21st Century C: Error 64 on OSX When Using Make

To end 2015, I decided to finally learn C, instead of making it a 2016 resolution! I had previously done the ‘Learn C The Hard Way’ tutorials, taken about a year off, and thus forgotten everything.

Rather than re-do the same material, I decided to get ’21st Century C’ from O’Reilly and work through that. Unfortunately, there is an error/misprint in the very beginning chapters that makes doing the exercises near impossible on OSX. This error manifests itself as c99: invalid argument 'all' to -W Error 64. If you encounter this error on OSX (I’m using OSX 10.11.2 El Capitan as of writing this post), here are three methods for fixing the issue.

Error 64!

When the discussion of using Makefiles begins on page 15, there is a discussion of the “smallest practicable makefile”, which is just six lines long:

1
2
3
4
5
6
7
P=program_name
OBJECTS=
CFLAGS= -g -Wall -O3
LDLIBS=
CC=c99

$(P): $(OBJECTS)

Unfortunately, this doesn’t quite work on OSX. Page 11 in the book sort-of references that a fix is needed, but the directions aren’t so clear…

Error 64, solution 1: Book Fix, updated

To use the book fix, you are supposed to:

  1. Create a file named c99
  2. Put the lines gcc -std=c99 $\* OR clang $\* in the c99 file
  3. Add the file to your PATH in Terminal (such as export PATH="/Users/computeruser:$PATH" if the c99 file were located in /Users/computeruser directory)
  4. Run chmod +x c99 on the file to make it executable

Once you add this work-around to your PATH, then open a fresh Terminal window (or run source .bash_profile to refresh the Bash settings), you should be able to use Make to compile your C code.

But to be honest, this seems like a really weird “fix” to me, as it overrides the C compiler settings for any program run via Terminal. I prefer one of two alternate solutions.

Error 64, solution 2: Makefile Change

As I was researching this, a helpful Twitter user noted:

When you switch the ‘c99’ reference to just ‘cc’ in the Makefile, everything works fine. Here’s the subtlety different, corrected Makefile:

1
2
3
4
5
6
7
P=program_name
OBJECTS=
CFLAGS= -g -Wall -O3
LDLIBS=
CC=cc

$(P): $(OBJECTS)

Error 64, solution 3: Switch to Clang

The final solution I came across is rather than using the GCC compiler, you can use an alternate compiler called Clang, which is also generally available on OSX (especially with XCode installed). Like solution 2 above, the Makefile is just subtlety different:

1
2
3
4
5
6
7
P=program_name
OBJECTS=
CFLAGS= -g -Wall -O3
LDLIBS=
CC=clang

$(P): $(OBJECTS)

Whether to use GCC or Clang as your compiler is really beyond the scope of this blog post; as 21st Century C discusses, it really shouldn’t matter (especially when you are just learning the mechanics of the language).

Error 64, Be Gone!

There’s not really much more to say at this point; this blog post is mainly documentation for anyone who comes across this error in the future. I’ve continued on through the book using Clang, but suffice to say, it’s not the compiler that writes poor-quality, non-compiling code, it’s the user. Ah, the fun of learning 🙂

