Two words: load balancing -
Is our chosen platform designed for set-based bulk processing, or load balancing? Both at the software and hardware levels? The load-balancing engine (and attendant SMP hardware) are simply the wrong architecture for large-scale bulk processing. There's no way to "properly" configure the wrong architecture.
Let's level-set the difference here. In an SMP-based scenario, our engineers have to carefully configure the hardware to garner the very best performance from it. We don't have that option in Netezza, because the hardware is pre-configured. Rather in Netezza, we gain power by how we organize and configure the data. We don't really have this option in an SMP-based model, because the database engine software pre-defines how we will organize the information (through index structures) and we cannot affect our fate without the indexes. Let's see the contrasts summarized:
Netezza - no indexes, no hardware config, performance is derived from data configuration
SMP machine - high hardware config, index-depedent, no ability to affect performance with data configuration
In short, the two performance tuning models are not only polar opposites, Netezza is far more adaptable and flexible because it is easier to reconfigure data than to reconfigure hardware.
I am continually impressed with the valiant attempts of various platform aficionados who assert, claim and champion the notion that "properly" configured SMP-based hardware is the the only issue in evaluating competitive performance between platforms. In short. a properly configured <name your platform here> is just as viable as the IBM Netezza platform. Just name your components and off you go.
Of course, most folks who are making these claims are not hardware aficionados at all. Now, I appreciate software folks because at heart I am one of them, but I cut my teeth as an software engineer on solutions that aligned high-powered hardware with other high-powered hardware, all the while respecting the fact that the software I was creating was actually orchestrating and controlling the interaction between these gravity-bending machines, not physically moving the "payload" as it were. Nothing, absolutely nothing could move the data faster than the hardware. We inherently know this, yet many seem to think that software products can overcome this issue by using RAM and other creative methods to accelerate the effect of software operation.
So before I launch into a more complex rant on this, a picture is worth 1000 words (at least). In Netezza Transformation I offered up some graphics for contrast and compare (and alluded to them in another blog entry here).
In the depiction (right) we have CPUs (on the top) and disk drives (on the bottom). The pipeline in between them is the
general-purpose backplane of the hardware configuration, which may include a SAN interface, optical or 10gigE networking or other connection mechanism to transfer data between the server's CPUs and the SAN's disk drives. Even if these disk drives are local on the machine containing the CPUs, this backplane is still the connector between them.
Now we will load a data file containing 100 billion rows of information, some 25 terabytes in size. This is a medium data size for big-data aficionados. The data will necessarily enter the machine from an external network connection, into the software engine (runnning on the CPUs) which will deliver the data onto the assigned location of the disk drives. Seems like a very simplistic and remedial explanation doesn't it?
Now we will query this data. Our ad-hoc and reporting users will want to scan and re-scan this information. Note how now the bottleneck is actually the hardware itself. The data must be pulled in-total from the disk drives, through this backplane and into CPU memory before it can be examined or qualifed. Even if we use index structures, the more complex the query, the more likely we will encounter a full-table-scan. How long, do we suppose, it would take for this configuration to scan 25 terabytes? (Keep in mind that all 25 terabytes has to move through the backplane).
A server-level MPP model would suggest placing two of these configurations side-by-side and coordinating them. In short, one of the server frames would contain some portion of the information while another frame contained the other. We could imagine placing multiples of these side-by-side to create an MPP grid of sorts. This is the essential secret-sauce of many Java grids and other grid-based solutions. Divide the data across a general-purpose grid and then coordinate the grid for loading and querying.
But notice how deeply we are burying the data under many layers of administered complexity. Sure, we can do this, but is it practical and sustainable? I've seen setups like this that served one application (and served it well) but it was an inaccessible island of capability that served no other masters. As general-purpose as all of its parts were, it had been purpose-built and purpose-deployed for a single solution that required the most heavy lifting at the time of its inception. Now that it is in place, other solutions around it are growing in the capacity needs to serve the grid, and none of them have access to the power within the grid. The grid becomes starved from the outside-in. No satellite solutiion can feed it or consume it at the rate it can process data, and it has no extensibility to support their processing missions.
So now we come full circle, we have a "properly configured" one-trick-pony. Over time, the expense and risk of this pony will become self-evident. Parts will break. Data will get lost. Lots of moving parts, especially general-purpose moving parts that are out-in-the-open, only increases the total failure points of the entire solution. Debugging and troubleshooting at the system level become matters of high-engineering, not simple administration. As noted above, in the environment where I cut-my-teeth, I was surrounded by these high-end engineers because it was the nature of the project. I noticed that once the project went into a packaging mode to deploy and maintain, these engineers moved-on and cast their cares on the shoulders of the junior engineers who backfilled them. This was a struggling existence for them, because the complexity of the solution did not lend itself to simple maintenance by junior engineers.
The Caractacus Potts adventure begins! You know Potts from the movie Chitty-Chitty-Bang-Bang. We thrilled to his inventions, and laughed when they did not deliver. A "simple" machine to crack eggs, cook them and deliver-up breakfast worked fine for everyone but him, delivering a plate of two uncooked eggs still in their shells. The puzzled look on his face told us he recognized the problem but did not know where to look for resolution. With so many moving parts, it could be anywhere. This is a classic outcome of "eccentric innovation" and "eccentric engineering" a.k.a "skunkworks". More importantly, the innovation only solves one problem (e.g. egg-centric breakfast) not a general-purpose solution platform.
