The Intel investment in Cloudera
Intel recently made a huge investment in Cloudera, stated facts about which start:
- $740 million …
- … for 18% of the company …
- … as part of an overall $900 million round.
- SEC filings coming soon with more details.
Give or take stock preferences, etc., that’s around a $4.1 billion valuation post-money, but Cloudera does say it now has “most of $1 billion” in the bank.
Cloudera further told me when I visited last Friday that the majority of the Intel investment is net new money. (I presume that the rest of the round is net-new as well.) Hence, I conclude that previous investors sold in the aggregate less than 10% of total holdings to Intel. While I’m pretty sure Mike Olson is buying himself a couple of nice toys, in most respects it’s business-as-usual at Cloudera, with the same investors, directors and managers they had before. By way of contrast, many of the “cashing-out” rumors going around are OBVIOUSLY absurd, unless you think Intel acquired a much larger fraction of Cloudera than it actually did.
That said, Intel spent a lot of money, and in connection with the investment there’s a tight Cloudera/Intel partnership. In particular, Read more
| Categories: Cloudera, Hadoop, Intel | 5 Comments |
Cloudera, Impala, data warehousing and Hive
There’s much confusion about Cloudera’s SQL plans and beliefs, and the company has mainly itself to blame. That said, here’s what I think is going on.
- Hive is good at some tasks and terrible at others.
- Hive is good at batch data transformation.
- Hive is bad at ad-hoc query, unless you really, really need Hive’s scale and low license cost. One example, per Eli Collins: Facebook has a 500 petabyte Hive warehouse, but jokes that on a good day an analyst can run 6 queries against it.
- Impala is meant to be good at what Hive is bad at – i.e., fast-response query. (Cloudera mentioned reliable 100 millisecond response times for at least one user.)
- Impala is also meant to be good at what Hive is good at, and will someday from Cloudera’s standpoint completely supersede Hive, but Cloudera is in no hurry for that day to arrive. Hive is more mature. Hive still has more SQL coverage than Impala. There’s a lot of legacy investment in Hive. Cloudera gets little business advantage if a customer sunsets Hive.
- Impala is already decent at some tasks analytic RDBMS are commonly used for. Cloudera insists that some queries run very quickly on Impala. I believe them.
- Impala is terrible at others, including some of the ones most closely associated with the concept of “data warehousing”. Data modeling is a big zero right now. Impala’s workload management, concurrency and all that are very immature.
- There are some use cases for which SQL-on-Hadoop blows away analytic RDBMS, for example ones involving data transformations – perhaps on multi-structured data – that are impractical in RDBMS.
And of course, as vendors so often do, Cloudera generally overrates both the relative maturity of Impala and the relative importance of the use cases in which its offerings – Impala or otherwise – shine.
Related links
- A survey of SQL/Hadoop integration (February, 2014)
- The cardinal rules of DBMS development (March, 2013)
| Categories: Cloudera, Data warehousing, Facebook, Hadoop, SQL/Hadoop integration, Workload management | 4 Comments |
Spark on fire
Spark is on the rise, to an even greater degree than I thought last month.
- Numerous clients and other companies I talk with have adopted Spark, plan to adopt Spark, or at least think it’s likely they will. In particular:
- A number of analytic-stack companies are joining ClearStory in using Spark. Most of the specifics are confidential, but I hope some will be announced soon.
- MapR has joined Cloudera in supporting Spark, and indeed — unlike Cloudera — is supporting the full Spark stack.
- Mike Olson of Cloudera is on record as predicting that Spark will be the replacement for Hadoop MapReduce. Just about everybody seems to agree, except perhaps for Hortonworks folks betting on the more limited and less mature Tez. Spark’s biggest technical advantages as a general data processing engine are probably:
- The Directed Acyclic Graph processing model. (Any serious MapReduce-replacement contender will probably echo that aspect.)
- A rich set of programming primitives in connection with that model.
- Support also for highly-iterative processing, of the kind found in machine learning.
- Flexible in-memory data structures, namely the RDDs (Resilient Distributed Datasets).
- A clever approach to fault-tolerance.
