Welcome!

Blog Feed Post

Getting the maximum performance of your Java processes

therore-concurrent provides self-tuning thread-pools helping you to make the most of your system.

Recently, I have been working in the optimization of an OLTP system. The software has a SEDA architecture (Staged Event Driven Architecture) with lots of threads doing little works. I had to fight with the hard task of adjusting a hundred of parameters. Each of those parameters affected some others and so on.

For example if the number of concurrent database connections is set too low, it would cause a contention in getting connections. On the contrary, if that number is set too high, it could cause a lock-contention in the database when the threads want to access to some shared resources (index, row, block, etc.)

Even more, not always the processing of an event requires the same type of resources. A sudden change in the type of events that are being treated, can turn an optimal configuration into a suboptimal.

One of the most significant parameters is the number of threads assigned for each component. It is difficult to choose a good value if you don’t know how much the threads use each type of resource and how much are they coupled between each other.

Usually certain tasks have a higher priority and should be processed as soon as possible. This further complicates the choice of the configuration. Enforcing priorities and maximizing throughputs are opposite goals therefore it is necessary to define the scope of both.

In my experience a huge configurability can work against you. In a medium/big SOA system with a lot of service communications and complex workload profiles that even change over time, is almost impossible to get the optimal value for each of those parameters. Because of that I found interesting to develop a library that might be able to adapt quickly at runtime in order to make the most of the system.

Self-tuning thread-pool

Nowadays creating threads manually is not very common. Instead of that, thread-pools are frequently used. A thread-pool manages the creation and allocation of threads. JDK comes with some interesting and useful classes for managing threads. I list two of the most important:

  • ThreadPoolExecutor is a very flexible and configurable thread-pool that supports customization of queue size, minimum and maximum pool size, keep-alive time, etc.
  • Executors is a convenient class that creates thread-pools for the most usual cases.

I have developed the library therore-concurrent that takes advantage of those classes and extends some functionalities. The library contains analogous to the above classes.

  • SelfTuningExecutorService is a thread-pool that implements a mechanism for searching a good value for the pool size. The algorithm tries to maximize the throughput respecting the thread-pool priorities.
  • SelfTuningExecutors acts as the factory of SelfTuningExecutorService. It is recommended to use it as a singleton.

The following charts show how quickly SelfTuningExecutorService finds the optimal value.

selftuning_poolsize_executions_chart

Using SelfTuningExecutors directly

  • Add the dependency to the pom
  • <dependency>
        <groupId>net.therore</groupId>
        <artifactId>therore-concurrent</artifactId>
        <version>1.1.0</version>
    </dependency>
    

  • The following snippet shows how can it be used.
  • SelfTuningExecutors executors = SelfTuningExecutors.defaultSelfTuningExecutors();
    ExecutorServicce service = executors.newSelfTuningExecutor("executor-for-test", corePoolSize, initPoolSize
           , maximumPoolSize, priority, queueSize);
    service.execute(task);
    

The only new parameters are initPoolSize and priority.

  • initPoolSize is the initial amount of threads assigned to the pool.
  • priority is a positive number that works for SelfTuningExecutorService to limit the number of threads of this pool regarding others.

Integrating SelfTuningExecutors with Quartz Scheduler

Quartz-Scheduler has his own thread-pool interface and its name is “ThreadPool” (not surprise). The class SelfTuningThreadPool that is in the artifact therore-concurrent-quartz implements such interface. Integrating it is very easy, follow these steps:

  • Add the dependency to the pom
  • <dependency>
        <groupId>net.therore</groupId>
        <artifactId>therore-concurrent-quartz</artifactId>
        <version>1.1.0</version>
    </dependency>
    

  • Change the configuration properties of quartz
  • # org.quartz.threadPool.class = org.quartz.simpl.SimpleThreadPool
    # org.quartz.threadPool.threadCount = 1
    # org.quartz.threadPool.threadPriority = 5
    org.quartz.threadPool.class = net.therore.concurrent.quartz.SelfTuningThreadPool
    org.quartz.threadPool.corePoolSize = 1
    org.quartz.threadPool.initPoolSize = 1
    org.quartz.threadPool.maximumPoolSize = 100
    org.quartz.threadPool.priority = 5
    org.quartz.threadPool.queueSize = 2
    

Integrating SelfTuningExecutors with Apache Camel

I love Apache Camel. It offers a lot of components supporting integration with different technologies. But if none of them actually help you yet, it’s pretty easy to make your own component.

