the field values, timestamps, etc. of the measurement fields under the target monitoring dimension can be extracted from the target data. The target monitoring metric and the target monitoring dimension are concatenated to obtain a data identifier, so that field data with the same data identifier are grouped together. For field data with the same data identifier, the metric value of the target monitoring metric under the target monitoring dimension can be determined based on a dynamic time window.

[0067] For example, grouping can be performed based on data identifiers. This operation can distribute data streams with the same data identifier to the same task management instance for processing. Grouping achieves load balancing, preventing a single instance from having excessively large amounts of data. It also allows for grouping data based on different metrics and dimensions, facilitating subsequent computations.

[0068] In this embodiment, target data is accurately filtered from streaming data based on the filtering logic in the indicator configuration information, and field values, associated monitoring dimensions, and timestamps are extracted. Then, a unique data identifier is generated by concatenating the monitoring indicators and dimensions, and field data is obtained on this basis. This enables fine-grained differentiation and targeted processing of mixed traffic from multiple services. This not only ensures the accuracy and contextual consistency of the monitoring indicator calculation, but also provides a traceable data foundation for subsequent real-time aggregation, time-series storage, and cross-dimensional analysis. It can improve the flexibility, scalability, and business adaptability of the streaming monitoring system.

[0069] As an example, when the monitoring system's computing engine starts or runs, it reads Redis service connection information (such as Redis address, port, etc.) and metric configuration identifiers from the configuration file. Based on this service connection information, the computing engine establishes a network connection with the Redis service, sends a configuration retrieval request to the Redis service, obtains the metric configuration set corresponding to the metric configuration identifier, and distributes the metric configuration set to downstream processing operators. The processing operators continuously check if the metric configuration set has arrived. Before the metric configuration set arrives, the processing operators cache the subscribed streaming data; after the metric configuration set arrives, they process the cached data first, then process the real-time data. Since the metric configuration set may contain multiple monitoring metrics, the processing operators need to extract dimension fields and metric fields for each business traffic separately. To distinguish data from different businesses and different dimensions, the processing operators can concatenate the name of the monitoring metric with the value of the dimension field, i.e., the monitoring dimension, as a data identifier (a unique identifier for the data). This data identifier, along with the metric field value, timestamp, etc., is passed to the downstream computing operators, which then calculate the metric value of the monitoring metric. Here, the processing operators and downstream computing operators are operators within the computing engine.

[0070] Therefore, the computing engine uses Redis as a broadcast data source to distribute the metric configurations under the specified metric configuration identifier to the downstream processing operators. It can be seen that by using broadcast data streams, the filtering logic can be updated in real time, which can meet the personalized needs of monitoring and improve the flexibility of monitoring.

[0071] In this embodiment, by acquiring the indicator configuration information and corresponding message queue identifier of the target monitoring indicator, the data source and processing logic are dynamically associated. On the one hand, the access channel of streaming data is accurately located; on the other hand, the raw streaming data is filtered and fields are extracted based on the indicator configuration information. This allows for the efficient generation of field data that meets monitoring requirements, decoupling of data consumption from business logic, and support for parallel processing of multiple indicators and multiple services. It can also improve the flexibility, scalability, and configuration-driven capability of the streaming monitoring system, providing accurate and targeted data input for real-time indicator calculation and downstream applications.

[0072] Step 202: Determine the current water level time based on the timestamp.

[0073] In this application, the current water level time is the latest timestamp in the observed data. For example, the observed data may include currently arriving field data, processed field data, etc. Here, processed field data refers to the field data of the measurement field under the target monitoring dimension corresponding to the target monitoring indicator that has already been processed.

[0074] For example, the timestamp can be compared with the water level time before the field data arrived. If the timestamp is greater than or equal to the water level time before the field data arrived, the water level time before the field data arrived can be updated to the timestamp, that is, the timestamp is used as the current water level time. If the timestamp is less than the water level time before the field data arrived, the water level time can be kept unchanged, that is, the water level time before the field data arrived is the current water level time.

[0075] Step 203: Determine the target type of the field data based on the relationship between the timestamp and the current water level time.

[0076] For example, the target type can be used to indicate whether the field data is delayed data. For example, the target type can include delayed type, non-delayed type, etc. If the target type is delayed, the field data can be considered delayed data; if the target type is non-delayed, the field data can be considered real-time data.

