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:
- 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.
- 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.
- 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.
- 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…