There was very interesting presentation by one professor. I'm not sure about what university, but he seemed to know his work.

He talked about how databse world is about to change. ACID is really expensive in terms of resources, and so are the more difficult things about relational schema (foreign keys, checks, etc). And architecture of classic RDBMSes is pretty wasteful -- they use on-disk format but cache it in memory.

He talked about how there are basically three new paths for DBMSes to follow. 1) Some drop the restrictions to become faster. This is the NoSql stuff, because you don't really need ACID for writing to Facebook wall.

This is called NoSql database.

2) OLAP, in data warehousing, the usual way to do things is that you load ridiculous amount of data into database, and then run analytical queries, that tend to heavily use aggregation and sometimes use just few dimmensions, while the DWH data tend to be pretty wide.

For this, column store makes perfect sense. It is not very quick on writes, but it can do very fast aggregation and selection of just few columns.

This is called Column store.

3) In OLTP, you need throughtput, but the question is, how big are your data, and how fast do they grow? Because RAM tends to get bigger exponentially, while how many customers you have will probably grow linearly or maybe fuster, but not much. So your data could fit into memory, now, or in future.

This allows you to make very fast database. All you need to do is to switch the architecture to memory-based, store data in memory format in memory and on disk. You don't read the disk, you just use it to store the data on shutdown.

This is called Main memory database.

No, that was the presentation. It was awesome, and if someone can find it, please give us a link! My search-fu was not strong enouhg.

...

What interests me is that we have NoSql databases for some time already, and we have at least one huge (are very expensive) column store: Teradata. But this seems to be first actual Main memory database.

My dream would be to switch Postgres to main memory or column store mode, but I guess that's not happening very soon :)

I'm a little skeptical:

- a bunch of the novel components (the UPS aware persistence layer, for example) aren't actually built yet

- they're pushing for people to build businesses on it already. I would characterize it as "bleeding-edge with bits of glass glued on", so this doesn't seem entirely honest.

- there's mostly a lot of breathless talk about how great and fast and scalable it is, but no mention of CAP theorem. To boil down their feature set, it's an in-memory RDBMS using the Actor model.

> UPS systems will stay active for a few minutes, based on their capacity, and the manager process will gracefuly shut down each daemon and write data to disk storage. This will ensure durability--even against power failure or system crash--while still maintaining in memory performance.

How does a UPS ensure durability against system or program crashes, disk corruption in large clusters, and other failures that can affect a simple write()?

> The real killer for database performance is synchronous transaction log writes. Even with the fastest underlying storage, this activity is the limiting factor for database write performance. InfiniSQL avoids this limiting factor while still retaining durability

How do you plan to implement this (since it appears it hasn't been implemented)? What is your fundamental insight about synchronous transaction logs that makes InifiSQL capable of being durable while (presumably) not having a synchronously written transaction log? If your answer is the UPS, please see my first question.

Edit: I don't see any mention of Paxos anywhere. Could you explain what you're using for consensus?

GNU AFPL?

Clause 13 is a real pain to deal with when exposing this over the network.

I guess the developer wants to sell a license (like the mysql java client GPL'ing).

Can't blame him, he needs to get paid.

In-memory distributed database? VoltDB is already way past 500Ktx/sec on a 12-node cluster.

On their site though, it says no sharding and that it can do these 500Ktx/sec even when each transaction involves data on multiple nodes. Does this performance degrade directly in relation to the number of nodes a tx needs to touch?

A simple, straightforward, wire-level description of how things work when coordinating and performing transactions across would be very useful. There's a lot of excited talk about actors, but nothing that really examines why this is faster, or any sort of technical analysis.

Looking at the "About and Goals" section of their docs http://www.infinisql.org/docs/overview/#idp37033600

I can't seem to find the word "Reliable" or any variation thereof anywhere in there.

In fact, that word is no where to be found on the blog post or on the entire InfiniSQL page (not in the Overview, Guides, Reference or even FAQ). I find this quite remarkable since reliability is the true virtue of an RDBMS, not speed or even capacity. At least that's what PostgreSQL aims for and this being another RDBMS, and is also open source, I see it as InfiniSQL's only direct competitor.

It's nice that this is scalable, apparently, to ridiculous levels, but if I can't retrieve what I store in exactly the same shape as I stored it, then that's a bit of a buzz kill for me.

Can we have some assurance that this is the case?

There's a note on "Durability" and a shot at log file writing for transactions, and presumably InfiniSQL uses concurrency and replicas, to provide it. In the Data Storage section, it mentions that InfiniSQL is still an in-memory database for the most part http://www.infinisql.org/docs/overview/#idp37053600

What they're describing is a massively redundant, UPS backed, in-memory cache.

Am I wrong?

The write bottleneck for traditional databases has never been the write-ahead log, with group commit and a battery-backed RAID controller you'll have a hard time saturating the disk with log writes. The bottleneck has always been random I/O induced by in-place updating indexes based on B-trees. You don't need to be in-memory if you use better data structures. TokuDB and TokuMX are proof of that.

