\documentclass[letterpaper,english]{article} %\documentclass[letterpaper,twocolumn,english]{article} \usepackage[T1]{fontenc} \usepackage[latin1]{inputenc} \usepackage{graphicx} \usepackage{geometry} \geometry{verbose,letterpaper,tmargin=1in,bmargin=1in,lmargin=1in,rmargin=1in} \makeatletter \usepackage{babel} \begin{document} \title{LLADD Outline } \author{Russell Sears \and ... \and Eric Brewer} \maketitle \begin{enumerate} \item Abstract \subsection*{Abstract} % todo/rcs Need to talk about collection api stuff / generalization of ARIES / new approach to application development Although many systems provide transactionally consistent data management, existing implementations are generally monolithic and tied to a higher-level DBMS, limiting the scope of their usefulness to a single application or a specific type of problem. As a result, many systems are forced to ``work around'' the data models provided by a transactional storage layer. Manifestations of this problem include ``impedance mismatch'' in the database world and the limited number of data models provided by existing libraries such as Berkeley DB. In this paper, we describe a light-weight, easily extensible library, LLADD, that allows application developers to develop scalable and transactional application-specific data structures. We demonstrate that LLADD is simpler than prior systems, is very flexible and performs favorably in a number of micro-benchmarks. We also describe, in simple and concrete terms, the issues inherent in the design and implementation of robust, scalable transactional data structures. In addition to the source code, we have also made a comprehensive suite of unit-tests, API documentation, and debugging mechanisms publicly available.% \footnote{http://lladd.sourceforge.net/% } \item Introduction \begin{enumerate} % rcs: The original intro is left intact in the other file; it would be too hard to merge right now. % This paragraph is a too narrow; the original was too vague \item {\bf Current transactional systems handle conventional workloads well, but object persistence mechanisms are a mess, as are {}``version oriented'' data stores requiring large, efficient atomic updates.} \item {\bf {}``Impedance mismatch'' is a term that refers to a mismatch between the data model provided by the data store and the data model required by the application. A significant percentage of software development effort is related to dealing with this problem. Related problems that have had less treatment in the literature involve mismatches between other performance-critical and labor intensive programming primitives such as concurrency models, error handling techniques and application development patterns.} % rcs: see ##1## in other file for more examples \item {\bf Past trends in the Database community have been driven by demand for tools that allow extremely specialized (but commercially important!) types of software to be developed quickly and inexpensively. {[}System R, OODBMS, benchmarks, streaming databases, etc{]} This has led to the development of large, monolithic database severs that perform well under many circumstances, but that are not nearly as flexible as modern programming languages or typical in-memory data structure libraries {[}Java Collections, STL{]}. Historically, programming language and software library development has focused upon the production of a wide array of composable general purpose tools, allowing the application developer to pick algorithms and data structures that are most appropriate for the problem at hand.} \item {\bf In the past, modular database and transactional storage implementations have hidden the complexities of page layout, synchronization, locking, and data structure design under relatively narrow interfaces, since transactional storage algorithms' interdependencies and requirements are notoriously complicated.} \item {\bf With these trends in mind, we have implemented a modular version of ARIES that makes as few assumptions as possible about application data structures or workload. Where such assumptions are inevitable, we have produced narrow APIs that allow the application developer to plug in alternative implementations of the modules that comprise our ARIES implementation. Rather than hiding the underlying complexity of the library from developers, we have produced narrow, simple API's and a set of invariants that must be maintained in order to ensure transactional consistency, allowing application developers to produce high-performance extensions with only a little effort.} \end{enumerate} \item {\bf 2.Prior work} \begin{enumerate} \item{\bf Databases' Relational model leads to performance / representation problems.} On the database side of things, relational databases excel in areas where performance is important, but where the consistency and durability of the data are crucial. Often, databases significantly outlive the software that uses them, and must be able to cope with changes in business practices, system architectures, etc.~\cite{relational} Databases are designed for circumstances where development time may dominate cost, many users must share access to the same data, and where security, scalability, and a host of other concerns are important. In many, if not most circumstances these issues are less important, or even irrelevant. Therefore, applying a database in these situations is likely overkill, which may partially explain the popularity of MySQL~\cite{mysql}, which allows some of these constraints to be relaxed at the discretion of a developer or end user. \item{\bf OODBMS / XML database systems provide models tied closely to PL or hierarchical formats, but, like the relational model, these models are extremely general, and might be inappropriate for applications with stringent performance demands, or that use these models in a way that cannot be supported well with the database system's underlying data structures.