This document contains the transcript from a presentation titled "So You Wanna Go Fast?" by Tyler Treat. Some of the key topics discussed include measuring performance using tools like pprof, how different language features in Go like channels, interfaces, and memory management can impact performance, and techniques for writing concurrent and multi-core friendly code in Go like using read-write mutexes. The overall message is that performance depends greatly on the specific situation and trade-offs must be considered between concurrency, memory usage, and execution speed. Measuring first is emphasized to guide any optimizations.
The document summarizes key concepts about optionals in Swift, including:
Optionals represent possibly missing values and can be nil. Non-optional types cannot be nil. Functions can return optionals to indicate possible missing values. Optionals must be unwrapped to access their underlying value. Forced unwrapping with ! should be avoided. Optional binding with if let is safer for testing and unwrapping. Optional chaining with ? allows working with chained optionals in a concise way.
When one server just isn’t enough, how can you scale out? In this webinar, you'll learn how to build out the capacity of your website. You'll see a variety of scalability approaches and some of the advanced capabilities of NGINX Plus.
View full webinar on demand at http://nginx.com/resources/webinars/nginx-load-balancing-software/
The document discusses HTTP caching in Android. It describes the different HTTP client libraries available in Android - java.net, Apache HttpClient, and android.webkit. It focuses on caching with Apache HttpClient and describes the Apache HttpClient Cache module, which implements HTTP 1.1 caching. Test results show that using the CachingHttpClient can improve performance over the DefaultHttpClient by caching HTTP responses.
HTTP caching involves storing copies of resources near clients to serve future requests faster. Caching can happen locally on a client or through shared proxies. Effective caching requires expiration dates, validation of cached responses, and invalidation of cached responses when content changes. Caching allows servers to scale to many users by offloading work to clients and proxies. The HTTP protocol and technologies like ESI were designed to support caching while handling dynamic content.
Content caching is one of the most effective ways to dramatically improve the performance of a web site. In this webinar, we’ll deep-dive into NGINX’s caching abilities and investigate the architecture used, debugging techniques and advanced configuration. By the end of the webinar, you’ll be well equipped to configure NGINX to cache content exactly as you need.
View full webinar on demand at http://nginx.com/resources/webinars/content-caching-nginx/
Operations: Production Readiness Review – How to stop bad things from HappeningAmazon Web Services
The document provides an overview of key areas to review for production readiness including architecture design, monitoring, logging, documentation, alerting, service level agreements, expected throughput, testing, and deployment strategy. It summarizes best practices and considerations for each area such as using circuit breakers in monitoring, consistent logging formats, storing documentation near code, automating level 1 operations, and strategies for testing, deployments, and managing error budgets.
Streaming Data Analytics with Amazon Redshift and Kinesis FirehoseAmazon Web Services
by Joyjeet Banerjee, Enterprise Solutions Architect, AWS
Evolving your analytics from batch processing to real-time processing can have a major business impact, but ingesting streaming data into your data warehouse requires building complex streaming data pipelines. Amazon Kinesis Firehose solves this problem by making it easy to transform and load streaming data into Amazon Redshift so that you can use existing analytics and business intelligence tools to extract information in near real-time and respond promptly. In this session, we will dive deep using Amazon Kinesis Firehose to load streaming data into Amazon Redshift reliably, scalably, and cost-effectively. Level: 200
by Joyjeet Banerjee, Enterprise Solutions Architect, AWS
Amazon Aurora is a MySQL- and PostgreSQL-compatible database engine that combines the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. In this deep dive session, we’ll discuss best practices and explore new features in areas like high availability, security, performance management and database cloning. Level 300
Apache Spark Streaming + Kafka 0.10 with Joan ViladrosarieraSpark Summit
Spark Streaming has supported Kafka since it’s inception, but a lot has changed since those times, both in Spark and Kafka sides, to make this integration more fault-tolerant and reliable.Apache Kafka 0.10 (actually since 0.9) introduced the new Consumer API, built on top of a new group coordination protocol provided by Kafka itself. So a new Spark Streaming integration comes to the playground, with a similar design to the 0.8 Direct DStream approach. However, there are notable differences in usage, and many exciting new features. In this talk, we will cover what are the main differences between this new integration and the previous one (for Kafka 0.8), and why Direct DStreams have replaced Receivers for good. We will also see how to achieve different semantics (at least one, at most one, exactly once) with code examples. Finally, we will briefly introduce the usage of this integration in Billy Mobile to ingest and process the continuous stream of events from our AdNetwork.
