DavidBirmingham 2700043KNU Tags:  performance transformation elt netezza sql etl transform 5,313 Views
I have noted in prior posts several of the "banes" of in-the-box data processing, not the least of which is harnessing the mechanics and nuances of the SQL statement itself. After all, the engine of in-the-box is a series of insert/select SQL statements. I've also noted that we need to squeeze the latency out of the inter-query handoff and management. These are important factors for efficiency, scalability and adaptability.
But this article deals primarily with "adaptive" SQL, that is, the ability to surgically and dynamically control the SQL, the paths of flow between SQL statements, their timings and the ability to conditionally execute them.
I am drawing a contrast between this approach and the common "wired" ETL application. In the wired application of an ETL tool, all components are known and flow-paths predefined. If we want to shut off a particular component or flow, we'd better make that decision at startup because we won't get to do this later. A benefit here is that if we add or change a flow-path, the ETL tool's dependency analysis will (usually) detect it and give it a thumbs-up or thumbs-down. We can (and do) perform this kind of design-time analysis, but what of dynamic run-time analysis?
Case in point: One group performs trickle-feed of data from a change-data-capture, so on any given loadng cycle, we don't know which files will show up. Not to worry in an ETL tool, since we would just build a separate mini-app to deal with the issues. The mini-app would key on the arrival of a specific file, process the file and present results to the database. This is a very typical implementation. But with hundreds of potential files, it's also logistically very daunting and hard to get the various streams to inter-operate. In fact, an ETL tool quickly reduces to "sphagetti-graphics" and the graphical user interface is just in-our-way at that point.
Case in point: One group has multiple query paths/flows where sql statements build one-to-the-next for the final outcome. These can follow a wide range of paths not unlike a labyrinth depending on a variety of different factors. The problem is, these factors aren't known until run-time and only appear in fleeting form as the data is processed. How do we capture these elements and use them as steering logic? In an ETL tool, our options are limited to none. In this particular case, three primary paths of logic were available each time the flows ran. Sometimes all three paths ran end-to-end. Sometimes only one, or two would run, or perhaps none-at-all. The starting conditions and unfolding data conditions determined the execution path.
But we have another name for this don't we? Isn't this just plain vanilla "computer programming"? Where the data shows up and we use the encountered-data and encountered-conditions to guide the IF-THEN-ELSE logic to conclusion? The problem you see, is that we are so accustomed to using IF-THEN-ELSE at the ROW/COLUMN level, we cannot imagine what this would look like at the SET level. Ahh, the conditional logic driving SETS is unique and distinct from that which drives basic elements. But then again, we can only scale in sets, not the basic elements. THis is where the dynamic nature of conditional-sets is invaluable.
But this isn't really about conditional sets, either. Only that conditional sets are a necessary capability and we have to account for them along with many other subtle nuances. Let's follow:
We have an external file and we load this into an intermediate/staging table (TABLE-A) in preparation for processing.
Now we build another target intermediate table (TABLE-B) and an insert/select statement to move / shape the data logically and physically from TABLE-A to TABLE-B.
From here we have several more similar operations, so we build intermediate tables for their results as well, such as TABLE-C, TABLE-D and TABLE-E
TABLE-A >>> TABLE-B >>> TABLE-C >>> TABLE-D >>> TABLE-E
Now let's say we have another chain of work starting from TABLE-V:
TABLE-V >>> TABLE-W >>> TABLE-X >>> TABLE-Y >>> TABLE-Z
Now something interesting happens, in that the developers sense a pattern that allows them to reuse certain logic if they only put these quantities into a couple of working tables, which we will call TABLE-G and TABLE-H, and now the flows look like this:
TABLE-A >>> TABLE-B >>>>>>>>TABLE-C >>> TABLE-D >>> TABLE-E
TABLE-V >>> TABLE-W >>>>>>>>TABLE-X >>> TABLE-Y >>> TABLE-Z
Notice how TABLE-G is feeding TABLE-C and TABLE-H is feeding TABLE-X, so that each of them have a 2-table dependency.
