The Persistent Object System Metastore: Persistence Via Metaprogramming

Total Page:16

File Type:pdf, Size:1020Kb

The Persistent Object System Metastore: Persistence Via Metaprogramming The Persistent Object System MetaStore: Persistence via Metaprogramming Arthur H. Lee UUCS-92-027 Department of Computer Science University of Utah Salt Lake City, UT 84112 USA June 10, 1992 Abstract Object-intensive applications require persistence of complex objects. Many of these applica­ tions also use vast amounts of data, often exceeding a machine’s virtual memory. MetaStore is a portable, persistent object system designed to solve these problems. MetaStore uses the metaprogramming facilities of the metaobject protocol to add per­ sistence to the Common Lisp Object System. This approach leaves the semantics of CL OS unchanged, requires only minimal syntactic changes to existing programs, and needs no com­ piler support. In the resulting language, programmers can define both classes and slots to be persistent. MetaStore then handles persistence at the metaobject level. MetaStore focuses on the persistence of passive data by providing a virtual object mem­ ory. It tries to keep an appropriate number of objects in memory to maintain system per­ formance at an acceptable level, and allows programmers to tune performance by specifying object and slot clustering. MetaStore currently supports only limited aspects of class evolu­ tion. MetaStore addresses a range of issues in implementing persistence, including object iden­ tity, addressing mechanisms, shared objects and structures, dirty bits, queries, transaction management, version control, object base garbage collection, and object clustering. Based on the initial implementation of MetaStore, we show performance measurements and propose improvements to the metaobject protocol of CLOS. CDRS CAD system, an object-intensive application at Evans & Sutherland Computer Corp., will soon be ported to MetaStore. The Persistent Object System MetaStore: Persistence via Metaprogramming by Arthur H. Lee A dissertation submitted to the faculty of The University of Utah in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Computer Science The University of Utah August 1992 Copyright © Arthur H. Lee 1992 All Rights Reserved THE UNIVERSITY OF UTAH GRADUATE SCHOOL SUPERVISORY COMMITTEE APPROVAL of a dissertation submitted by Arthur H. Lee This dissertation has been read by each member of the following supervisory committee and by majority vote has been found to be satisfactory. /*7f2L <9 , Ganf/E. Lindstrom THE UNIVERSITY OF UTAH GRADUATE SCHOOL FINAL READING APPROVAL To the Graduate Council of The University of Utah: I have read the dissertation o f___________ Arthur H. Lee___________ in its final form and have found that (1) its format, citations, and bibliographic style are consistent and acceptable; (2) its illustrative materials including figures, tables, and charts are in place; and (3) the final manuscript is satisfactory to the Supervisory Committee and is ready for submission to the Graduate School. .-Aae*- _________________ Date Jos\ej>h) L. Zash#ry ^3 Chair, Supervisory Committee Approved for the Major Department " I KflMAfl-4 C- U&asI za S Thomas C. Henderson Chair/Dean Approved for the Graduate Council S. j&Lu A ct B. Gale Dick Dean of The Graduate School Abstract Object-intensive applications require persistence of complex objects. Many of these applications also use vast amounts of data, often exceeding a machine’s virtual memory. MetaStore is a portable, persistent object system designed to solve these problems. MetaStore uses the metaprogramming facilities of the metaobject protocol to add persistence to the Common Lisp Object System. This approach leaves the semantics of CLOS unchanged, requires only minimal syntactic changes to existing programs, and needs no compiler support. In the resulting language, programmers can define both classes and slots to be persistent. MetaStore then handles persistence at the metaobject level. MetaStore focuses on the persistence of passive data by providing a virtual object memory. It tries to keep an appropriate number of objects in memory to maintain system performance at an acceptable level, and allows programmers to tune performance by specifying object and slot clustering. MetaStore currently supports only limited aspects of class evolution. MetaStore addresses a range of issues in implementing persistence, including object identity, addressing mechanisms, shared objects and structures, dirty bits, queries, transaction management, version control, object base garbage collection, and object clustering. Based on the initial implementation of MetaStore, we show performance mea­ surements and propose improvements to the metaobject protocol of CLOS. CDRS CAD system, an object-intensive application at Evans & Sutherland Computer Corp., will soon be ported to MetaStore. To Joyce, Chris, and Jamie. Contents Acknowledgments..................................................................................................xiii 1. Introduction....................................................................................................... 1 1.1 Object Persistence................................................................................... 3 1.1.1 CDRS: An Object-intensive Application................................ 3 1.1.2 Understanding The P ro b le m ................................................... 4 1.2 Related Work ......................................................................................... 6 1.2.1 Interlisp ...................................................................................... 6 1.2.2 P S -A lg o l...................................................................................... 7 1.2.3 M n em e......................................................................................... 8 1.2.4 P C L O S ......................................................................................... 9 1.3 M eta S tore............................................................................................... 10 1.3.1 Research Goals............................................................................ 10 1.3.2 Language Extension via Metaprogramming......................... 11 1.3.3 Persistent Object S to r e ............................................................ 12 1.3.4 MetaStore Architecture............................................................ 