The presentation at DevFest Tokyo 2017 / @__timakin__
An introduction of blockchain and why go is nice to implement blockchain.
Additionally described about the blockchain projects that are based on Go.
An introduction and future of Ruby coverage librarymametter
Ruby's current test coverage feature, coverage.so, only measures line coverage. The speaker proposes expanding it to support function and branch coverage in Ruby 2.5. This would involve updating the coverage.so API to return additional coverage data types and structure the output data in a more extensible way. A preliminary demo applying the new coverage.so to Ruby code showed it can integrate with C code coverage from GCOV and display results in LCOV format. The speaker seeks feedback on the proposed API design to finalize it for Ruby 2.5.
神に近づくx/net/context (Finding God with x/net/context)guregu
This document discusses different approaches to building an authentication middleware in Go web applications. It begins with using the standard library, then explores Goji and its request context. It settles on using the x/net/context package and kami router, which allow sharing database connections and authentication objects across requests and tests through the request context. Middleware is defined hierarchically in kami. This approach avoids global variables and simplifies testing.
1) Mercari has transitioned some services to microservices architecture running on Kubernetes in the US region to improve development velocity.
2) Key challenges in operating microservices include deployment automation using Spinnaker, and observability of distributed systems through request tracing, logging, and metrics.
3) The architecture is still evolving with discussions on service mesh and chaos engineering to improve reliability in the face of failures. Microservices adoption is just beginning in the JP region.
The document discusses using gRPC and Protocol Buffers to build fast and reliable APIs, describing how gRPC uses Protocol Buffers to define service interfaces and handle serialization, and allows building clients and servers in various languages that can communicate over the network through language-independent services. It provides examples of using gRPC to define and call both unary and streaming RPC services from Swift clients and servers.
Andrew Betts Web Developer, The Financial Times at Fastly Altitude 2016
Running custom code at the Edge using a standard language is one of the biggest advantages of working with Fastly’s CDN. Andrew gives you a tour of all the problems the Financial Times and Nikkei solve in VCL and how their solutions work.
The document discusses building apps for the Google Assistant using Google Cloud Functions and Actions on Google. It provides an overview of the architecture, development workflow, and ways for users to discover apps. Key points include using Cloud Functions as a serverless environment to handle requests, the Actions Console for configuration, and in-dialogue discovery or the Assistant Directory as ways for users to find actions.
Spark Streaming Programming Techniques You Should Know with Gerard MaasSpark Summit
At its heart, Spark Streaming is a scheduling framework, able to efficiently collect and deliver data to Spark for further processing. While the DStream abstraction provides high-level functions to process streams, several operations also grant us access to deeper levels of the API, where we can directly operate on RDDs, transform them to Datasets to make use of that abstraction or store the data for later processing. Between these API layers lie many hooks that we can manipulate to enrich our Spark Streaming jobs. In this presentation we will demonstrate how to tap into the Spark Streaming scheduler to run arbitrary data workloads, we will show practical uses of the forgotten ‘ConstantInputDStream’ and will explain how to combine Spark Streaming with probabilistic data structures to optimize the use of memory in order to improve the resource usage of long-running streaming jobs. Attendees of this session will come out with a richer toolbox of techniques to widen the use of Spark Streaming and improve the robustness of new or existing jobs.
Distributed systems are not strictly an engineering problem. It’s far too easy to assume a backend development concern, but the reality is there are implications at every point in the stack. Often the trade-offs we make lower in the stack in order to buy responsiveness bubble up to the top—so much, in fact, that it rarely doesn’t impact the application in some way.
Distributed systems affect the user. We need to shift the focus from system properties and guarantees to business rules and application behavior. We need to understand the limitations and trade-offs at each level in the stack and why they exist. We need to assume failure and plan for recovery. We need to start thinking of distributed systems as a UX problem.
Tyler Treat looks at distributed systems through the lens of user experience, observing how architecture, design patterns, and business problems all coalesce into UX. Tyler also shares system design anti-patterns and alternative patterns for building reliable and scalable systems with respect to business outcomes.
