Example usage for org.apache.spark.sql.types DataTypes TimestampType

Introduction

In this page you can find the example usage for org.apache.spark.sql.types DataTypes TimestampType.

Prototype

DataType TimestampType

To view the source code for org.apache.spark.sql.types DataTypes TimestampType.

Click Source Link

Document

Gets the TimestampType object.

Usage

From source file:KafkaSparkMongo.java

License:Apache License

public static void main(String[] args) throws Exception {
    if (args.length < 2) {
        System.err.println("Usage: JavaDirectKafkaWordCount <brokers> <topics>\n"
                + "  <brokers> is a list of one or more Kafka brokers\n"
                + "  <topics> is a list of one or more kafka topics to consume from\n\n");
        System.exit(1);//from   w  ww .  java  2  s.c  o m
    }

    String brokers = args[0];
    String topics = args[1];

    String UriMongo = "mongodb://localhost/streamSparkFinal.coll";
    dropDatabase(UriMongo);

    // Create the context with a 1 second batch size
    SparkConf sparkConf = new SparkConf().setAppName("JavaNetworkWordCount")
            .set("spark.app.id", "MongoSparkConnectorTour").set("spark.mongodb.input.uri", UriMongo)
            .set("spark.mongodb.output.uri", UriMongo);

    JavaStreamingContext ssc = new JavaStreamingContext(sparkConf, Durations.seconds(5));
    /** Create a JavaReceiverInputDStream on target ip:port and count the
     * words in input stream of \n delimited text (eg. generated by 'nc')
     * Note that no duplication in storage level only for running locally.
     * Replication necessary in distributed scenario for fault tolerance.
     */

    Set<String> topicsSet = new HashSet<>(Arrays.asList(topics.split(",")));
    Map<String, String> kafkaParams = new HashMap<>();
    kafkaParams.put("metadata.broker.list", brokers);

    // Create direct kafka stream with brokers and topics
    JavaPairInputDStream<String, String> messages = KafkaUtils.createDirectStream(ssc, String.class,
            String.class, StringDecoder.class, StringDecoder.class, kafkaParams, topicsSet);

    messages.print();

    JavaDStream<String> lines = messages.map(x -> x._2());

    JavaDStream<Tuple7<String, String, String, String, String, String, String>> words = lines.map(y -> {
        String[] wordy = SPACE.split(y);
        return new Tuple7<>(wordy[0], wordy[1], wordy[2], wordy[3], wordy[4], wordy[5], wordy[6]);
    });

    words.foreachRDD(rdd -> {

        List<StructField> subFields = new ArrayList<>();
        subFields.add(DataTypes.createStructField("X", DataTypes.DoubleType, true));
        subFields.add(DataTypes.createStructField("Y", DataTypes.DoubleType, true));
        subFields.add(DataTypes.createStructField("z", DataTypes.DoubleType, true));

        List<StructField> fields = new ArrayList<>();
        fields.add(DataTypes.createStructField("Serial", DataTypes.StringType, true));
        fields.add(DataTypes.createStructField("Zone", DataTypes.StringType, true));
        fields.add(DataTypes.createStructField("Group", DataTypes.StringType, true));
        fields.add(DataTypes.createStructField("coord", DataTypes.createStructType(subFields), true));
        fields.add(DataTypes.createStructField("Time", DataTypes.TimestampType, true));

        StructType schema = DataTypes.createStructType(fields);

        SparkSession spark = JavaSparkSessionSingleton.getInstance(rdd.context().getConf());

        JavaRDD<Row> rowRDD = rdd
                .map(palabra -> RowFactory.create(palabra._1(), palabra._2(), palabra._3(),
                        RowFactory.create(Double.parseDouble(palabra._4()), Double.parseDouble(palabra._5()),
                                Double.parseDouble(palabra._6())),
                        Timestamp.from(Instant.parse(palabra._7()))));