} Object-oriented databases are more focused on facilitating the development of complex applications that require reliable storage and may take advantage of less-flexible, more efficient data models, as they often only interact with a single application, or a handful of variants of that application.~\cite{lamb} \item{\bf Berkeley DB provides a lower level interface, increasing performance, and providing efficient tree and hash based data structures, but hides the details of storage management and the primitives provided by its transactional layer from developers. Again, only a handful of data formats are made available to the developer.} %rcs: The inflexibility of databases has not gone unnoticed ... or something like that. Still, there are many applications where MySQL is too inflexible. In order to serve these applications, a host of software solutions have been devised. Some are extremely complex, such as semantic file systems, where the file system understands the contents of the files that it contains, and is able to provide services such as rapid search, or file-type specific operations such as thumb-nailing, automatic content updates, and so on. Others are simpler, such as Berkeley~DB,~\cite{berkeleyDB, bdb} which provides transactional storage of data in unindexed form, or in indexed form using a hash table or tree. LRVM is a version of malloc() that provides transactional memory, and is similar to an object-oriented database but is much lighter weight, and more flexible~\cite{lrvm}. \item {\bf Incredibly scalable, simple servers CHT's, google fs?, ...} Finally, some applications require incredibly simple, but extremely scalable storage mechanisms. Cluster hash tables are a good example of the type of system that serves these applications well, due to their relative simplicity, and extremely good scalability characteristics. Depending on the fault model on which a cluster hash table is implemented, it is quite plausible that key portions of the transactional mechanism, such as forcing log entries to disk, will be replaced with other durability schemes, such as in-memory replication across many nodes, or multiplexing log entries across multiple systems. This level of flexibility would be difficult to retrofit into existing transactional applications, but is often important in the environments in which these applications are deployed. \item {\bf Implementations of ARIES and other transactional storage mechanisms include many of the useful primitives described below, but prior implementations either deny application developers access to these primitives {[}??{]}, or make many high-level assumptions about data representation and workload {[}DB Toolkit from Wisconsin??-need to make sure this statement is true!{]}} \end{enumerate} \item {\bf 3.Architecture } % rcs:The last paper contained a tutorial on how to use LLADD, which % should be shortend or removed from this version, so I didn't paste it % in. However, it made some points that belong in this section % see: ##2## \begin{enumerate} % % need block diagram here. 4 blocks: % % App specific: % % - operation wrapper % - operation redo fcn % % LLADD core: % % - logger % - page file % % lock manager, etc can come later... % \item {\bf {}``Core LLADD'' vs {}``Operations''} A LLADD operation consists of some code that manipulates data that has been stored in transactional pages. These operations implement high-level actions that are composed into transactions. They are implemented at a relatively low level, and have full access to the ARIES algorithm. Applications are implemented on top of the interfaces provided by an application-specific set of operations. This allows the the application, the operation, and LLADD itself to be independently improved. We have implemented a number of extremely simple, high performance general purpose data structures for our sample applications, and as building blocks for new data structures. Example data structures include two distinct linked list implementations, and an extendible array. Surprisingly, even these simple operations have important performance characteristics that are not provided by existing systems. \item {\bf ARIES provides {}``transactional pages'' } \begin{enumerate} \item {\bf Diversion on ARIES semantics } %rcs: Is this the best way to describe this? \item {\bf Non-interleaved transactions vs. Nested top actions vs. Well-ordered writes.