13 1.4 Roadm ap................................................................................................... 13 2. Preliminaries.................................................................................................... 15 2.1 Object-Oriented Program m ing............................................................ 15 2.1.1 Encapsulation............................................................................ 16 2.1.2 Dynamic B in d in g ...................................................................... 17 2.1.3 Inheritance................................................................................... 19 2.2 Persistent Object M odel......................................................................... 20 2.2.1 Mandatory Features................................................................... 21 2.2.2 Optional Features...................................................................... 22 2.3 Metaprogramming................................................................................... 22 2.4 Persistence Schemes............................................................................... 25 2.4.1 Addressing Mechanisms............................................................ 26 2.4.2 Incremental S a v es...................................................................... 26 2.4.3 Shared Structures...................................................................... 27 2.4.4 Orthogonal Persistence............................................................ 29 2.4.5 Selective Persistence................................................................... 30 2.4.6 Granularity of Persistence......................................................... 31 2.4.7 P ortability................................................................................... 31 2.4.8 Summary...................................................................................... 32 3. Language E xtension via M etaprogram m ing.............................................33 3.1 Overview of M e t a ...................................................................................... 33 3.2 Programmer’s View of MetaStore ...................................................... ... 37 3.2.1 Programming C onventions...................................................... ... 37 3.2.2 Semantic C hanges......................................................................... 39 3.3 Object Identity ......................................................................................... 39 3.4 Object Persistence...................................................................................... 40 3.5 Structure of Memory O b jects................................................................... 41 3.6 Husks and Granularity of P ersisten ce................................................... 42 3.7 Addressing Mechanisms......................................................................... ... 44 3.7.1 Associative OID Addressing ...................................................... 45 3.7.2 Pointer S w izzlin g ......................................................................... 45 3.8 Metaobject Protocol in A ction ............................................................ ... 46 3.8.1
Recommended publications
  • Encapsulation and Data Abstraction
    Data abstraction l Data abstraction is the main organizing principle for building complex software systems l Without data abstraction, computing technology would stop dead in its tracks l We will study what data abstraction is and how it is supported by the programming language l The first step toward data abstraction is called encapsulation l Data abstraction is supported by language concepts such as higher-order programming, static scoping, and explicit state Encapsulation l The first step toward data abstraction, which is the basic organizing principle for large programs, is encapsulation l Assume your television set is not enclosed in a box l All the interior circuitry is exposed to the outside l It’s lighter and takes up less space, so it’s good, right? NO! l It’s dangerous for you: if you touch the circuitry, you can get an electric shock l It’s bad for the television set: if you spill a cup of coffee inside it, you can provoke a short-circuit l If you like electronics, you may be tempted to tweak the insides, to “improve” the television’s performance l So it can be a good idea to put the television in an enclosing box l A box that protects the television against damage and that only authorizes proper interaction (on/off, channel selection, volume) Encapsulation in a program l Assume your program uses a stack with the following implementation: fun {NewStack} nil end fun {Push S X} X|S end fun {Pop S X} X=S.1 S.2 end fun {IsEmpty S} S==nil end l This implementation is not encapsulated! l It has the same problems as a television set
    [Show full text]
  • 9. Abstract Data Types
    COMPUTER SCIENCE COMPUTER SCIENCE SEDGEWICK/WAYNE SEDGEWICK/WAYNE 9. Abstract Data Types •Overview 9. Abstract Data Types •Color •Image processing •String processing Section 3.1 http://introcs.cs.princeton.edu http://introcs.cs.princeton.edu Abstract data types Object-oriented programming (OOP) A data type is a set of values and a set of operations on those values. Object-oriented programming (OOP). • Create your own data types (sets of values and ops on them). An object holds a data type value. Primitive types • Use them in your programs (manipulate objects). Variable names refer to objects. • values immediately map to machine representations data type set of values examples of operations • operations immediately map to Color three 8-bit integers get red component, brighten machine instructions. Picture 2D array of colors get/set color of pixel (i, j) String sequence of characters length, substring, compare We want to write programs that process other types of data. • Colors, pictures, strings, An abstract data type is a data type whose representation is hidden from the user. • Complex numbers, vectors, matrices, • ... Impact: We can use ADTs without knowing implementation details. • This lecture: how to write client programs for several useful ADTs • Next lecture: how to implement your own ADTs An abstract data type is a data type whose representation is hidden from the user. 3 4 Sound Strings We have already been using ADTs! We have already been using ADTs! A String is a sequence of Unicode characters. defined in terms of its ADT values (typical) Java's String ADT allows us to write Java programs that manipulate strings.
