Databricks, Spark and BDAS
Discussion of BDAS (Berkeley Data Analytics Systems), especially Spark and related projects, and also of Databricks, the company commercializing Spark.
- I spent three weeks in California on a hybrid personal/business trip. I had a bunch of meetings, but not three weeks’ worth.
- The timing was awkward for most companies I wanted to see. No blame accrues to those who didn’t make themselves available.
- I came back with a nasty cough. Follow-up phone calls aren’t an option until next week.
- I’m impatient to start writing. Hence tonight’s posts. But it’s difficult for a man and his cough to be productive at the same time.
A running list of recent posts is:
- As a companion to this post, I’m publishing a very long one on vendor lock-in.
Subjects I’d like to add to that list include:
- Spark (it’s prospering).
- Databricks (ditto, appearances to the contrary notwithstanding).
- Flink (it’s interesting as the streaming technology it’s now positioned to be, rather than the overall Spark alternative it used to be positioned as but which the world didn’t need).
- DataStax, MemSQL, Zoomdata, and Neo Technology (also prospering).
- Cloudera (multiple topics, as usual).
- Analytic SQL engines (“traditional” analytic RDBMS aren’t doing well).
- Enterprises’ inconsistent views about vendor lock-in.
- Microsoft’s reinvention (it feels real).
- Metadata (it’s ever more of a thing).
- Machine learning (it’s going to be a big portion of my research going forward).
- Transitions to the cloud — this subject affects almost everything else.
In a companion introduction to Kafka post, I observed that Kafka at its core is remarkably simple. Confluent offers a marchitecture diagram that illustrates what else is on offer, about which I’ll note:
- The red boxes — “Ops Dashboard” and “Data Flow Audit” — are the initial closed-source part. No surprise that they sound like management tools; that’s the traditional place for closed source add-ons to start.
- “Schema Management”
- Is used to define fields and so on.
- Is not equivalent to what is ordinarily meant by schema validation, in that …
- … it allows schemas to change, but puts constraints on which changes are allowed.
- Is done in plug-ins that live with the producer or consumer of data.
- Is based on the Hadoop-oriented file format Avro.
Kafka offers little in the way of analytic data transformation and the like. Hence, it’s commonly used with companion products. Read more
|Categories: Data integration and middleware, Databricks, Spark and BDAS, EAI, EII, ETL, ELT, ETLT, Hadoop, Kafka and Confluent, Market share and customer counts, Streaming and complex event processing (CEP)||2 Comments|
Cloudera released Version 2 of Cloudera Director, which is a companion product to Cloudera Manager focused specifically on the cloud. This led to a discussion about — you guessed it! — Cloudera and the cloud.
Making Cloudera run in the cloud has three major aspects:
- Cloudera’s usual software, ported to run on the cloud platform(s).
- Cloudera Director, which for example launches cloud instances.
- Points of integration, e.g. taking information about security-oriented roles from the platform and feeding then to the role-based security that is specific to Cloudera Enterprise.
Features new in this week’s release of Cloudera Director include:
- An API for job submission.
- Support for spot and preemptable instances.
- High availability.
- Some cluster repair.
- Some cluster cloning.
I.e., we’re talking about some pretty basic/checklist kinds of things. Cloudera Director is evidently working for Amazon AWS and Google GCP, and planned for Windows Azure, VMware and OpenStack.
As for porting, let me start by noting: Read more
I’m on two overlapping posting kicks, namely “lessons from the past” and “stuff I keep saying so might as well also write down”. My recent piece on Oracle as the new IBM is an example of both themes. In this post, another example, I’d like to memorialize some points I keep making about business intelligence and other analytics. In particular:
- BI relies on strong data access capabilities. This is always true. Duh.
- Therefore, BI and other analytics vendors commonly reinvent the data management wheel. This trend ebbs and flows with technology cycles.
Similarly, BI has often been tied to data integration/ETL (Extract/Transform/Load) functionality.* But I won’t address that subject further at this time.
*In the Hadoop/Spark era, that’s even truer of other analytics than it is of BI.
My top historical examples include:
- The 1970s analytic fourth-generation languages (RAMIS, NOMAD, FOCUS, et al.) commonly combined reporting and data management.
