Russell Sears Eric Brewer UC Berkeley A Flexible, Extensible Transaction Framework Existing transactional systems are designed to handle specific workloads. Unfortunately, the implementations of these systems are monolithic and hide the transactional infrastructure underneath a SQL interface. Lower-level implementations such as Berkeley DB efficiently serve a wider variety of workloads and are built in a more modular fashion. However, they do not provide APIs to allow applications to build upon and modify low-level policies such as allocation strategies, page layout or details of recovery semantics. Furthermore, data structure implementations are typically not broken into separable, public APIs, which discourages the implementation of new transactional data structures. Contrast this approach to the handling of data structures within modern object-oriented programming languages such as C++ or Java. Such languages typically provide a large number of data storage algorithm implementations. These structures may be used interchangeably with application-specific data collections, and collection implementations may be composed into more sophisticated data structures. We have implemented LLADD (/yad/), an extensible transactional storage library that takes a composable and layered approach to transactional storage. Below, we present some of its high level features and performance characteristics and discuss our plans to extend the system into distributed domains. Finally we introduce our current research focus, the application of automated program verification and optimization techniques to application specific extensions. Such techniques should significantly enhance the usability and performance of our system, allowing application developers to implement sophisticated cross-layer optimizations easily. Overview of the LLADD Architecture ---------------------------------- General-purpose transactional storage systems are extremely complex and only handle specific types of workloads efficiently. However, new types of applications and workloads are introduced on a regular basis. This results in the implementation of specialized, ad-hoc data storage systems from scratch, wasting resources and preventing code reuse. Instead of developing a set of general purpose data structures that attempt to perform well across many workloads, we have implemented a lower-level API that makes it easy for application designers to implement specialized data structures. Essentially, we have implemented an extensible navigational database system. We believe that this system will support modern development practices and allows transactions to be used in a wider range of applications. While implementations of general-purpose systems often lag behind the requirements of rapidly evolving applications, we believe that our architecture's flexibility allows us to address such applications rapidly. Our system also seems to be a reasonable long-term solution in cases where the development of a general-purpose system is not economical. For example, XML storage systems are rapidly evolving but still fail to handle many types of applications. Typical bioinformatics data sets [PDB, NCBI, Gene Ontology] must be processed by computationally intensive applications with rigid data layout requirements. The maintainers of these systems are slowly transitioning to XML, which is valuable as an interchange format, and supported by many general purpose tools. However, many of the data processing applications that use these databases still must employ ad-hoc solutions for data management. Whether or not general purpose XML database systems eventually meet all of the needs of each of these distinct scientific applications, extensions implemented on top of a more flexible data storage implementation could have avoided the need for ad-hoc solutions, and could serve as a partial prototype for higher level implementations. LLADD is based upon an extensible version of ARIES but does not hard-code details such as page format or data structure implementation. It provides a number of "operation" implementations which consist of redo/undo methods and wrapper functions. The redo/undo methods manipulate the page file by applying log entries while the wrapper functions produce log entries. Redo methods handle all page file manipulation during normal forward operation, reducing the amount of code that must be developed in order to implement new data structures. LLADD handles the scheduling of redo/undo invocations, disk I/O, and all of the other details specified by the ARIES recovery algorithm, allowing operation implementors to focus on the details that are important to the functionality their extension provides. LLADD ships with a number of default data structures and layouts, ranging from byte-level page layouts to linear hashtables and application-specific recovery schemes and data structures. These structures were developed with reusability in mind, encouraging developers to compose existing operations into application-specific data structures. For example, the hashtable is implemented on top of reusable modules that implement a resizable array and two exchangeable linked-list variants. In other work, we show that the system is competitive with Berkeley DB on traditional (hashtable based) workloads, and have shown significant performance improvements for less conventional workloads including custom data structure implementations, graph traversal algorithms and transactional object persistence workloads. The transactional object persistence system was based upon the observation that most object persistence schemes cache a second copy of each in-memory object in a page file, and often keep a third copy in operating system cache. By implementing custom operations that assume the program maintains a correctly implemented object cache, we allow LLADD to service object update requests without updating the page file. Since LLADD implements no-force, the only reason to update the page file is to service future application read requests. Therefore, we defer page file updates until the object is evicted from the application's object cache. This eliminates the need to maintain a large page cache in order to efficiently service write requests. We also leveraged our customizable log format to log differences to objects instead of