} % key point: locking + nested top action = 'normal' multithreaded %software development! (modulo 'obvious' mistakes like algorithmic %errors in data structures, errors in the log format, etc) % second point: more difficult techniques can be used to optimize % log bandwidth. _in ways that other techniques cannot provide_ % to application developers. Instead of providing a comprehensive discussion of ARIES, we will focus upon those features of the algorithm that are most relevant to a developer attempting to add a new set of operations. Correctly implementing such extensions is complicated by concerns regarding concurrency, recovery, and the possibility that any operation may be rolled back at runtime. We first sketch the constraints placed upon operation implementations, and then describe the properties of our implementation that make these constraints necessary. Because comprehensive discussions of write ahead logging protocols and ARIES are available elsewhere,~\cite{haerder, aries} we only discuss those details relevant to the implementation of new operations in LLADD. \subsection{Properties of an Operation\label{sub:OperationProperties}} Since transactions may be aborted, the effects of an operation must be reversible. Furthermore, aborting and committing transactions may be interleaved, and LLADD does not allow cascading aborts,% \footnote{That is, by aborting, one transaction may not cause other transactions to abort. To understand why operation implementors must worry about this, imagine that transaction A split a node in a tree, transaction B added some data to the node that A just created, and then A aborted. When A was undone, what would become of the data that B inserted?% } so in order to implement an operation, we must implement some sort of locking, or other concurrency mechanism that isolates transactions from each other. LLADD only provides physical consistency; due to the variety of locking systems available, and their interaction with application workload,~\cite{multipleGenericLocking} we leave it to the application to decide what sort of transaction isolation is appropriate. For example, it is relatively easy to build a strict two-phase locking lock manager~\cite{hierarcicalLocking} on top of LLADD, as needed by a DBMS, or a simpler lock-per-folder approach that would suffice for an IMAP server. Thus, data dependencies among transactions are allowed, but we still must ensure the physical consistency of our data structures, such as operations on pages or locks. Also, all actions performed by a transaction that committed must be restored in the case of a crash, and all actions performed by aborting transactions must be undone. In order for LLADD to arrange for this to happen at recovery, operations must produce log entries that contain all information necessary for undo and redo. An important concept in ARIES is the ``log sequence number'' or LSN. An LSN is essentially a virtual timestamp that goes on every page; it marks the last log entry that is reflected on the page, and implies that all previous log entries are also reflected. Given the LSN, LLADD calculates where to start playing back the log to bring the page up to date. The LSN goes on the page so that it is always written to disk atomically with the data on the page. ARIES (and thus LLADD) allows pages to be {\em stolen}, i.e. written back to disk while they still contain uncommitted data. It is tempting to disallow this, but to do so has serious consequences such as a increased need for buffer memory (to hold all dirty pages). Worse, as we allow multiple transactions to run concurrently on the same page (but not typically the same item), it may be that a given page {\em always} contains some uncommitted data and thus could never be written back to disk. To handle stolen pages, we log UNDO records that we can use to undo the uncommitted changes in case we crash. LLADD ensures that the UNDO record is durable in the log before the page is written back to disk and that the page LSN reflects this log entry. Similarly, we do not force pages out to disk every time a transaction commits, as this limits performance. Instead, we log REDO records that we can use to redo the change in case the committed version never makes it to disk. LLADD ensures that the REDO entry is durable in the log before the transaction commits. REDO entries are physical changes to a single page (``page-oriented redo''), and thus must be redone in the exact order. One unique aspect of LLADD, which is not true for ARIES, is that {\em normal} operations use the REDO function; i.e. there is no way to modify the page except via the REDO operation. This has the great property that the REDO code is known to work, since even the original update is a ``redo''. In general, the LLADD philosophy is that you define operations in terms of their REDO/UNDO behavior, and then build the actual update methods around those. Eventually, the page makes it to disk, but the REDO entry is still useful: we can use it to roll forward a single page from an archived copy. Thus one of the nice properties of LLADD, which has been tested, is that we can handle media failures very gracefully: lost disk blocks or even whole files can be recovered given an old version and the log. \subsection{Normal Processing} Operation implementors follow the pattern in Figure \ref{cap:Tset}, and need only implement a wrapper function (``Tset()'' in the figure, and register a pair of redo and undo functions with LLADD. The Tupdate function, which is built into LLADD, handles most of the runtime complexity. LLADD uses the undo and redo functions during recovery in the same way that they are used during normal processing. The complexity of the ARIES algorithm lies in determining exactly when the undo and redo operations should be applied. LLADD handles these details for the implementors of operations. \subsubsection{The buffer manager} LLADD manages memory on behalf of the application and prevents pages from being stolen prematurely. Although LLADD uses the STEAL policy and may write buffer pages to disk before transaction commit, it still must make sure that the UNDO log entries have been forced to disk before the page is written to disk. Therefore, operations must inform the buffer manager when they write to a page, and update the LSN of the page. This is handled automatically by the write