Developer Guide


This document describes the guts of jpype. It is intended lay out the architecture of the jpype code to aid intrepid lurkers to develop and debug the jpype code once I am run over by a bus. For most of this document I will use the royal we, except where I am giving personal opinions expressed only by yours truly, the author Thrameos.


When I started work on this project it had already existed for over 10 years. The original developer had intended a much larger design with modules to support multiple languages such as Ruby. As such it was constructed with three layers of abstraction. It has a wrapper layer over Java in C++, a wrapper layer for the Python api in C++, and an abstraction layer intended to bridge Python and other interpreted languages. This multilayer abstraction ment that every debugging call had to drop through all of those layers. Memory management was split into multiple pieces with Java controlling a portion of it, C++ holding a bunch of resources, Python holding additional resources, and HostRef controlling the lifetime of objects shared between the layers. It also had its own reference counting system for handing Java references on a local scale.

This level of complexity was just about enough to scare off all but the most hardened programmer. Thus I set out to eliminate as much of this as I could. Java already has its own local referencing system to deal in the form of LocalFrames. It was simply a matter of setting up a C++ object to hold the scope of the frames to eliminate that layer. The Java abstraction was laid out in a fashion somewhat orthagonally to the Java inheritance diagram. Thus that was reworked to something more in line which could be safely completed without disturbing other layers. The multilanguage abstraction layer was already pierced in multiple ways for speed. However, as the abastraction interwove throughout all the library it was a terrible lift to remove and thus required gutting the Python layer as well to support the operations that were being performed by the HostRef.

The remaining codebase is fairly slim and reasonably streamlined. This rework cut out about 30% of the existing code and sped up the internal operations. The Java C++ interface matches the Java class hierachy.


JPype is split into several distinct pieces.

jpype Python module

The majority of the front end logic for the toolkit is in Python jpype module. This module deals with the construction of class wrappers and control functions. The classes in the layer are all prefixed by J.

_jpype CPython module

The native module is supported by a CPython module called _jpype. The _jpype module is located in native/python and has C style classes with a prefix PyJP.

This CPython layer acts as a front end for passing to the C++ layer. It performs some error checking. In addition to the module functions in _JModule, the module has multiple Python classes to support the native jpype code such as _JClass, _JArray, _JValue, _JValue, etc.

CPython API wrapper

In addition to the exposed Python module layer, there is also a C++ wrapper for the Python API. This is located in native/python and has the prefix JPPy for all classes. jp_pythontypes wraps the required parts of the CPython API in C++ for use in the C++ layer.

C++ JNI layer

The guts that drive Java are in the C++ layer located in native/common. This layer has the namespace JP. The code is divided into wrappers for each Java type, a typemanager for mapping from Java names to class instances, support classes for proxies, and a thin JNI layer used to help ensure rigerous use of the same design patterns in the code. The primary responsibility of this layer is type conversion and matching of method overloads.

Java layer

In addition to the C++ layer, jpype has a native Java layer. This code is compiled as a “thunk” which is loaded into the JVM in the form of a a binary stored as a string. Code for Java is found in native/java. The Java layer is divided into two parts, a bootstrap loader and a jar containing the support classes. The Java layer is responsible managing the lifetime of shared Python, Java, and C++ objects.

jpype module

The jpype module itself is made of a series of support classes which act as factories for the individual wrappers that are created to mirror each Java class. Because it is not possible to wrap all Java classes with staticly created wrappers, instead jpype dynamically creates Python wrappers as requested by the user.

The wrapping process is triggered in two ways. The user can manually request creating a class by importing a class wrapper with jpype.imports or JPackage or by manually invoking it with JClass. Or the class wrapper can be created automatically as a result of a return type or exception thrown to the user.

Because the classes are created dynamically, the class structure uses a lot of Python meta programming. Each class wrapper derives from the class wrappers of each of the wrappers corresponding to the Java classes that each class extends and implements. The key to this is to hacked mro. The mro orders each of the classes in the tree such that the most drived class methods are exposed, followed by each parent class. This must be ordered to break ties resulting from multiple inheritance of interfaces. The factory classes are grafted into the type system using __instancecheck__ and __subtypecheck__.

resource types

JPype largely maps to the same concepts as Python with a few special elements. The key concept is that of a Factory which serves to create Java resources dynamically as requested. For example there is no Python notation to create a int[][] as the concept of dimensions are fluid in Python. Thus a factory type creates the actual object instance type with JArray(JInt,2) Like Python objects, Java objects derives from a type object which is called JClass that serves as a meta type for all Java derived resources. Additional type like object JArray and JInterface serve to probe the relationships between types. Java object instances are created by calling the Java class wrapper just like a normal Python class. A number of pseudo classes serve as placeholders for Java types so that it is not necessary to create the type instance when using. These aliased classes are JObject, JString, and JException. Underlying all Java instances is the concept of a jvalue.


