Introduction While experimenting with some time series collections I needed a large data set to check that our aggregation queries don’t become a bottleneck in case of increasing data loads. We settled for 50 million documents since beyond this number we would consider sharding anyway. Each time event looks like this: As we wanted to get random values, we thought of generating them using JavaScript or Python (we could have tried in in Java, but we wanted to write it as fast as possible). We didn’t know which one will be faster… Read More