[0077] For example, a timestamp can be compared with the current water level time. If the timestamp is less than the current water level time, the target type can be determined to be a delayed type. If the timestamp is greater than or equal to the current water level time, the target type can be determined to be a non-delayed type.

[0078] Therefore, by introducing water level time, it is possible to effectively distinguish between delayed data and non-delayed data. When the timestamp of the field data is less than the current water level time, it is determined to be historical data that has arrived late; otherwise, it is regarded as real-time data. This can significantly improve the ability of the streaming processing system to balance timeliness and accuracy.

[0079] Step 204: Determine the target time window based on the target type.

[0080] As one possible implementation, the target type of the field data is a non-delayed type. If the current water level time is greater than or equal to the right boundary of the current time window, a new time window can be triggered, and the new time window is determined as the target time window, that is, the field data falls within the new time window. If the current water level time is less than the right boundary of the current time window, the current time window is determined as the target time window.

[0081] Therefore, by introducing water level time, the time water level is dynamically advanced based on the timestamp of the field data, and the time window to which the current data belongs is intelligently determined. When the current water level time crosses the right boundary of the current window, a new time window is automatically triggered. This ensures the accuracy of streaming computing results while effectively balancing real-time performance and completeness, avoiding data misalignment or loss caused by fixed window division, and significantly improving the reliability and timeliness of monitoring.

[0082] As another possible implementation, if the target type of the field data is a delay type, determine the target historical window to which the timestamp belongs from at least one historical time window within the time of the current water level that is equal to the maximum delay time, and determine the target historical window as the target time window.

[0083] Therefore, by constraining the maximum delay duration, when the field data belongs to the delayed type, it is possible to accurately locate the actual time window to which the data belongs by tracing back from the current water level time to the historical time window within the limited time range. Thus, under the premise of allowing a certain delay, delayed data can be accurately placed into the corresponding time window to ensure data accuracy. It ensures that late data is still correctly classified into the window corresponding to its logical occurrence time for processing. This not only ensures the accuracy of streaming calculation results, but also avoids data loss caused by infinite waiting or premature window closure. It can effectively improve the fault tolerance capability of the monitoring system for delayed data and the completeness of monitoring indicator calculation.

[0084] Step 205: Update the calculation results within the target time window based on the field values ​​of the measurement fields in the field data to obtain the indicator values ​​of the target monitoring indicators within the target time window.

[0085] In this application, step 205 can be implemented in any of the embodiments of this application, so it will not be described in detail here.

[0086] In some embodiments, if the target time window is the current time window, the calculation results within the current time window can be updated based on the field values.

[0087] In some embodiments, the state stores the state of all time windows from the current water level time to the maximum delay time. If the target time window is a target historical window, the calculation results within the target historical window can be updated according to the field values.

[0088] In some embodiments, if the target time window is a new time window, the calculation result within the new time window can be determined based on the field value.

[0089] Taking the calculation method of target monitoring indicators as accumulation as an example, when real-time data (that is, field data with timestamps greater than or equal to the current water level time) arrives, it can be checked whether the current water level time is greater than or equal to the right boundary of the current time window. If it is greater than the right boundary of the current time window, the current water level time will push to generate a new time window, and the field value of the measurement field will be accumulated into the new time window; if it is not greater than the right boundary of the current time window, the accumulated field value of the current field data will be accumulated into the calculation result of the current window.

[0090] When delayed data (i.e., field data with timestamps less than the current water level time) arrives, since the status stores the states of all time windows from the current time to the maximum delay duration, the difference between the timestamp and the current water level time can be calculated as the delay duration, and it is checked whether the delay duration is less than the maximum delay duration. If it is less than the maximum delay duration, it is placed into the corresponding time window based on the timestamp, and the metric field value is added to the calculation result of the corresponding window. If it is greater than the maximum delay duration, the field data can be discarded.

[0091] Step 206: Push the indicator values ​​of the target monitoring indicators within the target time window and the boundary information of the target time window to the time series database.

[0092] In this application, step 206 can be implemented in any of the embodiments of this application, so it will not be described in detail here.

[0093] In this embodiment, the current water level time is determined based on the timestamp in the field data, and the timestamp of the field data is compared with the latest timestamp in the observed data to dynamically determine whether it is delayed data. Based on this, it is classified into different target types and then accurately mapped to the corresponding time window. This effectively realizes the intelligent identification and repositioning of out-of-order or late data, ensuring the real-time performance of streaming computing while improving the accuracy of time window division and the completeness of monitoring indicator calculation.

