DBMS product categories
Analysis of database management technology in specific product categories. Related subjects include:
Ever more products try to integrate SQL with Hadoop, and discussions of them seem confused, in line with Monash’s First Law of Commercial Semantics. So let’s draw some distinctions, starting with (and these overlap):
- Are the SQL engine and Hadoop:
- Necessarily on the same cluster?
- Necessarily or at least most naturally on different clusters?
- How, if at all, is Hadoop invoked by the SQL engine? Specifically, what is the role of:
- HDFS (Hadoop Distributed File System)?
- Hadoop MapReduce?
- How, if at all, is the SQL engine invoked by Hadoop?
- If something is called a “connector”, then Hadoop and the SQL engine are most likely on separate clusters. Good features include (but these can partially contradict each other):
- A way of making data transfer maximally parallel.
- Query planning that is smart about when to process on the SQL engine and when to use Hadoop’s native SQL (Hive or otherwise).
- If something is called “SQL-on-Hadoop”, then Hadoop and the SQL engine are or should be on the same cluster, using the same nodes to store and process data. But while that’s a necessary condition, I’d prefer that it not be sufficient.
Let’s go to some examples. Read more
|Categories: Cloudera, Data integration and middleware, EAI, EII, ETL, ELT, ETLT, Hadapt, Hadoop, HBase, Hortonworks, MapReduce, Microsoft and SQL*Server, NewSQL, PostgreSQL, SQL/Hadoop integration, Teradata||23 Comments|
From time to time I like to do “what I’m working on” posts. From my recent blogging, you probably already know that includes:
- Hadoop (always, and please see below).
- Analytic RDBMS (ditto).
- NoSQL and NewSQL.
- Specifically, SQL-on-Hadoop
- Spark and other memory-centric technology, including streaming.
- Public policy, mainly but not only in the area of surveillance/privacy.
- General strategic advice for all sizes of tech company.
Other stuff on my mind includes but is not limited to:
1. Certain categories of buying organizations are inherently leading-edge.
- Internet companies have adopted Hadoop, NoSQL, NewSQL and all that en masse. Often, they won’t even look at things that are conventional or expensive.
- US telecom companies have been buying 1 each of every DBMS on the market since pre-relational days.
- Financial services firms — specifically algorithmic traders and broker-dealers — have been in their own technical world for decades …
- … as have national-security agencies …
- … as have pharmaceutical research departments.
Fine. But what really intrigues me is when more ordinary enterprises also put leading-edge technologies into production. I pester everybody for examples of that.
I think that most sufficiently large enterprise SaaS vendors should offer an appliance option, as an alternative to the core multi-tenant service. In particular:
- SaaS appliances address customer fears about security, privacy, compliance, performance isolation, and lock-in.
- Some of these benefits occur even if the appliance runs in the same data centers that host the vendor’s standard multi-tenant SaaS. Most of the rest occur if the customer can choose a co-location facility in which to place the appliance.
- Whether many customers should or will use the SaaS appliance option is somewhat secondary; it’s a check-mark item. I.e., many customers and prospects will be pleased that the option at least exists.
How I reached them
Core reasons for selling or using SaaS (Software as a Service) as opposed to licensed software start:
- The SaaS vendor handles all software upgrades, and makes them promptly. In principle, this benefit could also be achieved on a dedicated system on customer premises (or at the customer’s choice of co-location facility).
- In addition, the SaaS vendor handles all the platform and operational stuff — hardware, operating system, computer room, etc. This benefit is antithetical to direct customer control.
- The SaaS vendor only has to develop for and operate on a tightly restricted platform stack that it knows very well. This benefit is also enjoyed in the case of customer-premises appliances.
Conceptually, then, customer-premises SaaS is not impossible, even though one of the standard Big Three SaaS benefits is lost. Indeed:
- Microsoft Windows and many other client software packages already offer to let their updates be automagically handled by the vendor.
- In that vein, consumer devices such as game consoles already are a kind of SaaS appliance.
- Complex devices of any kind, including computers, will see ever more in the way of “phone-home” features or optional services, often including routine maintenance and upgrades.
But from an enterprise standpoint, that’s all (relatively) simple stuff. So we’re left with a more challenging question — does customer-premises SaaS make sense in the case of enterprise applications or other server software?
|Categories: Data warehouse appliances, HP and Neoview, salesforce.com, Software as a Service (SaaS), Surveillance and privacy||5 Comments|
Generalizing about SaaS (Software as a Service) is hard. To prune some of the confusion, let’s start by noting:
- SaaS has been around for over half a century, and at times has been the dominant mode of application delivery.
