As I noted here, I think that the IBM BLU Accelerator is a very nice piece of work. Readers of this blog are in the software business where any feature developed by any vendor can be developed in a relatively short period of time by any other vendor… and BLU certainly moves DB2 forward in the in-memory database space led by HANA… it narrowed the gap. But let’s look at the gap that remains.
First, IBM is touting the fact that BLU requires no proprietary hardware and suggests that HANA does. I do not really understand this positioning? HANA runs on servers from a long list of vendors and each vendor spins the HANA reference architecture a little differently. I suppose that the fact that there is a HANA reference architecture could be considered limiting… and I guess that there is no reference for BLU… maybe it runs anywhere… but let’s think about that.
If you decide to run BLU and put some data in-memory then certainly you need some free memory to store it. Assuming that you are not running on a server with excess memory this means that you need to buy more. If you are running on a blade that only supports 128GB of DRAM or less, then this is problematic. If you upgrade to a 256GB server then you might get a bit of free memory for a little data. If you upgrade to a fat server that supports 512GB of DRAM or more, then you would likely be within the HANA reference architecture set. There is no magic here.
One of the gaps is related: you cannot cluster BLU so the amount of data you can support in-memory is limited to a single node per the paragraphs above. HANA supports shared-nothing clustering and will scale out to support petabytes of data in-memory.
This limit is not so terribly bad if you store some of your data in the conventional DB2 row store… or in a columnar format on-disk. This is why BLU is an accelerator, not a full-fledged in-memory DBMS. But if the limit means that you can get only a small amount of data resident in-memory it may preclude you from putting the sort of medium-to-large fact tables in BLU that would benefit most from the acceleration.
You might consider putting smaller dimension tables in BLU…. but when you join to the conventional DB2 row store the column store tables are materialized as rows and the row database engine executes the join. You can store the facts in BLU in columnar format… but they may not reside in-memory if there is limited availability… and only those joins that do not use row store will use the BLU level 3 columnar features (see here for a description of the levels of columnar maturity). So many queries will require I/O to fetch data.
When you pull this all together: limited available memory on a single node, with large fact tables projecting in and out of disk storage, and joins pushed to the row store you can imagine the severe constraint for a real-world data warehouse workload. BLU will accelerate some stuff… but the application has to be limited to the DRAM dedicated to BLU.
It is only software… IBM will surely add BLU clustering (see here)… and customers will figure out that they need to buy the same big-memory servers that make up the HANA reference architecture to realize the benefits… For analytics, BLU features will converge over the next 2-3 years to make it ever more competitive with HANA. But in this first BLU release the use of in-memory marketing slogans and of tests that might not reflect a real-world workload are a little misleading.
Right now it seems that HANA might retain two architectural advantages:
- HANA real-time support for OLTP and analytics against a single table instance; and
- the performance of the HANA platform: where more application logic runs next to the DBMS, in the same address space, across a lightweight thread boundary.
It is only software… so even these advantages will not remain… and the changing landscape will provide fodder for bloggers for years to come.
References
- Here is a great series of blogs on BLU that shows how joins with the row store materializes columns as rows…
