Sorry the mail got sent before I completed. I have completed it now.
On Sun, Mar 17, 2013 at 1:51 PM, Mehul Chadha <[email protected]> wrote:
> Hi,
>
> I am doing some profiling of hadoop 1.0.3 under certain workloads for my
> research and I observed some very strange performance issues.
>
> I am doing a simple join on 2 tables, and the code works as follows. The
> smaller table is distributed to every mapper using DistributedCache. The
> large table is distributed by the split size on every mapper. The setup
> phase of mapper creates a hashmap from this small table and in the map
> function on every key iteration a get on the hashmap is done. If get
> returns not NULL then the output is written. No reducer is required for
> this benchmark. Following is the code for the mapper:
>
> public class Map extends Mapper<LongWritable, Text, Text, Text> {
> private HashMap<String, String> joinData = new HashMap<String,
> String>();
>
> public void map(LongWritable key, Text value, Context context)
> throws IOException, InterruptedException {
>
> String textvalue = value.toString();
> String[] tokens;
> tokens = textvalue.split(",");
> if (tokens.length == 2) {
> String joinValue = joinData.get(tokens[0]);
> if (null != joinValue) {
> context.write(new Text(tokens[0]), new Text(tokens[1] + ","
> + joinValue));
> }
> }
> }
>
> public void setup(Context context) {
> try {
> Path[] cacheFiles = DistributedCache.getLocalCacheFiles(context
> .getConfiguration());
>
> if (null != cacheFiles && cacheFiles.length > 0) {
> String line;
> String[] tokens;
> BufferedReader br = new BufferedReader(new FileReader(
> cacheFiles[0].toString()));
> try {
> while ((line = br.readLine()) != null) {
>
> tokens = line.split(",");
> if (tokens.length == 2) {
> joinData.put(tokens[0], tokens[1]);
> }
> }
>
> } finally {
> br.close();
> }
> }
>
> } catch (IOException e) {
> // TODO Auto-generated catch block
> e.printStackTrace();
> }
> }
> }
>
> The strange performance occurs in the following 2 cases: I create a small
> table which is 64MB and a larger table which is 640MB. There is 1 master
> and 5 slave nodes. The small table file on the local node is named as
> small_table and the large table file is named as large_table.
>
> Scenario 1:
>
./hadoop dfs -copyFromLocal ~/small_table
/user/csv/small_path/
./hadoop dfs -copyFromLocal ~/large_table
/user/csv/large_path/
hadoop jar hashjoin.jar hashjoindriver
/user/csv/small_path /user/csv/large_path/ /user/output
Time Taken: 1:28 sec
Scenario 2:
./hadoop dfs -copyFromLocal ~/small_table
/user/csv/small_path/small
./hadoop dfs -copyFromLocal ~/large_table
/user/csv/large_path/large
hadoop jar hashjoin.jar hashjoindriver
/user/csv/small_path/ /user/csv/large_path /user/output
Time Taken: 50 sec
I am finding it so strange and weird. I have verified this a hundred times
and it is always true.
