bonnahu:
How many regions does your table have ?
Are they evenly distributed ?

Cheers

On Thu, Dec 4, 2014 at 3:34 PM, Jörn Franke <[email protected]> wrote:

> Hi,
>
> What is your cluster setup? How mich memory do you have? How much space
> does one row only consisting of the 3 columns consume? Do you run other
> stuff in the background?
>
> Best regards
> Am 04.12.2014 23:57 schrieb "bonnahu" <[email protected]>:
>
>> 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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>>
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