  • RSiteCatalyst Version 1.4.13 Release Notes
  • RSiteCatalyst Version 1.4.12 (and 1.4.11) Release Notes
  • Self-Service Adobe Analytics Data Feeds!
  • RSiteCatalyst Version 1.4.10 Release Notes
  • WordPress to Jekyll: A 30x Speedup
  • Bulk Downloading Adobe Analytics Data
  • Adobe Analytics Clickstream Data Feed: Calculations and Outlier Analysis
  • Adobe: Give Credit. You DID NOT Write RSiteCatalyst.
  • RSiteCatalyst Version 1.4.8 Release Notes
  • Adobe Analytics Clickstream Data Feed: Loading To Relational Database
  • Calling RSiteCatalyst From Python
  • RSiteCatalyst Version 1.4.7 (and 1.4.6.) Release Notes
  • RSiteCatalyst Version 1.4.5 Release Notes
  • Getting Started: Adobe Analytics Clickstream Data Feed
  • RSiteCatalyst Version 1.4.4 Release Notes
  • RSiteCatalyst Version 1.4.3 Release Notes
  • RSiteCatalyst Version 1.4.2 Release Notes
  • Destroy Your Data Using Excel With This One Weird Trick!
  • RSiteCatalyst Version 1.4.1 Release Notes
  • Visualizing Website Pathing With Sankey Charts
  • Visualizing Website Structure With Network Graphs
  • RSiteCatalyst Version 1.4 Release Notes
  • Maybe I Don't Really Know R After All
  • Building JSON in R: Three Methods
  • Real-time Reporting with the Adobe Analytics API
  • RSiteCatalyst Version 1.3 Release Notes
  • Adobe Analytics Implementation Documentation in 60 Seconds
  • RSiteCatalyst Version 1.2 Release Notes
  • Clustering Search Keywords Using K-Means Clustering
  • RSiteCatalyst Version 1.1 Release Notes
  • Anomaly Detection Using The Adobe Analytics API
  • (not provided): Using R and the Google Analytics API
  • My Top 20 Least Useful Omniture Reports
  • For Maximum User Understanding, Customize the SiteCatalyst Menu
  • Effect Of Modified Bounce Rate In Google Analytics
  • Adobe Discover 3: First Impressions
  • Using Omniture SiteCatalyst Target Report To Calculate YOY growth
  • Google Analytics Individual Qualification (IQ) - Passed!
  • Google Analytics SEO reports: Not Ready For Primetime?
  • An Afternoon With Edward Tufte
  • Google Analytics Custom Variables: A Page-Level Example
  • Xchange 2011: Think Tank and Harbor Cruise
  • Google Analytics for WordPress: Two Methods
  • WordPress Stats or Google Analytics? Yes!
  • Parallelizing Distance Calculations Using A GPU With CUDAnative.jl
  • Building a Data Science Workstation (2017)
  • JuliaCon 2015: Everyday Analytics and Visualization (video)
  • Vega.jl, Rebooted
  • Sessionizing Log Data Using data.table [Follow-up #2]
  • Sessionizing Log Data Using dplyr [Follow-up]
  • Sessionizing Log Data Using SQL
  • Review: Data Science at the Command Line
  • Introducing Twitter.jl
  • Code Refactoring Using Metaprogramming
  • Evaluating BreakoutDetection
  • Creating A Stacked Bar Chart in Seaborn
  • Visualizing Analytics Languages With VennEuler.jl
  • String Interpolation for Fun and Profit
  • Using Julia As A "Glue" Language
  • Five Hard-Won Lessons Using Hive
  • Using SQL Workbench with Apache Hive
  • Getting Started With Hadoop, Final: Analysis Using Hive & Pig
  • Quickly Create Dummy Variables in a Data Frame
  • Using Amazon EC2 with IPython Notebook
  • Adding Line Numbers in IPython/Jupyter Notebooks
  • Fun With Just-In-Time Compiling: Julia, Python, R and pqR
  • Getting Started Using Hadoop, Part 4: Creating Tables With Hive
  • Tabular Data I/O in Julia
  • Hadoop Streaming with Amazon Elastic MapReduce, Python and mrjob
  • A Beginner's Look at Julia
  • Getting Started Using Hadoop, Part 3: Loading Data
  • Innovation Will Never Be At The Push Of A Button
  • Getting Started Using Hadoop, Part 2: Building a Cluster
  • Getting Started Using Hadoop, Part 1: Intro
  • Instructions for Installing & Using R on Amazon EC2
  • Video: SQL Queries in R using sqldf
  • Video: Overlay Histogram in R (Normal, Density, Another Series)
  • Video: R, RStudio, Rcmdr & rattle
  • Getting Started Using R, Part 2: Rcmdr
  • Getting Started Using R, Part 1: RStudio
  • Learning R Has Really Made Me Appreciate SAS