CN113176975A

CN113176975A - Monitoring data processing method and device, storage medium and electronic equipment

Info

Publication number: CN113176975A
Application number: CN202110341003.1A
Authority: CN
Inventors: 王占; 邹康; 闻英友; 吕昕东; 葛东; 窦丽莉
Original assignee: Neusoft Cloud Technology Co ltd; Neusoft Corp
Current assignee: Neusoft Cloud Technology Co ltd; Neusoft Corp
Priority date: 2021-03-30
Filing date: 2021-03-30
Publication date: 2021-07-27
Anticipated expiration: 2041-03-30
Also published as: CN113176975B

Abstract

The present disclosure relates to a method and an apparatus for processing monitoring data, a storage medium and an electronic device, and relates to the technical field of electronic information processing, including: the method comprises the steps of obtaining an initial monitoring graph corresponding to a target task, wherein the target task comprises a first number of processing flows, and the initial monitoring graph comprises a first number of nodes and a second number of directed edges. And generating an intermediate monitoring graph according to the initial monitoring graph, wherein the intermediate monitoring graph is a directed acyclic graph, determining the level of each node according to the intermediate monitoring graph, no directed edge exists between nodes with the same level in the intermediate monitoring graph, and the level of a source node of each directed edge is greater than the level of a final node of the directed edge. And generating a monitoring tree corresponding to the middle monitoring graph according to the hierarchy of each node, arranging each node in the monitoring tree according to the size sequence of the hierarchy of the node, displaying the nodes of the first number according to the structure indicated by the monitoring tree, and displaying each directed edge in the directed edges of the second number between the nodes at the two ends of the directed edge.

Description

Monitoring data processing method and device, storage medium and electronic equipment

Technical Field

The present disclosure relates to the field of electronic information processing technologies, and in particular, to a method and an apparatus for processing monitoring data, a storage medium, and an electronic device.

Background

With the continuous development of electronic information technology, cloud computing, big data and other technologies, various industries begin to adopt a distributed cluster deployment structure to realize various services. The business may include a plurality of processing flows, each processing flow may be implemented by a different application module, and accordingly, the application modules may use different development platforms and different programming languages, and may be distributed on a large number of servers. Each process flow needs to be monitored to know the execution process or the execution state of the service, so each process flow in the current task can be monitored by a distributed tracking tool (e.g., Dapper). In general, each processing flow is displayed by a distributed tracking tool in a form of a table or a graph, however, the relationship between the processing flows cannot be visually displayed in the form of the table, the processing flows displayed in the form of the graph are randomly arranged, the execution sequence between the processing flows cannot be clearly displayed, and a user cannot easily obtain effective information from the processing flows.

Disclosure of Invention

In order to solve the problems in the prior art, the present disclosure provides a method and an apparatus for processing monitoring data, a storage medium, and an electronic device.

According to a first aspect of the embodiments of the present disclosure, there is provided a method for processing monitoring data, the method including:

acquiring an initial monitoring graph corresponding to a target task, wherein the target task comprises a first number of processing flows, the initial monitoring graph comprises a first number of nodes and a second number of directed edges, each node corresponds to one processing flow, and each directed edge is used for indicating the data flow direction between the nodes at two ends of the directed edge;

generating an intermediate monitoring graph according to the initial monitoring graph, wherein the intermediate monitoring graph comprises a first number of the nodes and a third number of the directed edges and is a directed acyclic graph, and the third number is smaller than or equal to the second number;

determining the level of each node according to the intermediate monitoring graph, wherein the directed edges do not exist between the nodes with the same level in the intermediate monitoring graph, and the level of the source node of each directed edge in the intermediate monitoring graph is larger than the level of the final node of the directed edge;

generating a monitoring tree corresponding to the intermediate monitoring graph according to the hierarchy of each node, wherein the monitoring tree comprises a first number of nodes, a root node is the node with zero in-degree in the initial monitoring graph, a leaf node is the node with zero out-degree in the initial monitoring graph, and each node is arranged in the monitoring tree according to the size sequence of the hierarchy of the node;

and displaying the first number of nodes according to the structure indicated by the monitoring tree, and displaying each directed edge in the second number of directed edges between the nodes at two ends of the directed edge.

Optionally, before the generating an intermediate monitoring graph according to the initial monitoring graph, the method further includes:

carrying out topology sequencing on the initial monitoring graph to determine whether a loop exists in the initial monitoring graph according to a topology sequence output by the topology sequencing; alternatively, the first and second electrodes may be,

performing depth-first search on the initial monitoring graph to determine whether a loop exists in the initial monitoring graph according to a search result output by the depth-first search;

the generating of the intermediate monitoring graph according to the initial monitoring graph comprises:

if a loop exists in the initial monitoring graph, deleting the appointed edge from the initial monitoring graph to obtain the middle monitoring graph.

Optionally, the performing topology ranking on the initial monitoring graph includes:

deleting the nodes with the zero in-degree in the initial monitoring graph from the initial monitoring graph, and putting the nodes into a first sequence; deleting the nodes with zero out degree in the initial monitoring graph from the initial monitoring graph, and putting the nodes into a second sequence;

determining a first target node with the largest difference between out-degree and in-degree in the initial monitoring graph, deleting the first target node from the initial monitoring graph, and putting the first target node into the first sequence;

repeating the steps of determining the first target node with the largest difference between the out-degree and the in-degree in the initial monitoring graph, deleting the first target node from the initial monitoring graph, and placing the first target node into the first sequence until the in-degree or the out-degree of all the nodes in the initial monitoring graph is zero;

putting the nodes with the in-degree of zero in the initial monitoring graph into the first sequence, and putting the nodes with the out-degree of zero in the initial monitoring graph into the second sequence;

and splicing the first sequence and the second sequence to obtain the topological sequence.

Optionally, the determining a hierarchy of each node according to the intermediate monitoring graph includes:

setting the hierarchy of each second target node in the intermediate monitoring graph as a first hierarchy, wherein the second target node is a node with zero out degree in the intermediate monitoring graph;

for each second target node, determining a hierarchy of the third target node corresponding to the second target node according to the number of directed edges between the third target node and the second target node, where the third target node is any node in the intermediate monitoring graph that is not the second target node;

and determining the hierarchy of the third target node according to the hierarchy of each second target node corresponding to the third target node, wherein the hierarchy of the third target node is larger than the first hierarchy.

