The problem seems related to sampling, a short answer would be based on Spark RDD.sample

If RDD.sample is still too slow for your requirement, then maybe https://en.wikipedia.org/wiki/Reservoir_sampling is the direction to investigate, but not sure any existing implementation yet. Reservoir sampling - Wikipedia<https://en.wikipedia.org/wiki/Reservoir_sampling> en.wikipedia.org Reservoir sampling is a family of randomized algorithms for randomly choosing a sample of k items from a list S containing n items, where n is either a very large or unknown number. ________________________________ From: Liu, Ming (Ming) <ming....@esgyn.cn> Sent: Friday, April 13, 2018 12:16:07 AM To: user@hbase.apache.org Subject: how to get random rows from a big hbase table faster Hi, all, We have a hbase table which has 1 billion rows, and we want to randomly get 1M from that table. We are now trying the RandomRowFilter, but it is still very slow. If I understand it correctly, in the Server side, RandomRowFilter still need to read all 1 billions but return randomly 1% for them. But read 1 billion rows is very slow. Is this true? So is there any other better way to randomly get 1% rows from a given table? Any idea will be very appreciated. We don't know the distribution of the 1 billion rows in advance. Thanks, Ming