Benchmarks
All benchmarks are reproducible and based on JMH (Java Microbenchmark Harness) or equivalent tools.
Accessor Performance Benchmark
SJF4J's OBNT relies on reflection for flexible access to POJO/JOJO/JAJO.
Source: ReflectionBenchmark.java.
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Benchmark Mode Cnt Score Error Units
ReflectionBenchmark.reflection_ctor_native avgt 24 6.532 ± 0.512 ns/op baseline
ReflectionBenchmark.reflection_ctor_reflect avgt 24 10.107 ± 0.059 ns/op
ReflectionBenchmark.reflection_ctor_methodHandle avgt 24 9.156 ± 0.664 ns/op
ReflectionBenchmark.reflection_ctor_lambda avgt 24 6.067 ± 0.064 ns/op (0.93x faster)
ReflectionBenchmark.reflection_getter_native avgt 24 0.648 ± 0.018 ns/op baseline
ReflectionBenchmark.reflection_getter_reflect avgt 24 4.184 ± 0.027 ns/op
ReflectionBenchmark.reflection_getter_methodHandle avgt 24 3.104 ± 0.034 ns/op
ReflectionBenchmark.reflection_getter_lambda avgt 24 0.796 ± 0.024 ns/op (1.23x slower)
ReflectionBenchmark.reflection_setter_native avgt 24 0.764 ± 0.023 ns/op baseline
ReflectionBenchmark.reflection_setter_reflect avgt 24 4.376 ± 0.041 ns/op
ReflectionBenchmark.reflection_setter_methodHandle avgt 24 3.040 ± 0.006 ns/op
ReflectionBenchmark.reflection_setter_lambda avgt 24 0.996 ± 0.007 ns/op (1.30x slower)Summary:
- SJF4J uses lambda-based accessors to minimize reflection overhead, enabling dynamic object manipulation with near-native performance.
JSON Parsing Benchmark
This benchmark measures the additional structural overhead introduced by SJF4J on top of native JSON libraries.
SJF4J performs encoding and decoding on top of the underlying JSON parsers.
It adds structural capabilities to the OBNT model, such as JOJO, @NodeValue, and @AnyOf, while attempting to minimize additional overhead.
To bridge different JSON libraries, SJF4J provides three streaming integration modes:
- SHARED_IO — reuse a shared IO pipeline across parsers
- EXCLUSIVE_IO — delegate exclusive streaming control to the underlying library
- PLUGIN_MODULE — integrate through a dedicated module implementation
Performance characteristics vary depending on the backend and workload, and no single mode is universally optimal.
Sample JSON (~1 KB) with nested objects and arrays.
Source: ReadBenchmark.java
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Benchmark (streamingMode) Mode Cnt Score Error Units
Read.json_jackson_native_has_any N/A avgt 20 3.114 ± 0.123 us/op baseline
Read.json_jackson_facade_jojo SHARED_IO avgt 20 3.145 ± 0.063 us/op
Read.json_jackson_facade_jojo EXCLUSIVE_IO avgt 20 2.845 ± 0.038 us/op (0.91x faster)
Read.json_jackson_facade_jojo PLUGIN_MODULE avgt 20 3.055 ± 0.050 us/op (0.98x faster)
Read.json_jackson_native_pojo N/A avgt 20 1.661 ± 0.033 us/op baseline
Read.json_jackson_facade_pojo SHARED_IO avgt 20 2.263 ± 0.043 us/op
Read.json_jackson_facade_pojo EXCLUSIVE_IO avgt 20 2.104 ± 0.004 us/op
Read.json_jackson_facade_pojo PLUGIN_MODULE avgt 20 1.627 ± 0.021 us/op (0.98x faster)
-- no baseline
Read.json_gson_facade_jojo SHARED_IO avgt 20 3.692 ± 0.056 us/op
Read.json_gson_facade_jojo EXCLUSIVE_IO avgt 20 3.699 ± 0.065 us/op
Read.json_gson_facade_jojo PLUGIN_MODULE avgt 20 3.782 ± 0.031 us/op
Read.json_gson_native_pojo N/A avgt 20 2.560 ± 0.038 us/op baseline
Read.json_gson_facade_pojo SHARED_IO avgt 20 2.644 ± 0.029 us/op
Read.json_gson_facade_pojo EXCLUSIVE_IO avgt 20 2.512 ± 0.018 us/op (0.98x faster)
Read.json_gson_facade_pojo PLUGIN_MODULE avgt 20 2.553 ± 0.027 us/op
Read.json_fastjson2_native_has_any N/A avgt 20 2.174 ± 0.039 us/op baseline
Read.json_fastjson2_facade_jojo SHARED_IO avgt 20 2.481 ± 0.043 us/op
Read.json_fastjson2_facade_jojo EXCLUSIVE_IO avgt 20 2.337 ± 0.048 us/op
Read.json_fastjson2_facade_jojo PLUGIN_MODULE avgt 20 2.232 ± 0.038 us/op (1.03x slower)
Read.json_fastjson2_native_pojo N/A avgt 20 0.768 ± 0.004 us/op baseline
Read.json_fastjson2_facade_pojo SHARED_IO avgt 20 1.565 ± 0.119 us/op
Read.json_fastjson2_facade_pojo EXCLUSIVE_IO avgt 20 1.211 ± 0.032 us/op
Read.json_fastjson2_facade_pojo PLUGIN_MODULE avgt 20 0.780 ± 0.006 us/op (1.02x slower)
Read.json_jsonp_facade_jojo SHARED_IO avgt 20 4.694 ± 0.061 us/op
Read.json_jsonp_facade_pojo SHARED_IO avgt 20 3.275 ± 0.087 us/op
Read.json_simple_facade_jojo SHARED_IO avgt 20 8.372 ± 0.081 us/op
Read.json_simple_facade_pojo SHARED_IO avgt 20 7.635 ± 0.071 us/opSummary
Jackson — Default
PLUGIN_MODULE, SJF4J achieves near-parity performance with native Jackson.
When usingEXCLUSIVE_IO, SJF4J can be slightly faster with JOJO, but slower with POJO.Gson — Default
EXCLUSIVE_IO, SJF4J performs slightly faster than the native Gson.
Gson’s native POJO binding does not support extra properties, while SJF4J does.Fastjson2 — Default
PLUGIN_MODULE, SJF4J achieves near-parity performance with native Fastjson2.
Fastjson2 still has a noticeable advantage in POJO binding.Overall performance in our benchmarks roughly follows: Fastjson2 > Jackson > Gson > JSON-P (Parsson) > Simple (built-in)
Note: Regardless of the underlying JSON parser, SJF4J ensures consistent behavior at the API level.
JSON Path Benchmark
This benchmark compares SJF4J with Jayway JsonPath using JMH.
To keep the comparison fair, the main results focus on compile and query over the same in-memory model, rather than parse + query with different parser stacks.
Source: JsonPathCompareBenchmark.java
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Benchmark (expr) Mode Cnt Score Error Units
compile_jayway $.store.book[1].price avgt 16 170.368 ± 61.792 ns/op
compile_jayway $.store.bicycle.color avgt 16 101.054 ± 60.623 ns/op
compile_jayway $.store.book[*].author avgt 16 93.501 ± 0.951 ns/op
compile_jayway $..price avgt 16 43.169 ± 0.460 ns/op
compile_jayway $.store.book[?(@.price>10)].title avgt 16 423.596 ± 2.417 ns/op
compile_jayway $.store.book[0,2].title avgt 16 222.436 ± 22.619 ns/op
compile_sjf4j $.store.book[1].price avgt 16 101.364 ± 4.972 ns/op
compile_sjf4j $.store.bicycle.color avgt 16 60.924 ± 0.249 ns/op
compile_sjf4j $.store.book[*].author avgt 16 70.906 ± 2.251 ns/op
compile_sjf4j $..price avgt 16 32.863 ± 0.249 ns/op
compile_sjf4j $.store.book[?(@.price>10)].title avgt 16 377.336 ± 34.203 ns/op
compile_sjf4j $.store.book[0,2].title avgt 16 117.516 ± 4.035 ns/op
query_jsonnode_definite_jayway $.store.book[1].price avgt 16 249.902 ± 8.303 ns/op
query_jsonnode_definite_jayway $.store.bicycle.color avgt 16 223.630 ± 0.487 ns/op
query_jsonnode_definite_sjf4j $.store.book[1].price avgt 16 124.094 ± 1.636 ns/op
query_jsonnode_definite_sjf4j $.store.bicycle.color avgt 16 85.514 ± 6.435 ns/op
query_jsonnode_indefinite_jayway $.store.book[*].author avgt 16 857.577 ± 78.340 ns/op
query_jsonnode_indefinite_jayway $..price avgt 16 3785.889 ± 47.246 ns/op
query_jsonnode_indefinite_jayway $.store.book[?(@.price>10)].title avgt 16 1704.644 ± 23.227 ns/op
query_jsonnode_indefinite_jayway $.store.book[0,2].title avgt 16 464.221 ± 6.294 ns/op
query_jsonnode_indefinite_sjf4j $.store.book[*].author avgt 16 342.072 ± 1.608 ns/op
query_jsonnode_indefinite_sjf4j $..price avgt 16 1911.318 ± 30.105 ns/op
query_jsonnode_indefinite_sjf4j $.store.book[?(@.price>10)].title avgt 16 1264.369 ± 28.331 ns/op
query_jsonnode_indefinite_sjf4j $.store.book[0,2].title avgt 16 232.189 ± 2.363 ns/op
query_maplist_definite_jayway $.store.book[1].price avgt 16 247.470 ± 3.935 ns/op
query_maplist_definite_jayway $.store.bicycle.color avgt 16 143.477 ± 4.889 ns/op
query_maplist_definite_sjf4j $.store.book[1].price avgt 16 36.643 ± 0.087 ns/op
query_maplist_definite_sjf4j $.store.bicycle.color avgt 16 26.284 ± 0.042 ns/op
query_maplist_indefinite_jayway $.store.book[*].author avgt 16 1055.750 ± 8.188 ns/op
query_maplist_indefinite_jayway $..price avgt 16 2511.681 ± 18.358 ns/op
query_maplist_indefinite_jayway $.store.book[?(@.price>10)].title avgt 16 1866.102 ± 38.002 ns/op
query_maplist_indefinite_jayway $.store.book[0,2].title avgt 16 563.458 ± 18.850 ns/op
query_maplist_indefinite_sjf4j $.store.book[*].author avgt 16 147.899 ± 0.727 ns/op
query_maplist_indefinite_sjf4j $..price avgt 16 524.305 ± 19.131 ns/op
query_maplist_indefinite_sjf4j $.store.book[?(@.price>10)].title avgt 16 830.208 ± 13.403 ns/op
query_maplist_indefinite_sjf4j $.store.book[0,2].title avgt 16 110.346 ± 5.904 ns/op
query_pojo_definite_sjf4j $.store.book[1].price avgt 16 101.030 ± 2.058 ns/op
query_pojo_definite_sjf4j $.store.bicycle.color avgt 16 83.222 ± 2.614 ns/op
query_pojo_indefinite_sjf4j $.store.book[*].author avgt 16 338.385 ± 5.152 ns/op
query_pojo_indefinite_sjf4j $..price avgt 16 1085.178 ± 13.573 ns/op
query_pojo_indefinite_sjf4j $.store.book[?(@.price>10)].title avgt 16 1066.040 ± 12.833 ns/op
query_pojo_indefinite_sjf4j $.store.book[0,2].title avgt 16 227.114 ± 5.777 ns/op
query_jojo_definite_sjf4j $.store.book[1].price avgt 16 48.522 ± 0.231 ns/op
query_jojo_definite_sjf4j $.store.bicycle.color avgt 16 38.667 ± 0.294 ns/op
query_jojo_indefinite_sjf4j $.store.book[*].author avgt 16 209.093 ± 0.957 ns/op
query_jojo_indefinite_sjf4j $..price avgt 16 959.550 ± 14.941 ns/op
query_jojo_indefinite_sjf4j $.store.book[?(@.price>10)].title avgt 16 887.293 ± 16.033 ns/op
query_jojo_indefinite_sjf4j $.store.book[0,2].title avgt 16 130.565 ± 1.011 ns/opSJF4J vs Jayway
Geometric mean, lower is better:
| Benchmark group | SJF4J | Jayway | Result |
|---|---|---|---|
compile | 92.786 ns/op | 136.777 ns/op | SJF4J 1.47x faster |
query_jsonnode_definite | 103.013 ns/op | 236.401 ns/op | SJF4J 2.29x faster |
query_jsonnode_indefinite | 661.899 ns/op | 1266.047 ns/op | SJF4J 1.91x faster |
query_maplist_definite | 31.034 ns/op | 188.431 ns/op | SJF4J 6.07x faster |
query_maplist_indefinite | 290.317 ns/op | 1292.203 ns/op | SJF4J 4.45x faster |
In addition to
queryAPIs, SJF4J also providesmutationAPIs such asput,ensurePutandadd/replace/remove.
SJF4J Object Model Comparison
SJF4J can run JSON Path directly over multiple Java object models.
For native Java object graphs, Map/List is still fastest. JOJO stays close to Map/List and remains clearly ahead of plain POJO, especially on definite queries.
| Benchmark group | Map/List | JOJO | POJO |
|---|---|---|---|
definite | 31.034 ns/op | 43.315 ns/op | 91.695 ns/op |
indefinite | 290.317 ns/op | 390.459 ns/op | 546.050 ns/op |
Summary:
- SJF4J shows strong performance in
compileandqueryworkloads, and provides comprehensivemutationoperations. - Within SJF4J,
Map/Listgives the best raw speed, whileJOJOstays much closer toMap/Listthan plainPOJOdoes.
JSON Schema Validation Benchmark
According to the official JSON Schema Bowtie benchmark, performance can be evaluated locally using:
shell
bowtie perf -i java-sjf4j -i java-json-schema -i java-networknt-json-schema-validator -D draft2020-12Sample result:
text
Tests with Draft2020-12_MetaSchema
Test Name java-sjf4j java-json-schema java-networknt-json-schema-validator
──────────────────────────────────────────────────────────────────────────────────────────────────────────
Validating metaschema 35ms +- 2ms 74ms +- 5ms: 2.13x slower 74ms +- 3ms: 2.15x slower
Reference 2.13x slower 2.15x slower
Tests with OpenAPI_Spec_Schema
Test Name java-sjf4j java-networknt-json-schema-validator java-json-schema
──────────────────────────────────────────────────────────────────────────────────────────────────────────
Non-OAuth Scopes Example 52ms +- 4ms 76ms +- 4ms: 1.46x slower 98ms +- 6ms: 1.87x slower
Webhook Example 55ms +- 3ms 70ms +- 2ms: 1.28x slower 93ms +- 4ms: 1.7x slower
Reference 1.37x slower 1.78x slower
Tests with useless_keywords
Test Name java-sjf4j java-networknt-json-schema-validator java-json-schema
──────────────────────────────────────────────────────────────────────────────────────────────────────────
Beginning of schema 399ms +- 10ms 521ms +- 21ms: 1.31x slower 569ms +- 11ms: 1.43x slower
Middle of schema 390ms +- 5ms 520ms +- 12ms: 1.33x slower 564ms +- 6ms: 1.45x slower
End of schema 411ms +- 14ms 538ms +- 16ms: 1.31x slower 586ms +- 14ms: 1.42x slower
Valid 396ms +- 10ms 525ms +- 19ms: 1.33x slower 585ms +- 15ms: 1.48x slower
Reference 1.32x slower 1.44x slower
Tests with nested_schemas
Test Name java-sjf4j java-json-schema java-networknt-json-schema-validator
──────────────────────────────────────────────────────────────────────────────────────────────────────────
No of Levels 1 31ms +- 850us 69ms +- 4ms: 2.25x slower 70ms +- 2ms: 2.26x slower
No of Levels 4 29ms +- 2ms 72ms +- 3ms: 2.46x slower 73ms +- 3ms: 2.5x slower
No of Levels 7 31ms +- 535us 71ms +- 3ms: 2.27x slower 74ms +- 2ms: 2.36x slower
No of Levels 10 32ms +- 1ms 75ms +- 4ms: 2.37x slower 75ms +- 3ms: 2.36x slower
Reference 2.34x slower 2.37x slowerSummary:
- In Bowtie’s draft 2020-12 benchmark, SJF4J delivers high performance and consistently ranks among the top-tier of Java implementations.