This slide will explain about building blocks of JVM optimization for you java based application.
It explains basics of heap concepts and different type of java garbage collectors.
As service providers and primary code contributors in the Islandora Community, discoverygarden encounters customers who are ingesting, accessing, and storing high volumes of data. For example, a customer who had 150,000 objects in 2012 now has three million objects and expectations to grow to five million in the very short term. This is increasingly common.
As repositories grow in size they can encounter poor performance, particularly during large ingests and derivative generation. To accommodate growing repositories caching mechanisms, infrastructure changes, and code updates are necessary.
The presentation will explore customer case studies that demonstrate interim solutions and the extensive, ongoing research and development to find long-term solutions.
As service providers and primary code contributors in the Islandora Community, discoverygarden encounters customers who are ingesting, accessing, and storing high volumes of data. For example, a customer who had 150,000 objects in 2012 now has three million objects and expectations to grow to five million in the very short term. This is increasingly common.
As repositories grow in size they can encounter poor performance, particularly during large ingests and derivative generation. To accommodate growing repositories caching mechanisms, infrastructure changes, and code updates are necessary.
The presentation will explore customer case studies that demonstrate interim solutions and the extensive, ongoing research and development to find long-term solutions.
Choosing Right Garbage Collector to Increase Efficiency of Java Memory UsageJelastic Multi-Cloud PaaS
With microservices, cloud hosting, and vertical scaling in mind, we'll compare the top Java garbage collectors to see how efficiently they handle memory resources. Being configured smartly, Java can be cost-effective for all ranges of projects — from cloud-native startups to legacy enterprise applications. And the selected garbage collection algorithm is one of the main foundational bricks here, as its settings can influence the whole project. In this presentation, we share our experiences in tuning RAM usage in a Java process to make it more elastic and gain the benefits of faster scaling and lower total cost of ownership (TCO). The provided results of testing G1, Parallel, ConcMarkSweep, Serial, Shenandoah, ZGC, C4 and OpenJ9 garabage collectors while scaling Java applications vertically will help you to make the right choice for own projects.
Apache Cassandra Lunch #67: Moving Data from Cassandra to Datastax AstraAnant Corporation
In Apache Cassandra Lunch #67, we discussed how to move data from Open Source Cassandra to Datastax Astra using dsbulk/scylla migratory.
https://github.com/DataStax-Examples/dsbulk-to-astra/
Accompanying Blog: https://blog.anant.us/apache-cassandra-lunch-67-moving-data-from-cassandra-to-datastax-astra-with-dsbulk
Accompanying Youtube: https://youtu.be/0k7RBf5vi5M
Sign Up For Our Newsletter: http://eepurl.com/grdMkn
Join Cassandra Lunch Weekly at 12 PM EST Every Wednesday: https://www.meetup.com/Cassandra-DataStax-DC/events/
Cassandra.Link:
https://cassandra.link/
Follow Us and Reach Us At:
Anant:
https://www.anant.us/
Awesome Cassandra:
https://github.com/Anant/awesome-cassandra
Cassandra.Lunch:
https://github.com/Anant/Cassandra.Lunch
Email:
solutions@anant.us
LinkedIn:
https://www.linkedin.com/company/anant/
Twitter:
https://twitter.com/anantcorp
Eventbrite:
https://www.eventbrite.com/o/anant-1072927283
Facebook:
https://www.facebook.com/AnantCorp/
In Apache Cassandra Lunch #59: Functions in Cassandra, we discussed the functions that are usable inside of the Cassandra database. The live recording of Cassandra Lunch, which includes a more in-depth discussion and a demo, is embedded below in case you were not able to attend live.
Presentation slides from DevConf.cz 2017
Challenges, take-aways and recommendations on scaling up OpenShift's logging and metrics stack.
Authors:
Ricardo Lourenço:
https://www.linkedin.com/in/ricardopereira4it/
Elvir Kuric
https://www.linkedin.com/in/elvirkuric/
MariaDB Auto-Clustering, Vertical and Horizontal Scaling within Jelastic PaaSJelastic Multi-Cloud PaaS
Availability and performance have a direct business impact for most of the companies nowadays. No one wants to lose money because of occasional downtime or data loss. Thus, to minimize the risk and ensure an extra level of redundancy, clustering and automatic scaling should be used. In this video Ruslan Synytsky presented how Jelastic PaaS implemented auto-clustering of MariaDB by providing the customers with different replication options out-of-box with no need in manual configurations. It is also detailed how to automate vertical and horizontal scaling of databases running in the cloud.