Now we get to the end of the chain of work and learn that TABLE-Z has to leverage some data in TABLE-C! We don't want to rebuild TABLE-C just for TABLE-Z, but in an ETL Tool this data would be bound/locked inside a flow. We could redirect the flow to TABLE-Z, unless the flow to TABLE-Z is entirely conditional and we don't know it until we encounter TABLE-C. What if, for example, the results of TABLE-C are conditional and if the condition is realized, none of the components following TABLE-C are executed. However, we could have TABLE-Z see this absence as acceptable and continue on.
Okay, that's a lot of stuff that might have your head spinning about now, but the simplicity in resolving the above is already in our hands. In any flow model, upstream components essentially have a "parent" relationship to a downstream "child" component. This parent-child relationship pervades flows (and especially trees) and as we can readily see, the above chain-of-events looks a lot like a tree (more so than a flow).
More importantly, each node of the tree is a checkpointed stop. We must build the intermediate table, process data into it and move on, but once we persist the data, we have a checkpointed operation. This is why it behaves so beautifully as a flow and a tree.
Now let's say over the course of SDLC (regular maintenance), that a developer needs to add some more operations and connect other existing operations to their results. This is essentially just introducing new source tables in the where/join clause, but the table as to exist. In short, if we add a new table to the logic of TABLE-X, it will now be dependent upon its original tables and the new ones. (Its query will break if they are not present at run time).
It is easy enough (honestly) to perform a quick dependency-check over all of our queries to make sure that their various source tables are accounted for. In other words, an operation actually exists that will produce the table. What if we picked the wrong table or even misspelled it? At run-time we would know, but we would rather know before execution because it's a design-time issue. This may verify that logically we have a plan to create the dependent table, but it does not deal with the simple fact that conditional circumstances may forego the physical instantiation of the table. Transforms ultimately do not operate on intent, but on the presence of physical assets.
As another nuance, this creates a disparity between the design-time flow of data, and the run-time flow of data. If the run-time is governed (e.g. ETL tools) so that the dependencies and conditions are all evaluated at the start of the application, the design-time and run-time are more easily mated for review by an auditor or analyst. But if any part of it is dynamically conditional, we can see how this could practically nullify the design-time form of the flow. They would simply say, "I know what the flow would do by design, but I want to see what it actually did at run time, because the data isn't matching up". Aha - so "intent" counts for design review, but "intent" is not what puts physical data into the tables. Operational processes do that.
As noted above with the necessity for conditionality and reduction of inter-transform latency, we now have a need to weave together at run-time what the flows will actually do. The "source tables" for a given transform are found in the where/join phrase and these had better be present when the SQL launches or it will be a short ride indeed.
And now, what you did not expect - one of the most powerful ways to use a Netezza machine is to forego the "serialization" of these flows and allow them to launch asynchronously. We can certainly throttle how many are "live" at at time, but if any or all of them can launch independently, how on earth are we supposed to manage the case where one or two of them really are dependent on another one or more? Do we put these in a separate flow? Do we really want our developers to have to remember that if they put an additional dependency in a transform that they have to regard whether that preceding transform has actually executed successfully?
So that's the real trick, isn't it? If I have forty transforms and all of them could run asynchronously except for about ten of them, that can only run after their predecessor completes, I have several options to see to it that these secondary operations do not fail (because their predecessor has not executed yet).
I can serialize them in by putting them into separate flows (or branches). One of them kicks off and runs to completion while the next one waits. This is logically consistent but also inefficient. If those secondary transforms are co-located with the original set, the optimizer can run them when there is bandwidth rather than waiting until the end. It is also logistically unwieldy because a developer has to remember to that if a transform should gain a dependency, it has to be moved to the second flow.
