Memory-centric data management
Analysis of technologies that manage data entirely or primarily in random-access memory (RAM). Related subjects include:
- Oracle TimesTen
- solidDB
- QlikTech
- SAP‘s BI Accelerator
- Exasol
- Solid-state memory as a replacement for disk
Kognitio’s story today
I had dinner tonight with the Kognitio folks. So far as I can tell:
- Branding has been mercifully simplified. Everything is now called “Kognitio” (as opposed to, for example, “WX2″).
- Notwithstanding its long history of selling disk-based DBMS and denigrating memory-only configurations, Kognitio now says that in fact it’s always been an in-memory DBMS vendor.
- Notwithstanding its long history of selling (or attempting to sell) analytic DBMS, Kognitio wants to be viewed as an accelerator to your existing DBMS. This is apparently inspired in part by SAP HANA, notwithstanding that HANA’s direction is to involved into a hybrid OLTP/analytic general-purpose DBMS.
- Notwithstanding its lack of analytic platform features, Kognitio wants to be viewed as selling an analytic platform.
- Notwithstanding its memory-centric focus, Kognitio doesn’t want to compress data. Kognitio’s opinion — which to my knowledge is shared by few people outside Kognitio — seems to be that the CPU cost of compression/decompression isn’t justified by the RAM savings from compression.
- Kognitio still is pushing a cloud/SaaS (Software as a Service) story. Even if you want to use Kognitio (the product) on-premises, Kognitio (the company) calls that “private cloud” and offers to let you pay annually.
Kognitio believes that this story is appealing, especially to smaller venture-capital-backed companies, and backs that up with some frieNDA pipeline figures.
Between that success claim and SAP’s HANA figures, it seems that the idea of using an in-memory DBMS to accelerate analytics has legs. This makes sense, as the BI vendors — Qlik Tech excepted — don’t seem to be accomplishing much with their proprietary in-memory alternatives. But I’m not sure that Kognitio would be my first choice to fill that role. Rather, if I wanted to buy an unsuccessful analytic RDBMS to use as an in-memory accelerator, I might consider ParAccel, which is columnar, has an associated compression story, has always had a hybrid memory-centric flavor much as Kognitio has, and is well ahead of Kognitio in the analytic platform derby. That said, I’ll confess to not having talked with or heard much about ParAccel for a while, so I don’t know if they’ve been able maintain technical momentum any more than Kognitio has.
| Categories: Cloud computing, Data warehousing, Database compression, Kognitio, Memory-centric data management, ParAccel, Software as a Service (SaaS) | Leave a Comment |
Many kinds of memory-centric data management
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
Comments on Oracle’s third quarter 2012 earnings call
Various reporters have asked me about Oracle’s third quarter 2012 earnings conference call. Specific Q&A includes:
What did Oracle do to have its earnings beat Wall Street’s estimates?
Have a bad second quarter and then set Wall Street’s expectations too low for Q3. This isn’t about strong results; it’s about modest expectations.
Can Oracle be a leader in both hardware and software?
- It’s not inconceivable.
- The observation that Oracle, IBM, and Teradata all are pushing hardware-software combinations has been intriguing ever since IBM bought Netezza. (SAP really isn’t, however; ditto Microsoft.)
- I do think Oracle may be somewhat overoptimistic as to how cooperative the Sun user base will be in buying more high-end product and in paying more in maintenance for the gear they already have.
Beyond that, please see below.
What about Oracle in the cloud?
MySQL is an important cloud supplier. But Oracle overall hasn’t demonstrated much understanding of what cloud technology and business are all about. An expensive SaaS acquisition here or there could indeed help somewhat, but it seems as if Oracle still has a very long way to go.
Other comments
Other comments on the call, whose transcript is available, include: Read more
| Categories: Cloud computing, Exadata, Humor, In-memory DBMS, Oracle, SAP AG, Software as a Service (SaaS) | 5 Comments |
SAP HANA today
SAP HANA has gotten much attention, mainly for its potential. I finally got briefed on HANA a few weeks ago. While we didn’t have time for all that much detail, it still might be interesting to talk about where SAP HANA stands today.
