spark日志+hivesql
windows本地读取hive，需要在resource里面将集群中的hive-site.xml下载下来。
<?xml version="1.0" encoding="UTF-8"?>
<!--Autogenerated by Cloudera Manager-->
<configuration>
<property>
<name>hive.metastore.local</name>
<value>false</value>
</property>
<property>
<name>hive.metastore.uris</name>
<value>thrift://bn00:9083</value>
</property>
<property>
<name>hive.metastore.client.socket.timeout</name>
<value>300</value>
</property>
<property>
<name>hive.metastore.warehouse.dir</name>
<value>/user/hive/warehouse</value>
</property>
<property>
<name>hive.warehouse.subdir.inherit.perms</name>
<value>true</value>
</property>
<property>
<name>hive.auto.convert.join</name>
<value>true</value>
</property>
<property>
<name>hive.auto.convert.join.noconditionaltask.size</name>
<value>20971520</value>
</property>
<property>
<name>hive.optimize.bucketmapjoin.sortedmerge</name>
<value>false</value>
</property>
<property>
<name>hive.smbjoin.cache.rows</name>
<value>10000</value>
</property>
<property>
<name>hive.server2.logging.operation.enabled</name>
<value>true</value>
</property>
<property>
<name>hive.server2.logging.operation.log.location</name>
<value>/var/log/hive/operation_logs</value>
</property>
<property>
<name>mapred.reduce.tasks</name>
<value>-1</value>
</property>
<property>
<name>hive.exec.reducers.bytes.per.reducer</name>
<value>67108864</value>
</property>
<property>
<name>hive.exec.copyfile.maxsize</name>
<value>33554432</value>
</property>
<property>
<name>hive.exec.reducers.max</name>
<value>1099</value>
</property>
<property>
<name>hive.vectorized.groupby.checkinterval</name>
<value>4096</value>
</property>
<property>
<name>hive.vectorized.groupby.flush.percent</name>
<value>0.1</value>
</property>
<property>
<name>hive.compute.query.using.stats</name>
<value>true</value>
</property>
<property>
<name>hive.vectorized.execution.enabled</name>
<value>true</value>
</property>
<property>
<name>hive.vectorized.execution.reduce.enabled</name>
<value>false</value>
</property>
<property>
<name>hive.merge.mapfiles</name>
<value>true</value>
</property>
<property>
<name>hive.merge.mapredfiles</name>
<value>false</value>
</property>
<property>
<name>hive.cbo.enable</name>
<value>true</value>
</property>
<property>
<name>hive.fetch.task.conversion</name>
<value>minimal</value>
</property>
<property>
<name>hive.fetch.task.conversion.threshold</name>
<value>268435456</value>
</property>
<property>
<name>hive.limit.pushdown.memory.usage</name>
<value>0.1</value>
</property>
<property>
<name>hive.merge.sparkfiles</name>
<value>true</value>
</property>
<property>
<name>hive.merge.smallfiles.avgsize</name>
<value>16777216</value>
</property>
<property>
<name>hive.merge.size.per.task</name>
<value>268435456</value>
</property>
<property>
<name>hive.optimize.reducededuplication</name>
<value>true</value>
</property>
<property>
<name>hive.optimize.reducededuplication.min.reducer</name>
<value>4</value>
</property>
<property>
<name>hive.map.aggr</name>
<value>true</value>
</property>
<property>
<name>hive.map.aggr.hash.percentmemory</name>
<value>0.5</value>
</property>
<property>
<name>hive.optimize.sort.dynamic.partition</name>
<value>false</value>
</property>
<property>
<name>hive.execution.engine</name>
<value>mr</value>
</property>
<property>
<name>spark.executor.memory</name>
<value>1277794713</value>
</property>
<property>
<name>spark.driver.memory</name>
<value>966367641</value>
</property>
<property>
<name>spark.executor.cores</name>
<value>6</value>
</property>
<property>
<name>spark.yarn.driver.memoryOverhead</name>
<value>102</value>
</property>
<property>
<name>spark.yarn.executor.memoryOverhead</name>
<value>135</value>
</property>
<property>
<name>spark.dynamicAllocation.enabled</name>
<value>true</value>
</property>
<property>
<name>spark.dynamicAllocation.initialExecutors</name>
<value>1</value>
</property>
<property>
<name>spark.dynamicAllocation.minExecutors</name>
<value>1</value>
</property>
<property>
<name>spark.dynamicAllocation.maxExecutors</name>
<value>2147483647</value>
</property>
<property>
<name>hive.metastore.execute.setugi</name>
<value>true</value>
</property>
<property>
<name>hive.support.concurrency</name>
<value>true</value>
</property>
<property>
<name>hive.zookeeper.quorum</name>
<value>bn00,bn01,bn02</value>
</property>
<property>
<name>hive.zookeeper.client.port</name>
<value>2181</value>
</property>
<property>
<name>hive.zookeeper.namespace</name>
<value>hive_zookeeper_namespace_hive</value>
</property>
<property>
<name>hbase.zookeeper.quorum</name>
<value>bn00,bn01,bn02</value>
