Package: json-fingerprint
json-fingerprintCreate consistent and comparable fingerprints with secure hashes from unordered JSON data.A JSON fingerprint consists of three parts: the version of the underlying canonicalization algorithm, the hash function used and a hexadecimal digest of the hash function output. An example output could look like this:jfpv1$sha256$5815eb0ce6f4e5ab0a771cce2a8c5432f64222f8fd84b4cc2d38e4621fae86af.Fingerprint elementDescriptionjfpv1JSON fingerprint version identifier:jsonfingerprintversion1$JSON fingerprint element separatorsha256Hash function identifier (sha256, sha384 or sha512)5815eb0c...1fae86afThe secure hash function output in hexadecimal formatTable of Contentsv1 release checklist (jfpv1)InstallationExamplesCreate JSON fingerprintsDecode JSON fingerprintsMatch fingerprintsFind matches in fingerprint listsJSON normalizationAlternative specificationsJSON Fingerprint v1 (jfpv1)PerformanceExample 1: flat data structuresExample 2: nested data structuresExample 3: big JSON objectsRunning testsv1 release checklist (jfpv1)This is a list of high-level development and documentation tasks, which need to be completed prior to freezing the API for v1. Before v1, backward-incompatible changes to the API are possible. Since the jfpv1 spec is work in progress, the fingerprints may not be fully comparable between different0.y.zversions.NB:JSON fingerprints up untilv0.12.2ignored empty objects and arrays as values. This behavior was changed inv0.13.0which means that JSON fingerprints created with earlier versions may produce different and incomparable hashes depending on the presence of empty objects or arrays.Formalized and complete jfpv1 specificationJSON type supportPrimitives and literalsArraysObjectsFlattened "sibling-aware" internal data structureSupport nested JSON data structures with mixed typesSupport most common SHA-2 hash functionsSHA256SHA384SHA512Dynamic jfpv1 fingerprint comparison function (JSON string against a fingerprint)Performance characteristics that scale sufficientlyExtensive verification against potential fingerprint (hash) collisionsInstallationTo install the json-fingerprint package, runpip install json-fingerprint.ExamplesThe complete working examples below show how to use all core features of thejson_fingerprintpackage.Create JSON fingerprintsJSON fingerprints can be created with thecreate()function, which requires three arguments: input (valid JSON string), hash function (SHA256, SHA384 and SHA512 are supported) and JSON fingerprint version (1).importjsonimportjson_fingerprintfromjson_fingerprintimporthash_functionsjson_1=json.dumps([3,2,1,[True,False],{"foo":"bar"}])json_2=json.dumps([2,{"foo":"bar"},1,[False,True],3])# Different orderfp_1=json_fingerprint.create(input=json_1,hash_function=hash_functions.SHA256,version=1)fp_2=json_fingerprint.create(input=json_2,hash_function=hash_functions.SHA256,version=1)print(f"Fingerprint 1:{fp_1}")print(f"Fingerprint 2:{fp_2}")This will output two identical fingerprints regardless of the different order of the json elements:Fingerprint 1: jfpv1$sha256$2ecb0c919fcb06024f55380134da3bbaac3879f98adce89a8871706fe50dda03