methods that LLADD provides to operation implementors (such as writeRecord()). However, it is also possible to create your own low-level page manipulation routines, in which case these routines must follow the protocol. \subsubsection{Log entries and forward operation\\ (the Tupdate() function)\label{sub:Tupdate}} In order to handle crashes correctly, and in order to undo the effects of aborted transactions, LLADD provides operation implementors with a mechanism to log undo and redo information for their actions. This takes the form of the log entry interface, which works as follows. Operations consist of a wrapper function that performs some pre-calculations and perhaps acquires latches. The wrapper function then passes a log entry to LLADD. LLADD passes this entry to the logger, {\em and then processes it as though it were redoing the action during recovery}, calling a function that the operation implementor registered with LLADD. When the function returns, control is passed back to the wrapper function, which performs any post processing (such as generating return values), and releases any latches that it acquired. % \begin{figure} %\begin{center} %\includegraphics[% % width=0.70\columnwidth]{TSetCall.pdf} %\end{center} \caption{\label{cap:Tset}Runtime behavior of a simple operation. Tset() and redoSet() are extensions that implement a new operation, while Tupdate() is built in. New operations need not be aware of the complexities of LLADD.} \end{figure} This way, the operation's behavior during recovery's redo phase (an uncommon case) will be identical to the behavior during normal processing, making it easier to spot bugs. Similarly, undo and redo operations take an identical set of parameters, and undo during recovery is the same as undo during normal processing. This makes recovery bugs more obvious and allows redo functions to be reused to implement undo. Although any latches acquired by the wrapper function will not be reacquired during recovery, the redo phase of the recovery process is single threaded. Since latches acquired by the wrapper function are held while the log entry and page are updated, the ordering of the log entries and page updates associated with a particular latch will be consistent. Because undo occurs during normal operation, some care must be taken to ensure that undo operations obtain the proper latches. \subsection{Recovery} In this section, we present the details of crash recovery, user-defined logging, and atomic actions that commit even if their enclosing transaction aborts. \subsubsection{ANALYSIS / REDO / UNDO} Recovery in ARIES consists of three stages, analysis, redo and undo. The first, analysis, is implemented by LLADD, but will not be discussed in this paper. The second, redo, ensures that each redo entry in the log will have been applied to each page in the page file exactly once. The third phase, undo, rolls back any transactions that were active when the crash occurred, as though the application manually aborted them with the {}``abort'' function call. After the analysis phase, the on-disk version of the page file is in the same state it was in when LLADD crashed. This means that some subset of the page updates performed during normal operation have made it to disk, and that the log contains full redo and undo information for the version of each page present in the page file.% \footnote{Although this discussion assumes that the entire log is present, the ARIES algorithm supports log truncation, which allows us to discard old portions of the log, bounding its size on disk.% } Because we make no further assumptions regarding the order in which pages were propagated to disk, redo must assume that any data structures, lookup tables, etc. that span more than a single page are in an inconsistent state. Therefore, as the redo phase re-applies the information in the log to the page file, it must address all pages directly. This implies that the redo information for each operation in the log must contain the physical address (page number) of the information that it modifies, and the portion of the operation executed by a single redo log entry must only rely upon the contents of the page that the entry refers to. Since we assume that pages are propagated to disk atomically, the REDO phase may rely upon information contained within a single page. Once redo completes, we have applied some prefix of the run-time log. Therefore, we know that the page file is in a physically consistent state, although it contains portions of the results of uncommitted transactions. The final stage of recovery is the undo phase, which simply aborts all uncommitted transactions. Since the page file is physically consistent, the transactions may be aborted exactly as they would be during normal operation. \subsubsection{Physical, Logical and Phisiological Logging.} The above discussion avoided the use of some common terminology that should be presented here. {\em Physical logging } is the practice of logging physical (byte-level) updates and the physical (page number) addresses to which they are applied. {\em Physiological logging } is what LLADD recommends for its redo records. The physical address (page number) is stored, but the byte offset and the actual difference are stored implicitly in the parameters of the redo or undo function. These parameters allow the function to update the page in a way that preserves application semantics. One common use for this is {\em slotted pages}, which use an on-page level of indirection to allow records to be rearranged within the page; instead of using the page offset, redo operations use a logical offset to locate the data. This allows data within a single page to be re-arranged at runtime to produce contiguous regions of free space. LLADD generalizes this model; for example, the parameters passed to the function may utilize application specific properties in order to be significantly smaller than the physical change made to the page.