        Dataset<Row> wordsDataFrame = spark.createDataFrame(rowRDD, schema);
        wordsDataFrame.show();
        MongoSpark.write(wordsDataFrame).option("collection", "pruebaF").mode("append").save();
    });

    ssc.start();
    ssc.awaitTermination();
}

---

From source file:com.bosscs.spark.commons.utils.CellsUtils.java

License:Apache License

private static DataType getDataType(Object value) {
    Class cls = value.getClass();
    DataType dataType;// w w w  .ja v a2s . c o m
    if (cls.equals(String.class)) {
        dataType = DataTypes.StringType;
    } else if (cls.equals(Byte[].class)) {
        dataType = DataTypes.BinaryType;
    } else if (cls.equals(Boolean.class)) {
        dataType = DataTypes.BooleanType;
    } else if (cls.equals(Timestamp.class)) {
        dataType = DataTypes.TimestampType;
    } else if (cls.equals(Double.class)) {
        dataType = DataTypes.DoubleType;
    } else if (cls.equals(Float.class)) {
        dataType = DataTypes.FloatType;
    } else if (cls.equals(Byte.class)) {
        dataType = DataTypes.ByteType;
    } else if (cls.equals(Integer.class)) {
        dataType = DataTypes.IntegerType;
    } else if (cls.equals(Long.class)) {
        dataType = DataTypes.LongType;
    } else if (cls.equals(Short.class)) {
        dataType = DataTypes.ShortType;
    } else if (value instanceof List) {
        List listValue = (List) value;
        if (listValue.isEmpty()) {
            dataType = DataTypes.createArrayType(DataTypes.StringType);
        } else {
            dataType = DataTypes.createArrayType(getDataType(listValue.get(0)));
        }
    } else if (value instanceof Map) {
        Map mapValue = (Map) value;
        if (mapValue.isEmpty()) {
            dataType = DataTypes.createMapType(DataTypes.StringType, DataTypes.StringType);
        } else {
            Map.Entry entry = (Map.Entry) mapValue.entrySet().iterator().next();
            dataType = DataTypes.createMapType(getDataType(entry.getKey()), getDataType(entry.getValue()));
        }
    } else {
        dataType = DataTypes.StringType;
    }
    return dataType;
}

---

From source file:com.estonteco.spark.frames.conf.factory.creator.impl.AbstractDataFrameCreator.java

protected Object cast(String value, DataType dataType) {
    if (value == null || dataType == null) {
        return null;
    }//from www  .  j a v  a  2s. c  o  m
    if (dataType == DataTypes.StringType) {
        return value;
    }
    if (dataType == DataTypes.BooleanType) {
        return Boolean.valueOf(value);
    }
    if (dataType == DataTypes.ByteType) {
        return Byte.valueOf(value);
    }
    if (dataType == DataTypes.DateType) {
        try {
            return DateUtils.fromJavaDate(Date.valueOf(value));
        } catch (IllegalArgumentException ex) {
            ex.printStackTrace();
            return null;
        }
    }
    if (dataType == DataTypes.DoubleType) {
        return Double.valueOf(value);
    }
    if (dataType == DataTypes.FloatType) {
        return Float.valueOf(value);
    }
    if (dataType == DataTypes.LongType) {
        return Long.valueOf(value);
    }
    if (dataType == DataTypes.ShortType) {
        return Short.valueOf(value);
    }
    if (dataType == DataTypes.TimestampType) {
        // Throw away extra if more than 9 decimal places
        int periodIdx = value.indexOf(".");
        String str = value;
        if (periodIdx != -1 && str.length() - periodIdx > 9) {
            str = str.substring(0, periodIdx + 10);
        }
        try {
            return java.sql.Timestamp.valueOf(str);
        } catch (IllegalArgumentException ex) {
            ex.printStackTrace();
            return null;
        }
    }
    if (dataType == DataTypes.BinaryType) {
        return value.getBytes();
    }
    return null;
}

---

From source file:com.getcake.sparkjdbc.SparkJDBCServer.java

License:Apache License

private String loadSingleFileWithMeta(String registerTableName, String fullPathTableName, String metaFileName)
        throws IOException {
    DataFrame dynamicDataFrame;//from   w ww  .j a va2s .  co m
    long startTime, firstStartTime;
    float durSeconds, durMinutes;
    String respMsg;

    startTime = System.currentTimeMillis();
    firstStartTime = startTime;
    try {
        dynamicDataFrame = hiveContext.table(registerTableName);
        respMsg = "table " + registerTableName + " at " + fullPathTableName + " was already loaded";
        log(respMsg);
        return respMsg;
    } catch (Throwable exc) {
        // hiveContext.table does not declare that it throws NoSuchTableException, so cannot use it in catch clause and
        // have to check for it explicitly
        if (exc instanceof NoSuchTableException) {
            respMsg = "table " + registerTableName + " at " + fullPathTableName
                    + " was not loaded => load it next";
            log(respMsg);
        } else {
            throw exc;
        }
    }

