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water compressor oil grade
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Crewel yarn is a fine, 2-ply or single ply yarn traditionally made of wool, used specifically for crewel embroidery, a form of surface embroidery. Its origins trace back to at least the medieval period in England, where it was used in the Bayeux Tapestry. Crewel yarn's distinguishing feature is its lightweight and slightly twisted structure, making it suitable for creating intricate designs on linen and other fabrics. The yarn's thickness allows for the creation of textured and dimensional effects, which are characteristic of crewel work. Today, crewel yarn comes in a variety of fibers, including wool blends and synthetic varieties, expanding its versatility for different projects. Its wide palette of colors enables artists to work with a rich spectrum, producing vivid and detailed embroidered pieces. Crewel embroidery, traditionally used for decorating household items like curtains and pillows, benefits from the yarn's durability and the depth it can add to handcrafted designs.
Ilmenite is mined in Australia by Ginkgo Minerals in the Murray Basin of New South Wales and by Snapper Mining and Jacinth Ambrosia Mines in the Eucla Basin of South Australia.
ustralia's coastal titanium placer mine, a large titanium deposit.
The world famous rutile sand mine. The mining area is located on the central coast of eastern Australia.
Deploying a Python job on YARN (Yet Another Resource Negotiator) requires wrapping your Python script as a Hadoop Streaming job or utilizing a framework like PySpark. With Hadoop Streaming, the Python script is treated as a mapper and/or reducer. Firstly, ensure your Hadoop cluster and YARN are well configured. Then, use the `hadoop` command with the `streaming` option, specifying your Python scripts for `mapper` and `reducer` parameters. For example:
```
hadoop jar /path/to/hadoop-streaming.jar \
-files yourMapper.py,yourReducer.py \
-mapper yourMapper.py \
-reducer yourReducer.py \
-input yourInputPath \
-output yourOutputPath
```
For PySpark jobs, submit them using `spark-submit` with the `--master yarn` flag to ensure they run on YARN. Ensure your Python environment is consistent across all nodes for smooth execution. Deploying Python jobs on YARN allows leveraging YARN's resource management and scheduling capabilities, ensuring efficient resource use across the cluster.
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