Performance Issues Using ORDER to Reduce the Number of Out Files – Apache Pig 0.16 Amazon EMR

Often you have a simple ETL process (a Pig job in our example) that just applies a filter to the source data, performs some calculations and saves the result to a target table.

So it is a Map-only job (no Reduce phase is required) that generates N output files, where N is the number of map tasks.

For example:

set mapreduce.output.fileoutputformat.compress true

-- Read data from a Hive table
data_all = LOAD 'dmtolpeko.mcp' USING org.apache.hive.hcatalog.pig.HCatLoader();

-- Filter selects less than 2% of rows from the source table
data_filtered = FILTER data_all BY event_name == 'CLIENT_LOGIN'; 

-- Define out columns and save the results to S3
data_out = FOREACH data_filtered GENERATE app_id, payload, event_timestamp;
STORE data_out INTO 's3://epic/hive/dmtolpeko.db/mcp_client_login/';   

From the log you can see that the job used 231 mappers:

INFO mapred.FileInputFormat: Total input paths to process : 250
INFO  org.apache.pig.backend.hadoop.executionengine.util.MapRedUtil  - Total input paths (combined) to process : 231
INFO util.MapRedUtil: Total input paths (combined) to process : 231
INFO mapreduce.JobSubmitter: number of splits:231

And we can see that the output contains 231 data files (_SUCCESS is an empty file):

aws s3 ls s3://epic/hive/dmtolpeko.db/mcp_client_login/ --summarize

2018-03-01 22:22:33          0 _SUCCESS
2018-03-01 22:22:30      11165 part-m-00000.gz
2018-03-01 22:22:29      11107 part-m-00001.gz
2018-03-01 22:22:30      11346 part-m-00002.gz
2018-03-01 22:22:27       5697 part-m-00228.gz
2018-03-01 22:22:26       5686 part-m-00229.gz
2018-03-01 22:22:28       5480 part-m-00230.gz

Total Objects: 232
   Total Size: 1396533

This job produced a large number of very small files. What can we do to reduce the number of output files?
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Access Amazon S3 from Spark

The first step is to specify AWS Hadoop libraries when launching PySpark:

./bin/pyspark --packages org.apache.hadoop:hadoop-aws:2.7.1

Then before you can access objects on Amazon S3, you have to specify your access keys:

sc._jsc.hadoopConfiguration().set("fs.s3n.awsAccessKeyId", "<key>")

Now let’s open a file and calculate the number of rows in it:


For my sample file the result as follows: