I’m taking a few weeks defocused from work, as a kind of grandpaternity leave. That said, the venue for my Dances of Infant Calming is a small-but-nice apartment in San Francisco, so a certain amount of thinking about tech industries is inevitable. I even found time last Tuesday to meet or speak with my clients at WibiData, MemSQL, Cloudera, Citus Data, and MongoDB. And thus:
1. I’ve been sloppy in my terminology around “geo-distribution”, in that I don’t always make it easy to distinguish between:
- Storing different parts of a database in different geographies, often for reasons of data privacy regulatory compliance.
- Replicating an entire database into different geographies, often for reasons of latency and/or availability/ disaster recovery,
The latter case can be subdivided further depending on whether multiple copies of the data can accept first writes (aka active-active, multi-master, or multi-active), or whether there’s a clear single master for each part of the database.
What made me think of this was a phone call with MongoDB in which I learned that the limit on number of replicas had been raised from 12 to 50, to support the full-replication/latency-reduction use case.
2. Three years ago I posted about agile (predictive) analytics. One of the points was:
… if you change your offers, prices, ad placement, ad text, ad appearance, call center scripts, or anything else, you immediately gain new information that isn’t well-reflected in your previous models.
Subsequently I’ve been hearing more about predictive experimentation such as bandit testing. WibiData, whose views are influenced by a couple of Very Famous Department Store clients (one of which is Macy’s), thinks experimentation is quite important. And it could be argued that experimentation is one of the simplest and most direct ways to increase the value of your data.
3. I’d further say that a number of developments, trends or possibilities I’m seeing are or could be connected. These include agile and experimental predictive analytics in general, as noted in the previous point, along with: Read more
The general Tokutek strategy has always been:
- Write indexes efficiently, which …
- … makes it reasonable to have more indexes, which …
- … lets more queries run fast.
But the details of “writes indexes efficiently” have been hard to nail down. For example, my post about Tokutek indexing last January, while not really mistaken, is drastically incomplete.
Adding further confusion is that Tokutek now has two product lines:
- TokuDB, a MySQL storage engine.
- TokuMX, in which the parts of MongoDB 2.2 that roughly equate to a storage engine are ripped out and replaced with Tokutek code.
TokuMX further adds language support for transactions and a rewrite of MongoDB’s replication code.
So let’s try again. I had a couple of conversations with Martin Farach-Colton, who:
- Is a Tokutek co-founder.
- Stayed in academia.
- Is a data structures guy, not a database expert per se.
The core ideas of Tokutek’s architecture start: Read more
Last week, I edited press releases back-to-back-to-back for three clients, all with announcements at this week’s Percona Live. The ones with embargoes ending today are Tokutek and GenieDB.
Tokutek’s news is that they’re open sourcing much of TokuDB, but holding back hot backup for their paid version. I approve of this strategy — “doesn’t lose data” is an important feature, and well worth paying for.
I kid, I kid. Any system has at least a bad way to do backups — e.g. one that involves slowing performance, or perhaps even requires taking applications offline altogether. So the real points of good backup technology are:
- To keep performance steady.
- To make the whole thing as easy to manage as possible.
GenieDB is announcing a Version 2, which is basically a performance release. So in lieu of pretending to have much article-worthy news, GenieDB is taking the opportunity to remind folks of its core marketing messages, with catchphrases such as “multi-regional self-healing MySQL”. Good choice; indeed, I wish more vendors would adopt that marketing tactic.
Along the way, I did learn a bit more about GenieDB. In particular:
- GenieDB is now just backed by a hacked version of InnoDB (no more Berkeley DB Java Edition).
- Why hacked? Because GenieDB appends a Lamport timestamp to every row, which somehow leads to a need to modify how indexes and caching work.
- Benefits of the chamge include performance and simpler (for the vendor) development.
- An arguable disadvantage of the switch is that GenieDB no longer can use Berkeley DB’s key-value interface — but MySQL now has one of those too.
I also picked up some GenieDB company stats I didn’t know before — 9 employees and 2 paying customers.
|Categories: GenieDB, Market share and customer counts, MySQL, NewSQL, Open source, Tokutek and TokuDB||2 Comments|
I talked Friday with Deep Information Sciences, makers of DeepDB. Much like TokuDB — albeit with different technical strategies — DeepDB is a single-server DBMS in the form of a MySQL engine, whose technology is concentrated around writing indexes quickly. That said:
- DeepDB’s indexes can help you with analytic queries; hence, DeepDB is marketed as supporting OLTP (OnLine Transaction Processing) and analytics in the same system.
