CN104462288A - Path similarity analysis method and system - Google Patents
Path similarity analysis method and system Download PDFInfo
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- CN104462288A CN104462288A CN201410705903.XA CN201410705903A CN104462288A CN 104462288 A CN104462288 A CN 104462288A CN 201410705903 A CN201410705903 A CN 201410705903A CN 104462288 A CN104462288 A CN 104462288A
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
- G06F11/3409—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment for performance assessment
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
- G06F11/3447—Performance evaluation by modeling
Abstract
The embodiment of the invention discloses a path similarity analysis method and system. The method includes the steps that first tree-form data of reference data and second tree-form data of problem data are obtained, depth values of all nodes are obtained, and a first set and a second set are determined according to the depth values of all the nodes of the first tree-form data and the depth values of all the nodes of the second tree-form data respectively; the similarity weight of the reference data and the problem data is calculated according to the depth values. Through the method and the system, all the nodes of the first tree-form data of the reference data and all the nodes of the second tree-form data of the problem data can be expressed in a set mode, the similarity weight of the reference data and the problem data is calculated according to all the depth values, then the corresponding relationship between the reference data and the problem data is obtained, the performance positioning work can be automated by using an intelligent performance problem analysis module, the time for analyzing performance problems is greatly shortened, and the dependence on manpower is reduced.
Description
Technical field
The present invention relates to field of information processing, in particular a kind of similarity of paths analytical approach and system.
Background technology
In storage service, due to data the module of process or function different, therefore there is multiple different I/O path.Typical in when cache hits, data directly return from cache module; Cache not hiting data can continue to flow to lower module, and this is the I/O path that two classes are different.Due to the continuity of system development, often there is certain corresponding relation in the different I/O path between storage system different editions, as under same operative scenario, the IO path of baseline version and later release (also claiming problem version) may cause difference owing to adding a certain function, empirically think to there is corresponding relation between this two class IO path, baseline IO path sports problem IO path, also referred to as structural mutation.
Under fixing business scenario (as storage system), the process path (as read and write the path of IO) of request is fixing, performance change can trace back to request process path change (as read IO cache hit probability decline cause IO path elongated, the corresponding decline of performance).By the effective means that the change in analysis request path is analytical performance change.Path change mainly contains two kinds, and 1, time delay is suddenlyd change: benchmark IO path is identical with problem IO path structure, but the time delay between respective modules has significant difference; 2, structural mutation: benchmark IO path is different with problem IO path structure; For 2, in reference data and problem data, need to calculate maximum possible corresponding relation in the possible accidental data of multi-to-multi.For the searching of corresponding relation, there is no universally recognized method at present.
The widely used IO Path-tracking tools of current industry has Google dapper, X-Trace etc.Dapper is a kind of distributed tracking system, can request call path between tracking server cluster.For Dapper, such trace tool principle of brief description:
Dapper can follow the tracks of the request of user process path on the information of each node, as timestamp.Dapper utilizes application program or middleware to every bar request record global flag, and being connected in series in path is whereby a complete loops.The start time of the every bar track record service of Dapper and end time, the ID of each module and father ID, does not have the module of father ID to be root module, and ID is followed the tracks of in public one of all tracking, is an I/O request path by following the tracks of ID by these record serial connections.X-Trace is as internet host trace tool, and its principle is similar with dapper.Also the trace tool adopting same principle is had in unified storage system.
These trace tools are all follow the tracks of sometime or the IO path of time period, as the stable IO path of baseline version, and the problem IO path of problem version.Sudden change relations problems for the IO path of different time sections cannot solve, and due to the impact of difference on system between the different level in IO path be different, larger difference between the difference reflection version of upper strata IO, and the IO difference of lower floor may be fine difference between version causes, prior art ignores such difference, unifiedly calculates the difference of IO, and similarity identification certainty is not high, easily cause structural mutation corresponding relation to judge by accident, cause errors of analytical results larger.
Summary of the invention
Embodiments provide a kind of similarity of paths analytical approach and system.
Embodiment of the present invention first aspect provides a kind of similarity of paths analytical approach, comprising:
Obtain the first tree data of reference data and the second tree data of problem data respectively;
Travel through according to each node of the membership between identical traversal rule and each node to described first tree data and described second tree data the depth value obtaining each node, the described depth value of each node is for representing the level that each node is positioned in described first tree data or the second tree data;
The first set and the second set is determined respectively according to the depth value of each node of described first tree data and described second tree data, wherein, described first set comprises multiple the first corresponding with described depth value respectively subset, and the depth value of the node of described first tree data in each described first subset is identical, described second set comprises multiple the second corresponding with described depth value respectively subset, and the depth value of the node of described second tree data in each described second subset is identical;
The similarity weights of described reference data and described problem data are calculated according to the depth value corresponding with each described second subset with each described first subset sums.
In conjunction with the first aspect of the embodiment of the present invention, in the first implementation of the first aspect of the embodiment of the present invention,
Before the described basis depth value corresponding with each described second subset with each described first subset sums calculates the similarity weights of described reference data and described problem data, described method also comprises:
Determine the maximal value of each depth value corresponding with described first subset and determine the maximal value of each depth value corresponding with described second subset;
Determine that the smaller value in the maximal value of each depth value corresponding with described first subset and the maximal value of each depth value corresponding with described second subset is the first reference depth value;
Determine that the higher value in the maximal value of each depth value corresponding with described first subset and the maximal value of each depth value corresponding with described second subset is the second reference depth value;
Determine that initialization similarity weights W equals 0;
Determine that destination node number is c, wherein, described c equals the maximal value of the nodes in each described second subset of each described first subset sums.
