HDFS-DU is an interactive visualization of the Hadoop distributed file system. The project aims to monitor different snapshots for the entire HDFS system in an interactive way, showing the size of the folders, the rate at which the size increases / decreases, and to highlight inefficient file storage.
HDFS-DU provides the following in a web UI:
- A tree-map visualization where each node is a folder in HDFS. The area of each node can be relative to the size or number of descendants.
- A file-tree visualization showing the topology of the file system.
HDFS-DU is built using the following front-end technologies:
- D3.js - For file-tree visualization
- JavaScript InfoVis Toolkit - For tree-map visualization
Follow @hdfsdu on Twitter to stay in touch!
Below is a screenshot of the HDFS-DU UI. The UI is made up of two linked visualizations. The left visualization is a tree-map which shows parent-child relationships through containment. The right visualization is a file-tree, which displays two levels of depth from the current selected node in the file system. The file-tree visualization displays extra information for each node on hover.
You can drill down clicking on nodes in either the tree-map or the file-tree.
There are two possible layouts for the tree-map. In the first layout the area of a node is proportional to the total file size of its descendants. In the second layout the area of a node is proportional to the count of its descendants.
To compute the color of a node, its size, including all its descendants, is divided by the number of those descendants. The color is assigned using this value such that a lighter color means more files for a given size. This helps to highlight inefficient nodes which contain too many small files.
To get started with hdfs-du, first clone the hdfs-du GitHub repository:
git clone https://github.com/twitter/hdfs-du.git
cd hdfs-du
Next, you can try running the hdfs-du demo on your local machine. The demo starts a local web server which serves the front-end client resources and sample data. Start the demo with the following command and then browse to http://localhost:20000/index.html:
./demo.sh
To visualize your own cluster, you need to generate an HDFS-DU data set. Currently this is a multi-step process:
- Export the HDFS fsimage file for offline processing with the Offline Image Viewer
- Process the fsimage export with Pig.
- Post-process the pig data set.
Simplifying this process is certainly possible (hey, it was hack week :)
First, SSH to your secondary name node, dump the fsimage in delimited format, and copy to HDFS.
hadoop oiv -i /path/to/current/fsimage -o fsimage-delimited.tsv -p Delimited
hadoop fs -copyFromLocal fsimage-delimited.tsv .
Now let's process fsimage export. Uncomment the register statement in
pig/src/test/resources/hdfsdu.pig, build the UDF, and run Pig to process the fsimage export.
cd /path/to/hdfs-du/pig
mvn package
pig -param INPUT=fsimage-delimited.tsv -param OUTPUT=hdfsdu.out pig/src/test/resources/hdfsdu.pig
Lastly we need to copy the dataset local and perform a quick post-processing step.
hadoop fs -getmerge hdfsdu.out .
python src/main/python/leaf.py hdfsdu.out/hdfsdu.out > hdfsdu.data
Now we're ready to start HDFS-DU!
./start.sh /path/to/hdfsdu.data
Point your web browser to http://localhost:20000 and see what your cluster looks like.
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.
Here are some high-level goals we'd love to see contributions for:
- Improve the front-end client
- Create a new back-end for a different runtime environment
Copyright 2012 Twitter, Inc.
Licensed under the Apache License, Version 2.0: http://www.apache.org/licenses/LICENSE-2.0