    [Show full text]
  • Abstract Data Types
    Chapter 2 Abstract Data Types The second idea at the core of computer science, along with algorithms, is data. In a modern computer, data consists fundamentally of binary bits, but meaningful data is organized into primitive data types such as integer, real, and boolean and into more complex data structures such as arrays and binary trees. These data types and data structures always come along with associated operations that can be done on the data. For example, the 32-bit int data type is defined both by the fact that a value of type int consists of 32 binary bits but also by the fact that two int values can be added, subtracted, multiplied, compared, and so on. An array is defined both by the fact that it is a sequence of data items of the same basic type, but also by the fact that it is possible to directly access each of the positions in the list based on its numerical index. So the idea of a data type includes a specification of the possible values of that type together with the operations that can be performed on those values. An algorithm is an abstract idea, and a program is an implementation of an algorithm. Similarly, it is useful to be able to work with the abstract idea behind a data type or data structure, without getting bogged down in the implementation details. The abstraction in this case is called an \abstract data type." An abstract data type specifies the values of the type, but not how those values are represented as collections of bits, and it specifies operations on those values in terms of their inputs, outputs, and effects rather than as particular algorithms or program code.
    [Show full text]
  • Abstract Data Types (Adts)
    Data abstraction: Abstract Data Types (ADTs) CSE 331 University of Washington Michael Ernst Outline 1. What is an abstract data type (ADT)? 2. How to specify an ADT – immutable – mutable 3. The ADT design methodology Procedural and data abstraction Recall procedural abstraction Abstracts from the details of procedures A specification mechanism Reasoning connects implementation to specification Data abstraction (Abstract Data Type, or ADT): Abstracts from the details of data representation A specification mechanism + a way of thinking about programs and designs Next lecture: ADT implementations Representation invariants (RI), abstraction functions (AF) Why we need Abstract Data Types Organizing and manipulating data is pervasive Inventing and describing algorithms is rare Start your design by designing data structures Write code to access and manipulate data Potential problems with choosing a data structure: Decisions about data structures are made too early Duplication of effort in creating derived data Very hard to change key data structures An ADT is a set of operations ADT abstracts from the organization to meaning of data ADT abstracts from structure to use Representation does not matter; this choice is irrelevant: class RightTriangle { class RightTriangle { float base, altitude; float base, hypot, angle; } } Instead, think of a type as a set of operations create, getBase, getAltitude, getBottomAngle, ... Force clients (users) to call operations to access data Are these classes the same or different? class Point { class Point { public float x; public float r; public float y; public float theta; }} Different: can't replace one with the other Same: both classes implement the concept "2-d point" Goal of ADT methodology is to express the sameness Clients depend only on the concept "2-d point" Good because: Delay decisions Fix bugs Change algorithms (e.g., performance optimizations) Concept of 2-d point, as an ADT class Point { // A 2-d point exists somewhere in the plane, ..