In conjunction with the first implementation of the first aspect of the embodiment of the present invention, in the second implementation of the first aspect of the embodiment of the present invention,
The similarity weights that the described basis depth value corresponding with each described second subset with each described first subset sums calculates described reference data and described problem data comprise:
Determine whether current depth value is less than or equal to described first reference depth value successively according to the depth value order from low to high corresponding with each described second subset with each described first subset sums;
If so, then the SED value described in the first subset sums corresponding with described current depth value between the second subset is determined according to string editing distance algorithm;
If described SED value equals 0, then determine described similarity weights W=W+a^
(b-1)* 2c, wherein, the span of a is described current depth value for being greater than 0 and being less than 1, b, and b is more than or equal to 1 and is less than or equal to described first reference depth value;
If described SED value is not equal to 0, then determine described similarity weights W=W+a^
(b-1)* 2c/SED.
In conjunction with the third implementation of the first aspect of the embodiment of the present invention, in the third implementation of the first aspect of the embodiment of the present invention,
The similarity weights that the described basis depth value corresponding with each described second subset with each described first subset sums calculates described reference data and described problem data also comprise:
If when determining that current depth value is greater than described first reference depth value successively according to described depth value order from low to high, then determine whether described current depth value is less than or equal to described second reference depth value;
If so, described similarity weights W=W – a^ is then determined
(b-1), wherein, b is greater than described first reference depth value and is less than or equal to described second reference depth value.
Embodiment of the present invention second aspect provides a kind of system, comprising:
First acquiring unit, for the second tree data of the first tree data and problem data that obtain reference data respectively;
Second acquisition unit, for traveling through according to each node of the membership between identical traversal rule and each node to described first tree data and described second tree data the depth value obtaining each node, the described depth value of each node is for representing the level that each node is positioned in described first tree data or the second tree data;
First determining unit, for determining the first set and the second set according to the depth value of each node of described first tree data and described second tree data respectively, wherein, described first set comprises multiple the first corresponding with described depth value respectively subset, and the depth value of the node of described first tree data in each described first subset is identical, described second set comprises multiple the second corresponding with described depth value respectively subset, and the depth value of the node of described second tree data in each described second subset is identical;
Computing unit, for calculating the similarity weights of described reference data and described problem data according to the depth value corresponding with each described second subset with each described first subset sums.
In conjunction with embodiment of the present invention second aspect, in the first implementation of embodiment of the present invention second aspect,
Described system also comprises:
Second determining unit, for determining the maximal value of each depth value corresponding with described first subset and determining the maximal value of each depth value corresponding with described second subset;
3rd determining unit is the first reference depth value for the smaller value in the maximal value of the maximal value and each depth value corresponding with described second subset of determining each depth value corresponding with described first subset;
4th determining unit is the second reference depth value for the higher value in the maximal value of the maximal value and each depth value corresponding with described second subset of determining each depth value corresponding with described first subset;
5th determining unit, for determining that initialization similarity weights W equals 0;
6th determining unit, for determining that destination node number is c, wherein, described c equals the maximal value of the nodes in each described second subset of each described first subset sums.
In conjunction with the first implementation of embodiment of the present invention second aspect, in the second implementation of embodiment of the present invention second aspect,
Described computing unit comprises:
According to the depth value order from low to high corresponding with each described second subset with each described first subset sums, first determination module, for determining whether current depth value is less than or equal to described first reference depth value successively;
First computing module, if be less than or equal to described first reference depth value for current depth value, then determines the SED value described in the first subset sums corresponding with described current depth value between the second subset according to string editing distance algorithm;
Second computing module, if equal 0 for described SED value, then determines described similarity weights W=W+a^
(b-1)* 2c, wherein, the span of a is described current depth value for being greater than 0 and being less than 1, b, and b is more than or equal to 1 and is less than or equal to described first reference depth value;
3rd computing module, if be not equal to 0 for described SED value, then determines described similarity weights W=W+a^
(b-1)* 2c/SED.
In conjunction with the second implementation of embodiment of the present invention second aspect, in the third implementation of embodiment of the present invention second aspect,
Described computing unit also comprises:
Second determination module, if for determining that current depth value is greater than described first reference depth value successively according to described depth value order from low to high time, then determine whether described current depth value is less than or equal to described second reference depth value;
3rd determination module, if for determining that described current depth value is less than or equal to described second reference depth value, then determines described similarity weights W=W – a^
(b-1), wherein, b is greater than described first reference depth value and is less than or equal to described second reference depth value.
The embodiment of the invention discloses a kind of similarity of paths analytical approach and system.Embodiment of the present invention method comprises: obtain the first tree data of reference data and the second tree data of problem data respectively, and obtain the depth value of each node, determine the first set and the second set according to the depth value of each node of described first tree data and described second tree data respectively; The similarity weights of described reference data and described problem data are calculated according to described depth value.The similarity of paths analytical approach provided by the present embodiment, can represent in the mode of each node of the second tree data of the first tree data of reference data and problem data by set, and each set comprises and the depth value subclass for representing each node level, and the similarity weights of described reference data and described problem data are calculated according to each depth value, the similarity weights calculating obtained described reference data and described problem data by the present embodiment can determine the corresponding relation between reference data and problem data, and then utility problem intelligent analysis module can robotization performance positioning work, the time that very big raising performance issue is analyzed, reduce the dependence to manpower.