Optionally, the generating a monitoring tree corresponding to the intermediate monitoring graph according to the hierarchy of each node includes:

generating an initial monitoring tree according to the hierarchy of each node, wherein the nodes with the same hierarchy in the initial monitoring tree are positioned in the same layer;

and adjusting the number of levels separated between each node and the corresponding adjacent node according to the level of each node and the level of the corresponding adjacent node to obtain the monitoring tree, wherein the sum of the levels separated between each node and the corresponding adjacent node in the monitoring tree is minimum, and the adjacent node is the node with the directed edge between the adjacent node and the node.

Optionally, the adjusting, according to the hierarchy of each node and the hierarchy of the corresponding adjacent node, the number of hierarchies between the node and the corresponding adjacent node includes:

for each node, if the number of levels between the node and the corresponding adjacent node is greater than 1, reducing the number of levels between the node and the corresponding adjacent node to obtain an intermediate monitoring tree;

determining the compactness of each directed edge in the middle monitoring tree, and adjusting the directed edges with negative compactness to obtain the monitoring tree, wherein the compactness is determined according to two connected regions obtained by deleting the directed edges from the middle monitoring tree.

Optionally, before the reducing the number of levels between the node and the corresponding adjacent node, the adjusting the number of levels between the node and the corresponding adjacent node according to the level of each node and the level of the corresponding adjacent node, further includes:

determining a compact tree according to the initial monitoring tree, wherein the compact tree belongs to the initial monitoring tree, and the number of levels between each node and the corresponding adjacent node in the compact tree is equal to 1;

the reducing the number of levels between the node and the corresponding adjacent node comprises:

if the node points to the corresponding adjacent node and the node belongs to the compact tree, increasing the level of the adjacent node corresponding to the node;

and if the node points to the corresponding adjacent node and the node does not belong to the compact tree, reducing the level of the node.

Optionally, the determining the compactness of each directed edge in the intermediate monitoring tree, and adjusting the directed edge whose compactness is negative includes:

deleting the directed edge and the target directed edge from the middle monitoring tree aiming at each directed edge in the middle monitoring tree to obtain two connected regions, wherein the number of levels between the nodes at the two ends of the target directed edge is more than 1;

determining the compactness of the directed edge according to the number and the direction of the directed edge existing between the two connected areas in the middle monitoring tree;

and if the compactness of the directed edge is negative, increasing the level of the source node of the directed edge according to the compactness of the directed edge.

deleting the root node directed edge and the target directed edge from the intermediate monitoring tree aiming at the root node directed edge of the intermediate monitoring tree to obtain two connected areas, wherein the number of levels at two ends of the target directed edge, which are separated from each other, is more than 1, and the root node directed edge is the directed edge of which any source node in the intermediate monitoring tree is the root node; determining the compactness of the directed edges of the root nodes according to the number and the direction of the directed edges existing between the two connected areas in the middle monitoring tree;

for each directed edge in the middle monitoring tree, determining the compactness of the directed edge according to the compactness sum of other directed edges corresponding to the directed edge and the out-degree and in-degree of a source node of the directed edge, wherein any one end of the other directed edges is the source node of the directed edge;

According to a second aspect of the embodiments of the present disclosure, there is provided a processing apparatus for monitoring data, the apparatus including:

an obtaining module, configured to obtain an initial monitoring graph corresponding to a target task, where the target task includes a first number of processing flows, the initial monitoring graph includes a first number of nodes and a second number of directed edges, each node corresponds to one processing flow, and each directed edge is used to indicate a data flow direction between the nodes at two ends of the directed edge;

a processing module, configured to generate an intermediate monitoring graph according to the initial monitoring graph, where the intermediate monitoring graph includes a first number of the nodes and a third number of the directed edges, and is a directed acyclic graph, and the third number is less than or equal to the second number;

a determining module, configured to determine a level of each node according to the intermediate monitoring graph, where the directed edge does not exist between the nodes with the same level in the intermediate monitoring graph, and a level of a source node of each directed edge in the intermediate monitoring graph is greater than a level of a final node of the directed edge;

a generating module, configured to generate a monitoring tree corresponding to the intermediate monitoring graph according to a hierarchy of each node, where the monitoring tree includes a first number of nodes, a root node is the node with an in-degree of zero in the initial monitoring graph, a leaf node is the node with an out-degree of zero in the initial monitoring graph, and each node is arranged in the monitoring tree according to a size order of the hierarchy of the node;

and the display module is used for displaying the first number of nodes according to the structure indicated by the monitoring tree and displaying each directed edge in the second number of directed edges between the nodes at two ends of the directed edge.

Optionally, the apparatus further comprises:

a judging module, configured to perform topology sorting on the initial monitoring graph before generating an intermediate monitoring graph according to the initial monitoring graph, so as to determine whether a loop exists in the initial monitoring graph according to a topology sequence output by the topology sorting; or, performing depth-first search on the initial monitoring graph to determine whether a loop exists in the initial monitoring graph according to a search result output by the depth-first search;

the processing module is used for: if a loop exists in the initial monitoring graph, deleting the appointed edge from the initial monitoring graph to obtain the middle monitoring graph.

Optionally, the determining module is configured to:

Optionally, the determining module includes:

the initialization submodule sets the hierarchy of each second target node in the intermediate monitoring graph as a first hierarchy, and the second target nodes are nodes with zero out degree in the intermediate monitoring graph;

a first determining submodule, configured to determine, for each second target node, a hierarchy of the third target node corresponding to the second target node according to the number of directed edges between the third target node and the second target node, where the third target node is any node in the intermediate monitoring graph that is not the second target node;

a second determining submodule, configured to determine a hierarchy of the third target node according to a hierarchy of each second target node corresponding to the third target node, where the hierarchy of the third target node is greater than the first hierarchy.

Optionally, the generating module includes:

the first generation submodule is used for generating an initial monitoring tree according to the hierarchy of each node, and the nodes with the same hierarchy in the initial monitoring tree are positioned in the same layer;

and a second generation submodule, configured to adjust, according to a hierarchy of each node and a hierarchy of a corresponding adjacent node, a number of hierarchies spaced between the node and the corresponding adjacent node, so as to obtain the monitoring tree, where a sum of the numbers of hierarchies spaced between each node and the corresponding adjacent node in the monitoring tree is minimum, and the adjacent node is the node between which the directed edge exists.