    FileInputStream propFileInputStream;
    propFileInputStream = new FileInputStream(metaFileName);
    properties = new Properties();
    properties.load(propFileInputStream);

    Stream<Entry<Object, Object>> stream = properties.entrySet().stream();
    Map<String, String> options = stream.collect(Collectors.toMap(entry -> String.valueOf(entry.getKey()),
            entry -> String.valueOf(entry.getValue())));

    int numColumns = Integer.parseInt(properties.getProperty("numColumns"));
    StructField structFields[] = new StructField[numColumns];
    String colName, colType;
    StructField structField;

    for (int i = 1; i <= numColumns; i++) {
        colName = properties.getProperty("col" + i + ".name");
        colType = properties.getProperty("col" + i + ".type");
        switch (colType) {
        case "TimeStamp":
            structField = DataTypes.createStructField(colName, DataTypes.TimestampType, true);
            break;

        case "Date":
            structField = DataTypes.createStructField(colName, DataTypes.DateType, true);
            break;

        case "Float":
            structField = DataTypes.createStructField(colName, DataTypes.FloatType, true);
            break;

        case "Integer":
            structField = DataTypes.createStructField(colName, DataTypes.IntegerType, true);
            break;

        case "Long":
            structField = DataTypes.createStructField(colName, DataTypes.LongType, true);
            break;

        case "Short":
            structField = DataTypes.createStructField(colName, DataTypes.ShortType, true);
            break;

        case "Double":
            structField = DataTypes.createStructField(colName, DataTypes.DoubleType, true);
            break;

        case "Boolean":
            structField = DataTypes.createStructField(colName, DataTypes.BooleanType, true);
            break;

        case "Binary":
            structField = DataTypes.createStructField(colName, DataTypes.BinaryType, true);
            break;

        case "Byte":
            structField = DataTypes.createStructField(colName, DataTypes.ByteType, true);
            break;

        case "Null":
            structField = DataTypes.createStructField(colName, DataTypes.NullType, true);
            break;

        default:
            structField = DataTypes.createStructField(colName, DataTypes.StringType, true);
        }

        structFields[i - 1] = structField;
    }

    // dynamicDataFrame = hiveContext.read().format("com.databricks.spark.csv").
    //   option("header", Boolean.toString(headerInCSVFileFlag)).option("inferSchema", Boolean.toString(inferSchemaFlag)).load(fullPathTableName);
    // Map<String, String> options = new HashMap<>(properties);
    options.put("path", "file:///" + fullPathTableName);
    // options.put("header", "false");
    // options.put("delimiter", ",");

    // DataType dataType = new DataType ();
    /*
    StructField structField1 = DataTypes.createStructField("LogType", DataTypes.StringType, false);
    StructField structField2 = DataTypes.createStructField("EntryTime", DataTypes.TimestampType, false);
    StructField structField3 = DataTypes.createStructField("Code_Class", DataTypes.StringType, false);
    StructField structField4 = DataTypes.createStructField("Code_Method", DataTypes.StringType, false);
    StructField structField5 = DataTypes.createStructField("Log_Message", DataTypes.StringType, false);
    structFields[0] = structField1;
    structFields[1] = structField2;
    structFields[2] = structField3;
    structFields[3] = structField4;
    structFields[4] = structField5;
    */

