Data skipping
Way back in 2006, I wrote about a cool Netezza feature called the zone map, which in essence allows you to do partition elimination even in the absence of strict range partitioning.
Netezza’s substitute for range partitioning is very simple. Netezza features “zone maps,” which note the minimum and maximum of each column value (if such concepts are meaningful) in each extent. This can amount to effective range partitioning over dates; if data is added over time, there’s a good chance that the data in any particular date range is clustered, and a zone map lets you pick out which data falls in the desired data range.
I further wrote
… that seems to be the primary scenario in which zone maps confer a large benefit.
But I now think that part was too pessimistic. For example, in bulk load scenarios, it’s easy to imagine ways in which data can be clustered or skewed. And in such cases, zone maps can let you skip a large fraction of potential I/O.
Over the years I’ve said that other things were reminiscent of Netezza zone maps, e.g. features of Infobright, SenSage, InfiniDB and even Microsoft SQL Server. But truth be told, when I actually use the phrase “zone map”, people usually give me a blank look.
In a recent briefing about BLU, IBM introduced me to a better term — data skipping. I like it and, unless somebody comes up with a good reason not to, I plan to start using it myself. 🙂
| Categories: Data warehousing, IBM and DB2, Netezza, Theory and architecture | 12 Comments |
Some stuff I’m working on
1. I have some posts up on Strategic Messaging. The most recent are overviews of messaging, pricing, and positioning.
2. Numerous vendors are blending SQL and JSON management in their short-request DBMS. It will take some more work for me to have a strong opinion about the merits/demerits of various alternatives.
The default implementation — one example would be Clustrix’s — is to stick the JSON into something like a BLOB/CLOB field (Binary/Character Large Object), index on individual values, and treat those indexes just like any others for the purpose of SQL statements. Drawbacks include:
- You have to store or retrieve the JSON in whole documents at a time.
- If you are spectacularly careless, you could write JOINs with odd results.
IBM DB2 is one recent arrival to the JSON party. Unfortunately, I forgot to ask whether IBM’s JSON implementation was based on IBM DB2 pureXML when I had the chance, and IBM hasn’t gotten around to answering my followup query.
3. Nor has IBM gotten around to answering my followup queries on the subject of BLU, an interesting-sounding columnar option for DB2.
4. Numerous clients have asked me whether they should be active in DBaaS (DataBase as a Service). After all, Amazon, Google, Microsoft, Rackspace and salesforce.com are all in that business in some form, and other big companies have dipped toes in as well. Read more
It’s time to change around Monash Research’s mailing lists
Email delivery of posts has been screwed up; multiple people tell me they haven’t gotten their email for months. (In the future, please tell me of such difficulties!) So it’s time for a change, and I’m asking for your advice as to what you’d suggest for our mailing list.
Yes, I’m asking via a blog post, even thought the core problem is that people who want to see my posts via e-mail aren’t getting them. Please work with me on this anyway. 🙂
My two basic questions are:
- What should be the frequency of delivery? To date, it’s been nightly (at least in theory).
- What delivery technology should be used? To date, it’s been FeedBlitz.
1. The nightly scheduling has been an artifact of an RSS-to-email link that no longer seems stable. So I’m thinking of just manually pasting each post into a list email, in which case:
- Posts could be sent without delay.
- Every post would be delivered by separate mail. (As opposed to having only one post per night be mailed, while others just get linked to.)
It’s a bit more work for me, but probably nothing dire. Does lower latency sound good to everybody? 🙂
2. The main technical options seem to be: Read more
| Categories: About this blog | 4 Comments |
More on Actian/ParAccel/VectorWise/Versant/etc.
My quick reaction to the Actian/ParAccel deal was negative. A few challenges to my views then emerged. They didn’t really change my mind.
Amazon Redshift
Amazon did a deal with ParAccel that amounted to:
- Amazon got a very cheap license to a limited subset of ParAccel’s product …
- … so that it could launch a service called Amazon Redshift.
