Tag: EDW

MPP on HANA, Exadata, Teradata, and Netezza

6 May… There is a good summary of this post and on the comments here.  – Rob

17 April… A single unit of parallelism is a core plus a thread/process to feed it instructions plus a feed of data. The only exception is when the core uses hyper-threading… in which case 2 instructions can execute more-or-less at the same time… then a core provides 2 units of parallelism. All of the other stuff: many threads per core and many data shards/slices per thread are just techniques to keep the core fed. – Rob

16 April… I edited this to correct my loose use of the word “shard”. A shard is a physical slice of data and I was using it to represent a unit of parallelism. – Rob

I made the observation in this post that there is some inefficiency in an architecture that builds parallel streams that communicate on a single node across operating system boundaries… and these inefficiencies can limit the number of parallel streams that can be deployed. Greenplum, for example, no longer recommends deploying a segment instance per core on a single node and as a result not all of the available CPU can be applied to each query.

This blog will outline some other interesting limits on the level of parallelism in several products and on the definition of Massively Parallel Processing (MPP). Note that the level of parallelism is directly associated with performance.

On HANA a thread is built for each core… including a thread for each hyper-thread. As a result HANA will split and process data with 80 units of parallelism on a high-end 40-core Intel server.

Exadata deploys 12 cores per cell/node in the storage subsystem. They deploy 12 disk drives per node. I cannot see it clearly documented how many threads they deploy per disk… but it could not be more than 24 units of parallelism if they use hyper-threading of some sort. It may well be that there are only 12 units of parallelism per node (see here).

Updated April 16: Netezza deploys 8 “slices” per S-Blade… 8 units of parallelism… one for each FPGA core in the Twin times four (2X4) Twinfin architecture (see here). The next generation Netezza Striper will have 16-way parallelism per node with 16 Intel cores and 16 FPGA cores…

Updated April 17: Teradata uses hyper-threading (see here)… so that they will deploy 24 units of parallelism per node on an EDW 6700C (2X6X2) and  32 units of parallelism per node on an EDW 6700H (2X8X2).

You can see the different definitions of the word “massive” in these various parallel processing systems.

Note that the next generation of Xeon processors coming out later this year will have 8X15 processors or 120 cores on a fat node:

  • This will provide HANA with the ability to deploy 240 units of parallelism per node.
  • Netezza will have to find a way to scale up the FPGA cores per S-Blade to keep up. TwinFin will have to become QuadFin or DozenFin. It became HexadecaFin… see above. – Rob
  • Exadata will have to put 120 SSD/disk drive combos in each node instead of 12 if they want to maintain the same parallelism-to-disk ratio with 120 units of parallelism.
  • Teradata will have to find a way to get more I/O bandwidth on the problem if they want to deploy nodes with 120+ units of parallelism per node.

Most likely all but HANA will deploy more nodes with a smaller number of cores and pay the price of more servers, more power, more floor space, and inefficient inter-node network communications.

So stay tuned…

Some Unaudited HANA Performance Numbers

Fast
Fast (Photo credit: Allie’s.Dad)

The following performance numbers are being reported publicly for HANA:

  • HANA scans data at 3MB/msec/core
    • On a high-end 80-core server this translates to 240GB/sec per node
  • HANA inserts rows at 1.5M records/sec/core
    • Or 120M records/sec per node…
  • Aggregates 12M records/sec/core
    • Or 960M records per node…

These numbers seem reasonable:

  • A 100X improvement over disk-based scan (The recent EMC DCA announcement claimed 2.4GB/sec per node for Greenplum)…
  • Sort of standard OLTP insert speeds for a big server…
  • Huge performance gains for in-memory aggregation using columnar orientation and SIMD HPC instructions…

Note that these numbers are the basis for suggesting that there is a new low-TCO approach to BI that eliminates aggregate tables, materialized views, cubes, and indexes… and eliminates the operational overhead of computing these artifacts… and still provides a sub-second response for all queries.

