I should qualify that statement, actually. I was comparing scanning 1m KVs to getting 1m KVs when all KVs are returned.
As James Taylor pointed out to me privately: A fairer comparison would have been to run a scan with a filter that lets x% of the rows pass (i.e. the selectivity of the scan would be x%) and compare that to a multi Get of the same x% of the row. There we found that a Scan+Filter is more efficient that issuing multi Gets if x is >= 1-2%. Or in other words, translating many Gets into a Scan+Filter is beneficial if the Scan would return at least 1-2% of the rows to the client. For example: if you are looking for less than 10-20k rows in 1m rows, using muli Gets is likely more efficient. if you are looking for more than 10-20k rows in 1m rows, using a Scan+Filter is likely more efficient. Of course this is predicated on whether you have an efficient way to represent the rows you are looking for in a filter, so that would probably shift this slightly more towards Gets (just imaging a Filter that to encode 100k random row keys to be matched; since Filters are instantiated store there is another natural limit there). As I said below, the crux of the matter is having some histograms of your data, so that such a decision could be made automatically. -- Lars ________________________________ From: lars hofhansl <la...@apache.org> To: "user@hbase.apache.org" <user@hbase.apache.org> Sent: Monday, February 18, 2013 5:48 PM Subject: Re: Optimizing Multi Gets in hbase As it happens we did some tests around last week. Turns out doing Gets in batches instead of a scan still gives you 1/3 of the performance. I.e. when you have a table with, say, 10m rows and scanning take N seconds, then calling 10m Gets in batches of 1000 take ~3N, which is pretty impressive. Now, this is with all data in the cache! When the data is not in the cache and the Gets are random it is many orders of magnitude slower, as the Gets are sprayed all over the disk. In that case sorting the Gets and issuing scans would indeed be much more efficient. The Gets in a batch are already sorted on the client, but as N. says it is hard to determine when to turn many Gets into a Scan with filters automatically. Without statistics/histograms I'd even wager a guess that would be impossible to do. Imagine you issue 10000 random Gets, but your table has 10bn rows, in that case it is almost certain that the Gets are faster than a scan. Now image the Gets only cover a small key range. With statistics we could tell whether it would beneficial to turn this into a scan. It's not that hard to add statistics to HBase. Would do it as part of the compactions, and record the histograms in some table. You can always do that yourself. If you suspect you are touching most rows in a table/region, just issue a scan with a appropriate filter (may have to implement your own filter, though). Maybe we could a version of RowFilter that match against multiple keys. -- Lars ________________________________ From: Varun Sharma <va...@pinterest.com> To: user@hbase.apache.org Sent: Monday, February 18, 2013 1:57 AM Subject: Optimizing Multi Gets in hbase Hi, I am trying to batched get(s) on a cluster. Here is the code: List<Get> gets = ... // Prepare my gets with the rows i need myHTable.get(gets); I have two questions about the above scenario: i) Is this the most optimal way to do this ? ii) I have a feeling that if there are multiple gets in this case, on the same region, then each one of those shall instantiate separate scan(s) over the region even though a single scan is sufficient. Am I mistaken here ? Thanks Varun
