A Short DW DBMS Market History: HANA, Oracle, DB2, Netezza, Teradata, & Greenplum

Here is a quick review of tens years of data warehouse database competition… and a peek ahead…

Maybe ten years ago Netezza shook up the DW DBMS market with a parallel database machine that could compete with Teradata.

About six years ago Greenplum entered the market with a commodity-based product that was competitive… and then added column store to make it a price/performance winner.

A couple of years later Oracle entered with Exadata… a product competitive enough to keep the Oracle faithful on an Oracle product… but nothing really special otherwise.

Teradata eventually added a columnar feature that matched Greenplum… and Greenplum focussed away from the data warehouse space. Netezza could not match the power of columnar and could not get there so they fell away.

At this point Teradata was more-or-less back on top… although Greenplum and the other chipped away based on price. In addition, Hadoop entered the market and ate away at Teradata’s dominance in the Big Data space. The impact of Hadoop is well documented in this blog.

Three-to-four years ago SAP introduced HANA and the whole market gasped. HANA was delivering 1000X performance using columnar formats, memory to eliminate I/O, and bare-metal techniques that effectively loaded data into the processor in full cache lines.

Unfortunately, SAP did not take advantage of their significant lead in the general database markets. They focussed on their large installed base of customers… pricing HANA in a way that generated revenue but did not allow for much growth in market share. Maybe this was smart… maybe not… I was not privy to the debate.

Now Oracle has responded with in-memory columnar capability and IBM has introduced BLU. We might argue over which implementation is best… but clearly whatever lead SAP HANA held is greatly diminished. Further, HANA pricing makes it a very tough sell outside of its implementation inside the SAP Business Suite.

Teradata has provided a memory-based cache under its columnar capabilities… but this is not at the same level of sophistication as the HANA, 12c, BLU technologies which compute directly against compressed columnar data.

Hadoop is catching up slowly and we should expect that barring some giant advance from the commercial space that they will reach parity in the next 5 years or so (the will claim parity sooner… but if we require all of the capabilities offered to be present there is just no way to produce mature software any faster than 5 years).

Interestingly there is one player who seems to be advancing the state of the art. Greenplum has rolled out a best-in-class optimizer with Orca… and now has acquired Quickstep which may provide the state-of-the-art in bare metal columnar computing. When these come together Greenplum could once again bounce to the top of the performance, and the price/performance, stack. In addition, Greenplum has skinnied down and is running on an open source business model. They are very Hadoop-friendly.

It will be interesting to see if this open-source business model provides the revenue to drive advanced development… there is not really a “community” behind Greenplum development. It will also be interesting to see if the skinny business model will allow for the deployment of an enterprise-level sales force… but it just might. If Pivotal combines this new technology with a focus on the large EDW market… they may become a bigger player.

Note that was sort of dumb-luck that I posted about how Hadoop might impact revenues of big database players like Teradata right before Teradata posted a loss… but do not over think this and jump to the conclusion that Teradata is dying. They are the leader in their large space. They have great technology and they more-or-less keep up with the competition. But skinnier companies can afford to charge less and Teradata, who grew up in the days of big enterprise software, will have to skinny down like Greenplum. It will be much harder for Teradata than it was for Greenplum… and both companies will struggle with profitability for a while. But it is these technology and market dynamics that give us all something to think about, blog about, and talk about over beers…

Some Database Performance Concepts

I’m working on a new idea… it may or may not pan out… but here are some concepts for your consideration… with some thoughts on their performance implications.

First a reminder… a reality check. In my experience if you POC two databases at about the same price point…and one is 30% faster than the other, 1.3X, then 50% of the time the faster DBMS will win the business. If one DBMS is 2X faster… then it will win the business 90% of the time. The 10% where the faster product loses will be because of internal politics or, for an existing application, due to the migration costs. Note that IMO it is silly to do a POC if you know up front that you will not pick the winner.