Video recording of the session https://www.youtube.com/watch?v=6MND3feb5zM
Scaling Jakarta EE Applications Vertically and Horizontally with Jelastic PaaSJelastic Multi-Cloud PaaS
In this presentation, you'll find out what metrics should be tracked in order to meet the load requirements of application, how to finetune scaling triggers in order to efficiently handle different load levels, how to automate vertical and horizontal scaling of Jakarta EE applications running in the cloud.
Also, we share how to integrate load performance testing tools for adjusting horizontal scaling and making sure that your application can cope with production workloads.
Practical side is shown based on Jelastic PaaS https://jelastic.com/
The Java memory model and the Garbage Collector can drive you into serious problems if you don't know how it runs, defrags, and remove objects - this presentation is not updated for Java 8.
Presentation held at GRNET Digital Technology Symposium on November 5-6, 2018 at the Stavros Niarchos Foundation Cultural Center, Athens, Greece.
• Introduction to Ceph and its internals
• Presentation of GRNET's Ceph deployments (technical specs, operations)
• Usecases: ESA Copernicus, ~okeanos, ViMa
FOSDEM 2019: M3, Prometheus and Graphite with metrics and monitoring in an in...Rob Skillington
The world in which we monitor software is growing more complex every year. There are increasingly more ways to run server-side software, with many more independent services and more points of failures, the list goes on! On the plus side, there’s a lot of great tools and patterns being developed to try and make things simple to assess and understand. This talk covers how metrics and monitoring can be leveraged in a variety of different ways, auto-discovering applications and their usage of databases, caches, load balancers, etc, setting up and tearing down dashboards and monitoring automatically for services and instances, and more.
We’ll also talk about how you can accomplish all this with a global view of your systems using both Prometheus and Graphite with M3, our open source metrics platform. We’ll take a deep dive look at how we use M3DB, distributed aggregation with the M3 aggregator and the M3 Kubernetes operator to horizontally scale a metrics platform in a way that doesn’t cost outrageous amounts to run with a system that’s still sane to operate with petabytes of metrics data.
English version of the presentation we gave at Devoxx FR 2012.
In depth analysis on how java Garbage collector works and how to minimise pause in your application.
Choosing Right Garbage Collector to Increase Efficiency of Java Memory UsageJelastic Multi-Cloud PaaS
With microservices, cloud hosting, and vertical scaling in mind, we'll compare the top Java garbage collectors to see how efficiently they handle memory resources. Being configured smartly, Java can be cost-effective for all ranges of projects — from cloud-native startups to legacy enterprise applications. And the selected garbage collection algorithm is one of the main foundational bricks here, as its settings can influence the whole project. In this presentation, we share our experiences in tuning RAM usage in a Java process to make it more elastic and gain the benefits of faster scaling and lower total cost of ownership (TCO). The provided results of testing G1, Parallel, ConcMarkSweep, Serial, Shenandoah, ZGC, C4 and OpenJ9 garabage collectors while scaling Java applications vertically will help you to make the right choice for own projects.
Apache Cassandra Lunch #67: Moving Data from Cassandra to Datastax AstraAnant Corporation
In Apache Cassandra Lunch #67, we discussed how to move data from Open Source Cassandra to Datastax Astra using dsbulk/scylla migratory.
