hdfs-du | Visualize your HDFS cluster usage | Data Visualization library

by twitter-archive JavaScript Version: Current License: Apache-2.0

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kandi X-RAY | hdfs-du Summary

hdfs-du is a JavaScript library typically used in Analytics, Data Visualization, Docker, Hadoop applications. hdfs-du has a Permissive License and it has low support. However hdfs-du has 7 bugs and it has 1 vulnerabilities. You can download it from GitHub.

Visualize your HDFS cluster usage

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Quality

Security

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Support

hdfs-du has a low active ecosystem.

It has 224 star(s) with 86 fork(s). There are 140 watchers for this library.

It had no major release in the last 6 months.

There are 10 open issues and 0 have been closed. There are 5 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of hdfs-du is current.

Quality

hdfs-du has 7 bugs (4 blocker, 0 critical, 3 major, 0 minor) and 51 code smells.

Security

hdfs-du has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

hdfs-du code analysis shows 1 unresolved vulnerabilities (1 blocker, 0 critical, 0 major, 0 minor).

There are 3 security hotspots that need review.

License

hdfs-du is licensed under the Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

hdfs-du releases are not available. You will need to build from source code and install.

Installation instructions, examples and code snippets are available.

hdfs-du saves you 582 person hours of effort in developing the same functionality from scratch.

It has 1359 lines of code, 44 functions and 27 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

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hdfs-du Key Features

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hdfs-du Examples and Code Snippets

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Community Discussions

Trending Discussions on hdfs-du

Cluster hosts have more storage space than HDFS seems to recognize / have access to? How to increase HDFS storage use?

QUESTION

Cluster hosts have more storage space than HDFS seems to recognize / have access to? How to increase HDFS storage use?

Asked 2020-May-26 at 22:29

Having problem where HDFS (HDP v3.1.0) is running out of storage space (which is also causing problems with spark jobs hanging in ACCEPTED mode). I assume that there is some configuration where I can have HDFS use more of the storage space already present on the node hosts, but exactly what was not clear from quick googling. Can anyone with more experience help with this?

In Ambari UI, I see... (from ambari UI) (from NameNode UI).

Yet when looking at the overall hosts via ambari UI, there appears to be still a good amount of space left on the cluster hosts (the last 4 nodes in this list are the data nodes and each has a total of 140GB of storage space)

Not sure what setting are relevant, but here are the general setting in ambari: My interpretation of the "Reserved Space for HDFS" setting is that it shows there should be 13GB reserved for non-DFS (ie. local FS) storage, so does not seem to make sense that HDFS is already running out of space. Am I interpreting this wrongly? Any other HDFS configs that should be shown in this question?

Looking at the disk usage by HDFS, I see...

...

ANSWER

Answered 2020-May-26 at 21:28

You haven't mentioned if there is crappy data in /tmp for example that could be cleaned.

Each datanode has 88.33 GB of storage?

If so, you cannot just create new HDDs to become attached to the cluster and suddenly create space.

dfs.data.dir in hdfs-site is a comma-separated list of mounted volumes on each datanode.

To get more storage, you need to format and mount more disks, then edit that property.

Source https://stackoverflow.com/questions/62031318

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install hdfs-du

To get started with hdfs-du, first clone the hdfs-du GitHub repository:.

Support

Bug fixes, features, and documentation improvements are welcome! Please fork the project and send us a pull request on GitHub. You can submit issues on Github as well.

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