The HANA section of SAP’s website is a confusing and sometimes inaccurate mess. But an IBM whitepaper on SAP HANA gives some helpful background.
SAP HANA is positioned as an “appliance”. So far as I can tell, that really means it’s a software product for which there are a variety of emphatically-recommended hardware configurations — Intel-only, from what right now are eight usual-suspect hardware partners. Anyhow, the core of SAP HANA is an in-memory DBMS. Particulars include:
- Mainly, HANA is an in-memory columnar DBMS, based on SAP’s confusingly-renamed BI Accelerator/BW Accelerator. Analytics and most OLTP (OnLine Transaction Processing) go against the columnar part of HANA.
- The HANA DBMS also has an in-memory row storage option, used to store metadata, small tables, and so on.
- SAP HANA talks both SQL and MDX.
- The HANA DBMS is shared-nothing across blades or rack servers. I imagine that within an individual blade it’s shared everything. The usual-suspect data distribution or partitioning strategies are available — hash, range, round-robin.
- SAP HANA has what sounds like a natural disk-based persistence strategy — logs, snapshots, and so on. SAP says that this is synchronous enough to give ACID compliance. For some hardware partners, those “disks” are actually Fusion I/O cards.
- HANA is fault-tolerant “across servers”.
- Text support is “coming soon”, which makes sense, given that BI Accelerator was based on the TREX search engine in the first place. Inxight is also in the HANA text mix.
- You can put data into SAP HANA in a variety of obvious ways:
- Writing it directly.
- Trigger-based replication (perhaps from the DBMS that runs your SAP apps).
- Log-based replication (based on Sybase Replication Server).
- SAP Business Objects’ ETL tool.
SAP says that the row-store part is based both on P*Time, an acquisition from Korea some time ago, and also on SAP’s own MaxDB. The IBM white paper mentions only the MaxDB aspect. (Edit: Actually, see the comment thread below.) Based on a variety of clues, I conjecture that this was an aspect of SAP HANA development that did not go entirely smoothly.
Other SAP HANA components include: Read more
Comments on the analytic DBMS industry and Gartner’s Magic Quadrant for same
This year’s Gartner Magic Quadrant for Data Warehouse Database Management Systems is out.* I shall now comment, just as I did on the 2010, 2009, 2008, 2007, and 2006 Gartner Data Warehouse Database Management System Magic Quadrants, to varying extents. To frame the discussion, let me start by saying:
- In general, I regard Gartner Magic Quadrants as a bad use of good research.
- Illustrating the uselessness of — or at least poor execution on — the overall quadrant metaphor, a large majority of the vendors covered are lined up near the line x = y, each outpacing the one below in both of the quadrant’s dimensions.
- I find fewer specifics to disagree with in this Gartner Magic Quadrant than in previous year’s versions. Two factors jump to mind as possible reasons:
- This year’s Gartner Magic Quadrant for Data Warehouse Database Management Systems is somewhat less ambitious than others; while it gives as much company detail as its predecessors, it doesn’t add as much discussion of overall trends. So there’s less to (potentially) disagree with.
- Merv Adrian is now at Gartner.
- Whatever the problems may be with Gartner’s approach, the whole thing comes out better than do Forrester’s failed imitations.
*As of February, 2012 — and surely for many months thereafter — Teradata is graciously paying for a link to the report.
Specific company comments, roughly in line with Gartner’s rough single-dimensional rank ordering, include: Read more
Terminology: Data mustering
I find myself in need of a word or phrase that means bring data together from various sources so that it’s ready to be used, where the use can be analysis or operations. The first words I thought of were “aggregation” and “collection,” but they both have other meanings in IT. Even “data marshalling” has a specific meaning different from what I want. So instead, I’ll go with data mustering.
I mean for the term “data mustering” to encompass at least three scenarios:
- Integrated (relational) data warehouse.
- Big bit bucket.
- Big bit stream.
