Notes from a long trip, July 19, 2016
For starters:
- 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.
- Spark and Databricks are both prospering, and of course enhancing their technology as well.
- Ditto DataStax.
- Flink is 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.
Subjects I’d like to add to that list include:
- MemSQL, Zoomdata, and Neo Technology (also prospering).
- Cloudera (multiple topics, as usual).
- Analytic SQL engines (“traditional” analytic RDBMS aren’t doing well).
- 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.
Cassandra and privacy requirements
For starters:
- I’ve suggested in the past that multi-data-center capabilities are important for “data sovereignty”/geo-compliance.
- The need for geo-compliance just got a lot stronger, with the abolition of the European Union’s Safe Harbour rule for the US. If you collect data in multiple countries, you should be at least thinking about geo-compliance.
- Cassandra is an established leader in multi-data-center operation.
But when I made that connection and checked in accordingly with my client Patrick McFadin at DataStax, I discovered that I’d been a little confused about how multi-data-center Cassandra works. The basic idea holds water, but the details are not quite what I was envisioning.
The story starts:
- Cassandra groups nodes into logical “data centers” (i.e. token rings).
- As a best practice, each physical data center can contain one or more logical data center, but not vice-versa.
- There are two levels of replication — within a single logical data center, and between logical data centers.
- Replication within a single data center is planned in the usual way, with the principal data center holding a database likely to have a replication factor of 3.
- However, copies of the database held elsewhere may have different replication factors …
- … and can indeed have different replication factors for different parts of the database.
In particular, a remote replication factor for Cassandra can = 0. When that happens, then you have data sitting in one geographical location that is absent from another geographical location; i.e., you can be in compliance with laws forbidding the export of certain data. To be clear (and this contradicts what I previously believed and hence also implied in this blog):
- General multi-data-center operation is not what gives you geo-compliance, because the default case is that the whole database is replicated to each data center.
- Instead, you get that effect by tweaking your specific replication settings.
| Categories: Cassandra, Clustering, DataStax, HBase, NoSQL, Open source, Specific users, Surveillance and privacy | 3 Comments |
Basho and Riak
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.
For starters:
- 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
Introduction to Cloudera Kudu
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. 🙂
For starters:
- 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.
IT-centric notes on the future of health care
It’s difficult to project the rate of IT change in health care, because:
- Health care is suffused with technology — IT, medical device and biotech alike — and hence has the potential for rapid change. However, it is also the case that …
- … health care is heavily bureaucratic, political and regulated.
Timing aside, it is clear that health care change will be drastic. The IT part of that starts with vastly comprehensive electronic health records, which will be accessible (in part or whole as the case may be) by patients, care givers, care payers and researchers alike. I expect elements of such records to include:
- The human-generated part of what’s in ordinary paper health records today, but across a patient’s entire lifetime. This of course includes notes created by doctors and other care-givers.
- Large amounts of machine-generated data, including:
- The results of clinical tests. Continued innovation can be expected in testing, for reasons that include:
- Most tests exploit electronic technology. Progress in electronics is intense.
- Biomedical research is itself intense.
- In particular, most research technologies (for example gene sequencing) can be made cheap enough over time to be affordable clinically.
- The output of consumer health-monitoring devices — e.g. Fitbit and its successors. The buzzword here is “quantified self”, but what it boils down to is that every moment of our lives will be measured and recorded.
- The results of clinical tests. Continued innovation can be expected in testing, for reasons that include:
These vastly greater amounts of data cited above will allow for greatly changed analytics.
Read more
More notes on HBase
1. Continuing from last week’s HBase post, the Cloudera folks were fairly proud of HBase’s features for performance and scalability. Indeed, they suggested that use cases which were a good technical match for HBase were those that required fast random reads and writes with high concurrency and strict consistency. Some of the HBase architecture for query performance seems to be:
- Everything is stored in sorted files. (I didn’t probe as to what exactly the files were sorted on.)
- Files have indexes and optional Bloom filters.
- Files are marked with min/max field values and time stamp ranges, which helps with data skipping.
Notwithstanding that a couple of those features sound like they might help with analytic queries, the base expectation is that you’ll periodically massage your HBase data into a more analytically-oriented form. For example — I was talking with Cloudera after all — you could put it into Parquet.