Fingerprint 2: jfpv1$sha256$2ecb0c919fcb06024f55380134da3bbaac3879f98adce89a8871706fe50dda03Since JSON objects with identical data content and structure will always produce identical fingerprints, the fingerprints can be used effectively for various purposes. These include finding duplicate JSON data from a larger dataset, JSON data cache validation/invalidation and data integrity checking.Decode JSON fingerprintsJSON fingerprints can be decoded with thedecode()convenience function. It returns the version, hash function and secure hash in a tuple.importjson_fingerprintfp="jfpv1$sha256$2ecb0c919fcb06024f55380134da3bbaac3879f98adce89a8871706fe50dda03"version,hash_function,hash=json_fingerprint.decode(fingerprint=fp)print(f"JSON fingerprint version:{version}")print(f"Hash function:{hash_function}")print(f"Hash hex digest:{hash}")This will output the individual elements that make up a fingerprint as follows:JSON fingerprint version: 1
Hash function: sha256
Hash hex digest: 2ecb0c919fcb06024f55380134da3bbaac3879f98adce89a8871706fe50dda03Match fingerprintsThematch()is another convenience function that matches JSON data against a fingerprint, and returns eitherTrueorFalsedepending on whether the data matches the fingerprint or not. Internally, it will automatically choose the correct version and hash function based on thetarget_fingerprintargument.importjsonimportjson_fingerprintjson_1=json.dumps([3,2,1,[True,False],{"foo":"bar"}])json_2=json.dumps([3,2,1])# "target_fp" contains the JSON fingerprint of "json_1"target_fp="jfpv1$sha256$2ecb0c919fcb06024f55380134da3bbaac3879f98adce89a8871706fe50dda03"match_1=json_fingerprint.match(input=json_1,target_fingerprint=target_fp)match_2=json_fingerprint.match(input=json_2,target_fingerprint=target_fp)print(f"Fingerprint matches with json_1:{match_1}")print(f"Fingerprint matches with json_2:{match_2}")This will output the following:Fingerprint matches with json_1: True
Fingerprint matches with json_2: FalseFind matches in fingerprint listsThefind_matches()function takes a JSON string and a list of JSON fingerprints as input. It creates a fingerprint of the JSON input string of each different variant in the target list, and looks for matches in the fingerprint list. It can optionally also deduplicate the fingerprint input list, and results list.importjsonimportjson_fingerprint# "data" produces jfpv1 (SHA256): jfpv1$sha256$d119f4d8...b1710d9f# "data produces jfpv1 (SHA384): jfpv1$sha384$9bca46fd...fd0e2e9cjson_data=json.dumps({"foo":"bar"})fingerprints=[# SHA256 match"jfpv1$sha256$d119f4d8b802091520162b78f57a995a9ecbc88b20573b0c7e474072b1710d9f",# SHA256 match (duplicate)"jfpv1$sha256$d119f4d8b802091520162b78f57a995a9ecbc88b20573b0c7e474072b1710d9f",# SHA384 match("jfpv1$sha384$9bca46fd7ef7aa2e16e68978b5eb5c294bd5b380780e81bcb1af97d4b339bca""f7f6a622b2f1a955eea2fadb8fd0e2e9c"),# SHA256, not a match"jfpv1$sha256$73f7bb145f268c033ec22a0b74296cdbab1405415a3d64a1c79223aa9a9f7643",]# Output all matches, including the two identical SHA256 fingerprintsmatches=json_fingerprint.find_matches(input=json_data,fingerprints=fingerprints)print(*(f"\nMatch:{match[0:30]}..."formatchinmatches))# Output deduplicated matches, removing one redundant SHA256 matchdeduplicated_matches=json_fingerprint.find_matches(input=json_data,fingerprints=fingerprints,deduplicate=True)print(*(f"\nDeduplicated match:{match[0:30]}..."formatchindeduplicated_matches))This will output the following results, first the list with a duplicate and the latter with deduplicated results:Match: jfpv1$sha256$d119f4d8b80209152...
Match: jfpv1$sha256$d119f4d8b80209152...
Match: jfpv1$sha384$9bca46fd7ef7aa2e1...

Deduplicated match: jfpv1$sha384$9bca46fd7ef7aa2e1...
