Two subjects in one post, because they were too hard to separate from each other
Any sufficiently complex software is developed in modules and subsystems. DBMS are no exception; the core trinity of parser, optimizer/planner, and execution engine merely starts the discussion. But increasingly, database technology is layered in a more fundamental way as well, to the extent that different parts of what would seem to be an integrated DBMS can sometimes be developed by separate vendors.
Major examples of this trend — where by “major” I mean “spanning a lot of different vendors or projects” — include:
- The object/relational, aka universal, extensibility features developed in the 1990s for Oracle, DB2, Informix, Illustra, and Postgres. The most successful extensions probably have been:
- Geospatial indexing via ESRI.
- Full-text indexing, notwithstanding questionable features and performance.
- MySQL storage engines.
- MPP (Massively Parallel Processing) analytic RDBMS relying on single-node PostgreSQL, Ingres, and/or Microsoft SQL Server — e.g. Greenplum (especially early on), Aster (ditto), DATAllegro, DATAllegro’s offspring Microsoft PDW (Parallel Data Warehouse), or Hadapt.
- Splits in which a DBMS has serious processing both in a “database” layer and in a predicate-pushdown “storage” layer — most famously Oracle Exadata, but also MarkLogic, InfiniDB, and others.
- SQL-on-HDFS — Hive, Impala, Stinger, Shark and so on (including Hadapt).
Other examples on my mind include:
- Data manipulation APIs being added to key-value stores such as Couchbase and Aerospike.
- TokuMX, the Tokutek/MongoDB hybrid I just blogged about.
- NuoDB’s willing reliance on third-party key-value stores (or HDFS in the role of one).
- FoundationDB’s strategy, and specifically its acquisition of Akiban.
And there are several others I hope to blog about soon, e.g. current-day PostgreSQL.
In an overlapping trend, DBMS increasingly have multiple data manipulation APIs. Examples include: Read more
When we scheduled a call to talk about Sentry, Cloudera’s Charles Zedlewski and I found time to discuss other stuff as well. One interesting part of our discussion was around the processing “frameworks” Cloudera sees as most important.
- The four biggies are:
- MapReduce. Duh.
- SQL, specifically Impala. This is as opposed to the uneasy Hive/MapReduce layering.
- “Math” , which seems to mainly be through partnerships with SAS and Revolution Analytics. I don’t know a lot about how these work, but I presume they bypass MapReduce, in which case I could imagine them greatly outperforming Mahout.
- Stream processing (Storm) is next in line.
- Graph — e.g. Giraph — rises to at least the proof-of-concept level. Again, the hope would be that this well outperforms graph-on-MapReduce.
- Charles is also seeing at least POC interest in Spark.
- But MPI (Message Passing Interface) on Hadoop isn’t going anywhere fast, except to the extent it’s baked into SAS or other “math” frameworks. Generic MPI use cases evidently turn out to be a bad fit for Hadoop, due to factors such as:
- Low data volumes.
- Latencies in various parts of the system
HBase was artificially omitted from this “frameworks” discussion because Cloudera sees it as a little bit more of a “storage” system than a processing one.
Another good subject was offloading work to Hadoop, in a couple different senses of “offload”: Read more
|Categories: BDAS, Spark, and Shark, Cloudera, Complex event processing (CEP), Endeca, Hadoop, HP and Neoview, MapReduce, Predictive modeling and advanced analytics, RDF and graphs, Revolution Analytics, SAS Institute, Teradata||22 Comments|
- Developed by Cloudera.
- An Apache incubator project.
- Slated to be rolled into CDH — Cloudera’s Hadoop distribution — over the next couple of weeks.
- Only useful with Hive in Version 1, but planned to also work in the future with other Hadoop data access systems such as Pig, search and so on.
- Lacking in administrative scalability in Version 1, something that is also slated to be fixed in future releases.
Apparently, Hadoop security options pre-Sentry boil down to:
- Kerberos, which only works down to directory or file levels of granularity.
- Third-party products.
Sentry adds role-based permissions for SQL access to Hadoop:
- By server.
- By database.
- By table.