I can fully serialize them into a list, but this is the most inefficient since it "boxcars" the transforms and does not leverage the extra machine cycles we could have used to shrink the duration.
I can link them via their target table and source table, such that this relationship is dynamically identified and the flow path dynamically realized. If a given transform does not run (conditional failure) or simply fails to execute, the dependency breakage is dynamically known. What does this do? What if a given transform is supposed to use an incoming (intake) table if it is present (data was loaded) otherwise use a target-table's contents (e.g. trickle-feed, change-data-capture problem). This allows the transform to do its work with consistency but also have the ability to dynamically change its sources based on availability.
Now, we know ETL tools don't do this. Other tools may attempt to rise to this level of dynamic pathing, but the bottom line is that if those tools don't provide this kind of latency-reduction, high-throughput, dynamically adaptable model, they will not be able to leverage the full bandwidth of the machine. Trust me on this - the difference is between using 90 percent of the machine or only 10 percent at a time. That machine packs the virtual joules to make it happen, so let's make it happen.
When we originally developed our framework to wrap around some of these necessary functions, we had not considered these nuances of dynamic interdependence and frankly, ELT was so new that it didn't really matter. The overhead to execute "raw" SQL was zero, but we could not effectively parallelize/async the queries without losing control. Running async chains of transforms necessitated detailed control, but nobody had a decent algorithm for it, so once again Brightlight had to pioneer this capability. Our architecture allowed us to easily integrate these things into the substrate of the framework as a transparent function. This is the primary benefit of a framework, that the developers can continue to build their applications without disruption, but we can upgrade and enhance the framework to provide stronger and deeper functionality. Whether our framework is right for all applications is not the issue, but whether the complete implementation is right for Netezza. It's a powerful machine and we should not arbitrarily leave any cycles on the table.
Imagine slowly running out of steam because of latent implementation inefficiencies, then ultimately asking for a Netezza upgrade that, if the inefficiencies weren't present, the upgrade wouldn't be necessary. This has happened with more than one of our sites and rather than upgrading to all-new-hardware, we installed, converted and bought back an enormous amount of capacity. They eventually upgraded the hardware much later on, but for the right reasons.
DavidBirmingham 2700043KNU Tags:  twinfin performance enzee underground pressure netezza adaptation transformation 4,893 Views
So I published this book last Spring ('11) on how the Netezza machine is a change-agent. It initiates transformation upon people or products that happen to intersect with it. Most of the time this transformation makes the subject better. Sort of like how heavy-lifting of weights will make the body stronger. Or the pressure can crush the subject. Stress works that way. We could imagine the Netezza machine as the change-agent entering the environment. Everything brushing against it or interacting with it will have to step-up, beef-up or adapt. I sometimes hear the new players say things like "But if the Netezza machine could only.." That's like a Buck Private saying of his drill sargeant, "If he could only ..." No, the subject must consider that the Netezza machine is never the object of transformation but rather is the initiator of it. But it's not a harsh existence by any means. Products that can adapt are far-and-away better than before. Those that cannot adapt now, will eventually, or remain in their current tier.
Having been directly or indirectly alongside these sorts of product integrations and proof-of-concepts (POCs) numerous times, it's always an interesting ride. The vendor shows up ready-to-go with visions-of-sugarplums in their head. And the suits who show up with them, are salivating for the ink on the license agreement. In less than an hour into the POC, all of them have a very different opinion of their product than when they arrived. Their bravado is reduced to a shy, sort of sheepish spin. Throw them a bone, not everyone walks out of this ring intact. Some of them shake their fist at the Netezza machine. It is unimpressed. Others shake their fist at their own product. Alas, it is but virtual, inanimate matter. What is transforming now? The person in the seat.