    [Show full text]
  • Csci 658-01: Software Language Engineering Python 3 Reflexive
    CSci 658-01: Software Language Engineering Python 3 Reflexive Metaprogramming Chapter 3 H. Conrad Cunningham 4 May 2018 Contents 3 Decorators and Metaclasses 2 3.1 Basic Function-Level Debugging . .2 3.1.1 Motivating example . .2 3.1.2 Abstraction Principle, staying DRY . .3 3.1.3 Function decorators . .3 3.1.4 Constructing a debug decorator . .4 3.1.5 Using the debug decorator . .6 3.1.6 Case study review . .7 3.1.7 Variations . .7 3.1.7.1 Logging . .7 3.1.7.2 Optional disable . .8 3.2 Extended Function-Level Debugging . .8 3.2.1 Motivating example . .8 3.2.2 Decorators with arguments . .9 3.2.3 Prefix decorator . .9 3.2.4 Reformulated prefix decorator . 10 3.3 Class-Level Debugging . 12 3.3.1 Motivating example . 12 3.3.2 Class-level debugger . 12 3.3.3 Variation: Attribute access debugging . 14 3.4 Class Hierarchy Debugging . 16 3.4.1 Motivating example . 16 3.4.2 Review of objects and types . 17 3.4.3 Class definition process . 18 3.4.4 Changing the metaclass . 20 3.4.5 Debugging using a metaclass . 21 3.4.6 Why metaclasses? . 22 3.5 Chapter Summary . 23 1 3.6 Exercises . 23 3.7 Acknowledgements . 23 3.8 References . 24 3.9 Terms and Concepts . 24 Copyright (C) 2018, H. Conrad Cunningham Professor of Computer and Information Science University of Mississippi 211 Weir Hall P.O. Box 1848 University, MS 38677 (662) 915-5358 Note: This chapter adapts David Beazley’s debugly example presentation from his Python 3 Metaprogramming tutorial at PyCon’2013 [Beazley 2013a].
    [Show full text]
  • On the Interaction of Object-Oriented Design Patterns and Programming
    On the Interaction of Object-Oriented Design Patterns and Programming Languages Gerald Baumgartner∗ Konstantin L¨aufer∗∗ Vincent F. Russo∗∗∗ ∗ Department of Computer and Information Science The Ohio State University 395 Dreese Lab., 2015 Neil Ave. Columbus, OH 43210–1277, USA [email protected] ∗∗ Department of Mathematical and Computer Sciences Loyola University Chicago 6525 N. Sheridan Rd. Chicago, IL 60626, USA [email protected] ∗∗∗ Lycos, Inc. 400–2 Totten Pond Rd. Waltham, MA 02154, USA [email protected] February 29, 1996 Abstract Design patterns are distilled from many real systems to catalog common programming practice. However, some object-oriented design patterns are distorted or overly complicated because of the lack of supporting programming language constructs or mechanisms. For this paper, we have analyzed several published design patterns looking for idiomatic ways of working around constraints of the implemen- tation language. From this analysis, we lay a groundwork of general-purpose language constructs and mechanisms that, if provided by a statically typed, object-oriented language, would better support the arXiv:1905.13674v1 [cs.PL] 31 May 2019 implementation of design patterns and, transitively, benefit the construction of many real systems. In particular, our catalog of language constructs includes subtyping separate from inheritance, lexically scoped closure objects independent of classes, and multimethod dispatch. The proposed constructs and mechanisms are not radically new, but rather are adopted from a variety of languages and programming language research and combined in a new, orthogonal manner. We argue that by describing design pat- terns in terms of the proposed constructs and mechanisms, pattern descriptions become simpler and, therefore, accessible to a larger number of language communities.
