I remember my interview at Spotify where we discussed how to implement thumbnail display service in the most effective way. What we actually came to is something along the lines of this library.
I always like it when a company focus on their real problems in job interviews and manage to avoid the brain teaser trap. That way you can have a feeling about the job that you are going to work on there, and see if you really like both work and people.
Thanks :-) That is a go-to interview question for us and we actually all do it slightly differently and take it in different directions depending on your expertise or specialization.
It is really closely aligned to what our core service is (distributing and streaming files) and is a great chance to talk with the interviewee and figure out where their strengths are.
Totally encourage other people to interview this way. It's what I've done at the past few companies I've been at and really worked excellently — just think of a problem you're working or have worked on, and distill it into an interview problem.
I also like this style, but you have to be very mindful of the fact that you've been thinking about the problem 1000x more than the candidate. The Curse of Knowledge[1] haunts the interview process. I haven't tried it much, but maybe it would be better to always use a fresh problem in each interview that not even you had seen. Maybe selected from StackOverflow.
If you're familiar with thumbnail display you know Facebook has Haystack for efficient image serving. It goes very low end. Sparkey or CDB or BAM (mentioned in this post) could be much less complex and can do similar work. Why did they go Haystack route I dont get it.
Are you asking why they didn't use Haystack? Haystack isn't released publicly as far as I can tell (plus, just because another company writes something doesn't mean it's actually good).
Sorry not being clear enough. Yes I was kind of asking why they didnt try simpler options. When you look at their paper on Haystack you see that it's essentially a constant local database.
Another cool project is bam[1], a constant key/value server built on similar principles. A single input file (in this case, a TSV file instead of the SPL log file) and an index file. The cool thing about bam is that it uses the CMPH[2] library to generate a minimal perfect hash function over the keys in the input file before putting them in the index file.
Wow, didn't expect this to make a mention on the front page of HN today. I never did convince Etsy to let me deploy bam in production, but it's so simple that it should be doable without much fuss. I mainly built it as a proof-of-concept to show that serving static data does not have to be difficult – and that loading large static data sets into a relational database is a truly wasteful, terrible approach. Are you actually using bam "in anger"?
Bam looks really interesting, definitely a lot simpler than Sparkey, and the basic principle is the same. I have been hesitant to use perfect hashing for Sparkey since I wasn't sure how well it holds up for really large data sets (close to a billion keys). Impressive to write it in less than 300 lines of clean code!
There are a bunch of comments related to performance data - the code is available so nothing is stopping anyone from making an unbiased comparison. :)
That said, I intend to publish some sort of performance comparison code / results. The downside with me doing it is that: 1) I know the sparkey code much better than I know level-db or any other solution, so the tuning parameters will probably be suboptimal for the other solutions. 2) I will only focus on our specific usecase (write large bulks, do lots of random reads), which may seem a bit unfair to the most general solutions.
Here are some preliminary performance benchmarks from my regular workstation (Intel(R) Core(TM) i5-2400 CPU @ 3.10GHz, 8 GB RAM): http://pastebin.com/7buZVgdu
The sparkey usage is fairly optimized, but I just randomly put something together for the level-db, so consider the results extremely biased.
Where's the source code for your bench? How large are the records you're loading? How large are the keys? What's the insert order?
How does your test compare to http://symas.com/mdb/microbench/ ? If you're going to try to talk about numbers, talk about them in a meaningful context. Right now you're just handwaving.
I didn't add the level-db code to this benchmark however, since I
1) didn't want to manage that dependency
2) didn't know how to write optimized code for usage of it.
I'm using very small records, a couple of bytes of key and value. The insert order is strictly increasing (key_0, key_1, ...), though that doesn't really matter for sparkey since it uses a hash for lookup instead of ordered lists or trees.
As for the symas mdb microbench, I only looked at it briefly but it seems like it's not actually reading the value it's fetching, only doing the lookup of where it actually is, is that correct?
"MDB's zero-memcpy reads mean its read rate is essentially independent of the size of the data items being fetched; it is only affected by the total number of keys in the database."
Doing a lookup and not using the values seems like a very unrealistic usecase.
Here's the part of the benchmark I'm referring to:
for (int i = 0; i < reads_; i++) {
const int k = rand_.Next() % reads_;
key.mv_size = snprintf(ckey, sizeof(ckey), "%016d", k);
mdb_cursor_get(cursor, &key, &data, MDB_SET);
FinishedSingleOp();
}
It's a valid measurement of how long the DB takes to access a record. You can assume that the time required for an app to use the values will be the same across all DBs therefore it's omitted. All of the microbench code behaves the same on this point.