Indexes are not a good thing… A blog on TCO

In many of my posts I refer to the issues associated with building “extra” data structures to meet performance goals (see one of my first posts ever here). These extra structures are always a trade-off… slowing the performance of one function in order to speed up another. I thought that it might be helpful to be very clear about where I stand on this.

Indexes improve the performance of queries that address a small set of data. They also can improve join performance if your favorite optimizer can apply an index intersection to the execution plan for your queries. Indexes dramatically slow the performance of inserts, updates, and bulk data loads as they have to be maintained when data changes. You can mitigate the cost and update indexes in the background… the trade-off does not go away. Indexes are probably required for OLTP applications that pick out single rows.

Wouldn’t it be great if your favorite DBMS could resolve every query very fast without the overhead and operational effort associated with maintaining indexes? Certainly we should aspire to a read-optimized database, a data warehouse DBMS, that does not require indexes.

Vertica projections provide an optimized, materialized, view that improves the performance for a set of queries. The Vertica optimizer automatically selects the optimal projection. Vertica provides a very slick tool that builds projections based on the query set provided. I worded my post on Vertica a little vague… so let me be sure here to point out that  every Vertica query runs against a projection… so it is possible to have only one. In this case there is no additional overhead. Adding projections slows the data load process and increases the storage requirements. This is the trade-off.

Other databases offer materialized views. They make the same trade-off as above.

An OLAP cube is a physical structure that pre-aggregates data so that your query workload can avoid the aggregation. The best implementations of this express the cube as a materialized view so that queries can use the pre-aggregated data without explicitly pointing at a cube structure… the optimizer picks it for you. In addition the best implementations let you drill out of the cube to the detail records. These products have the update/delete/load issues of an index plus add an extra data latency issue as the data has to be aggregated on some interval… usually hours or days. Many products do not allow joins from a cube. You can see the trade-off. The Oracle Exalytics product materializes the aggregated cube on a separate server in-memory. This provides even more performance but adds the system and operational overhead of moving data across system boundaries.

Wouldn’t it be nice if you could query raw data and perform aggregation so fast that even against terabytes of data you could run any query with 3 second or less response without the overhead of building cubes?

You may build specialized table structures and pre-join, pre-aggregate, or pre-compute data to make a set of queries run fast. The cost of building and maintaining this sort of implementation versus just querying the base tables is the trade-off. Further, this approach is sort of a trap. You cannot build these structures for every query… if you did the business would conceive another critical query the next day that required work.

You can add indexes to the structures built using the technique above and provide very fast application-specific performance to a small set of queries. This is currently the favored approach when companies build iOS or Android apps as it provides the best possible performance… at a significant price.

Wouldn’t it be great if this was unnecessary… you could just scan so fast that mobile response service levels could be met from the base data regardless of the query.

You can deploy redundant data in operational data stores, data marts, cube servers, analytic data stores, and so on… with each specialized store providing performance for some limited set of queries at the cost of development and support ongoing. Each of these copies could deploy specialized database products that speed up that set of queries a little more. Again, this surround-the-EDW approach is a trap that leads to the proliferation of data marts and of database technologies.

Please do not take that last paragraph the wrong way… I believe that the worst possible approach is to blindly standardize on one or two database products. This trade-off makes life convenient for the IT department at the expense of performance and agility in the business. It is OK to have one or two favored products but IT must always serve the business to the best of their ability as a first priority… and sometime the new start-up has just the thing (remember that once Teradata was a start-up and DB2 on the mainframe was the IT standard…).

What I wish was that one or two products could solve all of the performance and functionality problems without the cost of building “extra” stuff… one product would be better that two. I like products that make the extra stuff “free”. Netezza does a nice job of making zone maps “free”, for example. Teradata and Greenplum provide the option of row store or column store for “free”. Vertica automatically build extra projections for “cheap”… and while there is a cost to the projection it at least does not require staff to tune it up. Oracle materialized views are “cheap”.

What I dislike are products that require DBAs to work harder and harder to apply all of the techniques above to meet performance SLAs. Each of these techniques trades off performance for development and operational expense.