https://github.com/DataStax-Examples/dsbulk-to-astra/
Accompanying Blog: https://blog.anant.us/apache-cassandra-lunch-67-moving-data-from-cassandra-to-datastax-astra-with-dsbulk
Accompanying Youtube: https://youtu.be/0k7RBf5vi5M
Sign Up For Our Newsletter: http://eepurl.com/grdMkn
Join Cassandra Lunch Weekly at 12 PM EST Every Wednesday: https://www.meetup.com/Cassandra-DataStax-DC/events/
Cassandra.Link:
https://cassandra.link/
Follow Us and Reach Us At:
Anant:
https://www.anant.us/
Awesome Cassandra:
https://github.com/Anant/awesome-cassandra
Cassandra.Lunch:
https://github.com/Anant/Cassandra.Lunch
Email:
solutions@anant.us
LinkedIn:
https://www.linkedin.com/company/anant/
Twitter:
https://twitter.com/anantcorp
Eventbrite:
https://www.eventbrite.com/o/anant-1072927283
Facebook:
https://www.facebook.com/AnantCorp/
In Apache Cassandra Lunch #59: Functions in Cassandra, we discussed the functions that are usable inside of the Cassandra database. The live recording of Cassandra Lunch, which includes a more in-depth discussion and a demo, is embedded below in case you were not able to attend live.
Presentation slides from DevConf.cz 2017
Challenges, take-aways and recommendations on scaling up OpenShift's logging and metrics stack.
Authors:
Ricardo Lourenço:
https://www.linkedin.com/in/ricardopereira4it/
Elvir Kuric
https://www.linkedin.com/in/elvirkuric/
MariaDB Auto-Clustering, Vertical and Horizontal Scaling within Jelastic PaaSJelastic Multi-Cloud PaaS
Availability and performance have a direct business impact for most of the companies nowadays. No one wants to lose money because of occasional downtime or data loss. Thus, to minimize the risk and ensure an extra level of redundancy, clustering and automatic scaling should be used. In this video Ruslan Synytsky presented how Jelastic PaaS implemented auto-clustering of MariaDB by providing the customers with different replication options out-of-box with no need in manual configurations. It is also detailed how to automate vertical and horizontal scaling of databases running in the cloud.
Video recording of the session https://www.youtube.com/watch?v=6MND3feb5zM
Scaling Jakarta EE Applications Vertically and Horizontally with Jelastic PaaSJelastic Multi-Cloud PaaS
In this presentation, you'll find out what metrics should be tracked in order to meet the load requirements of application, how to finetune scaling triggers in order to efficiently handle different load levels, how to automate vertical and horizontal scaling of Jakarta EE applications running in the cloud.
Also, we share how to integrate load performance testing tools for adjusting horizontal scaling and making sure that your application can cope with production workloads.
Practical side is shown based on Jelastic PaaS https://jelastic.com/
The Java memory model and the Garbage Collector can drive you into serious problems if you don't know how it runs, defrags, and remove objects - this presentation is not updated for Java 8.
Presentation held at GRNET Digital Technology Symposium on November 5-6, 2018 at the Stavros Niarchos Foundation Cultural Center, Athens, Greece.
• Introduction to Ceph and its internals
• Presentation of GRNET's Ceph deployments (technical specs, operations)
• Usecases: ESA Copernicus, ~okeanos, ViMa
FOSDEM 2019: M3, Prometheus and Graphite with metrics and monitoring in an in...Rob Skillington
The world in which we monitor software is growing more complex every year. There are increasingly more ways to run server-side software, with many more independent services and more points of failures, the list goes on! On the plus side, there’s a lot of great tools and patterns being developed to try and make things simple to assess and understand. This talk covers how metrics and monitoring can be leveraged in a variety of different ways, auto-discovering applications and their usage of databases, caches, load balancers, etc, setting up and tearing down dashboards and monitoring automatically for services and instances, and more.
We’ll also talk about how you can accomplish all this with a global view of your systems using both Prometheus and Graphite with M3, our open source metrics platform. We’ll take a deep dive look at how we use M3DB, distributed aggregation with the M3 aggregator and the M3 Kubernetes operator to horizontally scale a metrics platform in a way that doesn’t cost outrageous amounts to run with a system that’s still sane to operate with petabytes of metrics data.
English version of the presentation we gave at Devoxx FR 2012.
In depth analysis on how java Garbage collector works and how to minimise pause in your application.
The Java Memory Model describes how threads in the Java programming language interact through memory. Together with the description of single-threaded execution of code, the memory model provides the semantics of the Java programming language.
It is crucial for a programmer to know how, according to Java Language Specification, write correctly synchronized, race free programs.