Camel’s team has thought very well the threading model. They use the concept (and interface) of ThreadPoolProfile which is a kind of thread-pool-template that you can use to instantiate several pools with the same configuration. If that is not enough, you can program your own implementation of ExecutorServiceManager, the Camel’s thread-pool provider. Simplifying, think about it like the Executors class of the JDK.

I’ve just done that, SelfTunigExecutorServiceManager is the name of my own implementation of ExecutorServiceManager. It is located in other maven module therore-concurrent-camel. I’ll explain how to use it.

  • Add the dependency to the pom
  • <dependency>
        <groupId>net.therore</groupId>
        <artifactId>therore-concurrent-camel</artifactId>
        <version>1.1.0</version>
    </dependency>
    

  • The following snippet contains two connected routes with SEDA component and SelfTunigExecutorServiceManager
  • SelfTunigExecutorServiceManager executorManager = new SelfTunigExecutorServiceManager(context);
    context.setExecutorServiceManager(executorManager);
    ThreadPoolProfile profile = new ThreadPoolProfile();
    profile.setId("self-tuning-profile");
    profile.setMaxPoolSize(100);
    profile.setMaxQueueSize(100);
    profile.setDefaultProfile(true);
    executorManager.setDefaultThreadPoolProfile(profile);        
    
    final String sedaEndpointUri = "seda:myseda?blockWhenFull=true&size=1";
    context.addRoutes(new RouteBuilder() {
       @Override
       public void configure() throws Exception {
           from("direct:in")
           .to(sedaEndpointUri);
       }
    });
    context.addRoutes(new RouteBuilder() {
       @Override
       public void configure() throws Exception {
           from(sedaEndpointUri)
           .threads(1, 100)
           .to("bean:mybean");
       }
    });
    
    ProducerTemplate template = context.createProducerTemplate();
    context.start();
    for (int i=0; i<ITERATIONS; i++) {
       template.sendBody("direct:in", "dummy string");
    }
    

Summary

I have figured out that there are many elements that might turn into selftuning ones. I chose ThreadPool because from my point of view is one of the most important, used and easy to test element.

Moreover, most of the modern libraries and frameworks feature different ways to extend their factories, providers and templates. All of that aims to develop general purpose classes and integrate them with lots of frameworks.

Read the original blog entry...

More Stories By Alfredo Diaz

Alfredo Diaz is a Java EE Architect with over 10 years of experience. He is an expert in SOA, real-time processing, scalability and HA. He is an Agile enthusiast.

