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Spring Boot中spring.jpa.open-in-view与JDBCTemplate问题复现

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该问题的最小复现示例库名为osiv-reproducer,包含一个具有两个端点的Controller:/data-jdbc/{tenantId}/foo/{id}和/jdbc-template/{tenantId}/foo/{id},分别用于从Spring Data JDBC存储库加载Tenant和Foo实体,以及通过JDBCTemplate加载Foo实体。应用程序的数据库连接池大小为5,且spring.jpa.open-in-view默认设置为true。" 知识点一:Spring Boot和Spring Data JDBC Spring Boot是一个流行的Java框架,用于简化新Spring应用的初始搭建以及开发过程。它使用了特定的方式来配置和管理Spring应用,使得开发者可以更加快速和简便地创建独立的、生产级别的Spring基础应用。Spring Data JDBC是Spring Data项目的一部分,用于简化数据访问层的开发。与Spring Data JPA不同,Spring Data JDBC在处理数据时更为轻量级,它不会将整个实体加载到内存中,而是直接与数据库交互。 知识点二:JDBCTemplate JDBCTemplate是Spring框架提供的一个用于简化JDBC操作的类。它封装了JDBC的大部分操作,包括资源管理、错误处理、SQL语句执行等。通过使用JDBCTemplate,开发者可以避免编写大量样板代码,从而减少编码工作量和潜在的错误。同时,它也提供了更高级的抽象,使得开发者能够更加专注于业务逻辑的实现。 知识点三:spring.jpa.open-in-view spring.jpa.open-in-view是一个在Spring Boot中配置的参数,它的默认值为true。当设置为true时,Spring会在处理每一个HTTP请求时,自动开启一个数据库事务,并在请求处理完毕后关闭事务。这个特性可以确保在处理请求的过程中,所有的数据库访问都是在事务上下文中进行的,从而保证数据的一致性和完整性。但是,这个特性也可能会带来性能上的影响,因为它会保持数据库连接打开,直到整个HTTP请求处理完毕,这可能会增加数据库连接池的压力。 知识点四:数据库连接池 数据库连接池是一种常用的优化数据库连接的方法。它预先建立一定数量的数据库连接,将这些连接维护在一个池中,应用程序需要进行数据库操作时,直接从池中取出一个连接使用,使用完毕后再放回池中,而不是每次操作都创建一个新的连接。这样可以大大提高数据库操作的效率,并且减少数据库连接的频繁打开和关闭所带来的时间和资源消耗。该复现示例的数据库连接池大小被设置为5。 知识点五:Kotlin语言 Kotlin是一种运行在Java虚拟机上的静态类型编程语言,它与Java完全兼容,可以用来开发Android应用、服务器端应用等。Kotlin语言简洁、安全、富有表达力,并且在与Java的互操作性上做了很多优化,使得开发者可以轻松地在新的和现有的Java代码中使用Kotlin。在osiv-reproducer复现示例中,Kotlin被作为开发语言使用,从"【标签】"字段中可以看出。

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E:\python3.9.13\lib\site-packages\tensorflow\lite\python\convert.py:854: UserWarning: Statistics for quantized inputs were expected, but not specified; continuing anyway. warnings.warn( WARNING: All log messages before absl::InitializeLog() is called are written to STDERR W0000 00:00:1750592469.236287 1300 tf_tfl_flatbuffer_helpers.cc:365] Ignored output_format. W0000 00:00:1750592469.236390 1300 tf_tfl_flatbuffer_helpers.cc:368] Ignored drop_control_dependency. 2025-06-22 19:41:09.237337: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: C:\Users\27731\AppData\Local\Temp\tmpou2lior2 2025-06-22 19:41:09.249839: I tensorflow/cc/saved_model/reader.cc:52] Reading meta graph with tags { serve } 2025-06-22 19:41:09.249859: I tensorflow/cc/saved_model/reader.cc:147] Reading SavedModel debug info (if present) from: C:\Users\27731\AppData\Local\Temp\tmpou2lior2 I0000 00:00:1750592469.381787 1300 mlir_graph_optimization_pass.cc:425] MLIR V1 optimization pass is not enabled 2025-06-22 19:41:09.404910: I tensorflow/cc/saved_model/loader.cc:236] Restoring SavedModel bundle. 2025-06-22 19:41:10.235403: I tensorflow/cc/saved_model/loader.cc:220] Running initialization op on SavedModel bundle at path: C:\Users\27731\AppData\Local\Temp\tmpou2lior2 2025-06-22 19:41:10.478873: I tensorflow/cc/saved_model/loader.cc:471] SavedModel load for tags { serve }; Status: success: OK. Took 1241546 microseconds. 