- Spark is a major contender in streaming.
- There’s some cool machine-learning innovation using Spark.
- Spark 1.0 will drop by mid-May, Apache voters willin’ an’ the creek don’ rise. Publicity will likely ensue, with strong evidence of industry support.*
*Yes, my fingerprints are showing again.
The most official description of what Spark now contains is probably the “Spark ecosystem” diagram from Databricks. However, at the time of this writing it is slightly out of date, as per some email from Databricks CEO Ion Stoica (quoted with permission):
… but if I were to redraw it, SparkSQL will replace Shark, and Shark will eventually become a thin layer above SparkSQL and below BlinkDB.
With this change, all the modules on top of Spark (i.e., SparkStreaming, SparkSQL, GraphX, and MLlib) are part of the Spark distribution. You can think of these modules as libraries that come with Spark.
MongoDB is growing up
I caught up with my clients at MongoDB to discuss the recent MongoDB 2.6, along with some new statements of direction. The biggest takeaway is that the MongoDB product, along with the associated MMS (MongoDB Management Service), is growing up. Aspects include:
- An actual automation and management user interface, as opposed to the current management style, which is almost entirely via scripts (except for the monitoring UI).
- That’s scheduled for public beta in May, and general availability later this year.
- It will include some kind of integrated provisioning with VMware, OpenStack, et al.
- One goal is to let you apply database changes, software upgrades, etc. without taking the cluster down.
- A reasonable backup strategy.
- A snapshot copy is made of the database.
- A copy of the log is streamed somewhere.
- Periodically — the default seems to be 6 hours — the log is applied to create a new current snapshot.
- For point-in-time recovery, you take the last snapshot prior to the point, and roll forward to the desired point.
- A reasonable locking strategy!
- Document-level locking is all-but-promised for MongoDB 2.8.
- That means what it sounds like. (I mention this because sometimes an XML database winds up being one big document, which leads to confusing conversations about what’s going on.)
- Security. My eyes glaze over at the details, but several major buzzwords have been checked off.
- A general code rewrite to allow for (more) rapid addition of future features.
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
| Categories: Columnar database management, Couchbase, HBase, In-memory DBMS, Microsoft and SQL*Server, NewSQL, NoSQL, OLTP, Oracle, Parallelization, SAP AG | 18 Comments |
Wants vs. needs
In 1981, Gerry Chichester and Vaughan Merlyn did a user-survey-based report about transaction-oriented fourth-generation languages, the leading application development technology of their day. The report included top-ten lists of important features during the buying cycle and after implementation. The items on each list were very similar — but the order of the items was completely different. And so the report highlighted what I regard as an eternal truth of the enterprise software industry:
What users value in the product-buying process is quite different from what they value once a product is (being) put into use.
Here are some thoughts about how that comes into play today.
Wants outrunning needs
1. For decades, BI tools have been sold in large part via demos of snazzy features the CEO would like to have on his desk. First it was pretty colors; then it was maps; now sometimes it’s “real-time” changing displays. Other BI features, however, are likely to be more important in practice.
2. In general, the need for “real-time” BI data freshness is often exaggerated. If you’re a human being doing a job that’s also often automated at high speed — for example network monitoring or stock trading — there’s a good chance you need fully human real-time BI. Otherwise, how much does a 5-15 minute delay hurt? Even if you’re monitoring website sell-through — are your business volumes really high enough that 5 minutes matters much? eBay answered “yes” to that question many years ago, but few of us work for businesses anywhere near eBay’s scale.
Even so, the want for speed keeps growing stronger. 🙂
3. Similarly, some desires for elastic scale-out are excessive. Your website selling koi pond accessories should always run well on a single server. If you diversify your business to the point that that’s not true, you’ll probably rewrite your app by then as well.
4. Some developers want to play with cool new tools. That doesn’t mean those tools are the best choice for the job. In particular, boring old SQL has merits — such as joins! — that shiny NoSQL hasn’t yet replicated.
5. Some developers, on the other hand, want to keep using their old tools, on which they are their employers’ greatest experts. That doesn’t mean those tools are the best choice for the job either.