[0094] Figure 3This is a flowchart illustrating a method for monitoring streaming data according to another embodiment of this application.

[0095] like Figure 3 As shown, the monitoring method for this streaming data includes: Step 301: Obtain the field data of the measurement field under the target monitoring dimension corresponding to the target monitoring indicator.

[0096] Step 302: Determine the target time window to which the field data belongs based on the timestamp in the field data.

[0097] In this application, steps 301-302 can be implemented in any of the embodiments of this application, so they will not be described in detail here.

[0098] Since field data may arrive late, and if the delay is too long, monitoring this data may be meaningless. Therefore, in some embodiments, the validity of the field data can be determined first based on the timestamp in the field data. If the field data is determined to be valid, the target time window to which the field data belongs can then be determined based on the timestamp. This allows for the calculation of monitoring metric values ​​based on valid data, improving the accuracy of the calculation results.

[0099] For example, the following methods can be used to determine whether field data is valid: if the timestamp is greater than or equal to the current water level time, the field data is considered valid; if the timestamp is less than the current water level time, and the difference between the current water level time and the timestamp is less than the maximum delay duration, the field data is considered valid; if the timestamp is less than the current water level time, and the difference between the current water level time and the timestamp is greater than the maximum delay duration, the field data is considered invalid and can be discarded. Here, the current water level time is the latest timestamp in the observed data.

[0100] Therefore, by dynamically determining the validity of data based on the timestamp, combined with the current water level time and the preset maximum delay duration, the system can ensure the real-time performance of streaming computation while also addressing data disorder issues caused by network jitter and transmission delays. This can improve the accuracy of calculation results within the window and avoid underestimation or distortion of monitoring indicators due to prematurely discarding legitimate late data.

[0101] Step 303: Update the calculation results within the target time window based on the field values ​​of the measurement fields in the field data to obtain the indicator values ​​of the target monitoring indicators within the target time window.

[0102] In this application, step 303 can be implemented in any of the embodiments of this application, so it will not be described in detail here.

[0103] Step 304: In response to the current water level time being greater than or equal to the right boundary of the current time window, push the indicator values ​​of the target monitoring indicators within the target time window and the boundary information of the target time window to the time series database.

[0104] In this application, the current water level time can refer to the latest timestamp in the observed data. For example, the observed data may include currently received field data, processed field data, etc.

[0105] As a push scenario, if the target time window is the current time window and the current water level time is greater than or equal to the right boundary of the current time window, the indicator values ​​of the target monitoring indicators within the current time window and the boundary information of the current time window can be pushed to the time series database.

[0106] As another push scenario, if the target time window is a historical time window and the current water level time is greater than or equal to the right boundary of the current time window, the indicator values ​​of the target monitoring indicators within the target time window and the boundary information of the target time window can be pushed together with the indicator values ​​of the target monitoring indicators within the current time window and the boundary information of the current time window to the time series database.

[0107] Therefore, when the conditions are met, the monitoring indicator values ​​and their boundary information of the historical time window and the current time window are written into the time series database together, ensuring the integrity and traceability of cross-window indicator data. This not only preserves the calculation results of field data that are included in the historical window due to delays, but also maintains the continuity and consistency of time series data in the time series database, providing a reliable data foundation for downstream real-time analysis, alarms and retrospective diagnosis.

[0108] In addition, when the indicator value of the target monitoring indicator is updated within the historical time window, it is not immediately pushed to the time series data. Instead, it is pushed to the time series database together with the indicator value of the target monitoring indicator within the current time window and the boundary information of the current time window. This can reduce the number of transmissions and save transmission resources.

[0109] Understandably, if the target time window is a new time window, the new time window can be used as the current time window. Thus, when the current water level time is greater than or equal to the right boundary of the current time window, a new time window is opened. It can be considered that the calculation within the current time window has ended, and the indicator values ​​of the target monitoring indicators within the current time window, the boundary information of the current time window, etc., can be pushed to the time series database.

[0110] In this embodiment, when real-time data arrives, if the timestamp pushes the current water level time to trigger a new time window, triggering a new window means that the previous time window has been calculated. At this time, the calculation result of the previous time window can be pushed to the time series database. When delayed data arrives, the calculation result of the corresponding window of the delayed data will be updated. After the update, it will not be pushed to the time series database immediately. It can be pushed to the time series database together with the calculation result of the previous time window when the new window is triggered at the current water level time.