    StructType schema = new StructType(structFields);

    dynamicDataFrame = hiveContext.load("com.databricks.spark.csv", schema, options);

    durSeconds = (float) (System.currentTimeMillis() - startTime) / 1000F;
    durMinutes = durSeconds / 60F;
    log("loaded table " + fullPathTableName + " in seconds: " + durSeconds + " / in minutes: " + durMinutes);

    schema = dynamicDataFrame.schema();
    structFields = schema.fields();
    for (StructField structFieldLocal : structFields) {
        DataType dataType = structFieldLocal.dataType();
        logger.debug(structFieldLocal.name() + " - dataType: " + dataType.typeName());
    }

    startTime = System.currentTimeMillis();
    dynamicDataFrame.cache();
    durSeconds = (float) (System.currentTimeMillis() - startTime) / 1000F;
    durMinutes = durSeconds / 60F;
    log("cache table " + fullPathTableName + " in seconds: " + durSeconds + " / in minutes: " + durMinutes);

    startTime = System.currentTimeMillis();
    dynamicDataFrame.registerTempTable(registerTableName);

    durSeconds = (float) (System.currentTimeMillis() - startTime) / 1000F;
    durMinutes = durSeconds / 60F;
    log("registerTempTable table " + registerTableName + " in seconds: " + durSeconds + " / in minutes: "
            + durMinutes);

    durSeconds = (float) (System.currentTimeMillis() - firstStartTime) / 1000F;
    durMinutes = durSeconds / 60F;
    respMsg = "Completed loading table " + fullPathTableName + " in seconds: " + durSeconds + " / in minutes: "
            + durMinutes;
    log(respMsg);
    return respMsg;
}

---

From source file:com.getcake.sparkjdbc.SparkJDBCServer.java

License:Apache License

private String loadFilesWithMeta(String registerTableName, String fullPathTableName, String metaFileName,
        String fileListName) throws IOException {
    DataFrame combinedDynamicDataFrame = null, dynamicDataFrame = null;
    long startTime, firstStartTime;
    float durSeconds, durMinutes;
    String respMsg;/*from w ww . j a  v a  2s .co  m*/

    startTime = System.currentTimeMillis();
    firstStartTime = startTime;
    try {
        combinedDynamicDataFrame = hiveContext.table(registerTableName);
        respMsg = "table " + registerTableName + " at " + fullPathTableName + " was already loaded";
        log(respMsg);
        return respMsg;
    } catch (Throwable exc) {
        // hiveContext.table does not declare that it throws NoSuchTableException, so cannot use it in catch clause and
        // have to check for it explicitly
        if (exc instanceof NoSuchTableException) {
            respMsg = "table " + registerTableName + " at " + fullPathTableName
                    + " was not loaded => load it next";
            log(respMsg);
        } else {
            throw exc;
        }
    }

    FileInputStream propFileInputStream;
    propFileInputStream = new FileInputStream(metaFileName);
    properties = new Properties();
    properties.load(propFileInputStream);

    Stream<Entry<Object, Object>> stream = properties.entrySet().stream();
    Map<String, String> options = stream.collect(Collectors.toMap(entry -> String.valueOf(entry.getKey()),
            entry -> String.valueOf(entry.getValue())));

    int numColumns = Integer.parseInt(properties.getProperty("numColumns"));
    StructField structFields[] = new StructField[numColumns];
    String colName, colType;
    StructField structField;

    // structField = DataTypes.createStructField("File_Source", DataTypes.StringType, true);
    // structFields[0] = structField;

    for (int i = 1; i <= numColumns; i++) {
        colName = properties.getProperty("col" + i + ".name");
        colType = properties.getProperty("col" + i + ".type");
        switch (colType) {
        case "TimeStamp":
            structField = DataTypes.createStructField(colName, DataTypes.TimestampType, true);
            break;

        case "Date":
            structField = DataTypes.createStructField(colName, DataTypes.DateType, true);
            break;

        case "Float":
            structField = DataTypes.createStructField(colName, DataTypes.FloatType, true);
            break;

        case "Integer":
            structField = DataTypes.createStructField(colName, DataTypes.IntegerType, true);
            break;

        case "Long":
            structField = DataTypes.createStructField(colName, DataTypes.LongType, true);
            break;

        case "Short":
            structField = DataTypes.createStructField(colName, DataTypes.ShortType, true);
            break;

        case "Double":
            structField = DataTypes.createStructField(colName, DataTypes.DoubleType, true);
            break;

        case "Boolean":
            structField = DataTypes.createStructField(colName, DataTypes.BooleanType, true);
            break;

        case "Binary":
            structField = DataTypes.createStructField(colName, DataTypes.BinaryType, true);
            break;

        case "Byte":
            structField = DataTypes.createStructField(colName, DataTypes.ByteType, true);
            break;

        case "Null":
            structField = DataTypes.createStructField(colName, DataTypes.NullType, true);
            break;

        default:
            structField = DataTypes.createStructField(colName, DataTypes.StringType, true);
        }

        structFields[i - 1] = structField;
    }

    StructType schema = new StructType(structFields);

