CN117573943A - Data comparison method based on serialization similarity calculation - Google Patents
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Abstract
The invention discloses a data comparison method based on serialization similarity calculation, which comprises the steps of converting two parameter objects to be compared into two JSON objects; constructing an neglecting rule, circularly traversing the neglecting rule to process fields to be ignored in the two JSON objects, and respectively obtaining two preprocessed JSON objects; serializing the two preprocessed JSON objects into two character strings; calculating the minimum operation times of two character string transitions; and calculating the similarity according to the character string lengths of the two character strings and the minimum operation times. According to the invention, after converting the parameter object into the JSON object, the interference capability of part of fields is ignored through a custom neglecting rule, then the JSON object is serialized into the character string, and then the similarity of the character string is utilized to compare the parameter object.
Description
Technical Field
The invention relates to the technical field of data comparison, in particular to a data comparison method based on serialization similarity calculation.
Background
In the software development process, a scenario of automatically comparing access parameters of two interfaces (such as an HTTP interface, a Dubbo interface, an operation database interface, and a domain layer interface) is generally required. In a traffic playback scenario, it is often necessary to compare the incoming and outgoing parameters of the request interface, so as to determine whether the traffic playback result is successful. When playback fails, partial scenes are hoped to acquire parameters with higher similarity for use, so that follow-up processes can be guaranteed to continue to be executed.
The general situation of the input and output parameters aiming at the request interface in the service scene is a complex parameter object, for example, the type fields such as List, map, set, basic data type and array and the like can be contained, and various service self-defined objects can be nested in the parameter object in a multi-layer manner. Therefore, it is difficult to achieve a comparison of whether two complex parameter objects are identical.
In the prior art, two technical schemes are generally adopted to compare parameter objects, JSONs are used for sequencing through fields and then are serialized into JSONs, and then whether the JSONs are equal or not is directly judged; or directly obtaining the value of the corresponding field in a JAVA reflection mode, and then directly comparing whether the values of the fields are the same. The two modes have the following problems, and certain fields are not supported to be ignored firstly, and then data comparison is carried out; when the data comparison is realized in a reflection mode, the performance is not high, and unsupported abnormal scenes are easy to appear; complex object data comparison is easy to cause unsuccessful problems; when the data comparison matching fails, the object with the highest similarity cannot be obtained.
Disclosure of Invention
The invention aims to solve the technical problems that: the data comparison method based on the serialization similarity calculation solves the technical problems of low comparison performance, low success rate and the like of the existing complex parameter objects.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
a data comparison method based on serialization similarity calculation comprises the following steps:
step 1, converting two parameter objects to be compared into a first JSON object and a second JSON object respectively;
step 2, constructing an neglecting rule, circularly traversing the neglecting rule, setting values of fields to be ignored in the first JSON object and the second JSON object as Null, and respectively obtaining a first preprocessing JSON object and a second preprocessing JSON object;
step 3, respectively serializing the first preprocessed JSON object and the second preprocessed JSON object into a first character string and a second character string;
step 4, calculating the minimum operation times required by changing the first character string into the second character string;
step 5, calculating the similarity of the first character string and the second character string according to the character string length of the first character string and the second character string and the minimum operation times;
and 6, judging whether the similarity is 100%, if the similarity is 100%, the two parameter objects are consistent, otherwise, the two parameter objects are inconsistent.
Further, in step 2, for the non-array field to be ignored, combining the prompt and the field name of the non-array field to construct a rule of omitting; combining the prompt, the field name of the array field and the bracket to be ignored to construct an ignore rule; when a number exists in the brackets and the number is N, N is a positive integer, the N value under the current array field is ignored, and when no number exists in the brackets, all values under the current array field are ignored.
Further, in step 3, the first preprocessed JSON object and the second preprocessed JSON object are serialized into the first string and the second string, respectively, using Hessian serialization or JSON serialization.
Further, step 3 further includes: and sequencing English of all parameter names of the first pretreatment JSON object and the second pretreatment JSON object from small to large according to alphabetical order and numbers, and sequencing after sequencing.
Further, in step 4, the minimum number of operations required for changing the first character string to the second character string is calculated by using an edit distance algorithm.
Further, in step 4, each operation of the minimum number of operations is to modify 1 character.
Further, in step 5, the calculation formula of the similarity S is:
；
d represents the minimum number of operations required for changing the first character string to the second character string, and S1 (L) and S2 (L) represent the character string lengths of the first character string and the second character string, respectively.
Further, if the similarity is not 100%, judging whether two parameter objects with highest similarity need to be acquired for returning, and if not, directly ending; and if necessary, searching the two parameter objects with the highest similarity to simulate, obtaining a simulation result and returning.
Compared with the prior art, the invention has the following beneficial effects:
according to the invention, after converting the parameter object into the JSON object, the interference capability of part of fields is ignored through a custom neglecting rule, then the JSON object is serialized into the character string, and then the similarity of the character string is utilized to compare the parameter object. In addition, the invention also utilizes the similarity of the character strings to solve the problem that the abnormal scene is directly interrupted for playback by rapidly matching the parameter object with the highest similarity for replacement.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
Term interpretation:
a List is an ordered collection that holds duplicate elements. Each element in the List has a unique index through which the element can be accessed and modified.