- The term multi-tenancy is being used in several different ways.
- Multi-tenancy, in the purest sense, is inessential to SaaS. It’s simply an implementation choice that has certain benefits for the SaaS provider. And by the way, …
- … salesforce.com, the chief proponent of the theory that true multi-tenancy is the hallmark of true SaaS, abandoned that position this week.
- Internet-based services are commonly, if you squint a little, SaaS. Examples include but are hardly limited to Google, Twitter, Dropbox, Intuit, Amazon Web Services, and the company that hosts this blog (KnownHost).
- Some of the core arguments for SaaS’ rise, namely the various efficiencies of data center outsourcing and scale, apply equally to the public cloud, to SaaS, and to AEaaS (Anything Else as a Service).
- These benefits are particularly strong for inherently networked use cases. For example, you really don’t want to be hosting your website yourself. And salesforce.com got its start supporting salespeople who worked out of remote offices.
- In theory and occasionally in practice, certain SaaS benefits, namely the outsourcing of software maintenance and updates, could be enjoyed on-premises as well. Whether I think that could be a bigger deal going forward will be explored in future posts.
For smaller enterprises, the core outsourcing argument is compelling. How small? Well:
- What’s the minimum level of IT operations headcount needed for mission-critical systems? Let’s just say “several”.
- What does that cost? Fully burdened, somewhere in the six figures.
- What fraction of the IT budget should such headcount be? As low a double digit percentage as possible.
- What fraction of revenues should be spent on IT? Some single-digit percentage.
So except for special cases, an enterprise with less than $100 million or so in revenue may have trouble affording on-site data processing, at least at a mission-critical level of robustness. It may well be better to use NetSuite or something like that, assuming needed features are available in SaaS form.*
|Categories: Amazon and its cloud, Buying processes, Cloud computing, Data mart outsourcing, Data warehouse appliances, Data warehousing, Infobright, Netezza, Pricing, salesforce.com, Software as a Service (SaaS), Workday||3 Comments|
Relational DBMS used to be fairly straightforward product suites, which boiled down to:
- A big SQL interpreter.
- A bunch of administrative and operational tools.
- Some very optional add-ons, often including an application development tool.
Now, however, most RDBMS are sold as part of something bigger.
- Oracle has hugely thickened its stack, as part of an Innovator’s Solution strategy — hardware, middleware, applications, business intelligence, and more.
- IBM has moved aggressively to a bundled “appliance” strategy. Even before that, IBM DB2 long sold much better to committed IBM accounts than as a software-only offering.
- Microsoft SQL Server is part of a stack, starting with the Windows operating system.
- Sybase was an exception to this rule, with thin(ner) stacks for both Adaptive Server Enterprise and Sybase IQ. But Sybase is now owned by SAP, and increasingly integrated as a business with …
- … SAP HANA, which is closely associated with SAP’s applications.
- Teradata has always been a hardware/software vendor. The most successful of its analytic DBMS rivals, in some order, are:
- Netezza, a pure appliance vendor, now part of IBM.
- Greenplum, an appliance-mainly vendor for most (not all) of its existence, and in particular now as a part of EMC Pivotal.
- Vertica, more of a software-only vendor than the others, but now owned by and increasingly mainstreamed into hardware vendor HP.
- MySQL’s glory years were as part of the “LAMP” stack.
- Various thin-stack RDBMS that once were or could have been important market players … aren’t. Examples include Progress OpenEdge, IBM Informix, and the various strays adopted by Actian.
The 2013 Gartner Magic Quadrant for Operational Database Management Systems is out. “Operational” seems to be Gartner’s term for what I call short-request, in each case the point being that OLTP (OnLine Transaction Processing) is a dubious term when systems omit strict consistency, and when even strictly consistent systems may lack full transactional semantics. As is usually the case with Gartner Magic Quadrants:
- I admire the raw research.
- The opinions contained are generally reasonable (especially since Merv Adrian joined the Gartner team).
- Some of the details are questionable.
- There’s generally an excessive focus on Gartner’s perception of vendors’ business skills, and on vendors’ willingness to parrot all the buzzphrases Gartner wants to hear.
- The trends Gartner highlights are similar to those I see, although our emphasis may be different, and they may leave some important ones out. (Big omission — support for lightweight analytics integrated into operational applications, one of the more genuine forms of real-time analytics.)
Anyhow: Read more
Oracle announced its in-memory columnar option Sunday. As usual, I wasn’t briefed; still, I have some observations. For starters:
- Oracle, IBM (Edit: See the rebuttal comment below), and Microsoft are all doing something similar …
- … because it makes sense.