Accompanying drawing explanation
A kind of preferred embodiment flow chart of steps of the similarity of paths analytical approach that Fig. 1 provides for the embodiment of the present invention;
The another kind of preferred embodiment flow chart of steps of the similarity of paths analytical approach that Fig. 2 provides for the embodiment of the present invention;
A kind of preferred embodiment structural representation of the first tree data of the reference data that Fig. 3 provides for the embodiment of the present invention;
A kind of preferred embodiment structural representation of the system that Fig. 4 provides for the embodiment of the present invention;
The another kind of preferred embodiment structural representation of the system that Fig. 5 provides for the embodiment of the present invention;
The another kind of preferred embodiment structural representation of the system that Fig. 6 provides for the embodiment of the present invention.
Embodiment
Embodiments providing a kind of similarity of paths analytical approach, for solving the corresponding relation problem of the sudden change in path, thus realizing the positioning work of robotization performance, improve the time that performance issue is analyzed greatly, reduce the dependence to manpower.
Below in conjunction with shown in Fig. 1, the present embodiment is described in detail to the similarity of paths analytical approach provided:
As shown in Figure 1, described similarity of paths analytical approach comprises:
101, the first tree data of reference data and the second tree data of problem data is obtained respectively;
Namely the data of input are resolved, with the second tree data of the first tree data and problem data that obtain reference data respectively;
Wherein, the data preferably inputted are IO path, and certain the present embodiment is only described with the citing of IO path, and be not construed as limiting, the data of such as this input also can be the request path in distributed file system.
Concrete, the canonical form in the IO path of input can be the dot file layout of xml tree structure or graphviz software.
102, travel through according to each node of the membership between identical traversal rule and each node to described first tree data and described second tree data the depth value obtaining each node;
Wherein, the described depth value of each node is for representing the level that each node is positioned in described first tree data or the second tree data;
Namely the level of each node in described first tree data or described second tree data is represented by described depth value.
The present embodiment is not construed as limiting described traversal rule, as long as travel through according to identical traversal rule described first tree data and described second tree data, and can travel through to all nodes in described first tree data and described second tree data the depth value obtaining all nodes according to this traversal rule.
103, the first set and the second set is determined according to the depth value of each node of described first tree data and described second tree data respectively;
Wherein, described first set comprises multiple the first corresponding with described depth value respectively subset, and the depth value of the node of described first tree data in each described first subset is identical;
Described second set comprises multiple the second corresponding with described depth value respectively subset, and the depth value of the node of described second tree data in each described second subset is identical;
Namely represented in the mode of each node of the reference data of tree represenation and problem data by set by the step 103 shown in the present embodiment.
104, the similarity weights of described reference data and described problem data are calculated according to the depth value corresponding with each described second subset with each described first subset sums.
By the similarity weights of depth value Calculation Basis data corresponding with each node respectively and problem data, and then application IO path sudden change corresponding relation algorithm determines final path sudden change relation.
Concrete, namely in order to calculate the similarity analysis algorithm of the similarity weights of described reference data and described problem data based on each node carrying out representing in the mode of set, the node of different depth values is calculated, finally exports the similarity weights of two class IO path datas.
The similarity of paths analytical approach provided by the present embodiment, can represent in the mode of each node of the second tree data of the first tree data of reference data and problem data by set, and each set comprises and the depth value subclass for representing each node level, and the similarity weights of described reference data and described problem data are calculated according to each depth value, the similarity weights calculating obtained described reference data and described problem data by the present embodiment can determine the corresponding relation between reference data and problem data, and then utility problem intelligent analysis module can robotization performance positioning work, the time that very big raising performance issue is analyzed, reduce the dependence to manpower.
Below in conjunction with shown in Fig. 2 to realizing being positioned at the different algorithm of node of different levels to obtain being described in detail of similarity weights:
201, the first tree data of reference data and the second tree data of problem data is obtained respectively;
Specifically please step 101 as shown in Figure 1, does not repeat in the present embodiment.
202, travel through according to each node of the membership between identical traversal rule and each node to described first tree data and described second tree data the depth value obtaining each node;
Wherein, the described depth value of each node is for representing the level that each node is positioned in described first tree data or the second tree data;
The present embodiment is illustrated described traversal rule, need it is clear that, traversal rule shown in the present embodiment is only a kind of example, be not construed as limiting, as long as travel through according to identical traversal rule described first tree data and described second tree data, and can travel through to all nodes in described first tree data and described second tree data the depth value obtaining all nodes according to this traversal rule.
Below as shown in Figure 3, Fig. 3 is the example of the first tree data of the reference data got, need it is clear that, Figure 3 shows that the citing of tree data, be not construed as limiting, only traversal rule is illustrated by shown in Fig. 3.
