Shulou(Shulou.com)06/03 Report--

1. First you need to install hive. Refer to http://lqding.blog.51cto.com/9123978/1750967.

2. Add a configuration file under the configuration directory of spark so that Spark can access the metastore of hive.

Root@spark-master:/usr/local/spark/spark-1.6.0-bin-hadoop2.6/conf# vi hive-site.xml hive.metastore.uris thrift://spark-master:9083 Thrift uri for the remote metastore. Used by metastore client to connect to remote metastore.

3. Copy the MySQL jdbc driver to the lib directory of spark

Root@spark-master:/usr/local/hive/apache-hive-1.2.1/lib# cp mysql-connector-java-5.1.36-bin.jar / usr/local/spark/spark-1.6.0-bin-hadoop2.6/lib/

4. Start the metastore service of Hive

Root@spark-master:/usr/local/hive/apache-hive-1.2.1/bin#. / hive--service metastore & [1] 20518root@spark-master:/usr/local/hive/apache-hive-1.2.1/bin# SLF4J: Class path contains multiple SLF4J bindings.SLF4J: Found binding in [jar:file:/usr/local/hadoop/hadoop-2.6.0/share/hadoop/common/lib/slf4j-log4j12-1.7.5. Jarhammer / impl/StaticLoggerBinder.class] SLF4J: Found binding in [See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.SLF4J: Actual binding is of type [org.slf4j.impl.Log4jLoggerFactory] Starting Hive Metastore ServerSLF4J: Class path contains multiple SLF4J bindings] SLF4J: See .SLF4J: Found binding in [jar:file:/usr/local/hadoop/hadoop-2.6.0/share/hadoop/common/lib/slf4j-log4j12-1.7.5.jarbank jar:file:/usr/local/hadoop/hadoop-2.6.0/share/hadoop/common/lib/slf4j-log4j12 implexstop StaticLoggerBinder.class] SLF4J: Found binding in [JarJet fileGroupe LocalSparkAfter 1.6.0Find Hadoop2.6 LBINFOPP 1.6.0Flt assemblyMutel 1.6.0FHADOOop2.6.0.jarlagorgOnslf4jActionStaticLoggerBinder.class] SLF4J : See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.SLF4J: Actual binding is of type [org.slf4j.impl.Log4jLoggerFactory]

5. Start spark-shell

Root@spark-master:/usr/local/spark/spark-1.6.0-bin-hadoop2.6/bin#. / spark-shell-- master spark://spark-master:7077

Generate hiveContext

Scala > val hc = new org.apache.spark.sql.hive.HiveContext (sc)