    List<String> fileLlist = new ArrayList<>();
    try (BufferedReader br = Files.newBufferedReader(Paths.get(fileListName))) {

        //br returns as stream and convert it into a List
        fileLlist = br.lines().collect(Collectors.toList());

    } catch (IOException e) {
        e.printStackTrace();
    }

    for (String file : fileLlist) {
        options.put("path", "file:///" + file);
        dynamicDataFrame = hiveContext.load("com.databricks.spark.csv", schema, options);
        if (combinedDynamicDataFrame == null) {
            combinedDynamicDataFrame = dynamicDataFrame;
        } else {
            combinedDynamicDataFrame = combinedDynamicDataFrame.unionAll(dynamicDataFrame);
        }
    }

    durSeconds = (float) (System.currentTimeMillis() - startTime) / 1000F;
    durMinutes = durSeconds / 60F;
    log("loaded table " + fullPathTableName + " in seconds: " + durSeconds + " / in minutes: " + durMinutes);

    schema = combinedDynamicDataFrame.schema();
    structFields = schema.fields();
    for (StructField structFieldLocal : structFields) {
        DataType dataType = structFieldLocal.dataType();
        logger.debug(structFieldLocal.name() + " - dataType: " + dataType.typeName());
    }

    startTime = System.currentTimeMillis();
    combinedDynamicDataFrame.cache();
    durSeconds = (float) (System.currentTimeMillis() - startTime) / 1000F;
    durMinutes = durSeconds / 60F;
    log("cache table " + fullPathTableName + " in seconds: " + durSeconds + " / in minutes: " + durMinutes);

    startTime = System.currentTimeMillis();
    combinedDynamicDataFrame.registerTempTable(registerTableName);

    durSeconds = (float) (System.currentTimeMillis() - startTime) / 1000F;
    durMinutes = durSeconds / 60F;
    log("registerTempTable table " + registerTableName + " in seconds: " + durSeconds + " / in minutes: "
            + durMinutes);

    durSeconds = (float) (System.currentTimeMillis() - firstStartTime) / 1000F;
    durMinutes = durSeconds / 60F;
    respMsg = "Completed loading table " + fullPathTableName + " in seconds: " + durSeconds + " / in minutes: "
            + durMinutes;
    log(respMsg);
    return respMsg;
}

---

From source file:com.github.sadikovi.riff.tree.expression.TimestampExpression.java

License:Open Source License

@Override
public DataType dataType() {
    return DataTypes.TimestampType;
}

---

From source file:com.splicemachine.db.iapi.types.SQLTimestamp.java

License:Apache License

@Override
public StructField getStructField(String columnName) {
    return DataTypes.createStructField(columnName, DataTypes.TimestampType, true);
}

---

From source file:com.thinkbiganalytics.kylo.catalog.spark.sources.AbstractJdbcDataSetProvider.java

License:Apache License

/**
 * Scans the specified field and updates the specified high water mark.
 *///w w w . j  a  v a2  s  .  c o  m
@Nonnull
@VisibleForTesting
T updateHighWaterMark(@Nonnull final T dataSet, @Nonnull final String fieldName,
        @Nonnull final JdbcHighWaterMark highWaterMark, @Nonnull final KyloCatalogClient<T> client) {
    // Determine function to convert column to Long
    final DataType fieldType = schema(dataSet).apply(fieldName).dataType();
    final Function1<?, Long> toLong;

    if (fieldType == DataTypes.DateType) {
        toLong = new DateToLong();
    } else if (fieldType == DataTypes.TimestampType) {
        toLong = new TimestampToLong();
    } else {
        throw new KyloCatalogException("Unsupported column type for high water mark: " + fieldType);
    }