6. More generally, some enterprises insist on brand labels that add little value but lots of expense. Yes, there are many benefits to vendor consolidation, and you may avoid many headaches if you stick with not-so-cutting-edge technology. But “enterprise-grade” hardware failure rates may not differ enough from “consumer-grade” ones to be worth paying for.
| Categories: Benchmarks and POCs, Business intelligence, Cloud computing, Clustering, Data models and architecture, Data warehousing, NoSQL, Software as a Service (SaaS), Vertica Systems | 3 Comments |
Notes and comments, March 17, 2014
I have ever more business-advice posts up on Strategic Messaging. Recent subjects include pricing and stealth-mode marketing. Other stuff I’ve been up to includes:
The Spark buzz keeps increasing; almost everybody I talk with expects Spark to win big, probably across several use cases.
Disclosure: I’ll soon be in a substantial client relationship with Databricks, hoping to improve their stealth-mode marketing. 😀
The “real-time analytics” gold rush I called out last year continues. A large fraction of the vendors I talk with have some variant of “real-time analytics” as a central message.
Basho had a major change in leadership. A Twitter exchange ensued. 🙂 Joab Jackson offered a more sober — figuratively and literally — take.
Hadapt laid off its sales and marketing folks, and perhaps some engineers as well. In a nutshell, Hadapt’s approach to SQL-on-Hadoop wasn’t selling vs. the many alternatives, and Hadapt is doubling down on poly-structured data*/schema-on-need.
*While Hadapt doesn’t to my knowledge use the term “poly-structured data”, some other vendors do. And so I may start using it more myself, at least when the poly-structured/multi-structured distinction actually seems significant.
WibiData is partnering with DataStax, WibiData is of course pleased to get access to Cassandra’s user base, which gave me the opportunity to ask why they thought Cassandra had beaten HBase in those accounts. The answer was performance and availability, while Cassandra’s traditional lead in geo-distribution wasn’t mentioned at all.
Disclosure: My fingerprints are all over that deal.
In other news, WibiData has had some executive departures as well, but seems to be staying the course on its strategy. I continue to think that WibiData has a really interesting vision about how to do large-data-volume interactive computing, and anybody in that space would do well to talk with them or at least look into the open source projects WibiData sponsors.
I encountered another apparently-popular machine-learning term — bandit model. It seems to be glorified A/B testing, and it seems to be popular. I think the point is that it tries to optimize for just how much you invest in testing unproven (for good or bad) alternatives.
I had an awkward set of interactions with Gooddata, including my longest conversations with them since 2009. Gooddata is in the early days of trying to offer an all-things-to-all-people analytic stack via SaaS (Software as a Service). I gather that Hadoop, Vertica, PostgreSQL (a cheaper Vertica alternative), Spark, Shark (as a faster version of Hive) and Cassandra (under the covers) are all in the mix — but please don’t hold me to those details.
I continue to think that computing is moving to a combination of appliances, clusters, and clouds. That said, I recently bought a new gaming-class computer, and spent many hours gaming on it just yesterday.* I.e., there’s room for general-purpose workstations as well. But otherwise, I’m not hearing anything that contradicts my core point.
*The last beta weekend for The Elder Scrolls Online; I loved Morrowind.
Splunk and inverted-list indexing
Some technical background about Splunk
In an October, 2009 technical introduction to Splunk, I wrote (emphasis added):
Splunk software both reads logs and indexes them. The same code runs both on the nodes that do the indexing and on machines that simply emit logs.
It turns out that the bolded part was changed several years ago. However, I don’t have further details, so let’s move on to Splunk’s DBMS-like aspects.
I also wrote:
The fundamental thing that Splunk looks at is an increment to a log – i.e., whatever has been added to the log since Splunk last looked at it.
That remains true. Confusingly, Splunk refers to these log increments as “rows”, even though they’re really structured and queried more like documents.
I further wrote:
Splunk has a simple ILM (Information Lifecycle management) story based on time. I didn’t probe for details.
Splunk’s ILM story turns out to be simple indeed.
