Tag: Business intelligence

A Quick Five Minute Rule Update for In-memory Databases

6/26/2014: I fixed the calculation… I had an error in my spreadsheet. Sorry. The 2012 break-even point is 217 minutes. – Rob

Following on to my blog on the Five Minute Rule and in-memory databases here I decided to quickly and informally recalculate the 4KB break-even point based on current technology (rather than use the 2007 numbers) The results are as follows:

  • A 1TB SATA Disk with 4.2ms average latency and 126MB/s max transfer rate costs $100 here
  • A 4GB DDR3 ECC memory card costs $33 here (I picked fairly expensive ECC memory… I could have gone with the $18 average price mentioned here)
  • Apply the Gray/Putzolu formula: Break Even Interval = (Pages per MB of RAM/Accesses per Second per Disk) * ($ per Disk Drive/$ per MB of RAM)

And we find that today the break-even point for a 4KB block of data is 217 minutes…

Again… this means that for any 4KB block of data… or for any database table where there are 4KB blocks that are touched… within a 3+ hour window it is more cost-effective to keep the data in-memory than to move it back and forth from disk. If the data is compressed the duration increases with the compression so that a table with 2X compression should reside in-memory if accessed on the average every 110 minutes.

Decision Support Redux

In the late 1980’s and the early 1990’s the term for software that business users executed to run reports, fire off canned queries, and/or to explore data ad hoc was called “decision support” software. Later, and still today, the term “business intelligence” came into use.

I never understood the sense of the switch. The term “business intelligence” is vague… sort of fluffy and pretentious. “Decision support” implies a purpose. In the years when the switch from one term to the other was in progress, if you asked the question: what do you mean by “business intelligence” the answer was… it is “decision support”.

Today the analytics that underlie both terms are becoming more sophisticated, and they execute in near-real-time. It could be said that there is business intelligence in the process that acquires data, analyzes it, discovers a pattern, and applies a rule automatically as a result. But the software programmer who built the system was focused on automating the decision process… not on creating intelligence.

A clear focus on supporting complex decisions will increase the chances of delivering a return on your investment in analytics. “Intelligence” is not useful unless it is applied to make a better decision. I vote for a return to the phrase “decision support”.

OLAP is not advanced analytics

OLAP searches a set of pre-aggregated data… a cube. If the cube is large enough that you don’t bump into the edges you might think that your search is ad hoc… but that is an illusion. The set is prescribed not ad hoc.

In the 1980’s we sent paper reports out… they were moved on a pallet with a fork-lift. The reports aggregated key metrics to many levels in a hierarchy sliced and diced across many dimensions. Today we take the lines off the reports and store them digitally in a cube and provide tools to let users navigate the cube to build their reports. What they build looks, to a large extent, like the reports from the 80’s.

Data warehousing provides more data and better data… so there are more cubes, more dimensions, more reports… and hopefully more business intelligence. But these reports provide 1980’s quality business intelligence on a screen instead of on paper… bounded by the OLAP cube.

When you hear folks talk about data science and data mining and advanced analytics and optimization… they are talking about advanced mathematical treatment of the data… know that this is going to require technology that is beyond the capabilities of a OLAP engine.

Exalytics is a OLAP engine. Here are some Exalytics use cases from a proponent. They are about OLAP dashboards… good stuff… but hardly advanced analytics. Oracle says that Exalytics is engineered for Extreme Analytics. If we agree that “extreme” analytics is not in any way advanced… then I agree.

The Big Data Bang

There is still an open question over whether, after the Big Bang, there is enough mass in the Universe to slow the expansion and cause the universe to contract. While the Big Data Bang continues to expand the universe of bits and bytes… I would like to ask whether some of these numbers are overstated? I know that the sum of the bits and bytes is expanding but I wonder if the universe of information is expanding as much as we claim?

Note that by “information” I mean a unique combination of bits and bytes representing some new information. In other words, if the same information is copied redundantly over and over does that count?