- DeepDB is marketed as “designed for big data and the cloud”, with reference to “Volume, Velocity, and Variety”. What I could discern in support of that is mainly:
- DeepDB has been tested at up to 3 terabytes at customer sites and up to 1 billion rows internally.
- Like most other NewSQL and NoSQL DBMS, DeepDB is append-only, and hence could be said to “stream” data to disk.
- DeepDB’s indexes could at some point in the future be made to work well with non-tabular data.*
- The Deep guys have plans and designs for scale-out — transparent sharding and so on.
*For reasons that do not seem closely related to product reality, DeepDB is marketed as if it supports “unstructured” data today.
Other NewSQL DBMS seem “designed for big data and the cloud” to at least the same extent DeepDB is. However, if we’re interpreting “big data” to include multi-structured data support — well, only half or so of the NewSQL products and companies I know of share Deep’s interest in branching out. In particular:
- Akiban definitely does. (Note: Stay tuned for some next-steps company news about Akiban.)
- Tokutek has planted a small stake there too.
- Key-value-store-backed NuoDB and GenieDB probably leans that way. (And SanDisk evidently shut down Schooner’s RDBMS while keeping its key-value store.)
- VoltDB, Clustrix, ScaleDB and MemSQL seem more strictly tabular, except insofar as text search is a requirement for everybody. (Edit: Oops; I forgot about Clustrix’s approach to JSON support.)
Edit: MySQL has some sort of an optional NoSQL interface, and hence so presumably do MySQL-compatible TokuDB, GenieDB, Clustrix, and MemSQL.
Also, some of those products do not today have the transparent scale-out that Deep plans to offer in the future.
Hmm. I probably should have broken this out as three posts rather than one after all. Sorry about that.
Discussions of DBMS performance are always odd, for starters because:
- Workloads and use cases vary greatly.
- In particular, benchmarks such as the YCSB or TPC-H aren’t very helpful.
- It’s common for databases or at least working sets to be entirely in RAM — but it’s not always required.
- Consistency and durability models vary. What’s more, in some systems — e.g. MongoDB — there’s considerable flexibility as to which model you use.
- In particular, there’s an increasingly common choice in which data is written synchronously to RAM on 2 or more servers, then asynchronously to disk on each of them. Performance in these cases can be quite different from when all writes need to be committed to disk. Of course, you need sufficient disk I/O to keep up, so SSDs (Solid-State Drives) can come in handy.
- Many workloads are inherently single node (replication aside). Others are not.
MongoDB and 10gen
I caught up with Ron Avnur at 10gen. Technical highlights included: Read more
Perhaps the single toughest question in all database technology is: Which different purposes can a single data store serve well? — or to phrase it more technically — Which different usage patterns can a single data store support efficiently? Ted Codd was on multiple sides of that issue, first suggesting that relational DBMS could do everything and then averring they could not. Mike Stonebraker too has been on multiple sides, first introducing universal DBMS attempts with Postgres and Illustra/Informix, then more recently suggesting the world needs 9 or so kinds of database technology. As for me — well, I agreed with Mike both times.
Since this is MUCH too big a subject for a single blog post, what I’ll do in this one is simply race through some background material. To a first approximation, this whole discussion is mainly about data layouts — but only if we interpret that concept broadly enough to comprise:
- Every level of storage (disk, RAM, etc.).
- Indexes, aggregates and raw data alike.
To date, nobody has ever discovered a data layout that is efficient for all usage patterns. As a general rule, simpler data layouts are often faster to write, while fancier ones can boost query performance. Specific tradeoffs include, but hardly are limited to: Read more
Alternate title: TokuDB updates
Tokutek turns a performance argument into a functionality one. In particular, Tokutek claims that TokuDB does a much better job than alternatives of making it practical for you to update indexes at OLTP speeds. Hence, it claims to do a much better job than alternatives of making it practical for you to write and execute queries that only make sense when indexes (or other analytic performance boosts) are in place.
That’s all been true since I first wrote about Tokutek and TokuDB in 2009. However, TokuDB’s technical details have changed. In particular, Tokutek has deemphasized the ideas that:
- Vaguely justified the “fractal” metaphor, namely …
- … the stuff in that post about having one block each sized for each power of 2, …
- … which seem to be a form of what is more ordinarily called “cache-oblivious” technology.
