Workload management
Discussion of workload management technology, typically in analytic or mixed-workload DBMS.
Choices in analytic computing system design
When I posted a long list of architectural options for analytic DBMS, I left a couple of IOUs in for missing parts. One was in the area of what is sometimes called advanced-analytics functionality, which roughly speaking means aspects of analytic database management systems that are not directly related to conventional* SQL queries.
*Main examples of “conventional” = filtering, simple aggregrations.
The point of such functionality is generally twofold. First, it helps you execute analytic algorithms with high performance, due to reducing data movement and/or executing the analytics in parallel. Second, it helps you create and execute sophisticated analytic processes with (relatively) little effort.
For now, I’m going to refer to an analytic RDBMS that has been extended by advanced-analytics functionality as an analytic computing system, rather than as some kind of “platform,” although I suspect the latter term is more likely to wind up winning. So far, there have been five major categories of subsystem or add-on module that contribute to making an analytic DBMS a more fully-fledged analytic computing system:
- SQL extensions. Examples include SQL-2003 analytics (notably windowing), or vendor-specific temporal functionality.
- A framework for UDFs (User-Defined Functions) to further extend SQL. At its core, a relational DBMS is a big SQL interpreter. SQL, while powerful, only does a limited number of things. User-Defined Functions are new predicates in the SQL language that do additional things.
- An execution engine for analytic processes that is less coupled to the SQL engine than a pure UDF framework might be. The two main approaches are MapReduce (e.g. Aster Data) and general C++ libraries (Netezza, ParAccel).
- Libraries of pre-built analytic processes. Commonly included are statistics, (other machine learning), general linear algebra, and Monte Carlo analysis. Some of these functions are fully parallelized (perhaps tens per vendor). Others just play nicely with the vendor’s execution framework, in that a separate copy can be run on each node (up to thousands per vendor, for those who bring in open source statistics libraries).
- Development tools such as integrated development environments (IDEs). Aster keeps trying to convince me that having built a nice Eclipse IDE is a major competitive differentiation.
| Categories: Aster Data, MapReduce, Netezza, ParAccel, Parallelization, Predictive modeling and advanced analytics, Workload management | 8 Comments |
DB2 workload management
DB2 has added a lot of workload management features in recent releases. So when we talked Tuesday afternoon, Tim Vincent and I didn’t bother going through every one. Even so, we covered some interesting subjects in the area of DB2 workload management, including: Read more
| Categories: Data warehousing, IBM and DB2, Netezza, Workload management | 3 Comments |
Infobright’s Release 3.4
Infobright called a couple weeks ago to discuss, among other subjects, its subsequently-released Infobright Release 3.4. I made no effort to distinguish between community/open source and professional/chargeable editions, but leaving that aside, it seems fair to characterize Infobright 3.4 as having two overlapping primary themes:
- Performance and bottleneck cleanup.
- “Omigod, you mean you didn’t have that feature before?” cleanup.
That said, the traditional release for cleaning up the last huge gaps in an analytic DBMS product seems have become 4.0; recent examples include Aster Data, Vertica and Greenplum. Infobright seems on track to be another example of that rule.
Ack. Now that I’ve said that, other vendors are going to be tempted to accelerate their numbering so as to reach the 4.0 mark sooner …
A lot of Infobright performance enhancements are in the vein “We used to rely on generic MySQL for that, but now we do it ourselves, and it works a lot better.” Examples include: Read more
| Categories: Data warehousing, Infobright, MySQL, Workload management | 6 Comments |
A partial overview of Netezza database software technology
Netezza is having its user conference Enzee Universe in Boston Monday–Wednesday, June 21-23, and naturally will be announcing new products there, and otherwise providing hooks and inducements to get itself written about. (The preliminary count is seven press releases in all.) To get a head start, I stopped by Netezza Thursday for meetings that included a 3 ½ hour session with 10 or so senior engineers, and have exchanged some clarifying emails since. Read more
| Categories: Data warehouse appliances, Data warehousing, Netezza, Theory and architecture, Workload management | 15 Comments |
Two cornerstones of Oracle’s database hardware strategy
After several months of careful optimization, Oracle managed to pick the most inconvenient* day possible for me to get an Exadata update from Juan Loaiza. But the call itself was long and fascinating, with the two main takeaways being:
- Oracle thinks flash memory is the most important hardware technology of the decade, one that could lead to Oracle being “bumped off” if they don’t get it right.
- Juan believes the “bulk” of Oracle’s business will move over to Exadata-like technology over the next 5-10 years. Numbers-wise, this seems to be based more on Exadata being a platform for consolidating an enterprise’s many Oracle databases than it is on Exadata running a few Especially Big Honking Database management tasks.