Well, let's keep it simple- what about a simple summary report? You know, national sales data summarized to the national, regional, district and store levels? Wouldn't this require a complete scan of the table to glean all of this? In the SMP-based depiction (above) how could be expect such a scan to operate? Software would pull the data in-total from the disk drives, choking the backplane. Software would then summarize the aforementioned quantities, in memory if possible and then deliver the result. Frankly, such a report could take many hours to execute, and keep the machine busy the whole time. Even if we "grid" this, the query would swamp the grid. On a tricked-out Sun E10k, we have about 12 gb/second throughput. Putting some math to this, with 25 terabytes in tow, we could expect the table scan to complete in about 30 minutes even if the full complement of 64 processors is on board, because the solution is I/O bound, not CPU-bound (no new CPUs will make it run faster). However, in reality the software engine and all its overhead drain the energy from the machine and this query will run for hours, even if it's the only operation running on the machine. So I guess we really will need more CPUs to balance off the software drain of the engine itself. (sigh).
This is because: engines that run on SMP-based devices are inherently load-balancing engines, not bulk-processing engines. Their processes stop, negotiate and resume even if there's nothing else going on. Think of it like this: Where I live in the country, at 5am in the morning all of the traffic lights on the main road blink-yellow until about 6am. If I travel on that road before then, I can go the speed-limit for over half-an-hour before hitting the first traffic light in the next (larger) town. But if those traffic lights all operated normally, I could get stopped at each one, protracting my 30-minute journey by orders of magnitude as I wait on traffic lights even when no other traffic is present. SMP-based engines automatically thread this way, where a flow-based model does not. A load-balancing engine will force all of its processes to stop at a virtual traffic light, come up for air to make sure nothing else requires attention, then go back to work. Transactional models absolutely require this but it is anathema to bulk processing.
Now we contrast this with IBM Netezza, which is a purpose-built platform for general-purpose across all solutions requiring such a platform. We don't have a one-trick pony. This would mean (ultimately) any form of data warehouse or bulk-processing solution, but more importantly anything that requires fast-retrieval of data, especially while performing high-intensity on-demand analytics on it.
In the IBM Netezza architecture (depicted right) each CPU has a shared-nothing disk drive and its own RAM. On the original Mustang Series 10400 (with 432 of these on board) we have a machine that costs far less than the Sun E10k noted above. Likewise we could scan those 100 billion rows in less than ten minutes. It won't ever take any longer than ten minutes. If we boost the CPUs of the machine, say by adding another frame to it (200+) CPUs, it will boost the machine's speed by another 50 percent. Queries that took 6 minutes now will take less than 4 minutes. It is a deterministic/predictable model, and adding more frames to the Netezza platform is simple and inexpensive compared to the sheer labor dollars of eccentric engineering.
As for the depiction (right) it has 16 CPU/Disk combinations. To use round numbers, let's say we put 1.5 terabytes on each disk for a grand total of 24 terabytes. With this configuration, for any given query, the path to conclusion is only 1.5 terabytes worth of scan-time away. Once we initiate the query, each CPU will run independently and will scan its 1.5 terabytes. All of them will complete simultaneously, meaning that the total duration of the query was no longer than it took to scan the 1.5 terabytes (they are all scanning in parallel). Now boost this to 400 CPUs, where each one now only has about 63 gigabytes share of the load. One scan of the entire 24TB table takes no longer than the time to scan 63 GB (they are all in parallel). We can measure our disk read-speed here and get very consistent estimates of how long a query should take.
Also keep in mind that (in a prior blog entry on indexes) I noted that we can radically reduce these operations to a fraction of their total scan times. But in the example above, full summary of the data on sales-boundaries, how much is that worth if we could do the sales base on a date? Or based on a range of dates? Perhaps even comparing last-years Independence-Day sales to this years?
In an SMP-based configuration, the information engineers would suggest partitions. The partition (for an SMP engine) is an artificial performance prop that anticipates the user's query needs based on known use-cases. It bundles data (say on a date boundary) so queries against that date can be fenced by the partition boundary. The Netezza zone map, on the other hand, automatically tells the machine where to look, and where not to look, to go capture the information required by the query. No props, no use-case anticipation, just the flexibility we really need if we want to keep fire-breathing users happy without special engineering to anticipate their needs.
Zone maps allow the sales-related comparison above to arrive in mere seconds to the user's fingertips. On an SMP machine, at best, even with partitions, indexes and other performance props, will require a maxed-out power frame (all CPUs on deck) and the best anticipatory information engineering to provide a consistent experience that even hopes to compete with Netezza. Even after all that, it won't come back in seconds, and won't provide the nimble flexibility so sought-after by even the average data analyst.
The conclusion is that the overall cost of deployment, ownership and ease of maintenance for a Netezza machine utterly eclipse the potential promise of SMP-based solutions. For an analyst, all columns, tables and functions are "fair game" for query - all over the database, 24x7. A Netezza machine provides just that. On an SMP-based engine, the analyst has to agree with information engineers on their entry points and usage patterns, and these have to be engineered into the model in order to support the users. Once engineered, the solution will support only that user base. All other user bases will require their own engineering model. This is not sustainable, durable or manageable, which is why those who are steeped in it will gladly embrace a Netezza machine. Value is recognized on so many levels.
