Databricks, Spark and BDAS
Discussion of BDAS (Berkeley Data Analytics Systems), especially Spark and related projects, and also of Databricks, the company commercializing Spark.
MemSQL 4.0
I talked with my clients at MemSQL about the release of MemSQL 4.0. Let’s start with the reminders:
- MemSQL started out as in-memory OTLP (OnLine Transaction Processing) DBMS …
- … but quickly positioned with “We also do ‘real-time’ analytic processing” …
- … and backed that up by adding a flash-based column store option …
- … before Gartner ever got around to popularizing the term HTAP (Hybrid Transaction and Analytic Processing).
- There’s also a JSON option.
The main new aspects of MemSQL 4.0 are:
- Geospatial indexing. This is for me the most interesting part.
- A new optimizer and, I suppose, query planner …
- … which in particular allow for serious distributed joins.
- Some rather parallel-sounding connectors to Spark. Hadoop and Amazon S3.
- Usual-suspect stuff including:
- More SQL coverage (I forgot to ask for details).
- Some added or enhanced administrative/tuning/whatever tools (again, I forgot to ask for details).
- Surely some general Bottleneck Whack-A-Mole.
There’s also a new free MemSQL “Community Edition”. MemSQL hopes you’ll experiment with this but not use it in production. And MemSQL pricing is now wholly based on RAM usage, so the column store is quasi-free from a licensing standpoint is as well.
Notes, links and comments, May 2, 2015
I’m going to be out-of-sorts this week, due to a colonoscopy. (Between the prep, the procedure, and the recovery, that’s a multi-day disablement.) In the interim, here’s a collection of links, quick comments and the like.
1. Are you an engineer considering a start-up? This post is for you. It’s based on my long experience in and around such scenarios, and includes a section on “Deadly yet common mistakes”.
2. There seems to be a lot of confusion regarding the business model at my clients Databricks. Indeed, my own understanding of Databricks’ on-premises business has changed recently. There are no changes in my beliefs that:
- Databricks does not directly license or support on-premises Spark users. Rather …
- … it helps partner companies to do so, where:
- Examples of partner companies include usual-suspect Hadoop distribution vendors, and DataStax.
- “Help” commonly includes higher-level support.
However, I now get the impression that revenue from such relationships is a bigger deal to Databricks than I previously thought.
Databricks, by the way, has grown to >50 people.
3. DJ Patil and Ruslan Belkin apparently had a great session on lessons learned, covering a lot of ground. Many of the points are worth reading, but one in particular echoed something I’m hearing lots of places — “Data is super messy, and data cleanup will always be literally 80% of the work.” Actually, I’d replace the “always” by something like “very often”, and even that mainly for newish warehouses, data marts or datasets. But directionally the comment makes a whole lot of sense.
| Categories: Data integration and middleware, Databricks, Spark and BDAS, DataStax, Hadoop, Health care, Investment research and trading, Text | Leave a Comment |
Which analytic technology problems are important to solve for whom?
I hear much discussion of shortfalls in analytic technology, especially from companies that want to fill in the gaps. But how much do these gaps actually matter? In many cases, that depends on what the analytic technology is being used for. So let’s think about some different kinds of analytic task, and where they each might most stress today’s available technology.
In separating out the task areas, I’ll focus first on the spectrum “To what extent is this supposed to produce novel insights?” and second on the dimension “To what extent is this supposed to be integrated into a production/operational system?” Issues of latency, algorithmic novelty, etc. can follow after those. In particular, let’s consider the tasks: Read more
| Categories: Business intelligence, Data warehousing, Databricks, Spark and BDAS, Hadoop, Netezza, NoSQL, Predictive modeling and advanced analytics, Tableau Software | 1 Comment |
Cask and CDAP
For starters:
- Continuuity toured in 2012 and touted its “app server for Hadoop” technology.
- Continuuity recently changed its name to Cask and went open source.
- Cask’s product is now called CDAP (Cask Data Application Platform). It’s still basically an app server for Hadoop and other “big data” — ouch do I hate that phrase — data stores.
- Cask and Cloudera partnered.
- I got a more technical Cask briefing this week.
Also:
- App servers are a notoriously amorphous technology. The focus of how they’re used can change greatly every couple of years.
