Microservices Expo Authors: Mehdi Daoudi, Elizabeth White, Pat Romanski, Flint Brenton, Gordon Haff

Related Topics: Java IoT, Agile Computing, @DevOpsSummit

Java IoT: Blog Feed Post

Java’s Built-In Garbage Collection | @CloudExpo #Java #Cloud #DevOps

Sun Java’s initial garbage collector did nothing to improve the image of garbage collection

How Java's Built-In Garbage Collection Will Make Your Life Better (Most of the Time)
By Kirk Pepperdine

“No provision need be made for the user to program the return of registers to the free-storage list.”

This line (along with the dozen or so that followed it) is buried in the middle of John McCarthy’s landmark paper, “Recursive Functions of Symbolic Expressions and Their Computation by Machine,” published in 1960. It is the first known description of automated memory management.

In specifying how to manage memory in Lisp, McCarthy was able to exclude explicit memory management. Thus, McCarthy relieved developers of the tedium of manual memory management. What makes this story truly amazing is that these few words inspired others to incorporate some form of automated memory management—otherwise known as garbage collection (GC)—into more than three quarters of the more widely used languages and runtimes developed since then. This list includes the two most popular platforms, Java’s Virtual Machine (JVM) and .NET’s Common Language Runtime (CLR), as well as the up and coming Go Lang by Google. GC exists not just on big iron but on mobile platforms such as Android’s Dalvik, Android Runtime, and Apple’s Swift. You can even find GC running in your web browser as well as on hardware devices such as SSDs. Let’s explore some of the reasons why the industry prefers automated over manual memory management.

Automatic Memory Management’s Humble Beginnings
So, how did McCarthy devise automated memory management? First, the Lisp engine decomposed Lisp expressions into sub-expressions, and each S-expression was stored in a single word node in a linked list. The nodes were allocated from a free list, but they didn’t have to be returned to the free list until it was empty.

Once the free list was empty, the runtime traced through the linked list and marked all reachable nodes. Next, it scanned through the buffer containing all nodes, and returned unmarked nodes to the free list. With the free-list refilled, the application would continue on.

Today, this is known as a single-space, in-place, tracing garbage collection. The implementation was quite rudimentary: it only had to deal with an acyclic-directed graph where all nodes were exactly the same size. Only a single thread ran, and that thread either executed application code or the garbage collector. In contrast, today’s collectors in the JVM must cope with a directed graph with cycles and nodes that are not uniformly sized. The JVM is multi-threaded, running on multi-core CPUs, possibly multi-socketed motherboards. Consequently, today’s implementations are far more complex—to the point GC experts struggle to predict performance in any given situation.

Slow Going: Garbage Collection Pause Time
When the Lisp garbage collector ran, the application stalled. In the initial versions of Lisp it was common for the collector to take 30 to 40 percent of the CPU cycles. On 1960s hardware this could cause the application stall, in what is known as a stop-the-world pause, for several minutes. The benefit was that allocation had barely any impact on application throughput (the amount of useful work done). This implementation highlighted the constant battle between pause time and impact on application throughput that persists to this day.

In general, the better the pause time characteristic of the collector, the more impact it has on application throughput. The current implementations in Java all come with pause time/overhead costs. The parallel collections come with long pause times and low overheads, while the mostly concurrent collectors have shorter pause times and consume more computing resources (both memory and CPU).

The goal of any GC implementer is to maximize the minimum amount of processor time that mutator threads are guaranteed to receive, a concept known as minimum mutator utilization (MMU). Even so, current GC overheads can run well under 5 percent, versus the 15 to 20 percent overhead you will experience in a typical C++ application.

So why you don’t feel this overhead like you do in a Java application? Because the overhead is evenly spread throughout the C/C++ run time, it is perceptibly invisible to the end users. In fact the biggest complaint about managed memory is that it pauses your application at unpredictable times for an unpredictable amount of time.