In the earlier design, wrappers, primitives and objects were all seperate concepts. At the JNI layer these are unified by a common element called jvalue. A jvalue is a union of all primitives with the jobject. The jobject can represent anything derived from Java object including the pseudo class jstring.

This has been replaced with a Java slot concept which holds an instance of JPValue which holds a pointer to the C++ Java type wrapper and a Java jvalue union. We will discuss this object further in the CPython section.


The most challenging part in working with the jpype module other than the need to support both major Python versions with the same codebase is the bootstrapping of resources. In order to get the system working, we must pass the Python resources so the _jpype CPython module can acquire resources and then construct the wrappers for java.lang.Object and java.lang.Class. The key difficulty is that we need reflection to get methods from Java and those are part of java.lang.Class, but class inherits from java.lang.Object. Thus Object and the interfaces that Class inherits must all be created blindly. The order of bootstrapping is controlled by specific sequence of boot actions after the JVM is started in startJVM. The class instance class_ may not be accessed until after all of the basic class, object, and exception types have been loaded.


The key objects exposed to the user (JClass, JObject, and JArray) are each factory meta classes. These classes serve as the gate keepers to creating the meta classes or object instances. These factories inherit from the Java class meta and have a class_ instance inserted after the the JVM is started. They do not have exposed methods as they are shadows for action for actual Java types.

The user calls with the specified arguments to create a resource. The factory calls the __new__ method when creating an instance of the derived object. And the C++ wrapper calls the method with internally construct resource such as _JClass or _JValue. Most of the internal calls currently create the resource directly without calling the factories. The gateway for this is PyJPValue_create which delegates the process to the corresponding specialized type.


One of the aspects of the jpype design is elegance of the factory patterns. Rather than expose the user a large number of distinct concepts with different names, the factories provide powerfull functionality with the same syntax for related things. Boxing a primitive, casting to a specific type, and creating a new object are all tied together in one factory, JObject. By also making that factory an effective base class, we allow it to be used for issubtype and isinstance.

This philosophy is further enhanced by silent customizers which integrate Python functionality into the wrappers such that Java classes can be used effectively with Python syntax. Consistent use and misuse of Python concepts such as with for defining blocks such as try with resources and synchronized hide the underlying complexity and give the feeling to the user that the module is integrated completely as a solution such as jython.

When adding a new feature to the Python layer, consider carefully if the feature needs to be exposed a new function or if it can be hidden in the normal Python syntax.

JPype does somewhat break the Python naming conventions. Because Java and Python have very different naming schemes, at least part of the kit would have a different convention. To avoid having one portion break Python conventions and another part conform, we choose to use Java notation consistently throughout. Package names should be lower with underscores, classes should camel case starting upper, functions and method should be camel case starting lower. All private methods and classes start with a leading underscore and are not exported.


There was a major change in the way the customizers work between versions. The previous system was undocumented and has now been removed, but as someone may have used of it previously, we will contrast it with the revised system so that the customizers can be converted.

In the previous system, a global list stored all customizers. When a class was created, it went though the list and asked the class if it matched that class name. If it matched, it altered the dict of members to be created so when the dynamic class was finished it had the custome behavior. This system wasn’t very scalable as each customizer added more work to the class construction process.

The revised system works by storing a dictionary keyed to the class name. Thus the customizer only applies to the specific class targeted to the customizer. The customizer is specified using annotation of a prototype class making methods automatically copy onto the class. However, sometimes a customizer needs to be applied to an entire tree of classes such as all classes that implement java.util.List. To handle this case, the class creation system looks for a special method __java_init__ in the tree of base classes and calls it on the newly created class. Most of the time the customization was the same simple pattern so we added a sticky flag to build the initialization method directly. This method can alter the class to make it add the new behavior. Note the word alter. Where before we changed the member prior to creating the class, here we are altering the class. Thus the customizer is expected to monkey patch the existing class. There is only one pattern of monkey patching that works on both Python 2 and Python 3 so be sure to use the type.__setattr__ method of altering the class dictionary.

