How do you read an ORC file in Java? I'm wanting to read in a small file for some unit test output verification, but I can't find a solution.
Reading an ORC file in Java
Came across this and implemented one myself recently
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.hive.ql.io.orc.OrcFile;
import org.apache.hadoop.hive.ql.io.orc.Reader;
import org.apache.hadoop.hive.ql.io.orc.RecordReader;
import org.apache.hadoop.hive.serde2.objectinspector.StructField;
import org.apache.hadoop.hive.serde2.objectinspector.StructObjectInspector;
import java.util.List;
public class OrcFileDirectReaderExample {
public static void main(String[] argv)
{
try {
Reader reader = OrcFile.createReader(HdfsFactory.getFileSystem(), new Path("/user/hadoop/000000_0"));
StructObjectInspector inspector = (StructObjectInspector)reader.getObjectInspector();
System.out.println(reader.getMetadata());
RecordReader records = reader.rows();
Object row = null;
//These objects are the metadata for each column. They give you the type of each column and can parse it unless you
//want to parse each column yourself
List fields = inspector.getAllStructFieldRefs();
for(int i = 0; i < fields.size(); ++i) {
System.out.print(((StructField)fields.get(i)).getFieldObjectInspector().getTypeName() + '\t');
}
while(records.hasNext())
{
row = records.next(row);
List value_lst = inspector.getStructFieldsDataAsList(row);
StringBuilder builder = new StringBuilder();
//iterate over the fields
//Also fields can be null if a null was passed as the input field when processing wrote this file
for(Object field : value_lst) {
if(field != null)
builder.append(field.toString());
builder.append('\t');
}
//this writes out the row as it would be if this were a Text tab seperated file
System.out.println(builder.toString());
}
}catch (Exception e)
{
e.printStackTrace();
}
}
}
This saved me! Worked perfectly! –
Diaspora
What would be the dependency for HdfsFactory class? –
Agee
As per Apache Wiki, ORC file format was introduced in Hive 0.11.
So you will need Hive packages in your project source path to read ORC files. The package for the same are
org.apache.hadoop.hive.ql.io.orc.Reader;
org.apache.hadoop.hive.ql.io.orc.OrcFile
I have tried implementing a solution using these already but haven't had much luck. –
Lantha
read orc testcase
@Test
public void read_orc() throws Exception {
//todo do kerberos auth
String orcPath = "hdfs://user/hive/warehouse/demo.db/orc_path";
//load hdfs conf
Configuration conf = new Configuration();
conf.addResource(getClass().getResource("/hdfs-site.xml"));
conf.addResource(getClass().getResource("/core-site.xml"));
FileSystem fs = FileSystem.get(conf);
// custom read column
List<String> columns = Arrays.asList("id", "title");
final List<Map<String, Object>> maps = OrcUtil.readOrcFile(fs, orcPath, columns);
System.out.println(new Gson().toJson(maps));
}
OrcUtil
to read orc path with special columns
import java.io.IOException;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.HashMap;
import java.util.Iterator;
import java.util.List;
import java.util.Map;
import java.util.Properties;
import java.util.stream.Collectors;
import org.apache.commons.lang.StringUtils;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.fs.PathFilter;
import org.apache.hadoop.hive.ql.io.orc.OrcFile;
import org.apache.hadoop.hive.ql.io.orc.OrcInputFormat;
import org.apache.hadoop.hive.ql.io.orc.OrcSerde;
import org.apache.hadoop.hive.ql.io.orc.OrcSplit;
import org.apache.hadoop.hive.ql.io.orc.OrcStruct;
import org.apache.hadoop.hive.ql.io.orc.Reader;
import org.apache.hadoop.hive.serde2.SerDeException;
import org.apache.hadoop.hive.serde2.objectinspector.StructField;
import org.apache.hadoop.hive.serde2.objectinspector.StructObjectInspector;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.mapred.FileInputFormat;
import org.apache.hadoop.mapred.InputFormat;
import org.apache.hadoop.mapred.InputSplit;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.RecordReader;
import org.apache.hadoop.mapred.Reporter;
public class OrcUtil {
