Change your key to user_month. That will put all of the records for a user together so you will only need a single disk operation to read all of your data. Also, test the option of putting multiple months in a single row.
On Mon, Apr 25, 2011 at 7:59 PM, Weihua JIANG <weihua.ji...@gmail.com>wrote: > Hi all, > > We want to implement a bill query system. We have 20M users, the bill > for each user per month contains about 10 0.6K-byte records. We want > to store user bill for 6 months. Of course, user query focused on the > latest month reports. But, the user to be queried doesn't have hot > spot. > > We use CDH3U0 with 6 servers (each with 24G mem and 3 1T disk) for > data node and region server (besides the ZK, namenode and hmaster > servers). RS heap is 8G and DN is 12G. HFile max size is 1G. The > block cache is 0.4. > > The row key is month+user_id. Each record is stored as a cell. So, a > month report per user is a row in HBase. > > Currently, to store bill records, we can achieve about 30K record/second. > > However, the query performance is quite poor. We can only achieve > about 600~700 month_report/second. That is, each region server can > only serve query for about 100 row/second. Block cache hit ratio is > about 20%. > > Do you have any advice on how to improve the query performance? > > Below is some metrics info reported by region server: > 2011-04-26T10:56:12 hbase.regionserver: > RegionServer=regionserver50820, blockCacheCount=40969, > blockCacheEvictedCount=216359, blockCacheFree=671152504, > blockCacheHitCachingRatio=20, blockCacheHitCount=67936, > blockCacheHitRatio=20, blockCacheMissCount=257675, > blockCacheSize=2743351688, compactionQueueSize=0, > compactionSize_avg_time=0, compactionSize_num_ops=7, > compactionTime_avg_time=0, compactionTime_num_ops=7, flushQueueSize=0, > flushSize_avg_time=0, flushSize_num_ops=0, flushTime_avg_time=0, > flushTime_num_ops=0, fsReadLatency_avg_time=46, > fsReadLatency_num_ops=257905, fsSyncLatency_avg_time=0, > fsSyncLatency_num_ops=1726, fsWriteLatency_avg_time=0, > fsWriteLatency_num_ops=0, memstoreSizeMB=0, regions=169, > requests=82.1, storefileIndexSizeMB=188, storefiles=343, stores=169 > 2011-04-26T10:56:22 hbase.regionserver: > RegionServer=regionserver50820, blockCacheCount=42500, > blockCacheEvictedCount=216359, blockCacheFree=569659040, > blockCacheHitCachingRatio=20, blockCacheHitCount=68418, > blockCacheHitRatio=20, blockCacheMissCount=259206, > blockCacheSize=2844845152, compactionQueueSize=0, > compactionSize_avg_time=0, compactionSize_num_ops=7, > compactionTime_avg_time=0, compactionTime_num_ops=7, flushQueueSize=0, > flushSize_avg_time=0, flushSize_num_ops=0, flushTime_avg_time=0, > flushTime_num_ops=0, fsReadLatency_avg_time=44, > fsReadLatency_num_ops=259547, fsSyncLatency_avg_time=0, > fsSyncLatency_num_ops=1736, fsWriteLatency_avg_time=0, > fsWriteLatency_num_ops=0, memstoreSizeMB=0, regions=169, > requests=92.2, storefileIndexSizeMB=188, storefiles=343, stores=169 > 2011-04-26T10:56:32 hbase.regionserver: > RegionServer=regionserver50820, blockCacheCount=39238, > blockCacheEvictedCount=221509, blockCacheFree=785944072, > blockCacheHitCachingRatio=20, blockCacheHitCount=69043, > blockCacheHitRatio=20, blockCacheMissCount=261095, > blockCacheSize=2628560120, compactionQueueSize=0, > compactionSize_avg_time=0, compactionSize_num_ops=7, > compactionTime_avg_time=0, compactionTime_num_ops=7, flushQueueSize=0, > flushSize_avg_time=0, flushSize_num_ops=0, flushTime_avg_time=0, > flushTime_num_ops=0, fsReadLatency_avg_time=39, > fsReadLatency_num_ops=261070, fsSyncLatency_avg_time=0, > fsSyncLatency_num_ops=1746, fsWriteLatency_avg_time=0, > fsWriteLatency_num_ops=0, memstoreSizeMB=0, regions=169, > requests=128.77777, storefileIndexSizeMB=188, storefiles=343, > stores=169 > > > And we also tried to disable block cache, it seems the performance is > even a little bit better. And it we use the configuration 6 DN servers > + 3 RS servers, we can get better throughput at about 1000 > month_report/second. I am confused. Can any one explain the reason? > > Thanks > Weihua >