To the process that the first tree data shown in Fig. 3 travels through be specifically:
1), root node is read, degree of depth level=1 residing for record node;
Wherein, when reading the first tree data shown in Fig. 3, can determine that node A is root node, and the depth value of node A is recorded as 1;
Whether child node is also had under judging root node;
2) if comprise child node, then determine that this child node is first object node, and the number of child node under determining this first object node, if the number of child node is 1 under first object node, then first object node is equal with the depth value of child node under first object node; If the number of child node is greater than 1 under first object node, then under first object node, the depth value of child node is that first object node depth value adds 1, if this first object node simultaneously and at least one brotgher of node of described first object node while be connected with the child node under described first object node, then the depth value of the child node under described first object node is that the depth value of described first object node subtracts one;
3) record node depth value level, the sub-IO path of Recursion process, until determine to traverse the second destination node, this second destination node is terminal node;
Wherein, the principle recording node depth value can be:
As shown in Figure 3, namely whether also have child node under decision node A, visible, there are three child nodes (Node B, node C and node D) under node A;
As shown in Figure 3, first pointed to the child node B of node A by depth-first principle, and record the depth value level=level+1=2 of child node B;
Traversal rule shown in the present embodiment can in conjunction with the principle of depth-first and breadth First, and first the order of the first tree data namely shown in traversing graph 3 is order from left to right;
Namely determine that the depth value of Node B is after 2, according to depth-first principle, first the sub-IO path of Recursion process, namely determine whether the number of the child node of Node B is greater than 1, and the child node E of traverse node B, and the depth value level=level+1=3 of record child node E;
Determine the child node number of node E, according to the child node K of depth-first principle determination node E, and record the depth value level=level+1=4 of node K;
Determine that the child node of node K is N, the father node because of node N has multiple, then the depth value of node N is that the depth value of node K subtracts one, i.e. the depth value level=level-1=3 of record and the mono-N of Ei;
Determine that the child node of node N is node S, and node S is terminal node, again because the father node of node S has multiple, then determines that node S is the second destination node, and the depth value level=3-1=2 of record child node S;
4), determine that whether the second destination node N is the overall terminal of the first tree data, if not, then by the depth value of this second destination node press-in data stack, and preserve current subpath stacked data, point to the root node of this subpath, carry out another IO subpath traversal, until traverse the last item IO subpath;
Wherein, specifically determine described second destination node be whether the concrete mode of the overall terminal of the first tree data can be judge this second destination node under whether also have child node;
In shown in Fig. 3, also have child node Q under node S, can determine that node S is not the overall terminal of the first tree data, but the terminal node of subpath that node S is positioned at;
The depth value 2 of node S is pressed into data stack, and preserve the subpath stacked data traveled through, namely successively the depth value 1 of the depth value 3 of the depth value 4 of the depth value 3 of node N, node K, node E, the depth value 2 of Node B and node A is pressed into data stack;
Point to the root node A of the subpath be positioned at of node S, according to breadth First principle, carry out the traversal in another strip IO path, then traverse node F via node A and Node B successively, the depth value level=level+1=3 of record node F;
Child node because of node F only has one, then depth value is constant, then node F and node P is in the same level of the first tree data, then the depth value of node F is equal with the depth value of node P is 3;
The child node of node F is node S, and node S has traveled through, be then pressed in data stack by the depth value 3 of node P and the depth value 3 of node F successively;
Point to the root node A of the subpath be positioned at of node P, according to breadth First principle, carry out the traversal in another strip IO path, then traverse the child node C of node A via node A, and record the depth value level=level+1=2 of node C;
Can determine that node G and node C is positioned at same level according to above-mentioned traversal rule, then the depth value of node C and node G is 2, and node G is the second destination node, and node G is not overall terminal, and successively by the depth value of node G and node C press-in data stack;
According to breadth First principle, carry out the traversal in another strip IO path, then traverse the child node D of node A via node A, and record the depth value level=level+1=2 of node D;
The depth value level=level+1=3 of node H can be determined according to above-mentioned traversal rule;
Node H and node M are positioned at same level, then the depth value of node H and node M is 3, and node M is the second destination node;
Determine that the terminal node of this single sub path is node O, because also having child node Q under node O, then determine that node O is not overall terminal, and have multiple father node (node M, node I and node J) to connect because of node O, the then depth value level=level-1=2 of node O, even child node has multiple father node, then the depth value of child node is that father node depth value subtracts one;
Then record the depth value 2 of node O, and successively by the depth value of node O, node M, node H and node D press-in data stack;
According to breadth First principle, carry out the traversal in another IO path, namely traverse node I via node A, node D successively, from above-mentioned traversal rule, the depth value level=level+1=3 of node I;
The child node of node I is node O, because the depth value of node O has been pressed into data stack, then by the depth value of node I press-in data stack;
5) if traverse the last item subpath, then overall terminal is traversed successively, and by the depth value of overall terminal press-in data stack;
According to above-mentioned traversal rule, determine to traverse the subpath that node J is positioned at and be the last item subpath, then determine that the depth value of node J is 3, the depth value level=level-1=1 of node Q successively according to above-mentioned traversal rule, then successively the depth value 3 of the depth value 1 of node Q and node J is pressed in data stack successively;
The depth value of each node of the first tree data can be obtained according to above-mentioned traversal rule, the obtain manner of the depth value of each node of described second tree data asks for an interview the obtain manner of the depth value of each node of the first tree data, specifically repeat no more, need it is clear that, as long as described first tree data carries out traveling through according to identical traversal rule with described second tree data.
203, the first set and the second set is determined according to the depth value of each node of described first tree data and described second tree data respectively;
Carry out in ergodic process by step 202 Suo Shi to described first tree data, for described first tree data, successively by the depth value of each node press-in data stack, set up each first subset according to the sequencing in press-in data stack;
From the above, the first corresponding with depth value 1 subset is: { node A, node Q};
First subset corresponding with depth value 2 is: { node S, Node B, node G, node C, node O, node D};
First subset corresponding with depth value 3 is: { node N, node E, node P, node F, node M, node H, node I, node J};
First subset corresponding with depth value 4 is: { node K, node L};
Visible, described first set comprises multiple the first corresponding with described depth value respectively subset, and the depth value of the node of described first tree data in each described first subset is identical;
What the mode of specifically setting up of described second set asked for leave the first set sets up mode, specifically do not repeat in the present embodiment, as long as described second set comprises multiple the second corresponding with described depth value respectively subset, and the depth value of the node of described second tree data in each described second subset is identical.