Now to the concepts… Note that these are ballpark numbers to help you think about trade-offs…

102312_2108_HopFestP1Ex1.png

The latency to start fetching data from DRAM is 100 ns… from disk it is 10M ns. If we assume that a smart RDBMS pre-fetches 80% of the data into DRAM then we can assume that an in-memory DBMS has a 200,000X performance advantage over a disk-based system.

The latency to a Flash/SSD device is 100K-200K ns. With the same 80% pre-fetch assumption an in-memory DBMS will be 20,000X faster.

Note that neither of these models include data transfer times which will favor in-memory databases even more.

If we have a hybrid system with both disk and SSD and we assume that 90% of the reads hit the SSD and that both layers in the storage hierarchy achieve 80% pre-fetch then then the in-memory system will be 38,000X faster. If fewer than 90% of the reads hit the SSD, then the latency goes up quickly.

These numbers form the basis for selecting in-memory caches like Teradata’s Intelligent Memory option as well as in-memory offerings from IBM, Microsoft, Oracle and SAP.

For typical data warehouse workloads column compression will provide around a 2.5X performance boost over row compression. This has two implications: you will get 2.5X better performance using column storage and you will get 2.5X more data into the faster levels of your storage hierarchy… more in SSD and more in-memory.

If we assume that a typical query only touches 10% of the columns in the tables addressed… then column projection provides a 9X performance boost over a row store. Exadata does not support column projection in the storage layer… and other hybrid row-or-column systems provide it only for columnar tables.

If we assume that the average latency from the processor caches is 10ns (.5ns L1, 7ns, L2, 15ns L3) and the latency to DRAM is 100ns then an in-memory system which pre-fetches data effectively into the processor caches will be 10X faster than one which goes to DRAM. If we assume that a standard RDBMS which processes uncompressed standard data types (no vector processing) gets a 20% cache hit ratio then the advantage to a cache aware RDBMS which loads full cache lines is around 8X. HANA, BLU, and the Oracle in-memory products are cache aware and get this boost with some caveats.

BLU and the Oracle in-memory option are hybrid systems that often convert data to a row form for processing (see here for some data on Oracle). If we assume that they use the full columnar in-memory vector-based structures 50% of the time then these products will see a 4X performance boost. HANA recommends that all data be stored in a columnar form so it would often see the full 8x boost.

These vector-based processes also avoid the cost of decompression… and since they process compressed vector data they can fit more information into each cache line. There is another 20%-200% (1.2X-2X) boost here but I cannot estimate it closer than that.

Finally, the vector based processes use the high performance computing instruction sets (AVX2) offered on modern CPUs… and this provides another 10X+ boost. Again, BLU and Oracle will utilize the vector form less often than HANA so they will see a boost over products like Teradata… but not see as large a boost as HANA.

There are other features at play here… some products, like HANA, shard data in-memory to get all of the cores busy on each query. I have not been able to determine if BLU or Oracle in-memory are there yet? Note that this powerful feature will allow a single query to run as fast as possible… but the benefit is mitigated when there is a workload of multiple concurrent queries (if I have 4 cores and 4 queries running concurrently, one query per core, then the 4 queries will take only a little more time than if I run the 4 queries serially with each query using all 4 cores).

It is important to note that the Oracle In-memory option does not run in the storage component of an Exadata cluster… only on the RAC layer. It is unclear how this works with the in-memory option.

The bottom line is that in-memory systems are not all alike. You can sort of add up the multipliers to get in the ballpark on how much faster Teradata will be with the Intelligent Memory option… how much faster than that a hybrid row and vector-column system like BLU or Oracle In-Memory… and how much faster a pure in-memory system might be. One thing is for sure… these in-memory options will always make the difference in a POC… in every case including them or not will blow away the 2X rule I started with… and in every case the performance benefit will outweigh the extra cost… the price/performance is very likely to be there.

