I hear a lot of feedback on the use of CDC to put data into a PureData for Analytics, Powered By Netezza Technology device. In the other machines (traditional database engines) the data flies into the box, the CDC is on-and-off the machine in seconds. But in my Netezza machine, the CDC seems to grind. I have it running every fifteen minutes, they say, and the prior CDC instance is still running when the next instance kicks off. This is totally unacceptable. Maybe we shouldn't be using CDC for this?
Or maybe they just don't have it configured correctly?
There are two major PDA principles in play here. One is strategic and the other is tactical. Many people can look at the tactical principle and accept it because it is testable, repeatable and measurable. The strategic one however, they will hold their judgment on because it does not fit their paradigm of what a database should do. I'll save the strategic one for last, because its implications are further reaching.
The CDC operation will accumulate records into a cache and then apply these at the designated time interval. This micro-batch scenario fits Netezza databases well. The secondary part of this is that the actual operation will include a delete/insert combination to cover all deletes, updates and inserts. So when the operation is complete, the contents of the Netezza table will be identical to the contents of the source table at that point in time (even though we expect some latency, that's okay).
The critical piece is this: An update operation on a Netezza table is under-the-covers a full-record-delete and full-record-insert. It does not update the record in place. A delete operation is just a subset of this combination. This is why the CDC's delete/insert combination is able to perfectly handle all deletes, updates and inserts. The missing understanding however, is the distribution key.
If we have a body of records that we need to perform a delete operation with against another, larger table, and the larger table is distributed on RANDOM, think about what the delete operation must do in a mechanical sense. It must take every record in the body of incoming records and ship it to every SPU so that the data is visible to all dataslices. It must do this because the data is random and it cannot know where to find a given record to apply the operation - it could literally be anywhere and if the record is not unique, could exist on every dataslice. It's random after all. This causes a delete operation (and by corollary an update operation) to grind as it attempts to locate its targets.
Contrast this to a table that is distributed on a key, and we actually use the key in the delete operation (such as a primary key). The incoming body of records is divided by key, and only that key's worth of data is shipped to the dataslice sharing that key - the operation is lightning-fast. This is why we say - never, ever perform a delete or update on a random table, or on a table that doesn't share the distribution key of the data we intend to affect. Deletes and Updates must be configured to co-locate, or they will grind.
Now back to the CDC operation. Whenever I hear that the CDC operation is grinding, my first question is: Do you have the target Netezza tables distributed on the same primary keys of the source table? The answer is invariably no (we will discover why in a moment). So then I ask them, what would it take to get the tables distributed on the primary key? How much effort would it be? And they invariably answer, well, not much, but it would break our solution.
Why is that?
Because they are reporting from the same tables that the CDC is affecting. And when reporting from these tables, the distribution key has to face the way the reporting users will use the tables, not the way CDC is using the tables. This conversation often closes with a "thank you very much" because now they understand the problem and see it as a shortcoming of Netezza or CDC, but not a shortcoming of how they have implemented the solution.
Which brings us to the strategic principle: There is no such thing as a general purpose database in Netezza.
What are we witnessing here? The CDC is writing to tables that should be configured and optimized for its use. They are not so, because the reporting users want them configured and optimized for their own use. They are using the same database for two purposes because they are steeped in the "normalization" protocol prevalent in general-purpose systems - that the databases should be multi-use or general-purpose.
But is this really true in the traditional databases? If we were using Oracle, DB2, SQLServer - to get better performance out of the data model wouldn't we reconfigure it into a star schema and aggressively index the most active tables? This moves away from the transactional flavor of the original tables to a strongly purpose-built flavor.
Why is it that we think this model is to be ignored when moving to Netezza? Oddly, Netezza is a Data Warehouse Appliance - it was designed to circunscribe and simplify the most prevalent practices of data warehousing - not the least of which - is the principle that there is no such thing as a general-purpose database. In a traditional engine we would never attempt to use transactional tables for reporting - they are too slow for set-based operations and deep-analytics. Yet over here in the Netezza machine, somehow this principle is either set-aside or ignored - or perhaps the solution implementors are unaware of it - and so these seemingly inexplicable grinding mysteries arise and people scratch their heads and wonder what's wrong with the machine.
And again, they never wonder what's wrong with their solution.