204, determine the maximal value of each depth value corresponding with described first subset and determine the maximal value of each depth value corresponding with described second subset;
205, determine that the smaller value in the maximal value of each depth value corresponding with described first subset and the maximal value of each depth value corresponding with described second subset is the first reference depth value;
206, determine that the higher value in the maximal value of each depth value corresponding with described first subset and the maximal value of each depth value corresponding with described second subset is the second reference depth value;
Namely have the shown in above-mentioned steps first set known, the maximal value of each depth value corresponding with each first subset in described first set is 4; Suppose by identical traversal rule determine each depth value corresponding with each second subset in the second set maximum be 10, then determine that the first reference depth value be the 4, second reference depth value is 10.
207, determine that initialization similarity weights W equals 0;
Wherein, W represents the similarity weights of reference data and problem data, and W is less, and to represent similarity less, and 0 to represent two class data completely different.
208, determine that destination node number is c;
Wherein, described c equals the maximal value of the nodes in each described second subset of each described first subset sums.
Maximum with the nodes in the first subset corresponding with depth value 3 in the first set in the present embodiment, then can determine that c value is 8.
Need it is clear that, there is no the precedence relationship in sequential between the step 204 in the present embodiment to step 208, its order of carrying out is only a kind of citing in the present embodiment.
And the present embodiment is also a kind of citing to the setting of c, is not construed as limiting, at different application scenarioss with under requiring the difference of similarity, can set arbitrarily c value, such as also c value can be set as the numerical value such as 500,1000, namely the setting of the present embodiment to c is not construed as limiting.
209, determine whether current depth value is less than or equal to described first reference depth value, if so, then carry out step 210, if not, then carry out step 214 successively according to the depth value order from low to high corresponding with each described second subset with each described first subset sums;
Respectively each subset in the first set and the second set is traveled through, and the order of traversal is by depth value order from low to high, namely for above-mentioned the first set determined, first determine that { depth value of node A, node Q} is current depth value to the first subset, and namely current depth value is 1; Namely the depth value that current traversal analyzes is current depth value.
Determine whether current depth value 1 is less than or equal to described first reference depth value 4;
Determine that the first subset is { after node A, node Q}, the first subset { node S, Node B, node G, node C, node O, node D} is determined again by depth value order from low to high, then determine that { node N, node E, node P, node F, node M, node H, node I, node J}, until determine last first subset { node K, node L};
The traversal mode and first of the second set is gathered identical, namely determines whether current depth value is less than or equal to described first reference depth value, specifically repeats no more successively according to the depth value order from low to high corresponding with each described second subset.
210, the SED value described in the first subset sums corresponding with described current depth value between the second subset is determined according to string editing distance algorithm;
Wherein, string editing distance algorithm (SED, string edit distance), refer between two character strings, the minimum editing operation number of times needed for another is converted to by one, the editing operation of license comprises a character is replaced to another character, inserts a character, deletes a character three kinds.SED can the difference of quantitative measurement two character strings; Such as kitten is changed into sitting: first concrete conversion process can carry out sitten (k->s), carry out sittin (e->i) afterwards, finally carry out sitting (->g), visible SED=3; Namely a kind of Similarity Measure function of SED distance Chang Zuowei, such as, be used in natural language processing, spell check, in webpage similarity comparison, being applied in the analysis of IO similarity of paths is a kind of effective criterion, specifically asks for an interview shown in prior art, does not specifically repeat in the present embodiment.
When namely determining that current depth value equals 1, SED value between the first subset sums second subset corresponding with depth value 1, different and different according to the character string in the first subset sums second subset corresponding from depth value 1 of the result of concrete calculating SED value, then do not repeat in the present embodiment, how concrete be prior art according to node calculate SED value, repeats no more.
211, judge whether the SED value corresponding with described current depth value equals 0, if so, then carry out step 212, if not, then carry out step 213;
212, described similarity weights W=W+a^ is determined
(b-1)* 2c;
Wherein, the span of a is for being greater than 0 and being less than 1, and concrete numerical value is not construed as limiting in the present embodiment, such as, can be 1/2;
B is described current depth value, and b is more than or equal to 1 and is less than or equal to described first reference depth value.
213, described similarity weights W=W+a^ is determined
(b-1)* 2c/SED;
Via above-mentioned steps, depth value is all traveled through one time from 1 to described first reference depth value, and then export similarity weights, following step is carried out on the basis of these similarity weights exported;
214, determine whether described current depth value is less than or equal to described second reference depth value, if so, then carry out step 215, if not, then carry out step 216;
Described in the present embodiment, the second reference depth value is described for 10, namely when current depth value is greater than the first reference depth value, then judges whether current depth value is less than or equal to described second reference depth value further.
215, described similarity weights W=W – a^ is determined
(b-1);
Wherein, b is greater than described first reference depth value and is less than or equal to described second reference depth value.
216, process ends, and the similarity weights W finally determined is exported;
As in the present embodiment for as described in the second reference depth value be 10 to be described, then when current depth value is 10, can will be that 10 corresponding W export with depth value, and process ends.
Shown in the present embodiment, cause and depth value are be upper layer data reference data and problem data from 1 to each node that described first reference depth value is corresponding, depth value is lower data from being greater than described first reference depth value to being less than or equal to each node corresponding to the second reference depth value reference data and problem data, because upper side data and lower data are different on the impact of corresponding relation between reference data and problem data, so the present embodiment is when calculating with depth value from 1 to the similarity weights of each node corresponding to described first reference depth value, make to adopt different account forms to the node being arranged in data different levels by the setting of c, different weights are given when calculating similarity weights to make the node of different levels, thus improve the identification certainty of similarity, ensure the accurate judgement of structural mutation corresponding relation, thus improve precision of analysis.
