Analysis of open source DBMS vendor MySQL (recently acquired by Sun Microsystems), its products, and other products in the MySQL ecosystem. Related subjects include:
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.
GenieDB is one of the newer and smaller NewSQL companies. GenieDB’s story is focused on wide-area replication and uptime, coupled to claims about ease and the associated low TCO (Total Cost of Ownership).
GenieDB is in my same family of clients as Cirro.
The GenieDB product is more interesting if we conflate the existing GenieDB Version 1 and a soon-forthcoming (mid-year or so) Version 2. On that basis:
- GenieDB has three tiers.
- GenieDB’s top tier is the usual MySQL front-end.
- GenieDB’s bottom tier is either Berkeley DB or a conventional MySQL storage engine.
- GenieDB’s bottom tier stores your entire database at every node.
- If you replicate locally, GenieDB’s middle tier operates a distributed cache.
- If you replicate wide-area, GenieDB’s middle tier allows active-active/multi-master replication.
The heart of the GenieDB story is probably wide-area replication. Specifics there include: Read more
|Categories: Cache, Cloud computing, Clustering, GenieDB, Market share and customer counts, MySQL, NewSQL||4 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.
I’m usually annoyed by lists of year-end predictions. Still, a reporter asked me for some, and I found one kind I was comfortable making.
Trends that I think will continue in 2013 include:
Growing attention to machine-generated data. Human-generated data grows at the rate business activity does, plus 0-25%. Machine-generated data grows at the rate of Moore’s Law, also plus 0-25%, which is a much higher total. In particular, the use of remote machine-generated data is becoming increasingly real.
Hadoop adoption. Everybody has the big bit bucket use case, largely because of machine-generated data. Even today’s technology is plenty good enough for that purpose, and hence justifies initial Hadoop adoption. Development of further Hadoop technology, which I post about frequently, is rapid. And so the Hadoop trend is very real.
Application SaaS. The on-premises application software industry has hopeless problems with product complexity and rigidity. Any suite new enough to cut the Gordian Knot is or will be SaaS (Software as a Service).
Newer BI interfaces. Advanced visualization — e.g. Tableau or QlikView — and mobile BI are both hot. So, more speculatively, are “social” BI (Business Intelligence) interfaces.
Price discounts. If you buy software at 50% of list price, you’re probably doing it wrong. Even 25% can be too high.
MySQL alternatives. NoSQL and NewSQL products often are developed as MySQL alternatives. Oracle has actually done a good job on MySQL technology, but now its business practices are scaring companies away from MySQL commitments, and newer short-request SQL DBMS are ready for use.
|Categories: Business intelligence, Hadoop, MySQL, NewSQL, NoSQL, Open source, Oracle, Pricing, Software as a Service (SaaS), Surveillance and privacy||3 Comments|
In a call Monday with a prominent company, I was told:
- Teradata, Netezza, Greenplum and Vertica aren’t relational.
- Teradata, Netezza, Greenplum and Vertica are all data warehouse appliances.
That, to put it mildly, is not accurate. So I shall try, yet again, to set the record straight.
In an industry where people often call a DBMS just a “database” — so that a database is something that manages a database! — one may wonder why I bother. Anyhow …
1. The products commonly known as Oracle, Exadata, DB2, Sybase, SQL Server, Teradata, Sybase IQ, Netezza, Vertica, Greenplum, Aster, Infobright, SAND, ParAccel, Exasol, Kognitio et al. all either are or incorporate relational database management systems, aka RDBMS or relational DBMS.
2. In principle, there can be difficulties in judging whether or not a DBMS is “relational”. In practice, those difficulties don’t arise — yet. Every significant DBMS still falls into one of two categories:
- Was designed to do relational stuff* from the get-go, even if it now does other things too.
- Supports a lot of SQL.
- Was designed primarily to do non-relational things.*
- Doesn’t support all that much SQL.
*I expect the distinction to get more confusing soon, at which point I’ll adopt terms more precise than “relational things” and “relational stuff”.
3. There are two chief kinds of relational DBMS: Read more
I haven’t done a notes/link/comments post for a while. Time for a little catch-up.
1. MySQL now has a memcached integration story. I haven’t checked the details. The MySQL team is pretty hard to talk with, due to the heavy-handedness of Oracle’s analyst relations.
2. The Large Hadron Collider offers some serious numbers, including:
- 1 petabyte/second.
- 6 x 109 collisions/second.
- Only 1 in 1013 collision records kept (which I guess knocks things down to a 100 byte/second average, from the standpoint of persistent storage).