DavidBirmingham 2700043KNU Tags:  spu netezza distribution parallel skew 60 Comments 50,468 Views
One of the most significant questions to answer on the Netezza platform is that of "distribution" - what does it mean? how does it apply? and all that. If you are a Netezza aficionado, be warned that the following commentary serves as a bit of a primer for the un-initiated. However, feel free to make comments or suggestions to improve it, or pass it on if you like.
In the prior entry I offered up a simple graphic (right) as to how the Netezza architecture is laid out internally. In this depiction, each CPU is harnessing its own disk drive. In the original architecture, this was called a SPU, because the CPU was coupled with a disk drive and an FPGA (field-programmable gate array) that holds the architectural firmware "secret sauce" of the Netezza platform. The FPGA is still present and accounted for in the new architecture as the additional blade(s) on the blade server. Not to over-complicate things, so let's look at the logical and physical layout of the data itself and this will be a bit clearer.
When we define a table on the Netezza host, it logically exists in the catalog once. However, with the depicted CPU/disk combinations (right) we can see 16 of these available to store the information physically. If the table is defined with a distribution of "random", then the data will be evenly divided between all 16 of them. Let's say we have a table arriving that has 16 million records. Once loaded, each of these SPUs would be in control over 1 million records. So the table exists logically once, and physically multiple times. For a plain straight query on this configuration, any question we ask the table will initate the same query on all SPUs simultaneously. They will work on their portion of the data and return it an answer. This Single-Instruction-Multiple-Data model is a bread-and-butter power strategy of Massively Parallel computing. It is powerful because at any given moment, the answer we want is only as far away as the slowest SPU. This would mean as fast as the SPU can scan 1 million records, this is the total duration of the query. All of the SPUs will move at this speed, in parallel, so will finish at the same time.
What if we put another table on the machine that contains say, 32 million rows? (trying to keep even numbers here for clarity). The machine will load this table such that each SPU will contain 2 million rows each. If no other data is on the machine, we effectively have 3 million records per SPU loaded, but the data itself may be completely unrelated. Notice how we would not divide the data another way, like putting 16 million records on 6 of the SPUs and the other 32 million records on the remaining 10 SPUs. No, in massively parallel context, we want all the SPUs working together for every query. The more SPUs are working, the faster the query will complete.
Now some understand the Massively Parallel model to be thus: Multiple servers, each containing some domain of the information, and when a query is sent out it will find its way to the appropriate server and cause it to search its storage for the answer. This is a more stovepiped model than the Netezza architecture and will ultimately have logistics and hardware scalability as Achilles's heels, not strengths for parallel processing. Many sites are successful with such models. But they are very application-centric and not open for reuse for other unrelated applications. I noted above that the information we just loaded on the Netezza machine can be in unrelated tables, and databases and application suites, because the Netezza machine is general-purpose when it comes to handling its applicability for data processing. It is purpose-built when we talk about scale.
But let's take the 16-million-record vs 32-million record model above and assume that one of the tables is a transactional table (the 32 million) and one is a dimensional table (the 16 million). The dimensional table contains a key called "store_id" that is represented also on the transactional table such that we can join them together in various contexts. Will the Netezza machine do this, and how will it? After all, the data for the tables is spread out across 16 SPUs.
Well, we have the option of joining these "as is" or we can apply an even more interesting approach, that of using a distribution key. Here's where we need to exercise some analysis, because it appears as though the STORE_ID is what we want for a distribution, but this might skew the data. What does this mean? When we assign a distribution, the Netezza machine will then hash the distribution key into one of 16 values. Every time that particular key appears, it will always be assigned the same value and land on the same SPU. So now we can redistribute both of these random tables on STORE_ID and be certain that for say, SPU #5, all of the same store_id's for the dimension table and for the transaction table are physically co-located on the same disk. You probably see where this is going now.
Ahh, but what if we choose store_id and it turns out that a large portion of the IDs hash to a common SPU? What if we see, rather than 1 million records per SPU, we now see around 500k per SPU with one SPU radically overloaded with the remainder? This will make the queries run slow, because while all the SPUs but one will finish fast, in half the time of before, they will all be waiting on the one overworked SPU with all the extra data. This is called data skew and is detrimental to performance.
However, had we chosen a date or timestamp field, our skew may have been even worse. We might see that the data is physically distributed on the SPUs in a very even form. But when we go to query the data, we will likely use a date as part of the query, meaning that only a few SPUs, perhaps even one SPU, will actually participate in the answer while all the others sit idle. This is called process skew and is also detrimental to performance.
Those are just some things to watch out for. If we stay in the model of using business keys for distribution and using dates for zone maps (which I will save for another entry) we will usually have a great time with the data and few worries at all.
Back to the configuration on store_id's. If we now query these two tables using store_id in the query itself, Netezza will co-locate the join on the SPU and will only allow records to emit from this join if there is a match. This means that the majority of the work will be happening in the SPU itself before it ever attempts to do anything else with it. What if we additionally clamped the query by using a date-field on the transaction table? The transaction table would then be filtered on this column, further reducing the join load on the machine and returning the data even faster.