Latest Stories
DX World EXPO, LLC, a Lighthouse Point, Florida-based startup trade show producer and the creator of "DXWorldEXPO® - Digital Transformation Conference & Expo" has announced its executive management team. The team is headed by Levent Selamoglu, who has been named CEO. "Now is the time for a truly global DX event, to bring together the leading minds from the technology world in a conversation about Digital Transformation," he said in making the announcement.
"Space Monkey by Vivent Smart Home is a product that is a distributed cloud-based edge storage network. Vivent Smart Home, our parent company, is a smart home provider that places a lot of hard drives across homes in North America," explained JT Olds, Director of Engineering, and Brandon Crowfeather, Product Manager, at Vivint Smart Home, in this SYS-CON.tv interview at @ThingsExpo, held Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA.
SYS-CON Events announced today that Conference Guru has been named “Media Sponsor” of the 22nd International Cloud Expo, which will take place on June 5-7, 2018, at the Javits Center in New York, NY. A valuable conference experience generates new contacts, sales leads, potential strategic partners and potential investors; helps gather competitive intelligence and even provides inspiration for new products and services. Conference Guru works with conference organizers to pass great deals to gre...
DevOps is under attack because developers don’t want to mess with infrastructure. They will happily own their code into production, but want to use platforms instead of raw automation. That’s changing the landscape that we understand as DevOps with both architecture concepts (CloudNative) and process redefinition (SRE). Rob Hirschfeld’s recent work in Kubernetes operations has led to the conclusion that containers and related platforms have changed the way we should be thinking about DevOps and...
The Internet of Things will challenge the status quo of how IT and development organizations operate. Or will it? Certainly the fog layer of IoT requires special insights about data ontology, security and transactional integrity. But the developmental challenges are the same: People, Process and Platform. In his session at @ThingsExpo, Craig Sproule, CEO of Metavine, demonstrated how to move beyond today's coding paradigm and shared the must-have mindsets for removing complexity from the develop...
In his Opening Keynote at 21st Cloud Expo, John Considine, General Manager of IBM Cloud Infrastructure, led attendees through the exciting evolution of the cloud. He looked at this major disruption from the perspective of technology, business models, and what this means for enterprises of all sizes. John Considine is General Manager of Cloud Infrastructure Services at IBM. In that role he is responsible for leading IBM’s public cloud infrastructure including strategy, development, and offering m...
The next XaaS is CICDaaS. Why? Because CICD saves developers a huge amount of time. CD is an especially great option for projects that require multiple and frequent contributions to be integrated. But… securing CICD best practices is an emerging, essential, yet little understood practice for DevOps teams and their Cloud Service Providers. The only way to get CICD to work in a highly secure environment takes collaboration, patience and persistence. Building CICD in the cloud requires rigorous ar...
Companies are harnessing data in ways we once associated with science fiction. Analysts have access to a plethora of visualization and reporting tools, but considering the vast amount of data businesses collect and limitations of CPUs, end users are forced to design their structures and systems with limitations. Until now. As the cloud toolkit to analyze data has evolved, GPUs have stepped in to massively parallel SQL, visualization and machine learning.
"Evatronix provides design services to companies that need to integrate the IoT technology in their products but they don't necessarily have the expertise, knowledge and design team to do so," explained Adam Morawiec, VP of Business Development at Evatronix, in this SYS-CON.tv interview at @ThingsExpo, held Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA.
To get the most out of their data, successful companies are not focusing on queries and data lakes, they are actively integrating analytics into their operations with a data-first application development approach. Real-time adjustments to improve revenues, reduce costs, or mitigate risk rely on applications that minimize latency on a variety of data sources. In his session at @BigDataExpo, Jack Norris, Senior Vice President, Data and Applications at MapR Technologies, reviewed best practices to ...
Widespread fragmentation is stalling the growth of the IIoT and making it difficult for partners to work together. The number of software platforms, apps, hardware and connectivity standards is creating paralysis among businesses that are afraid of being locked into a solution. EdgeX Foundry is unifying the community around a common IoT edge framework and an ecosystem of interoperable components.
"ZeroStack is a startup in Silicon Valley. We're solving a very interesting problem around bringing public cloud convenience with private cloud control for enterprises and mid-size companies," explained Kamesh Pemmaraju, VP of Product Management at ZeroStack, 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.
Large industrial manufacturing organizations are adopting the agile principles of cloud software companies. The industrial manufacturing development process has not scaled over time. Now that design CAD teams are geographically distributed, centralizing their work is key. With large multi-gigabyte projects, outdated tools have stifled industrial team agility, time-to-market milestones, and impacted P&L stakeholders.
"Akvelon is a software development company and we also provide consultancy services to folks who are looking to scale or accelerate their engineering roadmaps," explained Jeremiah Mothersell, Marketing Manager at Akvelon, 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.
Enterprises are adopting Kubernetes to accelerate the development and the delivery of cloud-native applications. However, sharing a Kubernetes cluster between members of the same team can be challenging. And, sharing clusters across multiple teams is even harder. Kubernetes offers several constructs to help implement segmentation and isolation. However, these primitives can be complex to understand and apply. As a result, it’s becoming common for enterprises to end up with several clusters. Thi...