Zero Downtime JEE Architectures

This document provides guidance on scaling Apache Kafka clusters and tuning performance. It discusses expanding Kafka clusters horizontally across inexpensive servers for increased throughput and CPU utilization. Key aspects that impact performance like disk layout, OS tuning, Java settings, broker and topic monitoring, client tuning, and anticipating problems are covered. Application performance can be improved through configuration of batch size, compression, and request handling, while consumer performance relies on partitioning, fetch settings, and avoiding perpetual rebalances.

Microsoft technologies form the backbone of many Enterprise IT Infrastructures. Whether you are running Microsoft Exchange, Sharepoint, SQL Server or Active Directory; chances are you rely upon you these services for your mission critical needs. Solutions Architects and IT professionals will get an overview of the common Microsoft workloads running on AWS including approaches for server migrations, design and deployment of infrastructure services and maintenance and monitoring of those services once they are in production.

The document discusses using Oracle Database with Amazon Web Services. It outlines Amazon EC2, which allows users to provision virtual machines in Amazon's data centers, and Amazon S3 for storing and retrieving data. It then provides steps for deploying Oracle Database Express Edition on EC2, backing up databases to S3 using Oracle Recovery Manager, and storing database files and backups in S3 for cost effective storage.

Learn how Netflix Open Source components can help your company speed up development and grow to web-scale size

See how organisations have economically accelerated adoption of MongoDB across the enterprise using advanced techniques, tooling and DevOps.

Building Cloud-Native App Series - Part 7 of 11 Microservices Architecture Series Containers Docker Kind Kubernetes Istio - Pods - ReplicaSet - Deployment (Canary, Blue-Green) - Ingress - Service

The document provides an introduction and overview of Apache Kafka presented by Jeff Holoman. It begins with an agenda and background on the presenter. It then covers basic Kafka concepts like topics, partitions, producers, consumers and consumer groups. It discusses efficiency and delivery guarantees. Finally, it presents some use cases for Kafka and positioning around when it may or may not be a good fit compared to other technologies.

Amazon Web Services (AWS) are gaining popularity, and for good reasons. The Amazon Relational Database Service (AWS RDS) is getting a lot of attention, also for very good reasons. It is quite a compelling idea to have on-demand data services that do not require hiring DBA staff. The expectation is set that everything works like magic and will satisfy all of your enterprise database availability needs. If you want to build high-volume, business-critical applications, possibly with geographically-distributed audiences, you really want to think twice about using RDS. Continuent customers have a large number deployments in AWS running MySQL on AWS EC2 instances and they choose to rely upon Tungsten Clustering to provide high availability (HA) and disaster recovery (DR). We also support multi-site/multi-master operations and offer true zero-downtime MySQL operations. AGENDA - How does RDS handle failover? (Hint: Not very quickly) - How does RDS handle read scaling? (Hint: Not very well) - Can you do zero-downtime maintenance with RDS? (Hint: No) - Is RDS cheaper? (Hint: No, not really)

With the rapid advancements in the cloud computing techniques and growing maturity of Infrastructure as Code (IaC) in the DevOps space, attaining a zero downtime while deploying the latest updates to the applications and databases, has become a new norm in the IT industry. Blue-Green deployment model is a DevOps technique which helps to achieve a zero downtime by seamlessly switching to new version(green) when it is fully ready while the original version (blue) is still running. Making duplicate copies of entire cloud infrastructure at a rapid pace is only possible when it is written as code, rather than manually configured. Terraform is a declarative programming language which helps in writing infrastructure as code.In this session -1.We will provision a new AWS VPC, public and private subnets and security groups using Terraform code.2. We will then create a Document DB cluster from scratch and create a new DB using snapshot, by running the Terraform.3. We will then demonstrate the creation of an AWS Fargate based ECS cluster using Terraform script and then run a java based micro service on it, that uses AWS Document DB as the NoSQL database. 4. We will then simulate the Blue Green deployment model by creating an AWS Lambda function which deploys the latest updates and rolls back to the older version with zero downtime.

According to OpenStack users survey, Cloud Foundry is the 2nd most popular workload on OpenStack. You want to deploy Cloud Foundry on OpenStack or already have. What's next? Cloud Foundry continues to evolve with revolutionary changes, e.g move from bosh-micro to bosh-init, using the new eCPI, move to Diego etc. Same with OpenStack, e.g changes from Keystone v2 to v3, from Liberty to Mitaka, network plugins changes etc. Both IaaS and PaaS layers are changing frequently. How do you do in-place updates/upgrades/operational tasks without impacting user experience at both the layers? In this talk will discuss our lessons learnt operating hybrid Cloud Foundry deployments on top of OpenStack over the last two years and how we used underlying technologies to seamlessly operate them

MySQL data rules the cloud, but recent experience shows us that there's no substitute for maintaining copies of data, across availability zones and regions, when it comes to Amazon Web Services (AWS) data resilience. In this webinar, we discuss the multi-master capabilities of Continuent Tungsten to help you build and manage systems that spread data across multiple sites. We cover important topics such as setting up large scale topologies, handling failures, and how to handle data privacy issues like removing personally identifiable information or handling privacy law restrictions on data movement. We will conclude with a live demonstration of a distributed MySQL solution with Continuent Tungsten clusters working across multiple AWS availability zones and regions.

