Not to beat a dead horse, but I thought a bit more about the problem. If you want to do this all in HBase using a M/R job...
Lets define the following: SELECT * FROM A, B WHERE A.a = B.a AND A.b = B.b AND A.c = xxx AND A.d = yyy AND B.e = zzz Is the sample query. So our join key is "a|b" because we're matching on columns a and b. (The pipe is to delimit the columns, assuming the columns themselves don't contain pipes...) Our filters on A are c and d while e is the filter on B. So we want to do the following: M/R Map job 1 gets the subset from table A along with a set of unique keys. M/R Map job 2 gets the subset from table B along with a set of unique keys. M/R Map job 3 takes either set of unique keys as the input list and you split it based on the number of parallel mappers you want to use. You have a couple of options on how you want to proceed. In each Mapper.map() your input is a unique key. I guess you could create two scanners, one for tempTableA, and one for tempTableB. It looks like you can get the iterator for each result set, and then for each row in temp table A, you iterate through the result set from temp table B, writing out the joined set. The only problem is that your result set file isn't in sort order. So I guess you could take the output from this job and reduce it to get it in to sort order. Option B. Using HDFS files for temp 'tables'. You can do this... but you would still have to track the unique keys and also sort both the keys and the files which will require a reduce job. Now this is just my opinion, but if I use HBase, I don't have to worry about using a reducer except to order the final output set. So I can save the time it takes to do the reduce step. So I have to ask... how much time is spent by HBase in splitting and compacting the temp tables? Also can't you pre-split the temp table before you use them? Or am I still missing something? Note: In this example, you'd have to write an input format that takes a java list object (or something similar) as your input and then you can split it to get it to run in parallel. Or you could just write this on the client and split the list up and run the join in parallel threads on the client node. Or a single thread which would mean that it would run and output in sort order. HTH -Mike > Date: Wed, 1 Jun 2011 07:47:30 -0700 > Subject: Re: How to efficiently join HBase tables? > From: [email protected] > To: [email protected] > > > you somehow need to flush all in-memory data *and* perform a > > major compaction > > This makes sense. Without compaction the linear HDFS scan isn't > possible. I suppose one could compact 'offline' in a different Map > Reduce job. However that would have it's own issues. > > > The files do have a flag if they were made by a major compaction, > > so you scan only those and ignore the newer ones - but then you are trailing > > This could be ok in many cases. The key would be to create a sync'd > cut off point enabling a frozen point-in-time 'view' of the data. I'm > not sure how that would be implemented. > > On Wed, Jun 1, 2011 at 6:54 AM, Lars George <[email protected]> wrote: > > Hi Jason, > > > > This was discussed in the past, using the HFileInputFormat. The issue > > is that you somehow need to flush all in-memory data *and* perform a > > major compaction - or else you would need all the logic of the > > ColumnTracker in the HFIF. Since that needs to scan all storage files > > in parallel to achieve its job, the MR task would not really be able > > to use the same approach. > > > > Running a major compaction creates a lot of churn, so it is > > questionable what the outcome is. The files do have a flag if they > > were made by a major compaction, so you scan only those and ignore the > > newer ones - but then you are trailing, and you still do not handle > > delete markers/updates in newer files. No easy feat. > > > > Lars > > > > On Wed, Jun 1, 2011 at 2:41 AM, Jason Rutherglen > > <[email protected]> wrote: > >>> I'd imagine that join operations do not require realtime-ness, and so > >>> faster batch jobs using Hive -> frozen HBase files in HDFS could be > >>> the optimal way to go? > >> > >> In addition to lessening the load on the perhaps live RegionServer. > >> There's no Jira for this, I'm tempted to open one. > >> > >> On Tue, May 31, 2011 at 5:18 PM, Jason Rutherglen > >> <[email protected]> wrote: > >>>> The