It is possible to apply customizers after the class has already been created because we operate by monkey patching. But there is a limitation that there can only be one __java_init__ method and thus two customizers specifying a global behavior on the same class wrapper will lead to unexpected behavior.

_jpype CPython module

Diving deeper into the onion, we have the Python front end. This is divided into a number of distinct pieces. Each piece is found under native/python and is named according to the piece it provides. For example, PyJPModule is found in the file native/python/pyjp_module.cpp

Earlier versions of the module had all of the functionality in the modules global space. This functionality is now split into a number of classes. These classes each have a constructor that is used to create an instance which will correspond to a Java resource such as class, array, method, or value.

Jpype objects work with the inner layers by inheriting from a set of special _jpype classes. This class hiarachy is mantained by the meta class _jpype._JClass. The meta class does type hacking of the Python API to insert a reserved memory slot for the JPValue structure. The meta class is used to define the Java base classes:

  • _JClass - Meta class for all Java types which maps to a java.lang.Class extending Python type.

  • _JArray - Base class for all Java array instances.

  • _JObject - Base type of all Java object instances extending Python object.

  • _JNumberLong - Base type for integer style types extending Python int.

  • _JNumberFloat - Base type for float style types extending Python float.

  • _JNumberChar - Special wrapper type for JChar and java.lang.Character types extending Python float.

  • _JException - Base type for exceptions extending Python Exception.

  • _JValue - Generic capsule representing any Java type or instance.

These types are exposed to Python to implement Python functionality specific to the behavior expected by the Python type. Under the hood these types are largely ignored. Instead the internal calls for the Java slot to determine how to handle the type. Therefore, internally often Python methods will be applied to the “wrong” type as the requirement for the method can be satisfied by any object with a Java slot rather than a specific type.

See the section regarding Java slots for details.

PyJPModule module

This is the front end for all the global functions required to support the Python native portion. Most of the functions provided in the module are for control and auditing.

Resources are created by setting attributes on the _jpype module prior to calling startJVM. When the JVM is started each of th required resources are copied from the module attribute lists to the module internals. Setting the attributes after the JVM is started has no effect. Resources are verified to exist when the JVM is started and any missing resource are reported as an error.

_JClass class

The class wrappers have a metaclass _jpyep._JClass which serves as the guardian to ensure the slot is attached, provide for the inheritance checks, and control access to static fields and methods. The slot holds a java.lang.Class instance but it does not have any of the methods normally associate with a Java class instance exposed. A java.lang.Class instance can be converted to a Jave class wrapper using JClass.

_JMethod class

This class acts as descriptor with a call method. As a descriptor accessing its methods through the class will trigger its __get__ function, thus getting ahold of it within Python is a bit tricky. The __get__ mathod is used to bind the static unbound method to a particular object instance so that we can call with the first argument as the this pointer.

It has some reflection and diagnostics methods that can be useful it tracing down errors. The beans methods are there just to support the old properties API.

The naming on this class is a bit deceptive. It does not correspond to a single method but rather all the overloads with the same name. When called it passes to with the arguments to the C++ layer where it must be resolved to a specific overload.

This class is stored directly in the class wrappers.

_JField class

This class is a descriptor with __get__ and __set__ methods. When called at the static class layer it operates on static fields. When called on a Python object, it binds to the object making a this pointer. If the field is static, it will continue to access the static field, otherwise, it will provide access to the member field. This trickery allows both static and member fields to wrap as one type.

This class is stored directly in the class wrappers.

_JArray class

Java arrays are extensions of the Java object type. It has both methods associated with java.lang.Object and Python array functionality. Primitives have specialized implementations to allow for the Python buffer API.

_JMonitor class

This class provides synchronized to JPype. Instances of this class are created and held using with. It has two methods __enter__ and __exit__ which hook into the Python RAII system.

_JValue class

Java primitive and object instance derive from special Python derived types. These each have the Python functionality to be exposed and a Java slot. The most generic of these is _JValue which is simply a capsule holding the Java C++ type wrapper and a Java jvalue union. CPython methods for the PyJPValue apply to all CPython objects that hold a Java slot.

Specific implementation exist for object, numbers, characters, and exceptions. But fundimentally all are treated the same internally and thus the CPython type is effectively erased outside of Python.