I know that is skipped my usual referencing so that you can see where I pulled these numbers from… but most of this information is buried in posts here and there on my blog… and as I stated up front… these are ballpark numbers. Hopefully you can see the sense behind them… but if you think I’m off please comment back and I’ll try to adjust…

HANA, BLU, Hekaton, and Oracle 12c vs. Teradata and Greenplum – November 2013

Catch Me If You Can (musical)
(Photo credit: Wikipedia)

I would like to point out a very important section in the paper on Hekaton on the Microsoft Research site here. I will quote the section in total:

2. DESIGN CONSIDERATIONS 

An analysis done early on in the project drove home the fact that a 10-100X throughput improvement cannot be achieved by optimizing existing SQL Server mechanisms. Throughput can be increased in three ways: improving scalability, improving CPI (cycles per instruction), and reducing the number of instructions executed per request. The analysis showed that, even under highly optimistic assumptions, improving scalability and CPI can produce only a 3-4X improvement. The detailed analysis is included as an appendix. 

The only real hope is to reduce the number of instructions executed but the reduction needs to be dramatic. To go 10X faster, the engine must execute 90% fewer instructions and yet still get the work done. To go 100X faster, it must execute 99% fewer instructions. This level of improvement is not feasible by optimizing existing storage and execution mechanisms. Reaching the 10-100X goal requires a much more efficient way to store and process data. 

This is important because it confirms the difference in a Level 3 and a Level 2 columnar implementation as described here. It is just not possible for a Level 2 implementation with a row-based join engine to achieve the performance of a Level 3 implementation. This will allow the Level 3 implementations: HANA, BLU, Hekaton, and Oracle 12c to distance themselves from the Level 2 products: Teradata and Greenplum; by more than 10X… and this is a very significant advantage.

Related articles

The Squeeze Hits Teradata

The SqueezeTeradata stock is falling hard due to guidance here that they may miss revenue targets. Analysts are downgrading the stock… but mostly from Buy to Neutral.

Normally stock prices would not be a relevant topic for this blog… but in this case I believe that the Squeeze I suggested first in October of 2012 and then again in February of this year (here and here) has started to affect Teradata revenues.

Note that the Squeeze should affect Netezza, Exadata, and Greenplum as well… but the effect will not be so directly reflected in the stock prices of IBM, Oracle, or EMC/VMWare/GE.

 

Oracle 12c IMDB Announcement at OOW13

SAP4OOWI changed the picture to show you the billboard SAP bought on US101N right across from the O HQ…

– Rob

Larry Ellison announced a new in-memory capability for Oracle 12c last night. There is little solid information available but taken at face value the new feature is significant… very cool… and fairly capable.

In short it appears that users have the ability to pin a table into memory in a columnar format. The new feature provides level 3 (see here) columnar capabilities… data is stored compressed and processed using vector and SIMD instruction sets. The pinned data is a redundant copy of the table in-memory… so INSERT/UPDATE/DELETE and data loads queries will use the row store and data is copied and converted to the in-memory columnar format.

As you can imagine there are lots of open questions. Here are some… and I’ll try to sort out answers in the next several weeks:

  1. It seems that data is converted row-to-columnar in real-time using a 2-phased commit. This will significantly slow down OLTP performance. LE suggested that there was a significant speed-up for OLTP based on performance savings from eliminating indexes required for analytics. This is a little disingenuous, methinks… as you will most certainly see a significant degradation when you compare OLTP performance without indexes (or with a couple of OLTP-centric indexes) and with the in-memory columnar feature on to OLTP performance without the redundant copy and format to columnar effort. So be careful. The use case suggested: removing analytic indexes and using the in-memory column store is solid and real… but if you have already optimized for OLTP and removed the analytic indexes you are likely to see performance drop.
  2. It is not clear whether the columnar data is persisted to disk/flash. It seems like maybe it is not. This implies that on start-up or recovery data is read from the row store on-disk tables and logs and converted to columnar. This may significantly impact start-up and recovery for OLTP systems.
  3. It is unclear how columnar tables are joined to row tables. It seems that maybe this is not possible… or maybe there is a dynamic conversion from one form to another? Note that it was mentioned that is possible for columnar data to be joined to columnar data. Solving for heterogeneous joins would require some sophisticated optimization. I suspect that when any row table is mentioned in a query that the row join engine is used. In this case analytic queries may run significantly slower as the analytic indexes will have been removed.
  4. Because of this and of item #2 it is unclear how this feature plays with Exadata. For lots of reasons I suspect that they do not play well and that Exadata cannot use the new feature. For example, there is no mention of new extended memory options for the Exadata appliance… and you can be sure that this feature will require more memory.