So here is where we get lifting effects where other machines experience drag. For an SMP-based machine, adding joins can make the query run slower. On a Netezza machine, joins can serve as filter-effects, limiting the data returned by the query and increasing the machine's immediate information on where-not-to-look. Likewise the date-filter is another way we tell the machine where-not-to-look. As I have noted, the more clues we can give the machine as to where-not-to-look, the faster the query will behave.
I have personally witnessed cases where adding a join to a large table actually decreases the overall query time. Adding another one further decreases it. Adding another one even further and so on - five-joins later we were getting returns in less than five seconds where the original query was taking minutes. This filter-table and filter-join effect occurs as a free artifact of the Netezza architecture.
Distribution is simply a way to lay the data out on the disks so that we get as many SPUs working for us as possible on every single query. The more SPUs are working, the more hardware is being applied to the problem. The more hardware, the more the physics is working for us. Performance is in the physics, always and forever. Performance is never in the software.
While the above is powerful for read-query lift (co-located reading) there is another power-tool called the co-located write. If we want to perform an insert-select operation, or a create-table-as-select, we need to be mindful that certain rules apply when we create these tables. For example, if we want to perform a co-located read above and drop the result into an intermediate table, it is ideal for the intermediate table to be distributed on the same key as the tables we are reading from. Why is this? Because the Netezza machne will simply read the data from one portion of the disk and ultimately land it to another portion of the same disk. Once again it never leaves the hardware in order to affect the answer. If we were to distribute the target table on another key, the data will have to leave the SPU as it finds a new SPU home to align with its distribution key. If we actually need to redistribute, this is fine. But if we don't and this is an arbitrary configuration, we can buy back a lot of power just by co-locating the reads and writes for maximum hardware involvement.
So that's distribution at a 50,000 foot level. We will look at more details later, or hop over to the Netezza Community to my blog there where some of these details have already been vetted.
It is important to keep in mind that while distribution and co-location are keys to high(er) performance on the machine, the true hero here is the SPU architecture and how it applies to the problem at hand. We have seen cases where applying a distribution alone has provided dramatic effects, such as 30x and 40x boost because of the nature of the data. This is not typical however, since the co-located reads will usually provide only a percentage boost rather than an X-level boost. This is why we often suggest that people sort of clear-their-head of jumping directly into a distribution discussion and instead put together a workable data model with a random distribution. Once loaded, profile the various key candidates to get a feel for which ones work the best and which ones do not. We have seen some users struggle with the data only because they prematurely selected a distribution key that - unbeknownst to them - had a very high skew and made the queries run too slow. This protracted their workstreams and made all kinds of things take longer than they should have.
So at inception, go simple, and then work your way up to the ideal.
On multiple distribution keys:
Many question arise on how many distribution keys to use. Keep in mind that this is as much a physical choice as a functional one. If the chosen key provides good physical distribution, then there is no reason to use more keys. More granularity in a good distribution has no value. However, a distribution key MUST be used in a join in order to achieve co-location. So if we use multiple keys, we are committing to using all of them in all joins, and this is rarely the case. I would strongly suggest that you center on a single distribution key and only move to more than one if you have high physical skew (again, a physical not functional reason). The distribution is not an index - it is a hash. In multi-key distributions, the join does not first look at one column then the next - it looks at all three at once because they are hashed together for the distribution. Joined-at-the-hip if you will.
On leaving out a distribution key:
One case had three tables joining their main keys to get a functional answer. They were all distributed on the same key, which was a higher-level of data than the keys used in the join. The developer apparently thought that because the distribution keys are declared that the machine will magically use them behind the scenes with no additional effort from us. This is not the case. If the distribution key(s) (all of them) are not mentioned in the join, the machine will not attempt co-location. In this particular case, using the higher-level key would have extended the join somewhat but would not have changed the functional answer. Simply adding this column to the join reduced that query's duration by 90 percent. So even if a particular distribution key does not "directly" participate in the functional answer, it must directly participate in the join so as to achieve co-location. And if this does not change the functional outcome, we get the performance and the right answer.
How it affects concurrency:
Many times people will ask: Why is it that the query runs fast when it's running alone, but when it's running side-by-side with another instance of itself they both slow to a crawl? This is largely due to the lack of co-location in the query. When the query cannot co-locate, it must redistribute the data across the inter-blade network fabric so that all the CPUs are privy to all the data. This quickly saturates the fabric so that when another query launches, they start fighting over fabric bandwidth not the CPU bandwidth. In fact some have noted that the box appears to be asleep because the CPUs and drives aren't doing anything during this high-crunch cycle. That's right, and it's a telltale sign that your machine's resources are bottlenecked at the fabric and not the CPU or I/O levels. By co-locating the joins, we keep the data off the fabric and down in the CPUs where it belongs, and the query will close faster and can easily co-exist with other high-intensity queries.