- The best BI visualization technology of the 1980s, Executive Information Systems (EIS), was generally unsuccessful. The core reason was a lack of what we’d now call drilldown. Not coincidentally, EIS vendors — notably leader Comshare — didn’t do well at DBMS-like technology.
- Business Objects, one of the pioneers of the modern BI product category, rose in large part on the strength of its “semantic layer” technology. (If you don’t know what that is, you can imagine it as a kind of virtual data warehouse modest enough in its ambitions to actually be workable.)
- Cognos, the other pioneer of modern BI, depending on capabilities for which it needed a bundled MOLAP (Multidimensional OnLine Analytic Processing) engine.
- But Cognos later stopped needing that engine, which underscores my point about technology ebbing and flowing.
|Categories: Business intelligence, Business Objects, Cognos, Databricks, Spark and BDAS, EAI, EII, ETL, ELT, ETLT, Hadoop, Information Builders, MicroStrategy, Software as a Service (SaaS), Teradata||5 Comments|
Mike Stonebraker and Larry Ellison have numerous things in common. If nothing else:
- They’re both titanic figures in the database industry.
- They both gave me testimonials on the home page of my business website.
- They both have been known to use the present tense when the future tense would be more accurate.
I mention the latter because there’s a new edition of Readings in Database Systems, aka the Red Book, available online, courtesy of Mike, Joe Hellerstein and Peter Bailis. Besides the recommended-reading academic papers themselves, there are 12 survey articles by the editors, and an occasional response where, for example, editors disagree. Whether or not one chooses to tackle the papers themselves — and I in fact have not dived into them — the commentary is of great interest.
But I would not take every word as the gospel truth, especially when academics describe what they see as commercial market realities. In particular, as per my quip in the first paragraph, the data warehouse market has not yet gone to the extremes that Mike suggests,* if indeed it ever will. And while Joe is close to correct when he says that the company Essbase was acquired by Oracle, what actually happened is that Arbor Software, which made Essbase, merged with Hyperion Software, and the latter was eventually indeed bought by the giant of Redwood Shores.**
*When it comes to data warehouse market assessment, Mike seems to often be ahead of the trend.
**Let me interrupt my tweaking of very smart people to confess that my own commentary on the Oracle/Hyperion deal was not, in retrospect, especially prescient.
Mike pretty much opened the discussion with a blistering attack against hierarchical data models such as JSON or XML. To a first approximation, his views might be summarized as: Read more
I talked with Cloudera shortly ahead of today’s announcement of Cloudera 5.5. Much of what we talked about had something or other to do with SQL data management. Highlights include:
- Impala and Kudu are being donated to Apache. This actually was already announced Tuesday. (Due to Apache’s rules, if I had any discussion with Cloudera speculating on the likelihood of Apache accepting the donations, I would not be free to relay it.)
- Cloudera is introducing SQL extensions so that Impala can query nested data structures. More on that below.
- The basic idea for the nested datatype support is that there are SQL extensions with a “dot” notation to let you get at the specific columns you need.
- From a feature standpoint, we’re definitely still in the early days.
- When I asked about indexes on these quasi-columns, I gathered that they’re not present in beta but are hoped for by the time of general availability.
- Basic data skipping, also absent in beta, seems to be more confidently expected in GA.
- This is for Parquet first, Avro next, and presumably eventually native JSON as well.
- This is said to be Dremel-like, at least in the case of Parquet. I must confess that I’m not familiar enough with Apache Drill to compare the two efforts.
- Cloudera is increasing its coverage of Spark in several ways.
- Cloudera is adding support for MLlib.
- Cloudera is adding support for SparkSQL. More on that below.
- Cloudera is adding support for Spark going against S3. The short answer to “How is this different from the Databricks service?” is:
- More “platform” stuff from the Hadoop stack (e.g. for data ingest).
- Less in the way of specific Spark usability stuff.
- Cloudera is putting into beta what it got in the Xplain.io acquisition, which it unfortunately is naming Cloudera Navigator Optimizer. More on that in a separate post.
- Impala and Hive are getting column-level security via Apache Sentry.
- There are other security enhancements.
- Some policy-based information lifecycle management is being added as well.
