我喜欢从单个元组生成多个元组。我的意思是： 我有文件中包含以下数据。
>> cat data
ID | ColumnName1:Value1 | ColumnName2:Value2
所以我通过以下命令
加载它grunt >> A = load '$data' using PigStorage('|');
grunt >> dump A;
(ID,ColumnName1:Value1,ColumnName2:Value2)
现在我想把这个元组分成两个元组。
(ID, ColumnName1, Value1)
(ID, ColumnName2, Value2)
我可以将UDF与foreach一起使用并生成。有些事情如下吗？
grunt >> foreach A generate SOMEUDF(A)
编辑：
输入元组：（id1，column1，column2） 输出：两个元组（id1，column1）和（id2，column2）所以它是List还是应该返回一个Bag？
public class SPLITTUPPLE extends EvalFunc <List<Tuple>>
{
public List<Tuple> exec(Tuple input) throws IOException {
if (input == null || input.size() == 0)
return null;
try{
// not sure how whether I can create tuples on my own. Looks like I should use TupleFactory.
// return list of tuples.
}catch(Exception e){
throw WrappedIOException.wrap("Caught exception processing input row ", e);
}
}
}
这种做法是否正确？
答案 0 :(得分：10)
您可以编写UDF或使用带内置函数的PIG脚本。
例如：
-- data should be chararray, PigStorage('|') return bytearray which will not work for this example
inpt = load '/pig_fun/input/single_tuple_to_multiple.txt' as (line:chararray);
-- split by | and create a row so we can dereference it later
splt = foreach inpt generate FLATTEN(STRSPLIT($0, '\\|')) ;
-- first column is id, rest is converted into a bag and flatten it to make rows
id_vals = foreach splt generate $0 as id, FLATTEN(TOBAG(*)) as value;
-- there will be records with (id, id), but id should not have ':'
id_vals = foreach id_vals generate id, INDEXOF(value, ':') as p, STRSPLIT(value, ':', 2) as vals;
final = foreach (filter id_vals by p != -1) generate id, FLATTEN(vals) as (col, val);
dump final;
测试INPUT：
1|c1:11:33|c2:12
234|c1:21|c2:22
33|c1:31|c2:32
345|c1:41|c2:42
<强>输出
(1,c1,11:33)
(1,c2,12)
(234,c1,21)
(234,c2,22)
(33,c1,31)
(33,c2,32)
(345,c1,41)
(345,c2,42)
我希望它有所帮助。
干杯。
答案 1 :(得分：6)
这是UDF版本。我更喜欢退回BAG：
import java.io.IOException;
import org.apache.pig.EvalFunc;
import org.apache.pig.backend.executionengine.ExecException;
import org.apache.pig.data.BagFactory;
import org.apache.pig.data.DataBag;
import org.apache.pig.data.DataType;
import org.apache.pig.data.Tuple;
import org.apache.pig.data.TupleFactory;
import org.apache.pig.impl.logicalLayer.FrontendException;
import org.apache.pig.impl.logicalLayer.schema.Schema;
/**
* Converts input chararray "ID|ColumnName1:Value1|ColumnName2:Value2|.." into a bag
* {(ID, ColumnName1, Value1), (ID, ColumnName2, Value2), ...}
*
* Default rows separator is '|' and key value separator is ':'.
* In this implementation white spaces around separator characters are not removed.
* ID can be made of any character (including sequence of white spaces).