Java Memory Consistency Model - concepts and contextTomek Borek
Java Memory Consistency Model is a difficult topic.
It's useful in making sure that multi-threaded programs on multi-threaded cores will interact with each other (and through memory) in a consistent manner.
It's specification is damn hard (even according to folks with lots of concurrent experience, like Doug Lea) to read, understand and routinely follow without error.
This presentation talks about some fallacies surrounding memory model, explains it, offers definitions and reasons for it's existence. It ain't deep, it's more entry level stuff.
This presentation is primarily based on Oracle's "Java SE 6 HotSpot™ Virtual Machine Garbage Collection Tuning" document.
This introduces how Java manages memory using generations, available garbage collectors and how to tune them to achieve desired performance.
The workshop is based on several Nikita Salnikov-Tarnovski lectures + my own research. The workshop consists of 2 parts. The first part covers:
- different Java GCs, their main features, advantages and disadvantages;
- principles of GC tuning;
- work with GC Viewer as tool for GC analysis;
- first steps tuning demo;
- comparison primary GCs on Java 1.7 and Java 1.8
The second part covers:
- work with Off-Heap: ByteBuffer / Direct ByteBuffer / Unsafe / MapDB;
- examples and comparison of approaches;
The off-heap-demo: https://github.com/moisieienko-valerii/off-heap-demo
Nowadays people usually talk more about big data, internet of things, and other buzzwords on various conferences. However, sometimes developers tend to not pay enough attention to the core things such as garbage collection. After having a short discussion with many somewhat experienced Java developers I came to a conclusion that most of them do not know how many garbage collectors there are in the latest JVM, and under what circumstances each of them should be enabled. This presentation is aimed to improve or refresh people’s knowledge on this core topic, and share a real use case when it helped us to resolve production issue.
At first glance, writing concurrent programs in Java seems like a straight-forward task. But the devil is in the detail. Fortunately, these details are strictly regulated by the Java memory model which, roughly speaking, decides what values a program can observe for a field at any given time. Without respecting the memory model, a Java program might behave erratic and yield bugs that only occure on some hardware platforms. This presentation summarizes the guarantees that are given by Java's memory model and teaches how to properly use volatile and final fields or synchronized code blocks. Instead of discussing the model in terms of memory model formalisms, this presentation builds on easy-to follow Java code examples.
This presentation was given to the system adminstration team to give them an idea of how GC works and what to look for when there is abottleneck and troubles.
Java is finally elastic! OpenJDK improvements and new features in Garbage Collection technology resulted in enhancing Java vertical scaling and resource consumption. Now JVM can promptly return unused memory and, as result it can go up and down automatically. In this presentation, we cover the main achievements in vertical scaling direction, as well as share peculiarities and tuning details of different GCs. Find out how to make your Java environments more elastic to follow the load and lower down the total cost of ownership at a large scale.
Have you ever seen an OutOfMemoryError? I'm sure you have. But then, did you understood that line you copied from StackOverflow?
If you haven't, and if you want a gently introduction to the complex world of Java Garbage Collection this is your talk.
I'll talk about garbage collection concepts, the garbage collection in the Hotspot JVM (the default in Oraclel's JDK/JRE) and I'll try to put it in terms that any Java developer can grasp. The next time you'll face the dreaded 'OutOfMemoryError', at least, you'll know what are you up against.
What to do in case in which an application does not provide the desired performance? If you have ever had problems with optimizing the performance of Java applications, surely you had to invest a solid amount of time to find out the real cause for the problems, which included the involvement of administrators and developers. Is there a way to shorten the time required to find a solution, what free tools are available for this purpose and to check that you have finally solved the problem? In this presentation, we will try to provide answers to these questions with concrete real life examples.
Elastic JVM for Scalable Java EE Applications Running in Containers #Jakart...Jelastic Multi-Cloud PaaS
Being configured smartly, Java can be scalable and cost-effective for all ranges of projects — from cloud-native startups to legacy enterprise applications. During this session, we will share our experiences in tuning RAM usage in a Java process to make it more elastic and gain the benefits of faster scaling and lower total cost of ownership (TCO). With microservices, cloud hosting, and vertical scaling in mind, we'll compare the top Java garbage collectors to see how efficiently they handle memory resources. The provided results of testing G1, Parallel, ConcMarkSweep, Serial, Shenandoah, ZGC and OpenJ9 garbage collectors while scaling Java EE applications vertically will help you to make the right choice for own projects.
