Analysis of storage technologies, especially in the context of database management. Related subjects include:
Perhaps the single toughest question in all database technology is: Which different purposes can a single data store serve well? — or to phrase it more technically — Which different usage patterns can a single data store support efficiently? Ted Codd was on multiple sides of that issue, first suggesting that relational DBMS could do everything and then averring they could not. Mike Stonebraker too has been on multiple sides, first introducing universal DBMS attempts with Postgres and Illustra/Informix, then more recently suggesting the world needs 9 or so kinds of database technology. As for me — well, I agreed with Mike both times.
Since this is MUCH too big a subject for a single blog post, what I’ll do in this one is simply race through some background material. To a first approximation, this whole discussion is mainly about data layouts — but only if we interpret that concept broadly enough to comprise:
- Every level of storage (disk, RAM, etc.).
- Indexes, aggregates and raw data alike.
To date, nobody has ever discovered a data layout that is efficient for all usage patterns. As a general rule, simpler data layouts are often faster to write, while fancier ones can boost query performance. Specific tradeoffs include, but hardly are limited to: Read more
I talked with Cloudera yesterday about an unannounced technology, and took the opportunity to ask some non-embargoed questions as well. In particular, I requested an update to what I wrote last year about typical Hadoop hardware.
Cloudera thinks the picture now is:
- 2-socket servers, with 4- or 6-core chips.
- Increasing number of spindles, with 12 2-TB spindles being common.
- 48 gigs of RAM is most common, with 64-96 fairly frequent.
- A couple of 1GigE networking ports.
Discussion around that included:
- Enterprises had been running out of storage space; hence the increased amount of storage.
- Even more storage can be stuffed on a node, and at times is. But at a certain point there’s so much data on a node that recovery from node failure is too forbidding.
- There are some experiments with 10 GigE.
|Categories: Cloudera, Data warehouse appliances, Hadoop, MapR, Solid-state memory, Storage||7 Comments|
I took the opportunity of Teradata’s Aster/Hadoop appliance announcement to catch up with Teradata hardware chief Carson Schmidt. I love talking with Carson, about both general design philosophy and his views on specific hardware component technologies.
From a hardware-requirements standpoint, Carson seems to view Aster and Hadoop as more similar to each other than either is to, say, a Teradata Active Data Warehouse. In particular, for Aster and Hadoop:
- I/O is more sequential.
- The CPU:I/O ratio is higher.
- Uptime is a little less crucial.
The most obvious implication is differences in the choice of parts, and of their ratio. Also, in the new Aster/Hadoop appliance, Carson is content to skate by with RAID 5 rather than RAID 1.
I think Carson’s views about flash memory can be reasonably summarized as: Read more
|Categories: Aster Data, Data warehouse appliances, Data warehousing, Hadoop, Solid-state memory, Storage, Teradata||2 Comments|
I’m not at Oracle OpenWorld, but as usual that won’t keep me from commenting. My bottom line on the first night’s announcements is:
- At many large enterprises, Oracle has a lock on much of their IT efforts. (But not necessarily in the internet or investigative analytics areas.) Tonight’s announcements serve to strengthen that.
- Tonight’s announcements do little to help Oracle in other market segments.
1. At the highest level, my view of Oracle’s strategy is the same as it’s been for several years:
Clayton Christensen’s The Innovator’s Solution teaches us that Oracle should focus on selling a thick stack of technology to its highest-end customers, and that’s exactly what Oracle does focus on.
2. Tonight’s news is closely in line with what Oracle’s Juan Loaiza told me three years ago, especially:
- 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.
3. Oracle is confusing people with its comments on multi-tenancy. I suspect:
- What Oracle is talking about when it says “multi-tenancy” is more like consolidation than true multi-tenancy.
- Probably there are a couple of true multi-tenancy features as well.
4. SaaS (Software as a Service) vendors don’t want to use Oracle, because they don’t want to pay for it.* This limits the potential impact of Oracle’s true multi-tenancy features. Even so: Read more
|Categories: Business intelligence, Cloud computing, Columnar database management, Data warehouse appliances, Data warehousing, Exadata, Memory-centric data management, Oracle, Software as a Service (SaaS), Solid-state memory, Storage||9 Comments|
Three months ago, I pointed out that it is hard to generalize about memory-centric database management, because there are so many different kinds. That said, there are some basic points that I’d like to record as background for any future discussion of the subject, focusing on differences between disk and RAM. And while I’m at it, I’ll throw in a few comments about flash memory as well.
