Database compression
Analysis of technology that compresses data within a database management system. Related subjects include:
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 evolve 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 |
IBM DB2 10
Shortly before Tuesday’s launch of DB2 10, IBM’s Conor O’Mahony checked in for a relatively non-technical briefing.* More precisely, this is about DB2 for “distributed” systems, aka LUW (Linux/Unix/Windows); some of the features have already been in the mainframe version of DB2 for a while. IBM is graciously permitting me to post the associated DB2 10 announcement slide deck.
*I hope any errors in interpretation are minor.
Major aspects of DB2 10 include new or improved capabilities in the areas of:
- Compression.
- Analytic query performance.
- Data ingest.
- Multi-temperature data management.
- Workload management.
- Graph management/relationship analytics.
- Time-travel, bitemporal features, and bitemporal time-travel.
Of course, there are various other enhancements too, including to security (fine-grained access control), Oracle compatibility, and DB2 pureScale. Everything except the pureScale part is also reflected in IBM InfoSphere Warehouse, which is a near-superset of DB2.*
*Also, the data ingest part isn’t in base DB2.
| Categories: Data warehousing, Database compression, IBM and DB2, RDF and graphs, Solid-state memory, Workload management | 3 Comments |
Hardware and components — lessons from Teradata
I love talking with Carson Schmidt, chief of Teradata’s hardware engineering (among other things), even if I don’t always understand the details of what he’s talking about. It had been way too long since our last chat, so I requested another one. We were joined by Keith Muller, who I presume is pictured here. Takeaways included:
- Teradata performance growth was slow in the early 2000s, but has accelerated since then; Intel gets a lot of the credit (and blame) for that.
- Carson hopes for a performance “discontinuity” with Intel Ivy Bridge.
- Teradata is not afraid to use niche special-purpose chips.
- Teradata’s views can be taken as well-informed endorsements of InfiniBand and SAS 2.0.
| Categories: Data warehouse appliances, Data warehousing, Database compression, Solid-state memory, Storage, Teradata | 9 Comments |
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
Clarifying SAND’s customer metrics, positioning and technical story
Talking with my clients at SAND can be confusing. That said:
- I need to revise my figures for SAND’s customer count way downward.
- SAND finally has a reasonably clear positioning.
- SAND’s product actually seems to have a lot of features.
A few months ago, I wrote:
SAND Technology reported >600 total customers, including >100 direct.
Upon talking with the company, I need to revise that figure downward, from > 600 to 15.
Exasol update
I last wrote about Exasol in 2008. After talking with the team Friday, I’m fixing that now. 
The general theme was as you’d expect: Since last we talked, Exasol has added some new management, put some effort into sales and marketing, got some customers, kept enhancing the product and so on.
Top-level points included:
- Exasol’s technical philosophy is substantially the same as before, albeit not with as extreme a focus on fitting everything in RAM.
- Exasol believes its flagship DBMS EXASolution has great performance on a load-and-go basis.
- Exasol has 25 EXASolution customers, all in Germany.*
- 5 of those are “cloud” customers, at hosting providers engaged by Exasol.
- EXASolution database sizes now range from the low 100s of gigabytes up to 30 terabytes.
- Pretty much the whole company is in Nuremberg.
Compression in Sybase ASE 15.7
Sybase recently came up with Adaptive Server Enterprise 15.7, which is essentially the “Make SAP happy” release. Features that were slated for 2012 release, but which SAP wanted, were accelerated into 2011. Features that weren’t slated for 2012, but which SAP wanted, were also brought into 2011. Not coincidentally, SAP Business Suite will soon run on Sybase Adaptive Server Enterprise 15.7.
15.7 turns out to be the first release of Sybase ASE with data compression. Sybase fondly believes that it is matching DB2 and leapfrogging Oracle in compression rate with a single compression scheme, namely page-level tokenization. More precisely, SAP and Sybase seem to believe that about compression rates for actual SAP application databases, based on some degree of testing. Read more
| Categories: Database compression, Sybase | 5 Comments |
Hybrid-columnar soundbites
Busy couple of days talking with reporters. A few notes on hybrid-columnar analytic DBMS, all backed up by yesterday’s post on Teradata columnar:
- Oracle does not actually offer columnar I/O; the other three systems do. But see the “I won’t be surprised” part in yesterday’s Teradata post.
- Aster does not offer columnar compression; the other three do.
- EMC Greenplum and Teradata offer different kinds of ways to mix column and row storage in the same table; each has its advantages.
- Teradata generally has a more mature and capable offering than EMC Greenplum, for most purposes, whichever way you choose to organize your tables.
Edit: The Wall Street Journal got this wrong, writing that Teradata was the first-ever hybrid columnar system. Specifically, they wrote
While columnar technology has been around for years, Teradata says its product is unique because it allows users to include both columns and rows in the same database.
Googling on “Teradata To Unveil New Analytics Product To Speed Business Adoption” might get you around the paywall to see the offending piece.
| Categories: Aster Data, Columnar database management, Data warehousing, Database compression, Greenplum, Teradata | 2 Comments |
Teradata Columnar and Teradata 14 compression
Teradata is pre-announcing Teradata 14, for delivery by the end of this year, where by “Teradata 14″ I mean the latest version of the DBMS that drives the classic Teradata product line. Teradata 14′s flagship feature is Teradata Columnar, a hybrid-columnar offering that follows in the footsteps of Greenplum (now part of EMC) and Aster Data (now part of Teradata).
The basic idea of Teradata Columnar is:
- Each table can be stored in Teradata in row format, column format, or a mix.
- You can do almost anything with a Teradata columnar table that you can do with a row-based one.
- If you choose column storage, you also get some new compression choices.
| Categories: Archiving and information preservation, Columnar database management, Data warehousing, Database compression, Oracle, Rainstor, Teradata | 6 Comments |
Hadoop hardware and compression
A month ago, I posted about typical Hadoop hardware. After talking today with Eric Baldeschwieler of Hortonworks, I have an update. I also learned some things from Eric and from Brian Christian of Zettaset about Hadoop compression.
First the compression part. Eric thinks 6-10X compression is common for “curated” Hadoop data — i.e., the data that actually gets used a lot. Brian used an overall figure of 6-8X, and told of a specific customer who had 6X or a little more. By way of comparison, it sounds as if the kinds of data involved are like what Vertica claimed 10-60X compression for almost three years ago.
Eric also made an excellent point about low-value machine-generated data. I was suggesting that as Moore’s Law made sensor networks ever more affordable: Read more
| Categories: Cloudera, Database compression, Hadoop, Hortonworks, Storage, Vertica Systems, Zettaset | 9 Comments |