- Amazon also invested in ParAccel.
Some argue that this is great for ParAccel’s future prospects. I’m not convinced.
No doubt there are and will be Redshift users, evidently including Infor. But so far as I can tell, Redshift uses very standard SQL, so it doesn’t seed a ParAccel market in terms of developer habits. The administration/operation story is similar. So outside of general validation/bragging rights, Redshift is not a big deal for ParAccel.
OEMs and bragging rights
It’s not just Amazon and Infor; there’s also a MicroStrategy deal to OEM ParAccel — I think it’s the real ParAccel software in that case — for a particular service, MicroStrategy Wisdom. But unless I’m terribly mistaken, HP Vertica, Sybase IQ and even Infobright each have a lot more OEMs than ParAccel, just as they have a lot more customers than ParAccel overall.
This OEM success is a great validation for the idea of columnar analytic RDBMS in general, but I don’t see where it’s an advantage for ParAccel vs. the columnar leaders. Read more
| Categories: Actian and Ingres, Amazon and its cloud, Columnar database management, HP and Neoview, Market share and customer counts, ParAccel, Sybase, VectorWise, Vertica Systems | 7 Comments |
Goodbye VectorWise, farewell ParAccel?
Actian, which already owns VectorWise, is also buying ParAccel. The argument for why this kills VectorWise is simple. ParAccel does most things VectorWise does, more or less as well. It also does a lot more:
- ParAccel scales out.
- ParAccel has added analytic platform capabilities.
- I don’t know for sure, but I’d guess ParAccel has more mature management/plumbing capabilities as well.
One might conjecture that ParAccel is bad at highly concurrent, single-node use cases, and VectorWise is better at them — but at the link above, ParAccel bragged of supporting 5,000 concurrent connections. Besides, if one is just looking for a high-use reporting server, why not get Sybase IQ?? Anyhow, Actian hasn’t been investing enough in VectorWise to make it a major market player, and they’re unlikely to start now that they own ParAccel as well.
But I expect ParAccel to fail too. Reasons include:
- ParAccel’s small market share and traction.
- The disruption of any acquisition like this one.
- My general view of Actian as a company.
| Categories: Actian and Ingres, Columnar database management, Data warehousing, HP and Neoview, ParAccel, Sybase, VectorWise, Vertica Systems | 10 Comments |
Analytic application themes
I talk with a lot of companies, and repeatedly hear some of the same application themes. This post is my attempt to collect some of those ideas in one place.
1. So far, the buzzword of the year is “real-time analytics”, generally with “operational” or “big data” included as well. I hear variants of that positioning from NewSQL vendors (e.g. MemSQL), NoSQL vendors (e.g. AeroSpike), BI stack vendors (e.g. Platfora), application-stack vendors (e.g. WibiData), log analysis vendors (led by Splunk), data management vendors (e.g. Cloudera), and of course the CEP industry.
Yeah, yeah, I know — not all the named companies are in exactly the right market category. But that’s hard to avoid.
Why this gold rush? On the demand side, there’s a real or imagined need for speed. On the supply side, I’d say:
- There are vast numbers of companies offering data-management-related technology. They need ways to differentiate.
- Doing analytics at short-request speeds is an obvious data-management-related challenge, and not yet comprehensively addressed.
2. More generally, most of the applications I hear about are analytic, or have a strong analytic aspect. The three biggest areas — and these overlap — are:
- Customer interaction
- Network and sensor monitoring
- Game and mobile application back-ends
Also arising fairly frequently are:
- Algorithmic trading
- Anti-fraud
- Risk measurement
- Law enforcement/national security
- Healthcare
- Stakeholder-facing analytics
I’m hearing less about quality, defect tracking, and equipment maintenance than I used to, but those application areas have anyway been ebbing and flowing for decades.
MemSQL scales out
The third of my three MySQL-oriented clients I alluded to yesterday is MemSQL. When I wrote about MemSQL last June, the product was an in-memory single-server MySQL workalike. Now scale-out has been added, with general availability today.