There is a significant growth industry in deduplication software that can backup data without copying redundant information. The savings from these products is astounding. NetApp claims 70% of the unstructured data may be redundant (see here). Data Domain says that eliminating (and compressing) redundant data reduces storage requirements by 10X-30X (see here).  What’s up with that?

In the data warehouse space it is just as bad. The same data lives in OLTP systems, ETL staging areas, Operational Data Stores, Enterprise Data Warehouses, Data Marts, and now Hadoop clusters. The same information is replicated in aggregate tables, indexes, materialized views, and cubes.  If you go into many shops you can find 50TB of EDW data exploded into 500TB of sandboxes for the data scientists to play with. Data is stored in snapshots on an hourly basis where less than 10% of the data changes from hour to hour. There is redundancy everywhere. There is redundancy everywhere. 🙂

I believe that there is a data explosion… and I believe that it is significant… but  there is also a sort of laziness about copying data.

Soon we will see in production the first systems where a single copy of OLTP and EDW and analytic data can reside in the same platform and be shared. It will be sort of shocking to see the Big Data Bang slow a little…

HANA and ABAP

 

One more surprise…

In the past SAP applications have, in general, avoided using database features. Even a SELECT with a projection was out-of-bounds. They did not want to depend on any database, so they tended to pull all data from the data layer to the application layer and loop through the data using procedural languages like ABAP. You might say that they were religiously database agnostic. My mistake… you might say that we were religiously database agnostic. I have to get used to these new surroundings.

Besides the obvious attributes of HANA: in-memory, shared-nothing, MPP, and column-oriented… the aim is to move the application logic next to the data and into HANA.

Any of you who have labored to convert procedural code into set-based SQL will understand the issue here. There are hundreds of thousands or millions of lines of procedural code… often very simple loops… that have to be converted to SQL to make the HANA architecture support the SAP application portfolio.

The surprise is not that there is this outstanding issue.. nor is it the ambitious architecture designed to push the application deep into the database (we are not talking about SQL-based stored procedures… we are talking about the application). The surprise is that the HANA development team has built a state-of-the-art facility that programmatically converts procedural logic into its set-based equivalent (not necessarily into SQL but sometimes into a language that can execute in-parallel). This is not a tool requiring manual intervention… it is an automatic, mathematically provable, transformation.

Right now the technique is used to covert logic in stored-procedures and in ABAP. But I hope to see it applied in the optimizer to convert those ugly Oracle cursor loops on-the-fly.

You can read more here.

By the way… SAP will continue to support ABAP using the database as a file server… moving all of the data from the database server to the application server for processing. But you can imagine that… when running applications that use this powerful capability… over time HANA will emerge with a huge performance advantage over other databases…

Oracle should be worried.

 

Numbers Everyone Should Know

Some of you have seen me build simple models to do a reality-check on architecture (see here, for example). Here are some metrics from a great presentation by Jeff Dean, a Google fellow.

Numbers Everyone Should Know

L1 cache reference 0.5 ns
Branch mispredict 5 ns
L2 cache reference 7 ns
Mutex lock/unlock 25 ns
Main memory reference 100 ns
Compress 1K bytes with Zippy 3,000 ns
Send 2K bytes over 1 Gbps network 20,000 ns
Read 1 MB sequentially from memory 250,000 ns
Round trip within same datacenter 500,000 ns
Disk seek 10,000,000 ns
Read 1 MB sequentially from disk 20,000,000 ns
Send packet CA->Netherlands->CA 150,000,000 ns

Of note is the 120X difference between the cost of reading 1MB from memory and the cost of reading 1MB from disk…

The entire presentation may be found here: http://www.odbms.org/download/dean-keynote-ladis2009.pdf

Happy Modeling…

Who is Massively Parallel? HANA vs. Teradata and (maybe) Oracle

I have promised not to promote HANA heavily on this site… and I will keep that promise. But I want to share something with you about the HANA architecture that is not part of the normal marketing in-memory database (IMDB) message: HANA is parallel from its foundation.

What I mean by that is that when a query is executed in-memory HANA dynamically shards the data in-memory and lets each core start a thread to work on its shard.