[0111] The following is combined Figure 4 This application describes the method for pushing the values ​​of the monitoring indicators. Figure 4 This is a schematic diagram illustrating a time window push mechanism provided in an embodiment of this application. Figure 4 As shown, the window length is as follows Figure 4 As shown, streaming data is read from the message queue, field data is extracted from the streaming data, and aggregation operations (such as cumulative operations) are performed on each field data that falls within the corresponding window to obtain the aggregation result corresponding to the window. Based on the currently arriving field data, the current water level time is determined. If the current water level time exceeds the right boundary of window 4, the aggregation operation within window 4 can be considered to have ended. The aggregation result 4 of window 4 and the left boundary of window 4 can be pushed to the time series database, and a new window, i.e., window 5, is opened. The arriving field data falls in the new window 5.

[0112] In addition, the status stores the aggregation results of window 1, window 2, ..., window 5. If the arriving field data is delayed data, it falls into the corresponding time window according to the timestamp. The data can be accumulated into the aggregation result of the corresponding window based on the field value of the measurement field in the field data.

[0113] In this embodiment, if the current water level time is greater than or equal to the right boundary of the current time window, the indicator value of the target monitoring indicator within the target time window and the boundary information of the target time window can be pushed to the time series database. This can ensure the accuracy of the calculation results within the time window, achieve efficient collaboration between streaming processing and storage, and provide low-latency, highly consistent time series data support for real-time monitoring, alarms, and historical backtracking.

[0114] To facilitate understanding of the streaming data monitoring method in the embodiments of this application, the following will be combined with... Figure 5 To explain, Figure 5 This is a schematic diagram of a streaming data monitoring process provided in an embodiment of this application.

[0115] like Figure 5As shown, when the configuration information of the target monitoring metric is updated, it is broadcast to the processing operator. Through data subscription, streaming data is retrieved from the message queue and distributed to the processing operator. The processing operator can obtain target data through field extraction, traffic filtering, and term filtering, and extract the field values ​​of the target monitoring dimension and measurement field from the target data. The target monitoring metric and target monitoring dimension are concatenated to obtain a data identifier. This data identifier, along with the field value of the measurement field and the timestamp extracted from the target data, is sent to the calculation operator. To facilitate calculation, the field data can be grouped according to the data identifier, and field data belonging to the same group are sent to the same task management instance, such as the same calculation operator. The calculation operator can perform operations such as aggregation within a scrolling window, calculation on delayed data, and organizing the indicator format. Organizing the indicator format can refer to the indicator values ​​of the target monitoring metric within the window, the left boundary of the window, etc., being asynchronously pushed to the time-series database according to a certain format.

[0116] The streaming data monitoring method of this application can collect streaming data through a built-in message queue connector, store the filtering logic and the field configuration required for indicator calculation in a broadcast state, and distribute it to all downstream processing operators through a broadcast stream. Each processing operator filters the streaming data according to the received indicator configuration information, selects the target data, extracts the fields required for indicator calculation, and passes them to the downstream calculation operators. The calculation operators perform calculations on the data within the window based on a scrolling window to obtain the calculation results of the monitoring indicators. Finally, the exit operator writes the calculation results into a time-series database, which can generate indicator curves for visualization on the monitoring platform.

[0117] The streaming data monitoring method of this application can ensure that the pushed indicator data is ordered, solve the problem of out-of-order writing and overwriting, and can automatically recover from the state point and continue to process data when faced with anomalies.

[0118] The streaming data monitoring method of this application has the following technical effects: (1) Improve the timeliness of monitoring indicators to achieve "early detection and early loss prevention" for online problems. This solution reduces data delays in both the data collection and data calculation stages, which can improve traceability efficiency.

[0119] (2) Improve the flexibility of resource expansion and reduce expansion costs. This solution can be deployed on a PaaS (Platform as a Service) platform. Through the platform, vertical expansion of resources such as CPU, disk, and memory of the deployed instance can be achieved. The platform also supports horizontal expansion of the number of task management instances, instance migration, and other operations, simplifying the operation process of resource expansion and improving flexibility.

[0120] (3) When new aggregation logic is needed, since the aggregation logic configuration of this application can be stored in the broadcast data source, only the configuration needs to be added. There is no need for development, deployment, or online processes, which makes the operation convenient and reduces time costs.

[0121] To implement the above embodiments, this application also proposes a monitoring device for streaming data. Figure 6 This is a schematic diagram of the structure of a streaming data monitoring device provided in an embodiment of this application.