- By view.
for a variety of actions — selections, transformations, schema changes, etc. Sentry does this by examining a query plan and checking whether each step in the plan is permissible. Read more
Hortonworks did a business-oriented round of outreach, talking with at least Derrick Harris and me. Notes from my call — for which Rob Bearden* didn’t bother showing up — include, in no particular order:
- Hortonworks denies advanced acquisition discussions with either Microsoft and Intel. Of course, that doesn’t exactly contradict the widespread story of Intel having made an acquisition offer.
- As vendors usually do, Hortonworks denies the extreme forms of Cloudera’s suggestion that Hortonworks competitive wins relate to price slashing. But Hortonworks does believe that its license fees often wind up being lower than Cloudera’s, due especially to Hortonworks offering few extra-charge items than Cloudera.
- Hortonworks used a figure of ~75 subscription customers. This does not include OEM sales through, for example, Teradata, Microsoft Azure, or Rackspace. However, that does include …
- … a small number of installations hosted in the cloud — e.g. ~2 on Amazon Web Services — or otherwise remotely. Also, testing in the cloud seems to be fairly frequent, and the cloud can also be a source of data ingested into Hadoop.
- Since Hortonworks a couple of times made it seem that Rackspace was an important partner, behind only Teradata and Microsoft, I finally asked why. Answers boiled down to a Rackspace Hadoop-as-a-service offering, plus joint work to improve Hadoop-on-OpenStack.
- Other Hortonworks reseller partners seem more important in terms of helping customers consumer HDP (Hortonworks Data Platform), rather than for actually doing Hortonworks’ selling for it. (This is unsurprising — channel sales rarely are a path to success for a product that is also appropriately sold by a direct force.)
- Hortonworks listed its major industry sectors as:
- Web and retailing, which it identifies as one thing.
- Health care (various subsectors).
- Financial services, which it called “competitive” in the kind of tone that usually signifies “we lose a lot more than we win, and would love to change that”.
*Speaking of CEO Bearden, an interesting note from Derrick’s piece is that Bearden is quoted as saying “I started this company from day one …”, notwithstanding that the now-departed Eric Baldeschwieler was founding CEO.
In Hortonworks’ view, Hadoop adopters typically start with a specific use case around a new type of data, such as clickstream, sensor, server log, geolocation, or social. Read more
I chatted yesterday with the Hortonworks gang. The main subject was Hortonworks’ approach to SQL-on-Hadoop — commonly called Stinger — but at my request we cycled through a bunch of other topics as well. Company-specific notes include:
- Hortonworks founder J. Eric “Eric14″ Baldeschwieler is no longer at Hortonworks, although I imagine he stays closely in touch. What he’s doing next is unspecified, except by the general phrase “his own thing”. (Derrick Harris has more on Eric’s departure.)
- John Kreisa still is at Hortonworks, just not as marketing VP. Think instead of partnerships and projects.
- ~250 employees.
- ~70-75 subscription customers.
Our deployment and use case discussions were a little confused, because a key part of Hortonworks’ strategy is to support and encourage the idea of combining use cases and workloads on a single cluster. But I did hear:
- 10ish nodes for a typical starting cluster.
- 100ish nodes for a typical “data lake” committed adoption.
- Teradata UDA (Unified Data Architecture)* customers sometimes (typically?) jumping straight to a data lake scenario.
- A few users in the 10s of 1000s of nodes. (Obviously Yahoo is one.)
- HBase used in >50% of installations.
- Hive probably even more than that.
- Hortonworks is seeing a fair amount of interest in Windows Hadoop deployments.
*By the way — Teradata seems serious about pushing the UDA as a core message.
Ecosystem notes, in Hortonworks’ perception, included:
- Cloudera is obviously Hortonworks’ biggest distro competitor. Next is IBM, presumably in its blue-forever installed base. MapR is barely on the radar screen; Pivotal’s likely rise hasn’t yet hit sales reports.
- Hortonworks evidently sees a lot of MicroStrategy and Tableau, and some Platfora and Datameer, the latter two at around the same level of interest.
- Accumulo is a big deal in the Federal government, and has gotten a few health care wins as well. Its success is all about security. (Note: That’s all consistent with what I hear elsewhere.)