If we take a step back, what we will see are reports that leverage the CDC-based tables, but we will see a common theme, which I will cover in a part-2 of this article. The theme is one of "re integration" versus "pre-integration". That is, integration-on-demand rather than data that is pre-configured and pre-formulated into consumption-ready formats. What is a symptom of this? How about a proliferation of views that snap-together five or more tables with a prevalence of left-outer-joins? Or a prevalence of nested views (five, ten, fifteen levels deep) that attempt to reconfigure data on-demand (rather than pre-configure data for an integrate-once-consume-many approach?) Think also about the type of solution that performs real-time fetches from source systems, integrates the data on-the-fly and presents it to the user - this is another type of integration-on-demand that can radically debilitate source systems as they are hit-and-re-hit for the very same set-based extracts dozens or hundreds of times in a day.
I'll take a deep-dive on integration-on-demand in the next installment, but for now think about what our CDC-based solution has enticed us to do: We have now reconfigured the tables with a new distribution key that helps the reports run faster, but because this deviates from the primary-key design of the source tables (which CDC operates against) then the CDC operation will grind. And when it grinds, it will consume precious resources like the inter-SPU network fabric. The grinding isn't just a duration issue - it's inappropriately using resources that would otherwise be available to the reporting users.
What's missing here is a simple step after the CDC completes. Its a really simple step. It will cause the average "purist" data modeler and DBA to retch their lunch when they hear of it. It will cause the admins of "traditional" engines to look askance at the Netezza machine and wonder what they could have been thinking when they purchased it. But the ultimate users of the system, when they see the subsecond response of the reports and way their queries return in lightning fashion compared to the tens-of-minutes, or even hours - of the prior solution, these same DBAs, admins and modelers will want to embrace the mystery.
The mystery here is "scale". When dealing with tables that have tens of billions, or hundreds of billions of records, the standard purist protocols that rigorously and faithfully protect capacity in the traditionl engines - actually sacrifice capacity and performance in the Netezza engine. It's not that we want to set aside those protocols. We just want to adapt them for purposes of scale.
The "next step" we have to take is to formulate data structures that align with how the reporting users intend to query the data, then use the CDC data to fill them. It's not that the CDC product can do this for you. It gets the data to the box. This "next step" in the process is simply forwarding the CDC data to these newly formulated tables. When this happens, the pre-integration and pre-calculation opportunities are upon us, and we can use them to reduce the total workload of the on-demand query by including the pre-integration and pre-calculation into the new target tables. These tables are then consumption-ready, have far fewer joins (and the need for left-outer joins often fall by the way-side). After all, why perform the left-outer operations on-demand if we can perform them once, use Netezza's massively parallel power for it, and then when the users ask a question, the data they want is pre-formulated rather than re-formulated on demand.
This necessarily means we need to regard our databases in terms of "roles". Each role has a purpose - and we deliberately embrace the notion of purpose-built schemas, and deliver our solution from the enslavement of a general-purpose model. The CDC-facing tables with support CDC - we won't report from them. The reporting tables face the user - we won't CDC to them.
Keep in mind that this problem (of CDC to Netezza) can rear its head with other approaches also - such as streaming data with a replicator or ETL tool to simulate the same effect of CDC. Either way, the data arrives in the Netezza machine looking a lot like the source structures and aren't consumption-ready.
I worked with a group some years ago with a CDC-like solution, and they took the "next step" to heart, formulated a set of target tables that were separate from staging and then used an ETL tool to fill them. The protocol was simply this: The ETL tool sources the data and fills the staging tables, then the ETL sources the staging tables and fills the target tables. This provided the necessary separation, so functionally fulfilled the mission. The problem with the solution however, was that for the transformation leg, the ETL tool was yanking the data from the machine into the ETL tool's domain, reformulating it and then pushing it back onto the machine. The data actually met itself coming-and-going over the network. A fully parallelized table was being serialized, crunched and then re-paralellized into the machine. As the data grew, this operation became slower and slower. That's what we would expect right? The bottleneck now is the ETL tool. The proper way to do this, if an ETL tool must be involved, is to leverage it to send SQL statements to the machine, keep the data inside the box. The Netezza architecture can process data internally far faster than any ETL tool could ever hope - so why take it out and incur the additional penalty of network transportation?
The ETL tool aficionados will balk at such a suggestion because it is such a strong deviation from their paradigm. But this is why Netezza is a change-agent. It requires things that traditional engines do not because it solves problems in scales that traditional engines cannot. In fact, performing such transformations inside a traditional engine would be a very bad idea. The ETL tools are all configured and optimized to handle transformation one way - outside the box. This is because it is a general-purpose tool and works well with general-purpose engines. There is a theme here: the phrase "general purpose" has limited viability inside the Netezza machine. If we embrace this reality with a full head of steam, the Netezza technology can provide all of our users with a breathtaking experience and we will have a scalable and extensible back-end solution.