Tuesday, July 3, 2012

##bottlenecks in a high performance data "flow" (abinitio

#1 probably most common -- database, both read and write operations. Therefore, ETL solutions achieve superior throughput by taking data processing out of database. ETL uses DB mostly as dumb storage.
* write - if a database data "sink" is too slow, then entire pipe is limited by its throughput, just like sewage.
** relevant in high frequency trading, where every execution must be recorded
* read - if you must query a DB to enrich or lookup some thing, this read can be much slower than other parts of the pipe.

#2 (similarly) flat files. Write tends to be faster than database write. (Read is a completely different story.)
** used in high frequency trading
** used in high volume market data storage -- Sigma2 for example
So flat file writing is important in industry.

#? Web service

#? The above are IO-bound. In contrast, CPU-bound compute-intensive transform can (and do) also become bottlenecks.

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