DevOps 060: How to Have Everything at Your Fingertips Next Time Things Break
Play • 55 min

Charles Wood and Jeffrey Groman are joined by Phil Wilkins, author of Unified Logging with Fluentd to talk about logging, infrastructure, monitoring and how to get started.

Panel

  • Charles Wood
  • Jeffrey Groman

Guest

  • Phil Wilkins

Sponsors

Picks

airhacks.fm podcast with adam bien
airhacks.fm podcast with adam bien
Adam Bien
How Caffeine Cache Happened
An airhacks.fm conversation with Ben Manes (@benmanes) about: TRS 80, Tandy RadioShack 80 computer, never push the red button, playing Reader Rabbit on 287, the fascination with hardware, the experimentation with water cooling and thermopads, learning C++ and Java at the University in Chicago, starting with Java 1.4 at school, building corporate travel systems with Java 5, the six hour interview at Google with a binary search tree, working on CRM tool at Google, building an enterprise version of iGoogle in Java and GWT, using Guice and GWT GIN to implement iGoogle.next, using a perforce monorepo, perforce was replaced by internal system called "paper", using blaze and bezel build system, bezel is more distributed, one build file per package, starting at a logistics company with Java 15, the jetty, JAX-RS, keycloak,RESTEasy, jooq and google's guice, starting to write a cache in 2008, using memcached and Java Message System (JMS) for synchronization, Java 5 and the Concurrent Linked HashMap / LRU, building Google Guava cache, Concurrent HashMap was used by Apache Cassandra, Google's MapMaker is predecessor to Guava Cache, Caffeine work started in 2008, EHCache was not concurrent back then, Java 5 concurrent HashMap didn't scale well, Java 5 regions in HashMap were too big, there were too many entries per segment, Java 8 uses small hash bins and scales better, Caffeine builds on top of Java 8 ConcurrentHashMap, LRU and every reads is a write, cache policy can be lossy, using dynamically growing data structures, Caffeine uses Java Collections, Caffeine looks like a HashMap, Caffeine adapts automatically to the read-, write-, or mixed workload, Caffeine's configuration is descriptive, refresh policies, cache loader, expiration, asynchronous behavior, listeners, soft- and weak references were supposed to be the solution to everything, hit rates monitoring, micrometer, dropwizard, prometheus monitoring adapters are available, reasearch papers tend to lie, working with cockroachDB committers, Infinispan uses Caffeine, the bias against pre-made stack Ben Manes on twitter: @benmanes, Ben's GitHub account: github.com/ben-manes
1 hr 20 min
Java Pub House
Java Pub House
Freddy Guime & Bob Paulin
Episode 94. Oh, put on your hat Dr. Watson, we are sleuthing this Heap Dump
So it happens. Sometimes a Java program just "dies" with the dreaded Out-of-memory Exception. Sometimes, it leaves behind a "heap dump", or a copy of what the Java program had in memory when just before it throw the Out-of-memory exception. For some devs, a heap dump is stressful, because they think is a black box with only mysteries, but we are here to calm your fears! In this episode we show how Heap dumps are your friends! If you happen to have one, then is almost assured that you can find what caused the out-of-memory, and/or you can "see" what the real values of configuration were. For troubleshooting a production incident, a heap dump becomes invaluable since "nothing" hides from it. Doesn't matter where the code came from, a heap dump will have exactly what each variable was holding and who might be responsible for a memory leak! "Memory leak you say? I thought java didn't have memory leaks!". Well, that's the other part of this episode where we discuss while Java has way less worries than (say c++) on allocating and deallocating memory, you can still create Memory leaks by having strong referenced objects that are never removed (and if that sounded like mumbo jumbo, even a better excuse to listen to this episode) So, we survived 2020. Take a listen to this end-of-the-year episode, and be even more ready for whatever 2021 throws at us! http://www.javaoffheap.com/datadog We thank DataDogHQ for sponsoring this podcast episode Don't forget to SUBSCRIBE to our cool NewsCast OffHeap! http://www.javaoffheap.com/ How to capture a Heap Dump https://www.baeldung.com/java-heap-dump-capture Eclipse Memory Analyzer https://www.eclipse.org/mat/ VisualVM https://visualvm.github.io/ Do you like the episodes? Want more? Help us out! Buy us a beer! https://www.javapubhouse.com/beer And Follow us! https://www.twitter.com/javapubhouse
1 hr 6 min
Streaming Audio: A Confluent podcast about Apache Kafka
Streaming Audio: A Confluent podcast about Apache Kafka
Confluent, original creators of Apache Kafka®
Becoming Data Driven with Apache Kafka and Stream Processing ft. Daniel Jagielski
When it comes to adopting event-driven architectures, a couple of key considerations often arise: the way that an asynchronous core interacts with external synchronous systems and the question of “how do I refactor my monolith into services?” Daniel Jagielski, a consultant working as a tech lead/dev manager at VirtusLab for Tesco, recounts how these very themes emerged in his work with European clients.  