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1---
2title: Using DigitalOcean Spaces Object Storage with FUSE
3url: using-digitalocean-spaces-object-storage-with-fuse.html
4date: 2018-01-16T12:00:00+02:00
5type: post
6draft: false
7---
8
9Couple of months ago [DigitalOcean](https://www.digitalocean.com) introduced new
10product called
11[Spaces](https://blog.digitalocean.com/introducing-spaces-object-storage/) which
12is Object Storage very similar to Amazon's S3. This really peaked my interest,
13because this was something I was missing and even the thought of going over the
14internet for such functionality was in no interest to me. Also in fashion with
15their previous pricing this also is very cheap and pricing page is a no-brainer
16compared to AWS or GCE. [Prices are clearly and precisely defined and
17outlined](https://www.digitalocean.com/pricing/). You must love them for that
18:)
19
20## Initial requirements
21
22* Is it possible to use them as a mounted drive with FUSE? (tl;dr YES)
23* Will the performance degrade over time and over different sizes of objects?
24 (tl;dr NO&YES)
25* Can storage be mounted on multiple machines at the same time and be writable?
26 (tl;dr YES)
27
28> Let me be clear. This scripts I use are made just for benchmarking and are not
29> intended to be used in real-life situations. Besides that, I am looking into
30> using this approaches but adding caching service in front of it and then
31> dumping everything as an object to storage. This could potentially be some
32> interesting post of itself. But in case you would need real-time data without
33> eventual consistency please take this scripts as they are: not usable in such
34> situations.
35
36## Is it possible to use them as a mounted drive with FUSE?
37
38Well, actually they can be used in such manor. Because they are similar to [AWS
39S3](https://aws.amazon.com/s3/) many tools are available and you can find many
40articles and [Stackoverflow items](https://stackoverflow.com/search?q=s3+fuse).
41
42To make this work you will need DigitalOcean account. If you don't have one you
43will not be able to test this code. But if you have an account then you go and
44[create new
45Droplet](https://cloud.digitalocean.com/droplets/new?size=s-1vcpu-1gb&region=ams3&distro=debian&distroImage=debian-9-x64&options=private_networking,install_agent).
46If you click on this link you will already have preselected Debian 9 with
47smallest VM option.
48
49* Please be sure to add you SSH key, because we will login to this machine
50 remotely.
51* If you change your region please remember which one you choose because we will
52 need this information when we try to mount space to our machine.
53
54Instuctions on how to use SSH keys and how to setup them are available in
55article [How To Use SSH Keys with DigitalOcean
56Droplets](https://www.digitalocean.com/community/tutorials/how-to-use-ssh-keys-with-digitalocean-droplets).
57
58![DigitalOcean Droplets](/posts/do-fuse/fuse-droplets.png)
59
60After we created Droplet it's time to create new Space. This is done by clicking
61on a button [Create](https://cloud.digitalocean.com/spaces/new) (right top
62corner) and selecting Spaces. Choose pronounceable ```Unique name``` because we
63will use it in examples below. You can either choose Private or Public, it
64doesn't matter in our case. And you can always change that in the future.
65
66When you have created new Space we should [generate Access
67key](https://cloud.digitalocean.com/settings/api/tokens). This link will guide
68to the page when you can generate this key. After you create new one, please
69save provided Key and Secret because Secret will not be shown again.
70
71![DigitalOcean Spaces](/posts/do-fuse/fuse-spaces.png)
72
73Now that we have new Space and Access key we should SSH into our machine.
74
75```bash
76# replace IP with the ip of your newly created droplet
77ssh root@IP
78
79# this will install utilities for mounting storage objects as FUSE
80apt install s3fs
81
82# we now need to provide credentials (access key we created earlier)
83# replace KEY and SECRET with your own credentials but leave the colon between them
84# we also need to set proper permissions
85echo "KEY:SECRET" > .passwd-s3fs
86chmod 600 .passwd-s3fs
87
88# now we mount space to our machine
89# replace UNIQUE-NAME with the name you choose earlier
90# if you choose different region for your space be careful about -ourl option (ams3)
91s3fs UNIQUE-NAME /mnt/ -ourl=https://ams3.digitaloceanspaces.com -ouse_cache=/tmp
92
93# now we try to create a file
94# once you mount it may take a couple of seconds to retrieve data
95echo "Hello cruel world" > /mnt/hello.txt
96```
97
98After all this you can return to your browser and go to [DigitalOcean
99Spaces](https://cloud.digitalocean.com/spaces) and click on your created
100space. If file hello.txt is present you have successfully mounted space to your
101machine and wrote data to it.
102
103I choose the same region for my Droplet and my Space but you don't have to. You
104can have different regions. What this actually does to performance I don't know.