Optionally, the second generation submodule is configured to:

Optionally, the second generation submodule is further configured to:

before the reduction of the number of levels between the node and the corresponding adjacent node, determining a compact tree according to the initial monitoring tree, wherein the compact tree belongs to the initial monitoring tree, and the number of levels between each node in the compact tree and the corresponding adjacent node is equal to 1;

the second generation submodule is configured to:

Optionally, the second generation submodule is further configured to:

According to a third aspect of embodiments of the present disclosure, there is provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method of the first aspect of embodiments of the present disclosure.

According to a fourth aspect of the embodiments of the present disclosure, there is provided an electronic apparatus including:

a memory having a computer program stored thereon;

a processor for executing the computer program in the memory to implement the steps of the method of the first aspect of an embodiment of the disclosure.

According to the technical scheme, the method comprises the steps of firstly obtaining an initial monitoring graph comprising a first number of nodes and a second number of directed edges, wherein each node corresponds to one processing flow of a target task, and each directed edge is used for indicating the data flow direction between the nodes at two ends of the directed edge. And then generating an intermediate monitoring graph without loops according to the initial monitoring graph, and determining the hierarchy of each node according to the intermediate monitoring graph, wherein directed edges do not exist between nodes with the same hierarchy in the intermediate monitoring graph, and the hierarchy of a source node of each directed edge is larger than that of a final node of the directed edge. And generating a monitoring tree corresponding to the intermediate monitoring graph according to the hierarchy of the nodes, wherein the monitoring tree comprises a first number of nodes, and each node is arranged in the monitoring tree according to the size sequence of the hierarchy of the node. And finally, displaying the first number of nodes according to the structure indicated by the monitoring tree, and displaying the directed edge between the nodes at the two ends of the directed edge for each directed edge. According to the method and the device, each node in the initial monitoring graph corresponding to the target task is layered, and the monitoring tree is generated according to the size of the hierarchy of each node, so that the directed edges between the nodes are displayed according to the monitoring tree, the data flow direction between each processing flow in the target task can be clearly reflected, the execution sequence between each processing flow can be displayed in a layered mode, and the display efficiency and the definition are improved.

Additional features and advantages of the disclosure will be set forth in the detailed description which follows.

Drawings

The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure without limiting the disclosure. In the drawings:

FIG. 1 is a flow diagram illustrating a method of processing monitoring data in accordance with an exemplary embodiment;

FIG. 2 is a schematic diagram illustrating an initial monitoring graph and an intermediate monitoring graph in accordance with an exemplary embodiment;

FIG. 3 is a schematic diagram illustrating a monitoring tree in accordance with an exemplary embodiment;

FIG. 4 is a flow diagram illustrating another method of processing monitored data in accordance with an exemplary embodiment;

FIG. 5 is a schematic diagram illustrating a topological sequence in accordance with an exemplary embodiment;

FIG. 6 is a flow diagram illustrating another method of processing monitored data in accordance with an exemplary embodiment;

FIG. 7 is a schematic diagram illustrating another topological sequence in accordance with an illustrative embodiment;

FIG. 8 is a flow diagram illustrating another method of processing monitored data in accordance with an exemplary embodiment;

FIG. 9 is a schematic diagram illustrating a spanning monitoring tree in accordance with an exemplary embodiment;

FIG. 10 is a schematic diagram illustrating another spanning monitoring tree in accordance with an exemplary embodiment;

FIG. 11 is a block diagram illustrating a processing device for monitoring data in accordance with an exemplary embodiment;

FIG. 12 is a block diagram illustrating another monitoring data processing apparatus according to an exemplary embodiment;

FIG. 13 is a block diagram illustrating another monitoring data processing apparatus according to an exemplary embodiment;

FIG. 14 is a block diagram illustrating another monitoring data processing apparatus according to an exemplary embodiment;

FIG. 15 is a block diagram illustrating an electronic device in accordance with an example embodiment.

Detailed Description

Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.

Fig. 1 is a flow chart illustrating a method of processing monitoring data according to an exemplary embodiment, which may include the following steps, as shown in fig. 1:

step 101, an initial monitoring graph corresponding to a target task is obtained, the target task includes a first number of processing flows, the initial monitoring graph includes a first number of nodes and a second number of directed edges, each node corresponds to one processing flow, and each directed edge is used for indicating a data flow direction between nodes at two ends of the directed edge.

For example, the target task may be monitored by a distributed tracking tool to obtain an initial monitoring graph corresponding to the target task. The target task may be understood as a task to be monitored, which is implemented by using a distributed cluster deployment structure, where the task includes a first number of processing flows, each processing flow may be implemented by a different application module, and each application module may depend on each other or may be independent of each other, which is not limited in this disclosure. The initial monitoring Graph may be understood as a directed Graph (english: ordered Graph) output by the distributed tracking tool for showing the target task, wherein the directed Graph includes a first number of nodes and a second number of directed edges. Each node corresponds to a processing flow, and it is understood that in the initial monitoring graph, the node can represent the corresponding processing flow, and further, the node can also represent attribute information (e.g., execution state, application module information, IP address, etc.) of the corresponding processing flow. Each directed edge corresponds to a data flow direction in the target task, that is, the directed edge is represented in the target task, a processing flow corresponding to a source node of the directed edge sends data to a processing flow corresponding to an end node of the directed edge, or the processing flow corresponding to the source node of the directed edge accesses, and the processing flow corresponding to the end node of the directed edge. In the initial monitoring graph output by the distributed tracking tool, each node is often randomly arranged, as shown in (a) in fig. 2, the nodes are cluttered, and a user cannot comprehensively know a target task by looking up the initial monitoring graph, so that effective information is difficult to obtain.

And 102, generating an intermediate monitoring graph according to the initial monitoring graph, wherein the intermediate monitoring graph comprises a first number of nodes and a third number of directed edges, the intermediate monitoring graph is a directed acyclic graph, and the third number is smaller than or equal to the second number.

For example, since the loop increases the complexity of the topology and also affects the hierarchy of each node, after the initial monitoring graph is obtained, an intermediate monitoring graph without the loop may be generated according to the initial monitoring graph. First, it may be determined whether a loop exists in the initial monitoring Graph, and if the loop exists in the initial monitoring Graph, the specified edge may be deleted from the initial monitoring Graph to delete the loop in the initial monitoring Graph, so as to obtain a Directed Acyclic Graph (DAG), which is an intermediate monitoring Graph. If no loop exists in the initial monitoring graph, the initial monitoring graph can be directly used as an intermediate monitoring graph. That is, the intermediate monitoring graph is a directed acyclic graph and includes all of the first number of nodes and the third number of directed edges, where the third number is less than the second number (i.e., the specified edges are deleted) in the case where a loop exists in the initial monitoring graph, and the third number is equal to the second number in the case where no loop exists in the initial monitoring graph.

