I am trying to load a large Hbase table into SPARK RDD to run a SparkSQL
query on the entity. For an entity with about 6 million rows, it will take
about 35 seconds to load it to RDD. Is it expected? Is there any way to
shorten the loading process? I have been getting some tips from
http://hbase.apache.org/book/perf.reading.html to speed up the process,
e.g., scan.setCaching(cacheSize) and only add the necessary
attributes/column to scan. I am just wondering if there are other ways to
improve the speed?
Here is the code snippet:
SparkConf sparkConf = new
SparkConf().setMaster("spark://url").setAppName("SparkSQLTest");
JavaSparkContext jsc = new JavaSparkContext(sparkConf);
Configuration hbase_conf = HBaseConfiguration.create();
hbase_conf.set("hbase.zookeeper.quorum","url");
hbase_conf.set("hbase.regionserver.port", "60020");
hbase_conf.set("hbase.master", "url");
hbase_conf.set(TableInputFormat.INPUT_TABLE, entityName);
Scan scan = new Scan();
scan.addColumn(Bytes.toBytes("MetaInfo"), Bytes.toBytes("col1"));
scan.addColumn(Bytes.toBytes("MetaInfo"), Bytes.toBytes("col2"));
scan.addColumn(Bytes.toBytes("MetaInfo"), Bytes.toBytes("col3"));
scan.setCaching(this.cacheSize);
hbase_conf.set(TableInputFormat.SCAN, convertScanToString(scan));
JavaPairRDD<ImmutableBytesWritable, Result> hBaseRDD
= jsc.newAPIHadoopRDD(hbase_conf,
TableInputFormat.class, ImmutableBytesWritable.class,
Result.class);
logger.info("count is " + hBaseRDD.cache().count());
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