Unlike jvalue we hold the object type in the C++ JPValue object. The class reference is used to determine how to match the arguments to methods. The class may not correspond to the actual class of the object. Using a class other than the actual class serves to allow an object to be cast and thus treated like another type for the purposes of overloading. This mechanism is what allows the JObject factory to perform a typecast to make an object instance act like one of its base classes..

Java Slots

THe key to achieving reasonable speed within CPython is the use of slots. A slot is a dedicated memory location that can be accessed without consulting the dictionary or bases of an object. CPython achieve this by reserving space within the type structure and by using a set of bit flags so that it can avoid costly. The reserved space in order by number and thus avoids the need to access the dictionary while the bit flags serve to determine the type without traversing the __mro__ structure. We had to implement the same effect which deriving from a wide variety for Python types including type, object, int, long, and Exception. Adding the slot directly to the type and objects base memory does not work because these types all have different memory layouts. We could have a table look up based on the type but because we must obey both the CPython and the Java object hierarchy at the same time it cannot be done within the memory layout of Python objects. Instead we have to think outside the box, or rather outside the memory footprint of Python objects.

CPython faces the same conflict internally as inheritance often forces adding a dictionary or weak reference list onto a variably size type sych as long. For those cases it adds extract space to the basesize of the object and then ignores that space for the purposes of checking inheritance. It pairs this with an offset slot that allows for location of the dynamic placed slots. We cannot replicate this in the same way because the CPython interals are all specialize static members and there is no provision for introducting user defined dynamic slots.

Therefore, instead we will add extra memory outside the view of Python objects though the use of a custom allocator. We intercept the call to create an object allocation and then call the regular Python allocators with the extra memory added to the request. As our extrs slot has resource in the form of Java global references associated with it, we must deallocate those resource regardless of the type that has been extended. We perform this task by creating a custom finalize method to serve as the destructor. Thus a Java slot requires overriding each of tp_alloc, tp_free and tp_finalize. The class meta gatekeeper creates each type and verifies that the required hooks are all in place. If the user tries to bypass this it should produce an error.

In place of Python bit flags to check for the presence of a Java slot we instead test the slot table to see if our hooks are in place. We can test if the slot is present by looking to see if both tp_alloc and tp_finalize point to our Java slot handlers. This means we are still effectively a slot as we can test and access with O(1).

Accessing the slot requires testing if the slot exists for the object, then computing the sice of the object using the basesize and itemsize associate with the type and then offsetting the Python object pointer appropriately. The overall cost is O(1), though is slightly more heavy that directly accesssing an offset.

CPython API layer

To make creation of the C++ layer easier a thin wrapper over the CPython API was developed. This layer provided for handling the CPython referencing using a smart pointer, defines the exception handling for Python, and provides resource hooks for duck typing of the _jpype classes.

This layer is located with the rest of the Python codes in native/python, but has the prefix JPPy for its classes. As the bridge between Python and C++, these support classes appear in both the _jpype CPython module and the C++ JNI layer.

Exception handling

A key piece of the jpype interaction is the transfer of exceptions from Java to Python. To accomplish this Python method that can result in a call to Java must have a try block around the contents of the function.

We use a routine pattern of code to interact with Java to achieve this:

PyObject* dosomething(PyObject* self, PyObject* args)
   // Tell the logger where we are

   // Make sure there is a jvm to receive the call.

   // Make a resource to capture any Java local references
   JPJavaFrame frame;

   // Call our Java methods

   // Return control to Python
   return obj.keep();

   // Use the standard catch to transfer any exceptions back
   // to Python

All entry points from Python into _jpype should be guarded with this pattern.

There are exceptions to this pattern such as removing the logging, operating on a call that does not need the JVM running, or operating where the frame is already supported by the method being called.

Python referencing

One of the most miserable aspects of programming with CPython is the relative inconsistancy of referencing. Each method in Python may use a Python object or steal it, or it may return a borrowed reference or give a fresh reference. Similar command such as getting an element from a list and getting an element from a tuple can have different rules. This was a constant source of bugs requiring consultation of the Python manual for every line of code. Thus we wrapped all of the Python calls we were required to work with in jp_pythontypes.

Included in this wrapper is a Python reference counter called JPPyObject. Whenever an object is returned from Python it is immediately placed in smart pointer JPPyObject with the policy that it was created with such as use_, borrowed_, claim_ or call_.