Other shared-nothing implementations like Teradata and Greenplum, which are not built on a native parallel architecture, start multiple instances of the database to take advantage of multiple cores. If they can start an instance-per-core then they approximate the parallelism of a native implementation… at the cost of inter-instance communication. Oracle, to my knowledge, does not parallelize steps within a single instance… I could be wrong there so I’ll ask my readers to help?

As you would expect, for analytics and complex queries this architecture provides a distinct advantage. HANA customers are optimizing price models sub-second in-real-time with each quote instead of executing a once-a-week 12-hour modeling job.

June 11, 2013: You can find a more complete and up-to-date discussion of this topic here… – Rob

As you would expect HANA cannot yet stretch into the petabyte range. The current HANA sweet spot is for warehouses or marts is in the sub-TB to 20TB range.

Real-time Analytics and BI: Part 1 – Singing for my Dinner

Several months ago I was invited to a dinner attached to a data science summit… with the price being that I had to deliver a 5 minute talk… I had to sing for my dinner. The result was this thinking on real-time analytics and the Toyota Prius.

Real-time analytics implies two things:
 
  1. Changes in the data are evaluated continuously; and
  2. The results of the analysis are used or displayed continuously.
In a Toyota Prius we can see two examples of real-time analytics.
 
The first is in the anti-lock braking system. There data reflecting the pressure on the brake pedal and on rotation of each wheel is sent to a computer that analyzes the results and adjusts the brake pressure on each wheel so that all four wheels turn at the same rate and the car stops in a straight line.
 
Note that the analytics are real-time and the results are used immediately without human intervention. This is important. It makes little sense to spend the money to capture and analyze data in real-time if the results are not actionable in near-real-time.
 
Think for a moment about the BI systems built over the last 20 years. First we captured and analyzed monthly data… and acted on that data within a 30-day window. Then we increased the granularity of the data to weekly and slightly adjusted the reports to reflect the finer granularity… and acted on the data within 7 days. Then we adjusted the data to daily and acted on the results each day. Then we adjusted the data to hourly and reacted even more quickly. These changes often did not fundamentally change the business processes driven by the data… they just made the processes more sensitive to the fine-grained information.
 
But if the data-driven business process takes ten minutes to complete… for example it takes ten minutes for staff to pick inventory, package the results, and load a delivery truck; could there be a return on the investment expense of developing a continuous, real-time analytic? I think not. There may, however, be ROI associated with a new robotic pick, package, and load process…
 
There is another possibility… If sometimes the pick, package, and load takes ten minutes and sometimes it takes fifteen minutes then the best solution is to perform the analytics on the current state on-demand… when there are resources to support the process. This maximizes the use of the resources without changing the business process.
 
The point here is that real-time requires a re-think… or at least a deep-think. The business process may have to change significantly to support real-time analytics.
 
The second real-time system in the Prius illustrates the problem. On the dashboard the Prius displays, in real-time, the state of the hybrid gas-electric system. It shows whether the battery is charging or discharging… it shows whether the car is being driven using the electric or the internal-combustion engine. It is one of the most beautiful dashboard displays you have ever seen… and executives everywhere must look at it and wonder why they cannot get such a beautiful display of the state of their business… after-all…  BI dashboards are “the thing”.
 
But the Prius display is useless. There is no action you would take while driving based on this real-time display.From a decision-making view it represents useless and expensive flash (that helps to sell the Prius…).
 
So… approach real-time analytics with a deep-think. Look for opportunities like the anti-lock braking system where real-time analytics can be embedded into automatic business processes. Avoid flashy dashboards that do not present actionable data.
 
In-memory databases (IMDB) such as SAP HANA, Oracle TimesTen, and VMWare SQLFire promise to enable real-time analytics… and this promise is real… the opportunities can and will revolutionize the enterprise over time…  but a revolution is not the same old BI at a finer granularity… it is much more significant than that. Heads will roll.

