I've been getting more comfortable with using Go over the last month. For fun and practice, I had the idea to translate a Breadth-first search problem I had in one of my CS courses from C (see mazesolver). For this I need a queue. My instinct was to jerry rig one from slices, and I began to do this, but I wondered whether there was anything else better out there. After looking into it, I decided to test the efficiency of three ways of doing queues:
- Slices (pop with
e := s[0]ands = s[1:], push withs = append(s, n)) - A linked-list, using the
container/liststandard library module - The ring-buffer based eapache/queue
TL;DR: Use slices.
See raw-results.txt for the unformatted output of the benchmark tests.
| 1. n=10 (1000 iterations) | |
|---|---|
| list | 0m0.012s |
| queue | 0m0.010s |
| slice | 0m0.010s |
| 2. n=10, random insert (1000 iterations) | |
|---|---|
| list | 0m0.022s |
| queue | 0m0.020s |
| slice | 0m0.021s |
| 3. n=100 (1000 iterations) | |
|---|---|
| list | 0m0.078s |
| queue | 0m0.076s |
| slice | 0m0.059s |
| 4. n=100, random insert (1000 iterations) | |
|---|---|
| list | 0m0.199s |
| queue | 0m0.188s |
| slice | 0m0.174s |
| 5. n=1000 (1000 iterations) | |
|---|---|
| list | 0m0.813s |
| queue | 0m1.086s |
| slice | 0m0.777s |
| 6. n=1000, random insert (1000 iterations) | |
|---|---|
| list | 0m2.572s |
| queue | 0m2.104s |
| slice | 0m2.024s |
| 7. n=10000 (1000 iterations) | |
|---|---|
| list | 0m10.676s |
| queue | 0m11.711s |
| slice | 0m8.803s |
| 8. n=10000, random insert (1000 iterations) | |
|---|---|
| list | 0m29.539s |
| queue | 0m24.285s |
| slice | 0m23.293s |
| 9. n=100000 | |
|---|---|
| list | 0m0.074s |
| queue | 0m0.065s |
| slice | 0m0.063s |
| 10. n=100000, random insert | |
|---|---|
| list | 0m0.241s |
| queue | 0m0.205s |
| slice | 0m0.225s |
| 11. n=1000000 | |
|---|---|
| list | 0m0.844s |
| queue | 0m0.684s |
| slice | 0m0.614s |
| 12. n=1000000, random insert | |
|---|---|
| list | 0m2.152s |
| queue | 0m1.916s |
| slice | 0m2.081s |
| 13. n=10000000 | |
|---|---|
| list | 0m7.843s |
| queue | 0m6.955s |
| slice | 0m6.082s |
| 14. n=10000000, random insert | |
|---|---|
| list | 0m20.915s |
| queue | 0m19.353s |
| slice | 0m19.030s |
| 15. n=100000000 | |
|---|---|
| list | 1m16.619s |
| queue | 1m6.998s |
| slice | 1m1.921s |
| 16. n=100000000, random insert | |
|---|---|
| list | 3m42.505s |
| queue | 3m13.185s |
| slice | 3m6.901s |
Across 16 tests:
- Lists won zero 🏅, two 🥈 and fourteen 🥉
- eapache's ring-buffer queue won four 🏅, ten 🥈 and two 🥉
- Slices won thirteen 🏅, three 🥈 and zero 🥉
I conclude that unless you know better, you should use slices as a FIFO queue in Go.
In addition to these performance statistics, I noticed while running the tests that slices were the least resource-intensive (memory and CPU%), and lists were the most. On the final test with one hundred million initial elements and random insert, the list implementation used about 8 GB of memory+swap, the ring-buffer implementation about 5-6GB, and the slice implementation about 3GB.
Buffered queues?
If you have any criticism or feedback on these tests, please send me an email at ~smlavine/public-inbox@lists.sr.ht.