This policy means that the reference counter needs to be incremented and the start and the end. We must reference it because if we don’t and some Python call destroys the refernce out from under us, the system may crash and burn.


This policy means we were to be give a borrowed reference that we are expected to reference and unreference when complete, but the command that returned it can fail. Thus before reference it, the system must check if an error has occurred. If there is an error, it is promoted to an exception.


This policy is used when we are given a new object with is already referenced for us. Thus we are to steal the reference for the duration of our use and then dereference when we are done to keep it from leaking.


This policy both steals the reference and verifies there were no errors prior to continuing. Errors are promoted to exceptions when this reference is created.

If we need to pass an object which is held in a smart pointer to Python which requires a reference, we call keep on the reference which transfers control to a PyObject* and prevents the pointer from removing the reference. As the object handle is leaving our control keep should only be called the return statement. The smart pointer is not used on method passing in which the parent explicitly holds a reference to the Python object. As all tuples passed as arguments operate like this, that means much of the API accepts bare PyObject* as arguments. It is the job of the caller to hold the reference for its scope.

On CPython extensions

CPython is somewhat of a nightmare to program in. It is not that they did not try to document the API, but it is darn complex. The problems extend well beyond the reference counting system that we have worked around. In particular, the object model though well developed is very complex, often to get it to work you must follow letter for letter the example on the CPython user guide, and even then it may all go into the ditch.

The key problem is that there are a lot of very bad examples of how to write CPython extension modules out there. Often the these examples bypass the appropriate macro and just call the field, or skip the virtual table and try to call the Python method directly. It is true that these things do not break there example, but they are conditioned on these methods they are calling directly to be the right one for the job, but depends a lot on what the behavior of the object is supposed to be. Get it wrong and you get really nasty segfault.

CPython itself may be partly responsible for some of these problems. They generally seem to trust the user and thus don’t verify if the call makes sense. It is true that it will cost a little speed to be aggressive about checking the type flags and the allocator match, but not checking when the error happens, means that it fails far from the original problem source. I would hope that we have moved beyond the philosophy that the user should just to whatever they want so it runs as fast as possible, but that never appears to be the case. Of course, I am just opining from the outside of the tent and I am sure the issues are much more complicated it appears superficially. Then again if I can manage to provide a safe workspace while juggling the issues of multiple virtual machines, I am free to have opinions on the value of trading performance and safety.

In short when working on the extension code, make sure you do everything by the book, and check that book twice. Always go through the types virtual table and use the propery macros to access the resources. Miss one line in some complex pattern even once and you are in for a world of hurt. There are very few guard rails in the CPython code.

C++ JNI layer

The C++ layer has a number of tasks. It is used to load thunks, call JNI methods, provide reflection of classes, determine if a conversion is possible, perform conversion, match arguments to overloads, and convert return values back to Java.

Memory management

Java provides built in memory management for controlling the lifespan of Java objects that are passed through JNI. When a Java object is created or returned from the JVM it returns a handle to object with a reference counter. To manage the lifespan of this reference counter a local frame is created. For the duration of this frame all local references will continue to exist. To extend the lifespan either a new global reference to the object needs to be created, or the object needs to be kept. When the local frame is destroyed all local references are destroyed with the exception of an optional specified local return reference.

We have wrapped the Java reference system with the wrapper JPLocalFrame. This wrapper has three functions. It acts as a RAII (Resource acquisition is initialization) for the local frame. Further, as creating a local frame requires creating a Java env reference and all JNI calls require access to the env, the local frame acts as the front end to call all JNI calls. Finally as getting ahold of the env requires that the thread be attached to Java, it also serves to automatically attach threads to the JVM. As accessing an unbound thread will cause a segmentation fault in JNI, we are now safe from any threads created from within Python even those created outside our knowledge. (I am looking at you spyder)

Using this pattern makes the JPype core safe by design. Forcing JNI calles to be called using the frame ensures:

  • Every local reference is destroyed.

  • Every thread is properly attached before JNI is used.

  • The pattern of keep only one local reference is obeyed.

To use a local frame, use the pattern shown in this example.

jobject doSomeThing(std::string args)
    // Create a frame at the top of the scope
    JPLocalFrame frame;

    // Do the required work
    jobject obj =frame.CallObjectMethodA(globalObj, methodRef, params);

    // Tell the frame to return the reference to the outer scope.
    //   once keep is called the frame is destroyed and any
    //   call will fail.
    return frame.keep(obj);

Note that the value of the object returned and the object in the function will not be the same. The returned reference is owned by the enclosing local frame and points to the same object. But as its lifespan belongs to the outer frame, its location in memory is different. You are allowed to keep a reference that was global or was passed in, in either of those case, the outer scope will get a new local reference that points to the same object. Thus you don’t need to track the origin of the object.