So I have watched them scramble to make the product hit-the-mark. Patches? We don't need no stinkin' patches. Except for today, when they will be on the phone in high-intensity conversations with their "engine-room" begging for special releases while on-site. Alas such malaise could have been avoided if only they had connected their product - at least once - to a Netezza machine. In so many cases, they will claim that they have Netezza machines in-the-shop so they are prepared-and-all-that. It is revealed, sometimes within the first hour, that the product has never been connected to a Netezza machine. It doesn't even do the basics, or address the machine correctly. It is especially humorous to hear them speak in terms of scalability as though a terabyte is a high-water mark for them. One may well ask, why are we wasting our time with underpowered technology? Well, in point of fact, when placed next to the Netezza machine it's all underpowered, so really it's just a matter of degree.
Case in point, Enzees know that in order to copy data from one database to another, we have to connect to the target database (we can only write to the database we are connected to). And then use a fully-qualifed database/tablename to grab data from elsewhere - in the "select" phrase. Forsooth, their product wants to do it like "all the others do" and connect to the source, pushing data to the target. Staring numb at the white board in realization of this fundamental flaw, they mutter "If only Netezza could....". But that's not the point. They arrived on site, product CD in hand, without ever having performed even one test on real Netezza machine, or this issue (and others) would have hit them on the first operation. They would have pulled up a chair in their labs, started the process of integration and perhaps call the potential customer "Can we push the POC off until next week? We have some issues (insert fabricated storyline here) and need to do this later."
Cue swarming engineers. Transformation ensues.
Another case in point, many enterprise products are built to standards that are optimized for the target runtime server. That is, they fully intend to bring the data out of the machine, process it and send it back. One of my colleagues made a joke about Jim Carrey's "The Grinch" and the mayor's lament for a "Grinch-less" Christmas. Well, didn't the Grinch tell Cindy-Lou Who that in order to fix a light on the tree, he would take the tree, fix it and bring it back? Seems like a lot of hassle for one light? Why can't you fix it here and not take it anywhere? Enzees see the analogy unfolding. No, we don't want to take the data out, process it and put it back. We want "Grinch-less" processing, too. Fix the data where it already is.
Why do this? Well, in 6.0 version of the NPS Host, the compression engine could easily give us up to 32x compression on the disk. Or even a nominal 16x compression, meaning that our 80 terabytes is now 5TB of storage. And while we may have to de-compress it on the inside of the machine to process it, the machine is well-suited to moving these quantities around internally. Woe unto the light-of- heart who would pull the data out into-the-open, blooming it to its full un-compressed glory, on the network, CPU, the network again - just to process it and put it back.
Unprepared for the largesse of such data stores, our vendor contender's product centers on common scalar data types. Integer, character, varchar, date. No big deal. Connect to the Netezza machine and find out that the "common" database size is in the many billions and tens of billions of rows. A chocolate-and-vanilla software product without regard to a BigInt (8 byte) data type, cannot exceed the ceiling of 2 billion (that's the biggest a simple integer can hold). This does not bode well for integrating to a database with a minmum of ten billion records and that's just the smallest table. Having integers peppered throughout the software architecture by default - would require a sweeping overhaul to remediate. As the day wears on, we see them struggle with singleton inserts (a big No-No in Netezza circles) and lack of process control over the Netezza return states and status. These are not exotic or odd, but no two databases behave the same way. The moment that Netezza returned the row-count that it had successfully copied four billion rows, we watched the product crash because it could not store the row-count anywhere - the product had standardized on integers, not big integers, so the internal variable overflowed and tossed everything overboard. Quite unfortunately, this was a data-transfer product and performed destructive operations on the data (copy over there, delete the original source over here). So any hiccup meant that we could lose data, and lots of it.
Cue announceer: "And the not-ready-for-prime-time-players..."
Oh, and that "lose data and lots of it" needs to be underscored. In a database holding tens of billions of rows (hundreds of terabytes) of structured data, that is, each record in inventory, with fiducial, legal, contractual, perhaps even regulatory wrappers around it, and we're way, way past the coffin zone. Some of you recall the "coffin zone" is the point-of-no-return for an extreme rock-face climber. Cross that boundary and you can't climb down. But we're not climbing a rock face are we? The principle is the same. Lose that data and we'll get a visit from the grim reaper. He doesn't hold a sickle, just a pink slip in one hand and a cardboard box in the other (just big enough for empty a desk-full of personal belongings).