Topic include:
- The “truth” can be prohibitively expensive: When does strong consistency make sense, and when does it not? How do we reconcile this with application UX?
- Failure as an inevitability: If we can’t build perfect systems, what is “good enough”?
- Dealing with partial knowledge: Systems usually operate in the real world (e.g., an inventory application for a widget warehouse). How do we design for the “disconnect” between the real world and the system?
The document discusses Python programming language. Some key points:
- Python was invented by Guido Van Rossum and is a combination of various programming languages.
- It is a general purpose, high-level programming language that is interpreted, interactive, object-oriented and extensible.
- Python can be used for web applications, desktop applications, data analysis, artificial intelligence, and more. Popular companies that use Python include Google, YouTube, and NASA.
- Python code is portable and can run on various platforms. It has a simple syntax and is easy to learn.
Operations: Production Readiness Review – How to stop bad things from HappeningAmazon Web Services
The document provides an overview of key areas to review for production readiness including architecture design, monitoring, logging, documentation, alerting, service level agreements, expected throughput, testing, and deployment strategy. It summarizes best practices and considerations for each area such as using circuit breakers in monitoring, consistent logging formats, storing documentation near code, automating level 1 operations, and strategies for testing, deployments, and managing error budgets.
Streaming Data Analytics with Amazon Redshift and Kinesis FirehoseAmazon Web Services
by Joyjeet Banerjee, Enterprise Solutions Architect, AWS
Evolving your analytics from batch processing to real-time processing can have a major business impact, but ingesting streaming data into your data warehouse requires building complex streaming data pipelines. Amazon Kinesis Firehose solves this problem by making it easy to transform and load streaming data into Amazon Redshift so that you can use existing analytics and business intelligence tools to extract information in near real-time and respond promptly. In this session, we will dive deep using Amazon Kinesis Firehose to load streaming data into Amazon Redshift reliably, scalably, and cost-effectively. Level: 200
by Joyjeet Banerjee, Enterprise Solutions Architect, AWS
Amazon Aurora is a MySQL- and PostgreSQL-compatible database engine that combines the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. In this deep dive session, we’ll discuss best practices and explore new features in areas like high availability, security, performance management and database cloning. Level 300
Apache Spark Streaming + Kafka 0.10 with Joan ViladrosarieraSpark Summit
Spark Streaming has supported Kafka since it’s inception, but a lot has changed since those times, both in Spark and Kafka sides, to make this integration more fault-tolerant and reliable.Apache Kafka 0.10 (actually since 0.9) introduced the new Consumer API, built on top of a new group coordination protocol provided by Kafka itself. So a new Spark Streaming integration comes to the playground, with a similar design to the 0.8 Direct DStream approach. However, there are notable differences in usage, and many exciting new features. In this talk, we will cover what are the main differences between this new integration and the previous one (for Kafka 0.8), and why Direct DStreams have replaced Receivers for good. We will also see how to achieve different semantics (at least one, at most one, exactly once) with code examples. Finally, we will briefly introduce the usage of this integration in Billy Mobile to ingest and process the continuous stream of events from our AdNetwork.
The presentation at DevFest Tokyo 2017 / @__timakin__
An introduction of blockchain and why go is nice to implement blockchain.
Additionally described about the blockchain projects that are based on Go.
An introduction and future of Ruby coverage librarymametter
Ruby's current test coverage feature, coverage.so, only measures line coverage. The speaker proposes expanding it to support function and branch coverage in Ruby 2.5. This would involve updating the coverage.so API to return additional coverage data types and structure the output data in a more extensible way. A preliminary demo applying the new coverage.so to Ruby code showed it can integrate with C code coverage from GCOV and display results in LCOV format. The speaker seeks feedback on the proposed API design to finalize it for Ruby 2.5.
神に近づくx/net/context (Finding God with x/net/context)guregu
This document discusses different approaches to building an authentication middleware in Go web applications. It begins with using the standard library, then explores Goji and its request context. It settles on using the x/net/context package and kami router, which allow sharing database connections and authentication objects across requests and tests through the request context. Middleware is defined hierarchically in kami. This approach avoids global variables and simplifies testing.
