Concurrency in Go. Kubernetes Up and Running. Docker Cookbook. Essential Docker for ASP. Docker and Kubernetes for Java Developers: Scale, deploy, and monitor multi-container applications. Please enter your comment! The Go Programming Language is the authoritative resource for any programmer who wants to learn Go. It shows how to write clear and idiomatic Go to solve real-world problems.
Early chapters cover the structural elements of Go programs: syntax, control flow, data types, and the organization of a program into packages, files, and functions. The examples illustrate many packages from the standard library and show how to create new ones of your own. Later chapters explain the package mechanism in more detail, and how to build, test, and maintain projects using the go tool.
They explain the key principles of encapsulation, composition, and substitutability using realistic examples. Two chapters on concurrency present in-depth approaches to this increasingly important topic. The first, which covers the basic mechanisms of goroutines and channels, illustrates the style known as communicating sequential processes for which Go is renowned. The second covers more traditional aspects of concurrency with shared variables.
These chapters provide a solid foundation for programmers encountering concurrency for the first time. The final two chapters explore lower-level features of Go. One covers the art of metaprogramming using reflection. The other shows how to use the unsafe package to step outside the type system for special situations, and how to use the cgo tool to create Go bindings for C libraries.
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Concurrency master Anthony Williams presents examples and practical tasks in every chapter, including insights that will delight even the most experienced developer. No prior experience with concurrency required. Summary Concurrency in. NET teaches you how to build concurrent and scalable programs in. NET using the functional paradigm. This intermediate-level guide is aimed at developers, architects, and passionate computer programmers who are interested in writing code with improved speed and effectiveness by adopting a declarative and pain-free programming style.
About the Technology Unlock the incredible performance built into your multi-processor machines. Concurrent applications run faster because they spread work across processor cores, performing several tasks at the same time. Modern tools and techniques on the. NET platform, including parallel LINQ, functional programming, asynchronous programming, and the Task Parallel Library, offer powerful alternatives to traditional thread-based concurrency.
About the Book Concurrency in. NET teaches you to write code that delivers the speed you need for performance-sensitive applications. Featuring examples in both C and F , this book guides you through concurrent and parallel designs that emphasize functional programming in theory and practice. You'll start with the foundations of concurrency and master essential techniques and design practices to optimize code running on modern multiprocessor systems.
What's Inside The most important concurrency abstractions Employing the agent programming model Implementing real-time event-stream processing Executing unbounded asynchronous operations Best concurrent practices and patterns that apply to all platforms About the Reader For readers skilled with C or F. He has over 20 years' experience delivering cost-effective technology solutions in a competitive business environment. Table of Contents PART 1 - Benefits of functional programming applicable to concurrent programs Functional concurrency foundations Functional programming techniques for concurrency Functional data structures and immutability PART 2 - How to approach the different parts of a concurrent program The basics of processing big data: data parallelism, part 1 PLINQ and MapReduce: data parallelism, part 2 Real-time event streams: functional reactive programming Task-based functional parallelism Task asynchronicity for the win Asynchronous functional programming in F Functional combinators for fluent concurrent programming Applying reactive programming everywhere with agents Parallel workflow and agent programming with TPL Dataflow PART 3 - Modern patterns of concurrent programming applied Recipes and design patterns for successful concurrent programming Building a scalable mobile app with concurrent functional programming.
This Golang book will teach you how to construct idiomatic Go code that is reusable and highly performant. This book covers channels and goroutines for parallelism and concurrency to write high-performance code for distributed systems. What you will learn Organize and manipulate data effectively with clusters and job queues Explore commonly applied Go data structures and algorithms Write anonymous functions in Go to build reusable apps Profile and trace Go apps to reduce bottlenecks and improve efficiency Deploy, monitor, and iterate Go programs with a focus on performance Dive into memory management and CPU and GPU parallelism in Go Who this book is for This Golang book is a must for developers and professionals who have an intermediate-to-advanced understanding of Go programming, and are interested in improving their speed of code execution.
Summary Go in Practice guides you through 70 real-world techniques in key areas like package management, microservice communication, and more. About the Technology Go may be the perfect systems language.
Built with simplicity, concurrency, and modern applications in mind, Go provides the core tool set for rapidly building web, cloud, and systems applications.
If you know a language like Java or C , it's easy to get started with Go; the trick is finding the practical dirt-under-the-fingernails techniques that you need to build production-ready code. Sharing of data 3. Data Integrity 4. Data Security 5. Conflict resolution 6. Data Independence. Persistent Data: If data is removed from database due to some explicit request from user to remove.
