As I mentioned in the first post of the certification series, I took the next step up in LPI’s certification ladder and earned LPIC-2 (verify). Was it easy? Was it hard? Let’s find out.

LPIC-2 is awarded after passing two exams, but only if you already hold a LPIC-1 certification. In total, that means four exams to go from zero to LPIC-2.

QA teams sometimes send requests like this:

For testing purposes, I need the /opt/test/xxx directory limited to 10 Megabytes. This directory is used by the abc123 application, which runs as user tester.

Can a directory’s size be limited in Linux? Yes. You need to:

1. Use the directory as a mount point for a size-limited storage device.
2. Set mount options so the specified non-root user has full access.
3. Disable SELinux (easy) or allow that particular user to access data on mount points (complicated).

There are several options:

Starting a career in computer science is hard enough, and landing with a good company is mostly down to luck. I won’t argue with that: you do need plenty of luck to get off to a good start. My aim is simply to show where the train you’ve just boarded might take you, or might not. Switching trains is always possible, but it gets harder with age.

At a high level, companies fall into two main categories:

I recently earned my LPIC-1 certification (verify here). Here’s what I learned, in case you want to try it and aren’t sure where to start.

LPIC-1 is granted after passing two exams:

• Exam 101: more theory-oriented, with many general questions.
• Exam 102: more practice-oriented, with an emphasis on scripting, networking and security.

You can take these exams in any order, and the certification is awarded automatically once you’ve passed both. Higher levels require the lower-level certification first. For example, any LPIC-3 certification requires passing a total of 5 exams, again in any order.

Imagine an interviewer hands you a slip of paper with this on it:

def foo(bar=[])

No context, just the question: what’s wrong with this code?

Strictly speaking, nothing. It’s valid Python (aside from the missing colon and function body), and a bug is only a bug when the code behaves differently from what the programmer intended.

“Performance” is a broad term, so it helps to split it into categories first. Two questions do most of the work:

1. What do we want to monitor? The whole system, or one particular application?
2. What kind of monitoring do we need? Basic statistics gathered by the kernel, or in-depth analysis?

Combining the answers gives four categories, each with its own set of tools:

Do you think list comprehensions are a tricky topic? After reading this post, you may well change your mind about what counts as “complicated” in Python.

A lambda is a small function defined right where you need it, typically for light data-processing jobs. There are two main reasons to use them:

• Speed: a compiler can optimize them, first by eliminating an actual function call, and then by opening the door to further internal code re-arrangements.
• Brevity: you write less code.

Here is a lambda that squares a number:

Python dictionaries are built on hash tables. They can hold arbitrary key-value pairs, and the keys and values don’t have to share a single data type. Dictionaries are unordered, meaning no ordering between elements is defined, so you can’t rely on any particular sequence when iterating over them.

Note: Unless stated otherwise, all examples use Python 2 syntax.

D = {}

D = dict((i, False) for i in xrange(1, 11) if i % 2 == 0)   # Python 2
D = {i: False for i in range(1, 11) if i % 2 == 0}          # Python 3

Note: the second form is known as a dictionary comprehension.

Note: I have ran into this problem on Apache httpd, but it could just as easily affect any other HTTP server.

The idea behind “KeepAlive” is straightforward: instead of opening a new connection for every request, the browser opens one connection to the server and reuses it to fetch many resources, such as the main page, stylesheets, JavaScript files, and images. In theory, this cuts page load times and gives visitors a better experience. So far, so good.

Lists are ordered, linear collections that offer constant-time element lookup by index. They can grow or shrink, be searched, and be sorted (including with a custom comparison function). They also aren’t limited to one data type, so a single list can hold a mix of values. Python lists are zero-indexed.

Note: Unless stated otherwise, all examples use Python 2 syntax.

A = [0] * 10

A = [2**i for i in xrange(0, 10)]

Note 1: xrange in the example above produces the values 0 through 9, inclusive.