Execute sql

Scala > hc.sql ("show tables") .foreach (println) [sougou,false] [T1 from sougou false] scala > hc.sql ("select count (*) foreach") .foreach (println) 16-03-14 23:15:58 INFO parse.ParseDriver: Parsing command: select count (*) from sougou16/03/14 23:16:00 INFO parse.ParseDriver: Parse Completed16/03/14 23:16:01 INFO Configuration.deprecation: mapred.map.tasks is deprecated. Instead, use mapreduce.job.maps16/03/14 23:16:02 INFO storage.MemoryStore: Block broadcast_0 stored as values in memory (estimated size 474.9 KB, free 474.9 KB) 16-03-14 23:16:02 INFO storage.MemoryStore: Block broadcast_0_piece0 stored as bytes in memory (estimated size 41.6 KB, free 516.4 KB) 16-03-14 23:16:02 INFO storage.BlockManagerInfo: Added broadcast_0_piece0 in memory on 192.168.199.100 estimated size 41635 (size: 41.6 KB Free: 517.4 MB) 16-03-14 23:16:02 INFO spark.SparkContext: Created broadcast 0 from collect at: 3016-03-14 23:16:03 INFO mapred.FileInputFormat: Total input paths to process: 116-03-14 23:16:03 INFO spark.SparkContext: Starting job: collect at: 3016-03-14 23:16:03 INFO scheduler.DAGScheduler: Registering RDD 5 (collect at: 30) 16-03-14 23:16:03 INFO scheduler.DAGScheduler: Got job 0 (collect at: 30) with 1 output partitions16/03/14 23:16:03 INFO scheduler.DAGScheduler: Final stage: ResultStage 1 (collect at: 30) 16-03-14 23:16:03 INFO scheduler.DAGScheduler: Parents of final stage: List (ShuffleMapStage 0) 16-03-14 23:16:04 INFO scheduler.DAGScheduler: Missing parents: List (ShuffleMapStage 0) 16-03-14 23:16:04 INFO scheduler.DAGScheduler: Submitting ShuffleMapStage 0 (MapPartitionsRDD [5] at collect at: 30) Which has no missing parents16/03/14 23:16:04 INFO storage.MemoryStore: Block broadcast_1 stored as values in memory (estimated size 13.8 KB, free 530.2 KB) 16-03-14 23:16:04 INFO storage.MemoryStore: Block broadcast_1_piece0 stored as bytes in memory (estimated size 6.9 KB, free 537.1 KB) 16-03-14 23:16:04 INFO storage.BlockManagerInfo: Added broadcast_1_piece0 in memory on 192.168.199.100 free 41635 (size: 6.9 KB (free: 517.4 MB) 16-03-14 23:16:04 INFO spark.SparkContext: Created broadcast 1 from broadcast at DAGScheduler.scala:100616/03/14 23:16:04 INFO scheduler.DAGScheduler: Submitting 2 missing tasks from ShuffleMapStage 0 (MapPartitionsRDD [5] at collect at: 30) 16-03-14 23:16:04 INFO scheduler.TaskSchedulerImpl: Adding task set 0.0 with 2 tasks16/03/14 23:16:04 INFO scheduler.TaskSetManager: Starting task 0.0 in stage 0 (TID 0, spark-worker2, partition 0 not designed local (2152 bytes) 23:16:04 INFO scheduler.TaskSetManager on 16-03-14 (TID 1, spark-worker1, partition 1 in stage 2152 bytes) 16-03-14 23:16:05 INFO storage.BlockManagerInfo: Added broadcast_1_piece0 in memory on spark-worker2:55899 (size: 6.9 KB, free: 146.2 MB) 23:16:05 INFO storage.BlockManagerInfo: Added broadcast_1_piece0 in memory on spark-worker1:38231 (size: 146.2 KB) Free: 146.2 MB) 23:16:09 on 16-03-14 INFO storage.BlockManagerInfo: Added broadcast_0_piece0 in memory on spark-worker1:38231 (size: 41.6 KB, free: 146.2 MB) 16-03-14 23:16:10 INFO storage.BlockManagerInfo: Added broadcast_0_piece0 in memory on spark-worker2:55899 (size: 41.6 KB) Free: 146.2 MB) 23:16:16 on 16-03-14 INFO scheduler.TaskSetManager: Finished task 1.0 in stage (TID 1) in 12015 ms on spark-worker1 (1) 16-03-14 23:16:16 INFO scheduler.DAGScheduler: ShuffleMapStage 0 (collect at: 30) finished in 12.351 s16 * / 14 23:16:16 INFO scheduler.DAGScheduler: looking for newly runnable stages16/03/14 23:16:16 INFO scheduler.DAGScheduler: running: Set () 16-03-14 23:16:16 INFO scheduler.DAGScheduler: waiting: Set (ResultStage 1) 16-03-14 23:16:16 INFO scheduler.DAGScheduler: failed: Set () 16-03-14 23:16:16 INFO scheduler.DAGScheduler: Submitting ResultStage 1 (MapPartitionsRDD [8] at collect at: 30) Which has no missing parents16/03/14 23:16:16 INFO scheduler.TaskSchedulerImpl: Removed TaskSet, whose tasks have all completed, from pool 16-03-14 23:16:16 INFO storage.MemoryStore: Block broadcast_2 stored as values in memory (estimated size 12.9 KB, free 550.1 KB) 16-03-14 23:16:16 INFO storage.MemoryStore: Block broadcast_2_piece0 stored as bytes in memory (estimated size 550.1 KB Free 556.5 KB) 23:16:16 on 16-03-14 INFO storage.BlockManagerInfo: Added broadcast_2_piece0 in memory on 192.168.199.100 INFO storage.BlockManagerInfo 41635 (size: 6.4 KB) (free: 517.4 MB) 16-03-14 23:16:16 INFO spark.SparkContext: Created broadcast 2 from broadcast at DAGScheduler.scala:100616/03/14 23:16:16 INFO scheduler.DAGScheduler: Submitting 1 missing tasks from ResultStage 1 (MapPartitionsRDD [8] at collect at: 30) 16-03-14 23:16:16 INFO scheduler.TaskSchedulerImpl: Adding task set 1.0 with 1 tasks16/03/14 23:16:16 INFO scheduler.TaskSetManager: Starting task 517.4 in stage 23:16:16 (TID 2, spark-worker1, partition 0 focus local Bytes) 23:16:16 on 16-03-14 INFO storage.BlockManagerInfo: Added broadcast_2_piece0 in memory on spark-worker1:38231 (size: 1999 KB) Free: 146.1 MB) 16-03-14 23:16:17 INFO spark.MapOutputTrackerMasterEndpoint: Asked to send map output locations for shuffle 0 to spark-worker1:4356816/03/14 23:16:17 INFO spark.MapOutputTrackerMaster: Size of output statuses for shuffle 0 is 158bytes16/03/14 23:16:18 INFO scheduler.DAGScheduler: ResultStage 1 (collect at: 30) finished in 1.288 s16 Legend 03pm 14 23:16:18 INFO scheduler.TaskSetManager: Finished task 0.0 in stage 1.288 (TID 2) in 1279 ms On spark-worker1 (1 move 1) 23:16:18 on 16-03-14 INFO scheduler.TaskSchedulerImpl: Removed TaskSet 1.0 Whose tasks have all completed, from pool 16-03-14 23:16:18 INFO scheduler.DAGScheduler: Job 0 finished: collect at: 30, took 14.285673 s [1000000]

Compared with Hive, the speed is improved. If it is a complex statement, it will be faster than hive.

Scala > hc.sql ("select word,count (*) cnt from sougou group by word order by cnt desc limit 5") .foreach (println). 23:19:16 on 16-03-14 INFO scheduler.DAGScheduler: ResultStage 3 (collect at: 30) finished in 11.900 s16 INFO scheduler.DAGScheduler 14 23:19:16 INFO scheduler.DAGScheduler: Job 1 finished: collect at: 30 Took 17.925094 s16 TID 03 INFO scheduler.TaskSetManager 14 23:19:16 INFO scheduler.TaskSetManager: Finished task 195.0 in stage 3.0 in 696 ms on spark-worker2 16-03-14 23:19:16 INFO scheduler.TaskSchedulerImpl: Removed TaskSet 3.0, whose tasks have all completed, from pool [Baidu, 7564] [baidu,3652] [body Art, 2786] [father of Yan Ning, head of Guantao County, 2388] [4399 Mini Game, 2119]

Previously, it took nearly 110s to run with Hive, but only 17s with Spark SQL.

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