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Michael Smalley

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If you use OS X or Linux on your desktop/servers, you may be at a point where you have configured a lot of your own settings, configurations, or themes within dotfiles. For the uninformed, dotfiles are files in your home directory that begin with a dot, or full-stop character. This indicates to the operating system that they are hidden files, used to set configuration settings for tools like vim , or shells such as bash and zsh to name a few.

This tutorial does not go into the specifics of configuring your dotfiles. Instead, my goal is to provide you with a light introduction to Git version control, allowing you to maintain your dotfiles in a centralized repository on

What’s The Point?

If you aren’t convinced it’s worth your time to put your dotfiles into Git version control, consider this:

By storing your dotfiles in a Git repository, you’ll be able to use them on any OS X or Linux machine with Internet access.

This means that in addition to gaining the ability to revert back to a known-working setup should you misconfigure your files, you will also be able to work in an environment you’ve customized yourself. On almost any workstation or server, you’re a simple git clone away from the familiarity of your customizations. More on git clone later… For now, we’ll begin with an example.

The following is an example of the type of file we would manage with git . This is actually an abridged version of my own .vimrc . The full version is available for view on my public Github :

If you don’t have your own .vimrc in your home directory, you’re welcome to use mine. It is intentionally minimal, as it works standalone (you don’t need to do anything other than drop it into your home directory for it to work). That said, we’re going to do things a little differently from here on, so pay attention (if you aren’t already).

The typical location for dotfiles on an OS X or Linux machine would be in a users home directory, e.g. /home/smalleycreative/.vimrc . We aren’t typical though, are we? We are trying to be crafty.

For starters, we’ll be putting all of our dotfiles into a folder called dotfiles , like so: /home/smalleycreative/dotfiles/vimrc . Then, we’ll simply symlink to them from our home directory. To programs like vim and bash these symlinks are transparent. As far as these programs are concerned, our dotfiles will still appear to exist at the top-level of our home directory, even though they’ll be tucked away in the dotfiles directory.

Range iteration latency: Nike W Internationlist PRM SDEWomens Sneakers Dark Stucco University Red by9W8G
’s range iteration is significantly slower than RocksDB’s range iteration, when values are also retrieved from SSD. We didn’t expect this, and still don’t quite understand it. We expected some slowdown due to the need to do IOPS on SSD, while RocksDB does purely serial reads. But, given the 100K IOPS i3.large instance is capable of, we didn’t even come close to using that bandwidth, despite pre-fetching. This needs further work and investigation.

Range iteration latency:

On the other end of the spectrum, Juleya Womens Moccasins Shoes Faux Leather Loafers Flats Comfort Hollow SlipOn Boat Shoes Fashion Summer Driving Shoes Casual Sandals Walking Pumps 3544 Army Green ZjTshi
’s key-only iteration is blazingly faster than RocksDB or key-value iteration (latency is shown by the almost invisible red bar). This is quite useful in certain use cases we have at Dgraph, where we iterate over the keys, run filters and only retrieve values for a much smaller subset of keys.

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can do key-only iteration blazingly fast, things slow down when it also needs to do value lookups. Theoretically, this shouldn’t be the case. Amazon’s i3.large disk optimized instance can do 100,000 4KB block random reads per second. Based on this, we should be able to iterate 100K key-value pairs per second, in other terms six million key-value pairs per minute.

However, Nike Women’s 844963600 Tennis Shoes Pink Vivid Pink / Whiteblack BLWM2H
’s current implementation doesn’t produce SSD random read requests even close to this limit, and the key-value iteration suffers as a result. There’s a lot of room for optimization in this space.

Badger is currently slower when it comes to running compactions compared to RocksDB. Due to this, for a dataset purely containing smaller values, it is slower to load data to Badger . This needs more optimization.

Again in a dataset purely containing smaller values, the size of LSM tree would be significantly larger than RocksDB because Badger doesn’t run compression on LSM tree. This should be easy to add on if needed, and would make a great first-time contributor project.

1 Recent improvements to SSDs might make B+-trees a viable option. Since WiscKey paper was written, SSDs have made huge gains in random write performance. A new interesting direction would be to combine the value log approach, and keep only keys and value pointers in the B+-tree. This would trade LSM tree read-sort-merge sequential write compactions with many random writes per key update and might achieve the same write throughput as LSM for a much simpler design.

We have built an efficient key-value store, which can compete in performance against top of the line key-value stores in market. It is currently rough around the edges, but provides a solid platform for any industrial application, be it data storage or building another database.

We will be replacing Dgraph’s dependency on RocksDB soon with Badger ; making our builds easier, faster, making Dgraph cross-platform and paving the way for embeddable Dgraph. The biggest win of using Badger is a performant Go native key-value store. The nice side-effects are ~4 times faster Get and a potential 86% reduction in AWS bills, due to less reliance on RAM and more reliance on ever faster and cheaper SSDs.

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Dr. Bartholomew graduated with a B.A. in Psychology from Harvard University, where he was a three-year letter winner on the varsity football team. He then earned a Ph.D. in Exercise Science with an emphasis in Sport and Exercise Psychology from Arizona State University. He came to The University of Texas in 1996 and lives in Round Rock, TX with his wife Pam and daughters Blair and Emma.

Ph.D. in Exercise Science, Sport Psychology, Arizona State University, Tempe, 1996

B.A. in Psychology, Harvard University, Cambridge, 1989

Dr. Bartholomew is the director of the Exercise and Sport Psychology Laboratory. He has two lines of research. His primary interest is in the promotion of physical activity in the elementary school setting and the benefits of physical activity for academic performance. He is also interested in the impact of exercise on mental health, with a specific interest in the use of single bouts of exercise (aerobic and/or resistance) to improve mood and reduce reactivity to stress. His work has been funded by the Texas State Department of Health and the National Institutes of Health.

Resaland, GK., Fusche Moe, V., Bartholomew, JB., Andersen, LB., McKay, HA., Anderssen, SA. Aadland, E. (2018). Gender-specific effects of physical activity on children¹s academic performance: The Active Smarter Kids cluster randomized controlled trail. , , 171–176.

Bartholomew, JB., Jowers, JM., Roberts, G., Fall, AM., Errisuriz, VL. Vaughn, S. (2018). Active Learning Increases Children's Physical Activity across Demographic Subgroups. , , 1–9.

Dooley, E.E.., Golaszewski, N.M.. Bartholomew, J.B.. (2017). Estimating the Accuracies at Exercise Intensities: The Accuracy of Self-monitoring Heart Rate and Physical Activity Wearable Devices. , (e34). doi:10.2196/mhealth.7043.

Bartholomew, J.B.., Jowers, J.M.., Errisuriz, V.L.., Vaughn, S. Roberts, G. (2017). A Cluster Randomized Control Trial to Assess the Impact of Active Learning on Child Activity, Attention Control, and Academic Outcomes: The Texas I-CAN Trial. , , 81–86.

Resaland, GK., Aadland, E., Nilsen, AKO., Bartholomew, JB., Andersen, LB. Anderssen, SA. (2017). . , 1–9. doi:10.1111/sms.12955.

Lattimer, L., Bartholomew, J.B.., Jowers, E.M.. Korineck, E. (2016). Elementary school lunch categorization and correlations with dietitian recommendations. , , 43–49.

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