~\cite{physiological} {\em Logical logging } can only be used for undo entries in LLADD, and is identical to physiological logging, except that it stores a logical address (the key of a hash table, for instance) instead of a physical address. This allows the location of data in the page file to change, even if outstanding transactions may have to roll back changes made to that data. Clearly, for LLADD to be able to apply logical log entries, the page file must be physically consistent, ruling out use of logical logging for redo operations. LLADD supports all three types of logging, and allows developers to register new operations, which is the key to its extensibility. After discussing LLADD's architecture, we will revisit this topic with a concrete example. \subsection{Concurrency and Aborted Transactions} Section~\ref{sub:OperationProperties} states that LLADD does not allow cascading aborts, implying that operation implementors must protect transactions from any structural changes made to data structures by uncommitted transactions, but LLADD does not provide any mechanisms designed for long-term locking. However, one of LLADD's goals is to make it easy to implement custom data structures for use within safe, multi-threaded transactions. Clearly, an additional mechanism is needed. The solution is to allow portions of an operation to ``commit'' before the operation returns.\footnote{We considered the use of nested top actions, which LLADD could easily support. However, we currently use the slightly simpler (and lighter-weight) mechanism described here. If the need arises, we will add support for nested top actions.} An operation's wrapper is just a normal function, and therefore may generate multiple log entries. First, it writes an undo-only entry to the log. This entry will cause the \emph{logical} inverse of the current operation to be performed at recovery or abort, must be idempotent, and must fail gracefully if applied to a version of the database that does not contain the results of the current operation. Also, it must behave correctly even if an arbitrary number of intervening operations are performed on the data structure. Next, the operation writes one or more redo-only log entries that may perform structural modifications to the data structure. These redo entries have the constraint that any prefix of them must leave the database in a consistent state, since only a prefix might execute before a crash. This is not as hard as it sounds, and in fact the $B^{LINK}$ tree~\cite{blink} is an example of a B-Tree implementation that behaves in this way, while the linear hash table implementation discussed in Section~\ref{sub:Linear-Hash-Table} is a scalable hash table that meets these constraints. %[EAB: I still think there must be a way to log all of the redoes %before any of the actions take place, thus ensuring that you can redo %the whole thing if needed. Alternatively, we could pin a page until %the set completes, in which case we know that that all of the records %are in the log before any page is stolen.] \subsection{Summary} This section presented a relatively simple set of rules and patterns that a developer must follow in order to implement a durable, transactional and highly-concurrent data structure using LLADD: \begin{itemize} \item Pages should only be updated inside of a redo or undo function. \item An update to a page should update the LSN. \item If the data read by the wrapper function must match the state of the page that the redo function sees, then the wrapper should latch the relevant data. \item Redo operations should address pages by their physical offset, while Undo operations should use a more permanent address (such as index key) if the data may move between pages over time. \item An undo operation must correctly update a data structure if any prefix of its corresponding redo operations are applied to the structure, and if any number of intervening operations are applied to the structure. \end{itemize} Because undo and redo operations during normal operation and recovery are similar, most bugs will be found with conventional testing strategies. It is difficult to verify the final property, although a number of tools could be written to simulate various crash scenarios, and check the behavior of operations under these scenarios. Of course, such a tool could easily be applied to existing LLADD operations. Note that the ARIES algorithm is extremely complex, and we have left out most of the details needed to understand how ARIES works, or to implement it correctly. Yet, we believe we have covered everything that a programmer needs to know in order to implement new data structures using the functionality that ARIES provides. This was possible due to the encapsulation of the ARIES algorithm inside of LLADD, which is the feature that most strongly differentiates LLADD from other, similar libraries. We hope that this will increase the availability of transactional data primitives to application developers. \end{enumerate} \item {\bf Log entries as a programming primitive } %rcs: Not quite happy with existing text; leaving this section out for now. % % Need to make some