Skip to main content

Adventures restoring the Mac Book Air from a Time machine backup

Taking backups with Time Machine on Mac OS X is a breeze: you plug in the backup drive, and wait for the magic to happen. Restoring the backup to a misbehaving laptop though appears to be a different story. I had to go through multiple iterations before I finally got the data back on the laptop. Since my backup setup is not atypical with the exception of an encrypted drive and backups, I was surprised it took that many times to successfully restore the data.

Initially I tried restoring the backups by booting the Mac in recovery mode, and using the restore from Time Machine option. The restore started, but after roughly 12 hours it silently failed.

For my second attempt I decided to install Yosemite from scratch and use the user migration assistant to recover my data. After progressing for a long time, the restore silently failed as well.

My third attempt was a bit more drastic: I wiped out the drive, and attempted to restore the backup from Time Machine. That too failed after progressing for roughly 12 hours.

For my final attempt I decided to wipe out the drive, reformat the drive to a different file system--case-sensitive journaled unencrypted file system, install Yosemite from scratch, and use the user migration assistant to recover the data. For some reason that worked, and after the migration was complete, I turned on File Vault to encrypt the drive, and everything was back to normal again.
 

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 dr away: Dapper a Large-Scale Distributed Systems Tracing Infrastructure

Modern Internet scale applications are a challenge to monitor and diagnose. The applications are usually comprised of complex distributed systems that are built by multiple teams, sometimes using different languages and technologies. When one component fails or misbehaves, it becomes a nightmare to figure out what went wrong and where. Monitoring and tracing systems aim to make that problem a bit more tractable, and Dapper, a system by Google for large scale distributed systems tracing is one such system. The paper starts by setting the context for Dapper through the use of a real service: "universal search". In universal search, the user types in a query that gets federated to multiple search backends such as web search, image search, local search, video search, news search, as well as advertising systems to display ads. The results are then combined and presented back to the user. Thousands of machines could be involved in returning that result, and any poor p...

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...