DavidBirmingham 2700043KNU Tags:  anti-indexing zone netezza index distribution maps indexes 23,000 Views
At far back as the 2007 Enzee Universe conference in Boston, people have scratched their head on the notion of a large-scale database that actually boasts about the absence of index structures. After all, indexes are the mainstay of relational databases and we simply can't get by without them, right? This is a simple example of how the power and architecture of the technology frees us to think about data loading and storage, data retrieval and in-the-box processing in a completely different way.
Firstly, it's no rumor that Netezza has no indexes. And for those of us who can't stand dealing with them, this is a huge plus. One of the Enzees at the 2011 conference asked point-blank - "What does Netezza have that Oracle does not?" and the clear answers will arise in this and later blog entries, but for now the absence of maintenance of index structures, will do just fine. What does Netezza have?
Or rather, the vendor has spent enormous thought labor into simplification of complex matters so that we don't have to deal with them. We can get to the business of - well - the business. The application data and information that we want to spend more time with if only we weren't dealing with the technical administration of the database engine. This is one of the greatest weaknesses of the traditional relational database when it comes to data warehousing in general and bulk processing in particular. It is also one of their greatest weaknesses in the space of analytics. After all, we want to apply the analytics by selecting subject areas and data sets to analyze, but if we have to stand up lots of infrastructure to support this mission we are regressing toward functional hardening rather than functional flexibility. Brittleness ensues and someone asks for a refactor, a re-architecture or whatever. When this request arrives in relation to a complex, engineered, multi-terabyte (or say tens-of-terabytes) data store, many will see it as a mind-numbing proposal. For most infrastructures, the weight and complexity of the installation has overwhelmed the logistical capacity of the humans to ever hope reeling it back in. There's not enough power in the machine to help. This could take a very long time to remediate.
Not so with an appliance. Such big-data issues are its bread-and-butter zone. If we have issues with a particular data model or information construct, the machine has the power (and then some) to get us out of the bad direction and into the new one. How did the direction get "bad" in the first place? The business changed its information priorities since the inception of the original model, and now the original no longer serves it well. The business has moved, because that's what businesses do. The analysts found opportunities in the nuggets of information-gold, and now want direct access to that gold rather than having to navigate to it or salivate while waiting for it.
One of the core features of the Netezza platform is how the data is distributed on the disk drives. Because it is an MPP, each of the disk drives is its own self-contained, shared-nothing hardware structure. Contrast this to the common SMP-based platform where the CPUs exist in one duck pond and the disk drives exist in another duck pond. The data is then couriered between duck ponds through throttled estuaries. If we issue a query, all of the data has to be pulled through this estuary and presented to the CPU ducks so that the data can be manipulated and examined in software. It is a software engine on a general-purpose platform.
However, imagine the Netezza platform where each Intel CPU is mated with special hardware (an FPGA), a bounty of RAM to manipulate and cache data, a self-contained Linux operating system, and its own dedicated disk drive. Imagine also that the disk drive is itself divided into multiple sections, where the inner sections are used for internal processes like RAID, temp space and system data but the outermost ring is where user data is stored, offering the benefit of the fastest disk speed for the oft-accessed information. All of these little attention-to-detail aspect cause each of the CPUs to run at a much more powerful factor than their common SMP counterparts. Why? Because their data is co-located with the CPU, where with the SMP we have to drag the data out of one duck pond to get it close to the CPUs that may operate on it. And with SMP there is no such thing as dedicated CPU-to-disk access. The SMP CPUs are shared-everything, but also the disk drives - shared-everything at the hardware level but shared-nothing at the functional-logical level. Netezza's CPUs are shared-nothing at the functional-logical level and shared-nothing at the CPU/disk level.
In Netezza, let's imagine putting 100 of these CPU/disk combinations to work. When we form a table on the host, the table exists logically in the catalog in one place. But it exists physically in 100 places. If we load 100 million records into the machine distributed randomly, then each of the CPUs will "own" 1 million of those records. If we issue a query to the table, each of the CPUs will operate only on the million records it can see. So for any given query, we are not serially scanning 100M records. We are scanning 1M records 100 times.
Now some may object, that we're still scanning the data end-to-end, and for plain-vanilla queries, this is true. However, I recall the first time I performed a join on two tables that were 100M records each doing a one-to-one join on unique keys, on a machine with 24 of these CPUs and the answer returned in 13 seconds. This was a plain vanilla test, so your actual mileage may vary. However, I did the same test on the same machine with 1B records joined to 1B records and the answer returned in less than 300 seconds. Imagine attempting this kind of join on a traditional relational database and expecting a return in any time to actually use the result. (and no, I did not use co-location for this test - a matter for another blog entry)
We have an additional "for free" aspect of the machine called a zone map. If data is laid down in contiguous form (the way transactions arrive in a retail point-of-sale model for example) then the machine will automatically track these blocks and keep their locations in a "zone map". If we then query the database with a given date, the machine will ignore the places where it knows the data is not, and use the zone maps to locate the range of data where it needs to search.
