Skip to main content

MacOS Catalina, OneDrive, and case sensitive file systems

Over the weekend, I dusted off my old Macbook Air to search for some old family photos. I have not used the laptop for a long time, and it was completely out of charge. I plugged it in, and it quickly booted. Shortly after, I got bombarded with notifications that many of the applications needed updating, and that a new version of the OS was available.

 

I waited till I found the photos I was looking for, before attempting to upgrade anything. I also wanted to install OneDrive to get my old files to the cloud, so that I can access them from any of my devices, instead of dusting off old computers to get to them.

The MacOS upgrade experience has always been fantastic, and this one was no different. The OS upgrade files downloaded quickly and after a restart and a quick install, the Macbook Air was ready to go.  Upgrading the installed applications was also a breeze, however in the process I discovered that a large majority of the applications installed were not compatible with Catalina.

 

Since I have not used the Macbook Air in ages, it was an easy decision to delete these applications; I didn’t miss them, and in the future if I find I cannot access any of my legacy files, I can always find a converter, or a newer version of the application.

 

Next, I installed Office. The installation was smooth and quick, but when I tried to setup OneDrive, I got an error that OneDrive cannot create the folder in the location I selected. The error message mentioned that either I don’t have access to the folder, or that the filesystem is case-sensitive.

 

I checked the permissions on the folder, and made sure that I own it, and have read/write access to it. That did not solve the problem, and so I investigated the case-sensitivity issue.

Turns out that the directory I chose for OneDrive was on a filesystem that was case sensitive. I did not want to change that filesystem, but since the drive had a bit of space left, I fired off the “Disk Utility” application, and created a new volume for OneDrive. I chose “APFS(Encrypted)” format and limited the volume to 20GB.

The install went smoothly after that, and now my old files have a new life in the cloud.

Comments

Popular posts from this blog

Kindle Paperwhite

I have always been allergic to buying specialized electronic devices that do only one thing, such as the Kindle, the iPod, and fitness trackers. Why buy these when technology evolves so fast that a multi-purpose device such as the phone or a smart watch can eventually do the same thing, but with the convenience of updates that fix bugs and add functionality? So, I was shocked when this weekend I made an impulse buy and got the newest Kindle Paperwhite—a special purpose device for reading eBooks. I was walking past the Amazon store in the mall and saw that the newest Kindle Paperwhites were marked down by $40 for the holidays. The device looked good in the display, so I went in to look at it closely. The Paperwhite is small and light, with a 6” screen that is backlit and waterproof.   The text was crisp and readable, and in the ambient light, it felt like I am reading a printed book. I was sold and bought it on the spot. At home I have struggled to put it down. The bo...

A paper a day keeps the doctor away: NoDB

In most database systems, the user defines the shape of the data that is stored and queried using concepts such as entities and relations. The database system takes care of translating that shape into physical storage, and managing its lifecycle. Most of the systems store data in the form of tuples, either in row format, or broken down into columns and stored in columnar format. The system also stores metadata associated with the data, that helps with speedy retrieval and processing. Defining the shape of the data a priori, and transforming it from the raw or ingestion format to the storage format is a cost that database systems incur to make queries faster. What if we can have fast queries without incurring that initial cost? In the paper " NoDB: Efficient Query Execution on Raw Data Files ", the authors examine that question, and advocate a system (NoDB) that answers it. The authors start with the motivation for such a system. With the recent explosion of data...

A paper a day keeps the doctor away: MillWheel: Fault-Tolerant Stream Processing at Internet Scale

The recent data explosion, and the increase in appetite for fast results spurred a lot of interest in low-latency data processing systems. One such system is MillWheel, presented in the paper " MillWheel: Fault-Tolerant Stream Processing at Internet Scale ", which is widely used at Google. In MillWheel, the users specify a directed computation graph that describe what they would like to do, and write application code that runs on each individual node in the graph. The system takes care of managing the flow of data within the graph, persisting the state of the computation, and handling any failures that occur, relieving the users from that burden. MillWheel exposes an API for record processing, that handles each record in an idempotent fashion, with an exactly once delivery semantics. The system checkpoints progress with a fine granularity, removing the need to buffer data between external senders. The authors describe the system using the Zeitgeist produ...