How Many Days Is 212 Hours
How Many Days Is 212 Hours – HomeIndiaIndia reports 14,313 COVID-19 cases, 181 deaths in last 24 hours as active cases fall to 212-day low
India reports 14,313 COVID-19 cases, 181 deaths in last 24 hours as active cases fall to 212-day low
How Many Days Is 212 Hours
India reported 14,313 new cases of COVID-19 and 181 deaths in the last 24 hours. Data from the Ministry of Health on Tuesday morning indicate that 26,579 people recovered during that period. With this, the total number of active cases in the country has now fallen to 2.14 lakh – the lowest in 212 days.
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Will is the author of On Learning to Program, a blog for new software engineers entering the industry. Will is also a certified reviewer where he has caught hundreds of bugs and other critical issues for over 30 teams.
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What is Apache Spark? Apache Spark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Java, Scala, Python, and R, and an optimized engine that supports general implementation graphs. It also supports a rich set of higher-level tools including Spark SQL for SQL and structured data processing, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for incremental processing. -flow calculation and processing.
Apache Spark began as a research project at the UC Berkeley AMLab in 2009 and was launched in early 2010. Many of the ideas behind the system have been presented in various research papers over the years. After its release, Spark grew into a huge development community and transitioned to the Apache Software Foundation in 2013. Today, the project is jointly developed by a community of hundreds of developers from hundreds of organizations.”
At the time of publication, the Spark project has approximately 30,700 stars on GitHub, over 24,500 forks, and over 31,000 commits in the main development branch. The vast majority of Spark is written in Scala.
Spark has a large set of contributor guidelines on its project website. Specifically, the first few sections of the Contributor Guidelines cover ways to contribute to the project that don’t directly contribute to the core code—this includes helping other users via the mailing list or StackOverflow, testing releases, reviewing changes, contributing to the documentation, and filing bug reports.
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Under the “Review Code” contribution method in the Contributor Guidelines, we found our first interesting discovery – Spark has a custom UI for filtering PRs and displaying their status:
This UI allows the user to filter PRs by open and obsolete, the project component to which the PR relates (Documents, Streaming, SQL, Web UI, etc.) and classification – sorting by some significant PR attributes, such as JIRA issue number , author, last update, changeset size, Jenkins CI build status, and when the project committer last commented.
As a large agile project, this UI is a very useful addition for maintainers – as someone who has worked on agile systems before, PR management can be a nightmare, and being able to filter the PR list to only the subset of pull requests I’m interested in really is useful.
As we saw earlier looking at the Apache Superset, Spark has very strict naming rules for its pull requests. Incomplete pull requests must be prefixed
What We Can Learn About Code Review From The Apache Spark Project
, each PR must be prefixed with a JIRA issue identifier to associate the changes with a JIRA issue, such as
Looking at the Github actions for the repository, we can see how this custom UI is partially driven by Github tags:
The tags are applied when the files matching the pattern in the YAML configuration are changed in the PR. This is a great way to automate PR classification management, removing the headache for project managers of manually tagging each PR with relevant tags.
Spark’s pull request template is dedicated to helping reviewers. It’s quite long so I refrained from copying it here. There are a few things worth noting:
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This section focuses on ensuring that code reviewers have all the necessary context to understand the proposed change and links to relevant documentation they may need to understand any relevant system. One of the wrong assumptions of software development for any project like Spark with a lot of external integrations is that those who maintain the project are experts in those systems. In general, different commits may be maintained by different committers, or committers may have a general, but not specific, understanding of how commits should work. Requiring links to documentation is a good way to ensure that the author communicates his understanding of these integrations to the reviewer when relevant.
Often, code authors believe that the change set being evaluated contains all the context needed to understand the motivation for the change. Isn’t it obvious that this is a mistake? Is the use case not clear from the code? In most cases, the code needs further explanation. Especially in large, fast-paced projects like Spark, it’s likely that many reviewers have full context about most applications. Adding a good change rationale for reviewers can greatly speed up the code review process.
Oh, release management. A big challenge for open source maintainers is to update the documentation and provide good release notes when a new version is delivered. Asking PR authors to count user-related changes can greatly assist maintainers in generating these release notes and ensuring that PRs are captured in the correct release number – a project following semantic versioning or similar will have release numbering rules and if these descriptions correct, they will allow contributors to make good decisions about which release a particular PR should fall into
Apache Spark has a section in its Contributor Guidelines that covers Code Review Standards that are likely to apply when reviewing code. The fact that these standards are shared with new contributors first is a positive and something I think more projects (open source and closed source) should consider emulating.
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Some of the more interesting points here are in the “Negatives, Risks” section which discusses code changes that the project is unlikely to accept. I found the following interesting:
“Introduces complex new functionality, especially an API that needs to be supported” and “Changes the public API or semantics (rarely allowed)” – the maintainers seem to believe that the Spark project is functionally complete at this stage ( and after 30,000+ commits, I think they have reasons) and optimization for the stability of the existing project. Given the pain involved in supporting the transformation of a public API into a mainstream system, this is a worthy goal.
“Adds userspace functionality that doesn’t need to be maintained in Spark, but can be hosted externally and indexed by spark-packages.org” – Spark used the packaging system to allow extensions of the Spark project that can be maintained by other groups, outside of the core Committers in the main Apache project. This is interesting because it allows Spark to be extended without forcing additional changes to the core repository.
“Lots of changes in a “big bang” change” – smaller changes are easier to review and if they cause problems easier to roll back. This is a common best practice when developing software, especially when dealing with mature, complex systems.
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One of the positives is also worth noting – if “the change is discussed and known by the committer”, it is more likely to be integrated. On larger projects with a management team, input from the plant can be more trouble than it’s worth. If executors are contacted before proposing a change, they will have an opportunity to discuss the merits and convey additional requirements to the new associate before the work is done. This makes the outcome more likely to be positive rather than negative for the community.
Do you find it useful? Be sure to check out What We Can Learn About Code Review from the Apache Superset Project.
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