Let me explain what I mean by each. Read more
| Categories: Complex event processing (CEP), Data warehousing, Investment research and trading, Sybase, Teradata | 10 Comments |
Some big-vendor execution questions, and why they matter
When I drafted a list of key analytics-sector issues in honor of look-ahead season, the first item was “execution of various big vendors’ ambitious initiatives”. By “execute” I mean mainly:
- “Deliver products that really meet customers’ desires and needs.”
- “Successfully convince them that you’re doing so …”
- “… at an attractive overall cost.”
Vendors mentioned here are Oracle, SAP, HP, and IBM. Anybody smaller got left out due to the length of this post. Among the bigger omissions were:
- salesforce.com (multiple subjects).
- SAS HPA.
- The evolution of Hadoop.
StreamBase LiveView — push-based real-time BI
My clients at StreamBase are coming out with a new product line called LiveView, and I agreed they could launch it via this blog. Key points about StreamBase LiveView Version 1.0 include:
- LiveView is a business intelligence and alerting suite built on/in the rest of StreamBase’s technology, meant to operate on streaming data.
- LiveView is positioned by StreamBase as having a true push event-driven architecture rather than pull/poll.
- StreamBase LiveView is designed to query in-memory data and then have the results change in real time as the data set changes.
- The LiveView user interface is a rapidly changing work in progress.
- LiveView has other Version 1 limitations as well
- LiveView is targeted squarely at StreamBase’s financial trading core market until some of the Version 1 limitations are lifted.
The basic StreamBase LiveView pipeline goes something like: Read more
| Categories: Business intelligence, Complex event processing (CEP), Data warehousing, Memory-centric data management, StreamBase | 1 Comment |
StreamBase catchup
While I was cryptic in my general CEP/streaming catchup, I’ll say a bit more regarding StreamBase in particular. At the highest level, non-technically:
- StreamBase once planned to conquer the world.
- However, StreamBase really only sold effectively in the financial trading and intelligence markets.
- StreamBase retrenched, focusing almost exclusively on the financial trading market.
- With StreamBase LiveView, StreamBase is expanding from embedded operational analytics to do (also operational) business intelligence as well.
- StreamBase is hopeful that, perhaps starting with Version 2 or so, LiveView will be successful outside the financial trading market.
| Categories: Complex event processing (CEP), Investment research and trading, Parallelization, StreamBase | 2 Comments |
Very brief CEP/streaming catchup
When I agreed to launch the StreamBase LiveView product via DBMS 2, I planned to catch up on the whole CEP/streaming area first. Due to the power and internet outages last week, that didn’t entirely happen. So I’ll do a bit of that now, albeit more cryptically than I hoped and intended.
- The upshot of my what to call CEP thread in August was that “streaming” and “event processing” are not the same concept, but it so happens that they have the most traction where they intersect. That said, I both observe and endorse an apparent shift from “event” to “stream” as the core of the terminology, in a reversal of my opinion of several years ago.
- IBM continues to throw a lot of resources at its System S/ InfoSphere Streams product, but I haven’t heard yet of much marketplace success. That said, I believe IBM is still pretty serious about Streams, as one would expect from an effort whose code name so cheekily references System R. In particular, Streams shows up prominently on IBM’s top-level analytic architecture slide.
- Sybase recently released its ESP (Event Stream Processor) 5.0, which it says is the full merger of the Aleri and Coral8 predecessors. You can still get Sybase ESP without buying into the full Sybase RAP stack, and Sybase has no plans to change that.
- Sybase has discontinued all the business intelligence types of products Aleri and Coral8 were developing. Rather, Sybase is OEMing Panopticon, which it reports has been well received. Other than the discontinuation of the BI efforts, there seem to be few Aleri or Coral8 features missing from the merged Sybase ESP product.
- Truviso continues to be out of the picture.
- I have more to say about StreamBase separately.
- I have more to say about Sybase and IBM, which I’ll get to when I can.
- I have nothing new on Progress Apama. I also know little about any of the open source efforts.
Meanwhile, if you want to see technically nitty-gritty posts about the CEP/streaming area, you may want to look at my CEP/streaming coverage circa 2007-9, based on conversations with (among others) Mike Stonebraker, John Bates, and Mark Tsimelzon.