The changing of the value while pointing is another common problem. A routine error is to get a local reference, call NewGlobalRef and then keeping the local reference rather than the shiny new global reference it made. This is not like the Python reference system where you have the object that you can ref and unref. Thus make sure you always store only the global reference.

jobject global;

// we are getting a reference, may be local, may be global.
// either way it is borrowed and it doesn't belong to us.
void elseWhere(jvalue value)
  JPLocalFrame frame;

  // Bunch of code leading us to decide we need to
  // hold the resource longer.
  if (cond)
    // okay we need to keep this reference, so make a
    // new global reference to it.
    global = frame.NewGlobalRef(value.l);

But don’t mistake this as an invitation to make global references everywhere. Global reference are global, thus will hold the member until the reference is destroyed. C++ exceptions can lead to missing the unreference, thus global references should only happen when you are placing the Java object into a class member variable or a global variable.

To help manage global references, we have JPRef<> which holds a global reference for the duration of the C++ lifespace. This is the base class for each of the global reference types we use.

typedef JPRef<jclass> JPClassRef;
typedef JPRef<jobject> JPObjectRef;
typedef JPRef<jarray> JPArrayRef;
typedef JPRef<jthrowable> JPThrowableRef;

For functions that expect the outer scope to already have created a frame for this context, we use the pattern of extending the outer scope rather than creating a new one.

jobject doSomeThing(JPLocalFrame& frame, std::string args)
    // Do the required work
    jobject obj = frame.CallObjectMethodA(globalObj, methodRef, params);

    // We must not call keep here or we will terminate
    // a frame we do not own.
    return obj;

Although the system we have set up is “safe by design”, there are things that can go wrong is misused. If the caller fails to create a frame prior to calling a function that returns a local reference, the reference will go into the program scoped local references and thus leak. Thus, it is usually best to force the user to make a scope with the frame extension pattern. Second, if any JNI references that are not kept or converted to global, it becomes invalid. Further, since JNI recycles the reference pointer fairly quickly, it most likely will be pointed to another object whose type may not be expected. Thus, best case is using the stale reference will crash and burn. Worse case, the reference will be a live reference to another object and it will produce an error which seems completely irrelevant to anything that was being called. Horrible case, the live object does not object to bad call and it all silently proceeds down the road another two miles before coming to flaming death.

Moral of the story, always create a local frame even if you are handling a global reference. If passed or returned a reference of any kind, it is a borrowed reference belonging to the caller or being held by the current local frame. Thus it must be treated accordingly. If you have to hold a global use the appropraite JPRef class to ensure it is exception and dtor safe. For further information read native/common/jp_javaframe.h.

Type wrappers

Each Java type has a C++ wrapper class. These classes provide a number of methods. Primitives each have their own unit type wrapper. Object, arrays, and class instances share a C++ wrapper type. Special instances are used for java.lang.Object and java.lang.Class. The type wrapper are named for the class they wrap such as JPIntType.

Type conversion

For type conversion, a C++ class wrapper provides four methods.


This method must consult the supplied Python object to determine the type and then make a determination of whether a conversion is possible. It reports none_ if there is no possible conversion, explicit_ if the conversion is only acceptable if forced such as returning from a proxy, implicit_ if the conversion is possible and acceptable as part of an method call, or exact_ if this type converts without ambiguity. It is excepted to check for something that is already a Java resource of the correct type such as JPValue, or something this is implementing the behavior as an interface in the form of a JPProxy.


This method consults the type and produces a conversion. The order of the match should be identical to the canConvertToJava. It should also handle values and proxies.


This method takes a jvalue union and converts it to the corresponding Python wrapper instance.


This converts a Java object into a JPValue corresponding. This unboxes primitives.

Array conversion

In addition to converting single objects, the type rewrappers also serve as the gateway to working with arrays of the specified type. Five methods are used to work with arrays: newArrayInstance, getArrayRange, setArrayRange, getArrayItem, and setArrayItem.

Invocation and Fields

To convert a return type produced from a Java call, each type needs to be able to invoke a method with that return type. This corresponses the underlying JNI design. The methods invoke and invokeStatic are used for this purpose. Similarly accessing fields requires type conversion using the methods getField and setField.