[0122] like Figure 6 As shown, the monitoring device 600 for streaming data includes: The acquisition module 610 is used to acquire field data of the measurement fields under the target monitoring dimension corresponding to the target monitoring indicators; wherein, the field data is extracted from the streaming data of the target business system; The determination module 620 is used to determine the target time window to which the field data belongs based on the timestamp in the field data; The calculation module 630 is used to update the calculation results within the target time window based on the field value of the measurement field in the field data, so as to obtain the indicator value of the target monitoring indicator within the target time window. The push module 640 is used to push the indicator values ​​of the target monitoring indicators within the target time window and the boundary information of the target time window to the time series database; wherein, the time series database is used to monitor the target business system.

[0123] Optionally, module 620 is defined for: Determine the current water level time based on the timestamp; where the current water level time is the latest timestamp among the observed data. The target type of the field data is determined based on the relationship between the timestamp and the current water level time; the target type is used to indicate whether the field data is delayed data. Determine the target time window based on the target type.

[0124] Optionally, the target type is a non-delayed type, and the determination module 620 is used for: In response to the current water level being greater than or equal to the right boundary of the current time window, a new time window is triggered and set as the target time window; In response to the current water level being less than the right boundary of the current time window, the current time window is determined as the target time window.

[0125] Optionally, module 620 is defined for: In response to the target type being a delay type, the target historical window to which the timestamp belongs is determined from at least one historical time window within the time from the current water level that is equal to the maximum delay time. Define the target historical window as the target time window.

[0126] Optionally, module 620 is defined for: In response to a timestamp being less than the current water level time, the target type is determined to be a delayed type; In response to a timestamp greater than or equal to the current water level time, the target type is determined to be a non-delayed type.

[0127] Optionally, the push module 640 is used for: In response to the current water level being greater than or equal to the right boundary of the current time window, the indicator values ​​of the target monitoring indicators within the target time window and the boundary information of the target time window are pushed to the time series database. The current water level time is the latest timestamp among the observed data.

[0128] Optionally, the push module 640 is used for: In response to the target time window being a historical time window, the indicator values ​​of the target monitoring indicators within the target time window and the boundary information of the target time window, along with the indicator values ​​of the target monitoring indicators within the current time window and the boundary information of the current time window, are pushed to the time series database.

[0129] Optionally, module 620 is defined for: In response to determining that field data is valid based on the timestamp, the target time window to which the field data belongs is determined based on the timestamp.

[0130] Optionally, module 620 is defined for: The field data is determined to be valid if the timestamp is greater than or equal to the current water level time; where the current water level time is the latest timestamp among the observed data. If the timestamp is less than the current water level time, and the difference between the current water level time and the timestamp is less than the maximum delay duration, the field data is determined to be valid data.

[0131] Optionally, module 610 is used for: Obtain the target monitoring metrics configuration information and message queue identifier; Read streaming data from the target message queue corresponding to the message queue identifier; Based on the indicator configuration information, the streaming data is processed to obtain field data.

[0132] Optionally, the indicator configuration information includes the target monitoring indicator, filtering logic, measurement fields, and target monitoring dimensions. The acquisition module 610 is used for: Based on the filtering logic, the streaming data is filtered to obtain the target data; Extract field values, the target monitoring dimensions associated with the field values, and timestamps from the target data; The target monitoring indicators and target monitoring dimensions are concatenated to obtain data identifiers; Retrieve field data based on field value, timestamp, and data identifier.

[0133] Optionally, module 610 is used for: The configuration file is parsed to obtain the message queue identifier, the metric configuration identifier, and the service connection information corresponding to the metric configuration identifier; Obtain the set of indicator configurations corresponding to the indicator configuration identifier through the service connection information; Retrieve indicator configuration information from the indicator configuration set.

[0134] It should be noted that the explanation of the aforementioned streaming data monitoring method embodiment also applies to the streaming data monitoring device of this embodiment, so it will not be repeated here.

[0135] In this embodiment, the target time window to which each piece of streaming data belongs is accurately determined based on the timestamp in the field data, ensuring that each piece of streaming data is assigned to the correct calculation window. The calculation results within the window are dynamically updated based on the field value of the metric field, thereby generating accurate and consistent target monitoring indicator values. The indicator values ​​and their corresponding time window boundary information are written into the time series database. This not only ensures the orderliness and traceability of the indicator data in the time dimension, but also significantly improves the accuracy, timeliness, and controllability of the streaming monitoring system, providing a reliable data foundation for real-time business insights, anomaly alarms, and historical backtracking analysis.

