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.
Necessary complexity
When I’m asked to talk to academics, the requested subject is usually a version of “What should we know about what’s happening in the actual market/real world?” I then try to figure out what the scholars could stand to hear that they perhaps don’t already know.
In the current case (Berkeley next Tuesday), I’m using the title “Necessary complexity”. I actually mean three different but related things by that, namely:
- No matter how cool an improvement you have in some particular area of technology, it’s not very useful until you add a whole bunch of me-too features and capabilities as well.
- Even beyond that, however, the simple(r) stuff has already been built. Most new opportunities are in the creation of complex integrated stacks, in part because …
- … users are doing ever more complex things.
While everybody on some level already knows all this, I think it bears calling out even so.
I previously encapsulated the first point in the cardinal rules of DBMS development:
Rule 1: Developing a good DBMS requires 5-7 years and tens of millions of dollars.
That’s if things go extremely well.
Rule 2: You aren’t an exception to Rule 1.
In particular:
- Concurrent workloads benchmarked in the lab are poor predictors of concurrent performance in real life.
- Mixed workload management is harder than you’re assuming it is.
- Those minor edge cases in which your Version 1 product works poorly aren’t minor after all.
My recent post about MongoDB is just one example of same.
Examples of the second point include but are hardly limited to: Read more
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.
The worst database developers in the world?
If the makers of MMO RPGs (Massive Multi-Player Online Role-Playing Games) aren’t quite the worst database application developers in the world, they’re at least on the short list for consideration. The makers of Guild Wars didn’t even try to have decent database functionality. A decade later, when they introduced Guild Wars 2, the database-oriented functionality (auction house, real-money store, etc.) would crash for days at a time. Lord of the Rings Online evidently had multiple issues with database functionality. Now I’m playing Elder Scrolls Online, which on the whole is a great game, but which may have the most database screw-ups of all.
ESO has been live for less than 3 weeks, and in that time:
1. There’s been a major bug in which players’ “banks” shrank, losing items and so on. Days later, the data still hasn’t been recovered. After a patch, the problem if anything worsened.
2. Guild functionality has at times been taken down while the rest of the game functioned.
3. Those problems aside, bank and guild bank functionality are broken, via what might be considered performance bugs. Problems I repeatedly encounter include:
- If you deposit a few items, the bank soon goes into a wait state where you can’t use it for a minute or more.
- Similarly, when you try to access a guild — i.e. group — bank, you often find it in an unresponsive state.
- If you make a series of updates a second apart, the game tells you you’re doing things too quickly, and insists that you slow down a lot.
- Items that are supposed to “stack” appear in 2 or more stacks; i.e., a very simple kind of aggregation is failing. There are also several other related recurring errors, which I conjecture have the same underlying cause.
In general, it seems like that what should be a collection of database records is really just a list, parsed each time an update occurs, periodically flushed in its entirety to disk, with all the performance problems you’d expect from that kind of choice.
| Categories: Fun stuff, Games and virtual worlds | 28 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
| Categories: Columnar database management, Couchbase, HBase, In-memory DBMS, Microsoft and SQL*Server, NewSQL, NoSQL, OLTP, Oracle, Parallelization, SAP AG | 18 Comments |
DBMS2 revisited
The name of this blog comes from an August, 2005 column. 8 1/2 years later, that analysis holds up pretty well. Indeed, I’d keep the first two precepts exactly as I proposed back then:
- Task-appropriate data managers. Much of this blog is about task-appropriate data stores, so I won’t say more about them in this post.
- Drastic limitations on relational schema complexity. I think I’ve been vindicated on that one by, for example:
- NoSQL and dynamic schemas.
- Schema-on-read, and its smarter younger brother schema-on-need.
- Limitations on the performance and/or allowed functionality of joins in scale-out short-request RDBMS, and the relative lack of complaints about same.
- Funky database design from major Software as a Service (SaaS) vendors such as Workday and Salesforce.com.
- A whole lot of logs.
I’d also keep the general sense of the third precept, namely appropriately-capable data integration, but for that one the specifics do need some serious rework.
For starters, let me say: Read more
| Categories: About this blog, Business intelligence, Database diversity, EAI, EII, ETL, ELT, ETLT, Investment research and trading, NoSQL, Schema on need | 2 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 |
Real-time analytics for everybody, uniquely from us!!
In my latest post, I noted that
The “real-time analytics” gold rush I called out last year continues.
I also recently mocked the slogan
Analytics for everybody!
So when I saw today an email subject line
[Vendor X] to announce real-time analytics for everyone …
I laughed. Indeed, I snorted so loudly that Linda — who was on a different floor of our house — called to check that I was OK. 🙂
As the day progressed, I had a consulting call with a client, and what did I see on the first substantive slide? There were references to:
broader audience
and
real-time data analysis
The trends — real or imaginary — are melting into each other!
| Categories: Analytic technologies | Leave a Comment |