There was a new hardware system announced that uses this in-memory capability… If you add all of this up it may be that this is a system designed to run SAP applications. In fact, before the presentation on in-memory there was a long (-winded) presentation of a new Fujitsu system and the SAP SD benchmark was specifically mentioned. This was not likely an accident. So… maybe what we have is a counter to HANA for SAP apps… not a data warehouse at all.

As I said… we’ll see as the technical details emerge. If the architectural constraints 1-4 above hold then this will require some work for Oracle to compete with HANA for SAP apps or for data warehouse workloads…

The Fog is Getting Thicker…

English: San Francisco in fog
San Francisco in fog (Photo credit: Wikipedia)

I renamed this so that Teradata folks would not get here so often… its not really about Intelligent Memory… just prompted by it. The post on Intelligent Memory is here. – Rob

Two quick comments on Teradata’s recent announcement of Intelligent Memory.

First… very very cool. More on this to come.

Next… life is going to become very hard for my readers and for bloggers in this space. The notion of an in-memory database is becoming rightfully blurred… as is the notion of column store.

Oracle blurs the concepts with words like “database in-memory” and “hybrid column compression” which is neither an in-memory database or a column store.

Teradata blurs the concept with a strong offering that uses DRAM as a block-IO device (like the old RAM-disks we used to configure on our PCs).

Teradata and Greenplum blur the idea of a column store by adding columnar tables over their row store database engines.

I’m not a fan of the double-speak… but the ability of companies to apply the 80/20 rule to stretch their architectures and glue on new advanced technologies is a good thing for consumers.

But it becomes very hard to distinguish the products now.

In future blogs I’ll try to point out differences… but we’ll have to go a little deeper into the Database Fog.

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…

Thoughts on AWS Redshift…

English: In visible light, 4C 71.07 is less th...
English: In visible light, 4C 71.07 is less than impressive, just a distant speck of light. It’s in radio and in X-rays – and now, gamma rays – that this object really shines. (Photo credit: Wikipedia)

The shared-nothing architecture has, from the beginning, offered the promise of using hardware to solve performance problems rather than applying staff and tuning. By this I mean… if you can add nodes and scale out to improve query response then why not throw hardware at performance problems rather than build a fragile infrastructure of aggregate tables, cubes, pre-joined/de-normalized marts, materialized views, indexes, etc. Each of these performance workarounds are both expensive to build and expensive to operate.

There are several reasons, I think tuning has been more popular than scaling. Not in any particular order:

First, hardware vendors made it too hard to order/provision new nodes. You could not just press a button and buy capacity. Vendors wanted to charge you for terabytes when all you wanted might be CPU and Memory to fix the problem (see here, sigh). You had to negotiate a deal with a rep, work through your procurement group, wait weeks for delivery. Then, the hardware you have might not match the hardware for sale. New models could not be mixed with old nodes… so you had to consider a whole new cluster. The process was so not-agile. There have been attempts to fix this… and some of them are credible… but none are popular.

Next, the process to install the new nodes was moderately difficult… not rocket science but not seamless to be sure. Data had to move. Backups had to be reconfigured and sometimes old backups could not be easily restored to the new configuration. There was no easy way to burn in the new hardware and if it failed early there were issues reversing the process. It just was not considered an everyday operational process… it was the exception and that made it tough. This process too has improved over time but it never became a no-brainer.

Finally, buying hardware is a capital expense (CAPEX). Even if you had to pay more in people costs to do the hard work of tuning those were operational expenses… and funding was easier to get.