MemSQL’s flagship reference is Zynga, across 100s of servers. Beyond that, the company claims (to quote a late draft of the press release):
Enterprises are already using distributed MemSQL in production for operational analytics, network security, real-time recommendations, and risk management.
All four of those use cases fit MemSQL’s positioning in “real-time analytics”. Besides Zynga, MemSQL cites penetration into traditional low-latency markets — financial services (various subsectors) and ad-tech.
Highlights of MemSQL’s new distributed architecture start: Read more
Notes on TokuDB and GenieDB
Last week, I edited press releases back-to-back-to-back for three clients, all with announcements at this week’s Percona Live. The ones with embargoes ending today are Tokutek and GenieDB.
Tokutek’s news is that they’re open sourcing much of TokuDB, but holding back hot backup for their paid version. I approve of this strategy — “doesn’t lose data” is an important feature, and well worth paying for.
I kid, I kid. Any system has at least a bad way to do backups — e.g. one that involves slowing performance, or perhaps even requires taking applications offline altogether. So the real points of good backup technology are:
- To keep performance steady.
- To make the whole thing as easy to manage as possible.
GenieDB is announcing a Version 2, which is basically a performance release. So in lieu of pretending to have much article-worthy news, GenieDB is taking the opportunity to remind folks of its core marketing messages, with catchphrases such as “multi-regional self-healing MySQL”. Good choice; indeed, I wish more vendors would adopt that marketing tactic.
Along the way, I did learn a bit more about GenieDB. In particular:
- GenieDB is now just backed by a hacked version of InnoDB (no more Berkeley DB Java Edition).
- Why hacked? Because GenieDB appends a Lamport timestamp to every row, which somehow leads to a need to modify how indexes and caching work.
- Benefits of the chamge include performance and simpler (for the vendor) development.
- An arguable disadvantage of the switch is that GenieDB no longer can use Berkeley DB’s key-value interface — but MySQL now has one of those too.
I also picked up some GenieDB company stats I didn’t know before — 9 employees and 2 paying customers.
Related links
| Categories: GenieDB, Market share and customer counts, MySQL, NewSQL, Open source, Tokutek and TokuDB | 3 Comments |
Notes on Teradata systems
Teradata is announcing its new high-end systems, the Teradata 6700 series. Notes on that include:
- Teradata tends to get 35-55% (roughly speaking) annual performance improvements, as measured by its internal blended measure Tperf. A big part of this is exploiting new-generation Intel processors.
- This year the figure is around 40%.
- The 6700 is based on Intel’s Sandy Bridge.
- Teradata previously told me that Ivy Bridge — the next one after Sandy Bridge — could offer a performance “discontinuity”. So, while this is just a guess, I expect that next year’s Teradata performance improvement will beat this year’s.
- Teradata has now largely switched over to InfiniBand.
Teradata is also talking about data integration and best-of-breed systems, with buzzwords such as:
- Teradata Unified Data Architecture.
- Fabric-based computing, even though this isn’t really about storage.
- Teradata SQL-H.
| Categories: Data integration and middleware, Data warehouse appliances, Data warehousing, Pricing, SAS Institute, Teradata | 3 Comments |
Teradata SQL-H
As vendors so often do, Teradata has caused itself some naming confusion. SQL-H was introduced as a facility of Teradata Aster, to complement SQL-MR.* But while SQL-MR is in essence a set of SQL extensions, SQL-H is not. Rather, SQL-H is a transparency interface that makes Hadoop data responsive to the same code that would work on Teradata Aster …
*Speaking of confusion — Teradata Aster seems to use the spellings SQL/MR and SQL-MR interchangeably.
… except that now there’s also a SQL-H for regular Teradata systems as well. While it has the same general features and benefits as SQL-H for Teradata Aster, the details are different, since the underlying systems are.
I hope that’s clear. 🙂