More details about Garbage Collector types https://jelastic.com/blog/garbage-collection/
Free registration at Jelastic https://jelastic.com/
Virtual machines don't have to be slow, they don't even have to be slower than running native code.
All you have to do is write your code, lay back and let the JVM do its magic !
Learn about various JVM runtime optimizations and why is it considered one of the best VMs in the world.
ApacheCon2010: Cache & Concurrency Considerations in Cassandra (& limits of JVM)srisatish ambati
Cache & Concurrency considerations for a high performance Cassandra deployment.
SriSatish Ambati
Cassandra has hit it's stride as a distributed java NoSQL database! It's fast, it's in-memory, it's scalable, it's seda; It's eventually consistent model makes it practical for the large & growing volumes of unstructured data usecases. It is also time to run it through the filters of performance analysis. For starters it runs on the java virtual machine and inherits the capabilities and culpabilities of the platform. This presentation reviews the runtime architecture, cache behavior & performance of a real-world workload on Cassandra. We blend existing system & jvm tools to get a quick overview & a breakdown of hotspots in the get, put & update operations. We highlight the role played by garbage collection & fragmentation due to long lived objects; We investigate lock contention in the data structures under concurrent usage. Cassandra uses UDP for management & TCP for data: we look at robustness of the communication patterns during high spikes and cluster-wide events. We review Non-Blocking Hashmap modifications to Cassandra that improve concurrency & amplify performance of this frontrunner in the NoSQL space
ApacheCon2010 NA
Wed, 03 November 2010 15:00
cassandra
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
Contact with Dawood Bhai Just call on +92322-6382012 and we'll help you. We'll solve all your problems within 12 to 24 hours and with 101% guarantee and with astrology systematic. If you want to take any personal or professional advice then also you can call us on +92322-6382012 , ONLINE LOVE PROBLEM & Other all types of Daily Life Problem's.Then CALL or WHATSAPP us on +92322-6382012 and Get all these problems solutions here by Amil Baba DAWOOD BANGALI
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Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
3. Overview of java garbage collectors
• Live objects vs Dead objects
• Runs automatically by JVM
• Cannot force JVM to run gc (System.gc())
• Demon thread called garbage collector
• Involves 3 steps
– Mark
– Sweep/Delete
– Compacting / defragmentation (time consuming)
• Minor garbage collection vs Major garbage
collection
4. Heap concepts
• Heap is divided in different sections
– Young generation
• Eden space
• Survivor space from
• Survivor space to
– Old generation (tenured space)
• Use for caching and long term survivor objects
6. Garbage collector types
• Serial collector
– Single thread shared between application and GC
– Used for small heap
– Used for less responsive applications
• Concurrent collector
– Runs concurrent to your application,
– doesn’t wait for old generation to be full
– Pause application during mark operation
– Used for low pause application
• Parallel collector
– Uses multiple CPU cores to perform GC
– Uses multiple threads for mark, sweep and fragmentation operations
– Wait for old generation to be full
– Pause application during all operations
– Used for batch processing or high throughput applications
• G1GC
– Highly customizable, can specify the time you want to run concurrent and parallel processor
– More garbage area collects first
– Option to specify maximum pause timings
8. JVM Tuning options
• -XX:+UseSerialGC
• -XX:+UseParallelGC
• -XX:+UseParallelOldGC
• -XX:+UseConcMarkSweepGC
• -XX:+UseG1GC
• -Xmsvalue : min amount of heap allocated
• -Xmxvalue : max amount of heap allocated
• -XX:NewRatio=ratio of young vs old generation
• -XX:NewSize=memory for eden space
• -XX:MaxNewSize=size memory for new generation space
• -XX:Permsize=size, used to define space for meta data and static objects
• -XX:+PrintGCDetails prints garbage collector details
• -Xloggc:gc.log where gc.log is a filename to store gc logs
• -verbose:gc for debugging gc logs