One test after another either fails or reveals another product flaw. When the smoke clears, the "rock solid offering" complete with sales-slicks and slick-salesmen, is beaten and battered and ready for the showers. The product engineers must now overhaul their technology (transform it) and fortify it for Netezza, or remain in their tier. The Netezza machine has spoken, reset itself into a resting-stance, presses a fist into a palm, graciously bows, and with a terse, gutteral challenge of a sensei master, says: "Your Kung Fu is not strong!"
Now it's transformation-fu.
Superficially, this can look like a common product-integration firefight. But this kind of scramble tells a larger tale: They weren't really ready for the POC. This would be similar to an "expert" big-city fireman, supremely trained and battle-hardened in the art of firefighting and all its risks, joining Red Adair's oil-well -fire-fighting team ( a niche to be sure) and finding that none of the equipment or procedures he is familiar with apply any longer. He will have to unlearn what he knows in order to be effective on a radically larger scale. He might have been a superhero back home, faster than a speeding bullet, able to leap tall (burning) buildings in a single bound, but when he shows up at Red Adair's place, they will tell him to exchange his clothing for a fireproof form and get rid of the cape. Nobody's a hero around an oil-field fire. Heroes leave the site horizontally, feet-first. No exceptions.
Enzees have experienced a similar transformation (with a different kind of fire). The most-oft-asked questions at conferences are just that flavor: How do we bring newbees into the fold? How do we get them from thinking in row-based/transactional solutions into set-based solutions? How do we help them understand how to use sweeping-query-scans to process billions of rows? Or use one-rule-multiple-row approaches versus cursor-based multiple-rule-per-row? How do we get testers into a model of testing with key-based summaries instead of eyeballs-on-rows (when rows are in billions)?
We were dealing with a backup problem at one site because of a lack of external disk space. Commodity tools often use external disk space for this purpose, until they are connected to a Netezza machine and their admin tool complains that they need to add "another hundred terabytes" of workspace. We gulp, realizing that the workspace is only today a grand total of ten terabytes in size. And you need another hundred! Yeesh, you big-data-people!
Most of the universe outside the Enzee universe will never have to address problems on this scale. It is not the machine itself that is the niche. It is the problem/solution domain. Most of the commodity products that are stepping up are doing so only because it's clear that Netezza is here to stay and they need to step into Netezza's domain. I suppose at some point they expected Netezza to give them a call to start the integration process, but the Netezza Enzee Universe already had all that under control. It's amazing how lots-of-power can simplify hard tasks to the end of ignoring commodity products entirely.
Another case in point, a product vendor "popped over" with a couple of his newbee product guys and spent two weeks trying to get their product to play in-scale with Netezza. Before throwing in the towel, they offered up the common litany of observations. "No indexes? What the?" and "Netezza needs to change X", or the favorite "Nobody stores this much data in one place." The short version is, you brought a knife to a gun fight, as Sean Connery would assert, or perhaps, you brought a pick-axe and a rope to scale Mt. Everest. What were you thinking? You see, most people who have never heard of Netezza (I know, there really are folks out there who don't know about it, strange as is seems) do not understand the scale of data inside its enclosure. Billions of records? Tens of billions of records? A half-trillion records? Is that all you got?
We will watch a switch flip over in their brains as they assess what they are trying to bite off. A small group will embrace the problem and work toward harnessing the Netezza machine in every way possible. Another group will provide a bolt-on adapter for Netezza to interface to their core product engine. While another, larger group will assess the expense of such things, the marketplace they currently address, and conclude that they will for now remain in their current tier. This is like a 180-lb fighter climbing into the ring with a heavyweight, and walking away realiizing that they need to add some muscle, some speed, and some toughness or just stay in their own weight class and be successful there.