105
106Additional information on FUSE:
107
108* [Github project page for s3fs](https://github.com/s3fs-fuse/s3fs-fuse)
109* [FUSE - Filesystem in Userspace](https://en.wikipedia.org/wiki/Filesystem_in_Userspace)
110
111## Will the performance degrade over time and over different sizes of objects?
112
113For this task I didn't want to just read and write text files or uploading
114images. I actually wanted to figure out if using something like SQlite is viable
115in this case.
116
117### Measurement experiment 1: File copy
118
119```bash
120# first we create some dummy files at different sizes
121dd if=/dev/zero of=10KB.dat bs=1024 count=10 #10KB
122dd if=/dev/zero of=100KB.dat bs=1024 count=100 #100KB
123dd if=/dev/zero of=1MB.dat bs=1024 count=1024 #1MB
124dd if=/dev/zero of=10MB.dat bs=1024 count=10240 #10MB
125
126# now we set time command to only return real
127TIMEFORMAT=%R
128
129# now lets test it
130(time cp 10KB.dat /mnt/) |& tee -a 10KB.results.txt
131
132# and now we automate
133# this will perform the same operation 100 times
134# this will output results into separated files based on objecty size
135n=0; while (( n++ < 100 )); do (time cp 10KB.dat /mnt/10KB.$n.dat) |& tee -a 10KB.results.txt; done
136n=0; while (( n++ < 100 )); do (time cp 100KB.dat /mnt/100KB.$n.dat) |& tee -a 100KB.results.txt; done
137n=0; while (( n++ < 100 )); do (time cp 1MB.dat /mnt/1MB.$n.dat) |& tee -a 1MB.results.txt; done
138n=0; while (( n++ < 100 )); do (time cp 10MB.dat /mnt/10MB.$n.dat) |& tee -a 10MB.results.txt; done
139```
140
141Files of size 100MB were not successfully transferred and ended up displaying
142error (cp: failed to close '/mnt/100MB.1.dat': Operation not permitted).
143
144As I suspected, object size is not really that important. Sadly I don't have the
145time to test performance over periods of time. But if some of you would do it
146please send me your data. I would be interested in seeing results.
147
148**Here are plotted results**
149
150You can download [raw result here](/posts/do-fuse/copy-benchmarks.tsv).
151Measurements are in seconds.
152
153<script src="//cdn.plot.ly/plotly-latest.min.js"></script>
154<div id="copy-benchmarks"></div>
155<script>
156(function(){
157 var request = new XMLHttpRequest();
158 request.open("GET", "/posts/do-fuse/copy-benchmarks.tsv", true);
159 request.onload = function() {
160 if (request.status >= 200 && request.status < 400) {
161 var payload = request.responseText.trim();
162 var tsv = payload.split("\n");
163 for (var i=0; i<tsv.length; i++) { tsv[i] = tsv[i].split("\t"); }
164 var traces = [];
165 var headers = tsv[0];
166 tsv.shift();
167 Array.prototype.forEach.call(headers, function(el, idx) {
168 var x = [];
169 var y = [];
170 for (var j=0; j<tsv.length; j++) {
171 x.push(j);
172 y.push(parseFloat(tsv[j][idx].replace(",", ".")));
173 }
174 traces.push({ x: x, y: y, type: "scatter", name: el, line: { width: 1, shape: "spline" } });
175 });
176 var copy = Plotly.newPlot("copy-benchmarks", traces, { legend: {"orientation": "h"}, height: 400, margin: { l: 40, r: 0, b: 20, t: 30, pad: 0 }, yaxis: { title: "execution time in seconds", titlefont: { size: 12 } }, xaxis: { title: "fn(i)", titlefont: { size: 12 } } });
177 } else { }
178 };
179 request.onerror = function() { };
180 request.send(null);
181})();
182</script>
183
184As far as these tests show, performance is quite stable and can be predicted
185which is fantastic. But this is a small test and spans only over couple of
186hours. So you should not completely trust them.
187
188### Measurement experiment 2: SQLite performanse
189
190I was unable to use database file directly from mounted drive so this is a no-go
191as I suspected. So I executed code below on a local disk just to get some
192benchmarks. I inserted 1000 records with DROPTABLE, CREATETABLE, INSERTMANY,
193FETCHALL, COMMIT for 1000 times to generate statistics. As you can see
194performance of SQLite is quite amazing. You could then potentially just copy
195file to mounted drive and be done with it.