Known further, by the similarity of paths analytical approach shown in the present embodiment, different reference datas and problem data can be mapped, fill up the blank between Trace system and performance issue analysis tool, the effective positioning work helping properties of product problem.
Below in conjunction with Fig. 4, a kind of system for realizing route similarity analysis method that the present embodiment provides is described in detail:
As shown in Figure 4, described system comprises:
First acquiring unit 401, for the second tree data of the first tree data and problem data that obtain reference data respectively;
Second acquisition unit 402, for traveling through according to each node of the membership between identical traversal rule and each node to described first tree data and described second tree data the depth value obtaining each node, the described depth value of each node is for representing the level that each node is positioned in described first tree data or the second tree data;
First determining unit 403, for determining the first set and the second set according to the depth value of each node of described first tree data and described second tree data respectively, wherein, described first set comprises multiple the first corresponding with described depth value respectively subset, and the depth value of the node of described first tree data in each described first subset is identical, described second set comprises multiple the second corresponding with described depth value respectively subset, and the depth value of the node of described second tree data in each described second subset is identical;
Computing unit 404, for calculating the similarity weights of described reference data and described problem data according to the depth value corresponding with each described second subset with each described first subset sums.
By the system that the present embodiment provides, can represent in the mode of each node of the second tree data of the first tree data of reference data and problem data by set, and each set comprises and the depth value subclass for representing each node level, and the similarity weights of described reference data and described problem data are calculated according to each depth value, the similarity weights calculating obtained described reference data and described problem data by the present embodiment can determine the corresponding relation between reference data and problem data, and then utility problem intelligent analysis module can robotization performance positioning work, the time that very big raising performance issue is analyzed, reduce the dependence to manpower.
Continue to describe in detail with the system obtaining similarity weights to the different algorithm of node that can realize being positioned at different levels below in conjunction with shown in Fig. 5:
As shown in Figure 5, described system comprises:
First acquiring unit 501, for the second tree data of the first tree data and problem data that obtain reference data respectively;
Second acquisition unit 502, for traveling through according to each node of the membership between identical traversal rule and each node to described first tree data and described second tree data the depth value obtaining each node, the described depth value of each node is for representing the level that each node is positioned in described first tree data or the second tree data;
First determining unit 503, for determining the first set and the second set according to the depth value of each node of described first tree data and described second tree data respectively, wherein, described first set comprises multiple the first corresponding with described depth value respectively subset, and the depth value of the node of described first tree data in each described first subset is identical, described second set comprises multiple the second corresponding with described depth value respectively subset, and the depth value of the node of described second tree data in each described second subset is identical;
Second determining unit 504, for determining the maximal value of each depth value corresponding with described first subset and determining the maximal value of each depth value corresponding with described second subset;
3rd determining unit 505 is the first reference depth value for the smaller value in the maximal value of the maximal value and each depth value corresponding with described second subset of determining each depth value corresponding with described first subset;
4th determining unit 506 is the second reference depth value for the higher value in the maximal value of the maximal value and each depth value corresponding with described second subset of determining each depth value corresponding with described first subset;
5th determining unit 507, for determining that initialization similarity weights W equals 0;
6th determining unit 508, for determining that destination node number is c, wherein, described c equals the maximal value of the nodes in each described second subset of each described first subset sums;
Computing unit 509, for calculating the similarity weights of described reference data and described problem data according to the depth value corresponding with each described second subset with each described first subset sums;
Concrete, described computing unit 509 comprises:
According to the depth value order from low to high corresponding with each described second subset with each described first subset sums, first determination module 5091, for determining whether current depth value is less than or equal to described first reference depth value successively;
First computing module 5092, if be less than or equal to described first reference depth value for current depth value, then determines the SED value described in the first subset sums corresponding with described current depth value between the second subset according to string editing distance algorithm;
Second computing module 5093, if equal 0 for described SED value, then determines described similarity weights W=W+a^
(b-1)* 2c, wherein, the span of a is described current depth value for being greater than 0 and being less than 1, b, and b is more than or equal to 1 and is less than or equal to described first reference depth value;
3rd computing module 5094, if be not equal to 0 for described SED value, then determines described similarity weights W=W+a^
(b-1)* 2c/SED.
Second determination module 5095, if for determining that current depth value is greater than described first reference depth value successively according to described depth value order from low to high time, then determine whether described current depth value is less than or equal to described second reference depth value;
3rd determination module 5096, if for determining that described current depth value is less than or equal to described second reference depth value, then determines described similarity weights W=W – a^
(b-1), wherein, b is greater than described first reference depth value and is less than or equal to described second reference depth value.
Shown in the present embodiment, cause and depth value are be upper layer data reference data and problem data from 1 to each node that described first reference depth value is corresponding, depth value is lower data from being greater than described first reference depth value to being less than or equal to each node corresponding to the second reference depth value reference data and problem data, because upper side data and lower data are different on the impact of corresponding relation between reference data and problem data, so the present embodiment is when calculating with depth value from 1 to the similarity weights of each node corresponding to described first reference depth value, make to adopt different account forms to the node being arranged in data different levels by the setting of c, different weights are given when calculating similarity weights to make the node of different levels, thus improve the identification certainty of similarity, ensure the accurate judgement of structural mutation corresponding relation, thus improve precision of analysis.
Known further, by the similarity of paths analytical approach shown in the present embodiment, different reference datas and problem data can be mapped, fill up the blank between Trace system and performance issue analysis tool, the effective positioning work helping properties of product problem.
