这篇文章是针对我之前的问题建议的map reduce实现:"如何优化Hive中1个大文件/表的扫描以确认/检查是否包含在wkt几何形状中"
我并不精通编写map-reduce的java程序，主要使用Hive或者Pig或者spark在Hadoop生态系统中进行开发。为了给出手头任务的背景:我试图将每个纬度/经度ping与相应的ZIP邮政编码相关联。我有一个WKT多多边形形状文件(500 MB)与所有的zip信息。我已经在Hive中加载了这个，可以使用ST_Contains(多边形，点)进行连接。然而，它需要很长时间才能完成。为了克服这个瓶颈，我试图利用ESRI("https://github.com/Esri/gis-tools-for-hadoop/tree/master/samples/point-in-polygon-aggregation-mr")中的例子，通过构建一个四叉树索引来搜索从latlong在多边形中派生的点。
我已经设法编写代码，它阻塞了集群的Java堆内存。任何关于改进代码或寻找不同方法的建议将不胜感激:错误信息:错误:Java堆空间ApplicationMaster杀死的容器。应要求杀死集装箱。退出代码是143容器以非零退出代码143退出
我的代码:public class MapperClass extends Mapper<LongWritable, Text, Text, IntWritable> {
// column indices for values in the text file
int longitudeIndex;
int latitudeIndex;
int wktZip;
int wktGeom;
int wktLineCount;
int wktStateID;
// in boundaries.wkt, the label for the polygon is "wkt"
//creating ArrayList to hold details of the file
ArrayList<ZipPolyClass> nodes = new ArrayList<ZipPolyClass>();
String labelAttribute;
EsriFeatureClass featureClass;
SpatialReference spatialReference;
QuadTree quadTree;
QuadTreeIterator quadTreeIter;
BufferedReader csvWkt;
// class to store all the values from wkt file and calculate geometryFromWKT
public class ZipPolyClass {
public String zipCode;
public String wktPoly;
public String stateID;
public int indexJkey;
public Geometry wktGeomObj;
public ZipPolyClass(int ijk, String z, String w, String s ){
zipCode = z;
wktPoly = w;
stateID = s;
indexJkey = ijk;
wktGeomObj = GeometryEngine.geometryFromWkt(wktPoly, 0, Geometry.Type.Unknown);
}
}
//building quadTree Index from WKT multiPolygon and creating an iterator
private void buildQuadTree(){
quadTree = new QuadTree(new Envelope2D(-180, -90, 180, 90), 8);
Envelope envelope = new Envelope();
int j=0;
while(j<nodes.size()){
nodes.get(j).wktGeomObj.queryEnvelope(envelope);
quadTree.insert(j, new Envelope2D(envelope.getXMin(), envelope.getYMin(), envelope.getXMax(), envelope.getYMax()));
}
quadTreeIter = quadTree.getIterator();
}
/**
* Query the quadtree for the feature containing the given point
*
* @param pt point as longitude, latitude
* @return index to feature in featureClass or -1 if not found
*/
private int queryQuadTree(Point pt)
{
// reset iterator to the quadrant envelope that contains the point passed
quadTreeIter.resetIterator(pt, 0);
int elmHandle = quadTreeIter.next();
while (elmHandle >= 0){
int featureIndex = quadTree.getElement(elmHandle);
// we know the point and this feature are in the same quadrant, but we need to make sure the feature
// actually contains the point
if (GeometryEngine.contains(nodes.get(featureIndex).wktGeomObj, pt, spatialReference)){
return featureIndex;
}
elmHandle = quadTreeIter.next();
}
// feature not found
return -1;
}
/**
* Sets up mapper with filter geometry provided as argument[0] to the jar
*/
@Override
public void setup(Context context)
{
Configuration config = context.getConfiguration();
spatialReference = SpatialReference.create(4326);
// first pull values from the configuration
String featuresPath = config.get("sample.features.input");
//get column reference from driver class
wktZip = config.getInt("sample.features.col.zip", 0);
wktGeom = config.getInt("sample.features.col.geometry", 18);
wktStateID = config.getInt("sample.features.col.stateID", 3);
latitudeIndex = config.getInt("samples.csvdata.columns.lat", 5);
longitudeIndex = config.getInt("samples.csvdata.columns.long", 6);
FSDataInputStream iStream = null;
try {
// load the text WKT file provided as argument 0
FileSystem hdfs = FileSystem.get(config);
iStream = hdfs.open(new Path(featuresPath));
BufferedReader br = new BufferedReader(new InputStreamReader(iStream));
String wktLine ;
int i=0;
while((wktLine = br.readLine()) != null){
String [] val = wktLine.split("\|");
String qtZip = val[wktZip];
String poly = val[wktGeom];
String stID = val[wktStateID];
ZipPolyClass zpc = new ZipPolyClass(i, qtZip, poly, stID);
nodes.add(i,zpc);
i++; // increment in the loop before end
}
}
catch (Exception e)
{
e.printStackTrace();
}
finally
{
if (iStream != null)
{
try {
iStream.close();
} catch (IOException e) { }
}
}
// build a quadtree of our features for fast queries
if (!nodes.isEmpty()) {
buildQuadTree();
}
}
@Override
public void map(LongWritable key, Text val, Context context)
throws IOException, InterruptedException {
/*
* The TextInputFormat we set in the configuration, by default, splits a text file line by line.
* The key is the byte offset to the first character in the line. The value is the text of the line.
*/
String line = val.toString();
String [] values = line.split(",");
// get lat long from file and convert to float
float latitude = Float.parseFloat(values[latitudeIndex]);
float longitude = Float.parseFloat(values[longitudeIndex]);
// Create our Point directly from longitude and latitude
Point point = new Point(longitude, latitude);
int featureIndex = queryQuadTree(point);
// Each map only processes one record at a time, so we start out with our count
// as 1. Since we have a distinct record file we will not run reducer
IntWritable one = new IntWritable(1);
if (featureIndex >= 0){
String zipTxt =nodes.get(featureIndex).zipCode;
String stateIDTxt = nodes.get(featureIndex).stateID;
String latTxt = values[latitudeIndex];
String longTxt = values[longitudeIndex];
String pointTxt = point.toString();
String name;
name = zipTxt+"t"+stateIDTxt+"t"+latTxt+"t"+longTxt+ "t" +pointTxt;
context.write(new Text(name), one);
} else {
context.write(new Text("*Outside Feature Set"), one);
}
}
}
我能够解决内存不足的问题通过修改arrayList
创建一个类对象(大约50k)来保存文本文件的每一行，消耗了所有的java堆内存。在此更改之后，代码即使在1节点的虚拟沙箱中也能正常运行。我能够在6分钟内处理大约4000万行数据。