Petabyte-scale data management
Posts about database management for databases with petabytes of user data.
eBay’s two enormous data warehouses
A few weeks ago, I had the chance to visit eBay, meet briefly with Oliver Ratzesberger and his team, and then catch up later with Oliver for dinner. I’ve already alluded to those discussions in a couple of posts, specifically on MapReduce (which eBay doesn’t like) and the astonishingly great difference between high- and low-end disk drives (to which eBay clued me in). Now I’m finally getting around to writing about the core of what we discussed, which is two of the very largest data warehouses in the world.
Metrics on eBay’s main Teradata data warehouse include:
- >2 petabytes of user data
- 10s of 1000s of users
- Millions of queries per day
- 72 nodes
- >140 GB/sec of I/O, or 2 GB/node/sec, or maybe that’s a peak when the workload is scan-heavy
- 100s of production databases being fed in
Metrics on eBay’s Greenplum data warehouse (or, if you like, data mart) include:
- 6 1/2 petabytes of user data
- 17 trillion records
- 150 billion new records/day, which seems to suggest an ingest rate well over 50 terabytes/day
- 96 nodes
- 200 MB/node/sec of I/O (that’s the order of magnitude difference that triggered my post on disk drives)
- 4.5 petabytes of storage
- 70% compression
- A small number of concurrent users
| Categories: Analytic technologies, Data warehouse appliances, Data warehousing, eBay, Greenplum, Petabyte-scale data management, Teradata, Web analytics | 48 Comments |
Cloudera presents the MapReduce bull case
Monday was fire-drill day regarding MapReduce vs. MPP relational DBMS. The upshot was that I was quoted in Computerworld and paraphrased in GigaOm as being a little more negative on MapReduce than I really am, in line with my comment
Frankly, my views on MapReduce are more balanced than [my] weary negativity would seem to imply.
Tuesday afternoon the dial turned a couple notches more positive yet, when I talked with Michael Olson and Jeff Hammerbacher of Cloudera. Cloudera is a new company, built around the open source MapReduce implementation Hadoop. So far Cloudera gives away its Hadoop distribution, without charging for any sort of maintenance or subscription, and just gets revenue from professional services. Presumably, Cloudera plans for this business model to change down the road.
Much of our discussion revolved around Facebook, where Jeff directed a huge and diverse Hadoop effort. Apparently, Hadoop played much of the role of an enterprise data warehouse at Facebook — at least for clickstream/network data — including:
- 2 1/2 petabytes of data managed via Hadoop
- 10 terabytes/day of data ingested via Hadoop (Edit: Some of these metrics have been updated in a subsequent post about Facebook.)
- Ad targeting queries run every 15 minutes in Hadoop
- Dashboard roll-up queries run every hour in Hadoop
- Ad-hoc research/analytic Hadoop queries run whenever
- Anti-fraud analysis done in Hadoop
- Text mining (e.g., of things written on people’s “walls”) done in Hadoop
- 100s or 1000s of simultaneous Hadoop queries
- JSON-based social network analysis in Hadoop
Some Facebook data, however, was put into an Oracle RAC cluster for business intelligence. And Jeff does concede that query execution is slower in Hadoop than in a relational DBMS. Hadoop was also used to build the index for Facebook’s custom text search engine.
Jeff’s reasons for liking Hadoop over relational DBMS at Facebook included: Read more
Teradata’s Petabyte Power Players
As previously hinted, Teradata has now announced 4 of the 5 members of its “Petabyte Power Players” club. These are enterprises with 1+ petabyte of data on Teradata equipment. As is commonly the case when Teradata discusses such figures, there’s some confusion as to how they’re actually counting. But as best I can tell, Teradata is counting: Read more
| Categories: Data warehousing, eBay, Market share and customer counts, Petabyte-scale data management, Specific users, Teradata | 11 Comments |
Greenplum’s single biggest customer
Greenplum offered a bit of clarification regarding the usage figures I posted last night. Everything on the list is in production, except that:
- One Greenplum customer is at 400 terabytes now, and upgrading to >1 petabyte “as we speak.”
- Greenplum’s other soon-to-be >1 petabyte customer isn’t in production yet. (Greenplum previously told me that customer was in the process of loading data right now.)
| Categories: Data warehousing, Fox and MySpace, Greenplum, Petabyte-scale data management, Specific users | 3 Comments |
Greenplum is in the big leagues
After a March, 2007 call, I didn’t talk again with Greenplum until earlier this month. That changed fast. I flew out to see Greenplum last week and spent over a day with president/co-founder Scott Yara, CTO/co-founder Luke Lonergan, marketing VP Paul Salazar, and product management/marketing director Ben Werther. Highlights – besides some really great sushi at Sakae in Burlingame – start with an eye-opening set of customer proof points, such as: Read more
| Categories: Analytic technologies, Data warehouse appliances, Data warehousing, Greenplum, Petabyte-scale data management, PostgreSQL | 19 Comments |
Yahoo scales its web analytics database to petabyte range
Information Week has an article with details on what sounds like Yahoo’s core web analytics database. Highlights include:
- The Yahoo web analytics database is over 1 petabyte. They claim it will be in the 10s of petabytes by 2009.