196
197```python
198import time
199import sqlite3
200import sys
201
202if len(sys.argv) < 3:
203 print("usage: python sqlite-benchmark.py DB_PATH NUM_RECORDS REPEAT")
204 exit()
205
206def data_iter(x):
207 for i in range(x):
208 yield "m" + str(i), "f" + str(i*i)
209
210header_line = "%s\t%s\t%s\t%s\t%s\n" % ("DROPTABLE", "CREATETABLE", "INSERTMANY", "FETCHALL", "COMMIT")
211with open("sqlite-benchmarks.tsv", "w") as fp:
212 fp.write(header_line)
213
214start_time = time.time()
215conn = sqlite3.connect(sys.argv[1])
216c = conn.cursor()
217end_time = time.time()
218result_time = CONNECT = end_time - start_time
219print("CONNECT: %g seconds" % (result_time))
220
221start_time = time.time()
222c.execute("PRAGMA journal_mode=WAL")
223c.execute("PRAGMA temp_store=MEMORY")
224c.execute("PRAGMA synchronous=OFF")
225result_time = PRAGMA = end_time - start_time
226print("PRAGMA: %g seconds" % (result_time))
227
228for i in range(int(sys.argv[3])):
229 print("#%i" % (i))
230
231 start_time = time.time()
232 c.execute("drop table if exists test")
233 end_time = time.time()
234 result_time = DROPTABLE = end_time - start_time
235 print("DROPTABLE: %g seconds" % (result_time))
236
237 start_time = time.time()
238 c.execute("create table if not exists test(a,b)")
239 end_time = time.time()
240 result_time = CREATETABLE = end_time - start_time
241 print("CREATETABLE: %g seconds" % (result_time))
242
243 start_time = time.time()
244 c.executemany("INSERT INTO test VALUES (?, ?)", data_iter(int(sys.argv[2])))
245 end_time = time.time()
246 result_time = INSERTMANY = end_time - start_time
247 print("INSERTMANY: %g seconds" % (result_time))
248
249 start_time = time.time()
250 c.execute("select count(*) from test")
251 res = c.fetchall()
252 end_time = time.time()
253 result_time = FETCHALL = end_time - start_time
254 print("FETCHALL: %g seconds" % (result_time))
255
256 start_time = time.time()
257 conn.commit()
258 end_time = time.time()
259 result_time = COMMIT = end_time - start_time
260 print("COMMIT: %g seconds" % (result_time))
261
262 print
263 log_line = "%f\t%f\t%f\t%f\t%f\n" % (DROPTABLE, CREATETABLE, INSERTMANY, FETCHALL, COMMIT)
264 with open("sqlite-benchmarks.tsv", "a") as fp:
265 fp.write(log_line)
266
267start_time = time.time()
268conn.close()
269end_time = time.time()
270result_time = CLOSE = end_time - start_time
271print("CLOSE: %g seconds" % (result_time))
272```
273
274You can download [raw result here](/posts/do-fuse/sqlite-benchmarks.tsv). And
275again, these results are done on a local block storage and do not represent
276capabilities of object storage. With my current approach and state of the test
277code these can not be done. I would need to make Python code much more robust
278and check locking etc.
279
280<div id="sqlite-benchmarks"></div>
281<script>
282(function(){
283 var request = new XMLHttpRequest();
284 request.open("GET", "/posts/do-fuse/sqlite-benchmarks.tsv", true);
285 request.onload = function() {
286 if (request.status >= 200 && request.status < 400) {
287 var payload = request.responseText.trim();
288 var tsv = payload.split("\n");
289 for (var i=0; i<tsv.length; i++) { tsv[i] = tsv[i].split("\t"); }
290 var traces = [];
291 var headers = tsv[0];
292 tsv.shift();
293 Array.prototype.forEach.call(headers, function(el, idx) {
294 var x = [];
295 var y = [];
296 for (var j=0; j<tsv.length; j++) {
297 x.push(j);
298 y.push(parseFloat(tsv[j][idx].replace(",", ".")));
299 }
300 traces.push({ x: x, y: y, type: "scatter", name: el, line: { width: 1, shape: "spline" } });
301 });
302 var sqlite = Plotly.newPlot("sqlite-benchmarks", traces, { legend: {"orientation": "h"}, height: 400, margin: { l: 50, r: 0, b: 20, t: 30, pad: 0 }, yaxis: { title: "execution time in seconds", titlefont: { size: 12 } } });
303 } else { }
304 };
305 request.onerror = function() { };
306 request.send(null);
307})();
308</script>
309
310## Can storage be mounted on multiple machines at the same time and be writable?
311
312Well, this one didn't take long to test. And the answer is **YES**. I mounted
313space on both machines and measured same performance on both machines. But
314because file is downloaded before write and then uploaded on complete there
315could potentially be problems is another process is trying to access the same
316file.
317
318## Observations and conslusion
319
320Using Spaces in this way makes it easier to access and manage files. But besides
321that you would need to write additional code to make this one play nice with you
322applications.
323
324Nevertheless, this was extremely simple to setup and use and this is just
325another excellent product in DigitalOcean product line. I found this exercise
326very valuable and am thinking about implementing some sort of mechanism for
327SQLite, so data can be stored on Spaces and accessed by many VM's. For a project
328where data doesn't need to be accessible in real-time and can have couple of
329minutes old data this would be very interesting. If any of you find this
330proposal interesting please write in a comment box below or shoot me an email
331and I will keep you posted.
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