</property>
<property>
<name>hbase.zookeeper.property.clientPort</name>
<value>2181</value>
</property>
<property>
<name>hive.cluster.delegation.token.store.class</name>
<value>org.apache.hadoop.hive.thrift.MemoryTokenStore</value>
</property>
<property>
<name>hive.server2.enable.doAs</name>
<value>true</value>
</property>
<property>
<name>hive.server2.use.SSL</name>
<value>false</value>
</property>
<property>
<name>spark.shuffle.service.enabled</name>
<value>true</value>
</property>
</configuration>
代码部分如下：
import java.util.ArrayList;
import java.util.List;
import org.apache.log4j.Level;
import org.apache.logging.log4j.LogManager;
import org.apache.logging.log4j.Logger;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.function.FlatMapFunction;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.api.java.function.Function2;
import org.apache.spark.api.java.function.PairFunction;
import org.apache.spark.api.java.function.VoidFunction;
import org.apache.spark.sql.DataFrame;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.RowFactory;
import org.apache.spark.sql.SQLContext;
import org.apache.spark.sql.hive.HiveContext;
import org.apache.spark.sql.types.DataTypes;
import org.apache.spark.sql.types.StructField;
import org.apache.spark.sql.types.StructType;
import org.apache.spark.streaming.Durations;
import org.apache.spark.streaming.api.java.JavaDStream;
import org.apache.spark.streaming.api.java.JavaStreamingContext;
import scala.Tuple2;
import com.google.common.collect.Lists;
public class HiveAndSparkSQLApp {
private static final Logger logger = LogManager.getLogger(App.class);
static {
// 设置日志级别清理
org.apache.log4j.Logger.getLogger("org.apache.spark").setLevel(Level.WARN);
org.apache.log4j.Logger.getLogger("org.eclipse.jetty.server").setLevel(Level.OFF);
}
@SuppressWarnings("serial")
public static void main(String[] args) {
// 调试环境,spark UI:http://localhost:4040/executors/
SparkConf conf = new SparkConf().setMaster("local[4]").setAppName("test")
.set("spark.testing.memory", "1147480000");
// spark streaming context
JavaStreamingContext jssc = new JavaStreamingContext(conf, Durations.seconds(5));
// spark hive context
final HiveContext hiveContext = new HiveContext(jssc.sparkContext());
// spark SQL context
// final SQLContext sqlContext = SQLContext.getOrCreate(jssc.sparkContext().sc());
/**
* 远程的socket监听
* 在节点上，执行nc -lk 9998
* 若节点上没有安装nc工具，执行yum install nc.x86_64
* 之后直接发送消息即可
*/
JavaDStream<String> lines = jssc.socketTextStream("node0", 9998);
lines.foreachRDD(new VoidFunction<JavaRDD<String>>() {
@Override
public void call(JavaRDD<String> rdd) throws Exception {
// SQLContext sqlContext = SQLContext.getOrCreate(rdd.context());
JavaRDD<Row> rowRDD = rdd.map(new Function<String, Row>() {
@Override
public Row call(String t) throws Exception {
String[] splited = new String[] { System.currentTimeMillis() + "",
System.currentTimeMillis() + "", System.currentTimeMillis() + "" };
// 1.Row构建
return RowFactory.create(Long.valueOf(splited[0]), splited[1], Long.valueOf(splited[2]));
}
});
// 2.DF metadata专用结构体
// 对Row具体指定元数据信息。
List<StructField> structFields = new ArrayList<StructField>();
// 列名称 列的具体类型（Integer Or String） 是否为空一般为true，实际在开发环境是通过for循环，而不是手动添加
structFields.add(DataTypes.createStructField("id", DataTypes.LongType, true));
structFields.add(DataTypes.createStructField("name", DataTypes.StringType, true));
structFields.add(DataTypes.createStructField("age", DataTypes.LongType, true));
// 构建StructType,用于最后DataFrame元数据的描述
StructType structType = DataTypes.createStructType(structFields);
// 3.构建DF
DataFrame personsDF = hiveContext.createDataFrame(rowRDD, structType);
// 4.注册为临时表
personsDF.registerTempTable("test");
DataFrame result = hiveContext.sql("select * from test");
/**
* 对结果进行处理，包括由DataFrame转换成为RDD<Row>,以及结果的持久化
*/
List<Row> listRow = result.javaRDD().collect();
for (Row row : listRow) {
logger.error("row:" + row);
}
hiveContext.sql("insert into recommendation_system.t111 select id from test");
}
});
// 测试流，需要存在感~
lines.flatMap(new FlatMapFunction<String, String>() {
public Iterable<String> call(String msg) {
System.err.println(msg);
logger.error(msg);
return Lists.newArrayList(" ".split(msg));
}
}).mapToPair(new PairFunction<String, String, Integer>() {
@Override
public Tuple2<String, Integer> call(String t) throws Exception {
return new Tuple2<String, Integer>(t, 1);
}
}).reduceByKey(new Function2<Integer, Integer, Integer>() {
@Override
public Integer call(Integer v1, Integer v2) throws Exception {
return v1 + v2;
}
}).print();
jssc.start();
jssc.awaitTermination();
}
}
