"MEMSQL IS 30 TIMES FASTER THAN MYSQL."
It's an extremely invalid and biased comparison. You're quite literally comparing the speed of writing to RAM versus the speed of writing to disk. If you didn't make such ridiculous assertions, people would accept your product for the actual awesome things it does and not focus on refuting your bullshit claims.
memcached is probably 30 times faster than MySQL too, but they don't go around claiming it's a durable database.
edit: memSQL main page says "Durable by Default"
We have the durability interface and configuration options here http://developers.memsql.com/docs/1b/durability.html
This should give you a good idea about how durability will react to tuning, but it doesn't dive deep into the internal design. If there's enough interest (seems like there is) we'd be happy to discuss it in a blog post.
This is a huge differentiator we are talking about here. For someone who has been around databases for quite some time (of which I had spent about 5 years working on SQL Server engine) this is very exciting. Is it not?
This sounds like absurd cargo culting. I've never designed a database but parsing the SQL can not have ever been the bottleneck; the fact that you rarely write to disk is where any speed improvements can be found.
SELECT * FROM table WHERE id > 5 LIMIT 10;
is actually of the same form as this query:
SELECT * FROM table WHERE id > 10 LIMIT 5;
without actually parsing the two. I mean you will incur a parsing overhead either way. What I think MemSQL is trying to optimize out is the actual execution of the query. I mean rather than interpreting and running it with checks performed in every iteration, they are compiling the code. I don't know how much of a difference this would make without actually seeing the numbers.
As far as I know that's the same on just about all databases.
http://developers.memsql.com/docs/1b/faq.html gives some more details.
Do you mean you determine placeholders without actually parsing the SQL?
@jakejake The docs. seem to suggest that the benefits of prepared statements are won without using prepared statements in MemSQL.
Not sure which ones if any do this today, the optimisation might no longer be relevant - I spotted DBs doing this 10 years ago when CGIs that didn't bother with prepared statements were rife.
I don't get excited for 1% decreases in latency. I'm willing to bet money that the performance penalty behind parsing the sql queries is asymptotically a constant - or at least, a tiny fraction of the time spent computing the data set.
I feel especially confident in this because memsql never brags about the specific increase in speed. This can't be hard to measure.
You are making all these skeptical claims that are technically wrong. I suggest you download MemSQL, compile a query and read the generated C++ code. Then download MySQL source and look at that too. At that point you will be coming to your own conclusions and I'll be happy to resume this debate.
Some queries in databases are indeed CPU bound, but the general statement that well-optimized databases turn to be CPU bound is not correct like that. It all depends on the use case.
If you're thinking full table scans with complicated processing on a database that's entirely in memory, then yes, you'll be CPU bound. But many (most?) databases are not entirely in memory, and there's no reason they need to be. With reasonable memory and indexing, most queries boil down to only a few IOs/disk seeks, so you're looking at <50ms, which is entirely fine for your average web request.
That's the case for the majority of "get by ID" style queries, which in my experience really are the lions share of queries, and in particular are also what needs to be fast. A complicated reporting query can take a couple of seconds, but your "main page/item view" must be fast.
If you have random reads on a large database that cannot be kept in memory, this will be the majority of your workload. Those queries will spend all their time waiting for the disk, and more or less none interpreting your query tree, so optimizing the query tree processing part makes no sense at all.
TL;DR, in my experience optimizing query tree interpretation does not yield significant benefits for the majority of queries, and IMHO won't be a significant differentiator for MemSQL outside of marketing.
There are use cases where 50ms is way too long, and where jitter associated with disk I/O is not acceptable. Capital markets for example. You are looking for sub-millisecond latency to be in the intraday post-trade game, and single digit milliseconds for quote-to-trade. The number of instructions in the hot code path (and cycles per instruction) actually starts to matter. Lock-free sturcutres utilized for storage are also important. This combination gives MemSQL some rather unique low-latency properties that certain folks can exploit to their delight.
To your other point, random reads and key-value lookups is where MemSQL shines. Since you never wait on I/O and don't take locks, optimizing execution makes a lot of sense actually. All that's left is execution and network.
I'm not sure where you are getting this information. According to the MemSQL documentation, they have one and only one isolation level, READ COMMITTED. But READ COMMITTED takes write locks and blocks reads, but doesn't take any read locks. Blocking behaviour still (and should!) still occur.
It sounds like you are looking for dirty read behaviour, but from the literature MemSQL doesn't support READ UNCOMMITTED.
Update: Read a later FAQ on the MemSQL website. They are using MVCC, so write locks are irrelevant. My apologies to the MemSQL team.
It is addressed in this FAQ: http://developers.memsql.com/docs/1b/faq.html#c1-q4.
I didn't realise you had a new FAQ up on the website - I'll have a look.
Compiling queries to C++ is great for some BS bullet point on a website with BS claims of performance gains, but it's only a niche feature at the end of the day. There are more important things that should be getting tackled first: multi-node distribution, better fsync handling, etc.
1. http://www.sqlteam.com/article/what-query-plans-are-in-sql-s...
edit: incidentally, one of the corner cases I can think of is joining 10 tables, which gives you 10! combinations for the query processor to work through. This is irrelevant in MemSQL, because you can only join to another two tables, and you cannot do right outer for full outer joins.
The original DB2 for VSE and VM (not DB2 for z-series/390) which was the productized version of System R did exactly this - compiled to native code. Subsequent DB2 implementations chose not to go down this path - even for single OS systems like DB2/390 and DB2/400.
In any event, I'm skeptical if this is going to make very much of a difference for the following reasons:
1. The time spent by the runtime/interpreter in "evaluating" the operators or expressions is really small relative to the heavy lifting of the actual relational operators to say nothing of I/O etc.
2. Most serious implementations don't really interpret the plan using a giant switch statement. For instance, with PostgreSQL, the interpreter puts in function pointers on first iteration. Thereafter it's only slightly worse than compiled native code.
SK
When you compile to native code, you can essentially save the dynamic dispatch on query parts, plus all the other benefits you get from compilation.
E.g. if you do good type analysis, you can also compile to much more efficient data representations, which will again have benefits for memory access. The compiler can also inline code, do loop lifting, and all those goodies.
But overall I strongly agree with 1), any time spent doing execution is usually dwarfed by IO.
What does this mean? Does the SQL get converted into actual C++ source code, then compiled with an internal C++ compiler? That seems like a weird thing to do. Or is it translated directly into an AST? If yes, I can't imagine other db's not doing it? I don't understand the claim being made here.
I am also curious if there is some way the developers have demonstrated the benefits of this approach vs. what other systems do. Most of the CPU time in a simple query in VoltDB is spent in networking, validating and suffering the cache-miss pain of walking index data structures. I can't see clearly how compiling to native code makes any of that much faster.
In fact, making a plan completely stable can cause problems if the data distribution changes dramatically. I asked about this problem in a previous article about MemSQL, and the answer was that the plan won't expire unless you drop an index or add a new one. [1]