As as example of this, we know of a 150-billion-row table that is over 25 terabytes in size, distributed and zoned such that over 95 percent of its queries return in 5 seconds or less. For a traditional RDBMS, it would take more time than that just to get its index scans squared away so that it could even approach the storage table. This is also why the Netezza machine itself can be scaled into the petabyte zone while maintaining the same performance. No indexes are in the way to load it. No indexes are necessary to search it. Imagine now: the two most daunting aspects of data warehousing and analytics - loading the data and keeping up with the user base - have been washed away with the elimination of indexes. (Don't we turn indexes off in a traditional SMP database so it will load faster, and don't we chase a rainbow trying to index and re-index the tables to keep up with user needs?) Not with Netezza. Without indexes on the columns, all columns are fair game for searching. Without indexes in the way for loading, we can deliver information into the machine, a reprocess it while there, with no penalty from the use of indexes.
By this measure, this is an anti-indexing strategy because Netezza operates on the basic principle of where-not-to-look. In other words, if we can tell it where the data is not, it can zone-in and find the data. When we think about it, this is how a common brick-and-mortar warehouse works. If we showed up and asked for a box of nails, the attendant knows that he doesn't have to look in the parts of the warehouse that carry lawn equipment, hammers, saws or window draperies. He knows where the nails don't exist.
Contrast this to the common SMP-based indexed database, which uses exactly the opposite approach. The indexes are searched for the specific key and then this key is applied to the primary data store. This is why indexed structures in general cannot scale in the same manner as an anti-indexed structure. Keep in mind, with a Netezza platform it won't matter how much data we put on that 25-terabyte table. We could double, triple it or more - and it would always provide the answer in a consistent time frame. This is because from query-to-query - it's still not-looking in the same places, no matter how big those places get. It will still continue to ignore them because the data's not there and it already knows that.
I've had people tell me that there is no real difference in the SMP versus the MPP. The SMP, "properly configured" (they claim) is just as good as any old MPP. However, there is no "proper" way to configure a general purpose hardware architecture so that it will scale. The only way we could hope to coordinate these CPUs is in software, and the only way we can get data into the CPUs is by accessing the shared disk drives in another duck pond on completely different hardware. The SMP configuration is by definition the wrong configuration for scalable bulk processing of any kind, so there is no way to "properly configure" something that already the inappropriate storage mechanism. This would be no different than claiming that a "properly configured" VW Bug (Herbie notwithstanding) could be just as fast as a stock car. The VW Bug is not the wrong platform for general purpose transportation. But it's the wrong platform for model requiring high-scale and high-performance, just an an SMP-based RDBMS cannot scale with the same factor (for set-based bulk processing) as a machine (Netezza) specifically built for this mission. Only a purpose-built machine can ultimately harness this level of data, and only an appliance can remove the otherwise mind-numbing complexity of keeping it scalable.
In the next weeks leading up to IOD (where I will be speaking on most-things Netezza) I'll offer up some additional insights on the internals of the architecture and how it differs from the traditional platforms.
"You hate me don't you?" she whimpered, teary-eyed.
"What, the Lincoln Lawyer? You'll never get Matt to sign on -"
"No attorneys. People don't like to mix attorneys and bloodsucking, no matter how cute it sounds."
"Worked for Angel."
"Or not. He's in syndication now, right?"
"Are you saying," she swallowed hard, "That I'm a high maintenance character?"
"You would. I just don't see how Blade will change anything."
"He has more bandwidth and better technology. Lookit, how do you think Blade got so successful? He could find vamps practically anywhere, no matter how deep they tried to hide themselves."
"Yeah, how did he do that? And why can't we have writers that good?"
"Feature films, Spooki. Different realm altogether. Just ask Bella."
"Bella has hers, I have mine," she sighed, "So how does Blade do it? Haven't our writers gone to every extent to boost the story?"
"Simple. He knows where the vamps aren't. Not at the supermarket. Not at the cinema. Just by process of elimination, Blade can zone-in on his prey anywhere on the map. He's groomed for it. He doesn't have to go to every extent to find what he wants."
"There's just got to be some way we can reclaim our lead in all this. I can't believe we've been eclipsed."
"Hey, reclaim is a thing of the past. We groom our characters now. It's the only way to fly."
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 Tags:  index partition cluster search netezza maps cbt mview zone 1 Comment 21,464 Views
Once again this entry will serve as an intro to the un-initiated. I get a lot of pings from folks who are new to the stuff and want to know their way around the machine from the beginning. Zone maps are bread-and-butter power tools of the Netezza technology. The companion capabilities are the materialized view and the cluster base tables (which are new to v 6.0).
About three years ago I sat in on a special presentation to our firm from one of Netezza's competitors. They put up some power-point slides to compare their technology to "the others". Their product was a data warehousing product and all the "others" were "OLTP" products. The usual suspects were in this list of course, but oddly so was Netezza. But Netezza is not an OLTP machine in any sense of the word. How odd that this company did not know enough about its primary rival to discern this simple truth. In addition, this vendor made certain odd claims about Netezza that were either architectural false-assumptions or just misinformation. One of which was this "In a Netezza machine, the more data you add to a table, the slower the table's response will be." This is of course completely false. Netezza tables are like any data storage with one major takeaway that should not be missed:
Netezza tables may behave inconsistently if the data is not properly configured and deployed. Notice I did not say "if the hardware is not properly ...." but the data itself. Netezza already has the hardware configuration under control and there's nothing we can do to affect it. We can however, align our data with the hardware architecture and will receive the benefit of a consistent experience no matter how much the data grows. It's hard to imagine this kind of consistency on any platform, so stay with me here.
