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Relying on a single content delivery network for your site can impose a number of flexibility limitations. By diversifying your CDN providers you can put the power back in your hands, allowing you to get the best of both worlds in terms of performance, reliability and cost. In this talk Marcus and Laurie will present Etsy’s recent work integrating multiple CDN providers to their site delivery infrastructure. This presentation was delivered at Velocity Europe, November 2013

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While OAuth provides a number of defenses against traditional security risks, there are still other attacks you should be aware of. Join us as we cover how you can use the Salesforce1 Platform to encode untrusted data securely. We'll dive deep into how the browser parses HTML, JavaScript, and URL. In addition, we'll also define and demonstrate parsing contexts, orders, and how that will affect encoding of Cross Site Scripting(XSS) payloads.

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Description: The Java language, tools and frameworks have been the foundation of countless enterprise systems. Here at Salesforce we fully support Java and are part of the Java community. We believe developers should be able to build quality apps using Java. Unfortunately, many of us are stuck with inefficient and painful ways of using this toolchain. It is time for enterprises to move past the sucky ways they are using the Java platform. Based on James Ward's blog post, "Java Doesn't Suck", in this webinar James shares his views along with noted Java developer and author, Bruce Eckel, and will also walk through the suckiest parts of Java, providing recommendations for moving forward, while having spirited discussion along the way. Join us for this awesome Salesforce developer webinar, to learn how to build better apps using Java! Key Takeaways: ::Discover new Java tools that increase productivity. ::Learn modern practices for deploying Java applications. Intended Audience: This session is for Java developers that build web apps and HTTP APIs. Recommended Resources: https://developer.salesforce.com/events/webinars/the-java-best-practices-tools-performance-and-deployment

Java Best Practices - Tools, Performance, and Deployment

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Adobe’s Unified Profile System is the heart of its Experience Platform. It ingests TBs of data a day and is PBs large. As part of this massive growth we have faced multiple challenges in our Apache Spark deployment which is used from Ingestion to Processing. We want to share some of our learnings and hard earned lessons and as we reached this scale specifically with Structured Streaming. Know thy Lag While consuming off a Kafka topic which sees sporadic loads, its very important to monitor the Consumer lag. Also makes you respect what a beast backpressure is. Reading Data In Fan Out Pattern using minPartitions to Use Kafka Efficiently Overload protection using maxOffsetsPerTrigger More Apache Spark Settings used to optimize Throughput MicroBatching Best Practices Map() +ForEach() vs MapPartitons + forEachPartition Adobe Spark Speculation and its Effects Calculating Streaming Statistics Windowing Importance of the State Store RocksDB FTW Broadcast joins Custom Aggegators OffHeap Counters using Redis Pipelining

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Capacity planning for elastic cloud infrastructure platforms like OpenStack is critical for successful deployments. The proper sizing of compute resources within OpenStack allows for easier scheduling, optimal efficiency in hardware utilization, and consistency of resource allocation. Google Compute Engine and Amazon Web Services offer deterministic compute resources designed to meet both cloud provider business requirements and cloud consumer service-level requirements. In this session, we'll explore these public provider approaches, extend them to OpenStack, and provide sizing data and tools to help with your deployment. In this session, Keith Basil, Sean Cohen, and Tushar Katarki discuss: -Approaches for providing consistent compute service levels in OpenStack. -Building instance families for your workloads. -Sizing compute node for OpenStack. -Storage & Network sizing or elastic clouds - Capacity planning tools & benchmarks

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Devops is usually viewed from a traditional perspective of a collaboration of Dev, Ops, and QA, driven by the change in Culture, People, and Process. But how do you know where you stand and where to move? As in almost any field, data and metrics give you the gauges and instruments. In this talk, we’ll talk about the key measurements for the DevOps transformation process and provide you with 3 metrics you can start measuring tomorrow.

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For the last two decades, the amount of data we store, process, and analyze is ever growing. The last decade shows a higher focus on immediate feedback loop data pipeline, using technologies such as Complex Event Processing (CEP), Stream Processing, and Change Data Capture (CDC). Services such as Kafka or NATS are to be found in almost every new system (at least to some extent). To build a data pipeline, the number of technologies, frameworks, and platforms are endless. Getting the initial grasp of it all is much harder than expected, but together we can tackle it!

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Messages sind heutzutage überall. Egal ob JavaScript Frontends in Form von Events, oder Backends mit Kafka oder NATS Message Queues, wir wollen zwei Ziele erreichen, Separation of Concerns (unabhängige Einheiten) und Skalierbarkeit (oder in Frontends Freigabe von Resourcen). Da heute alles Responsive sein muss, brauchen wir Event-basierte Systeme. Also lasst uns gemeinsam die darunterliegenden Systeme erforschen, verstehen und Einsatzbereiche erarbeiten.