2025-06-22 19:41:10.657810: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable. Traceback (most recent call last): File "E:\pycharm\study\计算机视觉\test2.py", line 302, in <module> deployer.convert_to_tflite_with_metadata(tflite_path, quantize=True) File "E:\pycharm\study\计算机视觉\test2.py", line 255, in convert_to_tflite_with_metadata tflite_model = converter.convert() File "E:\python3.9.13\lib\site-packages\tensorflow\lite\python\lite.py", line 1250, in wrapper return self._convert_and_export_metrics(convert_func, *args, **kwargs) File "E:\python3.9.13\lib\site-packages\tensorflow\lite\python\lite.py", line 1202, in _convert_and_export_metrics result = convert_func(self, *args, **kwargs) File "E:\python3.9.13\lib\site-packages\tensorflow\lite\python\lite.py", line 1768, in convert saved_model_convert_result = self._convert_as_saved_model() File "E:\python3.9.13\lib\site-packages\tensorflow\lite\python\lite.py", line 1749, in _convert_as_saved_model return super(TFLiteKerasModelConverterV2, self).convert( File "E:\python3.9.13\lib\site-packages\tensorflow\lite\python\lite.py", line 1494, in convert return self._optimize_tflite_model( File "E:\python3.9.13\lib\site-packages\tensorflow\lite\python\convert_phase.py", line 215, in wrapper raise error from None # Re-throws the exception. File "E:\python3.9.13\lib\site-packages\tensorflow\lite\python\convert_phase.py", line 205, in wrapper return func(*args, **kwargs) File "E:\python3.9.13\lib\site-packages\tensorflow\lite\python\lite.py", line 1146, in _optimize_tflite_model model = self._quantize( File "E:\python3.9.13\lib\site-packages\tensorflow\lite\python\lite.py", line 760, in _quantize calibrated = calibrate_quantize.calibrate( File "E:\python3.9.13\lib\site-packages\tensorflow\lite\python\convert_phase.py", line 215, in wrapper raise error from None # Re-throws the exception. File "E:\python3.9.13\lib\site-packages\tensorflow\lite\python\convert_phase.py", line 205, in wrapper return func(*args, **kwargs) File "E:\python3.9.13\lib\site-packages\tensorflow\lite\python\optimize\calibrator.py", line 258, in calibrate self._feed_tensors(dataset_gen, resize_input=True) File "E:\python3.9.13\lib\site-packages\tensorflow\lite\python\optimize\calibrator.py", line 152, in _feed_tensors self._calibrator.FeedTensor(input_array) ValueError: Cannot set tensor: Got value of type UINT8 but expected type FLOAT32 for input 0, name: serving_default_input_layer_1:0

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E:\python3.9.13\python.exe E:\pycharm\study\计算机视觉\test2.py 2025-06-22 19:33:45.947350: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`. 2025-06-22 19:33:46.966126: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`. 2025-06-22 19:33:49.689406: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: SSE3 SSE4.1 SSE4.2 AVX AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. Saved artifact at 'C:\Users\27731\AppData\Local\Temp\tmpsq8u2rw_'. 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TensorSpec(shape=(), dtype=tf.resource, name=None) 2723522242688: TensorSpec(shape=(), dtype=tf.resource, name=None) E:\python3.9.13\lib\site-packages\tensorflow\lite\python\convert.py:854: UserWarning: Statistics for quantized inputs were expected, but not specified; continuing anyway. warnings.warn( WARNING: All log messages before absl::InitializeLog() is called are written to STDERR W0000 00:00:1750592036.782204 18108 tf_tfl_flatbuffer_helpers.cc:365] Ignored output_format. W0000 00:00:1750592036.782307 18108 tf_tfl_flatbuffer_helpers.cc:368] Ignored drop_control_dependency. 