As I have noted before… the performance SLAs for BI are about to become severe as companies try to support BI on mobile devices. The development and operational costs of tuning up; that is the TCO; will be significant unless better, faster, software infrastructure becomes available.

The TCO for a database that could eliminate these extra constructs and could eliminate the cost of developing and maintaining them; and could eliminate the architectural fragility these approaches imply… and replace this with a DBMS that holds base data which could satisfy all queries in seconds; delivering the business agility this implies… the TCO would be compelling.

I actually believe that the answer is available in the market today… this is no longer a pipe dream… more later…

My 2 Cents: Greenplum 1Q2013

Unripe plums
Unripe plums (Photo credit: Wikipedia)

Since my blogs tend to be in response to some stimulus they may not reflect a holistic view on any particular product. The “My 2 Cents” series will try to provide a broader view…

Please consider this as you read on…

Summary

From a technical perspective, Greenplum is my favorite data warehouse database. Built on the same architecture as Teradata (see here), the Greenplum team was able to extend the core of Postgres… first building out a shared-nothing architecture and then adding feature after feature… putting the heat on the other major players. Greenplum was the first row-based RDBMS to add full columnar support… and their data-loading capability is second-to-none.

Oddly they do not want to be in the data warehouse space. Their recent announcement (here) does not include any reference to data warehousing or business intelligence. The tweets from @Greenplum, the Greenplum website, and all things marketing are focussed on analytics and/or Hadoop. Even their page on data warehousing (here) has no articles on data warehousing. It is just not their target market. That is fine… the product is still a great EDW platform… but it is a worry.

Where They Win

The reason they target analytics is because they excel there. If your warehouse workload clogs because of big, complex, queries… Greenplum can win the day. Their data flow architecture, which keeps tuples moving from execution step to execution step without writing to spool provides them with the ability to beat the competition on analytics. They provide a very rich set of in-database analytics and some add-on capabilities to improve the productivity of your data scientist team.

Their data load architecture, which they call scatter-gather, is a big differentiator. If your problem is that you cannot get data loaded and reports out in your nightly batch window then the combination of scatter-gather and the ability to run big report queries is unbeatable.

Greenplum also has a unique solution for near-real-time. They marry Gemfire, an in-memory object-oriented database, with scatter-gather to move small batches of inserted data to Greenplum with a very small time delta. I do not believe this solution supports inserts or deletes as they have to be applied directly to the Greenplum database… but it is a nice capability for a certain class of problems.

Where They Lose

Greenplum, like Teradata, can be beat when the problem to be solved is narrow. In these cases, when the database supports a single application with a small number of queries or when it supports a narrowly focussed data mart, they are vulnerable to Netezza, Vertica, or even Exadata. It is also sometimes the case that a poorly designed POC can narrow the scope enough that Greenplum loses.

Greenplum can also lose when a full EDW is required. The basic architecture of the RDBMS is capable of supporting an EDW… but some of the operational features required… RASR, workload, incremental backup, etc. are not mature. This may well be the intentional result of their focus away from these features at analytics.

In the Market

Despite the worries Greenplum should be included in every POC. They will push Teradata hard in performance and in price/performance.

As noted here… I do not understand their market strategy. It seems that they are competing with themselves by offering Hadoop for analytics… but this cannot be a bad thing for customers even if it is an odd position in the market. The analytics market they favor is tough… relatively small (compared to the DW space)… emerging… there are several capable competitors… and the market is haunted by the same problem that killed the data mining market in the mid-1990’s… there are just not enough skilled data scientists (see here).

My Guess at the Future

I cannot guess at the future of Greenplum… They are being moved into a new business unit that could be spun into a new company that has a charter to build software for the cloud (see here). This is odd in several dimensions. First, as I noted here, the shared nothing architecture Greenplum is built on is not a perfect fit for the cloud. There are ways to get around this (maybe the topic for a future post?) but it will require development in a fundamentally new direction. Further, the new division seems to be a software-only venture. This makes the future of the EMC Greenplum Data Computing Appliance uncertain. I suppose that there will be announcements soon to clarify these questions… but the architectural disconnects make it likely that there will be some arm-waving for a while.