- The Yahoo web analytics database is based on PostgreSQL. So much for MySQL fanboys’ claims of Yahoo validation for their beloved toy … uh, let me rephrase that. The highly-regarded MySQL, although doing a great job for some demanding and impressive applications at Yahoo, evidently wasn’t selected for this one in particular. OK. That’s much better now.
- But the Yahoo web analytics database doesn’t actually use PostgreSQL’s storage engine. Rather, Yahoo wrote something custom and columnar.
- Yahoo is processing 24 billion “events” per day. The article doesn’t clarify whether these are sent straight to the analytics store, or whether there’s an intermediate storage engine. Most likely the system fills blocks in RAM and then just appends them to the single persistent store. If commodity boxes occasionally crash and lose a few megs of data — well, in this application, that’s not a big deal at all.
- Yahoo thinks commercial column stores aren’t ready yet for more than 100 terabytes of data.
- Yahoo says it got great performance advantages from a custom system by optimizing for its specific application. I don’t know exactly what that would be, but I do know that database architectures for high-volume web analytics are still in pretty bad shape. In particular, there’s no good way yet to analyze the specific, variable-length paths users take through websites.
| Categories: Analytic technologies, Columnar database management, Data warehousing, MySQL, Petabyte-scale data management, PostgreSQL, Specific users, Theory and architecture, Yahoo | 13 Comments |
Netezza targets 1 petabyte
Netezza is promising petabyte-scale appliances later this year, up from 100 terabytes. That’s user data (I checked), and assumes 2-3X compression, or a little less than they think is actually likely. I.e., they’re describing their capacity in the same kinds of terms other responsible vendors do. They haven’t actually built and tested any 1 petabyte systems internally yet, but they’ve gone over 100 terabytes.
Basically, this leaves Netezza’s high-end capability about 10X below Teradata’s. On the other hand, it should leave them capable of handling pretty much every Teradata database in existence. Read more
| Categories: Analytic technologies, Data warehouse appliances, Data warehousing, Netezza, Petabyte-scale data management, Teradata | Leave a Comment |
Marketing versus reality on the one-petabyte barrier
Usually, I don’t engage in the kind of high-speed quick-response blogging I have over the past couple of days from the Teradata Partners conference (and more generally have for the past week or so). And I’m not sure it’s working out so well.
For example, the claim that Teradata has surpassd the one-petabyte mark comes as quite a surprise to variety of Teradata folks, not to mention at least one reliable outside anonymous correspondent. That claim may indeed be true about raw disk space on systems sold. But the real current upper limit, according to CTO Todd Walter,* is 5-700 terabytes of user data. He thinks half a dozen or so customers are in that range. I’d guess quite strongly that three of those are Wal-Mart, eBay, and an unspecified US intelligence agency.
*Teradata seems to have quite a few CTOs. But I’ve seen things much sillier than that in the titles department, and accordingly shan’t scoff further — at least on that particular subject. 😉
On the other hand, if anybody did want to buy a 10 petabyte system, Teradata could ship them one. And by the way, the Teradata people insist Sybase’s claims in the petabyte area are quite bogus. Teradata claims to have had bigger internal systems tested earlier than the one Sybase writes about.
| Categories: Data warehouse appliances, Data warehousing, eBay, Petabyte-scale data management, Specific users, Sybase, Teradata | 3 Comments |
Yet more on petabyte-scale Teradata databases
I managed to buttonhole Teradata’s Darryl MacDonald again, to follow up on yesterday’s brief chat. He confirmed that there are more than one petabyte+ Teradata databases out there, of which at least one is commercial rather than government/classified. Without saying who any of them were, he dropped a hint suggestive of Wal-Mart. That makes sense, given that a 423 terabyte figure for Wal-Mart is now three years old, and Wal-Mart is in the news for its 4 petabyte futures. Yes, that news has tended to mention HP NeoView recently more than Teradata. But it seems very implausible that a NeoView replacement of Teradata has already happened, if if such a thing is a possibility for the future. So right now however much data Wal-Mart has on its path from 423 terabytes to 4 petabytes and beyond is probably collected mainly on Teradata machines.