2. The discussion of which kinds of data are originally put into HBase was a bit confusing.
- HBase is commonly used to receive machine-generated data. Everybody knows that.
- Cloudera drew a distinction between:
- Straightforward time series, which should probably just go into HDFS (Hadoop Distributed File System) rather than HBase.
- Data that is bucketed by entity, which likely should go into HBase. Examples of entities are specific users or devices.
- Cloudera also reminded me that OpenTSDB, a popular time series data store, runs over HBase.
OpenTSDB, by the way, likes to store detailed data and aggregates side-by-side, which resembles a pattern I discussed in my recent BI for NoSQL post.
3. HBase supports caching, tiered storage, and so on. Cloudera is pretty sure that it is publicly known (I presume from blog posts or conference talks) that: Read more
| Categories: Cloudera, eBay, Facebook, Hadoop, HBase, Market share and customer counts, NoSQL, Open source, Petabyte-scale data management, Specific users, Yahoo | 4 Comments |
BI for NoSQL — some very early comments
Over the past couple years, there have been various quick comments and vague press releases about “BI for NoSQL”. I’ve had trouble, however, imagining what it could amount to that was particularly interesting, with my confusion boiling down to “Just what are you aggregating over what?” Recently I raised the subject with a few leading NoSQL companies. The result is that my confusion was expanded. 🙂 Here’s the small amount that I have actually figured out.
As I noted in a recent post about data models, many databases — in particular SQL and NoSQL ones — can be viewed as collections of <name, value> pairs.
- In a relational database, a record is a collection of <name, value> pairs with a particular and predictable — i.e. derived from the table definition — sequence of names. Further, a record usually has an identifying key (commonly one of the first values).
- Something similar can be said about structured-document stores — i.e. JSON or XML — except that the sequence of names may not be consistent from one document to the next. Further, there’s commonly a hierarchical relationship among the names.
- For these purposes, a “wide-column” NoSQL store like Cassandra or HBase can be viewed much as a structured-document store, albeit with different performance optimizations and characteristics and a different flavor of DML (Data Manipulation Language).
Consequently, a NoSQL database can often be viewed as a table or a collection of tables, except that:
- The NoSQL database is likely to have more null values.
- The NoSQL database, in a naive translation toward relational, may have repeated values. So a less naive translation might require extra tables.
That’s all straightforward to deal with if you’re willing to write scripts to extract the NoSQL data and transform or aggregate it as needed. But things get tricky when you try to insist on some kind of point-and-click. And by the way, that last comment pertains to BI and ETL (Extract/Transform/Load) alike. Indeed, multiple people I talked with on this subject conflated BI and ETL, and they were probably right to do so.
| Categories: Business intelligence, Cassandra, EAI, EII, ETL, ELT, ETLT, HBase, MongoDB, NoSQL, Structured documents | 6 Comments |
Notes on HBase
I talked with a couple of Cloudera folks about HBase last week. Let me frame things by saying:
- The closest thing to an HBase company, ala MongoDB/MongoDB or DataStax/Cassandra, is Cloudera.
- Cloudera still uses a figure of 20% of its customers being HBase-centric.
- HBaseCon and so on notwithstanding, that figure isn’t really reflected in Cloudera’s marketing efforts. Cloudera’s marketing commitment to HBase has never risen to nearly the level of MongoDB’s or DataStax’s push behind their respective core products.
- With Cloudera’s move to “zero/one/many” pricing, Cloudera salespeople have little incentive to push HBase hard to accounts other than HBase-first buyers.
Also:
- Cloudera no longer dominates HBase development, if it ever did.
- Cloudera is the single biggest contributor to HBase, by its count, but doesn’t make a majority of the contributions on its own.
- Cloudera sees Hortonworks as having become a strong HBase contributor.
- Intel is also a strong contributor, as are end user organizations such as Chinese telcos. Not coincidentally, Intel was a major Hadoop provider in China before the Intel/Cloudera deal.
- As far as Cloudera is concerned, HBase is just one data storage technology of several, focused on high-volume, high-concurrency, low-latency short-request processing. Cloudera thinks this is OK because of HBase’s strong integration with the rest of the Hadoop stack.
