WTF ("Who to Follow") is Twitter's user recommendation service, which is responsible for creating millions of connections daily between users based on shared interests, common connections, and other related factors. This paper provides an architectural overview and shares lessons we learned in building and running the service over the past few years. Particularly noteworthy was our design decision to process the entire Twitter graph in memory on a single server, which significantly reduced architectural complexity and allowed us to develop and deploy the service in only a few months. At the core of our architecture is Cassovary, an open-source in-memory graph processing engine we built from scratch for WTF. Besides powering Twitter's user recommendations, Cassovary is also used for search, discovery, promoted products, and other services as well. We describe and evaluate a few graph recommendation algorithms implemented in Cassovary, including a novel approach based on a combination of random walks and SALSA. Looking into the future, we revisit the design of our architecture and comment on its limitations, which are presently being addressed in a second-generation system under development.

/pdf/wtf-the-who-to-follow-service-at-twitter-3j1yvrx2d4.pdf

WTF: the who to follow service at Twitter

Logs are imperative in the development and maintenance process of many software systems. They record detailed runtime information that allows developers and support engineers to monitor their systems and dissect anomalous behaviors and errors. The increasing scale and complexity of modern software systems, however, make the volume of logs explodes. In many cases, the traditional way of manual log inspection becomes impractical. Many recent studies, as well as industrial tools, resort to powerful text search and machine learning-based analytics solutions. Due to the unstructured nature of logs, a first crucial step is to parse log messages into structured data for subsequent analysis. In recent years, automated log parsing has been widely studied in both academia and industry, producing a series of log parsers by different techniques. To better understand the characteristics of these log parsers, in this paper, we present a comprehensive evaluation study on automated log parsing and further release the tools and benchmarks for easy reuse. More specifically, we evaluate 13 log parsers on a total of 16 log datasets spanning distributed systems, supercomputers, operating systems, mobile systems, server applications, and standalone software. We report the benchmarking results in terms of accuracy, robustness, and efficiency, which are of practical importance when deploying automated log parsing in production. We also share the success stories and lessons learned in an industrial application at Huawei. We believe that our work could serve as the basis and provide valuable guidance to future research and deployment of automated log parsing.

Tools and benchmarks for automated log parsing

Machine learning (ML) is widely used to derive useful information from large-scale data (such as user activities, pictures, and videos) generated at increasingly rapid rates, all over the world. Unfortunately, it is infeasible to move all this globally-generated data to a centralized data center before running an ML algorithm over it--moving large amounts of raw data over wide-area networks (WANs) can be extremely slow, and is also subject to the constraints of privacy and data sovereignty laws. This motivates the need for a geo-distributed ML system spanning multiple data centers. Unfortunately, communicating over WANs can significantly degrade ML system performance (by as much as 53.7× in our study) because the communication overwhelms the limited WAN bandwidth.

Our goal in this work is to develop a geo-distributed ML system that (1) employs an intelligent communication mechanism over WANs to efficiently utilize the scarce WAN bandwidth, while retaining the accuracy and correctness guarantees of an ML algorithm; and (2) is generic and flexible enough to run a wide range of ML algorithms, without requiring any changes to the algorithms.

To this end, we introduce a new, general geo-distributed ML system, Gaia, that decouples the communication within a data center from the communication between data centers, enabling different communication and consistency models for each. We present a new ML synchronization model, Approximate Synchronous Parallel (ASP), whose key idea is to dynamically eliminate insignificant communication between data centers while still guaranteeing the correctness of ML algorithms. Our experiments on our prototypes of Gaia running across 11 Amazon EC2 global regions and on a cluster that emulates EC2 WAN bandwidth show that Gaia provides 1.8-53.5× speedup over two state-of-the-art distributed ML systems, and is within 0.94-1.40× of the speed of running the same ML algorithm on machines on a local area network (LAN).

/pdf/gaia-geo-distributed-machine-learning-approaching-lan-speeds-1ccudwayze.pdf

Gaia: geo-distributed machine learning approaching LAN speeds

https://cyberleninka.org/article/n/325149.pdf

Reference Architecture and Classification of Technologies, Products and Services for Big Data Systems

Modern engineering incorporates smart technologies in all aspects of our lives. Smart technologies are generating terabytes of log messages every day to report their status. It is crucial to analyze these log messages and present usable information (e.g. patterns) to administrators, so that they can manage and monitor these technologies. Patterns minimally represent large groups of log messages and enable the administrators to do further analysis, such as anomaly detection and event prediction. Although patterns exist commonly in automated log messages, recognizing them in massive set of log messages from heterogeneous sources without any prior information is a significant undertaking. We propose a method, named LogMine, that extracts high quality patterns for a given set of log messages. Our method is fast, memory efficient, accurate, and scalable. LogMine is implemented in map-reduce framework for distributed platforms to process millions of log messages in seconds. LogMine is a robust method that works for heterogeneous log messages generated in a wide variety of systems. Our method exploits algorithmic techniques to minimize the computational overhead based on the fact that log messages are always automatically generated. We evaluate the performance of LogMine on massive sets of log messages generated in industrial applications. LogMine has successfully generated patterns which are as good as the patterns generated by exact and unscalable method, while achieving a 500× speedup. Finally, we describe three applications of the patterns generated by LogMine in monitoring large scale industrial systems.

LogMine: Fast Pattern Recognition for Log Analytics

In recent years, there has been a substantial amount of work on large-scale data analytics using Hadoop-based platforms running on large clusters of commodity machines. A less-explored topic is how those data, dominated by application logs, are collected and structured to begin with. In this paper, we present Twitter's production logging infrastructure and its evolution from application-specific logging to a unified "client events" log format, where messages are captured in common, well-formatted, flexible Thrift messages. Since most analytics tasks consider the user session as the basic unit of analysis, we pre-materialize "session sequences", which are compact summaries that can answer a large class of common queries quickly. The development of this infrastructure has streamlined log collection and data analysis, thereby improving our ability to rapidly experiment and iterate on various aspects of the service.

/pdf/the-unified-logging-infrastructure-for-data-analytics-at-2o98fqowdj.pdf

The unified logging infrastructure for data analytics at Twitter

The Unified Logging Infrastructure for Data Analytics at Twitter

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

George Lee

Author Tools

Chat about Author

Papers

The unified logging infrastructure for data analytics at Twitter

The Unified Logging Infrastructure for Data Analytics at Twitter