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An implementation may provide a way to execute a block of code in a nal eld safe context If an object is constructed within a nal eld safe context, the reads of a nal eld of that object will not be reordered with modi cations of that nal eld that occur within that nal eld safe context A nal eld safe context has additional protections If a thread has seen an incorrectly published reference to an object that allows the thread to see the default value of a nal eld, and then, within a nal- eld safe context, reads a properly published reference to the object, it will be guaranteed to see the correct value of the nal eld In the formalism, code executed within a nal- eld safe context is treated as a separate thread (for the purposes of nal eld semantics only) In an implementation, a compiler should not move an access to a nal eld into or out of a nal- eld safe context (although it can be moved around the execution of such a context, so long as the object is not constructed within that context) One place where use of a nal- eld safe context would be appropriate is in an executor or thread pool By executing each Runnable in a separate nal eld safe context, the executor could guarantee that incorrect access by one Runnable 577

Parsing is a fundamental activity in many programs, and for all but the most trivial cases, it is a challenging topic Parsing is often done when we need to read data that is stored in a custom format so that we can process it or perform queries on it Or we may be required to parse a DSL (Domain-Speci c Language) these are mini task-speci c languages that appear to be growing in popularity Whether we need to read data in a custom format or code written using a DSL, we will need to create a suitable parser This can be done by handcrafting, or by using one of Python s generic parsing modules Python can be used to write parsers using any of the standard computer science techniques: using regexes, using nite state automata, using recursive descent parsers, and so on All of these approaches can work quite well, but for data or DSLs that are complex for example, recursively structured and featuring operators that have different precedences and associativities they can be challenging to get right Also, if we need to parse many different data formats or DSLs, handcrafting each parser can be time-consuming and tedious to maintain Fortunately, for some data formats, we don t have to write a parser at all For example, when it comes to parsing XML, Python s standard library comes with DOM, SAX, and element tree parsers, with other XML parsers available as third-party add-ons

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an entry may be omitted and will then not be returned during a search Another popular programming method for handling attributes is just to enumerate over the values for the entry We will examine a version of prettyPrint a bit later that will use this method After reaching this point, we know the entry has the attribute, but we do not know if the attribute has a value Formally, LDAP does not allow attributes with no values, but it does allow attributes with a null value Many attributes in an LDAP directory can have multiple values For example, the telephoneNumber attribute could contain one or more telephone numbers The following block of code gets the attribute values and handles multivalue situations by calling LDAPAttributegetStringValues, which returns an Enumeration of the values:

In the d() method, the compiler is allowed to reorder the reads of x and the call to g() freely Thus, A()f() could return -1, 0 or 1

In fact, Python has built-in support for reading and writing a wide range of data formats, including delimiter-separated data with the csv module, Windows-style ini les with the configparser module, JSON data with the json module, and also a few others, as mentioned in 5 Python does not provide any built-in support for parsing other languages, although it does provide the shlex module which can be used to create a lexer for Unix shelllike mini-languages (DSLs), and the tokenize module that provides a lexer for Python source code And of course, Python can execute Python code using the built-in eval() and exec() functions 513

to a object o won't remove nal eld guarantees for other Runnables handled by the same executor 1754 Write Protected Fields Normally, nal static elds may not be modi ed However Systemin, Systemout, and Systemerr are nal static elds that, for legacy reasons, must be allowed to be changed by the methods SystemsetIn, SystemsetOut and SystemsetErr We refer to these elds as being write-protected to distinguish them from ordinary nal elds The compiler needs to treat these elds differently from other nal elds For example, a read of an ordinary nal eld is ``immune'' to synchronization: the barrier involved in a lock or volatile read does not have to affect what value is read from a nal eld Since the value of write-protected elds may be seen to change, synchronization events should have an effect on them Therefore, the semantics dictate that these elds be treated as normal elds that cannot be changed by user code, unless that user code is in the System class

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