Embodiment shown in Fig. 4 to Fig. 5 has been described in detail from the structure of angle to system of functions of modules entity, system the embodiment of the present invention is described in detail from hardware point of view below in conjunction with Fig. 6, ask for an interview Fig. 6, another embodiment of the system in the embodiment of the present invention comprises:
This system 600 specifically comprises:
Input media 601, output unit 602, processor 603 and storer 604 (wherein, the processor 603 shown in Fig. 6 can have one or more, is described in Fig. 6 for a processor 603);
In some embodiments of the invention, input media 601, output unit 602, processor 603 are connected by bus or alternate manner with storer 604, wherein, to be connected by bus in Fig. 6.
Processor 603 is for performing following steps:
For the second tree data of the first tree data and problem data that obtain reference data respectively;
For traveling through according to each node of the membership between identical traversal rule and each node to described first tree data and described second tree data the depth value obtaining each node, the described depth value of each node is for representing the level that each node is positioned in described first tree data or the second tree data;
For determining the first set and the second set according to the depth value of each node of described first tree data and described second tree data respectively, wherein, described first set comprises multiple the first corresponding with described depth value respectively subset, and the depth value of the node of described first tree data in each described first subset is identical, described second set comprises multiple the second corresponding with described depth value respectively subset, and the depth value of the node of described second tree data in each described second subset is identical;
For calculating the similarity weights of described reference data and described problem data according to the depth value corresponding with each described second subset with each described first subset sums.
In other embodiments of the present invention, described processor 603 is also for performing following steps:
For determining the maximal value of each depth value corresponding with described first subset and determining the maximal value of each depth value corresponding with described second subset;
Be the first reference depth value for the smaller value in the maximal value of the maximal value and each depth value corresponding with described second subset of determining each depth value corresponding with described first subset;
Be the second reference depth value for the higher value in the maximal value of the maximal value and each depth value corresponding with described second subset of determining each depth value corresponding with described first subset;
For determining that initialization similarity weights W equals 0;
For determining that destination node number is c, wherein, described c equals the maximal value of the nodes in each described second subset of each described first subset sums.
In other embodiments of the present invention, described processor 603 is also for performing following steps:
For determining whether current depth value is less than or equal to described first reference depth value successively according to the depth value order from low to high corresponding with each described second subset with each described first subset sums;
If be less than or equal to described first reference depth value for current depth value, then determine the SED value described in the first subset sums corresponding with described current depth value between the second subset according to string editing distance algorithm;
If equal 0 for described SED value, then determine described similarity weights W=W+a^
(b-1)* 2c, wherein, the span of a is described current depth value for being greater than 0 and being less than 1, b, and b is more than or equal to 1 and is less than or equal to described first reference depth value;
If be not equal to 0 for described SED value, then determine described similarity weights W=W+a^
(b-1)* 2c/SED.
In other embodiments of the present invention, described processor 603 is also for performing following steps:
If during for determining that current depth value is greater than described first reference depth value successively according to described depth value order from low to high, then determine whether described current depth value is less than or equal to described second reference depth value;
If for determining that described current depth value is less than or equal to described second reference depth value, then determine described similarity weights W=W – a^
(b-1), wherein, b is greater than described first reference depth value and is less than or equal to described second reference depth value.
Those skilled in the art can be well understood to, and for convenience and simplicity of description, the system of foregoing description, the specific works process of device and unit, with reference to the corresponding process in preceding method embodiment, can not repeat them here.
In several embodiments that the application provides, should be understood that, disclosed system, apparatus and method, can realize by another way.Such as, device embodiment described above is only schematic, such as, the division of described unit, be only a kind of logic function to divide, actual can have other dividing mode when realizing, such as multiple unit or assembly can in conjunction with or another system can be integrated into, or some features can be ignored, or do not perform.Another point, shown or discussed coupling each other or direct-coupling or communication connection can be by some interfaces, and the indirect coupling of device or unit or communication connection can be electrical, machinery or other form.
The described unit illustrated as separating component or can may not be and physically separates, and the parts as unit display can be or may not be physical location, namely can be positioned at a place, or also can be distributed in multiple network element.Some or all of unit wherein can be selected according to the actual needs to realize the object of the present embodiment scheme.
In addition, each functional unit in each embodiment of the present invention can be integrated in a processing unit, also can be that the independent physics of unit exists, also can two or more unit in a unit integrated.Above-mentioned integrated unit both can adopt the form of hardware to realize, and the form of SFU software functional unit also can be adopted to realize.
If described integrated unit using the form of SFU software functional unit realize and as independently production marketing or use time, can be stored in a computer read/write memory medium.Based on such understanding, the part that technical scheme of the present invention contributes to prior art in essence in other words or all or part of of this technical scheme can embody with the form of software product, this computer software product is stored in a storage medium, comprising some instructions in order to make a computer equipment (can be personal computer, server, or the network equipment etc.) perform all or part of step of method described in each embodiment of the present invention.And aforesaid storage medium comprises: USB flash disk, portable hard drive, ROM (read-only memory) (ROM, Read-OnlyMemory), random access memory (RAM, Random Access Memory), magnetic disc or CD etc. various can be program code stored medium.
The above, above embodiment only in order to technical scheme of the present invention to be described, is not intended to limit; Although with reference to previous embodiment to invention has been detailed description, those of ordinary skill in the art is to be understood that: it still can be modified to the technical scheme described in foregoing embodiments, or carries out equivalent replacement to wherein portion of techniques feature; And these amendments or replacement, do not make the essence of appropriate technical solution depart from the spirit and scope of various embodiments of the present invention technical scheme.
