In the two previous post we have seen how disk IO and network IO affects our ETLs. For both use cases we have seen several techniques that could be used to improve drastically performance and drive to an efficient resource usage:
Avoid IO disk at all.
Use buff/cache properly if IO disk couldn’t be avoided.
Optimize data download by choosing the right file format, use the Keep-Alive properly and parallelize network operations.
In this post we are going to put together network and processing operations to see the improvement in a complete workflow.
In the previous post I have focused in avoiding as much as possible IO on disk and if that was not possible using buff/cache as much as possible by grouping in time IO operations. This approach can make our ETL processes run X times faster. In the two examples the numbers where:
Avoiding IO at all was 11,3 times faster
Using buff/cache was almost 4 times faster
All the examples used a dataset already in the disk so no real network operation occurred. In this post I am going to focus on network operation using again GNU parallel.
Este sitio web utiliza cookies para que usted tenga la mejor experiencia de usuario. Si continúa navegando está dando su consentimiento para la aceptación de las mencionadas cookies y la aceptación de nuestra política de cookies, pinche el enlace para mayor información.plugin cookies