Garbage Collection Advancements
Sun Java’s initial garbage collector did nothing to improve the image of garbage collection. Its single-threaded, single-spaced implementation stalled applications for long periods of time and created a significant drag on allocation rates. It wasn’t until Java 2, when a generational memory pool scheme—along with parallel, mostly concurrent and incremental collectors—was introduced. While these collectors offered improved pause time characteristics, pause times continue to be problematic. Moreover, these implementations are so complex that it’s unlikely most developers have the experience necessary to tune them. To further complicate the picture, IBM, Azul, and RedHat have one or more of their own garbage collectors—each with their own histories, advantages and quirks. In addition, a number of companies including SAP, Twitter, Google, Alibaba, and others have their own internal JVM teams with modified versions of the Garbage collectors.

Costs and Benefits of Modern-Day Garbage Collection

Over time, an addition of alternate and more complex allocation paths led to huge improvements in the allocation overhead picture. For example, a fast-path allocation in the JVM is now approximately 30 times faster than a typical allocation in C/C++. The complication: Only data that can pass an escape analysis test is eligible for fast-path allocation. (Fortunately the vast majority of our data passes this test and benefits from this alternate allocation path.)

Another advantage is in the reduced costs and simplified cost models that come with evacuating collectors. In this scheme, the collector copies live data to another memory pool. Thus, there is no cost to recover short-lived data. This isn’t an invitation to allocate ad nauseam, because there is a cost for each allocation and high allocation rates trigger more frequent GC activity and accumulate extra copy costs. While evacuating collectors helps make GC more efficient and predictable, there are still significant resource costs.

That leads us to memory. Memory management demands that you retain at least five times more memory than manual memory management needs. There are times the developer knows for certain that data should be freed. In those cases, it is cheaper to explicitly free rather than have a collector reason through the decision. It was these costs that originally caused Apple to choose manual memory management for Objective-C. In Swift, Apple chose to use reference counting. They added annotations for weak and owned references to help the collector cope with circular references.

There are other intangible or difficult-to-measure costs that can be attributed to design decisions in the runtime. For example, the loss of control over memory layouts can result in application performance being dominated by L2 cache misses and cache line densities. The performance hit in these cases can easily exceed a factor of 10:1. One of the challenges for future implementers is to allow for better control of memory layouts.

Looking back at how poorly GC performed when first introduced into Lisp and the long and often frustrating road to its current state, it’s hard to imagine why anyone building a runtime would want to use managed memory. But consider that if you manually manage memory, you need access to the underlying reference system—and that means the language needs added syntax to manipulate memory pointers.

Languages that rely on managed memory consistently lack the syntax needed to manage pointers because of the memory consistency guarantee. That guarantee states that all pointers will point where they should without dangling (null) pointers waiting to blow up the runtime, if you should happen to step on them. The runtime can’t make this guarantee if developers are allowed to directly create and manipulate pointers. As an added bonus, removing them from the language removes indirection, one of the more difficult concepts for developers to master. Quite often bugs are a result of a developer engaged in the mental gymnastics required to juggle a multitude of competing concerns and getting it wrong. If this mix contains reasoning through application logic, along with manual memory management and different memory access modes, bugs likely appear in the code. In fact, bugs in systems that rely on manual memory management are among the most serious and largest source of security holes in our systems today.

To prevent these types of bugs the developer always has to ask, “Do I still have a viable reference to this data that prevents me from freeing it?” Often the answer to this question is, “I don’t know.” If a reference to that data was passed to another component in the system, it’s almost impossible to know if memory can safely be freed. As we all know too well, pointer bugs will lead to data corruption or, in the best case, a SIGSEGV.

Removing pointers from the picture tends to yield a code that is more readable and easier to reason through and maintain. GC knows when it can reclaim memory. This attribute allows projects to safely consume third-party components, something that rarely happens in languages with manual memory management.

At its best, memory management can be described as a tedious bookkeeping task. If memory management can be crossed off the to-do list, then developers tend to be more productive and produce far fewer bugs. We have also seen that GC is not a panacea as it comes with its own set of problems. But thankfully the march toward better implementations continues.