Specifically, in the case that a loop exists in the initial monitoring graph, the specified edge may be determined according to a topology sequence (english: Topological Order) corresponding to the initial monitoring graph. For example, the first number of nodes may be arranged in a preset direction (e.g., from left to right, or from right to left), so that as many directed edges as possible are the same as the preset direction, then the directed edges opposite to the preset direction may be determined as directed edges forming a loop, and the directed edges may be deleted as specified edges, so as to obtain the middle monitoring graph. The initial monitoring graph can also be searched through Depth First Search (English: Depth First Search, abbreviation: DFS), if a loop is detected to exist, any directed edge in the loop can be used as a specified edge to delete to destroy the loop, and after the whole DFS process is completed, an intermediate monitoring graph can be obtained. Taking the initial monitoring graph shown in (b) of fig. 2 as an example, the initial monitoring graph includes six nodes, namely, node 1, node 2, node 3, node 4, node 5, and node 6, and directed edges between the nodes. The directed edge pointing to node 3 from node 4 can be deleted as a designated edge to obtain an intermediate monitoring graph, as shown in (c) of fig. 2.

Step 103, determining the hierarchy of each node according to the intermediate monitoring graph, wherein directed edges do not exist between the same nodes in the intermediate monitoring graph, and the hierarchy of the source node of each directed edge in the intermediate monitoring graph is greater than the hierarchy of the final node of the directed edge.

For example, since there are no loops in the intermediate monitoring graph, a first number of nodes may be layered according to the intermediate monitoring graph to determine a hierarchy (which may be denoted rank) of each node. In the intermediate monitoring graph, directed edges do not exist between nodes with the same hierarchy, and the hierarchy of a source node of each directed edge in the intermediate monitoring graph is larger than that of a final node of the directed edge, that is, the hierarchy of each node is determined according to the directional relation between each node in the intermediate monitoring graph. For example, the level of a node with zero out degree in the intermediate monitoring graph may be determined as 0, then the level of a node with a directed edge between the node with zero out degree is determined as 1, and so on until the level of each node is obtained. Taking the intermediate monitoring graph shown in (c) in fig. 2 as an example, the nodes with out degrees of zero include: node 6 and node 4. First, the hierarchy of the node 4 and the node 6 is determined to be 0, directional edges exist between the node 5 and the node 6 and between the node 5 and the node 4, then the hierarchy of the node 5 is determined to be 1, directional edges exist between the node 3 and the node 5, then the hierarchy of the node 3 is determined to be 2, and directional edges exist between the node 1 and the node 3, then the hierarchy of the node 1 is determined to be 3. Meanwhile, there is a directed edge between node 2 and node 4, then the hierarchy of node 2 is determined to be 1. Thus, the levels corresponding to the nodes 1, 2, 3, 4, 5 and 6 are respectively as follows: 3. 1, 2, 0, 1, 0.

And 104, generating a monitoring tree corresponding to the middle monitoring graph according to the hierarchy of each node, wherein the monitoring tree comprises a first number of nodes, a root node is a node with zero in-degree in the initial monitoring graph, leaf nodes are nodes with zero out-degree in the initial monitoring graph, and each node is arranged in the monitoring tree according to the size sequence of the hierarchy of the node.

And 105, displaying the first number of nodes according to the structure indicated by the monitoring tree, and displaying each directed edge in the second number of directed edges between the nodes at the two ends of the directed edge.

For example, after determining the hierarchy of each node, a monitoring tree corresponding to the intermediate monitoring graph may be generated according to the size order of the hierarchy of each node, where the monitoring tree includes all the first number of nodes, where a root node is a node with an in-degree of zero in the initial monitoring graph, and a leaf node is a node with an out-degree of zero in the initial monitoring graph. It is understood that the first number of nodes is divided into a fourth number of levels, and then the monitoring tree is divided into the fourth number of levels, and then each node is filled into the monitoring tree according to the corresponding level. Taking the middle monitoring graph shown in (c) of fig. 2 as an example, six (i.e., the first number) nodes are divided into four (i.e., the fourth number) levels, and then the monitoring tree is also divided into four levels, node 1 is filled into level 3 of the monitoring tree, node 2 is filled into level 1 of the monitoring tree, and so on, and the obtained monitoring tree is shown in (a) of fig. 3. Then, the first number of nodes may be displayed according to the structure indicated by the monitoring tree, that is, the first number of nodes are hierarchically arranged and displayed, so that the execution sequence between each processing flow can be hierarchically displayed. And then displaying each directed edge in the second number of directed edges between nodes at two ends of the directed edge, so that all directed edges included in the initial monitoring graph can be displayed (including the designated edges deleted under the condition that the loop exists in the initial monitoring graph), and the data flow direction between each processing flow can be accurately reflected. The user can directly check the relation and the execution sequence among the processing flows, so that the state of the target task is comprehensively known, and the display efficiency and the definition are effectively improved. Taking (a) in fig. 2 as an example of the initial monitoring graph, the nodes and directed edges displayed through the above steps may be shown in (b) in fig. 3, and it can be seen that the relationship and execution sequence between the nodes are clear, and the user can quickly obtain effective information.

Further, after displaying the first number of nodes according to the structure indicated by the monitoring tree, the method may further include: and for each node, displaying the attribute information of the processing flow corresponding to the node on the node, thereby increasing the dimension of information display so that a user can acquire more information about the target task. Wherein, the attribute information may include: the execution state of the processing flow, the information of the application module corresponding to the processing flow, the flow size of the processing flow, the flow ID of the processing flow, the IP address of the server corresponding to the processing flow and the like. For example, the attribute information includes the execution state of the processing flow, the execution state can be represented by the color of the node, red represents a serious alarm, yellow represents a normal alarm, and blue represents a normal alarm, so that the user can quickly and intuitively determine the abnormal processing flow and quickly and accurately locate the problem according to the execution sequence of the abnormal processing flow in the target task.