- The basic idea is to take the technology that manages indexes — which are basically columns+pointers — and massage it into an actual column store. However …
- … the devil is in the details. See, for example, my May post on IBM’s version, called BLU, outlining all the engineering IBM did around that feature.
- Notwithstanding certain merits of this approach, I don’t believe in complete alternatives to analytic RDBMS. The rise of analytic DBMS oriented toward multi-structured data just strengthens that point.
I’d also add that Larry Ellison’s pitch “build columns to avoid all that index messiness” sounds like 80% bunk. The physical overhead should be at least as bad, and the main saving in administrative overhead should be that, in effect, you’re indexing ALL columns rather than picking and choosing.
Anyhow, this technology should be viewed as applying to traditional business transaction data, much more than to — for example — web interaction logs, or other machine-generated data. My thoughts around that distinction start:
- I argued back in 2011 that traditional databases will wind up in RAM, basically because …
- … Moore’s Law will make it ever cheaper to store them there.
- Still, cheaper != cheap, so this is a technology only to use with you most valuable data — i.e., that transactional stuff.
- These are very tabular technologies, without much in the way of multi-structured data support.
|Categories: Columnar database management, Data warehousing, IBM and DB2, Memory-centric data management, Microsoft and SQL*Server, OLTP, Oracle, SAP AG, Workday||5 Comments|
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
Hortonworks did a business-oriented round of outreach, talking with at least Derrick Harris and me. Notes from my call — for which Rob Bearden* didn’t bother showing up — include, in no particular order:
- Hortonworks denies advanced acquisition discussions with either Microsoft and Intel. Of course, that doesn’t exactly contradict the widespread story of Intel having made an acquisition offer.
- As vendors usually do, Hortonworks denies the extreme forms of Cloudera’s suggestion that Hortonworks competitive wins relate to price slashing. But Hortonworks does believe that its license fees often wind up being lower than Cloudera’s, due especially to Hortonworks offering few extra-charge items than Cloudera.
- Hortonworks used a figure of ~75 subscription customers. This does not include OEM sales through, for example, Teradata, Microsoft Azure, or Rackspace. However, that does include …
- … a small number of installations hosted in the cloud — e.g. ~2 on Amazon Web Services — or otherwise remotely. Also, testing in the cloud seems to be fairly frequent, and the cloud can also be a source of data ingested into Hadoop.
- Since Hortonworks a couple of times made it seem that Rackspace was an important partner, behind only Teradata and Microsoft, I finally asked why. Answers boiled down to a Rackspace Hadoop-as-a-service offering, plus joint work to improve Hadoop-on-OpenStack.
- Other Hortonworks reseller partners seem more important in terms of helping customers consumer HDP (Hortonworks Data Platform), rather than for actually doing Hortonworks’ selling for it. (This is unsurprising — channel sales rarely are a path to success for a product that is also appropriately sold by a direct force.)
- Hortonworks listed its major industry sectors as:
- Web and retailing, which it identifies as one thing.
- Health care (various subsectors).
- Financial services, which it called “competitive” in the kind of tone that usually signifies “we lose a lot more than we win, and would love to change that”.
*Speaking of CEO Bearden, an interesting note from Derrick’s piece is that Bearden is quoted as saying “I started this company from day one …”, notwithstanding that the now-departed Eric Baldeschwieler was founding CEO.
In Hortonworks’ view, Hadoop adopters typically start with a specific use case around a new type of data, such as clickstream, sensor, server log, geolocation, or social. Read more
Some subjects just keep coming up. And so I keep saying things like:
Most generalizations about “Big Data” are false. “Big Data” is a horrific catch-all term, with many different meanings.
Most generalizations about Hadoop are false. Reasons include:
- Hadoop is a collection of disparate things, most particularly data storage and application execution systems.
- The transition from Hadoop 1 to Hadoop 2 will be drastic.
- For key aspects of Hadoop — especially file format and execution engine — there are or will be widely varied options.
Hadoop won’t soon replace relational data warehouses, if indeed it ever does. SQL-on-Hadoop is still very immature. And you can’t replace data warehouses unless you have the power of SQL.
Note: SQL isn’t the only way to provide “the power of SQL”, but alternative approaches are just as immature.
Most generalizations about NoSQL are false. Different NoSQL products are … different. It’s not even accurate to say that all NoSQL systems lack SQL interfaces. (For example, SQL-on-Hadoop often includes SQL-on-HBase.)