I also asked specifically about OpenStack. Hortonworks is a member of the OpenStack project, contributes nontrivially to Swift and other subprojects, and sees Rackspace as an important partner. But despite all that, I think strong Hadoop/OpenStack integration is something for the indefinite future.
Hortonworks’ views about Hadoop 2.0 start from the premise that its goal is to support running a multitude of workloads on a single cluster. (See, for example, what I previously posted about Tez and YARN.) Timing notes for Hadoop 2.0 include:
- It’s been in preview/release candidate/commercial beta mode for weeks.
- Q3 is the goal; H2 is the emphatic goal.
- Yahoo’s been in production with YARN >8 months, and has no MapReduce 1 clusters left. (Yahoo has >35,000 Hadoop nodes.)
- The last months of delays have been mainly about sprucing up various APIs and protocols, which may need to serve for a similar multi-year period as Hadoop 1′s have. But there also was some YARN stabilization into May.
Frankly, I think Cloudera’s earlier and necessarily incremental Hadoop 2 rollout was a better choice than Hortonworks’ later big bang, even though the core-mission aspect of Hadoop 2.0 is what was least ready. HDFS (Hadoop Distributed File System) performance, NameNode failover and so on were well worth having, and it’s more than a year between Cloudera starting supporting them and when Hortonworks is offering Hadoop 2.0.
Hortonworks’ approach to doing SQL-on-Hadoop can be summarized simply as “Make Hive into as good an analytic RDBMS as possible, all in open source”. Key elements include: Read more
- Cloudera changed CEOs last week. Tom Reilly, late of ArcSight, is the new guy (I don’t know him), while Mike Olson’s titles become Chairman and Chief Strategy Officer. Mike told me Friday that Reilly had secretly been working with him for months.
- Mike shared good-sounding numbers with me. But little is for public disclosure except the stat >400 employees.
- There are always rumors of infighting at Cloudera, perhaps because from earliest days Cloudera was a place where tempers are worn on sleeves. That said, Mike denied stories of problems between him and COO Kirk Dunn, and greatly praised Kirk’s successes at large-account sales.
- Cloudera now self-identifies pretty clearly as an analytic data management company. The vision is multiple execution engines – MapReduce, Impala, something more memory-centric, etc. – talking to any of a variety of HDFS file formats. While some formats may be optimized for specific engines – e.g. Parquet for Impala – anything can work with more or less anything.*
- Mike told me that Cloudera didn’t have any YARN users in production, but thought there would be some by year-end. Even so, he thinks it’s fair to say that Cloudera users have substantial portions of Hadoop 2 in production, for example NameNode failover and HDFS (Hadoop Distributed File System) performance enhancements. Ditto HCatalog.
*Of course, there will always be exceptions. E.g., some formats can be updated on a short-request basis, while others can only be written to via batch conversions.
- There’s a widespread belief that Hortonworks is being shopped. Numerous folks – including me — believe the rumor of an Intel offer for $700 million. Higher figures and alternate buyers aren’t as widely believed.
- Views of MapR market traction, never high, are again on the downswing.
- IBM Big Insights seems to have some traction.
- In case there was any remaining doubt — DBMS vendors are pretty unanimous in agreeing that it makes sense to have Hadoop too. To my knowledge SAP hasn’t been as clear about showing a markitecture incorporating Hadoop as most of the others have … but then, SAP’s markitecture is generally less clear than other vendors’.
- Folks I talk with are generally wondering where and why Datameer lost its way. That still leaves Datameer ahead of other first-generation Hadoop add-on vendors (Karmasphere, Zettaset, et al.), in that I rarely hear them mentioned at all.
- I visited with my client Platfora. Things seem to be going very well.
- My former client Revelytix seems to have racked up some nice partnerships. (I had something to do with that. )
|Categories: Cloudera, Data warehousing, Datameer, Hadoop, Hortonworks, IBM and DB2, Intel, MapR, Market share and customer counts, Platfora, SAP AG, Zettaset||11 Comments|
I visited Cloudera Friday for, among other things, a chat about Impala with Marcel Kornacker and colleagues. Highlights included:
- Impala is meant to someday be a competitive MPP (Massively Parallel Processing) analytic RDBMS.