Next up… my 2 Cents on The Rest…

My 2 Cents: Netezza 1Q2013

The TwinFin Surf Board
The TwinFin Surf Board (Photo credit: tvanhoosear)

Since my blogs tend to be in response to some stimulus they may not reflect a holistic view on any particular product. The “My 2 Cents” series will try to provide a broader view…

Please consider this as you read on…

Summary

Netezza put a new spin on data warehousing… they made it easy. The Netezza software includes a unique clustered index feature called a zone map that is powerful and easy to use. They also use a FPGA co-processor to augment the CPUs, offloading data compression and projection. When both of these innovations combine Netezza is hard to beat.

Zone maps are powerful when they can be used in a query plan… but the hardware is only good, not great, when zone maps are not in the plan. FPGAs provided a huge boost when Netezza first came on the scene… but as discussed here they do not provide the same boost today. In addition, FPGAs may limit the ability of a Netezza cluster to handle concurrent queries (see here and especially the comments).

The IBM acquisition has opened up a market of Blue shops to Netezza… so they are selling… and as a result Netezza is here to stay.

Where They Win

Of course, Netezza will win in all-Blue shops.

Netezza wins when there is a naturally sequenced field in each big table that is also used in the predicate for most queries. For example, if data is naturally in date/time sequence and every query has a date/time constraint then Netezza is hard to beat. This is the case most often for focussed data marts or single application databases… so look for Netezza for these sort of problems.

Netezza wins when there are a relatively small number of concurrent queries… and they can win when the queries are complex… as long as the zone map is in the plan.

Netezza can win when the POC is designed such that zone maps may be used in the POC… for example when the POC models only a single data load and the data is pre-sorted… even when the real application would fragment the data (for example… data will not naturally enter the warehouse sequentially by customer number… the same customer will be represented time and again… but if you load once only for a POC then you can sort by customer number and use it in the query predicates).

Note that I am not saying that Netezza is a poor performer when zone maps are not used… it is good… but they would never win a POC if no queries used the zone map.

Where They Lose

Guess what? Netezza loses when the zone maps cannot be used or can be used for only a small fraction of the query workload. Note again that the use of a zone map depends on two factors: the data has to be in sequence over all time, and the queries must use the columns mapped in the predicate. If data enters the system out of sequence then the zone map fragments and eventually loses the ability to speed up queries (a few random out of sequence rows are OK).

This constraint makes it hard for Netezza to service data warehouses where, by definition, lots of different user constituencies come at the data from lots of different directions… rather than always using the path grooved with a zone map.

Netezza was designed when only Sybase IQ had columnar oriented tables… today columnar is in nearly every DW database and this allowed the competition to cut deeply into Netezza’s competitive, zone-map enabled, edge. Teradata columns, Greenplum columns, or the natural column stores can win even when zone maps are on target.

Bottom line: do a POC…

In the Market

I spend most of my time in the general market for data warehousing. You won’t see me offer much of an opinion on HANA for BW, for example… even though there are ten thousand plus BW warehouses I just do not see them in the places I work.

Before Netezza was acquired by IBM they were everywhere… in nearly every POC. Now… not so much. To a very large extent they seem to have been directed into the Blue-only customer base (now that I think about it the same thing happened to the Ascential Data Stage suite of ETL products).

My Guess at the Future

As I noted in the reference above… I think that Netezza will eventually go away from the co-processor strategy.

There have been rumors for several years of design that allowed multiple zone maps. This would be very important… but loading out-of-sequence data, which is the necessary the result, could be very slow.

Netezza has lost some of its edge as other technologies added columnar capabilities to their technologies… and Netezza is surely looking at this… but their architecture which includes an execution engine on the server and on the FPGA makes this more complex than you might suspect. Zone maps and two-stage optimization (one in the server and once in the FPGA) is cool… but a tight coupling of the tricks makes for a difficult time extending and adding new features.