Through observing organizations as they pivot toward becoming real time and event driven, Daniel identifies the benefits of using Apache Kafka® and stream processing for auditing, integration, pub/sub, and event streaming. He describes the differences between a provisioned cluster vs. managed cluster and the importance of this within the Kafka ecosystem. Daniel also dives into the risk detection platform used by Tesco, which he helped build as a VirtusLab consultant and that marries the asynchronous and synchronous worlds. As Tesco migrated from a legacy platform to event streaming, determining risk and anomaly detection patterns have become more important than ever. They need the flexibility to adjust due to changing usage patterns with COVID-19. In this episode, Daniel talks integrations with third parties, push-based actions, and materialized views/projects for APIs. Daniel is a tech lead/dev manager, but he’s also an individual contributor for the Apollo project (an ICE organization) focused on online music usage processing. This means working with data in motion; breaking the monolith (starting with a proof of concept); ETL migration to stream processing, and ingestion via multiple processes that run in parallel with record-level processing. EPISODE LINKS * Building an Apache Kafka Center of Excellence Within Your Organization ft. Neil Buesing  * Risk Management in Retail with Stream Processing * Event Sourcing, Stream Processing and Serverless * It’s Time for Streaming to Have a Maturity Model ft. Nick Dearden * Read Daniel Jagielski's articles on the Confluent blog * Join the Confluent Community * Learn more with Kafka tutorials, resources, and guides at Confluent Developer * Live demo: Kafka streaming in 10 minutes on Confluent Cloud * Use *60PDCAST* to get an additional $60 of free Confluent Cloud usage (details)
48 min
Azure DevOps Podcast
Azure DevOps Podcast
Jeffrey Palermo
Mark Fussell on Dapr 1.0 - Episode 130
Joining Jeffrey today is return guest, Mark Fussell! Mark works on the Azure Incubations Team and is the Product Manager for Dapr, the Distributed Application Runtime. He has been working at Microsoft for over 19 years and has been a passionate advocate for building microservice-based applications for the last 10 years. He has a proven track record of building innovative computing platforms, running large-scale cloud services, and starting new million-dollar businesses within corporations. Last time Mark was on the show, he and Jeffrey discussed Dapr and what it can do for developers. In this episode, Mark and Jeffrey discuss the new 1.0 release of Dapr. Mark shares how to build, test, deploy, and monitor an application that’s built and deployed using Dapr. He speaks about the team’s journey for the last six months with working on the 1.0 release, the new and exciting changes with the 1.0 release, and all that Dapr is currently capable of. Topics of Discussion: [:38] Be sure to visit AzureDevOps.Show for past episodes and show notes. [:50] About The Azure DevOps Podcast, Clear Measure, and Jeffrey’s offer to speak at virtual user groups. [1:16] About Jeffrey’s newest podcast, Architect Tips! [1:20] About today’s episode with return guest, Mark Fussell. [1:42] Jeffrey welcomes Mark Fussell back to The Azure DevOps Podcast. [2:03] Mark gives a rundown of what’s new at Microsoft, how he ended up on the Azure Incubations Team at Microsoft, and what the team works on. [3:15] An overview of Dapr. [5:08] The huge news for Dapr: the new 1.0 release. [5:41] Mark elaborates on the journey for the last six months with Dapr and what’s new and exciting with the 1.0 release. [7:07] Is Dapr aimed squarely at processes such as backend services with no UI (that either need to be triggered by something or to pop up and do something)? [9:19] Is Dapr only for Javascript apps? Is it for .NET developers? How is it positioned? [11:55] The strategy of Azure and the positioning of Dapr. [13:25] What are some of Dapr’s main goals? Can Dapr be as simple as a single backend process to a whole bunch of backend processes? [21:53] A word from Azure DevOps Podcast’s sponsor: Clear Measure. [22:24] Is there overlap with Dapr and open-source distributed application frameworks for .NET such as MassTransit and NServiceBus? Did the Azure Incubations Team discuss these when developing Dapr? [24:19] Jeffrey and Mark dive into the operational side of Dapr. Mark speaks about how to build, test, deploy, and monitor an application that’s built and deployed using Dapr. [28:24] Does Dapr integrate with Application Insights on its own set of custom events and custom metrics? [29:28] What does deploying with ASP.NET look like? Is it possible, with Dapr, that you would not need to deploy a second process (whether it be Windows Service, Azure Function, or Containers) and you can simply bundle it in with a regular app service web application deployment? [33:51] Mark provides an update on the status of Kubernetes in Azure. [37:04] Discussing the future of running and deploying to Azure. Mentioned in this Episode: Architect Tips — New video podcast! Azure DevOps Clear Measure (Sponsor) .NET DevOps for Azure: A Developer's Guide to DevOps Architecture the Right Way, by Jeffrey Palermo — Available on Amazon! bit.ly/dotnetdevopsebook — Click here to download the .NET DevOps for Azure ebook! Jeffrey Palermo’s Youtube Jeffrey Palermo’s Twitter — Follow to stay informed about future events! The Azure DevOps Podcast’s Twitter: @AzureDevOpsShow Mark Fussell’s LinkedIn Mark Fussell’s Twitter @MFussell Dapr Dapr on GitHubr Dapr for .NET Developers, by Robert Vettor, Sander Molenkamp, and Edwin van Wijk Azure DevOps Podcast Ep. 66: “Mark Fussell on the Distributed Application Runtime or Dapr” KEDA Azure Queue Storage Azure Service Bus MassTransit NService Bus Azure DevOps Podcast Ep. 128: “Simon Timms on Microservices Architecture” Azure Application Insights OpenTelemetry Collector ASP.NET Kubernetes Azure DevOps Podcast Ep. 110: “Stefan Schackow on What’s New in Azure App Service” “Microsoft’s Dapr Introduces Cloud Native Development to the Enterprise” | The New Stack “Microsoft's most useful open-source project for Kubernetes, Dapr hits the 1.0 primetime” | The Register “Distributed Application Runtime (Dapr) v1.0 Announced” | InfoQ “Microsoft’s Dapr open-source project to help developers build cloud-native apps hits 1.0” | TechCrunch “Microsoft’s open source Dapr hits prime time to help developers embrace microservices” | VentureBeat Want to Learn More? Visit AzureDevOps.Show for show notes and additional episodes.
41 min
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