While I had Cloudera on the phone, I asked a few questions about Impala adoption, specifically focused on concurrency. There was mention of: Read more
|Categories: Benchmarks and POCs, Cloudera, Data warehousing, Databricks, Spark and BDAS, Market share and customer counts, Petabyte-scale data management, Predictive modeling and advanced analytics, SQL/Hadoop integration||4 Comments|
Basho was on my (very short) blacklist of companies with whom I refuse to speak, because they have lied about the contents of previous conversations. But Tony Falco et al. are long gone from the company. So when Basho’s new management team reached out, I took the meeting.
- Basho management turned over significantly 1-2 years ago. The main survivors from the old team are 1 each in engineering, sales, and services.
- Basho moved its headquarters to Bellevue, WA. (You get one guess as to where the new CEO lives.) Engineering operations are very distributed geographically.
- Basho claims that it is much better at timely product shipments than it used to be. Its newest product has a planned (or at least hoped-for) 8-week cadence for point releases.
- Basho’s revenue is ~90% subscription.
- Basho claims >200 enterprise clients, vs. 100-120 when new management came in. Unfortunately, I forgot to ask the usual questions about divisions vs. whole organizations, OEM sell-through vs. direct, etc.
- Basho claims an average contract value of >$100K, typically over 2-3 years. $9 million of that (which would be close to half the total, actually), comes from 2 particular deals of >$4 million each.
Basho’s product line has gotten a bit confusing, but as best I understand things the story is:
- There’s something called Riak Core, which isn’t even a revenue-generating product. However, it’s an open source project with some big users (e.g. Goldman Sachs, Visa), and included in pretty much everything else Basho promotes.
- Riak KV is the key-value store previously known as Riak. It generates the lion’s share of Basho’s revenue.
- Riak S2 is an emulation of Amazon S3. Basho thinks that Riak KV loses efficiency when objects get bigger than 1 MB or so, and that’s when you might want to use Riak S2 in addition or instead.
- Riak TS is for time series, and just coming out now.
- Also in the mix are some (extra charge) connectors for Redis and Spark. Presumably, there are more of these to come.
- There’s an umbrella marketing term of “Basho Data Platform”.
Technical notes on some of that include: Read more
|Categories: Aerospike, Basho and Riak, Cassandra, Clustering, Couchbase, Databricks, Spark and BDAS, DataStax, HBase, Health care, Log analysis, MapR, Market share and customer counts, MongoDB, NoSQL, Pricing, Specific users, Splunk||Leave a Comment|
I last wrote about Couchbase in November, 2012, around the time of Couchbase 2.0. One of the many new features I mentioned then was secondary indexing. Ravi Mayuram just checked in to tell me about Couchbase 4.0. One of the important new features he mentioned was what I think he said was Couchbase’s “first version” of secondary indexing. Obviously, I’m confused.
Now that you’re duly warned, let me remind you of aspects of Couchbase timeline.
- 2 corporate name changes ago, Couchbase was organized to commercialize memcached. memcached, of course, was internet companies’ default way to scale out short-request processing before the rise of NoSQL, typically backed by manually sharded MySQL.
- Couchbase’s original value proposition, under the name Membase, was to provide persistence and of course support for memcached. This later grew into a caching-oriented pitch even to customers who weren’t already memcached users.
- A merger with the makers of CouchDB ensued, with the intention of replacing Membase’s SQLite back end with CouchDB at the same time as JSON support was introduced. This went badly.
- By now, however, Couchbase sells for more than distributed cache use cases. Ravi rattled off a variety of big-name customer examples for system-of-record kinds of use cases, especially in session logging (duh) and also in travel reservations.
- Couchbase 4.0 has been in beta for a few months.
Technical notes on Couchbase 4.0 — and related riffs — start: Read more
|Categories: Cache, Clustering, Couchbase, Data models and architecture, Databricks, Spark and BDAS, Exadata, Hadoop, MarkLogic, MongoDB, MySQL, NoSQL, Open source, Schema on need, Structured documents, Web analytics||1 Comment|
This is part of a three-post series on Kudu, a new data storage system from Cloudera.
- Part 1 is an overview of Kudu technology.
- Part 2 is a lengthy dive into how Kudu writes and reads data.
- Part 3 (this post) is a brief speculation as to Kudu’s eventual market significance.