Redshift changes the game here. Even if the Paraccel database is just OK (see here)… and if the overhead of running in the virtualized AWS environment makes it worse… it is still OK. You can provision new hardware in a couple of minutes. If Teradata is 25% faster than Paraccel for your query set… so what? You can add 25% more Redshift for a fraction of the extra cost of Teradata. Need more performance? Dial it in. Need permission? No problem because it is all OPEX dollars.

Redshift will deliver the flexibility to make scale out less expensive than tune it out. The TCO reductions from running a simple system where hardware solves performance problems instead of ETL and staff will be significant. This is how it always should have been.

The issue for Redshift will be… given the trend to reduce the data latency from operations to BI… can you move significant amounts of data from on-premise into the cloud fast enough to meet service level agreements?

Do not overlook Redshift… Amazon could be a player in the EDW space… But look for other databases to make inroads here as well. In-memory databases could work well in the cloud as they avoid some of the hardware abstraction required to access disks.

Mobile Clients Require High Performance BI Computing

English: An image of an iPad 2.
(Photo credit: Wikipedia)

I posted a blog on the SAP site here that discussed the implications of mobile clients. I want to re-emphasize the issue as it is crucial.

While at Greenplum we routinely replaced older EDW platforms and provided stunning performance. I recall one customer in particular where we were given a query that ran in 7 hours and Greenplum executed the query in seven seconds. This was exceptional… more typical were cases where we reduced run-times from several hours to under 30 minutes… to 10 minutes… to 5 minutes. I’m sure that every major competitor: Teradata, Greenplum, Netezza, and Exadata has similar stories to tell.

But 5 minutes will not cut it if you are servicing a mobile client where sub-second response to the device is a requirement… and 10 minutes is out of the question. It does not matter if it ran in 10 hours before… 10 minute response is not acceptable to a mobile device.

Today we see sub-second response delivered to our phones by custom applications built on special high-performance platforms designed specifically to service a mobile client: iPhones, iPads, and Android devices.

But what will we do about the BI applications built on commercial platforms which have just used every trick in the book to become one of the 5 minute stories mentioned above?

I think that there are only a couple of architectural choices.

  1. We can rewrite the high-value queries as custom applications using specialized infrastructure… at great expense… and leaving the vast majority of queries un-serviced.
  2. We can apply the 80/20 rule to get the easiest queries serviced with only 20% of the effort. But according to Murphy the 20% left will be the highest value queries.
  3. We can tack on expensive, specialized, accelerators to some queries… to those that can be accelerated… but again we leave too much behind.
  4. Or we can move to a general purpose high performance computing platform that can service the existing BI workload with sub-second response.

In-memory computing will play a role… Exalytics provides option #3… HANA option #4.

SSD devices may play a role… but the performance improvements being quoted by vendors who use SSD as a block I/O device is 10X or less. A 10X improvement applied to a query that was just improved to 10 minutes yields a 1 minute query… still not the expected level of service.

IT departments will have to evaluate the price/performance, not just the price, as they consider their next platform purchases. The definition of adequate response is changing… and the old adequate, at the least cost, may not cut it. Mobile clients are here to stay. The productivity gains expected from these devices is significant. High performance BI computing is going to be a requirement.

SQLFire, Exalytics, TimesTen, and HANA… a quick comparison

Gemfire

As you may have noticed I’m looking at in-memory databases (IMDB) these days… Here are some quick architectural observations on VMWare‘s SQLFire, Oracle’s Exalytics and TimesTen offerings, and SAP HANA.

It is worth noting up front that I am looking to see how these products might be used to build a generalized data mart or a data warehouse… In other words I am not looking to compare them for special case applications. This is important because each of these products has some extremely cool features that allow them to be applied to application-specific purposes with a narrow scope of data and queries… maybe in a later blog I can try to look at some narrow use-cases.

Further, to make this quick blog tractable I am going to assume that the mart/dw problem to be solved requires more data than can fit on one server node… and I am going to ignore features that let queries access data that resides on disk… in-memory or bust.

Finally I will assume that the SQL dialect supported is sufficient and not drill into details there. I will look at architecture not SQL features…

Simply put I am going to look at a three characteristics:

  • Will the architecture support ad hoc queries?
  • Does the architecture support scale-out?
  • Can we say anything with regards to price/performance expectations?