Docker based Hadoop provisioning - anywhere Janos Matyas Senior Director, Engineering Hortonworks

This document summarizes Denis Gundarev's presentation on how to build a Citrix infrastructure in the Amazon Web Services (AWS) cloud. The presentation covered: - An overview of AWS services like EC2, S3, VPC, RDS, and how to monitor with CloudWatch - Common Citrix deployment architectures on AWS like using NetScaler and AutoScaling - Limitations of running Citrix on AWS like lack of capacity management and client OS support - Guidelines for deploying Citrix on AWS like starting simple, proper sizing, and careful VPC planning

Introduction to Apache KafkaAnd Real-Time ETL for DBAs and others who are interested in new ways of working with relational databases

Migrating Oracle workloads to Azure requires understanding the workload and hardware requirements. It is important to analyze the workload using the Automatic Workload Repository (AWR) report to accurately size infrastructure needs. The right virtual machine series and storage options must be selected to meet the identified input/output and capacity needs. Rather than moving existing hardware, the focus should be migrating the Oracle workload to take advantage of cloud capabilities while ensuring performance and high availability.

This presentation covers the basics of what container orchestration is providing pros and cons of Docker Swarm, Kubernetes and Amazon ECS and outlining the terms and tools you will need to successfully use them.

Building and evolving a pervasive, global service requires a multi-disciplined approach that balances requirements with service availability, latency, data replication, compute capacity, and efficiency. In this session, we’ll follow the Netflix journey of failure, innovation, and ubiquity. We'll review the many facets of globalization and then delve deep into the architectural patterns that enable seamless, multi-region traffic management; reliable, fast data propagation; and efficient service infrastructure. The patterns presented will be broadly applicable to internet services with global aspirations.

The document discusses continuous delivery and zero downtime deployment. It describes automating the full deployment process from development to production multiple times a day without any downtime. This is achieved through continuous integration, maintaining deployment scripts and packages, managing database changes, and techniques like feature toggles, blue/green deployments, and state management. The goal is to enable fast and reliable releases of new features and fixes to users.

The document discusses moving towards zero downtime. It summarizes Vision's solutions which aim to [1] eliminate planned and unplanned downtime, [2] quickly recover data to any point in time with zero data loss, and [3] improve service levels and comply with regulations. Vision addresses these issues through data replication, virtualized failover, application protection, and cluster integration capabilities.

The document discusses potential issues with using MTBF/MTTF as the primary reliability metric for the defense and aerospace industries. It argues that MTBF/MTTF provides an incomplete view of reliability across the entire product lifecycle and can result in overly optimistic assessments. The document proposes using an alternative metric called Bx/Lx, which specifies the life point where no more than a certain percentage (like 10%) of failures have occurred. This provides a more comprehensive view of reliability focused on early failures. Overall, the document advocates updating reliability metrics and practices to better reflect physical failure mechanisms.

Explains what troubleshooting is, what skills are involved, and clears up some common misconceptions. Originally designed with IT Helpdesks in mind, but it could apply to any kind of troubleshooting. ========================= Wrote this a VERY long time ago! I always meant to revisit/revamp it, but never quite got round to it. But people seem to get value from it, so I'll leave it up :)

The document summarizes a presentation on predictive analytics given by Mike Watschke from SAP. The presentation covered: - SAP's predictive analytics solution which automates data preparation, modeling, and deployment tasks. - Questions from the analyst about whether SAP sees in-memory technology as critical, whether it has its own data preparation technology or partners, and whether the capability works with cloud, Hadoop, and streaming data. - Upcoming topics for future briefing room presentations, including business intelligence/analytics in March, big data in April, and cloud in May.

The document discusses techniques for achieving zero downtime deployments. It begins with an introduction and overview before covering specific methods such as blue-green deployments, canary releases, and rolling deployments. It also provides details on tools that can be used and considerations for deploying to web servers and databases. The document advocates combining different techniques into a hybrid 1/10/100 approach for deploying code changes to environments in a phased manner to minimize risk.

Obstacles encountered by teams are logged on obstacle boards at three levels - team, management, and executive. At the team level, the Scrum Master tries to resolve obstacles and logs them on a physical board. Unresolved obstacles are escalated to the management level board where managers work to find solutions. Obstacles that cannot be resolved by management are escalated to the executive level board where executives are responsible for resolving or dismissing them.

The document discusses strategies for deploying and releasing applications, including creating a release strategy, release plans, and managing the test and release process. It recommends stakeholders meet to define responsibilities, environments, deployment tools, and other factors. The release strategy should describe the deployment pipeline and processes for testing, approvals, and moving builds between environments. The release plan details automated steps for initial deployment, rollbacks, upgrades, and other lifecycle events. Tools can help model and manage moving builds through approval gates to different test stages and production.