Instance versus Type wrappers

Instances of individual Java classes are made from JPClass. However, two special sets of conversion rules are required. These are in the form of specializations JPObjectBaseClass and JPClassBaseClass corresponding to java.lang.Object and java.lang.Class.

Support classes

In addition to the type wrappers, there are several support classes. These are:


The typemanager serves as a dict for all type wrappers created during the operation.


Lifetime manager for Java and Python objects.


Proxies implement a Java interface in Python.


Loader for Java thunks.


Decodes and encodes Java UTF strings.


C++ typewrappers are created as needed. Instance of each of the primitives along with java.lang.Object and java.lang.Class are preloaded. Additional instances are created as requested for individual Java classes. Currently this is backed by a C++ map of string to class wrappers.

The typemanager provides a number lookup methods.

// Call from within Python
JPClass* JPTypeManager::findClass(const string& name)

// Call from a defined Java class
JPClass* JPTypeManager::findClass(jclass cls)

// Call used when returning an object from Java
JPClass* JPTypeManager::findClassForObject(jobject obj)


When a Python object is presented to Java as opposed to a Java object, the lifespan of the Python object must be extended to match the Java wrapper. The reference queue adds a reference to the Python object that will be removed by the Java layer when the garbage collection deletes the wrapper. This code is almost entirely in the Java library, thus only the portion to support Java native methods appears in the C++ layer.

Once started the reference queue is mostly transparent. registerRef is used to bind a Python object live span to a Java object.

void JPReferenceQueue::registerRef(jobject obj, PyObject* hostRef)


In order to call Python functions from within Java, a Java proxy is used. The majority of the code is in Java. The C++ code holds the Java native portion. The native implement of the proxy call is the only place in with the pattern for reflecting Python exceptions back into Java appears.

As all proxies are ties to Python references, this code is strongly tied to the reference queue.


This code is responsible for loading the Java class thunks. As it is difficult to ensure we can access a Java jar from within Python, all Java native code is stored in a binary thunk compiled into the C++ layer as a header. The class loader provides a way to load this embedded jar first by bootstrapping a custom Java classloader and then using that classloader to load the internal jar.

The classloader is mostly transparent. It provides one method called findClass which loads a class from the internal jar.

jclass JPClassLoader::findClass(string name)


Java concept of UTF is pretty much out of sync with the rest of the world. Java used 16 bits for its native characters. But this was inadequate for all of the unicode characters, thus longer unicode character had to be encoded in the 16 bit space. Rather the directly providing methods to convert to a standard encoding such as UTF8, Java used UTF16 encoded in 8 bits which they dub Modified-UTF8. JPEncoding deals with converting this unusual encoding into something that Python can understand.

The key method in this module is transcribe with signature

std::string transcribe(const char* in, size_t len,
    const JPEncoding& sourceEncoding,
    const JPEncoding& targetEncoding)

There are two encodings provided, JPEncodingUTF8 and JPEncodingJavaUTF8. By selecting the source and traget encoding transcribe can convert to or from Java to Python encoding.

Incidentally that same modified UTF coding is used in storing symbols in the class files. It seems like a really poor design choice given they have to document this modified UTF in multiple places. As far as I can tell the internal converter only appears on and

Java native code

At the lowest level of the onion is the native Java layer. Although this layer is most remote from Python, ironically it is the easiest layer to communicate with. As the point of jpype is to communicate with Java, it is possible to directly communicate with the jpype Java internals. These can be imported from the package org.jpype. The code for the Java layer is located in native/java. It is compiled into a jar in the build directory and then converted to a C++ header to be compiled into the _jpype module.

The Java layer currently houses the reference queue, a classloader which can load a Java class from a bytestream source, the proxy code for implementing Java interfaces, and a memory compiler module which allows Python to directly create a class from a string.


Because the relations between the layers can be daunting especially when things go wrong. The CPython and C++ layer have a built in logger. This logger must be enabled with a compiler switch to activate. To active the logger, touch one of the cpp files in the native directory to mark the build as dirty, then compile the jpype module with:

python develop --enable-tracing

Once built run a short test program that demonstrates the problem and capture the output of the terminal to a file. This should allow the developer to isolate the fault to specific location where it failed.

To use the logger in a function start the JP_TRACE_IN(function_name) which will open a try catch block.