This post would probably be better if I had actual numbers for the speeds of various kinds of silicon operations, but I’ll do what I can without them.
For most purposes, database speed is a function of a few kinds of number:
- CPU cycles consumed.
- I/O throughput.
- I/O wait time.
- Network throughput.
- Network wait time.
The amount of storage used is also important, both directly — storage hardware costs money — and because if you save storage via compression, you may get corresponding benefits in I/O. Power consumption and similar costs are usually tied to hardware efficiency; the less gear you use, the less floor space and cooling you may be able to get away with.
When databases move to RAM from spinning disk, major consequences include: Read more
|Categories: Database compression, Memory-centric data management, Solid-state memory, solidDB||6 Comments|
SanDisk has acquired my client Schooner Information Technology. Notes on that include:
- Schooner used to be a flash-based appliance company.
- Then Schooner pivoted to be a database software company with strong flash expertise.
- Then Schooner pivoted further to emphasize general modern OLTP (OnLine Transaction Processing) clustered goodness.
- SanDisk makes flash memory. That’s the fit.
- Specifically, Schooner is being put in the division that grew out of the acquisition of Pliant, which makes solid-state disks for database applications, and gets rave reviews from Teradata.
- Schooner had a few dozen customers, but not a lot of evident traction. Hence, I would imagine, the acquisition.
That’s about all I have at this time.
|Categories: Market share and customer counts, Schooner Information Technology, Solid-state memory||3 Comments|
This is part of a four-post series, covering:
- Annoying Hadoop marketing themes that should be ignored.
- Hadoop versions and distributions, and their readiness or lack thereof for production.
- In general, how “enterprise-ready” is Hadoop?
- HBase 0.92 (this post)
As part of my recent round of Hadoop research, I talked with Cloudera’s Todd Lipcon. Naturally, one of the subjects was HBase, and specifically HBase 0.92. I gather that the major themes to HBase 0.92 are:
- Performance, scalability, and so on.
- “Coprocessors”, which are like triggers or stored procedures.
- Security, as the first major application of co-processors.
HBase coprocessors are Java code that links straight into HBase. As with other DBMS extensions of the “links straight into the DBMS code” kind,* HBase coprocessors seem best suited for very sophisticated users and third parties.** Evidently, coprocessors have already been used to make HBase security more granular — role-based, per-column-family/per-table, etc. Further, Todd thinks coprocessors could serve as a good basis for future HBase enhancements in areas such as aggregation or secondary indexing. Read more
|Categories: Benchmarks and POCs, Cloudera, Hadoop, HBase, MapReduce, NoSQL, Open source, Storage, Theory and architecture||2 Comments|
Cloudant is one of the few NoSQL companies with >100 paying subscription customers. For starters:
- Cloudant’s core software is a fork of CouchDB.
- Cloudant only sells you software as a service.
- More precisely, whether Cloudant offers DBaaS (DataBase as a Service) or PaaS (Platform as a Service) or a “data layer” (Cloudant’s preferred terminology) depends on your taste in buzzwords.
- I gather that Cloudant (the company) wants to handle pretty much all your data management needs. But Cloudant (the product) isn’t there yet, especially on the analytic side.
- Before CouchDB and Membase joined together, Cloudant was positioned as the big(ger) data version of CouchDB.
Company demographics include:
- Cloudant is based in Boston.
- Cloudant started out as a Y Combinator company in 2008, and “got serious” in 2009.
- Cloudant now has ~20 employees.
- Management hires include a couple of former Vertica guys.
The Cloudant guys gave me some customer counts in May that weren’t much higher than those they gave me in February, and seem to have forgotten to correct the discrepancy. Oh well. The latter (probably understated) figures included ~160 paying customers, of which:
- ~100 were multitenant.
- ~60 were single tenant.
- 1 was on-premise (but still managed by Cloudant) because of privacy concerns.
The largest Cloudant deployments seem to be in the 10s of terabytes, across a very low double digit number of servers.
|Categories: Cloudant, Clustering, Couchbase, CouchDB, MapReduce, Market share and customer counts, NoSQL, Pricing, Specific users, Storage||2 Comments|
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:
- 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||6 Comments|
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||13 Comments|