An in-memory RDBMS hardly seems to be "infinitely scalable". How would this work with DBs in the terabyte size or larger?

This is what Clustrix (YC company) claims to do.

What's up with the weird coding standards? Include files named infinisql_*.h and #line statements... strange.

What is the difference to Teradata or Netezza except this is open source and lack the burden of universality yet?

Hi, Alan. Yes, many things are not built yet. Nowhere am I pushing anybody to build their business on it yet, but I am looking for hackers and early adopters/alpha testers. I've gone through pains on every doc that I've created that this is early, alpha, needs lots of work--including the 2nd paragraph of the linked-to article: "InfiniSQL is still in early stages of development--it already has many capabilities, but many more are necessary for it to be useful in a production environment."

Regarding CAP, I'm not addressing multi-site availability at this stage--I want to get single site fully operational, redundant, and so on.

And, yes, to boil it down, it's an in-memory RDBMS using the Actor model.

The most important feature is that it performs transactions involving records on multiple nodes better than anything. This is the workload that keystores functionally cannot do, and which other distributed RDBMS' suffer under. It's also open source, has over 100 pages of technical docs, and is functional enough for people to pound on with some workloads--but not something to put into production yet.

Hi, yid. UPS protects against multiple simultaneous system crashes. Single system crash gets failed over, no problem. If both UPS systems detect their upstream PDU's as being out, then the InfiniSQL management protocol will initiate graceful shutdown, including persisting to disk. For write() issues, at least intially, I think that stuff in commodity hardware (such as ECC memory) is sufficient protection in most cases. Attaching a high end storage array, or using ZFS, also protects against low level disk problems. I don't see those problems as needing to be solved for a 1.0 relase, but am very much open to contributions that address those issues any time you want!

The fundamental insight about not needing transaction logs is pretty simple actually: if the power is guaranteed to either stay on, or to allow the system to quiesce gracefully, then the cluster will not suddenly crash. That's the motivator for transaction logs--to make sure that the data will still be there if the system suddenly crashes. Get rid of the need for transaction logs, get rid of the transaction logs.

Regarding consensus, I expect that there will be a quorum protocol in use amongst an odd number greater than 2 of manager processes, each with redundant network and power. But the specific protocol I haven't ironed out. If there's something I can grab off the shelf then it may be preferable to implementing from scratch, but I haven't gotten there yet.

This stuff hasn't been implemented yet, but the core around which it can be implemented, has been.

Do I sense a volunteer? ;-)

Hi, Michael. Yes, VoltDB is very fast, but they self-admittedly do not perform well if transactions contain records spanning across multiple nodes. That is the key feature difference between InfiniSQL and VoltDB (along, of course, that their project is functionally much further along).

If you want more details about how things work when performing transactions, I think that the overview I created would be a good starting point. It probably doesn't have everything you'd ask for, but I hope that it answers some of your questions: http://www.infinisql.org/docs/overview/

And to answer your question about performance degradation pertaining to number of nodes each transaction touches, I have not done comprehensive benchmarks of InfiniSQL measuring that particular item. However, I do believe that as multi-node communication increases, throughput will tend to decrease--I expect that the degradation would be graceful, but further testing is required. The benchmark I've performed and referenced has 3 updates and a select in each transaction, all very likely to be on 3 different nodes.

I'd like to invite you to benchmark InfiniSQL in your own environment. I've included the scripts in the source distribution, as well as a guide on how I benchmarked, as well as details on the specific benchmarking I've done so far. All at http://www.infinisql.org

I'd be glad to assist in any way, give pointers, and so on, if there are tests that you'd like to do. I also plan to do further benchmarking over time, and I'll update the site's blog (and twitter, etc) as I do so.

Please communicate further with me if you're curious.

Thanks, Mark

I'm not talking about the speed of the array, rather a battery backed controller. With one of those, as long as you're under the sequential write bandwidth of the underlying array, it pretty much doesn't matter how much you fsync the log, so that bottleneck (commit frequency) goes away.

If you're planning to write data faster than disk bandwidth, then you have no hope of being durable and we're talking about problems too different to be worth comparing, and in that case I retract my comment.

I don't understand what distinction you're trying to make between the "array itself" and the "log file has a limit to how many blocks can be appended". Can you clarify what limit you're talking about?

>The log writes don't saturate the array itself--but the log file has a limit to how many blocks can be appended--even on fast arrays

yes, the issue usually isn't the transaction log append speed. Instead, it happens too frequently that the log is configured to be too small. A log file switch causes a flush of accumulated modified datablocks of tables and indexes [buffer cache flush in Oracle parlance] from RAM to disk. With small log file size, the flush happens too frequently for too small amounts of modified data - this is where GP mentioned random IO bites in the neck.