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Basic Python Mapping for Operations
As we saw in the Python mapping for interfaces, for each operation on an interface, the proxy class contains a corresponding method with the same name. To invoke an operation, you call it via the proxy. For example, here is part of the definitions for our file system:
module Filesystem
{
interface Node
{
idempotent string name();
}
// ...
}
The name operation returns a value of type string. Given a proxy to an object of type Node, the client can invoke the operation as follows:
node = ... # Initialize proxy name = node.name() # Get name via RPC
Normal and idempotent Operations in Python
You can add an idempotent qualifier to a Slice operation. As far as the signature for the corresponding proxy method is concerned, idempotent has no effect. For example, consider the following interface:
interface Example
{
string op1();
idempotent string op2();
}
The proxy class for this is:
class ExamplePrx(Ice.ObjectPrx): def op1(self, context=None): # ... def op2(self, context=None): # ...
Because idempotent affects an aspect of call dispatch, not interface, it makes sense for the two methods to look the same.
Passing Parameters in Python
In-Parameters in Python
All parameters are passed by reference in the Python mapping; it is guaranteed that the value of a parameter will not be changed by the invocation.
Here is an interface with operations that pass parameters of various types from client to server:
struct NumberAndString
{
int x;
string str;
}
sequence<string> StringSeq;
dictionary<long, StringSeq> StringTable;
interface ClientToServer
{
void op1(int i, float f, bool b, string s);
void op2(NumberAndString ns, StringSeq ss, StringTable st);
void op3(ClientToServer* proxy);
}
The Slice compiler generates the following proxy for this definition:
class ClientToServerPrx(Ice.ObjectPrx): def op1(self, i, f, b, s, context=None): # ... def op2(self, ns, ss, st, context=None): # ... def op3(self, proxy, context=None): # ...
Given a proxy to a ClientToServer interface, the client code can pass parameters as in the following example:
p = ... # Get proxy...
p.op1(42, 3.14f, True, "Hello world!") # Pass simple literals
i = 42
f = 3.14f
b = True
s = "Hello world!"
p.op1(i, f, b, s) # Pass simple variables
ns = NumberAndString()
ns.x = 42
ns.str = "The Answer"
ss = [ "Hello world!" ]
st = {}
st[0] = ns
p.op2(ns, ss, st) # Pass complex variables
p.op3(p) # Pass proxy
Out-Parameters in Python
As in Java, Python functions do not support reference arguments. That is, it is not possible to pass an uninitialized variable to a Python function in order to have its value initialized by the function. The Java mapping overcomes this limitation with the use of holder classes that represent each out parameter. The Python mapping takes a different approach, one that is more natural for Python users.
The semantics of out parameters in the Python mapping depend on whether the operation returns one value or multiple values. An operation returns multiple values when it has declared multiple out parameters, or when it has declared a non-void return type and at least one out parameter.
If an operation returns multiple values, the client receives them in the form of a result tuple. A non-void return value, if any, is always the first element in the result tuple, followed by the out parameters in the order of declaration.
If an operation returns only one value, the client receives the value itself.
Here again are the same Slice definitions we saw earlier, but this time with all parameters being passed in the out direction:
struct NumberAndString
{
int x;
string str;
}
sequence<string> StringSeq;
dictionary<long, StringSeq> StringTable;
interface ServerToClient
{
int op1(out float f, out bool b, out string s);
void op2(out NumberAndString ns,
out StringSeq ss,
out StringTable st);
void op3(out ServerToClient* proxy);
}
The Python mapping generates the following code for this definition:
class ServerToClientPrx(Ice.ObjectPrx): def op1(self, context=None): # ... def op2(self, context=None): # ... def op3(self, context=None): # ...
Given a proxy to a ServerToClient interface, the client code can receive the results as in the following example:
p = ... # Get proxy... i, f, b, s = p.op1() ns, ss, st = p.op2() stcp = p.op3()
The operations have no in parameters, therefore no arguments are passed to the proxy methods. Since op1 and op2 return multiple values, their result tuples are unpacked into separate values, whereas the return value of op3 requires no unpacking.
Parameter Type Mismatches in Python
Although the Python compiler cannot check the types of arguments passed to a function, the Ice run time does perform validation on the arguments to a proxy invocation and reports any type mismatches as a ValueError exception.
Null Parameters in Python
Some Slice types naturally have "empty" or "not there" semantics. Specifically, sequences, dictionaries, and strings all can be None, but the corresponding Slice types do not have the concept of a null value. To make life with these types easier, whenever you pass None as a parameter or return value of type sequence, dictionary, or string, the Ice run time automatically sends an empty sequence, dictionary, or string to the receiver.
This behavior is useful as a convenience feature: especially for deeply-nested data types, members that are sequences, dictionaries, or strings automatically arrive as an empty value at the receiving end. This saves you having to explicitly initialize, for example, every string element in a large sequence before sending the sequence in order to avoid a run-time error. Note that using null parameters in this way does not create null semantics for Slice sequences, dictionaries, or strings. As far as the object model is concerned, these do not exist (only empty sequences, dictionaries, and strings do). For example, it makes no difference to the receiver whether you send a string as None or as an empty string: either way, the receiver sees an empty string.
Optional Parameters in Python
Optional parameters use the same mapping as required parameters. The only difference is that Ice.Unset can be passed as the value of an optional parameter or return value. Consider the following operation:
optional(1) int execute(optional(2) string params, out optional(3) float value);
A client can invoke this operation as shown below:
i, v = proxy.execute("--file log.txt")
i, v = proxy.execute(Ice.Unset)
if v:
print("value = " + str(v)) # v is set to a value
A well-behaved program must always test an optional parameter prior to using its value. Keep in mind that the Ice.Unset marker value has different semantics than None. Since None is a legal value for certain Slice types, the Ice run time requires a separate marker value so that it can determine whether an optional parameter is set. An optional parameter set to None is considered to be set. If you need to distinguish between an unset parameter and a parameter set to None, you can do so as follows:
if optionalParam is Ice.Unset:
print("optionalParam is unset")
elif optionalParam is None:
print("optionalParam is None")
else:
print("optionalParam = " + str(optionalParam))
Exception Handling in Python
Any operation invocation may throw a run-time exception and, if the operation has an exception specification, may also throw user exceptions. Suppose we have the following simple interface:
exception Tantrum
{
string reason;
}
interface Child
{
void askToCleanUp() throws Tantrum;
}
Slice exceptions are thrown as Python exceptions, so you can simply enclose one or more operation invocations in a try-except block:
child = ... # Get child proxy...
try:
child.askToCleanUp()
except Tantrum, t:
print "The child says:", t.reason
Typically, you will catch only a few exceptions of specific interest around an operation invocation; other exceptions, such as unexpected run-time errors, will usually be handled by exception handlers higher in the hierarchy. For example:
import traceback, Ice
def run():
child = ... # Get child proxy...
try:
child.askToCleanUp()
except Tantrum, t:
print "The child says:", t.reason
child.scold() # Recover from error...
child.praise() # Give positive feedback...
try:
# ...
run()
# ...
except Ice.Exception:
traceback.print_exc()
This code handles a specific exception of local interest at the point of call and deals with other exceptions generically. (This is also the strategy we used for our first simple application in Hello World Application.)