In summary, the present disclosure first obtains an initial monitoring graph including a first number of nodes and a second number of directed edges, where each node corresponds to a processing flow of a target task, and each directed edge is used to indicate a data flow direction between nodes at two ends of the directed edge. And then generating an intermediate monitoring graph without loops according to the initial monitoring graph, and determining the hierarchy of each node according to the intermediate monitoring graph, wherein directed edges do not exist between nodes with the same hierarchy in the intermediate monitoring graph, and the hierarchy of a source node of each directed edge is larger than that of a final node of the directed edge. And generating a monitoring tree corresponding to the intermediate monitoring graph according to the hierarchy of the nodes, wherein the monitoring tree comprises a first number of nodes, and each node is arranged in the monitoring tree according to the size sequence of the hierarchy of the node. And finally, displaying the first number of nodes according to the structure indicated by the monitoring tree, and displaying the directed edge between the nodes at the two ends of the directed edge for each directed edge. According to the method and the device, each node in the initial monitoring graph corresponding to the target task is layered, and the monitoring tree is generated according to the size of the hierarchy of each node, so that the directed edges between the nodes are displayed according to the monitoring tree, the data flow direction between each processing flow in the target task can be clearly reflected, the execution sequence between each processing flow can be displayed in a layered mode, and the display efficiency and the definition are improved.

Fig. 4 is a flowchart illustrating another monitoring data processing method according to an exemplary embodiment, where as shown in fig. 4, before step 102, the method may further include:

and 106a, carrying out topology sequencing on the initial monitoring graph to determine whether a loop exists in the initial monitoring graph according to a topology sequence output by the topology sequencing. Alternatively, the first and second electrodes may be,

and 106b, performing depth-first search on the initial monitoring graph to determine whether a loop exists in the initial monitoring graph according to a search result output by the depth-first search.

Accordingly, the implementation manner of step 102 may be: if a loop exists in the initial monitoring graph, deleting the appointed edge from the initial monitoring graph to obtain an intermediate monitoring graph.

For example, before determining the intermediate monitoring graph, it is necessary to determine whether a loop exists in the initial monitoring graph. The initial monitoring graph may be processed by a topological sort or depth first search algorithm to determine whether a loop exists in the initial monitoring graph.

In one implementation, the initial monitoring graph may be topologically sorted to determine whether a loop exists in the initial monitoring graph according to a topological sequence output by the topological sorting:

step 1) deleting the nodes with zero in-degree in the initial monitoring graph from the initial monitoring graph, and putting the nodes into a first sequence. And deleting the nodes with zero out degree in the initial monitoring graph from the initial monitoring graph, and putting the nodes into a second sequence.

And 2) determining a first target node with the largest difference between the out-degree and the in-degree in the initial monitoring graph, deleting the first target node from the initial monitoring graph, and putting the first target node into a first sequence.

And 3) repeating the step 2) until the in-degree or out-degree of all the nodes in the initial monitoring graph is zero.

And 4) putting the nodes with the in-degree of zero in the initial monitoring graph into a first sequence, and putting the nodes with the out-degree of zero in the initial monitoring graph into a second sequence.

And 5) splicing the first sequence and the second sequence to obtain a topological sequence.

Taking the initial monitoring graph shown in (b) of fig. 2 as an example, the node 1 with the in-degree of zero is deleted from the initial monitoring graph and put into the first sequence, where the first sequence is [ node 1], and at the same time, the node 6 with the out-degree of zero is deleted from the initial monitoring graph and put into the second sequence, where the second sequence is [ node 6 ]. Then, the difference between the out-degree and the in-degree of the node 2, the node 3, the node 4 and the node 5 is respectively determined, wherein the difference between the out-degree (i.e. 2) and the in-degree (i.e. 1) of the node 3 is the largest, then the node 3 is determined to be the first target node, the node 3 is deleted from the initial monitoring graph, and the first sequence is put into the first sequence, and the first sequence is [ node 1, node 3 ]. And then determining the difference between the out-degree and the in-degree of the nodes 2, 4 and 5. At this point, the in-degree of nodes 2 and 5 is zero and the out-degree of node 4 is zero, then nodes 2 and 5 may be placed in a first sequence, at which point the first sequence is [ node 1, node 3, node 2, node 5], and node 4 may be placed in a second sequence, at which point the second sequence is [ node 4, node 6 ]. And finally, splicing the first sequence and the second sequence to obtain a topological sequence: [ node 1, node 3, node 2, node 5, node 4, node 6 ]. The topological sequence shown in fig. 5 can be obtained by arranging the six nodes from left to right according to the topological sequence. The directed edge from right to left (i.e., the directed edge pointing from the node 4 to the node 3, which is indicated by the dotted line in fig. 5) is the designated edge, and the designated edge is deleted, so that the intermediate monitoring graph shown in (c) in fig. 2 can be obtained.

In another implementation, a depth-first search may also be performed on the initial monitoring graph to determine whether a loop exists in the initial monitoring graph according to a search result output by the depth-first search.

For example, the initial monitoring graph may be DFS, and in the process of DFS for each node that has not been visited, if a node that has been visited before is visited again, it is determined that a loop exists in the initial monitoring graph. If the loop is detected to exist, any directed edge in the loop can be used as a designated edge to delete to destroy the loop, and after the whole DFS process is completed, an intermediate monitoring graph can be obtained.

Fig. 6 is a flowchart illustrating another monitoring data processing method according to an exemplary embodiment, and as shown in fig. 6, step 103 may include:

and step 1031, setting the hierarchy of each second target node in the intermediate monitoring graph as a first hierarchy, wherein the second target node is a node with zero out-degree in the intermediate monitoring graph.

Step 1032, for each second target node, determining a hierarchy of the third target node corresponding to the second target node according to the number of directed edges between the third target node and the second target node, where the third target node is any node in the intermediate monitoring graph that is not the second target node.

And 1033, determining the hierarchy of the third target node according to the hierarchy of each second target node corresponding to the third target node, wherein the hierarchy of the third target node is greater than the first hierarchy.

For example, the manner of determining the hierarchy of each node in the intermediate monitoring graph may be to first determine a second target node with zero out degree in the intermediate monitoring graph, where the second target node may be one or more. Thereafter, the hierarchy of each second target node is set to the first hierarchy, where the first hierarchy is the lowest layer (i.e., the lowest hierarchy), and may be set to 0 (or 1), for example. Then, for a third target node that is not the second target node, the hierarchy of the third target node corresponding to the second target node may be determined according to the number of directed edges between the third target node and the second target node. For example, if there are 3 directed edges between the third target node and a certain second target node, the hierarchy of the third target node corresponding to the second target node may be determined to be 0+ 3-3. The third target node may correspond to different hierarchies for different second target nodes, and the level of the third target node may be determined as the highest hierarchy in the corresponding second target nodes. For example, if the hierarchy of the third target node corresponding to the two second target nodes is 2 and 3, respectively, the hierarchy of the third target node may be determined to be 3.