* @author
*
*/
public class TupleToBagColumnValuePairs extends EvalFunc<DataBag> {
private static final TupleFactory tupleFactory = TupleFactory.getInstance();
private static final BagFactory bagFactory = BagFactory.getInstance();
//Row separator character. Default is '|'.
private String rowsSeparator;
//Column value separator character. Default i
private String columnValueSeparator;
public TupleToBagColumnValuePairs() {
this.rowsSeparator = "\\|";
this.columnValueSeparator = ":";
}
public TupleToBagColumnValuePairs(String rowsSeparator, String keyValueSeparator) {
this.rowsSeparator = rowsSeparator;
this.columnValueSeparator = keyValueSeparator;
}
/**
* Creates a tuple with 3 fields (id:chararray, column:chararray, value:chararray)
* @param outputBag Output tuples (id, column, value) are added to this bag
* @param id
* @param column
* @param value
* @throws ExecException
*/
protected void addTuple(DataBag outputBag, String id, String column, String value) throws ExecException {
Tuple outputTuple = tupleFactory.newTuple();
outputTuple.append(id);
outputTuple.append(column);
outputTuple.append( value);
outputBag.add(outputTuple);
}
/**
* Takes column{separator}value from splitInputLine, splits id into column value and adds them to the outputBag as (id, column, value)
* @param outputBag Output tuples (id, column, value) should be added to this bag
* @param id
* @param splitInputLine format column{separator}value, which start from index 1
* @throws ExecException
*/
protected void parseColumnValues(DataBag outputBag, String id,
String[] splitInputLine) throws ExecException {
for (int i = 1; i < splitInputLine.length; i++) {
if (splitInputLine[i] != null) {
int columnValueSplitIndex = splitInputLine[i].indexOf(this.columnValueSeparator);
if (columnValueSplitIndex != -1) {
String column = splitInputLine[i].substring(0, columnValueSplitIndex);
String value = null;
if (columnValueSplitIndex + 1 < splitInputLine[i].length()) {
value = splitInputLine[i].substring(columnValueSplitIndex + 1);
}
this.addTuple(outputBag, id, column, value);
} else {
String column = splitInputLine[i];
this.addTuple(outputBag, id, column, null);
}
}
}
}
/**
* input - contains only one field of type chararray, which will be split by '|'
* All inputs that are: null or of length 0 are ignored.
*/
@Override
public DataBag exec(Tuple input) throws IOException {
if (input == null || input.size() != 1 || input.isNull(0)) {
return null;
}
String inputLine = (String)input.get(0);
String[] splitInputLine = inputLine.split(this.rowsSeparator, -1);
if (splitInputLine.length > 1 && splitInputLine[0].length() > 0) {
String id = splitInputLine[0];
DataBag outputBag = bagFactory.newDefaultBag();
if (splitInputLine.length == 1) { // there is just an id in the line
this.addTuple(outputBag, id, null, null);
} else {
this.parseColumnValues(outputBag, id, splitInputLine);
}
return outputBag;
}
return null;
}
@Override
public Schema outputSchema(Schema input) {
try {
if (input.size() != 1) {
throw new RuntimeException("Expected input to have only one field");
}
Schema.FieldSchema inputFieldSchema = input.getField(0);
if (inputFieldSchema.type != DataType.CHARARRAY) {
throw new RuntimeException("Expected a CHARARRAY as input");
}
Schema tupleSchema = new Schema();
tupleSchema.add(new Schema.FieldSchema("id", DataType.CHARARRAY));
tupleSchema.add(new Schema.FieldSchema("column", DataType.CHARARRAY));
tupleSchema.add(new Schema.FieldSchema("value", DataType.CHARARRAY));
return new Schema(new Schema.FieldSchema(getSchemaName(this.getClass().getName().toLowerCase(), input), tupleSchema, DataType.BAG));
} catch (FrontendException exx) {
throw new RuntimeException(exx);
}
}
}
以下是它在PIG中的使用方法：
register 'path to the jar';
define IdColumnValue myPackage.TupleToBagColumnValuePairs();
inpt = load '/pig_fun/input/single_tuple_to_multiple.txt' as (line:chararray);
result = foreach inpt generate FLATTEN(IdColumnValue($0)) as (id1, c2, v2);
dump result;
使用行李编写UDF的好灵感见DataFu source code by LinkedIn
答案 2 :(得分：0)
您可以在STRSPLIT的输出上使用TransposeTupleToBag（来自DataFu lib的UDF）来获取包，然后将包放在FLATTEN中，以便为每个原始列创建单独的行。