1) Mercari has transitioned some services to microservices architecture running on Kubernetes in the US region to improve development velocity.
2) Key challenges in operating microservices include deployment automation using Spinnaker, and observability of distributed systems through request tracing, logging, and metrics.
3) The architecture is still evolving with discussions on service mesh and chaos engineering to improve reliability in the face of failures. Microservices adoption is just beginning in the JP region.
The document discusses using gRPC and Protocol Buffers to build fast and reliable APIs, describing how gRPC uses Protocol Buffers to define service interfaces and handle serialization, and allows building clients and servers in various languages that can communicate over the network through language-independent services. It provides examples of using gRPC to define and call both unary and streaming RPC services from Swift clients and servers.
Andrew Betts Web Developer, The Financial Times at Fastly Altitude 2016
Running custom code at the Edge using a standard language is one of the biggest advantages of working with Fastly’s CDN. Andrew gives you a tour of all the problems the Financial Times and Nikkei solve in VCL and how their solutions work.
The document discusses building apps for the Google Assistant using Google Cloud Functions and Actions on Google. It provides an overview of the architecture, development workflow, and ways for users to discover apps. Key points include using Cloud Functions as a serverless environment to handle requests, the Actions Console for configuration, and in-dialogue discovery or the Assistant Directory as ways for users to find actions.
Spark Streaming Programming Techniques You Should Know with Gerard MaasSpark Summit
At its heart, Spark Streaming is a scheduling framework, able to efficiently collect and deliver data to Spark for further processing. While the DStream abstraction provides high-level functions to process streams, several operations also grant us access to deeper levels of the API, where we can directly operate on RDDs, transform them to Datasets to make use of that abstraction or store the data for later processing. Between these API layers lie many hooks that we can manipulate to enrich our Spark Streaming jobs. In this presentation we will demonstrate how to tap into the Spark Streaming scheduler to run arbitrary data workloads, we will show practical uses of the forgotten ‘ConstantInputDStream’ and will explain how to combine Spark Streaming with probabilistic data structures to optimize the use of memory in order to improve the resource usage of long-running streaming jobs. Attendees of this session will come out with a richer toolbox of techniques to widen the use of Spark Streaming and improve the robustness of new or existing jobs.
Distributed systems are not strictly an engineering problem. It’s far too easy to assume a backend development concern, but the reality is there are implications at every point in the stack. Often the trade-offs we make lower in the stack in order to buy responsiveness bubble up to the top—so much, in fact, that it rarely doesn’t impact the application in some way.
Distributed systems affect the user. We need to shift the focus from system properties and guarantees to business rules and application behavior. We need to understand the limitations and trade-offs at each level in the stack and why they exist. We need to assume failure and plan for recovery. We need to start thinking of distributed systems as a UX problem.
Tyler Treat looks at distributed systems through the lens of user experience, observing how architecture, design patterns, and business problems all coalesce into UX. Tyler also shares system design anti-patterns and alternative patterns for building reliable and scalable systems with respect to business outcomes.
Topic include:
- The “truth” can be prohibitively expensive: When does strong consistency make sense, and when does it not? How do we reconcile this with application UX?
- Failure as an inevitability: If we can’t build perfect systems, what is “good enough”?
- Dealing with partial knowledge: Systems usually operate in the real world (e.g., an inventory application for a widget warehouse). How do we design for the “disconnect” between the real world and the system?
The document discusses Python programming language. Some key points:
- Python was invented by Guido Van Rossum and is a combination of various programming languages.
- It is a general purpose, high-level programming language that is interpreted, interactive, object-oriented and extensible.
- Python can be used for web applications, desktop applications, data analysis, artificial intelligence, and more. Popular companies that use Python include Google, YouTube, and NASA.
- Python code is portable and can run on various platforms. It has a simple syntax and is easy to learn.
The pervasiveness of cloud and containers has led to systems that are much more distributed and dynamic in nature. Highly elastic microservice and serverless architectures mean containers spin up on demand and scale to zero when that demand goes away. In this world, servers are very much cattle, not pets. This shift has exposed deficiencies in some of the tools and practices we used in the world of servers-as-pets. Specifically, there are questions around how we monitor and debug these types of systems at scale. And with the rise of DevOps and product mindset, making data-driven decisions is becoming increasingly important for agile development teams.
