Hi, We are relatively new to Hbase, and we are hitting a roadblock on our scan performance. I searched through the email archives and applied a bunch of the recommendations there, but they did not improve much. So, I am hoping I am missing something which you could guide me towards. Thanks in advance.
We are currently writing data and reading in an almost continuous mode (stream of data written into an HBase table and then we run a time-based MR on top of this Table). We currently were backed up and about 1.5 TB of data was loaded into the table and we began performing time-based scan MRs in 10 minute time intervals(startTime and endTime interval is 10 minutes). Most of the 10 minute interval had about 100 GB of data to process. Our workflow was to primarily eliminate duplicates from this table. We have maxVersions = 5 for the table. We use TableInputFormat to perform the time-based scan to ensure data locality. In the mapper, we check if there exists a previous version of the row in a time period earlier to the timestamp of the input row. If not, we emit that row. We looked at https://issues.apache.org/jira/browse/HBASE-4683 and hence turned off block cache for this table with the expectation that the block index and bloom filter will be cached in the block cache. We expect duplicates to be rare and hence hope for most of these checks to be fulfilled by the bloom filter. Unfortunately, we notice very slow performance on account of being disk bound. Looking at jstack, we notice that most of the time, we appear to be hitting disk for the block index. We performed a major compaction and retried and performance improved some, but not by much. We are processing data at about 2 MB per second. We are using CDH 4.2.1 HBase 0.94.2 and HDFS 2.0.0 running with 8 datanodes/regionservers(each with 32 cores, 4x1TB disks and 60 GB RAM). HBase is running with 30 GB Heap size, memstore values being capped at 3 GB and flush thresholds being 0.15 and 0.2. Blockcache is at 0.5 of total heap size(15 GB). We are using SNAPPY for our tables. A couple of questions: * Is the performance of the time-based scan bad after a major compaction? * What can we do to help alleviate being disk bound? The typical answer of adding more RAM does not seem to have helped, or we are missing some other config Below are some of the metrics from a Regionserver webUI: requestsPerSecond=5895, numberOfOnlineRegions=60, numberOfStores=60, numberOfStorefiles=209, storefileIndexSizeMB=6, rootIndexSizeKB=7131, totalStaticIndexSizeKB=415995, totalStaticBloomSizeKB=2514675, memstoreSizeMB=0, mbInMemoryWithoutWAL=0, numberOfPutsWithoutWAL=0, readRequestsCount=30589690, writeRequestsCount=0, compactionQueueSize=0, flushQueueSize=0, usedHeapMB=2688, maxHeapMB=30672, blockCacheSizeMB=1604.86, blockCacheFreeMB=13731.24, blockCacheCount=11817, blockCacheHitCount=27592222, blockCacheMissCount=25373411, blockCacheEvictedCount=7112, blockCacheHitRatio=52%, blockCacheHitCachingRatio=72%, hdfsBlocksLocalityIndex=91, slowHLogAppendCount=0, fsReadLatencyHistogramMean=15409428.56, fsReadLatencyHistogramCount=1559927, fsReadLatencyHistogramMedian=230609.5, fsReadLatencyHistogram75th=280094.75, fsReadLatencyHistogram95th=9574280.4, fsReadLatencyHistogram99th=100981301.2, fsReadLatencyHistogram999th=511591146.03, fsPreadLatencyHistogramMean=3895616.6, fsPreadLatencyHistogramCount=420000, fsPreadLatencyHistogramMedian=954552, fsPreadLatencyHistogram75th=8723662.5, fsPreadLatencyHistogram95th=11159637.65, fsPreadLatencyHistogram99th=37763281.57, fsPreadLatencyHistogram999th=273192813.91, fsWriteLatencyHistogramMean=6124343.91, fsWriteLatencyHistogramCount=1140000, fsWriteLatencyHistogramMedian=374379, fsWriteLatencyHistogram75th=431395.75, fsWriteLatencyHistogram95th=576853.8, fsWriteLatencyHistogram99th=1034159.75, fsWriteLatencyHistogram999th=5687910.29 key size: 20 bytes Table description: {NAME => 'foo', FAMILIES => [{NAME => 'f', DATA_BLOCK_ENCODING => 'NONE', BLOOMFI true LTER => 'ROW', REPLICATION_SCOPE => '0', COMPRESSION => 'SNAPPY', VERSIONS => '5', TTL => ' 2592000', MIN_VERSIONS => '0', KEEP_DELETED_CELLS => 'false', BLOCKSIZE => '65536', ENCODE_ ON_DISK => 'true', IN_MEMORY => 'false', BLOCKCACHE => 'false'}]}