If I were the King of Netezza and I could not find a reasonable way to extend beyond the two tricks that got me here I would go with the flow… I would position Netezza as an extremely easy-to-deploy data mart appliance and hook it tightly (i.e. build in some integration) along-side DB2 and Hadoop… and I would cede the EDW space to DB2 and the Big Data space to Hadoop.

Next up… my 2 Cents on Greenplum

May 1, 2013: Here is an update, or maybe a summary, of my view on Netezza… – Rob

Will Hadoop Eat Greenplum and Netezza?

If I were the Register I would have titled this: Raging Stuffed Elephant To Devour Two Warehouse Vendors… I love the Register… if you do not read it have a look…

This is a post is about the market implications of architecture…

Let us assume that Hadoop matures and finds a permanent place in the market. This is not certain with some folks expressing concern (here) and others boundless enthusiasm (here). So let’s assume… and consider where it might fit.

The SqueezeOne place is in the data warehouse market… This view says Hadoop replaces the DBMS for data warehouses. But the very mature BI/DW market requires a high level of operational integrity and Hadoop is not there yet… it is advancing rapidly as an enterprise platform and I believe it will get there… but it will be 3-4 years. This is the thinking I provided here that leads me to draw the picture in Figure 1.

It is not that I believe that Hadoop will consume the data warehouse market but I believe that very large EDW’s… those over 1PB… and maybe over 500TB will be compelled by the economics of “free” to move big warehouses to Hadoop. So Hadoop will likely move down into the EDW space from the top.

Another option suggests that Big Data will be a platform unto itself. In this view Hadoop will sit beside the existing BI/DW platform and feed that platform the results of queries that derive structure from unstructured data… and/or that aggregate Big Data into consumable chunks. This is where Hadoop sits today.

In data warehouse terms this positions Hadoop as a very large independent analytic data mart. Figure 2 depicts this. Note that an analytics data mart, and a Hadoop cluster, require far less in the way of operational infrastructure… they share very similar technical requirements.Hadoop Along Side

This leads me to the point of this post… if Hadoop becomes a very large analytic data mart then where will Greenplum and Netezza fit in 2-3 years? Both vendors are positioning themselves in the analytic space… Greenplum almost exclusively so. Both vendors offer integrated Hadoop products… Greenplum offers the Greenplum database and Hadoop in the same hardware cluster (see here for their latest announcement)… Netezza provides a Hadoop connector (here). But if you believe in Hadoop… as both vendors ardently do… where do their databases fit in the analytics space once Hadoop matures and fully supports SQL? In the next 3-4 years what will these RDBMSs offer in the big data analytics space that will be compelling enough to make the configuration in Figure 3 attractive?

Unified HadoopI know that today Hadoop cannot do all that either Netezza or Greenplum can do. I understand that Netezza has two positions in the market… as an analytic appliance and as a data mart appliance… so it may survive in the mart space. But the overlap of technical requirements between Hadoop and an analytic data mart… combined with the enormous human investment in Hadoop R&D, both in the core and in the eco-system… make me wonder about where “Big Data” analytic relational databases will fit?

Note that this is not a criticism of the Greenplum RDBMS. Greenplum is a very fine product, one of the best EDW platforms around. I’ll have more to say about it when I provide my 2 Cents… But if Figure 2 describes the end state for analytics in 2-3 years then where is the place for the Figure 3 architecture? If Figure 3 is the end state then I do not see where the line will be drawn between the analytic workload that requires Greenplum and that that will run on Hadoop? I barely can see it now… and I cannot see it at all in the near future.

Both EMC Greenplum and IBM seem to strongly believe in Hadoop… they must see the overlap in functionality and feel the market momentum of Hadoop. They must see, better than most, that Hadoop wins this battle.

