Hadoop
Discussion of Hadoop. Related subjects include:
Database diversity revisited
From time to time, I try to step back and build a little taxonomy for the variety in database technology. One effort was 4 1/2 years ago, in a pre-planned exchange with Mike Stonebraker (his side, alas, has since been taken down). A year ago I spelled out eight kinds of analytic database.
The angle I’ll take this time is to say that every sufficiently large enterprise needs to be cognizant of at least 7 kinds of database challenge. General notes on that include:
- I’m using the weasel words “database challenge” to evade questions as to what is or isn’t exactly a DBMS.
- One “challenge” can call for multiple products and technologies even within a single enterprise, let alone at different ones. For example, in this post the “eight kinds of analytic database” are reduced to just a single category.
- Even so, one product or technology may be well-suited to address a couple different kinds of challenges.
The Big Seven database challenges that almost any enterprise faces are: Read more
Teradata SQL-H, using HCatalog
When I grumbled about the conference-related rush of Hadoop announcements, one example of many was Teradata Aster’s SQL-H. Still, it’s an interesting idea, and a good hook for my first shot at writing about HCatalog. Indeed, other than the Talend integration bundled into Hortonworks’ HDP 1, Teradata SQL-H is the first real use of HCatalog I’m aware of.
The Teradata SQL-H idea is:
- Register your Hadoop data to HCatalog. I’ll confess to being unclear about the details of how that works, for example in the case of data that just doesn’t fit well into flat relational tables. Stay tuned for future posts. For now, I’ll just note that:
- HCatalog is closely based on Hive’s metadata management. If you’ve run Hive against the data, HCatalog should already know about it.
- HCatalog can handle Pig and HBase data as well.
- Write SQL DDL (Data Description Language) so that your Aster cluster knows about the data.
- Write any Teradata Aster SQL/MR against that data. Some of the execution will be done on the Hadoop cluster, but pulling data back into Aster may well be necessary.
At least in theory, Teradata SQL-H lets you use a full set of analytic tools against your Hadoop data, with little limitation except price and/or performance. Teradata thinks the performance of all this can be much better than if you just use Hadoop (35X was mentioned in one particularly favorable example), but perhaps much worse than if you just copy/extract the data to an Aster cluster in the first place.
So what might the use cases be for something like SQL-H? Offhand, I’d say:
- SQL-H use cases are probably focused in areas where copying the data to Aster in advance doesn’t make a lot of sense. So presumably …
- … the Hadoop clusters involved would hold a lot more data than you’d want to pay for storing in Teradata Aster. E.g., think of cases where Hadoop is used as a big bit bucket or archival data store.
- There could be a kind of investigative workflow. First you play around with the Hadoop data via SQL-H. Then when you think you’re onto something, you set up ETL (Extract/Transform/Load) to get the data into Aster and ratchet up the effort.
By way of contrast, the whole thing makes less sense for dashboarding kinds of uses, unless the dashboard users are very patient when they want to drill down.
Why I’m so forward-leaning about Hadoop features
In my recent series of Hadoop posts, there were several cases where I had to choose between recommending that enterprises:
- Go with the most advanced features any vendor was credibly advocating.
- Be more cautious, and only adopt features that have been solidly proven in the field.
I favored the more advanced features each time. Here’s why.
To a first approximation, I divide Hadoop use cases into two major buckets, only one of which I was addressing with my comments:
1. Analytic data management.* Here I favored features over reliability because they are more important, for Hadoop as for analytic RDBMS before it. When somebody complains about an analytic data store not being ready for prime time, never really working, or causing them to tear their hair out, what they usually mean is that:
- It couldn’t do the work that needed doing …
- … with reasonable performance and turnaround time …
- … without undue effort in administration and/or programming.
Those complaints are much, much, more frequent than “It crashed”. So it was for Netezza, DATAllegro, Greenplum, Aster Data, Vertica, Infobright, et al. So it also is for Hadoop. And how does one address those complaints? By performance and feature enhancements, of the kind that the Hadoop community is introducing at high speed. Read more
| Categories: Buying processes, Data warehousing, EAI, EII, ETL, ELT, ETLT, Hadoop, HBase, Hortonworks, Open source | 1 Comment |
Notes on HBase 0.92
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 |
“Enterprise-ready Hadoop”
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 (this post)?
- HBase 0.92.
The posts depend on each other in various ways.
Cloudera, Hortonworks, and MapR all claim, in effect, “Our version of Hadoop is enterprise-ready, unlike those other guys’.” I’m dubious.
- Hortonworks has considerably fewer features than Cloudera, along with less of a production or support track record. (Edit: HCatalog may be a significant exception.)
- I doubt Cloudera really believes or can support the apparent claim in its CDH 4 press release that Hadoop is now suitable for every enterprise, whereas last month it wasn’t.
- While MapR was early with some nice enterprise features, such as high availability or certain management UI elements — quickly imitated in Cloudera Enterprise — I don’t think it has any special status as “enterprise-ready” either.
