DavidBirmingham 2700043KNU Etiquetas:  performance transformation elt netezza sql etl transform 4.255 vistas
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 Etiquetas:  transform air etl netezza elt sql 4 Comentarios 5.456 vistas
For a brief history of why ELT (that is, in-box-data-processing) is even a topic of discussion, we must recognize that the high-powered appliances such as Netezza have not only made such implementation viable, but even desirable.
Just so we level-set on what one of these looks like, it's a SQL statement. Usually an insert/select but can also include updates and the like. Many of you recognize these as multi-stage operations inside a stored procedure. The sentiment of course is that such an animal can perform better inside-the-machine than to take it out of the machine (through an ETL tool), process it, and put it back. This may be true for smaller data sets, but you aren't reading this because you have smaller data sets, are you? Netezza users are big-game hunters. Pea-shooters are for small animals, but if we want big game, we'd better bring a bigger game with us, put on a bigger game face, and bring bigger equipment.
But is it really the size of the equipment? Netezza users know that the size and the architecture are keys to success. Dog-piling a bunch of CPUs together does not a power-frame make.
Okay, back to the storyline here - in-the-box SQL-transforms, in a traditional RDBMS platform, are the realm of small game. Once the game gets bigger, these transforms degrade in performance, and rather rapidly. Watch as a swarm of engineers tries to reconstruct and rebuild the procedures, the SQL, the tables and even upgrade the hardware in a futile attempt to achieve incrementally more power. Emphasis on incrementally-more. Not linearly more.
As they grow weary of this battle, the ETL tools start looking better and better. We reach a critical mass, and the ELT-in-the-box is summarily forsaken as we stand up the ETL tool.
Sometime after this transition, the data volumes once again overwhelm the environment. One thing leads to another, and one day the Netezza machine arrives on the floor. Hopes are high.
But notice the transition above - ELT was forsaken for ETL, likely never to return. But wait, now we really have the power to do the ELT SQL-transforms, but we've mentally and perhaps emotionally (yeah, verily even politically) moved away from SQL-transforms.
Some reading this might scratch their heads and think, What's he talking about? We've been doing ELT in the machines using (PLSQL, etc, name your approach here) for many years. Why would we shy away from it?
Why not use an ETL Tool? I mean, they handle push-down SQL-generation right?
I can perhaps summarize this situation in a single conversation I had with a ETL tool vendor who was hawking the capability for his own tool, and after showing me the mechanics of making one of these little jewels operate with aplomb, I asked him, "So what about doing a few hundred of these in a sequence, or branching them into multiple sequences?" The vendor rep looked almost hurt. "Why would you want to do that?"
Well, if we're really talking about migrating transforms into the machine, this can grow into hundreds of operations rather quickly. This situation apparently overwhelms the logistical capability of even the most powerful ETL tool. But I have hope that they will solve this situation. Eventually.
I am not holding out hope that it will happen soon, or voluntarily. These tool vendors have invested millions in the performance boosting of their own products and will not likely toss this investment on the flames of the appliance movement, even if said flames are the exhaust flames of the appliance's rocket engines. This is why I say "eventually". They don't really have a marketable reason to embrace this approach.
Another problem of the ETL tool is that it is so divorced from the appliance's infrastructure that it cannot control the cross-environment logistics. This is especially true of the "virtual transaction" - that is - multiple flows arriving in multiple tables that are each in context of one another, yet are individually shared-nothing operations. If one of the flows should fail, how do we back out of this? Can we do a rollback of the tables where their flows succeeded? No we cannot. We could certainly implement an approach, but this is neither inherent nor intrinsic to the ETL tool. We need a shared-nothing virtual transaction that will control all of the flow in a common context, commit them in that context and faithfully rollback the results in that same context. ETL tools don't go there. Unless we implement the tool that way. As an application. Once implemented, how do we reuse this for the next application, and the next? We can see that it's not part of the tool itself.
If next-generation "ELT" scenarios are to be successful, they need several very important capabilities that are simply non-optional and non-trivial:
I am sure the visitors of this blog have even more aspects of a "wish list" that they have implemented (perhaps painfully so) and want more control over the data, its processing logistics and error control and recovery. Feel free to add your own comments and suggestions here.
DavidBirmingham 2700043KNU Etiquetas:  transformation sql netezza amazon elt book 4.957 vistas
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.