    [Show full text]
  • Software II: Principles of Programming Languages
    Software II: Principles of Programming Languages Lecture 6 – Data Types Some Basic Definitions • A data type defines a collection of data objects and a set of predefined operations on those objects • A descriptor is the collection of the attributes of a variable • An object represents an instance of a user- defined (abstract data) type • One design issue for all data types: What operations are defined and how are they specified? Primitive Data Types • Almost all programming languages provide a set of primitive data types • Primitive data types: Those not defined in terms of other data types • Some primitive data types are merely reflections of the hardware • Others require only a little non-hardware support for their implementation The Integer Data Type • Almost always an exact reflection of the hardware so the mapping is trivial • There may be as many as eight different integer types in a language • Java’s signed integer sizes: byte , short , int , long The Floating Point Data Type • Model real numbers, but only as approximations • Languages for scientific use support at least two floating-point types (e.g., float and double ; sometimes more • Usually exactly like the hardware, but not always • IEEE Floating-Point Standard 754 Complex Data Type • Some languages support a complex type, e.g., C99, Fortran, and Python • Each value consists of two floats, the real part and the imaginary part • Literal form real component – (in Fortran: (7, 3) imaginary – (in Python): (7 + 3j) component The Decimal Data Type • For business applications (money)
    [Show full text]
  • P4 Sec 4.1.1, 4.1.2, 4.1.3) Abstraction, Algorithms and ADT's
    Computer Science 9608 P4 Sec 4.1.1, 4.1.2, 4.1.3) Abstraction, Algorithms and ADT’s with Majid Tahir Syllabus Content: 4.1.1 Abstraction show understanding of how to model a complex system by only including essential details, using: functions and procedures with suitable parameters (as in procedural programming, see Section 2.3) ADTs (see Section 4.1.3) classes (as used in object-oriented programming, see Section 4.3.1) facts, rules (as in declarative programming, see Section 4.3.1) 4.1.2 Algorithms write a binary search algorithm to solve a particular problem show understanding of the conditions necessary for the use of a binary search show understanding of how the performance of a binary search varies according to the number of data items write an algorithm to implement an insertion sort write an algorithm to implement a bubble sort show understanding that performance of a sort routine may depend on the initial order of the data and the number of data items write algorithms to find an item in each of the following: linked list, binary tree, hash table write algorithms to insert an item into each of the following: stack, queue, linked list, binary tree, hash table write algorithms to delete an item from each of the following: stack, queue, linked list show understanding that different algorithms which perform the same task can be compared by using criteria such as time taken to complete the task and memory used 4.1.3 Abstract Data Types (ADT) show understanding that an ADT is a collection of data and a set of operations on those data
    [Show full text]
  • Chapter 9 of Concrete Abstractions: an Introduction to Computer
    Out of print; full text available for free at http://www.gustavus.edu/+max/concrete-abstractions.html CHAPTER NINE Generic Operations 9.1 Introduction We described data abstraction in Chapter 6 as a barrier between the way a data type is used and the way it is represented. There are a number of reasons to use data abstraction, but perhaps its greatest advantage is that the programmer can rely on an abstract mental model of the data rather than worrying about such mundane details as how the data is represented. For example, we can view the game-state ADT from Chapter 6 as a snapshot picture of an evolving Nim game and can view lists as ®nite sequences of objects. The simpli®cation resulting from using abstract models is essential for many of the complicated problems programmers confront. In this chapter we will exploit and extend data abstraction by introducing generic operations, which are procedures that can operate on several different data types. We rely on our mental model of an ADT when pondering how it might be used in a program. To actually work with the data, however, we need procedures that can manipulate it; these procedures are sometimes called the ADT's interface.For example, all of the procedures Scheme provides for manipulating lists comprise the list type's interface. The barrier between an ADT's use and its implementation results directly from the programming discipline of using the interface procedures instead of explicitly referring to the underlying representation. The interface must give us adequate power to manipulate the data as we would expect, given our mental model of the data, but we still have some ¯exibility in how the interface is speci®ed.
    [Show full text]
  • Data Abstraction and Basic Data Structures Abstract Data Type ADT
    Data Abstraction and Basic Data Structures Abstract Data type • Improving efficiency by building better 4i is an instance of type T, i ∈ T 4 Data Structure 4e is an element of set S, e ∈ S 4o is an object of class C, o ∈ C • Object IN 4 Abstract Data Type • Abstract Data Type 4Structures: data structure declarations f Specification f Design 4Functions: operation definitions 4 Architecture [Structure, Function] • An ADT is identified as a Class • Abstract Data Types 4in languages such as C++ and Java 4 Lists, Trees • Designing algorithms and 4 Stacks, Queues TECH proving correctness of algorithms 4 Priority Queue, Union-Find 4based on ADT operations and specifications 4 Dictionary Computer Science ADT Specification Operations for ADT • The specification of an ADT describe how the operations • Constructors (functions, procedures, or methods) behave 4create a new object and return a reference to it 4 in terms of Inputs and Outputs • A specification of an operation consists of: • Access functions 4 Calling prototype 4return information about an object, but do not modify it 4 Preconditions • Manipulation procedures 4 Postconditions 4modify an object, but do not return information • The calling prototype includes • State of an object 4 name of the operation 4 parameters and their types 4current values of its data 4 return value and its types • Describing constructors and manipulation procedures • The preconditions are statements 4in terms of Access functions 4 assumed to be true when the operation is called. • Recursive ADT • The postconditions are statements 4if any of its access functions returns 4 assumed to be true when the operation returns.
