Application areas
Posts focusing on the use of database and analytic technologies in specific application domains. Related subjects include:
- Any subcategory
- (in Text Technologies) Specific application areas for text analytics
MongoDB users and use cases
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, NoSQL, Solid-state memory, Specific users, Splunk, Telecommunications, Web analytics | 13 Comments |
Remote machine-generated data
I refer often to machine-generated data, which is commonly generated inexpensively and in log-like formats, and is often best aggregated in a big bit bucket before you try to do much analysis on it. The term has caught on, to the point that perhaps it’s time to distinguish more carefully among different kinds of machine-generated data. In particular, I think it may be useful to distinguish between:
- Log-stream machine-generated data, when what you’re looking at — at least initially — is the entire output of verbose logging systems.
- Remote machine-generated data.
Here’s what I’m thinking of for the second category. I rather frequently hear of cases in which data is generated by large numbers of remote machines, which occasionally send messages home. For example: Read more
| Categories: Analytic technologies, Cloud computing, Log analysis, MySQL, Netezza, Splunk, Truviso | 2 Comments |
McObject and eXtremeDB
I talked with McObject yesterday. McObject has two product lines, both of which are something like in-memory DBMS — eXtremeDB, which is the main one, and Perst. McObject has been around since at least 2003, probably has no venture capital, and probably has a very low double-digit number of employees.*
*I could be wrong in those guesses; as small companies go, McObject is unusually prone to secrecy games.
As best I understand:
- eXtremeDB is something like an in-memory object-oriented DBMS, designed to be embeddable.
- However, much as with Objectivity and other old-school OODBMS, eXtremeDB winds up being more of a toolkit with which to build DBMS than a full DBMS.
- eXtremeDB has a few indexing schemes. The main one is good old B-trees. One customer wanted Patricia tries, so they’re in there. (Perhaps not coincidentally, solidDB relies on Patricia tries.) At least one wanted R-trees, so they’re in there too.
- eXtremeDB has long had the option of persistent logs.
- eXtremeDB newly has a hybrid memory-centric option, in which you can have more data in the database than fits into RAM.
- eXtremeDB newly has multi-master two-phase-commit clustering.
My guess three years ago that eXtremeDB might emerge as an alternative to solidDB seems to have been borne out. McObject CEO Steve Graves says that the core of McObject’s business is OEMs, in sectors such as telecom equipment and defense/aerospace. That’s exactly solidDB’s traditional market, except that solidDB got acquired by IBM and deemphasized it.
I’ve said before that if I were starting a SaaS effort — and it wasn’t just focused on analytics — I’d look at using a memory-centric OODBMS. Perhaps eXtremeDB is worth looking at in such scenarios.
| Categories: In-memory DBMS, McObject, Memory-centric data management, Object, Objectivity and Infinite Graph, solidDB, Telecommunications | 11 Comments |
HBase is not broken
It turns out that my impression that HBase is broken was unfounded, in at least two ways. The smaller is that something wrong with the HBase/Hadoop interface or Hadoop’s HBase support cannot necessarily be said to be wrong with HBase (especially since HBase is no longer a Hadoop subproject). The bigger reason is that, according to consensus, HBase has worked pretty well since the .90 release in January of this year.
After Michael Stack of StumbleUpon beat me up for a while,* Omer Trajman of Cloudera was kind enough to walk me through HBase usage. He is informed largely by 18 Cloudera customers, plus a handful of other well-known HBase users such as Facebook, StumbleUpon, and Yahoo. Of the 18 Cloudera customers using HBase that Omer was thinking of, 15 are in HBase production, one is in HBase “early production”, one is still doing R&D in the area of HBase, and one is a classified government customer not providing such details. Read more
| Categories: Cloudera, Derived data, Facebook, Hadoop, HBase, Log analysis, Market share and customer counts, Open source, Specific users, Web analytics | 6 Comments |
An odd claim attributed to Mike Stonebraker
This post has a sequel.
Last week, Mike Stonebraker insulted MySQL and Facebook’s use of it, by implication advocating VoltDB instead. Kerfuffle ensued. To the extent Mike was saying that non-transparently sharded MySQL isn’t an ideal way to do things, he’s surely right. That still leaves a lot of options for massive short-request databases, however, including transparently sharded RDBMS, scale-out in-memory DBMS (whether or not VoltDB*), and various NoSQL options. If nothing else, Couchbase would seem superior to memcached/non-transparent MySQL if you were starting a project today.