- As data streams in, Splunk adds it to the most recent — “hot” — bucket. Once a bucket is full, it becomes immutable — “warm” — and a new hot bucket is opened to receive data.
- Splunk executes queries against whichever of these time-slice buckets make sense, then unions results together as needed.
Finally, I wrote:
I get the impression that most Splunk entity extraction is done at search time, not at indexing time. Splunk says that, if a <name, value> pair is clearly marked, its software does a good job of recognizing same. Beyond that, fields seem to be specified by users when they define searches.
and
I have trouble understanding how Splunk could provide flexible and robust reporting unless it tokenized and indexed specific fields more aggressively than I think it now does.
The point of what I in October, 2013 called
a high(er)-performance data store into which you can selectively copy columns of data
and which Splunk enthusiastically calls its “High Performance Analytic Store” is to meet that latter need.
Inverted-list indexing
Inverted list technology is confusing for several reasons, which start: Read more
| Categories: Data models and architecture, NoSQL, SAP AG, Splunk, Structured documents, Text | 1 Comment |
Confusion about metadata
A couple of points that arise frequently in conversation, but that I don’t seem to have made clearly online.
“Metadata” is generally defined as “data about data”. That’s basically correct, but it’s easy to forget how many different kinds of metadata there are. My list of metadata kinds starts with:
- Data about data structure. This is the classical sense of the term. But please note:
- In a relational database, structural metadata is rather separate from the data itself.
- In a document database, each document might carry structure information with it.
- Other inputs to core data management functions. Two major examples are:
- Column statistics that inform RDBMS optimizers.
- Value ranges that inform partition pruning or, more generally, data skipping.
- Inputs to ancillary data management functions — for example, security privileges.
- Support for human decisions about data — for example, information about authorship or lineage.
What’s worse, the past year’s most famous example of “metadata”, telephone call metadata, is misnamed. This so-called metadata, much loved by the NSA (National Security Agency), is just data, e.g. in the format of a CDR (Call Detail Record). Calling it metadata implies that it describes other data — the actual contents of the phone calls — that the NSA strenuously asserts don’t actually exist.
And finally, the first bullet point above has a counter-intuitive consequence — all common terminology notwithstanding, relational data is less structured than document data. Reasons include:
- Relational databases usually just hold strings — or maybe numbers — with structural information being held elsewhere.
- Some document databases store structural metadata right with the document data itself.
- Some document databases store data in the form of (name, value) pairs. In some cases additional structure is imposed by naming conventions.
- Actual text documents carry the structure imposed by grammar and syntax.
Related links
- A lengthy survey of metadata kinds, biased to Hadoop (August, 2012)
- Metadata as derived data (May, 2011)
- Dataset management (May, 2013)
- Structured/unstructured … multi-structured/poly-structured (May, 2011)
| Categories: Data models and architecture, Hadoop, Structured documents, Surveillance and privacy, Telecommunications | 5 Comments |
MemSQL 3.0
Memory-centric data management is confusing. And so I’m going to clarify a couple of things about MemSQL 3.0 even though I don’t yet have a lot of details.* They are:
- MemSQL has historically been an in-memory row store, which as of last year scales out.
- It turns out that the MemSQL row store actually has two table types. One is scaled out. The other — called “reference” — is replicated on every node.
- MemSQL has now added a third table type, which is columnar and which resides in flash memory.
- If you want to keep data in, for example, both the scale-out row store and the column store, you’d have to copy/replicate it within MemSQL. And if you wanted to access data from both versions at once (e.g. because different copies cover different time periods), you’d likely have to do a UNION or something like that.
*MemSQL’s first columnar offering sounds pretty basic; for example, there’s no columnar compression yet. (Edit: Oops, that’s not accurate. See comment below.) But at least they actually have one, which puts them ahead of many other row-based RDBMS vendors that come to mind.
And to hammer home the contrast:
- IBM, Oracle and Microsoft, which all sell row-based DBMS meant to run on disk or other persistent storage, have added or will add columnar options that run in RAM.
- MemSQL, which sells a row-based DBMS that runs in RAM, has added a columnar option that runs in persistent solid-state storage.