Posts Tagged 'columnar database'

Oracle Exadata Storage Server. Part I.

Brute Force with Brains.
Here is a brief overview of the Oracle Exadata Storage Server key performance attributes:

  • Intelligent Storage. Ship less data due to query intelligence in the storage.
  • Bigger Pipes. Infiniband with Remote Direct Memory Access. 5x Faster than Fibre Channel.
  • More Pipes. Scalable, redundant I/O Fabric.

Yes, it’s called Oracle Exadata Storage Server and it really was worth the wait. I know it is going to take a while for the message to settle in, but I would like to take my first blog post on the topic of Oracle Exadata Storage Server to reiterate the primary value propositions of the solution.

  • Exadata is fully optimized disk I/O. Full stop! For far too long, it has been too difficult to configure ample I/O bandwidth for Oracle, and far too difficult to configure storage so that the physical disk accesses are sequential.
  • Exadata is intelligent storage. For far too long, Oracle Database has had to ingest full blocks of data from disk for query processing, wasting precious host processor cycles to discard the uninteresting data (predicate filtering and column projection).

Oracle Exadata Storage Server is Brute Force. A Brawny solution.
A single rack of the HP Oracle Database Machine (based on Oracle Exadata Storage Server Software) is configured with 14 Oracle Exadata Storage Server “Cells” each with 12 3.5″ hard drives for a total of 168 disks. There are 300GB SAS and 1TB SATA options. The database tier of the single-rack HP Oracle Database Machine consists of 8 Proliant DL360 servers with 2 Xeon 54XX quad-core processors and 32 GB RAM running Oracle Real Application Clusters (RAC). The RAC nodes are interconnected with Infiniband using the very lightweight Reliable Datagram Sockets (RDS) protocol. RDS over Infiniband is also the I/O fabric between the RAC nodes and the Storage Cells. With the SAS storage option, the HP Oracle Database Machine offers roughly 1 terabyte of optimal user addressable space per Storage Cell-14 TB total.

Sequential I/O
Exadata I/O is a blend of random seeks followed by a series of large transfer requests so scanning disk at rates of nearly 85 MB/s per disk drive (1000 MB/s per Storage Cell) is easily achieved. With 14 Exadata Storage Cells, the data-scanning rate is 14 GB/s. Yes, roughly 80 seconds to scan a terabyte-and that is with the base HP Oracle Database Machine configuration. Oracle Exadata Storage Software offers these scan rates on both tables and indexes and partitioning is, of course, fully supported-as is compression.

Comparison to “Old School”
Let me put Oracle Exadata Storage Server performance into perspective by drawing a comparison to Fibre Channel SAN technology. The building block of all native Fibre Channel SAN arrays is the Fibre Channel Arbitrated Loop (FCAL) to which the disk drives are connected. Some arrays support as few as 2 of these “back-end” loops, larger arrays support as many as 64. Most, if not all, current SAN arrays support 4 Gb FCAL back-end loops which are limited to no more than 400MB/s of read bandwidth. The drives connected to the loops have front-end Fibre Channel electronics and-forgetting FC-SATA drives for a moment-the drives themselves are fundamentally the same as SAS drives-given the same capacity and rotational speed. It turns out that SAS and Fibre drives, of the 300GB 15K RPM variety, perform pretty much the same for large sequential I/O. Given the bandwidth of the drives, the task of building a SAN-based system that isn’t loop-bottlenecked requires limiting the number of drives per loop to 5 (or 10 for mirroring overhead). So, to match a single rack configuration of the HP Oracle Database Machine with a SAN solution would require about 35 back-end drive loops! All of this math boils down to one thing: a very, very large high-end SAN array.

Choices, Choices: Either the Largest SAN Array or the Smallest HP Oracle Database Machine
Only the largest of the high-end SAN arrays can match the base HP Oracle Database Machine I/O bandwidth. And this is provided the SAN array processors can actually pass through all the I/O generated from a full complement of back-end FCAL loops. Generally speaking, they just don’t have enough array processor bandwidth to do so.

Comparison to the “New Guys on the Block”
Well, they aren’t really that new. I’m talking about Netezza. Their smallest full rack has 112 Snippet Processing Units (SPU) each with a single SATA disk drive-and onboard processor and FPGA components-for a total user addressable space of 12.5 TB. If the data streamed off the SATA drives at, say, 70 MB/s, the solution offers 7.8 GB/s-42% slower than a single-rack HP Oracle Database Machine.

Big, Efficient Pipes
Oracle Exadata Storage Server delivers I/O results directly into the address space of the Oracle Database Parallel Query Option processes using the Reliable Datagram Sockets (RDS) protocol over Infiniband. As such, each of the Oracle Real Application Clusters nodes are able to ingest a little over a gigabyte of streaming data per second at a CPU cost of less than 5%, which is less than the typical cost of interfacing with Fibre Channel host-bus adaptors via traditional Unix/Linux I/O calls. With Oracle Exadata Storage Server, the Oracle Database host processing power is neither wasted on filtering out uninteresting data, nor plucking out columns from the rows. There would, of course, be no need to project in a colum-oriented database but Oracle Database is still row-oriented.

Oracle Exadata Storage Server is Intelligence Storage. Brainy Software.
Oracle Exadata Storage Server truly is an optimized way to stream data to Oracle Database. However, none of the traditional Oracle Database features (e.g., partitioning, indexing, compression, Backup/Restore, Disaster Protection, etc) are lost when deploying Exadata. Combining data elimination (via partitioning) with compression further exploits the core architectural strengths of Exadata. But what about this intelligence? Well, as we all know, queries don’t join all the columns and few queries ever run without a WHERE predicate for filtration. With Exadata that intelligence is offloaded to storage. Exadata Storage Cells execute intelligent software that understands how to perform filtration as well as column projection. For instance, consider a query that cites 2 columns nestled in the middle of a 100-column  row and the WHERE predicate filters out 50% of the rows. With Exadata, that is exactly what is returned to the Oracle Parallel Query processes.

By this time it should start to make sense why I have blogged in the past the way I do about SAN technology, such as this post about SAN disk/array bottlenecking.  Configuring a high-bandwidth SAN requires a lot of care.

Yes, this is a very short, technically-light blog entry about Oracle Exadata Storage Server, but this is day one. I didn’t touch on any of the other really exciting things Exadata does in the areas of I/O Resource Management, offloaded online backup and offloaded join filters, but I will.


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All content is © Kevin Closson and "Kevin Closson's Blog: Platforms, Databases, and Storage", 2006-2013. Unauthorized use and/or duplication of this material without express and written permission from this blog’s author and/or owner is strictly prohibited. Excerpts and links may be used, provided that full and clear credit is given to Kevin Closson and Kevin Closson's Blog: Platforms, Databases, and Storage with appropriate and specific direction to the original content.

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