Analysis of storage technologies, especially in the context of database management. Related subjects include:
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||11 Comments|
MarkLogic is releasing MarkLogic 5. Key elements of the announcement are:
- More-of-the-same in line with MarkLogic’s core positioning.
- A new bi-directional Hadoop connector.
- A free MarkLogic Express edition, limited in license terms more than in actual features, as per Slide 27 of the deck MarkLogic graciously supplied for me to post.
Also, MarkLogic is early with a feature that most serious DBMS vendors will soon have – support for tiered storage, with writes going first to solid-state storage, then being flushed to disk via a caching-style algorithm.* And as befits a sometime search-engine-substitute, MarkLogic has finally licensed a large set of document filters, from an Australian company called Isys. Apparently, the special virtue of the Isys filters is that they’re good at extracting not only text, but metadata as well.
*If there’s a caching algorithm that doesn’t contain a major element of LRU (Least Recently Used), I don’t recall ever hearing about it.
MarkLogic seems to have settled on a positioning that, although distressingly buzzword-heavy, is at least partly based upon reality. The real part includes:
- MarkLogic is a serious, enterprise-class DBMS (see for example Slide 12 of the MarkLogic deck) …
- … which has been optimized from the getgo for poly-structured data.
- MarkLogic can and does scale out to handle large amounts of data.
- MarkLogic is a general-purpose DBMS, suitable for both short-request and analytic tasks.
- MarkLogic is particularly well suited for analyses with long chains of “progressive enhancement” (MarkLogic’s favorite term when talking about derived data).
- MarkLogic often plays the role of a content assembler and/or search engine, and the people who use MarkLogic in those ways are commonly doing things that can be described as research and analysis.
Based on that reality, MarkLogic talks a lot about Volume, Velocity, Variety, Big Data, unstructured data, semi-structured data, and big data analytics.
|Categories: Hadoop, Market share and customer counts, MarkLogic, Scientific research, Solid-state memory, Structured documents, Text||1 Comment|
It is widely rumored that there will be a leadership change at HP (Meg Whitman in, Leo Apotheker out). In connection with that, I found myself holding forth on points such as:
- HP needs to make outstanding enterprise systems again.
- They fell away from that target under Mark Hurd, but they surely can hit it again, based on the remnants of DEC (Digital Equipment Corporation), Tandem, the higher-end part of Compaq, and of course the original HP systems group.
- In particular:
- Rumors say that Oracle Exadata 1 boxes, made by HP, were much lower quality than Exadata 2 boxes made by Sun.
- HP Neoview was a waste of good engineering talent.
- I’d like to see a few excellent Vertica appliances.
- I hope the SAP HANA appliances go well, whenever HANA finally becomes a serious product.
- The general move from disk to solid-state memory should offer some opportunities.
Once again, I’m working with an OLTP SaaS vendor client on the architecture for their next-generation system. Parameters include:
- 100s of gigabytes of data at first, growing to >1 terabyte over time.
- High peak loads.
- Public cloud portability (but they have private data centers they can use today).
- Simple database design — not a lot of tables, not a lot of columns, not a lot of joins, and everything can be distributed on the same customer_ID key.
- Stream the data to a data warehouse, that will grow to a few terabytes. (Keeping only one year of OLTP data online actually makes sense in this application, but of course everything should go into the DW.)
So I’m leaning to saying: Read more
|Categories: Analytic technologies, Cloud computing, Clustering, Data warehousing, dbShards and CodeFutures, Facebook, Infobright, MySQL, OLTP, Open source, Parallelization, Software as a Service (SaaS), Solid-state memory||13 Comments|
Kaminario, which used to be in the business of solid state storage via DRAM, now is emphasizing hybrid DRAM/flash storage appliances instead. The reason is evidently price. Per terabyte of primary storage (before mirroring onto disk and so on):
- A Kaminario K2 DRAM-only appliance costs $100K.
- A Kaminario K2 flash-only appliance costs $30K (but nobody buys that configuration).
- A typical Kaminario K2 hybrid DRAM/flash appliance might cost $35K (which tells us that there’s a lot more flash than DRAM).
Kaminario positions DRAM as where you focus your most write-intensive/ bottlenecking loads, such as logging or temp space, with the primary benefit being performance and a secondary benefit being slowing the wear on your flash.
My Couchbase business update with Bob Wiederhold was very interesting, but it didn’t answer much about the actual Couchbase product. For that, I talked with Dustin Sallings. We jumped around a lot, and some important parts of the Couchbase product haven’t had their designs locked down yet anyway. But here’s at least a partial explanation of what’s up.
memcached is a way to cache data in RAM across a cluster of servers and have it all look logically like a single memory pool, extremely popular among large internet companies. The Membase product — which is what Couchbase has been selling this year — adds persistence to memcached, an obvious improvement on requiring application developers to write both to memcached and to non-transparently-sharded MySQL. The main technical points in adding persistence seem to have been:
- A persistent backing store (duh), namely SQLite.
- A change to the hashing algorithm, to avoid losing data when the cluster configuration is changed.
Couchbase is essentially Membase improved by integrating CouchDB into it, with the main changes being:
- Changing the backing store to CouchDB (duh). This will be in the first Couchbase release.
- Adding cross data center replication on CouchDB’s consistency model. This will not, I believe, be in the first Couchbase release.
- Offering CouchDB’s programming and query interfaces as an option. So far as I can tell, this will be implemented straightforwardly in the first Couchbase release, with elegance planned for later down the road.
Let’s drill down a bit into Membase/Couchbase clustering and consistency. Read more
|Categories: Cache, Clustering, Couchbase, memcached, Memory-centric data management, MySQL, Parallelization, Solid-state memory||6 Comments|
I spoke with Eliot Horowitz and Max Schierson of 10gen last month about MongoDB users and use cases. The biggest clusters they came up with weren’t much over 100 nodes, but clusters an order of magnitude bigger were under development. The 100 node one we talked the most about had 33 replica sets, each with about 100 gigabytes of data, so that’s in the 3-4 terabyte range total. In general, the largest MongoDB databases are 20-30 TB; I’d guess those really do use the bulk of available disk space. Read more
|Categories: Data models and architecture, Games and virtual worlds, Log analysis, MongoDB and 10gen, NoSQL, Solid-state memory, Specific users, Splunk, Telecommunications, Web analytics||12 Comments|
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.
|Categories: Cloudera, Database compression, Hadoop, Hortonworks, Storage, Vertica Systems, Zettaset||10 Comments|