That said, “enterprise-ready Hadoop” really is an important topic.
So what does it mean for something to be “enterprise-ready”, in whole or in part? Common themes in distinguishing between “enterprise-class” and other software include:
- Usable by our existing staff.
- Sufficiently feature-rich.
- Integrates well with the rest of our environment.
- Fits well into our purchasing and vendor relations model.
- Well-supported.
- Sufficiently reliable, proven, and secure — which is to say, “safe”.
For Hadoop, as for most things, these concepts overlap in many ways. Read more
| Categories: Buying processes, Cloudera, Clustering, Hadoop, HBase, Hortonworks, MapR, MapReduce, Open source | 9 Comments |
Hadoop distributions: CDH 4, HDP 1, Hadoop 2.0, Hadoop 1.0 and all that
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 (this post).
- In general, how “enterprise-ready” is Hadoop?
- HBase 0.92.
The posts depend on each other in various ways.
My clients at Cloudera and Hortonworks have somewhat different views as to the maturity of various pieces of Hadoop technology. In particular:
- Cloudera introduced CDH 4* and Hortonworks introduced HDP 1*, both timed for the recent Hadoop Summit.
- CDH 4 is based mainly on Hadoop 2.0, which Cloudera says it has tested extensively.
- HDP 1 is based on Hadoop 1.0, on the theory that nobody has properly tested Hadoop 2.0, which is still characterized as “alpha”.
- CDH 4 boasts sub-second NameNode failover.
- Hortonworks is partnering with third parties such as VMware to address the high-availability problems caused by failover potentially taking several minutes.
- Hadoop 2.0 and CDH 4 also incorporate improvements to NameNode scalability, HDFS (Hadoop Distributed File System) performance, HBase performance, and HBase functionality.
- As does CDH 4, HDP 1 includes HCatalog, an extension of Hive technology that serves as a more general metadata store. (Edit: Actually, see the comment thread below.)
- Hortonworks thinks HCatalog is a big deal in improving Hadoop data management and connectivity, and already has a Talend partnership based on HCatalog. Cloudera is less sure, especially in HCatalog’s current form.
- HDP 1 includes Ambari, an Apache open source competitor to Cloudera Manager (the closed-source part of Cloudera Enterprise). Hortonworks concedes a functionality gap between Ambari and Cloudera Manager, but perhaps a smaller one than Cloudera sees.
- Hortonworks thinks Ambari being open source means better integration with other management platforms. Cloudera touts the integration features and integrations of Cloudera Manager 4.
- Nobody seems confident that MapReduce 2 is ready for prime time. While it’s in CDH 4, so is MapReduce 1.
*”CDH” stands, due to some trademarking weirdness, for “Cloudera’s Distribution including Apache Hadoop”. “HDP” stands for “Hortonworks Data Platform”.
| Categories: Amazon and its cloud, Cloudera, Hadoop, Hortonworks, MapReduce, Market share and customer counts | 14 Comments |
Hadoop marketing themes that deserve to be ignored
This is part of a four-post series, covering:
- Annoying Hadoop marketing themes that should be ignored (this post).
- Hadoop versions and distributions, and their readiness or lack thereof for production.
- In general, how “enterprise-ready” is Hadoop?
- HBase 0.92.
The posts depend on each other in various ways.
I am subjected to much Hadoop marketing. Indeed, I even help various clients inflict Hadoop marketing upon the world. But a guy’s got to draw a line somewhere, and there are certain Hadoop marketing themes that I just refuse to take seriously.
1. Big data. I think the term “big data” long ago jumped the shark. If a firm uses the term “big data”, I teeth-grittingly let it pass. But if they send me PR email offering to “explain” the benefits or “real meaning” of “big data”, my response is apt to be unkind.
2. Conference-timed news. I’ve never liked the custom of multiple vendors piling announcements into the same conference week. It seems like a calculated strategy to ensure getting the least possible mind share and attention — unless, of course, your announcement is so lame that brief mentions in conference-week roundups are the most visibility you can hope to get. Even so, many vendors make the marketing choice to pile on. Fine. But I’ll write in response if and when I feel like it.
3. Contribution Olympics. The Urinary Olympics as to who contributed more lines of code, patches, whatever to various Hadoop sub-projects got pretty silly; and although it peaked last year, elements of it are with us still. I do see two scenarios where the whole discussion might have genuine value, namely:
- When two vendors — typically Hortonworks and Cloudera — differ about a particular Hadoop sub-project, I’m inclined to believe the one who asserts “Well, we built most of and then extensively tested the last release of this, and it does what we say it does.”
- If you have a specialized desire to see a particular aspect of Hadoop hacked, there are a limited number of developers who are best-suited to do it for you, and you might be best served to deal with the company that employs (most of) them.
