Blog - LINE ENGINEERING

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Troubleshooting Data Engineering Software
Keiji Yoshida2019-03-26
I am a data engineer at LINE Data Labs.
Hello, I'm Keiji Yoshida. I currently work as a data engineer at LINE Data Labs. In this posting, I'd like to share a few cases on data engineering software troubleshooting: Apache Hadoop YARN ResourceManager failover Apache Hadoop HDFS NameNode failover Apache Zeppelin Notebook scheduler issue Apache Zeppelin deadlock issue Apache Spark SQL performance issue Troubleshooting failover issue in Apache Hadoop YARN ResourceManager Let's start with the failover issue in
- Hadoop
- Spark
- Zeppelin
EuroSTAR Conference 2018
Tony Lu2019-03-20
I am a service QA engineer at LINE Taiwan QA team and am interested in software test technology and methodology.
What is EuroSTAR Conference 2018 EuroSTAR is an annual Software Testing Conference hosted in Europe, in November. The topics of the conference covered vary from improving software development process, like DevOps and Agile, or AI related automation testing, and also the mindset building-up to overcome recognization biases. For the details of this yearly Software Testers Festival, see the conference website here. In 2018, the conference was held in The Hague, Netherlands, from Novemb
Synchronizing largely geographically distributed teams with Lint
Daniel Vila Teissiere2019-03-13
Android engineer at LINE Android messaging app.
It's well known that as a team grows, the communication overhead increases geometrically until it takes up most of the time of the members. Static analysis can help us reduce the time cost, streamline the code review process, as well as to keep big teams synchronized. As you can imagine, in LINE we face challenging problems on a daily basis, one of them being communication. Keeping teams in different parts of the world synchronized is the key to speed up delivery, so anything we can do to
TensorFlow.js
Jun2019-03-08
I am a front-end developer at LINE. I'm interested in programming languages.
Greetings! This is Jun and I'm a front-end developer at LINE. Recently the front-end development scene is booming with new technologies and it's becoming difficult to catch up with all those new things. Personally I'm onto machine learning (ML). As part of today's topic as "implementing machine learning in front-end web development", let me share my little experiment on machine learning, using TensorFlow.js. Getting started I used TensorFlow.js to build a simple classification model for
- frontend
- UIT
- TensorFlow
LIFF + TIC-80
seisuke2019-03-07
LINE Engineer
Hello, this is Seisuke. I work at LINE Fukuoka as an engineer for LINE app for Android. Our office held a hackathon for LINERs in November 2018, at which I had a chance to try out TIC-80, a fantasy console, with LIFF. I wanted to share some of the lessons I learned through the event. LINE Fukuoka annually holds a hackathon for LINERs. The way it works is this; someone with an idea submits a proposal, and then four to five developers who are interested in a particular idea make up a team to imp
- LIFF
- TIC-80
Wrapping a native SDK for Unity: our challenges and choices
Wei Wang2019-03-06
Wei Wang is a professional developer who focuses on iOS now. He has strong knowledge of Swift, Objective-C and network programming. With these skills, he created quite a few widely used applications and frameworks.
I am Wei Wang from the LINE SDK development team. Last year at LINE Dev Day 2018, we released our new open source LINE SDK for iOS and Android. The SDK provides a modern way to integrate LINE Login and several APIs into your app, helping you create an engaging and personalized user experience. In addition to app developers, we realized we needed to support mobile game developers as well. Games make up more than 50% of the apps in the App Store and Google Play, and generate the m
LINE의 OpenJDK 적용기: 호환성 확인부터 주의 사항까지
YongChan Kwon2019-02-25
SRE 엔지니어, JVM 성능 관리, AWX 운영
2018년 오라클의 라이선스 체계가 변경되면서 2019년 1월 이후 더 이상 무료로 Oracle JDK를 사용할 수 없게 되었습니다. 이에 LINE 내부에서는 사전에 OpenJDK를 적용하기 위해 필요한 사항이나 검토 항목을 정리하기 위하여 TF(task force)를 구성하였고, 진행된 내용과 사용된 기술적인 사항들을 정리하고자 이 글을 작성하게 되었습니다. 시작하기 전에 본격적인 이야기를 시작하기 전에 OpenJDK를 검토하면서 느낀 부분이 있어 옛날 옛적 이야기를 적어봅니다. 2000년 초반만 해도 OSS(Open Source Software)의 신뢰도에 대한 평가는 극과 극이었고, 공공 기관이나 큰 규모의 사업장에서 실제 OSS를 적용하기에는 쉽지 않은 시기였습니다. 예를 들어 Apache HTTPD Server(이하 Apache 웹 서버)의 Prefork MPM(Multi-Processing Modules)의 메모리 누수 문제와 당시 신
- Site Reliability Engineering
LINE Security Bug Bounty Program Report 2018
Kazuhiro Kubota2019-02-19
I hunt for vulnerabilities at LINE.
Hello, this is Kazuhiro Kubota from the LINE Security Department. Last time we brought you the report for the first half of 2018. Today, I'd like to summarize the results of the LINE Security Bug Bounty program for 2018. Number of reports in 2018 In 2018, a total of 88 reports qualified for the program. This figure is almost twice as many as the 45 cases in 2017. Indeed, we did expand our scope of the program from the previous year, 2017, but we think t
- Security
- Bug Bounty
conflr: Publishing R Markdown documents on Confluence
Hiroaki Yutani2019-02-13
Software Engineer at Data Analysis team, Data Labs.
I'm Hiroaki Yutani from Data Analysis team, Data Labs. Today, I'm happy to announce conflr (pronounced as confl-R), an open source R package for publishing R Markdown documents on Atlassian Confluence. In this post, I'll briefly explain the motivation; why we need this, and how to use conflr. A gap between R Markdown and Confluence Confluence is one of the greatest tools for documentation. Our team also utilizes it for sharing reports and various kinds of knowledge. On the
Introducing Rekcurd for Clova
Keigo Hattori2019-02-13
I am Hattori(@keigohtr). I develop NLU (Natural Language Understanding) for Clova at LINE.
Machine learning (ML) is getting more and more popular these days. You probably are using it already at your work, or have been encouraged to have a go at it. Thanks to its popularity, we see more tools and frameworks for machine learning emerging. Not only that, anyone can learn linear regression, logistic regression as well as various deep learning algorithms such as Perceptron, Adaboost, Random Forest, Support Vector Machine and XGBoost. Although we are seeing growth in tools and framew