*The big problem with VoltDB, last I checked, was its reliance on Java stored procedures to get work done.
Pleasantries continued in The Register, which got an amazing-sounding quote from Mike. If The Reg is to be believed — something I wouldn’t necessarily take for granted — Mike claimed that he (i.e. VoltDB) knows how to solve the distributed join performance problem. Read more
| Categories: Cache, Clustering, Couchbase, Games and virtual worlds, In-memory DBMS, memcached, Michael Stonebraker, MySQL, Parallelization, Theory and architecture, VoltDB and H-Store | 20 Comments |
Petabyte-scale Hadoop clusters (dozens of them)
I recently learned that there are 7 Vertica clusters with a petabyte (or more) each of user data. So I asked around about other petabyte-scale clusters. It turns out that there are several dozen such clusters (at least) running Hadoop.
Cloudera can identify 22 CDH (Cloudera Distribution [of] Hadoop) clusters holding one petabyte or more of user data each, at 16 different organizations. This does not count Facebook or Yahoo, who are huge Hadoop users but not, I gather, running CDH. Meanwhile, Eric Baldeschwieler of Hortonworks tells me that Yahoo’s latest stated figures are:
- 42,000 Hadoop nodes …
- … holding 180-200 petabytes of data.
Eight kinds of analytic database (Part 2)
In Part 1 of this two-part series, I outlined four variants on the traditional enterprise data warehouse/data mart dichotomy, and suggested what kinds of DBMS products you might use for each. In Part 2 I’ll cover four more kinds of analytic database — even newer, for the most part, with a use case/product short list match that is even less clear. Read more
Eight kinds of analytic database (Part 1)
Analytic data management technology has blossomed, leading to many questions along the lines of “So which products should I use for which category of problem?” The old EDW/data mart dichotomy is hopelessly outdated for that purpose, and adding a third category for “big data” is little help.
Let’s try eight categories instead. While no categorization is ever perfect, these each have at least some degree of technical homogeneity. Figuring out which types of analytic database you have or need — and in most cases you’ll need several — is a great early step in your analytic technology planning. Read more
Citrusleaf RTA
Citrusleaf has released an add-on product called Citrusleaf RTA (Real-Time Attribution). It’s to be used when:
- You want to update dashboards within a minute.
- You want to update predictive models fairly quickly (within the hour?), although it’s not clear to me how much the models are being updated or changed with that latency.
The metrics envisioned are:
- 100 or so ad impressions per person …
- … for 1 billion or so people …
- … stored for 30-90 days …
- … where each ad impression is a fairly short record …
- … stored on disk …
- … but indexed in a way so that the index can fit into RAM.
- 50-100,000 writes per second. (I didn’t ask on what amount of hardware.)
- Several hundred reads per second.
A consistent relational schema is NOT assumed.
Citrusleaf’s solution is:
- Have one index entry for each of the 1 billion people.
- Bang each new object/record to disk. Include in it a pointer to the previous object/record for the same person.
- Each time a new object/record is added, update the index in place so that it now points to the new once. Hence, the index is sized according to the number of people, not according to the total number of objects/records.
- Eventually let objects/records age off in the obvious way.
The downside is that when you do read 100 objects/records per person, you might need to do 100 seeks.
Temporal data, time series, and imprecise predicates
I’ve been confused about temporal data management for a while, because there are several different things going on.
- Date arithmetic. This of course has been around for a very long — er, for a very long time.
- Time-series-aware compression. This has been around for quite a while too.
- “Time travel”/snapshotting — preserving the state of the database at previous points in time. This is a matter of exposing (and not throwing away) the information you capture via MVCC (Multi-Version Concurrency Control) and/or append-only updates (as opposed to update-in-place). Those update strategies are increasingly popular for pretty much anything except update-intensive OLTP (OnLine Transaction Processing) DBMS, so time-travel/snapshotting is an achievable feature for most vendors.
- Bitemporal data access. This occurs when a fact has both a transaction timestamp and a separate validity duration. A Wikipedia article seems to cover the subject pretty well, and I touched on Teradata’s bitemporal plans back in 2009.
- Time series SQL extensions. Vertica explained its version of these to me a few days ago. I imagine Sybase IQ and other serious financial-trading market players have similar features.
In essence, the point of time series/event series SQL functionality is to do SQL against incomplete, imprecise, or derived data.* Read more