- Partly for that reason, I was unimpressed by Continuuity’s original hype-filled positioning.
So far as I can tell:
- Cask’s current focus is to orchestrate job flows, with lots of data mappings.
- This is supposed to provide lots of developer benefits, for fairly obvious reasons. Those are pitched in terms of an integration story, more in a “free you from the mess of a many-part stack” sense than strictly in terms of data integration.
- CDAP already has a GUI to monitor what’s going on. A GUI to specify workflows is coming very soon.
- CDAP doesn’t consume a lot of cycles itself, and hence isn’t a real risk for unpleasant overhead, if “overhead” is narrowly defined. Rather, performance drags could come from …
- … sub-optimal choices in data mapping, database design or workflow composition.
Quick update on Tachyon
I’m on record as believing that:
- Hadoop needs a memory-centric storage grid.
- Tachyon is a strong candidate to fill the role.
That said:
- It’s an open secret that there will be a Tachyon company. However, …
- … no details have been publicized. Indeed, the open secret itself is still officially secret.
- Tachyon technology, which just hit 0.6 a couple of days ago, still lacks many features I regard as essential.
- As a practical matter, most Tachyon interest to date has been associated with Spark. This makes perfect sense given Tachyon’s origin and initial technical focus.
- Tachyon was in 50 or more sites last year. Most of these sites were probably just experimenting with it. However …
- … there are production Tachyon clusters with >100 nodes.
As a reminder of Tachyon basics: Read more
| Categories: Clustering, Databricks, Spark and BDAS, Hadoop, Memory-centric data management | 3 Comments |
Databricks and Spark update
I chatted last night with Ion Stoica, CEO of my client Databricks, for an update both on his company and Spark. Databricks’ actual business is Databricks Cloud, about which I can say:
- Databricks Cloud is:
- Spark-as-a-Service.
- Currently running on Amazon only.
- Not dependent on Hadoop.
- Databricks Cloud, despite having a 1.0 version number, is not actually in general availability.
- Even so, there are a non-trivial number of paying customers for Databricks Cloud. (Ion gave me an approximate number, but is keeping it NDA until Spark Summit East.)
- Databricks Cloud gets at data from S3 (most commonly), Redshift, Elastic MapReduce, and perhaps other sources I’m forgetting.
- Databricks Cloud was initially focused on ad-hoc use. A few days ago the capability was added to schedule jobs and so on.
- Unsurprisingly, therefore, Databricks Cloud has been used to date mainly for data exploration/visualization and ETL (Extract/Transform/Load). Visualizations tend to be scripted/programmatic, but there’s also an ODBC driver used for Tableau access and so on.
- Databricks Cloud customers are concentrated (but not unanimously so) in the usual-suspect internet-centric business sectors.
- The low end of the amount of data Databricks Cloud customers are working with is 100s of gigabytes. This isn’t surprising.
- The high end of the amount of data Databricks Cloud customers are working with is petabytes. That did surprise me, and in retrospect I should have pressed for details.
I do not expect all of the above to remain true as Databricks Cloud matures.
Ion also said that Databricks is over 50 people, and has moved its office from Berkeley to San Francisco. He also offered some Spark numbers, such as: Read more
Where the innovation is
I hoped to write a reasonable overview of current- to medium-term future IT innovation. Yeah, right. 🙂 But if we abandon any hope that this post could be comprehensive, I can at least say:
1. Back in 2011, I ranted against the term Big Data, but expressed more fondness for the V words — Volume, Velocity, Variety and Variability. That said, when it comes to data management and movement, solutions to the V problems have generally been sketched out.
- Volume has been solved. There are Hadoop installations with 100s of petabytes of data, analytic RDBMS with 10s of petabytes, general-purpose Exadata sites with petabytes, and 10s/100s of petabytes of analytic Accumulo at the NSA. Further examples abound.
- Velocity is being solved. My recent post on Hadoop-based streaming suggests how. In other use cases, velocity is addressed via memory-centric RDBMS.
- Variety and Variability have been solved. MongoDB, Cassandra and perhaps others are strong NoSQL choices. Schema-on-need is in earlier days, but may help too.
2. Even so, there’s much room for innovation around data movement and management. I’d start with:
- Product maturity is a huge issue for all the above, and will remain one for years.
- Hadoop and Spark show that application execution engines:
- Have a lot of innovation ahead of them.
