We present a new data structure, called a random-access list, that supports array lookup and update operations in O(log n) time, while simultaneously providing O(1) time list operations (cons, head, tail). A closer analysis of the array operations improves the bound to O(minfi; log ng) in the worst case and O(log i) in the expected case, where i is the index of the desired element. Empirical evidence suggests that this data structure should be quite efficient in practice. 1 Introduction Lists are the primary data structure in every functional programmer 's toolbox. They are simple, convenient, and usually quite efficient. The main drawback of lists is that accessing the ith element requires O(i) time. In such situations, functional programmers often find themselves longing for the efficient random access of arrays. Unfortunately, arrays can be quite awkward to implement in a functional setting, where previous versions of the array must be available even after an update. Since arra...

We introduce data-structural bootstrapping, a technique to design data structures recursively, and use it to design confluently persistent deques. Our data structure requires O(log 3 k) worstcase time and space per deletion, where k is the total number of deque operations, and constant worst-case time and space for other operations. Further, the data structure allows a purely functional implementation, with no side effects. This improves a previous result of Driscoll, Sleator, and Tarjan. 1 An extended abstract of this paper was presented at the 4th ACM-SIAM Symposium on Discrete Algorithms, 1993. 2 Supported by a Fannie and John Hertz Foundation fellowship, National Science Foundation Grant No. CCR-8920505, and the Center for Discrete Mathematics and Theoretical Computer Science (DIMACS) under NSF-STC88-09648. 3 Also affiliated with NEC Research Institute, 4 Independence Way, Princeton, NJ 08540. Research at Princeton University partially supported by the National Science Foundatio...

Traditional techniques for designing and analyzing amortized data structures in an imperative setting are of limited use in a functional setting because they apply only to singlethreaded data structures, yet functional data structures can be non-single-threaded. In earlier work, we showed how lazy evaluation supports functional amortized data structures and described a technique (the banker's method) for analyzing such data structures. In this paper, we present a new analysis technique (the physicist's method) and show how one can sometimes derive a worst-case data structure from an amortized data structure by appropriately scheduling the premature execution of delayed components. We use these techniques to develop new implementations of FIFO queues and binomial queues. 1 Introduction Functional programmers have long debated the relative merits of strict versus lazy evaluation. Although lazy evaluation has many benefits [11], strict evaluation is clearly superior in at least one area:...

We describe an efficient, purely functional implementation of deques with catenation. In addition to being an intriguing problem in its own right, finding a purely functional implementation of catenable deques is required to add certain sophisticated programming constructs to functional programming languages. Our solution has a worst-case running time of O(1) for each push, pop, inject, eject and catenation. The best previously known solution has an O(log k) time bound for the k deque operation. Our solution is not only faster but simpler. A key idea used in our result is an algorithmic technique related to the redundant digital representations used to avoid carry propagation in binary counting.

Traditional database management systems perform updates-in-place and use logs and periodic checkpointing to efficiently achieve atomicity and durability. In this Thesis we shall present a different method, Shades, for achieving atomicity and durability using a copy-on-write policy instead of updates-in-place. We shall also present index structures and the implementation of Shines, a persistent functional programming language, built on top of Shades. Shades includes real-time generational garbage collection. Real-timeness is achieved by collecting only a small part, a generation, of the database at a time. Contrary to previously presented persistent garbage collection algorithms, Shades has no need to maintain metadata (remembered sets) of intra-generation pointers on disk since the metadata can be reconstructed during recovery. This considerably reduces the amount of disk writing. In conjunction with aggressive commit grouping, efficient index structures, a design specialized to a main memory environment, and a carefully crafted implementation of Shines, we have achieved surprisingly high performance, handsomely beating commercial database management systems.

The design and implementation of efficient aggregate data structures has been an important issue in functional programming. It is not clear how to select a good representation for an aggregate when access patterns to the aggregate are highly variant, or even unpredictable. Previous approaches rely on compile--time analyses or programmer annotations. These methods can be unreliable because they try to predict program behaviors before they are executed. We propose a probabilistic approach, which is based on Markov processes, for automatic selection of data representations. The selection is modeled as a random process moving in a graph with weighted edges. The proposed approach employs coin tossing at run--time to aid choosing suitable data representations. The transition probability function used by the coin tossing is constructed in a simple and common way from a measured cost function. We show that, under this setting, random selection of data representations can be quite effective. Th...