Examples with TypeInfoFactory.timestampTypeInfo org.apache.hadoop.hive.serde2.typeinfo.TypeInfoFactory.timestampTypeInfo used on opensource projects

Example 6 with TypeInfoFactory.timestampTypeInfo

use of org.apache.hadoop.hive.serde2.typeinfo.TypeInfoFactory.timestampTypeInfo in project hive by apache.

the class TestGenericUDFOPMinus method testTimestampMinusIntervalDayTime.

@Test
public void testTimestampMinusIntervalDayTime() throws Exception {
    GenericUDFOPMinus udf = new GenericUDFOPMinus();
    TimestampWritable left = new TimestampWritable(Timestamp.valueOf("2001-01-02 2:3:4.567"));
    HiveIntervalDayTimeWritable right = new HiveIntervalDayTimeWritable(HiveIntervalDayTime.valueOf("1 2:3:4.567"));
    ObjectInspector[] inputOIs = { PrimitiveObjectInspectorFactory.writableTimestampObjectInspector, PrimitiveObjectInspectorFactory.writableHiveIntervalDayTimeObjectInspector };
    DeferredObject[] args = { new DeferredJavaObject(left), new DeferredJavaObject(right) };
    PrimitiveObjectInspector oi = (PrimitiveObjectInspector) udf.initialize(inputOIs);
    Assert.assertEquals(TypeInfoFactory.timestampTypeInfo, oi.getTypeInfo());
    TimestampWritable res = (TimestampWritable) udf.evaluate(args);
    Assert.assertEquals(Timestamp.valueOf("2001-01-01 00:00:00"), res.getTimestamp());
}

Example 7 with TypeInfoFactory.timestampTypeInfo

use of org.apache.hadoop.hive.serde2.typeinfo.TypeInfoFactory.timestampTypeInfo in project hive by apache.

the class DruidSerDe method inferSchema.

/* Select query */
private void inferSchema(SelectQuery query, List<String> columnNames, List<PrimitiveTypeInfo> columnTypes, String address) throws SerDeException {
    // Timestamp column
    columnNames.add(DruidTable.DEFAULT_TIMESTAMP_COLUMN);
    columnTypes.add(TypeInfoFactory.timestampTypeInfo);
    // Dimension columns
    for (DimensionSpec ds : query.getDimensions()) {
        columnNames.add(ds.getOutputName());
        columnTypes.add(TypeInfoFactory.stringTypeInfo);
    }
    // The type for metric columns is not explicit in the query, thus in this case
    // we need to emit a metadata query to know their type
    SegmentMetadataQueryBuilder builder = new Druids.SegmentMetadataQueryBuilder();
    builder.dataSource(query.getDataSource());
    builder.merge(true);
    builder.analysisTypes();
    SegmentMetadataQuery metadataQuery = builder.build();
    // Execute query in Druid
    SegmentAnalysis schemaInfo;
    try {
        schemaInfo = submitMetadataRequest(address, metadataQuery);
    } catch (IOException e) {
        throw new SerDeException(e);
    }
    if (schemaInfo == null) {
        throw new SerDeException("Connected to Druid but could not retrieve datasource information");
    }
    for (String metric : query.getMetrics()) {
        columnNames.add(metric);
        columnTypes.add(DruidSerDeUtils.convertDruidToHiveType(schemaInfo.getColumns().get(metric).getType()));
    }
}

Example 8 with TypeInfoFactory.timestampTypeInfo

use of org.apache.hadoop.hive.serde2.typeinfo.TypeInfoFactory.timestampTypeInfo in project hive by apache.

the class TestMapJoinOperator method doTestString.

public void doTestString(long seed, VectorMapJoinVariation vectorMapJoinVariation) throws Exception {
    int rowCount = 10000;
    HiveConf hiveConf = new HiveConf();
    String[] bigTableColumnNames = new String[] { "b1" };
    TypeInfo[] bigTableTypeInfos = new TypeInfo[] { TypeInfoFactory.stringTypeInfo };
    int[] bigTableKeyColumnNums = new int[] { 0 };
    String[] smallTableValueColumnNames = new String[] { "sv1", "sv2" };
    TypeInfo[] smallTableValueTypeInfos = new TypeInfo[] { TypeInfoFactory.dateTypeInfo, TypeInfoFactory.timestampTypeInfo };
    int[] bigTableRetainColumnNums = new int[] { 0 };
    int[] smallTableRetainKeyColumnNums = new int[] {};
    int[] smallTableRetainValueColumnNums = new int[] { 0, 1 };
    SmallTableGenerationParameters smallTableGenerationParameters = new SmallTableGenerationParameters();
    // ----------------------------------------------------------------------------------------------
    MapJoinTestDescription testDesc = new MapJoinTestDescription(hiveConf, vectorMapJoinVariation, bigTableColumnNames, bigTableTypeInfos, bigTableKeyColumnNums, smallTableValueColumnNames, smallTableValueTypeInfos, bigTableRetainColumnNums, smallTableRetainKeyColumnNums, smallTableRetainValueColumnNums, smallTableGenerationParameters);
    // Prepare data.  Good for ANY implementation variation.
    MapJoinTestData testData = new MapJoinTestData(rowCount, testDesc, seed, seed * 10);
    executeTest(testDesc, testData);
}

