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Articles by Ray Chiang
Utilisation Of Singapore's Scheme Of Arrangement Mechanism By Indian Companies Undertaking A "Reverse Flip" To India
In recent times, a number of Indian startups initially domiciled abroad, including some in Singapore, have undertaken a "reverse flip" — a process of redomiciling their parent entities back to India. This strategic move back home aligns with their aspirations to tap into India's burgeoning capital markets and favourable regulatory environment. Furthermore, this is supported by investor sentiment whereby both Indian and global investors also acknowledge India's economic growth potential.
Flexible Work Arrangements - Next Steps For Employers
The recently announced Tripartite Guidelines on Flexible Work Arrangements Requests (Guidelines) require employers to, as far as reasonably practicable, consider employees' formal requests for flexible work arrangements (FWA) from 1 December 2024 (Effective Date). The three types of FWAs that can be requested are (i) flexible place of work, (ii) flexible working hours (with no change in total working hours and workload) and (iii) flexible workloads.
The new Work Injury Compensation Act – steps towards enhanced protection for employees and employers | JD Supra
Updated: May 25, 2018: JD Supra is a legal publishing service that connects experts and their content with broader audiences of professionals, journalists and associations.
Extensive amendments to the Employment Act in the pipeline | JD Supra
JD Supra is a legal publishing service that connects experts and their content with broader audiences of professionals, journalists and associations. This Privacy Policy describes how JD Supra, LLC ("JD Supra" or "we," "us," or "our") collects, uses and shares personal data collected from visitors to our website (located at www.jdsupra.com) (our "Website") who view only publicly-available content as well as subscribers to our services (such as our email digests or author tools)(our "Services").
Apache Hadoop 3.0.0-alpha2 Released - Cloudera Engineering Blog
The Apache Hadoop project announced the release of 3.0.0-alpha2 on January 25th, 2017. This is the second alpha release in the 3.0.0 release series leading up to 3.0.0 GA, and incorporates 857 new fixes, improvements, and features since 3.0.0-alpha1 last September. It’s worth reading our previous blog post about 3.0.0-alpha1; in this post, we’ll discuss the new improvements that landed in alpha2. The pain of classpath isolation has been experienced by many Java developers.
Untangling Apache Hadoop YARN, Part 5: Using FairScheduler queue properties - Cloudera Engineering Blog
Previously in Part 4, we described the most commonly used FairScheduler properties in Apache Hadoop. In Part 5, we’ll provide some examples to show how properties can be used, individually and in combination, to achieve commonly desired behavior such as application prioritization and organizing queues. Summary: Create a “best effort” queue that runs applications when the cluster is underutilized.
Untangling Apache Hadoop YARN, Part 4: Fair Scheduler Queue Basics
In this installment, we provide insight into how the Fair Scheduler works, any why it works the way it does. In Part 3 of this series, you got a quick introduction to Fair Scheduler, one of the scheduler choices in Apache Hadoop YARN (and the one recommended by Cloudera). In Part 4, we will cover most of the queue properties, some examples of their use, as well as their limitations. By default, the Fair Scheduler uses a single default queue.
Untangling Apache Hadoop YARN, Part 3: Scheduling
In Parts 1 and 2, we covered the basics of YARN resource allocation. In this installment, we’ll provide an overview of cluster scheduling and introduce the Fair Scheduler, one of the scheduler choices available in YARN. A standalone computer can have several CPU cores, each running a single process, but there can be as many as a few hundred processes running simultaneously.
Untangling Apache Hadoop YARN, Part 2
A new installment in the series about the tangled ball of thread that is YARN In Part 1 of this series, we covered the fundamentals of clusters of YARN. In Part 2, you’ll learn about other components than can run on a cluster and how they affect YARN cluster configuration. As shown in the previous post, a YARN cluster can be configured to use up all the resources on the cluster.
Untangling Apache Hadoop YARN, Part 1
In this multipart series, fully explore the tangled ball of thread that is YARN. YARN (Yet Another Resource Negotiator) is the resource management layer for the Apache Hadoop ecosystem. YARN has been available for several releases, but many users still have fundamental questions about what YARN is, what it’s for, and how it works.
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