[0136] According to embodiments of this application, this application also provides an electronic device, a readable storage medium, and a computer program product.

[0137] Figure 7 A schematic block diagram of an example electronic device 700 that can be used to implement embodiments of this application is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the application described and / or claimed herein.

[0138] like Figure 7As shown, device 700 includes a computing unit 701, which can perform various appropriate actions and processes based on a computer program stored in ROM (Read-Only Memory) 702 or a computer program loaded from storage unit 708 into RAM (Random Access Memory) 703. RAM 703 can also store various programs and data required for the operation of device 700. The computing unit 701, ROM 702, and RAM 703 are interconnected via bus 704. I / O (Input / Output) interface 705 is also connected to bus 704.

[0139] Multiple components in device 700 are connected to I / O interface 705, including: input unit 706, such as keyboard, mouse, etc.; output unit 707, such as various types of monitors, speakers, etc.; storage unit 708, such as disk, optical disk, etc.; and communication unit 709, such as network card, modem, wireless transceiver, etc. Communication unit 709 allows device 700 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0140] The computing unit 701 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 701 include, but are not limited to, CPUs (Central Processing Units), GPUs (Graphics Processing Units), various special-purpose AI (Artificial Intelligence) computing chips, various computing units running machine learning model algorithms, DSPs (Digital Signal Processors), and any suitable processor, controller, microcontroller, etc. The computing unit 701 performs the various methods and processes described above, such as the method for monitoring streaming data. For example, in some embodiments, the method for monitoring streaming data can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 708. In some embodiments, part or all of the computer program can be loaded and / or installed on device 700 via ROM 702 and / or communication unit 709. When the computer program is loaded into RAM 703 and executed by the computing unit 701, one or more steps of the method for monitoring streaming data described above can be performed. Alternatively, in other embodiments, the computing unit 701 may be configured to perform a method for monitoring streaming data by any other suitable means (e.g., by means of firmware).

[0141] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, FPGAs (Field Programmable Gate Arrays), ASICs (Application-Specific Integrated Circuits), ASSPs (Application-Specific Standard Products), SOCs (System-on-Chips), CPLDs (Complex Programmable Logic Devices), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0142] The program code used to implement the methods of this application may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0143] In the context of this application, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, RAM, ROM, EPROM (Electrically Programmable Read-Only Memory) or flash memory, optical fiber, CD-ROM (Compact Disc Read-Only Memory), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0144] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (Cathode-Ray Tube) or LCD (Liquid Crystal Display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0145] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include LANs (Local Area Networks), WANs (Wide Area Networks), the Internet, and blockchain networks.

[0146] Computer systems can include clients and servers. Clients and servers are generally geographically separated and typically interact via communication networks. The client-server relationship is established by computer programs running on the respective computers and having a client-server relationship with each other. A server can be a cloud server, also known as a cloud computing server or cloud host, a hosting product within the cloud computing service ecosystem, addressing the shortcomings of traditional physical hosts and VPS (Virtual Private Server) services, such as high management difficulty and weak business scalability. Servers can also be servers for distributed systems or servers incorporating blockchain technology.

[0147] According to an embodiment of this application, this application also provides a computer program product that, when an instruction processor in the computer program product is executed, performs the streaming data monitoring method proposed in the above embodiments of this application.

[0148] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this application can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this application can be achieved, and this is not limited herein.

[0149] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for monitoring streaming data, comprising: Obtain the field data of the measurement fields under the target monitoring dimension corresponding to the target monitoring metric; wherein, the field data is extracted from the streaming data of the target business system; Based on the timestamp in the field data, determine the target time window to which the field data belongs; Based on the field value of the measurement field in the field data, the calculation result within the target time window is updated to obtain the indicator value of the target monitoring indicator within the target time window; The indicator values ​​of the target monitoring indicators within the target time window and the boundary information of the target time window are pushed to the time series database; wherein, the time series database is used to monitor the target business system.

2. The method as described in claim 1, wherein, The step of determining the target time window to which the field data belongs based on the timestamp in the field data includes: The current water level time is determined based on the timestamp; wherein the current water level time is the latest timestamp in the observed data. The target type of the field data is determined based on the relationship between the timestamp and the current water level time; wherein, the target type is used to indicate whether the field data is delayed data; The target time window is determined based on the target type.