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Farms are simple. A farm, a building or two, maybe a barn. Done. You’d wish. Monitoring farms and barns is a tedious task. No farm looks like the other and water distribution, next to other elements, has grown generically. A little bit like the good old legacy systems we all love. With the additional complication of keeping track of topology changes, typical building automation systems are out of the scope. See how clevabit integrated neo4j, PostgreSQL and TimescaleDB to bring observability to farms and what I learned along the way. And there were a lot of “this time it works” moments.

Of Farm Topologies and Time-Series Data

Smart devices taking over our living rooms, our bed rooms, and, in general, our life. It has never been more important to build secure devices, but most companies seem to fail, and they fail hard. We (only) build systems for farms and barns, and still, I wanted security for Cow-stumers. Building a mostly secure system is fairly simple. There is a good set of low-hanging fruits. Building a really locked down system is tough, though. Much harder than expected. Here is what I learned.

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Time-series data, or data being associated with its respective time of occurrence, is everywhere. From the obvious cases, such as metrics, observability, IoT data, all the way to logs, invoicing, or payment records. While storing some of these in relational databases is standard practice, people often reach for specific time-series databases when volume gets high. But imagine if you could have all of them in the same database: PostgreSQL.

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Building, deploying and operating application systems for high scale and failure tolerance is the supreme field of software engineering. While Continuous Integration (CI) and oftentimes also Continuous Delivery (CD) have become a part of commonly used build pipelines, monitoring and observability is still often an afterthought or manually configured. To keep up with containers being started and stopped for version upgrades, scaling up and down or to mitigate failure situations, monitoring needs to automate all the tasks to react to infrastructure changes and find issues before users being impacted. People today expect “Oops-Less Operation”, or do you want your bank to be offline?

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Continuous Integration, Continuous Delivery, Continuous Monitoring! These days CI and CD are commonly used mechanics to achieve fast turn-around times for high-demand applications. Microservices architectures and highly dynamic envrionments (based on Kubernetes, Docker, …), however, come with a whole different set of problems. Systems, that not only appear and disappear dynamically (e.g. autoscaling), but most commonly tend to be written using multiple different programming languages, are hard to monitor from the point of view that matters: User Requests and User Experience. but the answer is simple; Continuous Monitoring (CM). Let's build a polyglot microservices infrastructure. A way to monitor and trace multi-service requests will be demonstrated using Instana’s automatic discovery system.

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As we all know Java is the best language in the world, except there is Go. Go is just so much more, isn’t it? The syntax is so concise and meaningful, the compiler is so much more helpful and the rules are all over it. We will uncovering the bitter truth, the 5 reasons, that every Java developer should know about Go. We’ll present why Go is just the better programming language and why the hype around Go is all real. Let your eyes be to opened and your brain to explode. Sarcasm included.

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Everyone knows there isn't just one way of doing things. This is also true for web-administrated Embedded Devices and a lot of different ways to attack the implementation were taken before the combination of Golang and Typescript manifested. Plenty of the tries started by missing knowledge, inability, the hate of some programming languages or just plainly on size requirements. Over Java and C/C++ to Go+Lua, Go+JavaScript and the final decision on Go and Typescript, we follow the adventure of an embedded framework and the arising problems. Pros and Cons but also the feeling for a Java developer and new horizons are given.

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The way from monolithic to micro service architectures can hard. Overall micro services are not the all holy grail to just solve all your issues. You need to be aware that you need the right developers and the right toolset. Oh and not to forget, moving state to authorization systems doesn't mean your application is really stateless :) Anyhow micro services are a great architecture and this deck is a short introduction on why we need to change our application architectures and what pitfalls you you have when introducing the idea of micro services.

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You never heard of sun.misc.Unsafe? You may be using it without realising it. Are you using Spring or Hibernate? 90% of todays applications use sun.misc.Unsafe directly or indirectly. Unsafe is meant to be an API internal to the JVM but since it turned out to be extremely powerful, more and more frameworks have used it. This talk will show different usecases of sun.misc.Unsafe and other inherently unsafe possibilities in Java. Some are commonly used in production environments like fast Serialization, Offheap or to build lock-free algorithms, some not suitable for production but fun to play with breaking the rules of what you can do in Java. After the talk you’ll understand what is possible and what might help you, but not to forget, this talk is given with a big warning: Don’t Try That At Home!

Unsafe Java World - Crossing the Borderline - JokerConf 2014 Saint Petersburg

Today’s amounts of collected data are showing a nearly exponential growth. More than 75% of all the data have been collected in the past 5 years. To store this data and process it in an appropriate time you need to partition the data and parallelize the processing of reports and analytics. This talk will demonstrate how to parallelize data processing using Hazelcast and it’s underlying distributed data structures. With a quick introduction into the different terms and some short live coding examples we will make the journey into the distributed computing. Sourcecode of the demonstrations are available here: 1. https://github.com/noctarius/hazelcast-mapreduce-presentation 2. https://github.com/noctarius/hazelcast-distributed-computing

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