public static List<Map<String, Object>> readOrcFile(FileSystem fs, String orcPath, List<String> readColumns)
throws IOException, SerDeException {
JobConf jobConf = new JobConf();
for (Map.Entry<String, String> entry : fs.getConf()) {
jobConf.set(entry.getKey(), entry.getValue());
}
FileInputFormat.setInputPaths(jobConf, orcPath);
FileInputFormat.setInputPathFilter(jobConf, ((PathFilter) path1 -> true).getClass());
InputSplit[] splits = new OrcInputFormat().getSplits(jobConf, 1);
InputFormat<NullWritable, OrcStruct> orcInputFormat = new OrcInputFormat();
List<Map<String, Object>> rows = new ArrayList<>();
for (InputSplit split : splits) {
OrcSplit orcSplit = (OrcSplit) split;
System.out.printf("read orc split %s%n", ((OrcSplit) split).getPath());
StructObjectInspector inspector = getStructObjectInspector(orcSplit.getPath(), jobConf, fs);
List<? extends StructField> readFields = inspector.getAllStructFieldRefs()
.stream().filter(e -> readColumns.contains(e.getFieldName())).collect(Collectors.toList());
// 49B file is empty
if (orcSplit.getLength() > 49) {
RecordReader<NullWritable, OrcStruct> recordReader = orcInputFormat.getRecordReader(orcSplit, jobConf, Reporter.NULL);
NullWritable key = recordReader.createKey();
OrcStruct value = recordReader.createValue();
while (recordReader.next(key, value)) {
Map<String, Object> entity = new HashMap<>();
for (StructField field : readFields) {
entity.put(field.getFieldName(), inspector.getStructFieldData(value, field));
}
rows.add(entity);
}
}
}
return rows;
}
private static StructObjectInspector getStructObjectInspector(Path path, JobConf jobConf, FileSystem fs)
throws IOException, SerDeException {
OrcFile.ReaderOptions readerOptions = OrcFile.readerOptions(jobConf);
readerOptions.filesystem(fs);
Reader reader = OrcFile.createReader(path, readerOptions);
String typeStruct = reader.getObjectInspector().getTypeName();
System.out.println(typeStruct);
List<String> columnList = parseColumnAndType(typeStruct);
String[] fullColNames = new String[columnList.size()];
String[] fullColTypes = new String[columnList.size()];
for (int i = 0; i < columnList.size(); ++i) {
String[] temp = columnList.get(i).split(":");
fullColNames[i] = temp[0];
fullColTypes[i] = temp[1];
}
Properties p = new Properties();
p.setProperty("columns", StringUtils.join(fullColNames, ","));
p.setProperty("columns.types", StringUtils.join(fullColTypes, ":"));
OrcSerde orcSerde = new OrcSerde();
orcSerde.initialize(jobConf, p);
return (StructObjectInspector) orcSerde.getObjectInspector();
}
private static List<String> parseColumnAndType(String typeStruct) {
int startIndex = typeStruct.indexOf("<") + 1;
int endIndex = typeStruct.lastIndexOf(">");
typeStruct = typeStruct.substring(startIndex, endIndex);
List<String> columnList = new ArrayList<>();
List<String> splitList = Arrays.asList(typeStruct.split(","));
Iterator<String> it = splitList.iterator();
while (it.hasNext()) {
StringBuilder current = new StringBuilder(it.next());
String currentStr = current.toString();
boolean left = currentStr.contains("(");
boolean right = currentStr.contains(")");
if (!left && !right) {
columnList.add(currentStr);
continue;
}
if (left && right) {
columnList.add(currentStr);
continue;
}
if (left && !right) {
while (it.hasNext()) {
String next = it.next();
current.append(",").append(next);
if (next.contains(")")) {
break;
}
}
columnList.add(current.toString());
}
}
return columnList;
}
}
Try this for getting ORCFile rowcount...
private long getRowCount(FileSystem fs, String fName) throws Exception {
long tempCount = 0;
Reader rdr = OrcFile.createReader(fs, new Path(fName));
StructObjectInspector insp = (StructObjectInspector) rdr.getObjectInspector();
Iterable<StripeInformation> iterable = rdr.getStripes();
for(StripeInformation stripe:iterable){
tempCount = tempCount + stripe.getNumberOfRows();
}
return tempCount;
}
//fName is hdfs path to file.
long rowCount = getRowCount(fs,fName);
Can you mention the imports, the external libraries? The function alone is difficult to reuse unless these are mentioned. –
Jamijamie
© 2022 - 2024 — McMap. All rights reserved.