CloudBurst 2019 - Indexing and searching NuGet.org with Azure Functions and Search

Speaker: Christoph Petrausch Event: inovex Brownbag 06. November 2015 weiter Vorträge von inovex: https://www.inovex.de/de/content-pool/vortraege

Warren Strange, Principal Systems Engineer, ForgeRock, presents a Breakout Session on the ELK Stack at the 2014 IRM Summit in Phoenix, Arizona.

Logstash is a tool for managing logs that allows for input, filter, and output plugins to collect, parse, and deliver logs and log data. It works by treating logs as events that are passed through the input, filter, and output phases, with popular plugins including file, redis, grok, elasticsearch and more. The document also provides guidance on using Logstash in a clustered configuration with an agent and server model to optimize log collection, processing, and storage.

1. Logstash is an open source tool for collecting, processing, and storing logs and other event data. It allows centralized collection and parsing of logs from various sources before sending them to Elasticsearch for storage and indexing. 2. Kibana provides visualization and search capabilities on top of the logs stored in Elasticsearch, allowing users to easily explore and analyze log data. 3. The combination of Logstash, Elasticsearch, and Kibana provides a replacement for commercial log management tools like Splunk, with the ability to collect, parse, store, search, and visualize logs from many different sources in a centralized way.

This talk covers the basics of centralizing logs in Elasticsearch and all the strategies that make it scale with billions of documents in production. Topics include: - Time-based indices and index templates to efficiently slice your data - Different node tiers to de-couple reading from writing, heavy traffic from low traffic - Tuning various Elasticsearch and OS settings to maximize throughput and search performance - Configuring tools such as logstash and rsyslog to maximize throughput and minimize overhead

The document discusses reactive functional programming with Java 8 on Android N. It introduces reactive programming concepts like Observables and Subscribers. It provides an example of using RxJava to find PNG images in a folder and load them into a gallery, as compared to the vanilla Java approach. It also demonstrates creating Observables, Subscribers, transforming streams, handling REST responses, and subscribing to streams. Specifically, it shows an example of clicking a button to get a user's followers from GitHub, get details on each follower, filter by company, and update the UI with results.

This document introduces the ELK stack, which consists of Elasticsearch, Logstash, and Kibana. It provides instructions on setting up each component and using them together. Elasticsearch is a search engine that stores and searches data in JSON format. Logstash is an agent that collects logs from various sources, applies filters, and outputs to Elasticsearch. Kibana visualizes and explores the logs stored in Elasticsearch. The document demonstrates setting up each component and running a proof of concept to analyze sample log data.

This document provides an overview of the ELK stack, including Logstash for collecting and parsing logs, Elasticsearch for indexing logs, and Kibana for visualizing logs. It discusses using the open source ELK stack as an alternative to Splunk and provides instructions for getting started with a basic ELK implementation.

This document provides an overview of Retrofit, an open source library for Android and Java that allows making REST API calls in a simple and efficient manner. It discusses how to initialize Retrofit with an endpoint URL and adapter, define API methods using annotations, handle requests and responses both synchronously and asynchronously, and convert JSON responses to Java objects using Gson. Code samples are provided throughout to demonstrate common Retrofit tasks like making GET requests, handling API parameters and headers, and subscribing to asynchronous Observable responses.

This document discusses the ELK stack, which consists of Elasticsearch, Logstash, and Kibana. It provides an overview of each component, including that Elasticsearch is a search and analytics engine, Logstash is a data collection engine, and Kibana is a data visualization platform. The document then discusses setting up an ELK stack to index and visualize application logs.

LogStash is a tool for ingesting, processing, and storing data from various sources into Elasticsearch. It includes plugins for input, filter, and output functionality. Common uses of LogStash include parsing log files, enriching events, and loading data into Elasticsearch for search and analysis. The document provides an overview of LogStash and demonstrates how to install it, configure input and output plugins, and create simple and advanced processing pipelines.

This document discusses experiences using the ELK stack (Elasticsearch, Logstash, Kibana) and D3.js for large log analysis and visualization. It begins with an overview of network traffic logging at Kasetsart University, which generates over 30 terabytes of log data per day. It then demonstrates setting up an ELK testbed to index these logs in real-time for fast search and exploration in Kibana. Finally, it shows how D3.js can be used to create dynamic, real-time visualizations of the logged data.

Speaker: Dominik Helleberg, inovex MobileTech Conference 2015 September 2015 Mehr Vorträge: https://www.inovex.de/de/content-pool/vortraege/

This document discusses Logstash, an open source tool for collecting, parsing, and storing log files. It can ingest logs from various sources using inputs, apply filters to parse and transform log events, and output the structured data to destinations like Elasticsearch for search and analysis. The document provides an overview of Logstash's core functionality and components, demonstrates simple usage examples, and discusses integrating it with Kibana for visualizing and exploring log data. It also shares some lessons learned in production usage and points to additional resources.

Retrofit is a type-safe REST client library for Android and Java that allows defining REST APIs as Java interfaces. It simplifies HTTP communication by converting remote APIs into declarative interfaces. It supports synchronous, asynchronous, and observable API consumption. The Retrofit library was created by Square.

A case study of the usage of Gradle in the Ratpack web framework. First, we'll examine the Ratpack Gradle plugins, including their functionality, implementation, and testing. Next, we'll examine the build script for the Ratpack project itself. Here, we'll discuss various details of the project's build, including handling multiple projects, multiple types of testing, support for multiple styles of target hardware (developer workstations, cloud CI), and more. For each, we'll go over the desired behavior, how it was achieved, and why it was necessary.