Set is an unordered Set of disallowed repeating elements.
Map is a set of key-value pair mappings. Each key can only correspond to one value, but different keys can correspond to the same value.
Hessian is a lightweight serialization tool library that uses a compact binary format to serialize data.
JSON (JavaScript Object Notation, JS object profile) is a lightweight data exchange format.
A JSON Object refers to an Object created in JAVA using a JSON library to process JSON format data.
Java beans are a common Java class.
Null refers to a particular value that is used to indicate that a variable is absent or not.
The present invention will be described in further detail with reference to the accompanying drawings, in order to make the objects, technical solutions and advantages of the present invention more apparent. It will be apparent that the described embodiments are only some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1, a data comparison method based on serialization similarity calculation includes the following steps:
step 1, converting two parameter objects to be compared into a first JSON object and a second JSON object respectively;
step 2, constructing an neglecting rule, circularly traversing the neglecting rule, setting values of fields to be ignored in the first JSON object and the second JSON object as Null, and respectively obtaining a first preprocessing JSON object and a second preprocessing JSON object;
step 3, respectively serializing the first preprocessed JSON object and the second preprocessed JSON object into a first character string and a second character string;
step 4, calculating the minimum operation times required by changing the first character string into the second character string;
step 5, calculating the similarity of the first character string and the second character string according to the character string length of the first character string and the second character string and the minimum operation times;
and 6, judging whether the similarity is 100%, if the similarity is 100%, the two parameter objects are consistent, otherwise, the two parameter objects are inconsistent.
In the embodiment, in step 1, two parameter objects to be compared are respectively converted into a first JSON object and a second JSON object, where the parameter objects to be compared include various basic types, arrays, list, map, set, multi-layer nested java bean objects, and the like, and the JSON objects are JSONObject or JSONArray.
In the embodiment, in step 2, through circularly traversing the constructed ignore rule, the values of the fields to be ignored in the first JSON object and the second JSON object are set to Null, and Null realizes the unification of the ignored field values, so that the subsequent data comparison result is not interfered.
Preferably, the ignore rule is unified with a prompt, which is merely a prompt to enter a command, and is not part of the command itself. For the non-array field to be ignored, combining the prompt (for example, $) and the field name of the non-array field to construct an ignore rule, if a Long type userId field exists in the parameter object user, if the judgment of the field needs to be ignored, the expression of the ignore rule is as follows: user. For an array field to be ignored, combining a prompt (for example $), a field name of the array field and a bracket (in the cross section), and constructing an ignore rule, if a roleBoodes field of an array type exists in a parameter object, if judgment of the field needs to be ignored, an expression of the ignore rule is as follows: user. When a number exists in the brackets and the number is N, N is a positive integer, the N value under the current array field is ignored, such as $user. RoleNodes [2], and the 2 nd value under the roleNodes field is ignored; when there are no numbers in brackets, this means that all values under the current array field, such as $user.
In the embodiment, in step 3, the first preprocessed JSON object and the second preprocessed JSON object are respectively serialized into the first string and the second string, and the comparison of whether the two complex objects are consistent is converted into the comparison of whether the two strings are consistent.
Preferably, the first pre-processed JSON object and the second pre-processed JSON object are serialized into the first string S1 and the second string S2, respectively, using Hessian serialization or JSON serialization.
In this embodiment, step 3 further includes: and sequencing English of all parameter names of the first pretreatment JSON object and the second pretreatment JSON object from small to large according to alphabetical order and numbers, and sequencing after sequencing. When serialization is used, because the sequence of parameters can affect the character strings of the result after serialization, if the sequence of the two parameters is inconsistent, the difference between the results of subsequent calculation of the first character string S1 and the second character string S2 can be directly caused. Therefore, before serialization, english of all parameter names is firstly sequenced from small to large according to alphabetical order and numbers, and then serialization is performed after sequencing, so that errors of results caused by inconsistent sequences are reduced.
In this embodiment, step 4 is to calculate the minimum number of operations required for changing the first character string to the second character string, and the minimum number of operations is also called the edit distance between the two character strings. If the values of the corresponding parameters of the two objects are the same, the serialized strings should also be the same.
Preferably, the minimum number of operations required to change the first string S1 to the second string S2 is calculated using an edit distance algorithm (Levenshtein Distance).
Preferably, each operation of the minimum number of operations is to modify 1 character.
In this embodiment, step 5 calculates the similarity between the first string and the second string according to the string lengths and the minimum number of operations of the first string and the second string. The minimum operation number calculated in step 4 indicates the number of times the first string S1 is changed to the minimum operation character required for the second string S2, and the maximum operation number is the maximum value of the string lengths of the first string S1 and the second string S, for which the similarity of the two strings is calculated according to the string length S1 (L) of the first string S1, the string length S2 (L) of the second string S2, and the minimum operation number.