The initial monitoring graph includes 7 nodes, namely a node A, a node B, a node C, a node D, a node E, a node F and a node G, and the corresponding topological sequence is shown in FIG. 7. First, the second target nodes are determined to be a node D, a node F and a node G, and the corresponding levels are 0, 0 and 0. For node D, there is a directed edge between node B and node D, so the level of node D corresponding to node B is 1, and there are two directed edges between node a and node D, so the level of node D corresponding to node a is 2. For the node F, the level of the node F corresponding to the node E is 1, the level of the node F corresponding to the node B and the node C is 2, and the level of the node F corresponding to the node a is 3. For the node G, the level of the node G corresponding to the node C is 1, and the level of the node G corresponding to the node a is 2. Then, the level of the node D corresponding to the node a is 2, the level of the node F corresponding to the node a is 3, the level of the node G corresponding to the node G is 2, and the level of the node a may be the maximum value thereof, that is, 3, and so on, the levels of the node a, the node B, the node C, the node D, the node E, the node F, and the node G may be: 3. 2, 0, 1, 0.

Fig. 8 is a flowchart illustrating another monitoring data processing method according to an exemplary embodiment, and as shown in fig. 8, step 104 may include:

step 1041, generating an initial monitoring tree according to the hierarchy of each node, where the nodes with the same hierarchy in the initial monitoring tree are located in the same layer.

Step 1042, according to the hierarchy of each node and the hierarchy of the corresponding adjacent node, adjusting the number of the hierarchies between the node and the corresponding adjacent node to obtain a monitoring tree, where the sum of the numbers of the hierarchies between each node and the corresponding adjacent node in the monitoring tree is the minimum, and the adjacent node is a node having a directed edge with the node.

For example, after determining the hierarchy of each node, an initial monitoring tree may be generated. It is understood that the first number of nodes are divided into a fourth number of levels, and then the initial monitoring tree is divided into the fourth number of levels, and then each node is filled into the initial monitoring tree according to the corresponding level, so that the same level of nodes in the initial monitoring tree are located in the same layer. Taking the topological sequence shown in fig. 7 as an example, the obtained hierarchies of the node a, the node B, the node C, the node D, the node E, the node F, and the node G are respectively: 3. 2, 0, 1, 0. The initial monitoring tree generated according to the hierarchy of 7 nodes is shown as (a) in fig. 9.

There may be cases where one or more directed edges span multiple levels in the initial monitoring tree (which may also be understood as directed edges that are too long in length), resulting in an initial monitoring tree that is not compact enough. As shown in fig. 9 (a), the node B points to the directed edge of the node D, spans three levels of 2, 1, and 0, and is too long. Therefore, the number of levels between each node and the corresponding adjacent node can be adjusted according to the level of each node and the level of the corresponding adjacent node, so as to reduce the number of levels between each node and the corresponding adjacent node as much as possible (i.e. shorten the length of each directed edge as much as possible), thereby obtaining the monitoring tree. The monitoring tree is the most compact, that is, the number of levels between each node and the corresponding adjacent node in the first number of nodes is calculated, and the obtained number of levels is summed to obtain the minimum result.

The following describes how to adjust the number of layer levels between each node and the corresponding adjacent node:

in an application scenario, the implementation of step 1042 may include:

and 6) aiming at each node, if the number of the levels between the node and the corresponding adjacent node is more than 1, reducing the number of the levels between the node and the corresponding adjacent node to obtain the intermediate monitoring tree.

For example, the level number of the layer between each node and the corresponding adjacent node may be determined for each node. If the number of levels between the node and the corresponding adjacent node is greater than 1, it indicates that the length of the directed edge between the node and the corresponding adjacent node is too long, and the number of levels between the node and the corresponding adjacent node can be reduced (i.e., the length of the directed edge between the node and the corresponding adjacent node is shortened), so as to obtain the intermediate monitoring tree.

Specifically, before step 6), step 1042 may further include:

and 8) determining a compact tree according to the initial monitoring tree, wherein the compact tree belongs to the initial monitoring tree, and the number of levels between each node and the corresponding adjacent node in the compact tree is equal to 1. That is, the compact tree is derived from the initial monitoring tree, wherein the number of levels between nodes at both ends of each directed edge included in the compact tree is equal to 1.

Correspondingly, the implementation manner of step 6) may be:

and if the node points to the corresponding adjacent node and the node belongs to the compact tree, increasing the level of the adjacent node corresponding to the node. And if the node points to the corresponding adjacent node and the node does not belong to the compact tree, reducing the level of the node.

Taking the initial monitoring tree shown in (a) of fig. 9 as an example, a compact tree is generated, as shown in (B) of fig. 9, which includes node a, node B, node C, node E, and node F. If the number of levels between the node B and the adjacent node D is 2-0 ═ 2, which is greater than 1, the node B points to the node D, and the node B belongs to the compact tree, then the node D may be moved up by one level (i.e., the level of the node D is modified to 1), and similarly, the node G may be moved up by one level (i.e., the level of the node G is modified to 1), and the intermediate monitoring tree shown in (c) of fig. 9 may be obtained.

And 7) determining the compactness of each directed edge in the middle monitoring tree, adjusting the directed edges with negative compactness to obtain the monitoring tree, and determining the compactness according to two connected areas obtained by deleting the directed edges from the middle monitoring tree.

For example, in order to further avoid the situation that the length of the directed edge is still too long, the compactness of each directed edge in the middle monitoring tree may be respectively determined, and if a directed edge whose compactness is negative exists, the directed edge may be adjusted to obtain a monitoring tree with the smallest sum of the hierarchical numbers of each node and the corresponding adjacent node. And determining the compactness according to the two connected regions obtained by deleting the directed edge from the middle monitoring tree.

The implementation manner of the step 7) can include the following two types:

the first method is as follows:

firstly, deleting the directed edge and the target directed edge from the middle monitoring tree aiming at each directed edge in the middle monitoring tree to obtain two connected areas, wherein the number of the levels between the nodes at the two ends of the target directed edge is more than 1.

And then, determining the compactness of the directed edge according to the number and the direction of the directed edges existing between the two connected areas in the middle monitoring tree.

And finally, if the compactness of the directed edge is negative, increasing the level of the source node of the directed edge according to the compactness of the directed edge.