In this talk, we discuss a new approach to system monitoring and data collection: the observability pipeline. For organizations that are heavily siloed, this approach can help empower teams when it comes to operating their software. The observability pipeline provides a layer of abstraction that allows you to get operational data such as logs and metrics everywhere it needs to be without impacting developers and the core system. Unlocking this data can also be a huge win for the business with things like auditability, business analytics, and pricing. Lastly, it allows you to change backing data systems easily or test multiple in parallel. With the amount of data and the number of tools modern systems demand these days, we'll see how the observability pipeline becomes just as essential to the operations of a service as the CI/CD pipeline.
From learning how to code (2 weeks) then jumping in to EmberJS. A look into what it's like to be a beginning developer and how easy EmberJS is to use with very little knowledge.
This talk provides light insight on Ember-CLI, components, add-ons, and troubleshooting code.
Video: vimeo.com/144527585
Aaron Patterson's talk from Future Insights Live 2014 in Las Vegas: "The Web is constantly changing, and technology is constantly evolving. With these ideas in mind, let's look at what Ruby and Rails not only can do, but must do in order to push technology forward on the web."
Miss his talk? Join us at a future show: www.futureofwebapps.com. Sign up for our newsletter at futureinsights.com and get 15% off your next conference.
The document provides an overview and examples of using the Trafaret Python library for data validation and conversion. Some key points:
- Trafaret validates data and converts it to the expected type
- It allows defining validation rules via simple functions and is easy to extend
- Examples show validating dictionaries, lists, integers, strings, and custom data types
- Trafaret can be used to validate configuration, API requests/responses, and model properties
What is observability and how is it different from traditional monitoring? How do we effectively monitor and debug complex, elastic microservice architectures? In this interactive discussion, we’ll answer these questions. We’ll also introduce the idea of an “observability pipeline” as a way to empower teams following DevOps practices. Lastly, we’ll demo cloud-native observability tools that fit this “observability pipeline” model, including Fluentd, OpenTracing, and Jaeger.
Traditional Operations isn’t going away, it’s just retooling. The move from on-premise to cloud means Ops, in the classical sense, is largely being outsourced to cloud providers. What’s left is a thin but crucial slice between cloud providers and the products built by development teams, encompassing infrastructure and deployment automation, configuration management, log management, and monitoring and instrumentation—all through the lens of self-service.
Join me as I share my vision for the future of Operations as an organizational competency and how it relates to DevOps. We will discuss where industry practices are headed while sharing some real-world stories—the good and the bad—of applying these practices at Workiva. The intended outcome of this talk is to leave listeners with a better understanding of what an effective modern engineering organization looks like, including patterns and best practices, and the path to reaching it. The end goal is an organization which delivers value to customers reliably, efficiently, and continuously.
Ops is dead, long live Ops!
Building a Distributed Message Log from Scratch - SCaLE 16xTyler Treat
Apache Kafka has shown that the log is a powerful abstraction for data-intensive applications. It can play a key role in managing data and distributing it across the enterprise efficiently. Vital to any data plane is not just performance, but availability and scalability. In this session, we examine what a distributed log is, how it works, and how it can achieve these goals. Specifically, we'll discuss lessons learned while building NATS Streaming, a reliable messaging layer built on NATS that provides similar semantics. We'll cover core components like leader election, data replication, log persistence, and message delivery. Come learn about distributed systems!
Building a Distributed Message Log from ScratchTyler Treat
Apache Kafka has shown that the log is a powerful abstraction for data-intensive applications. It can play a key role in managing data and distributing it across the enterprise efficiently. Vital to any data plane is not just performance, but availability and scalability. In this session, we examine what a distributed log is, how it works, and how it can achieve these goals. Specifically, we'll discuss lessons learned while building NATS Streaming, a reliable messaging layer built on NATS that provides similar semantics. We'll cover core components like leader election, data replication, log persistence, and message delivery. Come learn about distributed systems!
This document summarizes a talk given by Tyler Treat about using simple solutions for complex distributed systems problems. Some key points:
- Distributed systems are inherently asynchronous and unreliable, but many try to build them as if they are synchronous.
- Exact delivery guarantees are expensive and impossible at scale. Replayable and idempotent delivery are better alternatives.