ELK Stack workshop covers real-world use cases and works with the participants to - implement them. This includes Elastic overview, Logstash configuration, creation of dashboards in Kibana, guidelines and tips on processing custom log formats, designing a system to scale, choosing hardware, and managing the lifecycle of your logs.

Which NuGet package was that type in again? In this session, let's build a "reverse package search" that helps finding the correct NuGet package based on a public type. Together, we will create a highly-scalable serverless search engine using Azure Functions and Azure Search that performs 3 tasks: listening for new packages on NuGet.org (using a custom binding), indexing packages in a distributed way, and exposing an API that accepts queries and gives our clients the best result.

Which NuGet package was that type in again? In this session, let's build a "reverse package search" that helps to find the correct NuGet package based on a public type name. Together, we will create a highly-scalable serverless search engine using Azure Functions and Azure Search that performs 3 tasks: listening for new packages on NuGet.org (using a custom binding), indexing packages in a distributed way, and exposing an API that accepts queries and gives our clients the best result. Expect code, insights into the service design process, and more!

Which NuGet package was that type in again? In this session, let's build a "reverse package search" that helps finding the correct NuGet package based on a public type. Together, we will create a highly-scalable serverless search engine using Azure Functions and Azure Search that performs 3 tasks: listening for new packages on NuGet.org (using a custom binding), indexing packages in a distributed way, and exposing an API that accepts queries and gives our clients the best result. https://blog.maartenballiauw.be/post/2019/07/30/indexing-searching-nuget-with-azure-functions-and-search.html

Everybody is consuming NuGet packages these days. It’s easy, right? But how can we create and share our own packages? What is .NET Standard? How should we version, create, publish and share our package? Once we have those things covered, we’ll look beyond what everyone is doing. How can we use the NuGet client API to fetch data from NuGet? Can we build an application plugin system based on NuGet? What hidden gems are there in the NuGet server API? Can we create a full copy of NuGet.org? Good questions! In this talk, we will get them answered.

Everybody is consuming or producing NuGet packages these days. It’s easy, right? We’ll look beyond what everyone is doing. How can we use the NuGet client API to fetch data from NuGet? Can we build an application plugin system based on NuGet? What hidden gems are there in the NuGet server API? Can we create a full copy of NuGet.org?

- What are Internal Developer Portal (IDP) and Platform Engineering? - What is Backstage? - How Backstage can help dev to build developer portal to make their job easier Jirayut Nimsaeng Founder & CEO Opsta (Thailand) Co., Ltd. Youtube Record: https://youtu.be/u_nLbgWDwsA?t=850 Dev Mountain Tech Festival @ Chiang Mai November 12, 2022

This document summarizes Nuxeo's Release 8.1 including new tools for launching performance tests on Nuxeo clusters, an instant share feature for temporarily granting access without account creation, Live Connect integration for Box file sharing, and expanded Elasticsearch integration. It also discusses Nuxeo Docker images, a Nuxeo code generator, a Polymer sample app, updated REST and automation clients, and upcoming branch management features.

This presentation was given at the Boston Django meetup on November 16, and surveyed several leading PaaS providers including Stackato, Dotcloud, OpenShift and Heroku. For each PaaS provider, I documented the steps necessary to deploy Mezzanine, a popular Django-based CMS and blogging platform. At the end of the presentation, I do a wrap-up of the different providers and provide a comparison matrix showing which providers have which features. This matrix is likely to go out-of-date quickly because these providers are adding new features all the time.

The document provides an overview of OGCE (Open Grid Computing Environment), which develops and packages reusable software components for science portals. Key components described include services, gadgets, tags, and how they fit together. Installation and usage of the various OGCE components is discussed at a high level.

With distributed tracing, we can track requests as they pass through multiple services, emitting timing and other metadata throughout, and this information can then be reassembled to provide a complete picture of the application’s behavior at runtime - Read more in https://blog.buoyant.io/2016/05/17/distributed-tracing-for-polyglot-microservices/ and https://www.rookout.com/

This document discusses Elsevier's SciVerse platform and developer network. It introduces SciVerse as a social network for scientific search and content that uses OpenSocial standards. It describes how SciVerse extends Apache Shindig to make apps contextual. It also discusses SciVerse's framework and content APIs that allow apps to access scientific content and metadata. Finally, it provides examples of object-oriented JavaScript coding and using the APIs to build mashups with third-party services.

This document discusses Elsevier's SciVerse platform and developer network. It introduces SciVerse as a social network for scientific search and content that uses OpenSocial standards. It describes how SciVerse extends Apache Shindig to make apps contextual. It also discusses SciVerse's framework and content APIs that allow apps to access scientific content and metadata. Finally, it provides examples of object-oriented JavaScript coding and using the APIs to build mashups with third-party services.

This document discusses using Nutch, an open source web crawler, with Scala. It provides an overview of Nutch's architecture and how plugins can be written in Scala to extend its functionality. As an example, it describes how Scala was used to build a plugin for an aggregator application that crawls multiple suppliers, parses content to extract details, and passes this data to an actor for processing. The solution was able to crawl 5 suppliers and collect over 500k records using Nutch and 823 lines of Scala code.