- At the moment, it is not one. For example, Impala lacks any meaningful form of workload management or query optimization.
- While Impala will run against any HDFS (Hadoop Distributed File System) file format, claims of strong performance assume that the data is in Parquet …
- … which is the replacement for the short-lived Trevni …
- … and which for most practical purposes is true columnar.
- Impala is also meant to be more than an RDBMS; Parquet and presumably in the future Impala can accommodate nested data structures.
- Just as Impala runs against most or all HDFS file formats, Parquet files can be used by most Hadoop execution engines, and of course by Pig and Hive.
- The Impala roadmap includes workload management, query optimization, data skipping, user-defined functions, hash distribution, two turtledoves, and a partridge in a pear tree.
Data gets into Parquet via batch jobs only — one reason it’s important that Impala run against multiple file formats — but background format conversion is another roadmap item. A single table can be split across multiple formats — e.g., the freshest data could be in HBase, with the rest is in Parquet.
|Categories: Cloudera, Columnar database management, Data models and architecture, Data warehousing, Database compression, Hadoop, HBase, MapReduce, Market share and customer counts, Specific users, Workload management||6 Comments|
A few days ago I posted Daniel Abadi’s thoughts in a discussion of Hadapt, Microsoft PDW (Parallel Data Warehouse)/PolyBase, Pivotal/Greenplum Hawq, and other SQL-Hadoop combinations. This is Dave DeWitt’s response. Emphasis mine.
|Categories: Benchmarks and POCs, Cloudera, Clustering, Data warehousing, Greenplum, Hadapt, Hadoop, MapReduce, Microsoft and SQL*Server, PostgreSQL||5 Comments|
My client Syncsort:
- Is an ETL (Extract/Transform/Load) vendor, whose flagship product DMExpress was evidently renamed to DMX.
- Has a strong history in and fondness for sort.
- Has announced a new ETL product, DMX-h ETL Edition, which uses Hadoop MapReduce to parallelize DMX by controlling a copy of DMX that resides on every data node of the Hadoop cluster.*
- Has also announced the closely-related DMX-h Sort Edition, offering acceleration for the sorts inherent in Map and Reduce steps.
- Contributed a patch to Apache Hadoop to open up Hadoop MapReduce to make all this possible.
*Perhaps we should question Syncsort’s previous claims of having strong multi-node parallelism already.
The essence of the Syncsort DMX-h ETL Edition story is:
- DMX-h inherits the various ETL-suite trappings of DMX.
- Syncsort claims DMX-h has major performance advantages vs., for example, Hive- or Pig-based alternatives.
- With a copy of DMX on every node, DMX-h can do parallel load/export.
I talk with a lot of companies, and repeatedly hear some of the same application themes. This post is my attempt to collect some of those ideas in one place.
1. So far, the buzzword of the year is “real-time analytics”, generally with “operational” or “big data” included as well. I hear variants of that positioning from NewSQL vendors (e.g. MemSQL), NoSQL vendors (e.g. AeroSpike), BI stack vendors (e.g. Platfora), application-stack vendors (e.g. WibiData), log analysis vendors (led by Splunk), data management vendors (e.g. Cloudera), and of course the CEP industry.
Yeah, yeah, I know — not all the named companies are in exactly the right market category. But that’s hard to avoid.
Why this gold rush? On the demand side, there’s a real or imagined need for speed. On the supply side, I’d say:
- There are vast numbers of companies offering data-management-related technology. They need ways to differentiate.
- Doing analytics at short-request speeds is an obvious data-management-related challenge, and not yet comprehensively addressed.
2. More generally, most of the applications I hear about are analytic, or have a strong analytic aspect. The three biggest areas — and these overlap — are:
- Customer interaction
- Network and sensor monitoring
- Game and mobile application back-ends
Also arising fairly frequently are:
- Algorithmic trading
- Risk measurement
- Law enforcement/national security
- Stakeholder-facing analytics
I’m hearing less about quality, defect tracking, and equipment maintenance than I used to, but those application areas have anyway been ebbing and flowing for decades.