Sat.Jun 01, 2024 - Fri.Jun 07, 2024

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Hibernate Validator vs Regex vs Manual Validation: Which One Is Faster?

DZone

While I was coding for a performance back-end competition , I tried a couple of tricks, and I was wondering if there was a faster validator for Java applications, so I started a sample application. I used a very simple scenario: just validate the user's email.

Java 278
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Turbocharge Dynatrace App development with the new Visual Studio Code extension `Dynatrace Apps`

Dynatrace

As an app developer, you have many recurring tasks: starting the development server, creating app functions, querying data stored in Grail, managing app configurations, and building and deploying apps. Sound familiar? The VS Code extension Dynatrace Apps is here to streamline your development process and simplify app building. App configuration and build management Let’s start with the basics: Use the App Configuration form (select Configure app in the VS Code Project tree) to set up all necessa

Code 242
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What Is RabbitMQ: Key Features and Uses

Scalegrid

RabbitMQ is an open-source message broker that simplifies inter-service communication by ensuring messages are effectively queued, delivered, and processed across diverse applications. It employs the Advanced Message Queuing Protocol (AMQP) to provide reliable, scalable message passing, crucial for modern applications dealing with large-scale, complex data flows.

IoT 130
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Flaky Tests Overhaul at Uber

Uber Engineering

Dive into how we tackled flakiness among thousands of tests in our CI pipelines with a modularized and configurable approach across our diverse codebase, strengthening reliability of testing infrastructure, improving efficiency in identifying and resolving issues.

Testing 104
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Master the Art of Querying Data on Amazon S3

DZone

In an era where data is the new oil, effectively utilizing data is crucial for the growth of every organization. This is especially the case when it comes to taking advantage of vast amounts of data stored in cloud platforms like Amazon S3 - Simple Storage Service, which has become a central repository of data types ranging from the content of web applications to big data analytics.

Big Data 278
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Auto-adaptive thresholds for AI-driven quality gating

Dynatrace

The latest release of the Site Reliability Guardian incorporates assistance from Dynatrace Davis® AI to automatically derive appropriate threshold targets and adjust them over time to protect your quality improvements. This process, known as auto-adaptive thresholding, eliminates the need to define a static threshold upfront. Instead, it derives the suitable thresholds from previous validation results.

Metrics 232
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Administering a Patroni Managed PostgreSQL Cluster

Percona

There are quite a number of methods these days for installing Patroni. I want to discuss the next step, which is how to start tuning a running system.The manner of updating a running Postgres server typically involves editing the standard Postgres files: postgresql.conf postgresql.auto.conf pg_hba.

Tuning 93

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Managing Architectural Tech Debt

DZone

When I think about technical debt , I still remember the first application I created that made me realize the consequences of an unsuitable architecture. It happened back in the late 1990s when I was first getting started as a consultant. The client had requested the use of the Lotus Notes platform to build a procurement system for their customers. Using the Lotus Notes client and a custom application, end-users could make requests that would be tracked by the application and fulfilled by the pr

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Dynatrace SaaS release notes version 1.293

Dynatrace

We have released Dynatrace version 1.293. To learn what’s new, have a look at the release notes. The post Dynatrace SaaS release notes version 1.293 appeared first on Dynatrace news.

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Controlling Resource Consumption on a PostgreSQL Server Using Linux cgroup2

Percona

Multi-tenancy/co-hosting is always challenging. Running multiple PG instances could help to reduce the internal contention points (scalability issues) within PostgreSQL. However, the load caused by one of the tenants could affect other tenets, which is generally referred to as the “Noisy Neighbor” effect.

Servers 93
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Presentation: LIquid: A Large-Scale Relational Graph Database

InfoQ

Scott Meyer discusses LIquid, the graph database built to host LinkedIn, serving a ~15Tb graph at ~2M QPS.

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Best Practices for Batch Processing in IBM App Connect Enterprise as a Service

DZone

Batch processing is a capability of App Connect that facilitates the extraction and processing of large amounts of data. Sometimes referred to as data copy , batch processing allows you to author and run flows that retrieve batches of records from a source, manipulate the records, and then load them into a target system. This post provides recommendations for designing flows that use batch processing.

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Upgrading M3DB from v1.1 to v1.5

Uber Engineering

Innovating with Impact: Learn about our transformative journey upgrading the M3DB Database and enhancing Uber’s observability stack.

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GCache and Record-Set Cache Encryption in Percona XtraDB Cluster – Part One

Percona

Keeping Your Data Safe: An Introduction to Data-at-Rest Encryption in Percona XtraDB Cluster.Data breaches are a major concern today. Whether you’re a business owner or value your privacy, the thought of your sensitive information being exposed is unsettling. Fortunately, data-at-rest encryption can help.