For example, the target directed edge in the intermediate monitoring tree may be determined first, and then for each directed edge in the intermediate monitoring tree except the target directed edge, the directed edge and the target directed edge may be deleted from the intermediate monitoring tree first, and at this time, two connected regions may be obtained. And then determining the number and direction of the directed edges existing between the two connected areas in the middle monitoring tree, thereby obtaining the compactness of the directed edges. Specifically, a connected region including the source node of the directed edge may be used as the source region, a connected region including the final node of the directed edge may be used as the target region, and the compactness of the directed edge is the number of directed edges pointing from the source region to the target region — the number of directed edges pointing from the target region to the source region. Finally, if the compactness of the directed edge is negative, the level of the source node of the directed edge can be increased according to the compactness of the directed edge.

Taking the middle monitoring tree shown in (a) in fig. 10 as an example, the middle monitoring tree includes a node a, a node b, a node c, a node d, a node e, a node f, a node g, and a node h. Firstly, determining target directed edges with the hierarchical number greater than 1 between nodes at two ends: edge ae (representing node a pointing to node e), and edge ad (representing node a pointing to node d). Taking the compactness of the edge fh (indicating that the node f points to the node h) as an example, the edge ae, the edge ad and the edge fh can be deleted from the intermediate monitoring tree to obtain two connected regions, wherein the source region comprises: node d, node e, node f, the target area includes: in the intermediate monitoring tree, the number of directed edges pointing from the source region to the target region is 1 (i.e., edge fh), the number of directed edges pointing from the target region to the source region is 2 (i.e., edges ae and ad), and the compactness of the edge fh is 1-2 to-1. The compactness of the edge fh is negative, then the level of the source node of the edge fh (i.e., node f) can be increased by the absolute value of the compactness of the directed edge, i.e., the level of the node f is increased by 1. For another example, taking compactness of the edge ab (representing that the node a points to the node b), the edge ae, the edge ad, and the edge ab may be deleted from the intermediate monitoring tree to obtain two connected regions, where the source region includes: the node a, the target area includes: node b, node c, node d, node e, node f, node g, and node h. The number of directed edges pointing from the source region to the target region is 3 (i.e., edge ab, edge ae, edge ad), the number of directed edges pointing from the target region to the source region is 0, and the compactness of edge ab is 3-0 ═ 3. The compactness of side ab is positive, so no adjustment is needed to side ab. The above steps are performed for each directed edge, and a monitoring tree can be obtained as shown in (b) in fig. 10.

The second method comprises the following steps:

firstly, aiming at a root node directed edge of an intermediate monitoring tree, deleting the root node directed edge and a target directed edge from the intermediate monitoring tree to obtain two connected areas, wherein the number of levels at two ends of the target directed edge is more than 1, and the root node directed edge is a directed edge of which any source node in the intermediate monitoring tree is a root node. And determining the compactness of the directed edges of the root nodes according to the quantity and the direction of the directed edges existing between the two communicated regions in the middle monitoring tree.

And then, aiming at each directed edge in the middle monitoring tree, determining the compactness of the directed edge according to the compactness sum of other directed edges corresponding to the directed edge and the out-degree and in-degree of the source node of the directed edge, wherein any end of the other directed edges is the source node of the directed edge.

By way of example, the compactness of each directed edge may also be determined in another manner. The target directed edge in the middle monitoring tree may be determined first, and then, for each root node directed edge in the middle monitoring tree, the root node directed edge and the target directed edge may be deleted from the middle monitoring tree first, and at this time, two connected regions may be obtained. The root node directed edge is a directed edge in which any source node in the middle monitoring tree is a root node, and the root node directed edge is not a target directed edge. The manner of determining the compactness of the directed edge of the root node is the same as that in the first manner, and is not described herein again.

Then, for each directed edge which is not the root directed edge and is not the target directed edge in the middle monitoring tree, the compactness of the directed edge can be determined according to the sum of the compactness of other directed edges corresponding to the directed edge and the out-degree and in-degree of the source node of the directed edge, any one end of the other directed edges is the source node of the directed edge, and the other directed edges are not the directed edge. Specifically, the compactness of the directed edge is the sum of the compactness of the other directed edges + the out-degree of the source node of the directed edge-the in-degree of the source node of the directed edge. Finally, if the compactness of the directed edge is negative, the level of the source node of the directed edge can be increased according to the compactness of the directed edge.

The intermediate monitoring tree shown in (a) in fig. 10 is also taken as an example. Firstly, determining a target directed edge: edge ae and edge ad. The edge ab is a root node directed edge, and the corresponding compactness is 3. Taking an edge bc (indicating that the node b points to the node c) as an example, the other directed edges corresponding to the edge bc are the edges ab, the out-degree of the source node (i.e., the node b) of the edge bc is 1, the in-degree of the node b is 1, and then the compactness of the edge bc is 3+ 1-3.

Fig. 11 is a block diagram illustrating a processing apparatus for monitoring data according to an exemplary embodiment, and as shown in fig. 11, the apparatus 200 may include the following modules:

an obtaining module 201, configured to obtain an initial monitoring graph corresponding to a target task, where the target task includes a first number of processing flows, the initial monitoring graph includes a first number of nodes and a second number of directed edges, each node corresponds to one processing flow, and each directed edge is used to indicate a data flow direction between nodes at two ends of the directed edge.

A processing module 202, configured to generate an intermediate monitoring graph according to the initial monitoring graph, where the intermediate monitoring graph includes a first number of nodes and a third number of directed edges, and is a directed acyclic graph, and the third number is smaller than or equal to the second number.

The determining module 203 is configured to determine a hierarchy of each node according to the intermediate monitoring graph, where there is no directed edge between nodes of the same hierarchy in the intermediate monitoring graph, and a hierarchy of a source node of each directed edge in the intermediate monitoring graph is greater than a hierarchy of a final node of the directed edge.

The generating module 204 is configured to generate a monitoring tree corresponding to the intermediate monitoring graph according to the hierarchy of each node, where the monitoring tree includes a first number of nodes, a root node is a node with an in-degree of zero in the initial monitoring graph, leaf nodes are nodes with an out-degree of zero in the initial monitoring graph, and each node is arranged in the monitoring tree according to the size sequence of the hierarchy of the node.

The display module 205 is configured to display the first number of nodes according to the structure indicated by the monitoring tree, and display each directed edge in the second number of directed edges between nodes at two ends of the directed edge.

