The History of Parallel Database Architectures

Parallel computing is not a new invention, being available now for more than 30 years. Parallel database architectures followed soon.

Everything started with the shared memory architecture, which is still widely used this day in servers:

shared memory.

The shared memory architecture is a multiprocessor system with a common memory, a single operating system and a shared disk or storage area network for storing the data. Processes are scattered across the CPU’s.

One significant disadvantage of the shared memory architecture is that it is not scaling very well and therefore, even with modern multi-core processors, you will not find any setup containing more than a few hundred of processors.

Current databases are making use of other two architectures, namely the shared disk and the shared nothing architecture (Teradata is based on the shared nothing architecture):

shared nothing

Both architectures overcome the problem of limited scaling.

In the shared disk architecture, the processors communicate over a network, but still, only a shared disk or storage area network exists for storing the data. In a shared disk architecture, therefore, you can find two networks: the network for inter-processor communication and the storage system for communication with the disk.

The shared nothing architecture uses local disks for each processor. Only the network for processor communication exists.

Databases based on any of this architecture are built to execute queries in parallel, but in the shared nothing architecture, the data is additionally distributed across the disks.

Everybody familiar with Teradata will easily spot the relationship between the shared nothing architecture and the AMPs (processors), drives and the BYNET  (processor communication network).

One important goal for any shared nothing architecture based database system is the even distribution of data across the available disks. We all know how Teradata is doing this, namely by hashing the primary index columns and storing the rows on the related AMPs.

A column-store database would instead  distribute different columns across the disks. Still, the goal is the same: even data distribution to optimally exploit parallelism.

Parallel databases and fault tolerance or why Hadoop rules

Parallel databases are nowadays state of the art. But there is one issue which can’t be handled in a good way by parallel databases (based on any of the described architectures): Fault tolerance.

All architectures behave okay when there are a limited amount of processors available. The statistical probability of processor failure is small in this case.

The situation would look quite different in the event of a system with thousands of processors. Chance of a processor failure would be near to 100%.

Parallel databases based on one of the three described architectures fundamentally are not fault tolerant, that’s  why Teradata, for example, offers Hot Standby Nodes doing nothing else than replicating the data across a network. I would consider this a very costly approach, but it is a usual method (not only for Teradata).

If we are comparing parallel databases with the Map Reduce Framework (please take a look at my related article for further details), we will quickly spot the parallel file system (HDFS) and fault handling as a good base for fault tolerance.

It is easy to recognize that long running batch operations fit much better Map Reduce than a parallel database system like Teradata.

I guess now it becomes clearer why all big database vendors start playing in the big data arena.  Big data needs a different architecture.

Currently, the trend is “Hadoop over SQL” to make the transition as smooth as possible as SQL is the primary interaction language for most database systems.

We will see what the next years will bring. Teradata’s shot is SQL-H, other database vendors develop their integrations. None of them is currently really convincing me.

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