Cache 89
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April talk video posted: “Safety, Security, Safety[sic] and C/C++[sic]”

Sutter's Mill

Many thanks to ACCU for inviting me back again this April. It was my first time back to ACCU (and only my second trip to Europe) since the pandemic began, and it was a delight to see many ACCUers in person again for the first time in a few years. I gave this talk, which is now up on YouTube here: It’s an evolved version of my March essay “C++ safety, in context.” I don’t like just repeating material, so the essay and the talk each covers things that the other doesn’

C++ 63
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Observations on Cloud-Native Observability: A Journey From the Foundations of Observability to Surviving Its Challenges at Scale

DZone

Editor's Note: The following is an article written for and published in DZone's 2024 Trend Report, Cloud Native: Championing Cloud Development Across the SDLC. Cloud native and observability are an integral part of developer lives. Understanding their responsibilities within observability at scale helps developers tackle the challenges they are facing on a daily basis.

Cloud 200
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Take a Clone it will last longer

Percona Community

So cloning is a great subject. I mean we clone sheep, we can clone human organs in time we might be able to clone humans, but thats a topic for scientist and philosophers. What is MySQL Replicaion: The MySQL clone plugin can be used to replicate data from a MySQL server to another MySQL server, and it supports replication. The cloning process creates a physical snapshot of the data, including tables, schemas, tablespaces, and data dictionary metadata.

Servers 57
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Keeping an Eye on the Eye: Self-Monitoring for Percona Monitoring and Management

Percona

Percona Monitoring and Management (PMM) is a powerful tool for keeping your databases healthy, but what about PMM itself? While it comes pre-packaged as an appliance, PMM’s internal workings can be complex. Many users, including our internal teams, frequently ask: “How can I tell if everything inside PMM is functioning properly?

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Taming the Test Beast: Effective Strategies for Streamlining Software Testing

Kovair

Testing stands as a formidable gatekeeper in the relentless demand for high-quality software. Testing is key to identifying defects, ensuring functionality, and ultimately delivering a.

Testing 52
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Telemetry Pipelines Workshop: Avoiding Telemetry Data Loss With Fluent Bit

DZone

Are you ready to get started with cloud-native observability with telemetry pipelines? This article is part of a series exploring a workshop guiding you through the open source project Fluent Bit, what it is, a basic installation, and setting up the first telemetry pipeline project. Learn how to manage your cloud-native data from source to destination using the telemetry pipeline phases covering collection, aggregation, transformation, and forwarding from any source to any destination.

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Modernizing Uber’s Batch Data Infrastructure with Google Cloud Platform

Uber Engineering

Did you know that Uber is transitioning to the cloud? Learn more about how we’re modernizing our batch data infrastructure with Google Cloud Platform.

Google 50
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End of Life Alert: CentOS 7 and Debian 10 Approaching EOL

Percona

The End-of-Support date for CentOS 7 and Debian 10 is coming soon. According to their respective release life cycles, the EOL date for both systems is June 30th, 2024. This announcement has some implications for support for Percona software running on these operating systems.

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Queuing Theory for Software Engineers

DZone

“Start With Why” Queues are a built-in mechanism everywhere in today's software. Not being familiar with the basics of queuing theory will prevent you from understanding the relations between latency and throughput , high-level capacity estimations, and workload optimization. Knowing the internals of queuing models is actually not that hard to grasp.

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NET 9 and C# 13: New Features and Improvements

DZone

The development of the.NET platform and C# language moves forward with the launch of.NET 9 and C# 13, introducing a range of enhancements and advancements to boost developer efficiency, speed, and safety. This article delves into upgrades and new features in these releases giving developers a detailed look.

C++ 130
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Downsampling Metrics in Percona Monitoring and Management: Saving Space and Improving Performance

Percona

Downsampling is the process by which we can selectively prune (discard, summarize, or recalculate) data from a series of samples in order to decrease how much storage is consumed.

Metrics 84
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MongoDB Partitioning: Best Practices for Scalability and Performance

Percona

MongoDB’s flexibility and speed make it a popular database choice, but as your data grows, managing and querying massive datasets can become challenging. This is where partitioning, also known as sharding, comes to the rescue.Partitioning strategically divides your data collection into smaller, more manageable chunks.

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Uber Becomes Kotlin™ Foundation Silver Member

Uber Engineering

We are thrilled to join the Kotlin Foundation, underscoring our commitment to the Kotlin community and our belief in Kotlin as a core part of the tech stack that has helped make Uber successful.

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How Uber Accomplishes Job Counting  At Scale

Uber Engineering

Have more rows than you can count on two hands? Don’t feel like using approximations? Learn how Uber uses Apache Pinot™ to count!

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DataK9: Auto-categorizing an exabyte of data at field level through AI/ML

Uber Engineering

Discover Uber’s pioneering DataK9 project, leveraging AI and ML to categorize data at scale and on a granular level.

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