Combined with Impala, Kudu is (among other things) an attempt to build a no-apologies analytic DBMS (DataBase Management System) into Hadoop. My reactions to that start:
- It’s plausible; just not soon. What I mean by that is:
- Success will, at best, be years away. Please keep that in mind as you read this otherwise optimistic post.
- Nothing jumps out at me to say “This will never work!”
- Unlike when it introduced Impala — or when I used to argue with Jeff Hammerbacher pre-Impala — this time Cloudera seems to have reasonable expectations as to how hard the project is.
- There’s huge opportunity if it works.
- The analytic RDBMS vendors are beatable. Teradata has a great track record of keeping its product state-of-the-art, but it likes high prices. Most other strong analytic RDBMS products were sold to (or originated by) behemoth companies that seem confused about how to proceed.
- RDBMS-first analytic platforms didn’t do as well as I hoped. That leaves a big gap for Hadoop.
I’ll expand on that last point. Analytics is no longer just about fast queries on raw or simply-aggregated data. Data transformation is getting ever more complex — that’s true in general, and it’s specifically true in the case of transformations that need to happen in human real time. Predictive models now often get rescored on every click. Sometimes, they even get retrained at short intervals. And while data reduction in the sense of “event extraction from high-volume streams” isn’t that a big deal yet in commercial apps featuring machine-generated data — if growth trends continue as much of us expect, it’s only a matter of time before that changes.
Of course, this is all a bullish argument for Spark (or Flink, if I’m wrong to dismiss its chances as a Spark competitor). But it also all requires strong low-latency analytic data underpinnings, and I suspect that several kinds of data subsystem will prosper. I expect Kudu-supported Hadoop/Spark to be a strong contender for that role, along with the best of the old-school analytic RDBMS, Tachyon-supported Spark, one or more contenders from the Hana/MemSQL crowd (i.e., memory-centric RDBMS that purport to be good at analytics and transactions alike), and of course also whatever Cloudera’s strongest competitor(s) choose to back.
This is part of a three-post series on Kudu, a new data storage system from Cloudera.
- Part 1 (this post) is an overview of Kudu technology.
- Part 2 is a lengthy dive into how Kudu writes and reads data.
- Part 3 is a brief speculation as to Kudu’s eventual market significance.
Cloudera is introducing a new open source project, Kudu,* which from Cloudera’s standpoint is meant to eventually become the single best underpinning for analytics on the Hadoop stack. I’ve spent multiple hours discussing Kudu with Cloudera, mainly with Todd Lipcon. Any errors are of course entirely mine.
*Like the impala, the kudu is a kind of antelope. I knew that, because I enjoy word games. What I didn’t know — and which is germane to the naming choice — is that the kudu has stripes.
- Kudu is an alternative to HDFS (Hadoop Distributed File System), or to HBase.
- Kudu is meant to be the underpinning for Impala, Spark and other analytic frameworks or engines.
- Kudu is not meant for OLTP (OnLine Transaction Processing), at least in any foreseeable release. For example:
- Kudu doesn’t support multi-row transactions.
- There are no active efforts to front-end Kudu with an engine that is fast at single-row queries.
- Kudu is rather columnar, except for transitory in-memory stores.
- Kudu’s core design points are that it should:
- Accept data very quickly.
- Immediately make that data available for analytics.
- More specifically, Kudu is meant to accept, along with slower forms of input:
- Lots of fast random writes, e.g. of web interactions.
- Streams, viewed as a succession of inserts.
- Updates and inserts alike.
- The core “real-time” use cases for which Kudu is designed are, unsurprisingly:
- Low-latency business intelligence.
- Predictive model scoring.
- Kudu is designed to work fine with spinning disk, and indeed has been tested to date mainly on disk-only nodes. Even so, Kudu’s architecture is optimized for the assumption that there will be at least some flash on the node.
- Kudu is designed primarily to support relational/SQL processing. However, Kudu also has a nested-data roadmap, which of course starts with supporting the analogous capabilities in Impala.
|Categories: Business intelligence, Cloudera, Columnar database management, Database compression, Databricks, Spark and BDAS, Hadoop, HBase, Predictive modeling and advanced analytics, Solid-state memory, SQL/Hadoop integration||7 Comments|