This document discusses implementing reliability strategies and engineering. It begins by explaining the importance of reliability in fields like aviation, defense, and energy where failure could lead to dangerous situations. It then discusses mechanical reliability and common failure modes. Reliability engineering is introduced as the study of reliability and life-cycle management. Several high-profile system failures are listed to emphasize the need for reliability in design. The document outlines various areas of reliability engineering and provides definitions of key terms. It gives examples of reliability calculations and discusses maintainability, availability, and quality. Analytical reliability techniques are also summarized, along with key points and steps to implement a reliability strategy.

Abusing the word "Reliability" was an annoying thing for me, it's not linked to submission date of a document nor the training programs, yes these procedure can help in undirect way to improve the reliability, but when you consider your reliability program sole on it, then you are not doing reliability anymore. So i decided to express my anger in peaceful way and i hope it can be a postive too. for that i'll start to write a post and i'll call it "Real Reliability" to bust the myth around reliability, and i'll start with my first enemy "MTBF". This for all the fed up guys from the wrong usage of "Reliability"

Continuing the series that started with maintenance technicians and supervisors, if you are new to the position of Operations Supervisor, what are some of the things you can begin working on immediately to improve reliability within the area you work?

Part 2 of how to measure reliability for part 1 https://www.linkedin.com/pulse/real-reliability-stop-abusing-ammar-alkhaldi-cbbss-

The document discusses the importance of including equipment operators in Reliability Centered Maintenance (RCM) analysis. Operators play a key role by identifying important failure modes that others may overlook related to equipment operation. They can provide valuable details in failure effect statements about process impacts. Operators also help determine downtime from failures and identify mitigation tasks, such as process monitoring, that are effective at improving reliability. The document argues that excluding operators results in an incomplete RCM analysis.

You wonder sometimes, is Reliability the same as Availability. Here's a sample, showing 2 ways to calculate Availability. (They are not the same, but at times we think so.)

The document discusses software availability and resiliency. It defines availability as the percentage of time a system is up and running. High availability systems aim for 99.999% uptime or less than 5 minutes of downtime per year. The document advocates for a reactive, message-driven approach to building resilient systems that can withstand failures through isolation, asynchronous communication, failure management techniques like circuit breakers and supervisors, and redundancy. The goal is to design systems that can continue processing transactions even when failures occur.

Most companies don’t measure mean time between failures (MTBF), even though it’s the most basic measurement that quantifies reliability. MTBF is the average time an asset functions before it fails. So, why don’t they measure MTBF? Let’s define reliability first before we go any further. Reliability: The ability of an item to perform a required function under stated conditions for a stated period of time So why don’t we measure Mean Time Between Failure. This articles discusses this issue.

A draft version of the paper that was eventually published as “J.A.Jones & J.A.Hayes, ”A comparison of electronic-reliability prediction models”, IEEE Transactions on reliability, June 1999, Volume 48, Number 2, pp 127-134” Provide with the kind permission of the author, J.A.Jones

MTBF is often misused and can be misleading. It is calculated as the average time between failures of a system, but does not represent the actual duration of failure-free periods. A better metric is reliability (R(t)), which shows the probability that a system will operate at a given time. Additionally, the document notes that MTBF is intended for repairable systems, while MTTF is a more accurate term for non-repairable systems, as it is calculated the same way as MTBF under certain assumptions about repair times and part lifetime distributions.

Solar trackers are the foundation of a utility-scale solar plant and their reliability affects energy production, uptime, and O&M costs; significantly impacting the economics of a project. In the near future it will become increasingly important for solar asset owners and investors to take tracker reliability into consideration. For tracker vendors, providing proven reliability and overall bankability of their systems will be a critical differentiator moving forward.

This document discusses efficient reliability demonstration tests that can reduce sample sizes and test times compared to conventional methods. It presents principles for test time reduction using degradation measurements during testing. Methods are provided for calculating optimal test plans that minimize costs while meeting reliability requirements and risk constraints. Decision rules are given for terminating tests early based on degradation measurements and risk estimates. An example application demonstrates how the approach can significantly reduce testing costs.

TubeMogul grew from few servers to over two thousands servers and handling over one trillion http requests a month, processed in less than 50ms each. To keep up with the fast growth, the SRE team had to implement an efficient Continuous Delivery infrastructure that allowed to do over 10,000 puppet deployment and 8,500 application deployment in 2014. In this presentation, we will cover the nuts and bolts of the TubeMogul operations engineering team and how they overcome challenges.

This document discusses various patterns for scaling web applications. It begins by describing single machine, two-tier, and multi-tier web application architectures. It then covers message bus and service-oriented architectures. The document discusses scaling up approaches like identifying bottlenecks, utilizing bottlenecks, adjusting non-bottlenecks, elevating bottlenecks, and reviewing processes. Specific scaling techniques covered include caching, threading and queues, and using content delivery networks.