`com.google.inject.internal.Sets`

instead of `com.google.common.collect.Sets`

. Is it too much to ask for people to at least look at the code that is getting generated?
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## Tuesday, November 29, 2011

###
Damn auto-completion

## Saturday, November 12, 2011

###
Top-K Selection

## Friday, November 11, 2011

###
Broken pipe

###
Scala, regex, and null

I really wish people would be more careful when using the auto-completion features of IDEs. Though auto-completion does provide some time savings it is also a frequent contributor to sloppy coding. After updating some packages our system broke because someone had incorrectly imported

`com.google.inject.internal.Sets`

instead of `com.google.common.collect.Sets`

. Is it too much to ask for people to at least look at the code that is getting generated?
Labels:
musing,
programming

Ok, so I need to select the top-K values from a list of size N where K is much smaller than N. Two approaches that immediately come to mind are:

As a variant on option 2 a colleague proposed using a small heap that would have at most K elements at any given time. When the heap is full an element would be popped off before a new element could be added. So if I wanted the top-K smallest values I would use a max-heap and if the next value from the input is smaller than the largest value on the heap I would pop off the largest value and insert the smaller value. The running time for this approach is O(N*lg(K)).

For my use case both N and K are fairly small. The size of N will be approximately 10,000 elements and K will typically be 10, but can be set anywhere from 1 to 100. A C++ test program that implements all three approaches and tests them for various sizes of K is provided at the bottom of this post. The table below shows the results for K equal to 10, 50, and 100. You can see that all three approaches have roughly the same running time and increasing the size of K has little impact on the actual running time.

Here is the chart for all values of K:

So clearly the third approach with the small heap is the winner. With a small K it is essentially linear time and an order of magnitude faster than the naive sort. The graph shows both the average time and the 99th-percentile so you can see the variation in times is fairly small. This first test covers the sizes for my use case, but out of curiosity, I also tested the more interesting case with a fixed size K and varying the size of N. The graph for N from 100,000 to 1,000,000 in increments of 100,000 tells the whole story:

Source code:

- Sort the list and select the first K elements. Running time O(N*lg(N)).
- Insert the elements of the list into a heap and pop the top K elements. Running time O(N + K*lg(N)).

As a variant on option 2 a colleague proposed using a small heap that would have at most K elements at any given time. When the heap is full an element would be popped off before a new element could be added. So if I wanted the top-K smallest values I would use a max-heap and if the next value from the input is smaller than the largest value on the heap I would pop off the largest value and insert the smaller value. The running time for this approach is O(N*lg(K)).

For my use case both N and K are fairly small. The size of N will be approximately 10,000 elements and K will typically be 10, but can be set anywhere from 1 to 100. A C++ test program that implements all three approaches and tests them for various sizes of K is provided at the bottom of this post. The table below shows the results for K equal to 10, 50, and 100. You can see that all three approaches have roughly the same running time and increasing the size of K has little impact on the actual running time.

K=10 | K=50 | K=100 | |
---|---|---|---|

sort | 0.468977 | 0.466918 | 0.467177 |

fullheap | 0.099325 | 0.102839 | 0.106735 |

smallheap | 0.018063 | 0.028948 | 0.040435 |

Here is the chart for all values of K:

So clearly the third approach with the small heap is the winner. With a small K it is essentially linear time and an order of magnitude faster than the naive sort. The graph shows both the average time and the 99th-percentile so you can see the variation in times is fairly small. This first test covers the sizes for my use case, but out of curiosity, I also tested the more interesting case with a fixed size K and varying the size of N. The graph for N from 100,000 to 1,000,000 in increments of 100,000 tells the whole story:

Source code:

#include <algorithm> #include <cstdlib> #include <ctime> #include <iostream> #include <numeric> #include <vector> using namespace std; typedef void (*topk_func)(vector<int> &, vector<int> &, int); bool greater_than(const int &v1, const int &v2) { return v1 > v2; } void topk_sort(vector<int> &data, vector<int> &top, int k) { sort(data.begin(), data.end()); vector<int>::iterator i = data.begin(); int j = 0; for (; j < k; ++i, ++j) { top.push_back(*i); } } void topk_fullheap(vector<int> &data, vector<int> &top, int k) { make_heap(data.begin(), data.end(), greater_than); for (int j = 0; j < k; ++j) { top.push_back(*data.begin()); pop_heap(data.begin(), data.end(), greater_than); data.pop_back(); } } void topk_smallheap(vector<int> &data, vector<int> &top, int k) { for (vector<int>::iterator i = data.begin(); i != data.end(); ++i) { if (top.size() < k) { top.push_back(*i); push_heap(top.begin(), top.end()); } else if (*i < *top.begin()) { pop_heap(top.begin(), top.end()); top.pop_back(); top.push_back(*i); push_heap(top.begin(), top.end()); } } sort_heap(top.begin(), top.end()); } void run_test(const string &name, topk_func f, int trials, int n, int k) { vector<double> times; for (int t = 0; t < trials; ++t) { vector<int> data; for (int i = 0; i < n; ++i) { data.push_back(rand()); } clock_t start = clock(); vector<int> top; f(data, top, k); clock_t end = clock(); times.push_back(1000.0 * (end - start) / CLOCKS_PER_SEC); } sort(times.begin(), times.end()); double sum = accumulate(times.begin(), times.end(), 0.0); double avg = sum / times.size(); double pct90 = times[static_cast<int>(trials * 0.90)]; double pct99 = times[static_cast<int>(trials * 0.99)]; cout << name << " " << k << " " << avg << " " << pct90 << " " << pct99 << endl; } int main(int argc, char **argv) { cout << "method k avg 90-%tile 99-%tile" << endl; int trials = 1000; int n = 10000; for (int k = 1; k <= 100; ++k) { run_test("sort", topk_sort, trials, n, k); run_test("fullheap", topk_fullheap, trials, n, k); run_test("smallheap", topk_smallheap, trials, n, k); } return 0; }

Labels:
cpp,
programming

If you are using python to write a script, please properly handle the broken pipe exception. We have set of command line tools at work that are extremely annoying to use because if you pipe the output through other standard tools, e.g., head, it spits out a worthless exception about a broken pipe. Consider the following test script:

Pipe it into head and look at the output:

I know there was a broken pipe and I don't care. Just swallow the worthless exception so I can see the meaningful output. This is probably the number one reason I often mutter "damn python crap" when using some of these tools. So if you are writing scripts in python, please be considerate and handle the broken pipe exception. Here is an example for quick reference:

#!/usr/bin/env python import sys buffer = "" for i in range(100000): buffer += "%d\n" % i sys.stdout.write(buffer)

Pipe it into head and look at the output:

$ ./test.py | head -n1 0 ERROR:root:damn Traceback (most recent call last): File "./test.py", line 6, in <module> sys.stdout.write(buffer) IOError: [Errno 32] Broken pipe

I know there was a broken pipe and I don't care. Just swallow the worthless exception so I can see the meaningful output. This is probably the number one reason I often mutter "damn python crap" when using some of these tools. So if you are writing scripts in python, please be considerate and handle the broken pipe exception. Here is an example for quick reference:

#!/usr/bin/env python import errno import sys try: buffer = "" for i in range(100000): buffer += "%d\n" % i sys.stdout.write(buffer) except IOError, e: if e.errno != errno.EPIPE: raise e

Labels:
programming,
python

One of the perks of using scala is that I hardly ever see a NullPointerException unless I'm working with java libraries. The primary reason is because most scala libraries tend to use Option rather than

I just assumed that

`null`

. However, while using a regex with pattern matching I was surprised by a NullPointerException when trying to look at the result of an optional capture group. Consider the following example:scala> val Pattern = "(a)(b)?".r Pattern: scala.util.matching.Regex = (a)(b)? scala> "a" match { case Pattern(a, b) => printf("[%s][%s]%n", a, b) } [a][null] scala> "ab" match { case Pattern(a, b) => printf("[%s][%s]%n", a, b) } [a][b]

I just assumed that

`b`

would be of type `Option[String]`

. There is probably a good reason for this travesty, my guess would be something about making it work with the type system, but after using scala for a while it just seems wrong to be getting a `null`

value.
Labels:
programming,
scala

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