A number of years ago I did some deep immersion in a health-care expert system for Value Health Sciences. The objective was to directly apply and enforce the codes found in the CPT code book used by physicians (those are the codes the doc will check on the sheet he hands to you before he leaves the office). Insurance companies have standard billing/invoicing arrangements with the physicians and these codes are the centerpiece. For VHS, their medical practitioners had set up rules for characterizing (and properly re-characterizing) a claim. For example, if a physician billed codes 1,2 and 4 but he was supposed to have billed these all as 1 and 5 (because code 5 bundles 2 and 4 to reduce cost) the system would have a rule in it that looked something like:
1,2,4 = 1, 5
The difficulty here of course, with some 100,000 codes and over 40,000 rules, is to organize them so that they are quickly accessible. By design, the first code in the rule (eg. code "1" above) was considered an "index" code. In other words, any and all rules that would use the code "1" as an anchor would also make "1" the first code. This lended itself to the opportunity to "vector" the rules. That is, create an in-memory array, indexed by the given code, and a list of rules would be handily available at that vector point. So that all we had to do was look at the claim (e.g. codes 1,2,4) fetch the various candidate rules from their vector lists ( the list for code 1 might have 3 rules, likewise for the others, reducing our total candidate rules from 40,000 to only 9 rules). We could then run the rules and dispatch the claim. When I had first received this model, the system was physically running through all 40,000 rules to find the right match. This vectored approach reduced the total rules, but also eliminated guesswork. And with all that, boosted the power and created a very consistent turnaround time for each patient claim.
The vector list above is a common way in programming to organize information so that - if we have something in our hands already - like a filter or a vector based on a natural key - we can quickly find where we need to start looking and eliminate the places where we are not interested. While this sounds a lot like a common index-structure approach, it's actually a process-of-elimination approach. The two have similar qualities, so I will delineate them here at a high level.
An index is a surgical approach to finding data by logging its record-based keys and a record locator into a separately maintained structure. By finding keys, we find physical records. This is important for transactional systems that need fast record-level turnaround. It is not ideal however, for analytics or data warehousing where the vast majority of queries don't care about individual records at all, but about answers that span records. This alternative approach of vectoring, is a way to bucket the information so that we're not attempting to specifically locate the record itself, but the bucket where the record may be located, that is, derive a group of candidates upon which we will take further action.
When we think about this, the candidate approach, as opposed to the surgical approach is the only way to scale anything that has to deal with multiple-record sets (set-based processing). Engines that are geared to examine one-record-at-a-time even if it is to aggregate or perform multi-record operations with the single-records it retrieves, cannot compete with an engine that is designed to defacto treat the records as multiples from the outset. Transactional versus bulk. The transactional engine is inherently slower and non-scalable compared to the bulk engine (for set-based operations).
I noted in a prior entry that Netezza's anti-indexing strategy heavily leverages "where not to look" because no matter how large a table gets, this "vectoring" or "candidate" approach will guarantee that our response time is very consistent. How does this apply to zone maps?
Recall the diagram with the CPU/disk combinations. Each of these disks is divided into its smallest actionable physical portion (an extent) and the extent then becomes the currency-of-the-realm when we're talking about searching for data. The machine automatically tracks the data that is added to an extent, and when we generate-statistics, every extent is accounted for in a zone map. The map has basic statistics on the columns in the table at the extent level, such as the high/low values for a column in the given extent.
Extent ColA ColB ColC
1 200 A B
1 300 A B
1 400 C D
1 500 E F
2 200 F G
2 400 H I
2 600 J K
3 100 L M
3 200 J O
Note the above layouts of the extents. If we were to look at the high/low values of each, we would find zone maps like this:
Extent ColA High ColA Low
1 500 200
2 600 200
3 200 100
So now if we ask a question to the machine based on say, a ColA value of 200, the machine will search all three extents. It has no choice because the zone map is telling it that pertinent data exists in all three.
Now let's physically sort the data by ColA
Extent ColA ColB ColC
1 100 L M
1 200 A B
1 200 F G
1 200 J O
2 300 A B
2 400 C D
2 400 H I
3 500 E F
3 600 J K
Now if we take a look at the zone map, we would see something like:
Extent ColA High ColA Low
1 200 100
2 400 300
3 600 500
So that now if we query the table for values of 200, the machine searches only one extent, not all of them. (Now, this is a simple example, because a given value could extend across many extents). Likewise a given disk has thousands of extents upon which to store the data, so it can spread the information out into much smaller, manageable chunks.
The point, however, is that by organizing the data this way, the zone maps reduce the total searchable extents. Keep in mind that we don't have to sort the data each time we want to use it. For many warehouses, the data naturally arrives in this contiguous form (for example, transaction date in a retail model). The Netezza machine will track all integers and dates automatically without us having to do anything additional to enable it. So we might take a penalty once to sort the data at the outset, then benefit from the data's natural arrival and collation.
This effectively creates the aforementioned vectored effect. It also guarantees a consistent experience because the data associated with the given value (say 200 above) isn't going anywhere. It will always be in the same location, tracked by the same zone map. Anytime we want to get back to the records associated with value 200, the zone map will serve them up. If the response time last week was 2 seconds, we could quadruple the size of the table and it will still be 2 seconds, because the data itself didn't go anywhere.