entire copies of objects. With these optimizations, we showed a 2-3x performance improvement over Berkeley DB on object persistence across our benchmarks, and a 3-4x improvement over an in-process version of MySQL with the InnoDB backend. (A traditional MySQL setup that made use of a separate server process was prohibitively slow. InnoDB provided the best performance among MySQL's durable storage managers.) Furthermore, our system uses memory more efficiently, increasing its performance advantage in situations where the size of system memory is a bottleneck. We leave systematic performance tuning of LLADD to future work, and believe that further optimizations will improve our performance on these benchmarks significantly. Because of its natural integration into standard system software development practices, we think that LLADD can be naturally extended into networked and distributed domains. For example, typical write-ahead-logging protocols implicitly implement machine independent, reorderable log entries in order to implement logical undo. These two properties have been crucial in past system software designs, including data replication, distribution, and conflict resolution algorithms. Therefore, we plan to provide a networked, logical redo log as an application-level primitive, and to explore system designs that leverage this approach. Current Research Focus ---------------------- LLADD's design assumes that application developers will implement high-performance transactional data structures. However, these data structures are notoriously difficult to implement correctly. Our current research attempts to address these concerns. For our infrastructure to be generally useful the functionality that it provides should be efficient, reliable and applicable to new application domains. We believe that improvements to the development process can address each of these goals. Application developers typically have a limited amount of time to spend implementing and verifying application-specific storage extensions, but bugs in these extensions have dire consequences. Also, while data structure algorithms tend to be simple and easily understood, performance tuning and verification of implementation correctness is extremely difficult. Recovery-based algorithms must behave correctly during forward operation and also under arbitrary recovery scenarios. Behavior during recovery is particularly difficult to verify due to the large number of materialized page file states that could occur after a crash. Fortunately, write-ahead-logging schemes such as ARIES make use of nested-top-actions to vastly simplify the problem. Given the correctness of page-based physical undo and redo, logical undo may assume that page spanning operations are applied to the data store atomically. Existing work in the static-analysis community has verified that device driver implementations correctly adhere to complex operating system kernel locking schemes[SLAM]. We would like to formalize LLADD's latching and logging APIs, so that these analyses will be directly applicable to LLADD. This would allow us to verify that data structure behavior during recovery is equivalent to the behavior that would result if an abort() was issued on each prefix of the log that is generated during normal forward operation. By using coarse latches that are held throughout entire logical operation invocations, we can drastically reduce the size of this space, allowing conventional state-state based search techniques (such as randomized or exhaustive state-space searches, or unit testing techniques) to be practical. It has been shown that such coarse-grained latching can yield high-performance concurrent data structures if semantics-preserving optimizations such as page prefetching are applied [ARIES/IM]. A separate approach to the static analysis of LLADD extensions uses compiler optimization techniques. Software built on top of layered APIs frequently makes calls to low level functions that result in repeated work. A common example in LLADD involves loops over data with good locality in the page file. The vast majority of the time, these loops result in a series of high level API calls that continually pin and unpin the same underlying data. The code for each of these high level API calls could be copied into many different variants with different pinning/unpinning and latching/unlatching behavior, but this would greatly complicate the API that application developers must work with, and complicate any application code that made use of such optimizations. Compiler optimization techniques such as code hoisting and partial common subexpression elimination solve analogous problems to remove redundant computations. Code hoisting moves code outside of loops and conditionals, while partial common subexpression elimination inserts checks that decide at runtime whether a particular computation is redundant. We hope to extend such techniques to reduce the number of buffer manager and locking calls made by existing code. In situations where memory is abundant, these calls are a significant performance bottleneck, especially for read-only operations. Similar optimization techniques are applicable to application code. Local LLADD calls are simply normal function calls. Therefore it may even be possible to apply the transformations that these optimizations perform to application code that is unaware of the underlying storage implementation. This class of optimizations would be very difficult to implement with existing transactional storage systems but should significantly improve application performance. We hope to validate our ideas about static analysis by incorporating them into our development process as we increase the reliability and overall quality of LLADD's implementation and its APIs. Our architecture provides a set of tools that allow applications to implement custom transactional data structures and page layouts. This avoids "impedance mismatch," simplifying applications and providing appropriate applications with performance that is comparable or superior to other general-purpose solutions. By adding support for automated code verification and transformations we hope to make it easy to produce correct extensions and to allow simple, maintainable implementations to compete with special purpose, hand-optimized code.