- Others who may be inclined to disagree are in several cases doing projects on top of HBase to extend its reach. (In particular, please see the discussion below about Apache Phoenix and Trafodion, both of which want to offer relational-like functionality.)
| Categories: Cloudera, Clustering, Data models and architecture, Database diversity, Hadoop, HBase, Hortonworks, HP and Neoview, Intel, Market share and customer counts, NoSQL, Open source | 4 Comments |
Context for Cloudera
Hadoop World/Strata is this week, so of course my clients at Cloudera will have a bunch of announcements. Without front-running those, I think it might be interesting to review the current state of the Cloudera product line. Details may be found on the Cloudera product comparison page. Examining those details helps, I think, with understanding where Cloudera does and doesn’t place sales and marketing focus, which given Cloudera’s Hadoop market stature is in my opinion an interesting thing to analyze.
So far as I can tell (and there may be some errors in this, as Cloudera is not always accurate in explaining the fine details):
- CDH (Cloudera Distribution … Hadoop) contains a lot of Apache open source code.
- Cloudera has a much longer list of Apache projects that it thinks comprise “Core Hadoop” than, say, Hortonworks does.
- Specifically, that list currently is: Hadoop, Flume, HCatalog, Hive, Hue, Mahout, Oozie, Pig, Sentry, Sqoop, Whirr, ZooKeeper.
- In addition to those projects, CDH also includes HBase, Impala, Spark and Cloudera Search.
- Cloudera Manager is closed-source code, much of which is free to use. (I.e., “free like beer” but not “free like speech”.)
- Cloudera Navigator is closed-source code that you have to pay for (free trials and the like excepted).
- Cloudera Express is Cloudera’s favorite free subscription offering. It combines CDH with the free part of Cloudera Manager. Note: Cloudera Express was previously called Cloudera Standard, and that terminology is still reflected in parts of Cloudera’s website.
- Cloudera Enterprise is the umbrella name for Cloudera’s three favorite paid offerings.
- Cloudera Enterprise Basic Edition contains:
- All the code in CDH and Cloudera Manager, and I guess Accumulo code as well.
- Commercial licenses for all that code.
- A license key to use the entirety of Cloudera Manager, not just the free part.
- Support for the “Core Hadoop” part of CDH.
- Support for Cloudera Manager. Note: Cloudera is lazy about saying this explicitly, but it seems obvious.
- The code for Cloudera Navigator, but that’s moot, as the corresponding license key for Cloudera Navigator is not part of the package.
- Cloudera Enterprise Data Hub Edition contains:
- Everything in Cloudera Basic Edition.
- A license key for Cloudera Navigator.
- Support for all of HBase, Accumulo, Impala, Spark, Cloudera Search and Cloudera Navigator.
- Cloudera Enterprise Flex Edition contains everything in Cloudera Basic Edition, plus support for one of the extras in Data Hub Edition.
In analyzing all this, I’m focused on two particular aspects:
- The “zero, one, many” system for defining the editions of Cloudera Enterprise.
- The use of “Data Hub” as a general marketing term.
| Categories: Cloudera, Data warehousing, Databricks, Spark and BDAS, Hadoop, HBase, Hortonworks, Open source, Pricing | 2 Comments |
NoSQL vs. NewSQL vs. traditional RDBMS
I frequently am asked questions that boil down to:
- When should one use NoSQL?
- When should one use a new SQL product (NewSQL or otherwise)?
- When should one use a traditional RDBMS (most likely Oracle, DB2, or SQL Server)?
The details vary with context — e.g. sometimes MySQL is a traditional RDBMS and sometimes it is a new kid — but the general class of questions keeps coming. And that’s just for short-request use cases; similar questions for analytic systems arise even more often.
My general answers start:
- Sometimes something isn’t broken, and doesn’t need fixing.
- Sometimes something is broken, and still doesn’t need fixing. Legacy decisions that you now regret may not be worth the trouble to change.
- Sometimes — especially but not only at smaller enterprises — choices are made for you. If you operate on SaaS, plus perhaps some generic web hosting technology, the whole DBMS discussion may be moot.
In particular, migration away from legacy DBMS raises many issues: Read more