Rittman Mead - Delivered Intelligence

In my Log
Buffer posting at the end of last week I made a passing
reference to SAP’s
new BI Accelerator,
and made the flippant comment “Yeah, right..,” about the claim that it
did away with the need to tune or pre-summarize your data warehouse.
The article did however pique my interest, and having had a look at the
technology more, it is actually rather interesting.

If you have a look around the ‘net, there were in fact several
news
reports about the product when it was launched a couple of weeks ago.
Some of them are a bit hyperbolic – “SAP’s
Agassi Confirms Oracle Killer Plans, Dishes on SOA”
for example – but what SAP are actually offering looks fairly
intriguing, a data warehouse appliance that has many of the features
found in current Oracle offerings – the OLAP Option, TimesTen
in-memory database, bitmap indexes and IOTs, but wrapped up in an
offering squarely aimed at their customers running SAP Business
Warehouse.

What the product appears to be is an appliance – combination
of
hardware and software aimed at performing a particular, defined task -
to speed up the queries performed by SAP Business Warehouse, SAP’s
add-on to their ERP suite that performs ODS-style queries against their
data (SAP BW is due to be a supported datasource in the upcoming Maui
release of Oracle BI Suite Enterprise Edition). SAP BI Accelerator is
delivered either on IBM or HP blade servers containing Intel Xeon
“Woodcrest” 64-bit CPUs, the Linux Operating system and the BI
Accelerator application. It’s “hot pluggable” into the SAP Netweaver BI
architecture, and when plugged in, starts to cache queries and improve
the performance of ad-hoc BI queries.

The way the accelerator works is to capture the data requested
by
the Netweaver-based application, parse it and tokenize it, and store it
in a column-based data store in a similar way to Sybase IQ
and SAND.
Like Sybase IQ, it achieves impressive levels of data compression due
to the column-based nature of it’s storage (blocks contain columns of
data, not rows, sorted and tokenized making it easy to compress data of
low cardinality), sorted and ordered and then stored in a way where the
data is effectively the index. This is then horizontally partitioned
across multiple blade servers in a similar way to Oracle RAC and ASM
(shared data storage, with fact data processed across all blade nodes
but dimensions having a particular node affinity), and then loaded into
RAM to create an in-memory database. Apart from the reduced need for
disk storage, column-based databases can work particularly well with
data warehouses as a particular query, requesting just a small subset
of the measures in a fact table, will request a much smaller amount of
data from the cache as blocks contain just the data for a particular
column, not the entire row with all the other measures that you don’t
really need.

This architecture diagram from the Database
Research Project in Germany shows how the product works under
the covers:

SAP have contracted Winter Corporation to produce
some benchmarks
which look fairly impressive, and certainly other people who’ve worked
with in-memory and column-based databases seem to be pretty impressed
with the technology. I’m not so sure about the more wilder claims about
the technology – that it can do away with the need for a relational
database completely for SAP customers – column-based databases might
work well for decision support but they’re a terrible choice for OLTP
applications, and in terms of in-memory databases, what happens when
you pull the plug out? You’ve got to persist the data somewhere – but
it certainly looks like an interesting technology, and one that no
doubt SAP will roll-out when in a competitive situation with Oracle
when it comes to selling ERP suites with built in analytics. Where it
falls short, I’d say, compared to products such as the OLAP Option is
probably around calculations, time-series analysis, multi-dimensional queries and so on, but as a
competitor to summarization technologies such as materialized views,
OLAP servers and even third-party products such as Hyperroll, and as a way of getting fast access to detail-level data, it
looks interesting. Watch this space, as they say.

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