Well, some DB's "load" the value in some way before giving it to the user, so that time is implicitly measured for those types of DB's but not for others, so I don't think it's a particularly fair comparison. I think Tokyo Cabinet gives you a pointer to newly allocated memory, at least for compressed data (but I am not completely sure about this). Like LMDB, Sparkey also does no processing of the value for uncompressed data, but for compressed data some decompression needs to take place in the iterator buffer (I guess that's equivalent to your cursor object). Even worse, if this is done lazily upon value retrieval, the cost is completely hidden in the benchmark.
In any case, I think the easiest way to get a fair benchmark is to at least iterate over the value, possibly also compare it. If that time turns out to be significant (perhaps even dominant) compared to the actual lookup time, then further optimization of the actual storage layer is pretty meaningless.
This was run on my Dell M4400 laptop, Intel Q9300 2.53GHz quadcore CPU, 8GB RAM. The maximum DB size is around 4GB so this is a purely in-memory test. Your hash lookup is faster than the B+tree, but with compression you lose the advantage.
I was surprised to see LevelDB (https://code.google.com/p/leveldb/) was missing from the list of storage solutions you tried, because it seems optimal for your use-case. Were you aware of it?
I'm not sure about the optimal use-case match. Sparkey is for "mostly static" datasets where on disk structures are generated by a batch process and pushed to servers providing read only access to the data to consumers.
leveldb on the other hand, supports concurrent writes and provides features to handle data consistency and cheap gradual reindexing.
Right, this is actually the first commit - it's just that the history was squashed before publish - we had to remove sensitive Spotify specific things in it, and it seemed easiest to just do a big squash.
The problem is, that so many projects for so many things exists. It is hard to find the matching project, thats why many people invent the wheele again.
I wish more projects would follow this kind of readme format, at least somewhat. There are so many new things that popup on HN but have very little information about all the whats and whys I should care
What problem are you solving?
If existing solutions existed what hurdles did you face with them and how did you overcome them with your custom solution?
How do you compare from a performance view? (granted they still need to do this, but at least put in a section about it)
Indeed, from the README: We used to rely a lot on CDB (which is a really great piece of software). It performed blazingly quick and produces compact files. We only stopped using it when our data started growing close to the 4 GB limit
Similar also to DiscoDB, which does support compression, and uses perfect hashing for constant time-lookup with minimal disk access.
Not only that but it provides lightning-fast conjunctive normal form queries, a.k.a logical combinations of primitive keys. Plus it has Python / Erlang bindings.
I'm baffled by the choice of using the GNU autofools chain just to include a Doxygen target in the Makefile. The whole thing is essentially straight up C with just 1 library dependency.
The command line argument processing is also quite haphazardly done, it's not like it using getopt or whatever that poses compatibility issues. Is writing and packaging with a Makefile that difficult?
We used to have a "simple" makefile, but once we started to support multiple development environments (OSX, various Linux flavors) it got more and more complex. Autotools actually brings a lot of functionality as part of the package and is what people expect. I'm no fan, but it works for our use case.
They wrote a database to solve an operational need. From experience I can tell you that's an endeavour you should strife to spend as little time on as possible.
I think it's a miracle they produced something they feel comfortable sharing with the world. If you write a database in house, and the tool chain and the argument processing are the only things done haphazardly, then hats off to you :)
Whoa I sense passive-aggressiveness :) I'm still waiting for those benchmarks. The code is very clean and simple I was just picking bones. The use case seems to be overly specific tho. Is there any other example use cases where this library could be useful?
This looks interesting. Can be used for deduplication by keeping this hash table on disk for large amount of data
I wrote something like this to optimize disk seeks heavily by returning a reference of 8 byte and keeping a hashtable in memory. A mostly-append only records store that allowing mutations of same key and by rounding size of blobs by power of 2. Written to optimize storage layer for Membase.
I have now created a very simple benchmark suite to give you some rough performance numbers, and updated the README to include some sample numbers for one specific machine.
I'm struggling to find something that it does that a webserver pointed at the filesystem doesn't do (with hash-ids for file names). I'm wondering if that's all it is, with a bit of logic to write the files in the correct structure.
From the description: Sparkey is an extremely simple persistent key-value store. You could think of it as a read-only hashtable on disk and you wouldn't be far off.
I always like it when a company focus on their real problems in job interviews and manage to avoid the brain teaser trap. That way you can have a feeling about the job that you are going to work on there, and see if you really like both work and people.