My 2 Cents: Oracle Exadata 1Q2013

English: The logo of Oracle Corporation de:Bil...
(Photo credit: Wikipedia)

Since my blogs tend to be in response to some stimulus they may not reflect a holistic view on any particular product. The “My 2 Cents” series will try to provide a broader view…

To help pay the bills please consider this as you read on…

Summary

OK, I hate Oracle marketing (see here and here). They are happy to skirt the edge of the credible too often. But let’s be real… Exadata was a very smart move… even if it a flawed product. The flaws are painful but not fatal… and Oracle can now play in the data warehouse space in places they could not play before. I do not believe that Exadata is a strong competitor as you will see below… it will not win many “fair” POCs… but the fight will be more than close enough to make customers with existing Oracle warehouses pick Exadata once they consider the cost of migration. This is tough… it means that customers are locked in to a relatively weak alternative… and every Oracle customer (and every Teradata customer and every SQL Server customer and every DB2 customer) should consider the long-term costs of vendor lock-in. But each customer has to weigh this for themselves… and this evaluation of the cost of lock-in is about neither architecture nor marketing…

Where They Win

First and foremost Exadata wins when there is an existing data warehouse or data mart on Oracle that will have to be migrated. My recommendation to customers is that they think about this carefully before they engage other vendors. It is a waste of everybody’s time to consider alternatives when in the end no alternative has a chance… and it is a double waste to do a POC when even a big technical win by a competitor cannot win them the business.

Exadata can win technically when the data “working set” is small. This allows Exadata to keep the hot data in SSD and in memory and better still, in the RAC layer. This allows Oracle to win POCs where that can suggest a subset of the EDW data is all that is required.

Exadata can win when the queries required, or tested, contain highly selective predicates that can be pushed down in the first steps of the explain plan. Conversely, Exadata bonks when lots of data must be pulled to the RAC layer to perform a join step.

Where They Lose

Everyone who has an Exadata system or who is considering one should view the two videos here. The architectural issues are apparent… and you can then consider the impact for your workload.

As noted above… in an Exadata execution plan the early simple table scans and projection are executed in the storage layer… subsequent steps occur in the RAC layer… if lots of data has to be moved up then the cluster chokes.

There are times when the architectural limitations are just too large and a migration is required to meet the response time requirements for the business. This often happens when Exadata is to support a single application rather than a data warehouse workload… In other words, if the cost of migrating away from Oracle is small, either because the applications to be moved are small or because an automated tool is available to mitigate the cosy or because the migration costs are subsidized by another source, then Exadata can lose even when there is a migration required.

Exadata can be beat on price… unless you count the cost of migration.

In the Market

For the reasons above, Exadata wins for current Oracle customers. There was a honeymoon when Exadata was winning some greenfield deals against other competitors… but these are now more rare.

My Guess at the Future

I think that the basic architecture of Exadata is defensible… having a split configuration is , after all, not completely foreign. Teradata and Greenplum and others use master nodes split from data nodes… and this is where is I predict we’ll see Oracle go. Over time, more execution steps will move to the storage layer and out of the RAC layer and in the end, Exadata will look ever more like a shared-nothing implementation. This just has to be the architectural way forward for Exadata (but don’t expect LE to stand up anytime soon and admit that he was wrong all of these years about the value of a shared-nothing architecture).

Phil has alerted us that there will be some OLTP/BI enhancements coming (see the comments section here)… which stole away a prediction I would have made otherwise.

The bottlenecks pointed out by Kevin Closson (as above and more here) need to be addressed… but to some extent these issues are the result of hardware constraints… and the combination of better hardware configurations and the push-down of more execution steps can mitigate many of the issues.

It will be a while before the Exadata architecture evolves to a point where the product is more competitive… and from now to then I think the World will be as I described it above… Oracle zealots will pick Exadata either as a religious stance or to avoid the cost of a migration… others will mostly go elsewhere…

Coming next… my 2 Cents on Netezza…

My 2 Cents: Teradata 1Q2013

Since my blogs tend to be in response to some stimulus they may not reflect a holistic view on any particular product. The “My 2 Cents” series will try to provide a broader view…

Teradata Storage Rack
Teradata Storage Rack (Photo credit: pchow98)

Summary

Despite my criticisms of some of their market positions (here, here, here, and here) Teradata provides the single best data warehouse platform in the market, hands-down. As an EDW, or data mart it is the best. It will be very competitive as an analytics mart and/or as an operational data store. It has a very complete eco-system of utilities and offers a robust set of Reliability, Availability, Serviceability, and Recoverability (RASR) features to make the eco-system solid. Performance is very good… Teradata should win more POCs than they lose… and they have become more competitive on price… so their price/performance is good if not great.

