本文整理了Java中org.apache.spark.sql.hive.HiveContext.sql()方法的一些代码示例，展示了HiveContext.sql()的具体用法。这些代码示例主要来源于Github/Stackoverflow/Maven等平台，是从一些精选项目中提取出来的代码，具有较强的参考意义，能在一定程度帮忙到你。HiveContext.sql()方法的具体详情如下：
包路径：org.apache.spark.sql.hive.HiveContext
类名称：HiveContext
方法名：sql

HiveContext.sql介绍

暂无

代码示例

代码示例来源：origin: Impetus/Kundera

@Override
public void registerTable(EntityMetadata m, SparkClient sparkClient)
{
  sparkClient.sqlContext.sql("use " + m.getSchema());
}

代码示例来源：origin: Impetus/Kundera

/**
 * Gets the data frame.
 * 
 * @param query
 *            the query
 * @param m
 *            the m
 * @param kunderaQuery
 *            the kundera query
 * @return the data frame
 */
public DataFrame getDataFrame(String query, EntityMetadata m, KunderaQuery kunderaQuery)
{
  PersistenceUnitMetadata puMetadata = kunderaMetadata.getApplicationMetadata().getPersistenceUnitMetadata(
      persistenceUnit);
  String clientName = puMetadata.getProperty(DATA_CLIENT).toLowerCase();
  SparkDataClient dataClient = SparkDataClientFactory.getDataClient(clientName);
  if (registeredTables.get(m.getTableName()) == null || !registeredTables.get(m.getTableName()))
  {
    dataClient.registerTable(m, this);
    registeredTables.put(m.getTableName(), true);
  }
  // at this level temp table or table should be ready
  DataFrame dataFrame = sqlContext.sql(query);
  return dataFrame;
}

代码示例来源：origin: Impetus/Kundera

@Override
public boolean persist(List listEntity, EntityMetadata m, SparkClient sparkClient)
{
  try
  {
    Seq s = scala.collection.JavaConversions.asScalaBuffer(listEntity).toList();
    ClassTag tag = scala.reflect.ClassTag$.MODULE$.apply(m.getEntityClazz());
    JavaRDD personRDD = sparkClient.sparkContext.parallelize(s, 1, tag).toJavaRDD();
    DataFrame df = sparkClient.sqlContext.createDataFrame(personRDD, m.getEntityClazz());
    sparkClient.sqlContext.sql("use " + m.getSchema());
    if (logger.isDebugEnabled())
    {
      logger.info("Below are the registered table with hive context: ");
      sparkClient.sqlContext.sql("show tables").show();
    }
    df.write().insertInto(m.getTableName());
    return true;
  }
  catch (Exception e)
  {
    throw new KunderaException("Cannot persist object(s)", e);
  }
}

代码示例来源：origin: org.apache.camel/camel-spark

@Override
public void process(Exchange exchange) throws Exception {
  HiveContext hiveContext = resolveHiveContext();
  String sql = exchange.getIn().getBody(String.class);
  Dataset<Row> resultFrame = hiveContext.sql(sql);
  exchange.getIn().setBody(getEndpoint().isCollect() ? resultFrame.collectAsList() : resultFrame.count());
}

代码示例来源：origin: ddf-project/DDF

@Override
public SqlResult sql(String command, Integer maxRows, DataSourceDescriptor dataSource) throws DDFException {
 // TODO: handle other dataSources and dataFormats
 DataFrame  rdd = this.getHiveContext().sql(command);
 Schema schema = SparkUtils.schemaFromDataFrame(rdd);
 String[] strResult = SparkUtils.df2txt(rdd, "\t");
 return new SqlResult(schema,Arrays.asList(strResult));
}

代码示例来源：origin: Quetzal-RDF/quetzal

public static void main( String[] args )
 {       
//   	SparkConf conf = new SparkConf().setAppName("App-mt").setMaster("local[2]");
//      	SparkConf conf = new SparkConf().setAppName("App-mt").setMaster("spark://Kavithas-MBP.home:7077");
   SparkConf conf = new SparkConf().setAppName("App-mt").setMaster("spark://kavithas-mbp.watson.ibm.com:7077");
 
   JavaSparkContext sc = new JavaSparkContext(conf);
   
   HiveContext sqlContext = new HiveContext(sc.sc());
   DataFrame urls = sqlContext.read().json("/tmp/urls.json");
   urls.registerTempTable("urls");
   DataFrame temp = sqlContext.sql("select * from urls");
   temp.show();
   
     sqlContext.sql("add jar /tmp/quetzal.jar");
   sqlContext.sql("create temporary function webservice as 'com.ibm.research.rdf.store.utilities.WebServiceGetUDTF'");
   DataFrame drugs = sqlContext.sql("select webservice(\"drug,id,action\", \"url\", \"\", \"GET\", \"xs=http://www.w3.org/2001/XMLSchema\", \"//row\",\"drug\",\"./drug\","
       + " \"<string>\", \"id\", \"./id\",\"<string>\", \"action\", \"./action\", \"<string>\", url) as (drug, drug_typ, id, id_typ, action, action_typ) from urls");
   drugs.show();
   System.out.println("Num rows:" + drugs.count());
 }

代码示例来源：origin: ddf-project/DDF

@Override
public SqlTypedResult sqlTyped(String command, Integer maxRows, DataSourceDescriptor dataSource) throws  DDFException {
 DataFrame rdd = ((SparkDDFManager) this.getManager()).getHiveContext().sql(command);
 Schema schema = SparkUtils.schemaFromDataFrame(rdd);
 int columnSize = schema.getNumColumns();
 Row[] rddRows = rdd.collect();
 List<List<SqlTypedCell>> sqlTypedResult = new ArrayList<List<SqlTypedCell>>();
 // Scan every cell and add the type information.
 for (int rowIdx = 0; rowIdx < rddRows.length; ++rowIdx) {
  List<SqlTypedCell> row = new ArrayList<SqlTypedCell>();
  for (int colIdx = 0; colIdx < columnSize; ++ colIdx) {
   // TODO: Optimize by reducing getType().
   row.add(new SqlTypedCell(schema.getColumn(colIdx).getType(), rddRows[rowIdx].get(colIdx).toString()));
  }
  sqlTypedResult.add(row);
 }
 return new SqlTypedResult(schema, sqlTypedResult);
}

代码示例来源：origin: ddf-project/DDF

for(Column column: categoricalColumns) {
 String sqlCmd = String.format("select distinct(%s) from %s where %s is not null", column.getName(), this.getDDF().getTableName(), column.getName());
 DataFrame sqlresult = sqlContext.sql(sqlCmd);
 Row[] rows = sqlresult.collect();
 List<String> values = new ArrayList<>();
DataFrame sqlResult = sqlContext.sql(sql);
Row[] rows = sqlResult.collect();
Row result = rows[0];

代码示例来源：origin: ddf-project/DDF

@Override
public DDF sql2ddf(String command, Schema schema, DataSourceDescriptor dataSource, DataFormat dataFormat) throws DDFException {
 //    TableRDD tableRdd = null;
 //    RDD<Row> rddRow = null;
 DataFrame rdd = this.getHiveContext().sql(command);
 if (schema == null) schema = SchemaHandler.getSchemaFromDataFrame(rdd);
 DDF ddf = this.getManager().newDDF(this.getManager(), rdd, new Class<?>[]
         {DataFrame.class}, null, schema);
 ddf.getRepresentationHandler().cache(false);
 ddf.getRepresentationHandler().get(new Class<?>[]{RDD.class, Row.class});
 return ddf;
}