And by the way, Oracle doesn’t make its storage-tier software available to run on anything than Oracle-designed boxes. At the moment, that means Exadata Versions 1 and 2. Since Exadata is by far Oracle’s best DBMS offering (at least in theory), that means Oracle’s best database offering only runs on specific Oracle-sold hardware platforms. Read more
Aster Data 4.0 and the evolution of “advanced analytic(s) servers”
Since Linda and I are leaving on vacation in a few hours, Aster Data graciously gave me permission to morph its “12:01 am Monday, November 2” embargo into “late Friday night.”
Aster Data is officially announcing the 4.0 release of nCluster. There are two big pieces to this announcement:
- Aster is offering a slick vision for integrating big-database management and general analytic processing on the same MPP cluster, under the not-so-slick name “Data-Application Server.”
- Aster is also offering a sophisticated vision for workload management.
In addition, Aster has matured nCluster in various ways, for example cleaning up a performance problem with single-row updates.
Highlights of the Aster “Data-Application Server” story include: Read more
| Categories: Aster Data, Cloud computing, Data warehousing, EAI, EII, ETL, ELT, ETLT, MapReduce, Market share and customer counts, Teradata, Theory and architecture, Workload management | 9 Comments |
Netezza on concurrency and workload management
I visited Netezza Friday for what was mainly an NDA meeting. But while I was there I asked where Netezza stood on concurrency, workload management, and rapid data mart spin-out. Netezza’s claims in those regards turned out to be surprisingly strong.
In the biggest surprise, Netezza claimed at least one customer had >5,000 simultaneous users, and a second had >4,000. Both are household names. Other unspecified Netezza customers apparently also have >1,000 simultaneous users. Read more
| Categories: Data warehouse appliances, Data warehousing, Netezza, Teradata, Theory and architecture, Workload management | 13 Comments |
Some DB2 highlights
I chatted with IBM Thursday, about recent and imminent releases of DB2 (9.5 through 9.7). Highlights included:
- DB2 is getting Oracle emulation, which I posted about separately.
- IBM says that it had >50 new DB2 data warehouse customers last year. I neglected to ask how many of these had been general-purpose DB2 customers all along.
- By “data warehouse customer” I mean a user for InfoSphere Warehouse, which previously was called DB2’s DPF (Data Partitioning Feature). Apparently, this includes both logical and physical partitioning. E.g., DB2 isn’t shared-nothing without this feature.
- IBM is proud of DB2’s compression, which it claims commonly reaches 70-80%. It calls this “industry-leading” in comparison to Oracle, SQL Server, and other general-purpose relational DBMS.
- DB2 compression’s overall effect on performance stems from a trade-off between I/O (lessened) and CPU burden (increased). For OLTP workloads, this is about a wash. For data warehousing workloads, IBM says 20% performance improvement from compression is average.
- DB2 now has its version of one of my favorite Oracle security features, called Label Based Access Control. A label-control feature can make it much easier to secure data on a row-by-row, value-by-value basis. The obvious big user is national intelligence, followed by financial services. IBM says the health care industry also has interest in LBAC.
- Also in the security area, IBM reworked DB2’s audit feature for 9.5
- I think what I heard in our discussion of DB2 virtualization is:
- Increasingly, IBM is seeing production use of VMware, rather than just test/development.
- IBM believes it is a much closer partner to VMware than Oracle or Microsoft is, because it’s not pushing its own competing technology.
- Generally, virtualization is more important for OLTP workloads than data warehousing ones, because OLTP apps commonly only need part of the resources of a node while data warehousing often wants the whole node.
- AIX data warehousing is an exception. I think this is because AIX equates to big SMP boxes, and virtualization lets you spread out the data warehousing processing across more nodes, with the usual parallel I/O benefits.
- When IBM talks of new autonomic/self-tuning features in DB2, they’re used mainly for databases under 1 terabyte in size. Indeed, the self-tuning feature set doesn’t work with InfoSphere Warehouse.
- Even with the self-tuning feature it sounds as if you need at least a couple of DBA hours per instance per week, on average.
- DB2 on Linux/Unix/Windows has introduced some enhanced workload management features analogous to those long found in mainframe DB2. For example, resource allocation rules can be scheduled by time. (The point of workload management is to allocate resources such as CPU or I/O among the simultaneous queries or other tasks that contend for them.) Workload management rules can have thresholds for amounts of resources consumed, after which the priority for a task can go up (“Get it over with!”) or down (“Stop hogging my system!”).