The JPype tracer can be augmented with the Python tracing module to give a very good picture of both JPype and Python states at the time of the crash. To use the Python tracing, start Python with…

python -m trace --trace


Some of the tests require additional instrumentation to run, this can be enabled with the enable-coverage option:

python develop --enable-coverage

Debugging issues

If the tracing function proves inadequate to identify a problem, we often need to turn to a general purpose tool like gdb or valgrind. The JPype core is not easy to debug. Python can be difficult to properly monitor especially with tools like valgrind due to its memory handling. Java is also challenging to debug. Put them together and you have the mother of all debugging issues. There are a number of complicating factors. Let us start with how to debug with gdb.

Gdb runs into two major issues, both tied to the signal handler. First, Java installs its own signal handlers that take over the entire process when a segfault occurs. This tends to cause very poor segfault stacktraces when examining a core file, which often is corrupt after the first user frame. Second, Java installs its signal handlers in such as way that attempting to run under a debugger like gdb will often immediately crash preventing one from catching the segfault before Java catches it. This makes for a catch 22, you can’t capture a meaningful non-interactively produced core file, and you can’t get an interactive session to work.

Fortunately there are solutions to the interactive session issue. By disabling the SIGSEGV handler, we can get past the initial failure and also we can catch the stack before it is altered by the JVM.

gdb -ex 'handle SIGSEGV nostop noprint pass' python

Thus far I have not found any good solutions to prevent the JVM from altering the stack frames when dumping the core. Thus interactive debugging appears to be the best option.

There are additional issues that one should be aware of. Open-JDK 1.8 has had a number of problems with the debugger. Starting JPype under gdb may trigger, may trigger the following error.

gdb.error: No type named nmethod.

There are supposed to be fixes for this problem, but none worked for me. Upgrading to Open-JDK 9 appears to fix the problem.

Another complexity with debugging memory problems is that Python tends to hide the problem with its allocation pools. Rather than allocating memory when a new object is request, it will often recycle and existing object which was collect earlier. The result is that an object which turns out is still live becomes recycled as a new object with a new type. Thus suddenly a method which was expected to produce some result instead vectors into the new type table, which may or may not send us into segfault land depending on whether the old and new objects have similar memory layouts.

This can be partially overcome by forcing Python to use a different memory allocation scheme. This can avoid the recycling which means we are more likely to catch the error, but at the same time means we will be excuting different code paths so we may not reach a similar state. If the core dump is vectoring off into code that just does not make sense it is likely caused by the memory pools. Starting Python 3, it is possible to select the memory allocation policy through an enviroment variable. See the PYTHONMALLOC setting for details.

Future directions

Although the majority of the code has been reworked for JPype 0.7, there is still further work to be done. Almost all Java constructs can be exercised from within Python, but Java and Python are not static. Thus, we are working on further improvements to the jpype core focusing on making the package faster, more efficient, and easier to maintain. This section will discuss a few of these options.

Java based code is much easier to debug as it is possible to swap the thunk code with an external jar. Further, Java has much easier management of resources. Thus pushing a portion of the C++ layer into the Java layer could further reduce the size of the code base. In particular, deciding the order of search for method overloads in C++ attempts to reconstruct the Java overload rules. But these same rules are already available in Java. Further, the C++ layer is designed to make many frequent small calls to Java methods. This is not the preferred method to operate in JNI. It is better to have specialized code in Java which preforms large tasks such as collecting all of the fields needed for a type wrapper and passing it back in a single call, rather than call twenty different general purpose methods. This would also vastly reduce the number of jmethods that need to be bound in the C++ layer.

The world of JVMs is currently in flux. Jpype needs to be able to support other JVMs. In theory, so long a JVM provides a working JNI layer, there is no reason the jpype can’t support it. But we need loading routines for these JVMs to be developed if there are differences in getting the JVM launched.

There is a project page on github shows what is being developed for the next release. Series 0.6 was usable, but early versions had notable issues with threading and internal memory management concepts had to be redone for stability. Series 0.7 is the first verion after rewrite for simplication and hardening. I consider 0.7 to be at the level of production quality code suitable for most usage though still missing some needed features. Series 0.8 will deal with higher levels of Python/Java integration such as Java class extension and pickle support. Series 0.9 will be dedicated to any additional hardening and edge cases in the core code as we should have complete integration. Assuming everything is completed, we will one day become a real boy and have a 1.0 release.