    [Show full text]
  • Approaches to Composition and Refinement in Object-Oriented Design
    Approaches to Composition and Refinement in Object-Oriented Design Marcel Weiher Matrikelnummer 127367 Diplomarbeit Fachbereich Informatik Technische Unversitat¨ Berlin Advisor: Professor Bernd Mahr August 21, 1997 Contents 1 Introduction 4 1.1 Object Orientation 4 1.2 Composition and Refinement 5 2 State of the Art 7 2.1 Frameworks 7 2.1.1 Framework Construction 8 2.1.2 Reusing Frameworks 9 2.2 Design Patterns 10 2.2.1 Reusing Patterns 10 2.2.2 Communicating Patterns 12 2.3 Commentary 13 3 Enhanced Interfaces 14 3.1 Interface Typing 14 3.2 The Refinement Interface 15 3.2.1 Protocol Dependencies 15 3.2.2 Reuse Contracts 18 3.3 Interaction Interfaces 21 3.3.1 Interaction Protocols 21 3.3.2 Protocol Specification and Matching 22 3.4 Commentary 23 4 Orthogonalization 24 4.1 Delegation 24 4.1.1 Prototypes Languages 25 4.1.2 Composition Filters 25 4.2 Mixins 27 4.2.1 Mixins and Multiple Inheritance 28 4.2.2 Nested Dynamic Mixins 28 4.3 Extension Hierarchies 29 4.3.1 Extension Operators 29 4.3.2 Conflicts: Detection, Resolution, Avoidance 30 4.4 Canonic Components 31 4.4.1 Components 31 4.4.2 Views 32 4.5 Commentary 33 1 5 Software Architecture 34 5.1 Architectural Styles 34 5.1.1 Pipes and Filters 35 5.1.2 Call and Return 36 5.1.3 Implicit Invocation 36 5.1.4 Using Styles 37 5.2 Architecture Description Languages 38 5.2.1 UniCon 38 5.2.2 RAPIDE 39 5.2.3 Wright 40 5.2.4 ACME 43 5.3 Commentary 43 6 Non-linear Refinement 45 6.1 Domain Specific Software Generators 45 6.1.1 A Domain Independent Model 46 6.1.2 Implementation 47 6.2 Subject Oriented
    [Show full text]
  • 1 Introduction
    Concise Notes on Data Structures and Algorithms Introduction 1 Introduction 1.1 What Are Data Structures and Algorithms? If this book is about data structures and algorithms, then perhaps we should start by defining these terms. We begin with a definition for “algorithm.” Algorithm: A finite sequence of steps for accomplishing some computational task. An algorithm must • Have steps that are simple and definite enough to be done by a computer, and • Terminate after finitely many steps. This definition of an algorithm is similar to others you may have seen in prior computer science courses. Notice that an algorithm is a sequence of steps, not a program. You might use the same algorithm in different programs, or express the same algorithm in different languages, because an algorithm is an entity that is abstracted from implementation details. Part of the point of this course is to introduce you to algorithms that you can use no matter what language you program in. We will write programs in a particular language, but we are really studying the algorithms, not their implementations. The definition of a data structure is a bit more involved. We begin with the notion of an abstract data type. Abstract data type (ADT): A set of values (the carrier set), and operations on those values. Here are some examples of ADTs: Boolean—The carrier set of the Boolean ADT is the set {true, false}. The operations on these values are negation, conjunction, disjunction, conditional, is equal to, and perhaps some others. Integer—The carrier set of the Integer ADT is the set {…, -2, -1, 0, 1, 2, …}, and the operations on these values are addition, subtraction, multiplication, division, remainder, is equal to, is less than, is greater than, and so on.
    [Show full text]