I recommend a POC for most customers in most cases… you can often save 20%-30% in a competitive situation.. but if you don’t have any special requirements… if you are building a standard BI/DW eco-system then Teradata would be the only vendor I would trust without a POC.

Where They Win

Now that they support columnar tables and columnar projection Teradata should win way more POCs than they lose (before columnar support they could lose to the column stores or to hybrids like Greenplum). The Teradata optimizer is very robust. It efficiently solves for a broad array of queries, and for a mixed workload that cuts across the data is many ways. This makes Teradata well-suited as the platform for an EDW.

Every RDBMS has a sweet spot where they win… so Teradata will not win every POC. But if you POC for an EDW and you prove with a full contingent of data, with queries that cut across the data in several ways, with a fair emulation of data loading, querying, loading , and querying… with a full workload… Teradata is tough to beat.

Where They Lose

The shared-nothing architecture is an imperfect fit on a single node… so other players can win smaller data warehouses that can fit on 1-2 nodes. In addition, they can be beat for very large configurations (1PB and above…) by Hadoop.

Teradata can be beat when the workload consists of very complex queries and/or where the problem to be solved requires fantastic response on a small number of CPU-intensive queries… this is a side-effect of spooling the intermediate results to a block device.

Teradata can be beat when data is trickled in at a high, continuous, rate.

Teradata can be beat when a query set goes through the data in a narrow way, using a single index or the equivalent, as might be the case for a data mart.

Teradata can be beat on price.

In the Market

For the reasons above, Teradata is the leader in the DW platform market. Recent competition from Exadata, Netezza, Greenplum, Vertica… and now HANA… has cut margins but not impacted business growth too much. Competitors have projected Teradata’s demise for 20 years now… but the product continues to set the standard.

As noted here, I believe that Hadoop will squeeze Teradata at the 1PB level and above…

My Guess at the Future

Teradata has three architectural challenges to address… and I suspect they will manage all three more-or-less.

First, the old architecture which was designed for very small DRAM configurations forces unnecessary I/O in violation of Gray and Putzolu’s Five Minute Rule (see here). This will be mitigated in the short-term by writing spool to SSD devices… and in the medium term by writing spool to NVRAM. If these mitigations are not sufficient then Teradata may have to consider re-engineering in a data flow scheme… but this will be tough.

Next, there are several advances in network technology coming in the next 2-3 years… and software defined networks will impact the space as well. ByNet may have served its purpose… providing Teradata with a significant edge for 20+ years… but Teradata may consider moving to an off-the-shelf network (see here).

Finally, a truly active data warehouse requires support for simultaneous OLTP and BI workloads… I would expect Teradata to build in the sort of hybrid OLTP/BI table capability now supported by both Vertica and HANA… and quasi-supported by Gemfire/Greenplum.

Teradata has some interesting business challenges as their margins shrink… and one of those challenges is that their expensive 3-person relationship/technical/industry sales team approach will face some pressure. But it is these sales teams that also provide Teradata an edge. They are the only databases vendor who can field team after team of veterans who understand both the technology and the vertical space.

If I were King of Teradata I might try to push downstream and build a configuration optimized for the low end. This would not be a high-margin hardware business but it would sell services and increase market share.

Getting started with Hadoop… Enhance Your Data Warehouse Eco-system

Gartner thinks that the Big Data hype is going to die down a little for the lack of progress… (see here) Companies without web-scale, big, data are finding it hard to do anything commercially interesting… still CIO’s sense that Hadoop is going to become important. This post provides a suggestion that might help you to get started.

Hadoop goes here

In most data warehouse eco-systems there is an area, a staging place, where data lands after it is extracted from the source and before it is transformed. Sometimes the staging area and the ETL process are continuous and data flows through the ETL hardware system without seeming to land… but it usually is written somewhere.