Deduplicated match: jfpv1$sha256$d119f4d8b80209152...JSON normalizationThe jfpv1 JSON fingerprint function transforms the data internally into a normalized (canonical) format before hashing the output.Alternative specificationsMost existing JSON normalization/canonicalization specifications and related implementations operate on three key aspects: data structures, values and data ordering. While the ordering of key-value pairs (objects) is straightforward, issues usually arise from the ordering of arrays.The JSON specifications, including the most recentRFC 8259, have always considered the order of array elements to bemeaningful. As data gets serialized, transferred, deserialized and serialized again throughout various systems, maintaining the order of array elements becomes impractical if not impossible in many cases. As a consequence, this makes the creation and comparison of secure hashes of JSON data across multiple systems a complex process.JSON Fingerprint v1 (jfpv1)The jfpv1 specification takes a morevalue-orientedapproach toward JSON normalization and secure hash creation: values and value-related metadata bear most significance when JSON data gets normalized into the jfpv1 format. The original JSON data gets transformed into a flattened list of small objects, which are then hashed and sorted, and ultimately hashed again as a whole.In practice, the jfpv1 specification purposefully ignores the original order of data elements in an array. The jfpv1 specification focuses instead on verifying that the following aspects of JSON datasets being compared match:All values in the compared datasets are identicalThe values exist in identical paths (arrays, object key-value pairs)In the case of arrays, each array gets a unique hash identifier based on the data elements it holds. This way, each flattened value "knows" to which array it belongs to. This identifier is called asibling hashbecause it is derived from each array element's value as well as its neighboring values.PerformanceThe JSON fingerprint v1 specification and its first implementation have been designed with a primary focus on functional utility over performance. There are some performance-related characteristics that are good to be aware of:Due to the way the internalsibling hashesare computed, highly nested data structures will increase the processing time significantlyThe amount of data in a single data element, or the number of elements in a flat array, is much less meaningful performance-wise than the overall depth of the data structureBelow are some examples of the performance impact when processing different types of data structures.Example 1: flat data structuresProcessing an array of arrays with the maximum depth of 2 levels for each datum:importjsonimporttimeimportjson_fingerprintfromjson_fingerprintimporthash_functionsdata=json.dumps([[1,2],[3,4],[5,6],[7,8],[9,10],[11,12],])start_time=time.time_ns()# Measure time in nanosecondsiterations=1000foriinrange(iterations):json_fingerprint.create(input=data,hash_function=hash_functions.SHA256,version=1)end_time=time.time_ns()duration=round(((end_time-start_time)/iterations/1000000),2)# To millisecondsprint(f"Average processing time per JSON fingerprint:{duration}milliseconds")Performance test results:Average processing time per JSON fingerprint: 0.27 millisecondsAs seen in the test results, flat data structures perform well on modern computer hardware.Example 2: nested data structuresProcessing a nested array of arrays with datums11and127 levels deep in the data structure:importjsonimporttimeimportjson_fingerprintfromjson_fingerprintimporthash_functionsdata=json.dumps([[1,2,[3,4,[5,6,[7,8,[9,10,[11,12]]]]]],])start_time=time.time_ns()# Measure time in nanosecondsiterations=1000foriinrange(iterations):json_fingerprint.create(input=data,hash_function=hash_functions.SHA256,version=1)end_time=time.time_ns()duration=round(((end_time-start_time)/iterations/1000000),2)# To millisecondsprint(f"Average processing time per JSON fingerprint:{duration}milliseconds")Performance test results:Average processing time per JSON fingerprint: 2.75 millisecondsCompared to the flat data structure with the same amount of data, the nesting of arrays increased the processing time tenfold.Example 3: big JSON objectsProcessing a dynamically generated JSON object of three different sizes:~256KiB,~512KiB, and~1MiB:importjsonimporttimeimportjson_fingerprintfromjson_fingerprintimporthash_functionsdeftest_performance(data:str,size:str)->None:start_time=time.time_ns()# Measure time in nanosecondsiterations=1000foriinrange(iterations):json_fingerprint.create(input=data,hash_function=hash_functions.SHA256,version=1)end_time=time.time_ns()duration=round(((end_time-start_time)/iterations/1000000),2)# To millisecondsprint(f"Average processing time per JSON fingerprint ({size}):{duration}milliseconds")text="abcdefg "*16384# a single text element (~128KiB)text_list=["hijklmn "*128foriinrange(128)]# 128 * 1KiB text elements (~128KiB)test_performance(json.dumps({"text":text,"text_list":text_list}),"~256KiB")test_performance(json.dumps({"text":text*2,"text_list":text_list*2}),"~512KiB")test_performance(json.dumps({"text":text*4,"text_list":text_list*4}),"~1MiB")Performance test result:Average processing time per JSON fingerprint (~256KiB): 2.91 milliseconds
Average processing time per JSON fingerprint (~512KiB): 5.42 milliseconds
Average processing time per JSON fingerprint (~1MiB): 11.18 millisecondsProcessing fairly sizeable JSON objects with text content in a flat structure scales linearly.Running testsThe entire internal test suite of json-fingerprint is included in its distribution package. If you wish to run the internal test suite, install the package and run the following command:python -m json_fingerprint.tests.runIf all tests ran successfully, this will produce an output similar to the following:...............................
----------------------------------------------------------------------
Ran 31 tests in 0.008s

OK