- Real-time filtering by a cluster of several thousand machines, over a 25 nanosecond period.
3. One application area we don’t talk about much for analytic technologies is education. However: Read more
|Categories: Cache, memcached, Memory-centric data management, MySQL, Open source, Petabyte-scale data management, RDF and graphs, Scientific research||Leave a Comment|
Oracle wants you to help you migrate from Microsoft SQL Server to MySQL. I was asked for comment, and replied:
- There are many SQL Server/Windows uses for which MySQL/Linux would do just as well. (Edit: But see the comments below.)
- However, I’m not sure in how many cases it would be worth the trouble of migration.
- Many Microsoft users have adopted a thick Windows-based stack. MySQL migration doesn’t address them.
- At the other extreme, if your application is really trivial, why bother moving?
- A few Seattle-area internet companies may have adopted SQL Server and now be wondering why. For them, this offer could be appealing.
Am I missing anything?
It feels like time to write about Clustrix, which I last covered in detail in May, 2010, and which is releasing Clustrix 4.0 today. Clustrix and Clustrix 4.0 basics include:
- Clustrix makes a short-request processing appliance.
- As you might guess from the name, Clustrix is clustered — peer-to-peer, with no head node.
- The Clustrix appliance uses flash/solid-state storage.
- Traditionally, Clustrix has run a MySQL-compatible DBMS.
- Clustrix 4.0 introduces JSON support. More on that below.
- Clustrix 4.0 introduces a bunch of administrative features, and parallel backup.
- Also in today’s announcement is a Rackspace partnership to offer Clustrix remotely, at monthly pricing.
- Clustrix has been shipping product for about 4 years.
- Clustrix has 20 customers in production, running >125 Clustrix nodes total.
- Clustrix has 60 people.
- List price for a (smallest size) Clustrix system is $150K for 3 nodes. Highest-end maintenance costs 15%.
- There’s also a $100K version meant for high availability/disaster recovery. Over half of Clustrix’s customers use off-site disaster recovery.
- Clustrix is raising a C round. Part of it has already been raised from insiders, as a kind of bridge.
The biggest Clustrix installation seems to be 20 nodes or so. Others seem to have 10+. I presume those disaster recovery customers have 6 or more nodes each. I’m not quite sure how the arithmetic on that all works; perhaps the 125ish count of nodes is a bit low.
Clustrix technical notes include: Read more
|Categories: Cloud computing, Clustering, Clustrix, Database compression, Market share and customer counts, MySQL, OLTP, Pricing, Structured documents||4 Comments|
In August 2010, I wrote about Workday’s interesting technical architecture, highlights of which included:
- Lots of small Java objects in memory.
- A very simple MySQL backing store (append-only, <10 tables).
- Some modernistic approaches to application navigation.
- A faceted approach to BI.
I caught up with Workday recently, and things have naturally evolved. Most of what we talked about (by my choice) dealt with data management, business intelligence, and the overlap between the two.
It is now reasonable to say that Workday’s servers fall into at least seven tiers, although we talked mainly about five that work together as a kind of giant app/database server amalgamation. The three that do noteworthy data management can be described as:
- In-memory objects and transactions. This is similar to what Workday had before.
- Persistent MySQL. Part of this is similar to what Workday had before. In addition, Workday is now storing certain data in tables in the ordinary relational way.
- In-memory caching and indexing. This has three aspects:
- Indexes for the ordinary relational tables, organized in interesting ways.
- Indexes for Workday’s search-box navigation (as per my original Workday technical post, you can search across objects, task-names, etc.).
- Compressed copies of the Java objects, used to instantiate other servers as needed. The most obvious uses of this are:
- Recovery for the object/transaction tier.
- Launch for the elastic compute tier. (Described below.)
Two other Workday server tiers may be described as: Read more
I’m frequently asked to generalize in some way about in-memory or memory-centric data management. I can start:
- The desire for human real-time interactive response naturally leads to keeping data in RAM.
- Many databases will be ever cheaper to put into RAM over time, thanks to Moore’s Law. (Most) traditional databases will eventually wind up in RAM.
- However, there will be exceptions, mainly on the machine-generated side. Where data creation and RAM data storage are getting cheaper at similar rates … well, the overall cost of RAM storage may not significantly decline.
Getting more specific than that is hard, however, because:
- The possibilities for in-memory data storage are as numerous and varied as those for disk.
- The individual technologies and products for in-memory storage are much less mature than those for disk.
- Solid-state options such as flash just confuse things further.
Consider, for example, some of the in-memory data management ideas kicking around. Read more