Otherwise, however, I pay little attention to claims like “We thought this scheme up 2 years ago, and hence we’re the experts on whether it’s now ready for production.”
| Categories: Cloudera, Hadoop, Hortonworks | 12 Comments |
Metamarkets’ back-end technology
This is part of a three-post series:
- Introduction to Metamarkets and Druid
- Druid overview
- Metamarkets’ back-end technology (this post)
The canonical Metamarkets batch ingest pipeline is a bit complicated.
- Data lands on Amazon S3 (uploaded or because it was there all along).
- Metamarkets processes it, primarily via Hadoop and Pig, to summarize and denormalize it, and then puts it back into S3.
- Metamarkets then pulls the data into Hadoop a second time, to get it ready to be put into Druid.
- Druid is notified, and pulls the data from Hadoop at its convenience.
By “get data read to be put into Druid” I mean:
- Build the data segments (recall that Druid manages data in rather large segments).
- Note metadata about the segments.
That metadata is what goes into the MySQL database, which also retains data about shards that have been invalidated. (That part is needed because of the MVCC.)
By “build the data segments” I mean:
- Make the sharding decisions.
- Arrange data columnarly within shard.
- Build a compressed bitmap for each shard.
When things are being done that way, Druid may be regarded as comprising three kinds of servers: Read more
Notes on the analysis of large graphs
This post is part of a series on managing and analyzing graph data. Posts to date include:
- Graph data model basics
- Relationship analytics definition
- Relationship analytics applications
- Analysis of large graphs (this post)
My series on graph data management and analytics got knocked off-stride by our website difficulties. Still, I want to return to one interesting set of issues — analyzing large graphs, specifically ones that don’t fit comfortably into RAM on a single server. By no means do I have the subject figured out. But here are a few notes on the matter.
How big can a graph be? That of course depends on:
- The number of nodes. If the nodes of a graph are people, there’s an obvious upper bound on the node count. Even if you include their houses, cars, and so on, you’re probably capped in the range of 10 billion.
- The number of edges. (Even more important than the number of nodes.) If every phone call, email, or text message in the world is an edge, that’s a lot of edges.
- The typical size of a (node, edge, node) triple. I don’t know why you’d have to go much over 100 bytes post-compression*, but maybe I’m overlooking something.
*Even if your graph has 10 billion nodes, those can be tokenized in 34 bits, so the main concern is edges. Edges can include weights, timestamps, and so on, but how many specifics do you really need? At some point you can surely rely on a pointer to full detail stored elsewhere.
The biggest graph-size estimates I’ve gotten are from my clients at Yarcdata, a division of Cray. (“Yarc” is “Cray” spelled backwards.) To my surprise, they suggested that graphs about people could have 1000s of edges per node, whether in:
- An intelligence scenario, perhaps with billions of nodes and hence trillions of edges.
- A telecom user-analysis case, with perhaps 100 million nodes and hence 100s of billions of edges.
Yarcdata further suggested that bioinformatics use cases could have node counts higher yet, characterizing Bio2RDF as one of the “smaller” ones at 22 billion nodes. In these cases, the nodes/edge average seems lower than in people-analysis graphs, but we’re still talking about 100s of billions of edges.
Recalling that relationship analytics boils down to finding paths and subgraphs, the naive relational approach to such tasks would be: Read more
| Categories: Analytic technologies, Aster Data, Data models and architecture, Hadoop, Health care, MapReduce, RDF and graphs, Scientific research, Telecommunications, Yarcdata and Cray | 20 Comments |
Notes on the Hadoop and HBase markets
I visited my clients at Cloudera and Hortonworks last week, along with scads of other companies. A few of the takeaways were:
- Cloudera now has 220 employees.
- Cloudera now has over 100 subscription customers.
- Over the past year, Cloudera has more than doubled in size by every reasonable metric.
- Over half of Cloudera’s customers use HBase, vs. a figure of 18+ last July.
- Omer Trajman — who by the way has made a long-overdue official move into technical marketing — can no longer keep count of how many petabyte-scale Hadoop clusters Cloudera supports.
- Cloudera gets the majority of its revenue from subscriptions. However, professional services and training continue to be big businesses too.
- Cloudera has trained over 12,000 people.
- Hortonworks is training people too.
- Hortonworks now has 70 employees, and plans to have 100 or so by the end of this quarter.
- A number of those Hortonworks employees are executives who come from seriously profit-oriented backgrounds. Hortonworks clearly has capitalist intentions.
- Hortonworks thinks a typical enterprise Hadoop cluster has 20-50 nodes, with 50-100 already being on the large side.
- There are huge amounts of Elastic MapReduce/Hadoop processing in the Amazon cloud. Some estimates say it’s the majority of all Amazon Web Services processing.
- I met with 4 young-company clients who I regard as building vertical analytic stacks (WibiData, MarketShare, MetaMarkets, and ClearStory). All 4 are heavily dependent on Hadoop. (The same isn’t as true of older companies who built out a lot of technology before Hadoop was invented.)
- There should be more HBase information at HBaseCon on May 22.
- If MapR still has momentum, nobody I talked with has noticed.