IBM is presenting a DB2 Tech Talk that compares the BLU Accelerator to HANA. There are several mistakes and some odd thinking in the pitch so let me address the issues as a way to explain some things about HANA and about BLU. This blog will consider what data needs to be in-memory.
IBM like several others, continues to repeat a talking point along the lines of: “We believe that you should not have to fit all of you active data in memory…”. Let’s think about this…
Note that in the current release HANA has a constraint that all of the data in a single column, the entire vector that represents the data in that column, must be in-memory before it can be operated on. If the table is partitioned and partition-elimination is applied then the data in the partition for the column must be loaded in-memory. This is a real constraint that will be removed in a subsequent release… but it is not a very severe constraint if you think about it.
But let’s be clear… HANA does not require all data to be in-memory… it will read data from peripheral devices in and out as required just as BLU does.
Now what does this mean? Let’s walk through some scenarios.
First, let’s imagine a customer with 10TB of user data, per the scenario IBM discusses. Let’s not get into a whose product compresses better discussion and assume that both BLU and HANA will get 4X compression… so there is 2.5TB of user data to be processed.
Now let’s imagine a system with only a very little memory available for data. In other words, let’s configure both BLU and HANA so that they are full columnar databases, but not in-memory databases. In this case BLU would operate by doing constant I/O without constraint and HANA would fail whenever it could not fit a required column in memory. Note that HANA might not fail at all… it would depend on whether there was a large single un-partitioned column that was required.
This scenario is really silly though… HANA is an in-memory database, designed to keep data in-memory from the start… so SAP would not support this imaginary configuration. The fact that you could make BLU work out of memory is not really relevant as nowhere does IBM position, or reference, BLU as a disk-based column store add-on… you would just use DB2.
Now let’s configure a system to IBM’s specification with 400GB of memory. IBM does not really say how much of this memory is available to BLU for data… but for the sake of argument let’s ignore the system requirements and assume that BLU uses one-half, 200GB, as work space to process queries so that 200GB is available to store data in-memory. As you will see it does not really matter in this argument whether I am spot on here or not. So using IBM’s recommendation there is now a 200GB cache that can be used as data is paged in and out. Anyone who has ever used a data warehouse knows that caching does not work well for BI queries as each query touches large enough volumes of the data to flush the cache… so BLU will effectively be performing I/O for most queries and is back to being an out-of-memory columnar database. Note that this flushing issue is why the in-memory capabilities from Oracle and Teradata pin certain tables into memory. In this scenario HANA will operate exactly as BLU does with the constraint that any single column that in a compressed form exceeds 200GB will not be able to be processed.
Finally let’s configure a system with 5TB of memory per SAP’s recommendation for HANA. In this case BLU and HANA both fit all of the data in-memory… with 2.5TB of compressed user data in and 2.5TB of work space… and there is no I/O. This is an in-memory DBMS.
But according to the IBM Power 770 spec (here) there is no way to get 5TB of memory on a single p770 node… so to match HANA and eliminate all I/O they would require two nodes… but BLU cannot be deployed on a cluster… so on they would have to deploy on a single node and perform I/O on 20% of the data. The latency for SSD I/O is 200Kns and for disk it is 10Mns… for DRAM it is 100ns and HANA loads full cache lines so that the average latency is under 20ns… so the penalty paid by BLU is severe and it will never keep up with HANA.
There is more bunk around recommendations for the number of cores but I can make no sense of it at all so I do not know where to begin to debunk it. SAP recommends high-end Intel servers to run HANA. In the scenario above we would recommend multiple servers… soon enough there will be Haswell servers with 6TB of DRAM and this case will run on one node.
I have stated repeatedly that anytime a vendor presents a slide comparing their product to their competitors you should immediately throw them out… it will always be twisted. Don’t trust them. And don’t trust me as I work for SAP. But hopefully you can see some logic in my case. If you need an IMDB then you need memory. If you are short of memory then the IMDB operates like a columnar RDBMS with a memory cache. If you are running a BI query workload then you need to pin data in the cache or the system will thrash. Because of this SAP recommends that you get enough memory to get all of the data in… we recommend that you operate our in-memory database product in-memory…
This really the point of the post. The Five Minute Rule informs us about what data should be in-memory (see here). An in-memory database is designed from the bottom up to manage hot data in-memory. The in-memory add-ons being offered over legacy systems are very capable and should not be ignored… and as the price of memory drops the Five Minute Rule will suggest that data in-memory will account for and ever larger percentage of your EDW. But to offer an in-memory capability and recommend that you should keep the bulk of the data on disk is silly… and to state that your product has a competitive advantage because you do not recommend that all of the data managed by your in-memory feature be kept in-memory is silliness squared.