2025-06-22 19:33:56.783212: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: C:\Users\27731\AppData\Local\Temp\tmpsq8u2rw_ 2025-06-22 19:33:56.795014: I tensorflow/cc/saved_model/reader.cc:52] Reading meta graph with tags { serve } 2025-06-22 19:33:56.795037: I tensorflow/cc/saved_model/reader.cc:147] Reading SavedModel debug info (if present) from: C:\Users\27731\AppData\Local\Temp\tmpsq8u2rw_ I0000 00:00:1750592036.912750 18108 mlir_graph_optimization_pass.cc:425] MLIR V1 optimization pass is not enabled 2025-06-22 19:33:56.934221: I tensorflow/cc/saved_model/loader.cc:236] Restoring SavedModel bundle. 2025-06-22 19:33:57.833679: I tensorflow/cc/saved_model/loader.cc:220] Running initialization op on SavedModel bundle at path: C:\Users\27731\AppData\Local\Temp\tmpsq8u2rw_ 2025-06-22 19:33:58.061998: I tensorflow/cc/saved_model/loader.cc:471] SavedModel load for tags { serve }; Status: success: OK. Took 1278790 microseconds. 2025-06-22 19:33:58.314124: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable. fully_quantize: 0, inference_type: 6, input_inference_type: UINT8, output_inference_type: UINT8 E:\python3.9.13\lib\site-packages\tensorflow\lite\python\interpreter.py:457: UserWarning: Warning: tf.lite.Interpreter is deprecated and is scheduled for deletion in TF 2.20. Please use the LiteRT interpreter from the ai_edge_litert package. See the [migration guide](https://ai.google.dev/edge/litert/migration) for details. warnings.warn(_INTERPRETER_DELETION_WARNING) INFO: Created TensorFlow Lite XNNPACK delegate for CPU. Traceback (most recent call last): File "E:\pycharm\study\计算机视觉\test2.py", line 529, in <module> tflite_deployer = ObjectRecognitionDeployer( File "E:\pycharm\study\计算机视觉\test2.py", line 25, in __init__ self.load_tflite_model(model_path) File "E:\pycharm\study\计算机视觉\test2.py", line 40, in load_tflite_model self.print_model_metadata(model_path) File "E:\pycharm\study\计算机视觉\test2.py", line 44, in print_model_metadata displayer = metadata.MetadataDisplayer.with_model_file(model_path) File "E:\python3.9.13\lib\site-packages\tensorflow_lite_support\metadata\python\metadata.py", line 719, in with_model_file return cls.with_model_buffer(f.read()) File "E:\python3.9.13\lib\site-packages\tensorflow_lite_support\metadata\python\metadata.py", line 735, in with_model_buffer raise ValueError("The model does not have metadata.") ValueError: The model does not have metadata.E:\python3.9.13\python.exe E:\pycharm\study\计算机视觉\test2.py 2025-06-22 19:33:45.947350: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`. 2025-06-22 19:33:46.966126: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`. 2025-06-22 19:33:49.689406: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: SSE3 SSE4.1 SSE4.2 AVX AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. Saved artifact at 'C:\Users\27731\AppData\Local\Temp\tmpsq8u2rw_'. The following endpoints are available: * Endpoint 'serve' args_0 (POSITIONAL_ONLY): TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='input_layer_1') Output Type: TensorSpec(shape=(None, 5), dtype=tf.float32, name=None) Captures: 2723523238368: TensorSpec(shape=(), dtype=tf.resource, name=None) 2723523274704: TensorSpec(shape=(), dtype=tf.resource, name=None) 2723523275760: TensorSpec(shape=(), dtype=tf.resource, name=None) 2723523272944: TensorSpec(shape=(), dtype=tf.resource, name=None) 2723523274528: TensorSpec(shape=(), dtype=tf.resource, name=None) 2723523368912: TensorSpec(shape=(), dtype=tf.resource, name=None) 2723523401328: TensorSpec(shape=(), dtype=tf.resource, name=None) 2723523402384: TensorSpec(shape=(), dtype=tf.resource, name=None) 2723523399744: TensorSpec(shape=(), dtype=tf.resource, name=None) 2723523401152: TensorSpec(shape=(), dtype=tf.resource, name=None) 2723523417712: TensorSpec(shape=(), dtype=tf.resource, name=None) 2723523441936: 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TensorSpec(shape=(), dtype=tf.resource, name=None) 2723522242688: TensorSpec(shape=(), dtype=tf.resource, name=None) E:\python3.9.13\lib\site-packages\tensorflow\lite\python\convert.py:854: UserWarning: Statistics for quantized inputs were expected, but not specified; continuing anyway. warnings.warn( WARNING: All log messages before absl::InitializeLog() is called are written to STDERR W0000 00:00:1750592036.782204 18108 tf_tfl_flatbuffer_helpers.cc:365] Ignored output_format. W0000 00:00:1750592036.782307 18108 tf_tfl_flatbuffer_helpers.cc:368] Ignored drop_control_dependency. 2025-06-22 19:33:56.783212: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: C:\Users\27731\AppData\Local\Temp\tmpsq8u2rw_ 2025-06-22 19:33:56.795014: I tensorflow/cc/saved_model/reader.cc:52] Reading meta graph with tags { serve } 2025-06-22 19:33:56.795037: I tensorflow/cc/saved_model/reader.cc:147] Reading SavedModel debug info (if present) from: C:\Users\27731\AppData\Local\Temp\tmpsq8u2rw_ I0000 00:00:1750592036.912750 18108 mlir_graph_optimization_pass.cc:425] MLIR V1 optimization pass is not enabled 2025-06-22 19:33:56.934221: I tensorflow/cc/saved_model/loader.cc:236] Restoring SavedModel bundle. 2025-06-22 19:33:57.833679: I tensorflow/cc/saved_model/loader.cc:220] Running initialization op on SavedModel bundle at path: C:\Users\27731\AppData\Local\Temp\tmpsq8u2rw_ 2025-06-22 19:33:58.061998: I tensorflow/cc/saved_model/loader.cc:471] SavedModel load for tags { serve }; Status: success: OK. Took 1278790 microseconds. 2025-06-22 19:33:58.314124: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable. fully_quantize: 0, inference_type: 6, input_inference_type: UINT8, output_inference_type: UINT8 E:\python3.9.13\lib\site-packages\tensorflow\lite\python\interpreter.py:457: UserWarning: Warning: tf.lite.Interpreter is deprecated and is scheduled for deletion in TF 2.20. Please use the LiteRT interpreter from the ai_edge_litert package. See the [migration guide](https://ai.google.dev/edge/litert/migration) for details. warnings.warn(_INTERPRETER_DELETION_WARNING) INFO: Created TensorFlow Lite XNNPACK delegate for CPU. Traceback (most recent call last): File "E:\pycharm\study\计算机视觉\test2.py", line 529, in <module> tflite_deployer = ObjectRecognitionDeployer( File "E:\pycharm\study\计算机视觉\test2.py", line 25, in __init__ self.load_tflite_model(model_path) File "E:\pycharm\study\计算机视觉\test2.py", line 40, in load_tflite_model self.print_model_metadata(model_path) File "E:\pycharm\study\计算机视觉\test2.py", line 44, in print_model_metadata displayer = metadata.MetadataDisplayer.with_model_file(model_path) File "E:\python3.9.13\lib\site-packages\tensorflow_lite_support\metadata\python\metadata.py", line 719, in with_model_file return cls.with_model_buffer(f.read()) File "E:\python3.9.13\lib\site-packages\tensorflow_lite_support\metadata\python\metadata.py", line 735, in with_model_buffer raise ValueError("The model does not have metadata.") ValueError: The model does not have metadata.

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