Exalytics is a smart-aggregate store that sits over an Oracle database to offload aggregate query workload (see my previous post here or the Rittman Mead post here which declares: “Oracle Exalytics uses a specially enhanced version of Oracle TimesTen, Oracle’s in-memory database, to cache commonly used aggregates used in dashboards, analyses and other BI objects.” Exalytics does not support a scale-out shared-nothing architecture but it can scale up by adding nodes with new aggregate data. Queries access data within the aggregate structure and it is not possible to join to data off the Exalytics node… so ad hoc is out. Within these limits, which preclude Exalytics from being considered as a general platform for a mart or warehouse, Exalytics provides dictionary-based compression which should provide around 5X compression to reduce the amount of memory required and reduce the amount of hardware required.

TimesTen can do more. It is a general RDBMS. But it was designed for OLTP. I assume that the reason that Oracle has not rolled it out as a general-purpose data mart or data warehouse has to do with constraints that grow from those OLTP architectural roots. For example, BI queries run longer and require more data than a OLTP query… and even with data in-memory temporary storage is required for each query… and memory utilization is a product of the amount of data required and the amount of time the data has to inhabit memory… so BI queries put far more pressure on an in-memory DBMS. There are techniques to mitigate this… but you have to build the techniques in from the ground up.

I imagine that this is why TimesTen works for Exalytics, though. A OLAP query against a pre-aggregated cube does not graze an entire mart or warehouse. It is contained and “small data” (for my wacky take re: Exalytics and Exadata see here).

TimeTen is not sharded… so scalability is an issue. Oracle gets around this nicely by allowing you to partition data across instances and have the application route queries to the appropriate server. But this approach will not support joins across partitions so it severely limits scalability in a general-purpose mart or warehouse.

SQLFire is a very interesting new product built on top of Gemfire… and therefore mature from the start. SQLFire is more scalable than TimesTen/Exalytics. It supports sharded data in a cluster of servers. But SQLFire has the limitation that it cannot join data across shards (they call them partitions… see here) so it will be hard to support ad hoc queries… They provide the ability to replicate tables to support any sort of joins. If, for example, you replicate small dimension tables to coexist with sharded fact tables all joins are supported. This solution is problematic if you have multiple fact tables which must be joined… and replication of data uses more memory… but SQLFire has the foundation in place to become BI-capable over time.

Performance in an in-memory database comes first and foremost from eliminating disk I/O. All three IMDB product provide this capability. Then performance comes from the efficient use of compression. TimeTen incorporates Oracles dictionary-based “columnar” compression (I so hate this term… it is designed to make people think that Oracle products are sort-of columnar… but so far they are not). Then performance comes from columnar projection… the ability to avoid touching all data in a row to process a query. Neither TimesTen nor SQLFire are columnar databases. Then performance comes from parallel execution. Neither TimesTen nor SQLFire can involve all cores on a single query to my knowledge.

Price comes from compression as well. The more highly compressed the data is the less memory required to store it. Further, if data can be used without decompressing it, then less working memory is required. As noted, TimesTen has a compression capability. SQLFire does not appear to compress data. Neither can use compressed data. Note that 2X compression cuts the amout of memory/hardware required in half or more… 4X cuts it to a quarter… and so on. So this is significant.

Now for some transparency… I started the research for this blog, and composed a 1st draft, last Spring while I was at EMC Greenplum. I am now at SAP working with HANA. So… I will not go into HANA at great length… but I will point out that: HANA fully supports a shared-nothing architetcture… so it is fully scalable; HANA is fully parallel and able to use all cores for each query; HANA fully supports columnar tables so it provides deep compression and the ability to use the compressed data in execution. This is not remarkable as HANA was designed from the bottom up to support both BI and OLTP workloads while TimesTen and SQLFire started from a purely OLTP architectural foundation.

References:

vFabric SQLFire User’s Guide

Oracle Times Ten In-Memory Database Architectural Overview