    // Create UDF and apply to field
    final String accumulableId = (highWaterMark.getName() != null) ? highWaterMark.getName()
            : UUID.randomUUID().toString();
    final Accumulable<JdbcHighWaterMark, Long> accumulable = accumulable(highWaterMark, accumulableId,
            new JdbcHighWaterMarkAccumulableParam(), client);

    final JdbcHighWaterMarkVisitor<?> visitor = new JdbcHighWaterMarkVisitor<>(accumulable, toLong);
    return map(dataSet, fieldName, visitor, fieldType);
}

---

From source file:com.thinkbiganalytics.spark.metadata.SqlTransformStage.java

License:Apache License

/**
 * Gets the Spark SQL data type for the specified JDBC data type.
 *///from  w  w  w.java 2 s  .  c o  m
@Nonnull
@SuppressWarnings("squid:S1479")
private DataType getCatalystType(final int sqlType, final int precision, final int scale,
        final boolean signed) {
    switch (sqlType) {
    case Types.BINARY:
    case Types.BLOB:
    case Types.LONGVARBINARY:
    case Types.VARBINARY:
        return DataTypes.BinaryType;

    case Types.BIT:
    case Types.BOOLEAN:
        return DataTypes.BooleanType;

    case Types.DATE:
        return DataTypes.DateType;

    case Types.DECIMAL:
    case Types.NUMERIC:
        if (precision != 0 || scale != 0) {
            return new DecimalType(precision, scale);
        } else {
            return DecimalType.SYSTEM_DEFAULT();
        }

    case Types.DOUBLE:
    case Types.REAL:
        return DataTypes.DoubleType;

    case Types.FLOAT:
        return DataTypes.FloatType;

    case Types.SMALLINT:
    case Types.TINYINT:
        return DataTypes.IntegerType;

    case Types.INTEGER:
        return signed ? DataTypes.IntegerType : DataTypes.LongType;

    case Types.ROWID:
        return DataTypes.LongType;

    case Types.BIGINT:
        return signed ? DataTypes.LongType : new DecimalType(20, 0);

    case Types.CHAR:
    case Types.CLOB:
    case Types.LONGNVARCHAR:
    case Types.LONGVARCHAR:
    case Types.NCHAR:
    case Types.NCLOB:
    case Types.NVARCHAR:
    case Types.REF:
    case Types.SQLXML:
    case Types.STRUCT:
    case Types.VARCHAR:
        return DataTypes.StringType;

    case Types.TIME:
    case Types.TIMESTAMP:
        return DataTypes.TimestampType;

    default:
        log.debug("Unsupported SQL type: {}", sqlType);
        return DataTypes.StringType;
    }
}

---

From source file:org.apache.carbondata.converter.SparkDataTypeConverterImpl.java

License:Apache License

private static org.apache.spark.sql.types.DataType convertCarbonToSparkDataType(DataType carbonDataType) {
    if (carbonDataType == org.apache.carbondata.core.metadata.datatype.DataTypes.STRING) {
        return DataTypes.StringType;
    } else if (carbonDataType == org.apache.carbondata.core.metadata.datatype.DataTypes.SHORT) {
        return DataTypes.ShortType;
    } else if (carbonDataType == org.apache.carbondata.core.metadata.datatype.DataTypes.INT) {
        return DataTypes.IntegerType;
    } else if (carbonDataType == org.apache.carbondata.core.metadata.datatype.DataTypes.LONG) {
        return DataTypes.LongType;
    } else if (carbonDataType == org.apache.carbondata.core.metadata.datatype.DataTypes.DOUBLE) {
        return DataTypes.DoubleType;
    } else if (carbonDataType == org.apache.carbondata.core.metadata.datatype.DataTypes.BOOLEAN) {
        return DataTypes.BooleanType;
    } else if (org.apache.carbondata.core.metadata.datatype.DataTypes.isDecimal(carbonDataType)) {
        return DataTypes.createDecimalType();
    } else if (carbonDataType == org.apache.carbondata.core.metadata.datatype.DataTypes.TIMESTAMP) {
        return DataTypes.TimestampType;
    } else if (carbonDataType == org.apache.carbondata.core.metadata.datatype.DataTypes.DATE) {
        return DataTypes.DateType;
    } else {/*w w w .java  2  s.co m*/
        return null;
    }
}

---

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