Preferably, the calculation formula of the similarity S is:
；
d represents the minimum number of operations required for changing the first string S1 to the second string S2, and S1 (L) and S2 (L) represent the string lengths of the first string S1 and the second string S2, respectively.
A similarity S of 100% indicates that the data values of the two objects are completely identical, whereas a similarity S of 0% indicates that the data values of the two objects are completely inconsistent, i.e. the higher the value of the similarity S, the more similar the two objects. If the two strings are identical, the minimum operation number D is 0, and the calculated similarity s=100%; on the contrary, if the two strings are completely different, the minimum operation number D is the maximum value of the string lengths of the first string S1 and the second string S, that is, the minimum operation number D is equal to Max (S1 (L), S2 (L)), so that the calculated similarity s=0%.
In this embodiment, if the similarity is not 100%, determining whether two parameter objects with the highest similarity need to be acquired for returning, and if not, directly ending; and if necessary, searching the two parameter objects with the highest similarity to perform simulation (Mock), obtaining a simulation result and returning.
For the scene of failure of comparison of two parameter objects, namely, when the similarity is not 100%, part of scenes need to be compatible with the scene of failure, and even if the comparison is failed, the parameter object with the best similarity is hoped to be acquired, so that the subsequent flow is executed. For example, in a flow playback scene, when the similarity of two request parameters (parameters in recording and playback) for the same interface is inconsistent, the program is expected to automatically acquire a parameter object with the highest similarity for use, so that the program is ensured not to be unable to continue with subsequent playback point verification due to the difference of one point. Therefore, according to the calculated similarity S, the present invention performs memory level caching (e.g. caching the last 10 records) on the recently recorded request access parameters. When the parameters are found to be inconsistent during playback, a record with the highest similarity S with the record is obtained according to the interface address and is selected as a temporary scheme, so that the capability of automatically repairing and using the abnormal scene is ensured as much as possible.
Finally, it should be noted that: the above embodiments are merely preferred embodiments of the present invention for illustrating the technical solution of the present invention, but not limiting the scope of the present invention; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the corresponding technical solutions; that is, even though the main design concept and spirit of the present invention is modified or finished in an insubstantial manner, the technical problem solved by the present invention is still consistent with the present invention, and all the technical problems are included in the protection scope of the present invention; in addition, the technical scheme of the invention is directly or indirectly applied to other related technical fields, and the technical scheme is included in the scope of the invention.
Claims (8)
1. The data comparison method based on the serialization similarity calculation is characterized by comprising the following steps of:
step 1, converting two parameter objects to be compared into a first JSON object and a second JSON object respectively;
step 2, constructing an neglecting rule, circularly traversing the neglecting rule, setting values of fields to be ignored in the first JSON object and the second JSON object as Null, and respectively obtaining a first preprocessing JSON object and a second preprocessing JSON object;
step 3, respectively serializing the first preprocessed JSON object and the second preprocessed JSON object into a first character string and a second character string;
step 4, calculating the minimum operation times required by changing the first character string into the second character string;
step 5, calculating the similarity of the first character string and the second character string according to the character string length of the first character string and the second character string and the minimum operation times;
and 6, judging whether the similarity is 100%, if the similarity is 100%, the two parameter objects are consistent, otherwise, the two parameter objects are inconsistent.
2. The method for comparing data based on serialization similarity calculation according to claim 1, wherein in step 2, for the non-array field to be ignored, combining the field names of the prompt and the non-array field to construct a rule for omitting; combining the prompt, the field name of the array field and the bracket to be ignored to construct an ignore rule; when a number exists in the brackets and the number is N, N is a positive integer, the N value under the current array field is ignored, and when no number exists in the brackets, all values under the current array field are ignored.
3. The method for comparing data based on serialization similarity calculation according to claim 1, wherein in step 3, the first preprocessed JSON object and the second preprocessed JSON object are serialized into the first string and the second string, respectively, using Hessian serialization or JSON serialization.
4. The method of claim 1, wherein the step 3 further comprises: and sequencing English of all parameter names of the first pretreatment JSON object and the second pretreatment JSON object from small to large according to alphabetical order and numbers, and sequencing after sequencing.
5. The method of claim 1, wherein in step 4, the minimum number of operations required for changing the first string to the second string is calculated using an edit distance algorithm.
6. The method of claim 1, wherein in step 4, each operation of the minimum number of operations is 1 character modified.
7. The method for comparing data based on serialized similarity calculation of claim 1, wherein in step 5, the calculation formula of similarity S is:
；
d represents the minimum number of operations required for changing the first character string to the second character string, and S1 (L) and S2 (L) represent the character string lengths of the first character string and the second character string, respectively.
8. The method for comparing data based on serialized similarity calculation of claim 1, further comprising determining whether two parameter objects with highest similarity need to be acquired for return if the similarity is not 100%, and ending directly if not; and if necessary, searching the two parameter objects with the highest similarity to simulate, obtaining a simulation result and returning.
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