The fact is that often enterprises only move data to their data warehouse that will be consumed by a user query. Often users want to see only lightly aggregated data in which case aggregation is part of the ETL process… the raw detail is lost. A great example of this comes from the telecommunications space. Call details may be aggregated into a call record… and often call records are sufficient to support a telco’s business processes.

But sometimes the detail is important. In this case the staging area needs to become a raw data warehouse… a place where piles of data may be stored inexpensively for a time… possibly for a long time.

This is where Hadoop comes in. Hadoop uses inexpensive hardware and very inexpensive software. It can become your staging area and your raw data warehouse with little effort. In subsequent phases, you can build up a library of the jobs that need to look at raw data. You might even start to build up a series of transformations and aggregations that might eventually replace your ETL system.

This is what Sears Holdings is up to (see here).

As I suggested in an earlier post, the economics of Hadoop make it the likely repository for big data. Using Hadoop as the staging area for your data warehouse data might provide a low risk way to get started with Hadoop… with an ROI… preparing your staff for other Hadoop things to come…

 

HANA Support for OLTP and BI In a Single Table

Aerial view of Hana, Maui
Aerial view of Hana, Maui (Photo credit: Wikipedia)

This is a rehash of my post for SAP here… I thought you might find it interesting as it describes the architecture HANA uses to support OLTP and BI against a single table.

A couple of points to think about:

  • If you have only one database structure you can optimize for only one query; e.g. the OLTP query is fast against a OLTP structure but slow against a BI structure… or visa versa.
  • If you have two structures you have to ETL the data between the two at some cost. There is cost in keeping a replica of the data, cost in developing, administering, and executing the ETL process. In addition there is a lost opportunity cost hidden in the latency of the data. You cannot see the current state of the business by querying the BI data as some data has not yet been ETL’d across.
  • OLTP performance is normally paramount; so the perfect system would not compromise that performance or compromise it only a little.

Let’s look at the HANA approach to this at a high level.

HANA provides a single view of a table to an application or a user, but under-the-covers each table includes a OLTP optimized part, a BI optimized part, and a mechanism for moving data from one part to the other

When a transaction hits the system; inserts, updates, and deletes are processed in the OLTP part with no performance penalty. The read portion of the OLTP query accesses the read-optimized internal structure with no performance penalty. Note that reading a single column in a column store, which is the key for the transaction, is roughly equivalent to reading an index structure on top of a standard disk-based DBMS. Except the column is always in-memory which means I/O is never required. This provides the HANA system with an advantage over a disk-based system. Disk I/O is 120+ times slower than memory access so even an index is unlikely to beat in-memory. See here for some numbers you should know.

After the transaction is committed into the internal, OLTP-optimized part, a process starts that moves the data to the BI optimized part. This is called a delta merge as the OLTP portion holds all of the changes, the delta, in the data set.

When a BI query starts it can limit the scan to only partitions in the BI optimized part, or if real-time data is required it can scan both parts. The small portion of the scan that accesses the OLTP/delta portion is sub-optimal when compared to the scan of the BI part,  but not slow at all as the data is all in-memory.

We can tease the performance apart as follows:

  1. There is a OLTP insert/update/delete “write” portion… and HANA executes this like any OLTP database, as fast as an OLTP RDBMS, with a commit after a write-to-log;
  2. There is a OLTP select “read” portion… and HANA performs this in the in-memory column store faster than many OLTP databases… and scans the delta structure as fast as any OLTP database;
  3. There is a delta merge from the OLTP write-optimized part to the BI read-optimized column store that is hundreds to tens of thousands of times faster than any ETL tool; and
  4. There is a BI select portion that scans the in-memory column store hundreds to thousands of times faster than a disk-based BI database.
  5. If the BI query requires access to real-time data then an in-memory scan of the delta file is required… there is no analogy to this in a system with separate OLTP and BI tables.
The implementation uses MVCC instead of locks.

Nice.