points the old text did not make: % % - log optimizations (for space) can be very important. % - many small writes % - large write of small diff % - app overwrites page many times per transaction (for example, database primary key) % We have solutions to #1 and 2. A general solution to #3 involves 'scrubbing' a logical log of redundant operations. % % - Talk about virtual async log thing... \item {\bf Error handling with compensations as {}``abort() for C''} % stylized usage of Weimer -> cheap error handling, no C compiler modifications... \item {\bf Concurrency models are fundamentally application specific, but record/page level locking and index locks are often a nice trade-off} \item {\bf {}``latching'' vs {}``locking'' - data structures internal to LLADD are protected by LLADD, allowing applications to reason in terms of logical data addresses, not physical representation. Since the application may define a custom representation, this seems to be a reasonable tradeoff between application complexity and performance.} \end{enumerate} \item {\bf Applications } \begin{enumerate} \item {\bf Atomic file-based transactions. Prototype blob implementation using force, shadow copies (it is trivial to implement given transactional pages). File systems that implement atomic operations may allow data to be stored durably without calling flush() on the data file. Current implementation useful for blobs that are typically changed entirely from update to update, but smarter implementations are certainly possible. The blob implementation primarily consists of special log operations that cause file system calls to be made at appropriate times, and is simple, so it could easily be replaced by an application that frequently update small ranges within blobs, for example.} \item {\bf Index implementation - modular hash table. Relies on separate linked list, expandable array implementations.} \subsection{Array List} % Example of how to avoid nested top actions \subsection{Linked Lists} % Example of two different page allocation strategies. % Explain how to implement linked lists w/out NTA's (even though we didn't do that)? \subsection{Linear Hash Table\label{sub:Linear-Hash-Table}} % The implementation has changed too much to directly reuse old section, other than description of linear hash tables: Linear hash tables are hash tables that are able to extend their bucket list incrementally at runtime. They work as follows. Imagine that we want to double the size of a hash table of size $2^{n}$, and that the hash table has been constructed with some hash function $h_{n}(x)=h(x)\, mod\,2^{n}$. Choose $h_{n+1}(x)=h(x)\, mod\,2^{n+1}$ as the hash function for the new table. Conceptually we are simply prepending a random bit to the old value of the hash function, so all lower order bits remain the same. At this point, we could simply block all concurrent access and iterate over the entire hash table, reinserting values according to the new hash function. However, because of the way we chose $h_{n+1}(x),$ we know that the contents of each bucket, $m$, will be split between bucket $m$ and bucket $m+2^{n}$. Therefore, if we keep track of the last bucket that was split, we can split a few buckets at a time, resizing the hash table without introducing long pauses while we reorganize the hash table~\cite{lht}. We can handle overflow using standard techniques; LLADD's linear hash table uses linked lists of overflow buckets. % Implementation simple! Just slap together the stuff from the prior two sections, and add a header + bucket locking. \item {\bf Asynchronous log implementation/Fast writes. Prioritization of log writes (one {}``log'' per page) implies worst case performance (write, then immediate read) will behave on par with normal implementation, but writes to portions of the database that are not actively read should only increase system load (and not directly increase latency)} \item {\bf Custom locking. Hash table can support all of the SQL degrees of transactional consistency, but can also make use of application-specific invariants and synchronization to accommodate deadlock-avoidance, which is the model most naturally supported by C and other programming languages.} %Many plausible lock managers, can do any one you want. %too much implemented part of DB; need more 'flexible' substrate. \end{enumerate} \item {\bf Validation } \begin{enumerate} \item {\bf Comparison of transactional primatives (best case for each operator)} \item {\bf Serialization Benchmarks (Abstract log) } {\bf Need to define application semantics workload (write heavy w/ periodic checkpoint?) that allows for optimization.} {\bf All of these graphs need X axis dimensions. Number of (read/write?) threads, maybe?} {\bf Graph 1: Peak write throughput. Abstract log wins (no disk i/o, basically, measure contention on ringbuffer, and compare to log I/O + hash table insertions.)} {\bf Graph 2: Measure maximum average write throughput: Write throughput vs. rate of log growth. Spool abstract log to disk. Reads starve, or read stale data. } {\bf Graph 3: Latency @ peak steady state write throughput. Abstract log size remains constant. Measure read latency vs. queue length. This will show the system's 'second-order' ability to absorb spikes. } \item {\bf Graph traversal benchmarks: Bulk load + hot and cold transitive closure queries} \item {\bf Hierarchical Locking - Proof of concept} \item {\bf TPC-C (Flexibility) - Proof of concept} % Abstract syntax tree implementation? \item {\bf Sample Application. 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