Hive-HBase integration allows you to create Hive tables that are > >>>> backed > >>>> by HBase > >>> > >>> In addition, HBase can be made to go faster for MapReduce jobs, if the > >>> HFile's could be used directly in HDFS, rather than proxying through > >>> the RegionServer. > >>> > >>> I'd imagine that join operations do not require realtime-ness, and so > >>> faster batch jobs using Hive -> frozen HBase files in HDFS could be > >>> the optimal way to go? > >>> > >>> On Tue, May 31, 2011 at 1:41 PM, Patrick Angeles <[email protected]> > >>> wrote: > >>>> On Tue, May 31, 2011 at 3:19 PM, Eran Kutner <[email protected]> wrote: > >>>> > >>>>> For my need I don't really need the general case, but even if I did I > >>>>> think > >>>>> it can probably be done simpler. > >>>>> The main problem is getting the data from both tables into the same MR > >>>>> job, > >>>>> without resorting to lookups. So without the theoretical > >>>>> MutliTableInputFormat, I could just copy all the data from both tables > >>>>> into > >>>>> a temp table, just append the source table name to the row keys to make > >>>>> sure > >>>>> there are no conflicts. When all the data from both tables is in the > >>>>> same > >>>>> temp table, run a MR job. For each row the mapper should emit a key > >>>>> which > >>>>> is > >>>>> composed of all the values of the join fields in that row (the value > >>>>> can be > >>>>> emitted as is). This will cause all the rows from both tables, with same > >>>>> join field values to arrive at the reducer together. The reducer could > >>>>> then > >>>>> iterate over them and produce the Cartesian product as needed. > >>>>> > >>>>> I still don't like having to copy all the data into a temp table just > >>>>> because I can't feed two tables into the MR job. > >>>>> > >>>> > >>>> Loading the smaller table in memory is called a map join, versus a > >>>> reduce-side join (a.k.a. common join). One reason to prefer a map join is > >>>> you avoid the shuffle phase which potentially involves several trips to > >>>> disk > >>>> for the intermediate records due to spills, and also once through the > >>>> network to get each intermediate KV pair to the right reducer. With a map > >>>> join, everything is local, except for the part where you load the small > >>>> table. > >>>> > >>>> > >>>>> > >>>>> As Jason Rutherglen mentioned above, Hive can do joins. I don't know if > >>>>> it > >>>>> can do them for HBase and it will not suit my needs, but it would be > >>>>> interesting to know how is it doing them, if anyone knows. > >>>>> > >>>> > >>>> The Hive-HBase integration allows you to create Hive tables that are > >>>> backed > >>>> by HBase. You can do joins on those tables (and also with standard Hive > >>>> tables). It might be worth trying out in your case as it lets you easily > >>>> see > >>>> the load characteristics and the job runtime without much coding > >>>> investment. > >>>> > >>>> There are probably some specific optimizations that can be applied to > >>>> your > >>>> situation, but it's hard to say without knowing your use-case. > >>>> > >>>> Regards, > >>>> > >>>> - Patrick > >>>> > >>>> > >>>>> -eran > >>>>> > >>>>> > >>>>> > >>>>> On Tue, May 31, 2011 at 22:02, Ted Dunning <[email protected]> > >>>>> wrote: > >>>>> > >>>>> > The Cartesian product often makes an honest-to-god join not such a > >>>>> > good > >>>>> > idea > >>>>> > on large data. The common alternative is co-group > >>>>> > which is basically like doing the hard work of the join, but involves > >>>>> > stopping just before emitting the cartesian product. This allows > >>>>> > you to inject whatever cleverness you need at this point. > >>>>> > > >>>>> > Common kinds of cleverness include down-sampling of problematically > >>>>> > large > >>>>> > sets of candidates. > >>>>> > > >>>>> > On Tue, May 31, 2011 at 11:56 AM, Michael Segel > >>>>> > <[email protected]>wrote: > >>>>> > > >>>>> > > So the underlying problem that the OP was trying to solve was how to > >>>>> join > >>>>> > > two tables from HBase. > >>>>> > > Unfortunately I goofed. > >>>>> > > I gave a quick and dirty solution that is a bit incomplete. They row > >>>>> key > >>>>> > in > >>>>> > > the temp table has to be unique and I forgot about the Cartesian > >>>>> > > product. So my solution wouldn't work in the general case. > >>>>> > > > >>>>> > > >>>>> > >>>> > >>> > >> > >