Integrated Data: A database can be a collection of data from different files and when any redundancy among those files are removed from database is said to be integrated data.
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Know how locks work in MySQL and how they relate to transactions. This book explains the major role that locks play in database systems, showing how locks are essential in allowing high-concurrency workloads. You will learn about lock access levels and lock granularities from the user level as well as table locks to record and gap locks.
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Interestingly, the introduction of read arcs in Petri nets has also led to several variants of the semantics. In this section, we present some of them, mainly taken from [14]. For simplicity, we assume that for every transition t, its context is disjoint from its preset and postset. A RPN is represented as a graph with two types of nodes: places circles and transitions rectangles. Presets are represented by arrows from places to transitions, postsets by arrows from transitions to places, and contexts by undirected edges, called read arcs, between places and transitions.
The initial marking is represented by tokens in places. The transition a, for instance, has p1 in its preset, p2 in its context and p4 in its postset. A Petri net starts in its initial marking M0.
For instance, the net in Fig. However, d can never fire because that would require to fire both a and b first, and firing one of a, b disables the other. One is the well-known step semantics [23], in which multiple transitions can fire simultaneously. This is typically the case of a and b in the net of Fig. The step semantics can be interpreted as first checking whether all members of a set of transitions can fire, and then firing them simultaneously.
We then recall the interval semantics introduced in [14], which allows a more liberal choice of checking and firing transitions in a set. We present the semantics under the assumption that the underlying net is safe even under these two semantics, which allow more possibilities than the atomic one.
A step is a set S of transitions of N. Sn of steps for which there exist markings M1 ,. A variant of step semantics, called maximal step semantics has received interest in the literature [25, 19]. In the example of Fig. However, transition d remains dead since none of these s-runs contains all of a, b, and c. The intuitive model underlying the step semantics is that all the transitions in the step can first check, in any order, whether they are enabled and not in conflict with one another, i.
Once the checks have been performed, they can all fire, again in any order. The notion introduced in Def. In this section, we generalize this idea.
The left-hand side of Fig. Similar ideas about splitting transitions can be found in several works, for instance in [44].
Intuitively, if we apply this construction to all transitions from Fig. The following Def. They can in turn be fired in any order, reaching the desired marking of split N.
For instance, for the net N from Fig. In this section, we shall introduce the interval semantics, which is more general than the step semantics, and whose interpretation on a net N does correspond to the feasible executions in split N.
Definition 12 Interval semantics, i-run Every a-run of split N is called i-run of N , or run of N under the interval semantics. Coming back to the example of Fig. Under the atomic semantics, a and b are in conflict, which prevents d from firing. Under the interval semantics, d can also fire. Definition 13 complete marking A marking of split N is complete if no pt is marked. In particular, the initial marking is complete.
Moreover, complete i-runs and only them lead to complete markings. We provide here a similar encoding of BNs into safe RPNs, with the explicit specification of the context of transitions, and with notations that will be used in Sect. The encoding can be easily generalized to multi- valued networks to safe RPNs, following [13, 32]. Let us consider a BN f of dimension n. Hereafter, Def. It is worth noticing that the resulting RPN can have a number of transitions exponential in the number of literals in the Boolean functions.
The comparison of both models also leads us into the opposite direction. In order to simplify the encodings, we additionally assume the RPNs to be loop-free, i. It is well known that loops can be replaced by read arcs without any effect on the atomic semantics.
Transporting the dynamics, i. A RPN transition typically has more than one output place, while the functions in BNs write on one single variable. Essentially, when components corresponding to the pre-condition and context of a transition t are marked, and if no other transition t0 is already occurring, the tth component of the BN can be updated to 1.
Then, the components related to the input and output places of t are updated in any order to apply their respective un-marking and marking. Once all these components have been updated, the tth component is updated to 0.
It results that a transition t is occurring, encoded by the value 1 of the tth component, if and only if either i no transition is occurring, and all components corresponding to places in the pre-condition and context of t have value 1, or ii t is already occurring and at least one input resp.
A component corresponding to a place p has value 1 if and only if either one of transition producing p is occurring, or if it has already value 1 and none transition consuming it is occurring.
Finally, one can remark that deciding the reachability in BNs is in PSPACE: given a BN of dimension n and the initial configuration x, let us define a counter using n bits, initially with value 0. Then, while the counter has value strictly less that 2n and the current configuration is not equal to y, non- deterministically apply an update, and increase the counter by one. For others, it may render configurations reachable, or exclude previously feasible paths.
We say that a network of the latter category is synchronism sensitive.
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