Rather, Tokutek’s new focus for getting the same benefits is to provide a separate buffer for each node of a b-tree. In essence, Tokutek is taking the usual “big blocks are better” story and extending it to indexes. TokuDB also uses block-level compression. Notes on that include: Read more
|Categories: Akiban, Database compression, Market share and customer counts, NewSQL, Tokutek and TokuDB||7 Comments|
I plan to write about several NewSQL vendors soon, but first here’s an overview post. Like “NoSQL”, the term “NewSQL” has an identifiable, recent coiner — Matt Aslett in 2011 — yet a somewhat fluid meaning. Wikipedia suggests that NewSQL comprises three things:
- OLTP- (OnLine Transaction Processing)/short-request-oriented SQL DBMS that are newer than MySQL.
- Innovative MySQL engines.
- Transparent sharding systems that can be used with, for example, MySQL.
I think that’s a pretty good working definition, and will likely remain one unless or until:
- SQL-oriented and NoSQL-oriented systems blur indistinguishably.
- MySQL (or PostgreSQL) laps the field with innovative features.
To date, NewSQL adoption has been limited.
- NewSQL vendors I’ve written about in the past include Akiban, Tokutek, CodeFutures (dbShards), Clustrix, Schooner (Membrain), VoltDB, ScaleBase, and ScaleDB, with GenieDB and NuoDB coming soon.
- But I’m dubious whether, even taken together, all those vendors have as many customers or production references as any of 10gen, Couchbase, DataStax, or Cloudant.*
That said, the problem may lie more on the supply side than in demand. Developing a competitive SQL DBMS turns out to be harder than developing something in the NoSQL state of the art.
As a follow-up to the latest Stonebraker kerfuffle, Derrick Harris asked me a bunch of smart followup questions. My responses and afterthoughts include:
- Facebook et al. are in effect Software as a Service (SaaS) vendors, not enterprise technology users. In particular:
- They have the technical chops to rewrite their code as needed.
- Unlike packaged software vendors, they’re not answerable to anybody for keeping legacy code alive after a rewrite. That makes migration a lot easier.
- If they want to write different parts of their system on different technical underpinnings, nobody can stop them. For example …
- … Facebook innovated Cassandra, and is now heavily committed to HBase.
- It makes little sense to talk of Facebook’s use of “MySQL.” Better to talk of Facebook’s use of “MySQL + memcached + non-transparent sharding.” That said:
- It’s hard to see why somebody today would use MySQL + memcached + non-transparent sharding for a new project. At least one of Couchbase or transparently-sharded MySQL is very likely a superior alternative. Other alternatives might be better yet.
- As noted above in the example of Facebook, the many major web businesses that are using MySQL + memcached + non-transparent sharding for existing projects can be presumed able to migrate away from that stack as the need arises.
Continuing with that discussion of DBMS alternatives:
- If you just want to write to the memcached API anyway, why not go with Couchbase?
- If you want to go relational, why not go with MySQL? There are many alternatives for scaling or accelerating MySQL — dbShards, Schooner, Akiban, Tokutek, ScaleBase, ScaleDB, Clustrix, and Xeround come to mind quickly, so there’s a great chance that one or more will fit your use case. (And if you don’t get the choice of MySQL flavor right the first time, porting to another one shouldn’t be all THAT awful.)
- If you really, really want to go in-memory, and don’t mind writing Java stored procedures, and don’t need to do the kinds of joins it isn’t good at, but do need to do the kinds of joins it is, VoltDB could indeed be a good alternative.
And while we’re at it — going schema-free often makes a whole lot of sense. I need to write much more about the point, but for now let’s just say that I look favorably on the Big Four schema-free/NoSQL options of MongoDB, Couchbase, HBase, and Cassandra.
A press person recently asked about:
… start-ups that are building technologies to enable MySQL and other SQL databases to get over some of the problems they have in scaling past a certain size. … I’d like to get a sense as to whether or not the problems are as severe and wide spread as these companies are telling me? If so, why wouldn’t a customer just move to a new database?
While that sounds as if he was asking about scale-out relational DBMS in general, MySQL or otherwise, short-request or analytic, it turned out that he was asking just about short-request scale-out MySQL. My thoughts and comments on that narrower subject include(d) but are not limited to: Read more
|Categories: Akiban, dbShards and CodeFutures, Investment research and trading, Kaminario, MySQL, NewSQL, Oracle, ScaleBase, ScaleDB, Schooner Information Technology, Solid-state memory, Tokutek and TokuDB, Web analytics||5 Comments|