Cloud Computing and Data Warehousing: Part 4 – IMDB Data Warehouse in a Cloud

In the previous blogs on this topic (Part 1, Part 2, Part 3) I suggested that:

  1. Shared-nothing is required for an EDW,
  2. An EDW is not usually under-utilized,
  3. There are difficulties in re-distributing sharded, shared-nothing data to provide elasticity, and
  4. A SAN cannot provide the same IO bandwidth per server as JBOD… nor hit the same price/performance targets.

Note that these issues are tied together. We might be able to spread the EDW workload over so many shards and so many SANs that the amount of I/O bandwidth per GB of EDW data is equal to or greater than that provided on a DW Appliance. This introduces other problems as there are typically overhead issues with a great many nodes. But it could work.

But what if we changed the architecture so that I/O was not the bottleneck? What if we built a cloud-based shared-nothing in-memory database (IMDB)? Now the data could live on SAN as it would only be read at start-up and written at shut-down… so the issues with the disk subsystem disappear… and issues around sharing the SAN disappear. Further, elasticity becomes feasible. With an IMDB we can add and delete nodes and re-distribute data without disk I/O… in fact it is likely that a column store IMDB could move column-compressed data without re-building rows. IMDB changes the game by removing the expense associated with disk I/O.

There is evidence emerging  that IMDB technology is going to change the playing field (see here).

Right now there are only a few IMDB products ready in the market:

  • TimeTen: which is not shared-nothing scalable, nor columnar, but could be the platform for a very small, 400GB or less (see here), cloud-based EDW;
  • SQLFire: which is semi-shared-nothing scalable (no joins across shards), not columnar, but could be the platform for a larger, maybe 5TB, specialized EDW;
  • ParAccel: which is shared-nothing scalable, columnar, but not fully an IMDB… but could be (see C. Monash here); or
  • SAP HANA: which is shared-nothing, IMDB, columnar and scalable to 100TB (see here).

So it is early… but soon enough we should see real EDWs in the cloud and likely on Amazon EC2, based on in-memory database technologies.

Cloud Computing and Data Warehousing: Part 3 – ParAccel on EC2

In the previous post here I suggested that a SAN-based, cloudy, EDW is about 4X the cost for the same performance over a data warehouse appliance.. and I described why. I have actually seen this comparison.

It is difficult to compare Amazon EC2 hardware to the hardware typically assembled in a shared-nothing EDW cluster whether the hardware is from HP, Dell, Sun, IBM, or Teradata. So let’s assume that Amazon gets a 20% edge due to huge volume purchases over your firm. Note that this is a significant edge since the hardware is a commodity. Further, lets assume that Amazon gets another 30% edge in TCO on system administration costs. This is the cost of staff to manage the Linux OS and the hardware components. This may also be generous to the Amazon side of the equation. The numbers are not important… you can put in whatever seems to model your situation best… if you work for a large efficient company the numbers may go down for EC2.

Lets also assume that you reserve and receive dedicated hardware on EC2. This will not be the case but lets continue to build a best-case scenario for EC2.

From these numbers we can assume that the EC2 configuration will be 3X the cost for the same performance as a dedicated purpose-built database cluster. Again this assumes that the EC2 hardware is dedicated so this number is optimistic.

So why would anyone do this? Because EC2 has no up-front capital expense associated… it is an operating expense. This is significant.

So what is the advantage of buying ParAccel on EC2? I’m unsure. ParAccel has not done particularly well in the marketplace… but it is not clear that this is a technology issue. The answer could lie in the fact that companies deploy ParAccel on EC2 for data mart or application-specific workloads that may not use 100% of the hardware resources provided?

I think that if you work through these three blogs you can get an idea of how to model the opportunity for yourself. If the ability to spend OPEX dollars with Amazon is important… even if you need 3X the hardware… then this is a very interesting way to go.

But do not imagine that you are getting the same performance with ParAccel on EC2 that you wold get with ParAccel on HP or Dell… for a fraction of the price. There is no architectural advantage in ParAccel on EC2 over Vertica or Greenplum or any other DBMS that can run on EC2… ParAccel is, however, trying something new and interesting… if you understand the trade-offs.

In the last blog of this series (here) I’ll discuss some new approaches that may change the game… including another interesting possibility for ParAccel going forward.