Go Lang’s new collector uses a combination of reference counting and tracing to reduce overheads and minimize pause times. Azul claims to have solved the GC pause problem by driving pause times down dramatically. Oracle and IBM keep working on collectors that they claim are better suited for very large heaps that contain significant amounts of data. RedHat has entered the fray with Shenandoah, a collector that aims to completely eliminate pause times from the run time. Meanwhile, Twitter and Google continue to improve the existing collectors so they continue to be competitive to the newer collectors.

Share “How Java’s Built-In Garbage Collection Will Make Your Life Better (Most of the Time)” On Your Site

The post How Java’s Built-In Garbage Collection Will Make Your Life Better (Most of the Time) appeared first on Application Performance Monitoring Blog | AppDynamics.

Read the original blog entry...

More Stories By AppDynamics Blog

In high-production environments where release cycles are measured in hours or minutes — not days or weeks — there's little room for mistakes and no room for confusion. Everyone has to understand what's happening, in real time, and have the means to do whatever is necessary to keep applications up and running optimally.

DevOps is a high-stakes world, but done well, it delivers the agility and performance to significantly impact business competitiveness.

@MicroservicesExpo Stories
For over a decade, Application Programming Interface or APIs have been used to exchange data between multiple platforms. From social media to news and media sites, most websites depend on APIs to provide a dynamic and real-time digital experience. APIs have made its way into almost every device and service available today and it continues to spur innovations in every field of technology. There are multiple programming languages used to build and run applications in the online world. And just li...
The dynamic nature of the cloud means that change is a constant when it comes to modern cloud-based infrastructure. Delivering modern applications to end users, therefore, is a constantly shifting challenge. Delivery automation helps IT Ops teams ensure that apps are providing an optimal end user experience over hybrid-cloud and multi-cloud environments, no matter what the current state of the infrastructure is. To employ a delivery automation strategy that reflects your business rules, making r...
"We started a Master of Science in business analytics - that's the hot topic. We serve the business community around San Francisco so we educate the working professionals and this is where they all want to be," explained Judy Lee, Associate Professor and Department Chair at Golden Gate University, in this SYS-CON.tv interview at 21st Cloud Expo, held Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA.
There is a huge demand for responsive, real-time mobile and web experiences, but current architectural patterns do not easily accommodate applications that respond to events in real time. Common solutions using message queues or HTTP long-polling quickly lead to resiliency, scalability and development velocity challenges. In his session at 21st Cloud Expo, Ryland Degnan, a Senior Software Engineer on the Netflix Edge Platform team, will discuss how by leveraging a reactive stream-based protocol,...
The general concepts of DevOps have played a central role advancing the modern software delivery industry. With the library of DevOps best practices, tips and guides expanding quickly, it can be difficult to track down the best and most accurate resources and information. In order to help the software development community, and to further our own learning, we reached out to leading industry analysts and asked them about an increasingly popular tenet of a DevOps transformation: collaboration.
We call it DevOps but much of the time there’s a lot more discussion about the needs and concerns of developers than there is about other groups. There’s a focus on improved and less isolated developer workflows. There are many discussions around collaboration, continuous integration and delivery, issue tracking, source code control, code review, IDEs, and xPaaS – and all the tools that enable those things. Changes in developer practices may come up – such as developers taking ownership of code ...
Cloud Governance means many things to many people. Heck, just the word cloud means different things depending on who you are talking to. While definitions can vary, controlling access to cloud resources is invariably a central piece of any governance program. Enterprise cloud computing has transformed IT. Cloud computing decreases time-to-market, improves agility by allowing businesses to adapt quickly to changing market demands, and, ultimately, drives down costs.
Modern software design has fundamentally changed how we manage applications, causing many to turn to containers as the new virtual machine for resource management. As container adoption grows beyond stateless applications to stateful workloads, the need for persistent storage is foundational - something customers routinely cite as a top pain point. In his session at @DevOpsSummit at 21st Cloud Expo, Bill Borsari, Head of Systems Engineering at Datera, explored how organizations can reap the bene...