Example 9 with TypeInfoFactory.timestampTypeInfo

use of org.apache.hadoop.hive.serde2.typeinfo.TypeInfoFactory.timestampTypeInfo in project hive by apache.

the class TestVectorGenericDateExpressions method testDateAddColScalar.

@Test
public void testDateAddColScalar() throws HiveException {
    for (PrimitiveCategory colType1 : dateTimestampStringTypes) testDateAddColScalar(colType1, true);
    VectorExpression udf = new VectorUDFDateAddColScalar(0, 0, 1);
    udf.setInputTypeInfos(new TypeInfo[] { TypeInfoFactory.stringTypeInfo, TypeInfoFactory.timestampTypeInfo });
    udf.transientInit();
    VectorizedRowBatch batch = new VectorizedRowBatch(2, 1);
    batch.cols[0] = new BytesColumnVector(1);
    batch.cols[1] = new LongColumnVector(1);
    BytesColumnVector bcv = (BytesColumnVector) batch.cols[0];
    byte[] bytes = "error".getBytes(utf8);
    bcv.vector[0] = bytes;
    bcv.start[0] = 0;
    bcv.length[0] = bytes.length;
    udf.evaluate(batch);
    Assert.assertEquals(batch.cols[1].isNull[0], true);
}

Example 10 with TypeInfoFactory.timestampTypeInfo

use of org.apache.hadoop.hive.serde2.typeinfo.TypeInfoFactory.timestampTypeInfo in project hive by apache.

the class TestVectorGenericDateExpressions method testDateDiffColScalar.

@Test
public void testDateDiffColScalar() throws HiveException {
    for (PrimitiveCategory colType1 : dateTimestampStringTypes) {
        for (PrimitiveCategory scalarType2 : dateTimestampStringTypes) {
            LongColumnVector date1 = newRandomLongColumnVector(10000, size);
            LongColumnVector output = new LongColumnVector(size);
            VectorizedRowBatch batch = new VectorizedRowBatch(2, size);
            batch.cols[0] = castTo(date1, colType1);
            batch.cols[1] = output;
            long scalar2 = newRandom(1000);
            validateDateDiff(batch, date1, scalar2, colType1, scalarType2);
            TestVectorizedRowBatch.addRandomNulls(date1);
            batch.cols[0] = castTo(date1, colType1);
            validateDateDiff(batch, date1, scalar2, colType1, scalarType2);
        }
    }
    VectorExpression udf;
    byte[] bytes = "error".getBytes(utf8);
    VectorizedRowBatch batch = new VectorizedRowBatch(2, 1);
    udf = new VectorUDFDateDiffColScalar(0, 0L, 1);
    udf.setInputTypeInfos(new TypeInfo[] { TypeInfoFactory.timestampTypeInfo, TypeInfoFactory.stringTypeInfo });
    batch.cols[0] = new BytesColumnVector(1);
    batch.cols[1] = new LongColumnVector(1);
    BytesColumnVector bcv = (BytesColumnVector) batch.cols[0];
    bcv.vector[0] = bytes;
    bcv.start[0] = 0;
    bcv.length[0] = bytes.length;
    udf.evaluate(batch);
    Assert.assertEquals(batch.cols[1].isNull[0], true);
    udf = new VectorUDFDateDiffColScalar(0, bytes, 1);
    udf.setInputTypeInfos(new TypeInfo[] { TypeInfoFactory.timestampTypeInfo, TypeInfoFactory.stringTypeInfo });
    udf.transientInit();
    batch.cols[0] = new LongColumnVector(1);
    batch.cols[1] = new LongColumnVector(1);
    udf.evaluate(batch);
    Assert.assertEquals(batch.cols[1].isNull[0], true);
}