Fig. 12 is a block diagram illustrating another monitoring data processing apparatus according to an exemplary embodiment, and as shown in fig. 12, the apparatus 200 may further include:

the judging module 206 is configured to perform topology sorting on the initial monitoring graph before generating the intermediate monitoring graph according to the initial monitoring graph, so as to determine whether a loop exists in the initial monitoring graph according to a topology sequence output by the topology sorting. Or, performing depth-first search on the initial monitoring graph to determine whether a loop exists in the initial monitoring graph according to a search result output by the depth-first search.

Correspondingly, the processing module 202 is configured to delete the specified edge from the initial monitoring graph to obtain the intermediate monitoring graph if a loop exists in the initial monitoring graph.

In an application scenario, the determining module 206 may be configured to perform the following steps:

Fig. 13 is a block diagram illustrating another monitoring data processing apparatus according to an exemplary embodiment, and as shown in fig. 13, the determining module 203 may include:

the initialization submodule 2031 is configured to set a hierarchy of each second target node in the intermediate monitoring graph to be the first hierarchy, where the second target node is a node with zero out degree in the intermediate monitoring graph.

The first determining submodule 2032 is configured to determine, for each second target node, a hierarchy of the third target node corresponding to the second target node according to the number of directed edges between the third target node and the second target node, where the third target node is any node in the intermediate monitoring graph that is not the second target node.

The second determining submodule 2033 is configured to determine, according to the hierarchy of each second target node corresponding to the third target node, the hierarchy of the third target node, where the hierarchy of the third target node is greater than the first hierarchy.

Fig. 14 is a block diagram illustrating another monitoring data processing apparatus according to an exemplary embodiment, and as shown in fig. 14, the generating module 204 may include:

the first generating submodule 2041 is configured to generate an initial monitoring tree according to the hierarchy of each node, where nodes in the initial monitoring tree that are the same in hierarchy are located in the same layer.

The second generating submodule 2042 is configured to adjust, according to the level of each node and the level of the corresponding adjacent node, the level number between the node and the corresponding adjacent node to obtain a monitoring tree, where the sum of the level numbers between each node and the corresponding adjacent node in the monitoring tree is the minimum, and the adjacent node is a node having a directed edge with the node.

In an application scenario, the second generation submodule 2042 may be configured to perform the following steps:

In another application scenario, the second generating submodule 2042 is further configured to perform:

and 8) before reducing the number of levels between the node and the corresponding adjacent node, determining a compact tree according to the initial monitoring tree, wherein the compact tree belongs to the initial monitoring tree, and the number of levels between each node and the corresponding adjacent node in the compact tree is equal to 1.

Correspondingly, the implementation manner of step 6) may be:

and if the node points to the corresponding adjacent node and the node belongs to the compact tree, increasing the level of the adjacent node corresponding to the node.

In another application scenario, the implementation manner of step 7) may be:

With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.

FIG. 15 is a block diagram illustrating an electronic device 300 according to an example embodiment. As shown in fig. 15, the electronic device 300 may include: a processor 301 and a memory 302. The electronic device 300 may also include one or more of a multimedia component 303, an input/output (I/O) interface 304, and a communication component 305.

The processor 301 is configured to control the overall operation of the electronic device 300, so as to complete all or part of the steps in the above-mentioned method for processing monitoring data. The memory 302 is used to store various types of data to support operation at the electronic device 300, such as instructions for any application or method operating on the electronic device 300 and application-related data, such as contact data, transmitted and received messages, pictures, audio, video, and the like. The Memory 302 may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk or optical disk. The multimedia components 303 may include a screen and an audio component. Wherein the screen may be, for example, a touch screen and the audio component is used for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may further be stored in the memory 302 or transmitted through the communication component 305. The audio assembly also includes at least one speaker for outputting audio signals. The I/O interface 304 provides an interface between the processor 301 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 305 is used for wired or wireless communication between the electronic device 300 and other devices. Wireless Communication, such as Wi-Fi, bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, NB-IOT, eMTC, or other 5G, etc., or a combination of one or more of them, which is not limited herein. The corresponding communication component 305 may therefore include: Wi-Fi module, Bluetooth module, NFC module, etc.

In an exemplary embodiment, the electronic Device 300 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components, and is used for executing the above Processing method of monitoring data.

In another exemplary embodiment, there is also provided a computer readable storage medium including program instructions, which when executed by a processor, implement the steps of the above-described monitoring data processing method. For example, the computer readable storage medium may be the memory 302 including the program instructions, which are executable by the processor 301 of the electronic device 300 to perform the above-mentioned monitoring data processing method.

In another exemplary embodiment, a computer program product is also provided, which contains a computer program executable by a programmable apparatus, the computer program having code portions for performing the above-mentioned method of processing monitoring data when executed by the programmable apparatus.

The preferred embodiments of the present disclosure are described in detail with reference to the accompanying drawings, however, the present disclosure is not limited to the specific details of the above embodiments, and various simple modifications may be made to the technical solution of the present disclosure within the technical idea of the present disclosure, and these simple modifications all belong to the protection scope of the present disclosure.

It should be noted that, in the foregoing embodiments, various features described in the above embodiments may be combined in any suitable manner, and in order to avoid unnecessary repetition, various combinations that are possible in the present disclosure are not described again.

In addition, any combination of various embodiments of the present disclosure may be made, and the same should be considered as the disclosure of the present disclosure, as long as it does not depart from the spirit of the present disclosure.

Claims

1. A method for processing monitoring data, the method comprising:

2. The method of claim 1, wherein prior to the generating an intermediate monitoring graph from the initial monitoring graph, the method further comprises:

3. The method of claim 2, wherein said topologically ordering said initial monitoring graph comprises:

4. The method of claim 1, wherein determining a hierarchy for each of the nodes based on the intermediate monitoring graph comprises:

5. The method according to claim 4, wherein the generating the monitoring tree corresponding to the intermediate monitoring graph according to the hierarchy of each node comprises:

6. The method of claim 5, wherein said adjusting the number of levels between each said node and its corresponding adjacent node according to the level of the node and the level of the corresponding adjacent node comprises:

7. The method of claim 6, wherein said adjusting the number of levels separated from each of said nodes and corresponding adjacent nodes according to the level of each of said nodes and the level of the corresponding adjacent node prior to said reducing the number of levels separated from the corresponding adjacent node further comprises:

8. A device for processing monitoring data, the device comprising:

9. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.

10. An electronic device, comprising:

a memory having a computer program stored thereon;

a processor for executing the computer program in the memory to carry out the steps of the method of any one of claims 1 to 7.