Contrast this to other heavy-lifting algorithms in software technologies. The Ab Initio platform, for example, will perform an in-memory Sort that will "intelligently" spill to disk when it runs out of memory. What this means is that it will take a predefined block of memory and attempt to perform all work inside it until it runs out. If so, it will manufacture 16 file locations within which to spill its overflow. These are organized with the same "vectored" approach as the sort keys would dictate. If these 16 files run out of space, they will further divide into 256 files to split the workload. Anyone working with the technology will tell you that this secondary split into 256 is to be avoided if at all possible because it negatively (and significantly) affects the performance of the sort. But we see the difference here. A general-purpose sort in software needs these 256 assets to functionally complete its mission, and adding more means less performance. Now compare this to Netezza which uses not just 256, but thousands of extents to divide the data out - and rather than experience the performance drag, will derive power and lift from this.
So let's apply something additional, especially for highly active tables. If we have a nightly batch window for processing, we likely have all the time we need to keep these zone maps healthy. But if we have an intraday model or even an aggressive micro-batch model, it is possible that the zone maps may be "violated". Note above that as long as the data arrives in contiguous chunks (like transaction date) the data will be set into the extents so that we minimize the total extents for a search. If a transaction date (for example) should appear outside of this contiguous range (say a record from last week's data has arrived) then it means the high/low range for that particular extent will be extended to include the errant record. It could mean that the given extent is also included in queries that don't related to either date, because the extent is simply being included because of its high-low range. That is, every query looking for this extent's primary transaction date, the errant transaction date, or any date in between them, will include this extent in the candidate list. While this is inconsequential in most cases, if it repeats over time it can slowly degrade the table's performance. Highly active micro-batch and intraday models have a tendency to see these effects if they remain unchecked or unguarded. But not to worry - the solution is simple.
What is daunting about this is that our highly-active-table might be a multi-billion-row beast that does not lend itself to re-sorting and in an intraday model, there's no time to re-sort the data anyhow.
A materialized view has the quality of being able to apply a virtual zone map to a table without physically re-sorting the information. All we have to do is pick the columns we want to zone map, and then form the materialized view with an order-by on those columns. Rebuilding one of these is usually very fast. In addition, we can be rebuilding it even while its prior version is active. At one site, we set up a process to rebuild the view thus:
Note that for the few moment that two materialized views are applied to two tables bears no risk at all. And if we drop the original materialized view, it will wait for all pending queries to stop - meaning that this won't break anything the users are doing (another necessity for highly active intraday models)
Important here is that the materialized views are not being used as surrogate tables, but as a means to virtualize the zone maps for faster response time on the table associated with the view. Another important aspect of the materialized view is that it can zone-map additional data types that the regular zone maps do not (e.g. varchars).
Let's say that we have a very active table and even re-creating the materialized view is too much uptake to maintain it. Not to worry, the materialized view has another quality that means we don't have to refresh it each time the table changes. The materialized view maintains its contents until the underpinning table changes. We then have the option to rebuild the view or use it "as is". This "as is" is usually agreeable for the short term, since when data is added to the underpinning table, the materialized view creates an extension to itself. Now whenever we query the table, it will examine the materialized view for default zone-map information but will also examine the extent. Considering that this extra extent is very small it is also insignificant in its overhead. So we can keep this in place for say, all day long and then clean up the materialized view in the evening. The materialized view's performance will not drag over the course of a day unless we do some serious re-churning of the underpinning data (which is not normal for common micro-batch models).
And now - Cluster-based tables
With all the above preliminary stuff out of the way, a discussion on CBTs is fairly straightforward. As of the 6.0 release, Netezza has provided this as a multi-dimensional capability for even more powerful searches. The data in the CBT is physically organized into extents based on keys. And when we perform a groom operation, the data is physically reshuffled so that the keys are physically co-located on the fewest extents possible. Like a self-enforcing zone map. only a lot more powerful. In a zone map approach, the primary way to get the data contiguous is to re-sort the data and the zone maps fall out for free. In a CBT, we organize the data and the execute groom. The initial groom will take awhile, but each subsequent groom will take inconsequential time. The data in the CBT can be organized on more than one key, but the keys can behave independently like a multi-dimensional effect.
Case in point - let's say we have 200 extents of data and the particular set of data we want spans only 10 of those extents (a zone map). We will ignore the other 190 extents and focus all the machine's energy on the 10 where the data is located. But wait, what if we have an additional key upon which to search, and the combination of the first key (10 extents) and second key reduces the total extents to only 2? That is, the first key appears in 10 extents but the first and second key appear in only 2 extents of those 10. Now we search only 2 extents. Again, we have organized and configured data, not hardware, to achieve the goal. We have told Netezza where-not-to-look and now we have zoomed in on a mere 2 extents out of 200.
But let's say that we add a lot more data to the table over the year, expanding this 200 extents to many hundreds if not thousands more. Can we see now that even if this happens, we can go back to the table again and again, ask the the same question as before, and only 2 of the extents will be used to search for data. This of course dispels the vendor-promulgated propaganda that Netezza tables get slower when we add more data.