- NATS is a simple, high performance, and highly available messaging system that embraces asynchronous communication.
- Workiva uses NATS as a messaging backplane between microservices for pub/sub, RPC, and load balancing. Running a local NATS daemon per VM improves performance.
Probabilistic algorithms for fun and pseudorandom profitTyler Treat
There's an increasing demand for real-time data ingestion and processing. Systems like Apache Kafka, Samza, and Storm have become popular for this reason. This type of high-volume, online data processing presents an interesting set of new challenges, namely, how do we drink from the firehose without getting drenched? Explore some of the fundamental primitives used in stream processing and, specifically, how we can use probabilistic methods to solve the problem.
The Economics of Scale: Promises and Perils of Going DistributedTyler Treat
What does it take to scale a system? We'll learn how going distributed can pay dividends in areas like availability and fault tolerance by examining a real-world case study. However, we will also look at the inherent pitfalls. When it comes to distributed systems, for every promise there is a peril.
From Mainframe to Microservice: An Introduction to Distributed SystemsTyler Treat
An introductory overview of distributed systems—what they are and why they're difficult to build. We explore fundamental ideas and practical concepts in distributed programming. What is the CAP theorem? What is distributed consensus? What are CRDTs? We also look at options for solving the split-brain problem while considering the trade-off of high availability as well as options for scaling shared data.
Biometric attendance systems allow organizations to meet their legal responsibility through exact tracking of employee work time estimation and overtime durations and absence documentation.
MariaDB Server comes with MariaDB Galera Cluster built-in, and it has a new release model, and a lot of new Galera Cluster features, since the replication layer within MariaDB Server is also rapidly changing. We are going to use 11.8 RC for this webinar. Come learn how about the new features, and how to use:
Vectors with Galera Cluster
Mix asynchronous replication with wsrep replication of Galera Cluster
Use features like system versioned tables with Galera Cluster
Allowlist for IST/SST requests in Galera Cluster
The JSON interface to wsrep node/SST state progress reporting
Move from unencrypted to TLS encryption with no downtime for Galera Cluster traffic
Wsrep_mode changes
Galera Cluster full GTID support for MariaDB Server, which includes replicating the MariaDB GTID to all nodes in the cluster
Making MariaDB Galera Cluster more robust with lots of bug fixes
Using MariaDB MaxScale with MariaDB Galera Cluster
Odoo WooCommerce Connector, Multiple Woocommerce store connectionAagam infotech
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VADY: Revolutionizing Business Intelligence with AI-Powered InsightsNewFangledVision
In today’s fast-paced business world, making data-driven decisions is essential. VADY AI analytics delivers AI-powered business intelligence, transforming raw data into real-time insights that drive growth. By leveraging data analytics for business, organizations can identify trends, optimize operations, and gain an AI-driven competitive advantage. With enterprise AI solutions, businesses can streamline workflows, automate decision-making, and improve overall efficiency. VADY’s context-aware AI analytics adapts to your industry, offering actionable intelligence tailored to your needs. Whether you’re in finance, marketing, or operations, VADY data analytics solutions provide AI-powered data visualization and automated data insights software to ensure that every business decision is backed by powerful analytics.
Alluxio Webinar | What’s New in Alluxio AI: 3X Faster Checkpoint File Creatio...Alluxio, Inc.
Alluxio Webinar
Feb. 25, 2025
For more Alluxio Events: https://www.alluxio.io/events/
Speaker:
Bill Hodak (VP of Marketing and Product Marketing, Alluxio)
Tom Luckenbach (Solutions Engineering Manager, Alluxio)
Join us to learn about the latest release of Alluxio Enterprise AI. In this webinar, we’ll provide an overviewof the new features and capabilities of Alluxio Enterprise AI, built to accelerate AI workloads and maximize GPU utilization.
Key highlights include:
- New caching mode accelerates AI checkpoints
- Advanced cache eviction policies provide fine-grained control
- Python SDK integrations enhance AI framework compatibility
- A demo of Alluxio accelerating AI training workloads in AWS
How to develop, run and optimize Spring Boot 3 application on AWS Lambda-OBI ...Vadym Kazulkin
In this talk I will present and compare several options of how to run Spring Boot 3 application on AWS Lambda using:
AWS Serverless Java Container
AWS Lambda Web Adapter
Spring Cloud Function and
Custom Docker Image.
