Data Sampling methods in the ALICE O$^2$ distributed processing system

The ALICE experiment at the CERN LHC focuses on studying the quark-gluon plasma produced by heavy-ion collisions. Starting from 2021, it will see its input data throughput increase a hundredfold, up to 3.5 TB/s. To cope with such a large amount of data, a new online-offline computing system, called O$^2$, will be deployed. It will synchronously compress the data stream by a factor of 35 down to 100 GB/s before storing it permanently. One of the key software components of the system will be the data Quality Control (QC). This framework and infrastructure is responsible for all aspects related to the analysis software aimed at identifying possible issues with the data itself, and indirectly with the underlying processing done both synchronously and asynchronously. Since analyzing the full stream of data online would exceed the available computational resources, a reliable and efficient sampling will be needed. It should provide a few percent of data selected randomly in a statistically sound manner with a minimal impact on the main dataflow. Extra requirements include e.g. the option to choose data corresponding to the same collisions over a group of computing nodes. In this paper the design of the O$^2$ Data Sampling software is presented. In particular, the requirements for pseudo-random number generators to be used for sampling decisions are highlighted, as well as the results of the benchmarks performed to evaluate different possibilities. Finally, a large scale test of the O$^2$ Data Sampling is reported.