Claims (8)
1. a similarity of paths analytical approach, is characterized in that, comprising:
Obtain the first tree data of reference data and the second tree data of problem data respectively;
Travel through according to each node of the membership between identical traversal rule and each node to described first tree data and described second tree data the depth value obtaining each node, the described depth value of each node is for representing the level that each node is positioned in described first tree data or the second tree data;
The first set and the second set is determined respectively according to the depth value of each node of described first tree data and described second tree data, wherein, described first set comprises multiple the first corresponding with described depth value respectively subset, and the depth value of the node of described first tree data in each described first subset is identical, described second set comprises multiple the second corresponding with described depth value respectively subset, and the depth value of the node of described second tree data in each described second subset is identical;
The similarity weights of described reference data and described problem data are calculated according to the depth value corresponding with each described second subset with each described first subset sums.
2. similarity of paths analytical approach according to claim 1, it is characterized in that, before the described basis depth value corresponding with each described second subset with each described first subset sums calculates the similarity weights of described reference data and described problem data, described method also comprises:
Determine the maximal value of each depth value corresponding with described first subset and determine the maximal value of each depth value corresponding with described second subset;
Determine that the smaller value in the maximal value of each depth value corresponding with described first subset and the maximal value of each depth value corresponding with described second subset is the first reference depth value;
Determine that the higher value in the maximal value of each depth value corresponding with described first subset and the maximal value of each depth value corresponding with described second subset is the second reference depth value;
Determine that initialization similarity weights W equals 0;
Determine that destination node number is c, wherein, described c equals the maximal value of the nodes in each described second subset of each described first subset sums.
3. similarity of paths analytical approach according to claim 2, is characterized in that, the similarity weights that the described basis depth value corresponding with each described second subset with each described first subset sums calculates described reference data and described problem data comprise:
Determine whether current depth value is less than or equal to described first reference depth value successively according to the depth value order from low to high corresponding with each described second subset with each described first subset sums;
If so, then the SED value described in the first subset sums corresponding with described current depth value between the second subset is determined according to string editing distance algorithm;
If described SED value equals 0, then determine described similarity weights W=W+a^
(b-1)* 2c, wherein, the span of a is described current depth value for being greater than 0 and being less than 1, b, and b is more than or equal to 1 and is less than or equal to described first reference depth value;
If described SED value is not equal to 0, then determine described similarity weights W=W+a^
(b-1)* 2c/SED.
4. similarity of paths analytical approach according to claim 3, is characterized in that, the similarity weights that the described basis depth value corresponding with each described second subset with each described first subset sums calculates described reference data and described problem data also comprise:
If when determining that current depth value is greater than described first reference depth value successively according to described depth value order from low to high, then determine whether described current depth value is less than or equal to described second reference depth value;
If so, described similarity weights W=W – a^ is then determined
(b-1), wherein, b is greater than described first reference depth value and is less than or equal to described second reference depth value.
5. a system, is characterized in that, comprising:
First acquiring unit, for the second tree data of the first tree data and problem data that obtain reference data respectively;
Second acquisition unit, for traveling through according to each node of the membership between identical traversal rule and each node to described first tree data and described second tree data the depth value obtaining each node, the described depth value of each node is for representing the level that each node is positioned in described first tree data or the second tree data;
First determining unit, for determining the first set and the second set according to the depth value of each node of described first tree data and described second tree data respectively, wherein, described first set comprises multiple the first corresponding with described depth value respectively subset, and the depth value of the node of described first tree data in each described first subset is identical, described second set comprises multiple the second corresponding with described depth value respectively subset, and the depth value of the node of described second tree data in each described second subset is identical;
Computing unit, for calculating the similarity weights of described reference data and described problem data according to the depth value corresponding with each described second subset with each described first subset sums.
6. system according to claim 5, is characterized in that, described system also comprises:
Second determining unit, for determining the maximal value of each depth value corresponding with described first subset and determining the maximal value of each depth value corresponding with described second subset;
3rd determining unit is the first reference depth value for the smaller value in the maximal value of the maximal value and each depth value corresponding with described second subset of determining each depth value corresponding with described first subset;
4th determining unit is the second reference depth value for the higher value in the maximal value of the maximal value and each depth value corresponding with described second subset of determining each depth value corresponding with described first subset;
5th determining unit, for determining that initialization similarity weights W equals 0;
6th determining unit, for determining that destination node number is c, wherein, described c equals the maximal value of the nodes in each described second subset of each described first subset sums.
7. system according to claim 6, is characterized in that, described computing unit comprises:
According to the depth value order from low to high corresponding with each described second subset with each described first subset sums, first determination module, for determining whether current depth value is less than or equal to described first reference depth value successively;
First computing module, if be less than or equal to described first reference depth value for current depth value, then determines the SED value described in the first subset sums corresponding with described current depth value between the second subset according to string editing distance algorithm;
Second computing module, if equal 0 for described SED value, then determines described similarity weights W=W+a^
(b-1)* 2c, wherein, the span of a is described current depth value for being greater than 0 and being less than 1, b, and b is more than or equal to 1 and is less than or equal to described first reference depth value;
3rd computing module, if be not equal to 0 for described SED value, then determines described similarity weights W=W+a^
(b-1)* 2c/SED.
8. system according to claim 7, is characterized in that, described computing unit also comprises:
Second determination module, if for determining that current depth value is greater than described first reference depth value successively according to described depth value order from low to high time, then determine whether described current depth value is less than or equal to described second reference depth value;
3rd determination module, if for determining that described current depth value is less than or equal to described second reference depth value, then determines described similarity weights W=W – a^
(b-1), wherein, b is greater than described first reference depth value and is less than or equal to described second reference depth value.
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