- Are tightly entwined with data management, and with data movement as well.
- Hadoop is due for another refactoring, focused on both in-memory and persistent storage.
- There are many issues in storage that can affect data technologies as well, including but not limited to:
- Solid-state (flash or post-flash) vs. spinning disk.
- Networked vs. direct-attached.
- Virtualized vs. identifiable-physical.
- Object/file/block.
- Graph analytics and data management are still confused.
3. As I suggested last year, data transformation is an important area for innovation. Read more
Notes on machine-generated data, year-end 2014
Most IT innovation these days is focused on machine-generated data (sometimes just called “machine data”), rather than human-generated. So as I find myself in the mood for another survey post, I can’t think of any better idea for a unifying theme.
1. There are many kinds of machine-generated data. Important categories include:
- Web, network and other IT logs.
- Game and mobile app event data.
- CDRs (telecom Call Detail Records).
- “Phone-home” data from large numbers of identical electronic products (for example set-top boxes).
- Sensor network output (for example from a pipeline or other utility network).
- Vehicle telemetry.
- Health care data, in hospitals.
- Digital health data from consumer devices.
- Images from public-safety camera networks.
- Stock tickers (if you regard them as being machine-generated, which I do).
That’s far from a complete list, but if you think about those categories you’ll probably capture most of the issues surrounding other kinds of machine-generated data as well.
2. Technology for better information and analysis is also technology for privacy intrusion. Public awareness of privacy issues is focused in a few areas, mainly: Read more
Hadoop’s next refactoring?
I believe in all of the following trends:
- Hadoop is a Big Deal, and here to stay.
- Spark, for most practical purposes, is becoming a big part of Hadoop.
- Most servers will be operated away from user premises, whether via SaaS (Software as a Service), co-location, or “true” cloud computing.
Trickier is the meme that Hadoop is “the new OS”. My thoughts on that start:
- People would like this to be true, although in most cases only as one of several cluster computing platforms.
- Hadoop, when viewed as an operating system, is extremely primitive.
- Even so, the greatest awkwardness I’m seeing when different software shares a Hadoop cluster isn’t actually in scheduling, but rather in data interchange.
There is also a minor issue that if you distribute your Hadoop work among extra nodes you might have to pay a bit more to your Hadoop distro support vendor. Fortunately, the software industry routinely solves more difficult pricing problems than that.
| Categories: Cloud computing, Databricks, Spark and BDAS, Hadoop, MapReduce, MemSQL, Software as a Service (SaaS) | 15 Comments |
Thoughts and notes, Thanksgiving weekend 2014
I’m taking a few weeks defocused from work, as a kind of grandpaternity leave. That said, the venue for my Dances of Infant Calming is a small-but-nice apartment in San Francisco, so a certain amount of thinking about tech industries is inevitable. I even found time last Tuesday to meet or speak with my clients at WibiData, MemSQL, Cloudera, Citus Data, and MongoDB. And thus:
1. I’ve been sloppy in my terminology around “geo-distribution”, in that I don’t always make it easy to distinguish between:
- Storing different parts of a database in different geographies, often for reasons of data privacy regulatory compliance.
- Replicating an entire database into different geographies, often for reasons of latency and/or availability/ disaster recovery,
The latter case can be subdivided further depending on whether multiple copies of the data can accept first writes (aka active-active, multi-master, or multi-active), or whether there’s a clear single master for each part of the database.
What made me think of this was a phone call with MongoDB in which I learned that the limit on number of replicas had been raised from 12 to 50, to support the full-replication/latency-reduction use case.
2. Three years ago I posted about agile (predictive) analytics. One of the points was:
… if you change your offers, prices, ad placement, ad text, ad appearance, call center scripts, or anything else, you immediately gain new information that isn’t well-reflected in your previous models.
Subsequently I’ve been hearing more about predictive experimentation such as bandit testing. WibiData, whose views are influenced by a couple of Very Famous Department Store clients (one of which is Macy’s), thinks experimentation is quite important. And it could be argued that experimentation is one of the simplest and most direct ways to increase the value of your data.
3. I’d further say that a number of developments, trends or possibilities I’m seeing are or could be connected. These include agile and experimental predictive analytics in general, as noted in the previous point, along with: Read more