How is DevOps going within your organization? If you need some help measuring just how well it is going, we have prepared a list of some key DevOps metrics to track. These metrics can help you understand how your team is doing over time. The word DevOps means different things to different people. Some say it a culture and every vendor in the industry claims that their tools help with DevOps. Depending on how you define DevOps, some of these metrics may matter more or less to you and your team.
"CA has been doing a lot of things in the area of DevOps. Now we have a complete set of tool sets in order to enable customers to go all the way from planning to development to testing down to release into the operations," explained Aruna Ravichandran, Vice President of Global Marketing and Strategy at CA Technologies, in this SYS-CON.tv interview at DevOps Summit at 21st Cloud Expo, held Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA.
"We are an integrator of carrier ethernet and bandwidth to get people to connect to the cloud, to the SaaS providers, and the IaaS providers all on ethernet," explained Paul Mako, CEO & CTO of Massive Networks, in this SYS-CON.tv interview at 21st Cloud Expo, held Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA.
"Grape Up leverages Cloud Native technologies and helps companies build software using microservices, and work the DevOps agile way. We've been doing digital innovation for the last 12 years," explained Daniel Heckman, of Grape Up in this SYS-CON.tv interview at 21st Cloud Expo, held Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA.
"NetApp's vision is how we help organizations manage data - delivering the right data in the right place, in the right time, to the people who need it, and doing it agnostic to what the platform is," explained Josh Atwell, Developer Advocate for NetApp, in this SYS-CON.tv interview at 20th Cloud Expo, held June 6-8, 2017, at the Javits Center in New York City, NY.
"Outscale was founded in 2010, is based in France, is a strategic partner to Dassault Systémes and has done quite a bit of work with divisions of Dassault," explained Jackie Funk, Digital Marketing exec at Outscale, in this SYS-CON.tv interview at 20th Cloud Expo, held June 6-8, 2017, at the Javits Center in New York City, NY.
"I focus on what we are calling CAST Highlight, which is our SaaS application portfolio analysis tool. It is an extremely lightweight tool that can integrate with pretty much any build process right now," explained Andrew Siegmund, Application Migration Specialist for CAST, in this SYS-CON.tv interview at 21st Cloud Expo, held Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA.
Let's do a visualization exercise. Imagine it's December 31, 2018, and you're ringing in the New Year with your friends and family. You think back on everything that you accomplished in the last year: your company's revenue is through the roof thanks to the success of your product, and you were promoted to Lead Developer. 2019 is poised to be an even bigger year for your company because you have the tools and insight to scale as quickly as demand requires. You're a happy human, and it's not just...
The enterprise data storage marketplace is poised to become a battlefield. No longer the quiet backwater of cloud computing services, the focus of this global transition is now going from compute to storage. An overview of recent storage market history is needed to understand why this transition is important. Before 2007 and the birth of the cloud computing market we are witnessing today, the on-premise model hosted in large local data centers dominated enterprise storage. Key marketplace play...
Cavirin Systems has just announced C2, a SaaS offering designed to bring continuous security assessment and remediation to hybrid environments, containers, and data centers. Cavirin C2 is deployed within Amazon Web Services (AWS) and features a flexible licensing model for easy scalability and clear pay-as-you-go pricing. Although native to AWS, it also supports assessment and remediation of virtual or container instances within Microsoft Azure, Google Cloud Platform (GCP), or on-premise. By dr...
With continuous delivery (CD) almost always in the spotlight, continuous integration (CI) is often left out in the cold. Indeed, it's been in use for so long and so widely, we often take the model for granted. So what is CI and how can you make the most of it? This blog is intended to answer those questions. Before we step into examining CI, we need to look back. Software developers often work in small teams and modularity, and need to integrate their changes with the rest of the project code b...
Kubernetes is an open source system for automating deployment, scaling, and management of containerized applications. Kubernetes was originally built by Google, leveraging years of experience with managing container workloads, and is now a Cloud Native Compute Foundation (CNCF) project. Kubernetes has been widely adopted by the community, supported on all major public and private cloud providers, and is gaining rapid adoption in enterprises. However, Kubernetes may seem intimidating and complex ...