Another case-in-point is the need for high-intensity data processing in-the-box in a continuous form, coupled with the need for the reporting environment to share the data once-processed, likewise coupled with the need for backup/restore/archive and perhaps a hot-swap failover protocol. We can do these things with smaller machines and their supporting vendor software products. But what about Netezza, with such daunting data sizes, adding the complexity of data processing?
At one site we had a TwinFin 48 (384 processors) and two TwinFin 24's (192 processors) with the '48 doing the heavy-lifting for both production roles. When it came time to get more hardware, the architects decided to get another '48 and split the roles, so that one of the machines would do hard-data-processing and simply replicate-final-results to the second '48, limiting its processing impact for any given movement. This was not the only part of their plan. They then set up replicators to make "hot" versions of each of these databases on the other server. This allowed them to store all of the data on both, providing a hot DR live/live configuration, but it would only cost them storage, not CPU power. Configured correctly, neither of the live databases would know the difference. Our replicators (nzDIF modules) seamlessly operated this using the Netezza compressed format to achieve an effective 6TB/hour inter-machine transfer rate, plenty of power for incremental/trickle feeds across the machines.
Some say "I want an enterprise product that I can use for all of my databases". Well, this is the problem isn't it? Netezza is not like "all of our other databases". Products that have a smashing time with the lower-volume environments start to think that a "big" version of one of those environments somehow qualifies their product to step-up. I am fond of noting that Ab Initio, at one site loading a commodity SMP RDBMS, was achieving fifteen million rows in two hours. Ab Initio can load data a lot faster than that (and is on record as the only technology that can feed Netezza's load capacity). So what was the problem? The choice of database hardware? Software? Disk space? Actually it was the mistaken belief that any of those can scale to the same heights as Netezza. I could not imagine, for example, that if fifteen million rows would take two hours, what about a billion rows (1300 hours? ). Netezza's cruising-speed is over a million rows a second from one stream, and can load multiple streams-at-a-time.
Many very popular enteprise products have not bothered to integrate with a Netezza machine, and many of those who have, provide some form of bolt-on adapter for it. It usually works, but because the problem domain is a niche, it's not on their "product radar". It's not "integrated as-one". What does this mean? Netezza's ecosystem, and now assimilated by IBM, through IBM's product genius and sheer integration muscle, will ultimately have a powerful stack for enterprise computing such that none of the other players will be able to catch up. If those vendors have not integrated by now, the goal-line to achieve it is even now racing ahead of them toward the horizon. Perhaps they won't catch up. Perhaps they won't keep up. Some products (e.g. nzDIF) are at the front-edge, but nzDIF is not a shrink-wrapped or download-and-go kind of toolkit. We use it to accelerate our clients and differentiate our approaches. It's a development platform, an operational environment and expert system (our best and brightest capture Netezza best practices directly into the core). This has certainly been a year where we've gotten the most requests for it. But there's only one way to get a copy.
Cue Red Adair.
"No capes!" - Edna Mode, clothing-designer-for-the-gods, Disney/Pixar's The Incredibles
The long-awaited "SQL" to Netezza Underground has hit Amazon.Com. Netezza Transformation
This book tackles some deeper issues around transforming our data warehouse, our approaches and even our thinking to align with the arrival of our brand-new appliance
No, it's not the appliance we'll transform, but that the appliance will transform us. Once again, a little tongue-in-cheek irony
The book offers working examples of the stuff people ask me about most often, like ELT/SQL-Transforms, checkpointing, exception handling (primary/foreign key), windowing (SCDs and deduplication) as well as a cookbook on more details to watch for in a migration project.
And of course, is replete with Case Study Short and a whole chapter on Case Studies. There's also an appendix at the end to offer up some simple scripting jump-start routines that can make bash so much easier.