I'll also discuss strategies how to optimize cold start of such Lambda function with AWS Custom Lambda Runtime based on GraalVM Native Image and AWS own offering SnapStart based on Firecracker microVM snapshot and restore and CRaC (Coordinated Restore at Checkpoint) runtime hooks. Moreover I'll present various (SnapStart) priming techniques to reduce the cold start even further. Is Spring Boot native support of CRaC also a potential optimization? I'll also discuss optimization strategies for the warm start/execution time of the Lambda function.
Evaluation as an Essential Component of the Generative AI LifecycleMaxim Salnikov
In this session, we’ll explore how systematic evaluation ensures generative AI applications are reliable, safe, and effective across their lifecycle. From selecting the right base model to rigorous pre-production testing and ongoing post-deployment monitoring, evaluation helps teams address risks like misinformation, biases, and security vulnerabilities. Learn how to integrate evaluation into every stage of development to build AI solutions that deliver high-quality user experiences, foster trust, and adapt seamlessly to real-world demands.
Copy & Paste the link: https://digitalcrackpro.com/wondershare-filmora-crack/
Wondershare Filmora Crack 14.3.15 + Key Free Download 2025
Wondershare Filmora Crack is a professional video editor that allows you to express your creativity while also surprising you with beautiful results. Furthermore, you will be able to incorporate your favorite music right into your film to create the appropriate atmosphere. You can also use the content and titles library to create stunning animated messages.
Disaster Recovery in Azure: Architecture and Best PracticesSan sri
Disaster Recovery in Azure: Architecture and Best Practices explores the critical role of Azure Disaster Recovery in ensuring business continuity. This article covers the essentials of Azure Site Recovery, detailing its benefits and solution architectures for both SMBs and large enterprises. Readers will learn about key components, best practices for implementation, and the importance of thorough planning and testing. Additionally, it highlights HexaCorp's integrated solutions for effective data protection. Discover how to safeguard your critical applications against potential disruptions.
Are you facing problems with inconsistent and scattered product data? It’s time to centralize, optimize, and transform your product information with Happiest Minds' Pimcore PIM capabilities.
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Google Cloud Build: Your Complete CI/CD Pipeline Solution in the Cloud
Discover how Google Cloud Build revolutionizes continuous integration and continuous delivery (CI/CD) with its fully managed, serverless platform. This comprehensive presentation explores Cloud Build's architecture, features, and real-world implementations for streamlined software delivery.
Learn how Cloud Build integrates with GitHub, Bitbucket, and Cloud Source Repositories, enabling automatic builds triggered by code commits. The platform supports multiple programming languages and frameworks, including Java, Python, Node.js, and Go, making it versatile for diverse development teams.
Through practical examples and demonstrations, attendees will learn to:
Set up automated build triggers for different environments
Implement parallel and sequential build steps
Integrate testing frameworks and quality gates
Manage artifacts across Google Cloud services
Monitor build metrics and optimize pipeline efficiency
The presentation includes case studies and advanced topics covering Cloud Build's integration with Cloud Run, Google Kubernetes Engine (GKE), and Cloud Functions. Perfect for developers, DevOps engineers, and technical leaders looking to streamline their development pipeline.
35. @tyler_treat
“Instead of explicitly using locks to mediate access
to shared data, Go encourages the use of channels
to pass references to data between goroutines.”
https://blog.golang.org/share-memory-by-communicating
94. @tyler_treat
“We generally don’t want sync/atomic to be used
at all…Experience has shown us again and again
that very very few people are capable of writing
correct code that uses atomic operations…”
—Ian Lance Taylor
105. @tyler_treat
1. Assign a generation, G1, to each
I-node (empty struct).
2. Add new node by copying I-node with
updated branch and generation then
GCAS, i.e. atomically:
- compare I-nodes to detect tree
mutations.
- compare root generations to detect
snapshots.
@tyler_treat
G2
G1
Ctrie
113. @tyler_treat
“Packages that import unsafe may depend on internal
properties of the Go implementation. We reserve the
right to make changes to the implementation that may
break such programs.”
https://golang.org/doc/go1compat