3. The method as described in claim 2, wherein, The target type is a non-delayed type, and determining the target time window based on the target type includes: In response to the current water level time being greater than or equal to the right boundary of the current time window, a new time window is triggered and the new time window is determined as the target time window; In response to the current water level time being less than the right boundary of the current time window, the current time window is determined as the target time window.

4. The method of claim 2, wherein, Determining the target time window based on the target type includes: In response to the target type being a delay type, the target historical window to which the timestamp belongs is determined from at least one historical time window within the maximum delay time from the current water level time. The target historical window is defined as the target time window.

5. The method of claim 2, wherein, The step of determining the target type of the field data based on the relationship between the timestamp and the current water level time includes: In response to the timestamp being less than the current water level time, the target type is determined to be a delay type; In response to the timestamp being greater than or equal to the current water level time, the target type is determined to be a non-delayed type.

6. The method of claim 1, wherein, The step of pushing the indicator values ​​of the target monitoring indicator within the target time window and the boundary information of the target time window to the time series database includes: In response to the current water level time being greater than or equal to the right boundary of the current time window, the indicator value of the target monitoring indicator within the target time window and the boundary information of the target time window are pushed to the time series database. The current water level time is the latest timestamp in the observed data.

7. The method of claim 6, wherein, The step of pushing the indicator values ​​of the target monitoring indicator within the target time window and the boundary information of the target time window to the time series database includes: In response to the target time window being a historical time window, the indicator values ​​of the target monitoring indicator within the target time window and the boundary information of the target time window, along with the indicator values ​​of the target monitoring indicator within the current time window and the boundary information of the current time window, are pushed to the time series database.

8. The method of claim 1, wherein, The step of determining the target time window to which the field data belongs based on the timestamp includes: In response to determining that the field data is valid based on the timestamp, the target time window to which the field data belongs is determined based on the timestamp.

9. The method of claim 8, wherein, The step of determining that the field data is valid based on the timestamp includes: In response to the timestamp being greater than or equal to the current water level time, the field data is determined to be valid data; wherein, the current water level time is the latest timestamp among the observed data; In response to the timestamp being less than the current water level time, and the difference between the current water level time and the timestamp being less than the maximum delay duration, the field data is determined to be valid data.

10. The method of claim 1, wherein, The process of obtaining the field data of the measurement field under the target monitoring dimension corresponding to the target monitoring metric includes: Obtain the indicator configuration information and message queue identifier of the target monitoring indicator; Based on the message queue identifier, read the streaming data from the target message queue corresponding to the message queue identifier; Based on the indicator configuration information, the streaming data is processed to obtain the field data.

11. The method of claim 10, wherein, The indicator configuration information includes the target monitoring indicator, filtering logic, the measurement field, and the target monitoring dimension. The step of processing the streaming data according to the indicator configuration information to obtain the field data includes: The streaming data is filtered according to the filtering logic to obtain the target data; Extract the field value, the target monitoring dimension associated with the field value, and the timestamp from the target data; The target monitoring indicators and target monitoring dimensions are concatenated to obtain data identifiers; The field data is obtained based on the field value, the timestamp, and the data identifier.

12. The method of claim 10, wherein, The step of obtaining the indicator configuration information and message queue identifier of the target monitoring indicator includes: The configuration file is parsed to obtain the message queue identifier, the indicator configuration identifier, and the service connection information corresponding to the indicator configuration identifier; The service connection information is used to obtain the set of indicator configurations corresponding to the indicator configuration identifier; The indicator configuration information is obtained from the indicator configuration set.

13. A monitoring device for streaming data, comprising: The acquisition module is used to acquire field data of the measurement fields under the target monitoring dimension corresponding to the target monitoring metric; wherein, the field data is extracted from the streaming data of the target business system; The determination module is used to determine the target time window to which the field data belongs based on the timestamp in the field data; The calculation module is used to update the calculation results within the target time window based on the field value of the measurement field in the field data, so as to obtain the indicator value of the target monitoring indicator within the target time window; The push module is used to push the indicator values ​​of the target monitoring indicators and the boundary information of the target time window to the time series database; wherein, the time series database is used to monitor the target business system.

14. An electronic device, comprising: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-12.

15. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-12.

16. A computer program product comprising a computer program that, when executed by a processor, implements the steps of the method according to any one of claims 1-12.

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