I'm learning elasticsearch now.
This slide is old, new version is here. -> http://blog.johtani.info/blog/2013/08/30/hold-first-elasticsearch-meetup-in-japan/
HTrace: Tracing in HBase and HDFS
HBase Meetup on the Night before Strata/HW @ Google in Chelsea
2014/10/15
NTT DATA
Masatake Iwasaki (OSS Professional Services)
I'm learning elasticsearch now.
This slide is old, new version is here. -> http://blog.johtani.info/blog/2013/08/30/hold-first-elasticsearch-meetup-in-japan/
HTrace: Tracing in HBase and HDFS
HBase Meetup on the Night before Strata/HW @ Google in Chelsea
2014/10/15
NTT DATA
Masatake Iwasaki (OSS Professional Services)
Scala.js can compile Scala code to JavaScript, allowing Scala code to run in browsers. The presenter ported a Node.js application called Nightwatch to Scala.js to see how it works. They encountered some obstacles like obscure bugs and difficulty switching between Scala and JavaScript contexts. While Scala.js provided some benefits like type safety and pattern matching, it also required more lines of code and time compared to the original JavaScript version. The presenter also looked at ScalaCSS for CSS but found it undocumented and difficult to use. They concluded Scala.js may not be worth it to replace JavaScript, and other tools like Sass are better for CSS than ScalaCSS. Scalat
Guess the Country - Playing with Twitter Streaming APIChris Birchall
(1) The document describes a project that collected over 285,000 geo-tagged tweets over 13 days using the Twitter Streaming API and extracted user names and countries to build a database linking names to countries. (2) It then used Spark to process the data and built a web interface that allows users to search for a name and get the most likely country. (3) Analysis of the data found the top countries by user count were the United States, United Kingdom, Indonesia and others, and the most common first and last names.
ScalaCache is a facade library that provides a simple API for caching methods across various cache systems like Google Guava, Ehcache, Memcached, and Redis. It uses macros to generate cache keys and automatically cache method return values. This avoids issues with traditional approaches using the Java API or Spring which involve runtime overhead or restrictions. ScalaCache aims to improve features like asynchronous support, versioning, custom serialization, and browser caching integration.
Hydra is a Hadoop-style distributed processing framework optimized for building and navigating tree data structures. It includes components for job control, task running, querying, and a distributed filesystem. To get started, users install prerequisites like RabbitMQ and Maven, clone and build the Hydra repository, start the local stack, seed sample data, and can then run sample jobs and queries to see results. The document provides tips for analyzing text files with Hydra and concludes that it is well suited for applications that involve working with tree data structures.
This document provides an overview of Gatling, an open source load testing tool. It can record scenarios from browser interactions similar to Selenium and run multiple scenarios simultaneously. The document discusses Gatling's scripting domain specific language (DSL) for defining scenarios, advanced features like loading data from files, and how to get started using Gatling by downloading, creating a simulation, and viewing reports. It also briefly mentions Gatling's internal architecture using Scala, Akka, and Netty.
This document discusses debugging and testing Elasticsearch systems. It provides tips for debugging issues like typos in mappings, setting up a local environment for testing, useful commands like analyze and explain, tuning queries, and testing strategies using Java and Ruby. The document emphasizes the importance of testing representative queries to ensure expected results and the ability to tune queries without breaking other queries. It also recommends using Elasticsearch plugins like Head for visualizing clusters and indices.
Phone Home: A client-side error collection systemChris Birchall
This document summarizes Phone Home, a system to collect client-side errors from users' browsers. The Phone Home client runs JavaScript in users' browsers to collect errors and send them via HTTP POST requests to the Phone Home server. The server is built with Scalatra and saves the error data to MongoDB. It was created to debug unreproducible jQuery errors in IE browsers. Features include error handling, page load timing collection, custom field support, and an admin UI with stats and a recent events list. The server uses CORS to allow cross-domain requests from the Phone Home client. Data can also be analyzed using the ltsv4s library to parse and query the log files in Scala.
3. ZooKeeperとは
“ZooKeeper is a centralized service for
maintaining configuration information, naming,
providing distributed synchronization, and
providing group services.”
と言うと…?