I am running a MapReduce job (and sometimes Spark) that generates hundreds of small part-r-0000x files in an HDFS directory. Having so many small files is degrading my NameNode performance and making it difficult to download the data. What is the standard command-line tool or programmatic approach to combine these into one large file? Should I be merging them directly within HDFS or during the download process to my local system?
3 answers
For a simple command-line solution, the most common tool is the getmerge command. You run hdfs dfs -getmerge /src/hdfs_directory /local/destination/file.txt. This fetches all the part files from the HDFS directory, concatenates them in order, and saves them as a single file on your local filesystem. It’s important to note that this happens on the local client machine, so ensure you have enough disk space locally to handle the combined file size. This is a staple technique in Cloud Technology for preparing data for local analysis or reporting.
What if I want the merged file to stay inside HDFS rather than downloading it locally? Is there a way to do that without moving data over the network to my client machine?
've used the Spark coalesce(1) method many times, but it can be very slow if your dataset is huge. For multi-terabyte merges, it's often better to merge them as a separate post-processing step using Hive or a dedicated compaction job.
I agree with Sarah; coalesce(1) can definitely become a bottleneck! For massive datasets, keeping them partitioned is usually better for parallel processing, but for final reports, getmerge or concat are the way to go.
Marcus, that’s a key requirement for big data at scale. You should use the hdfs dfs -concat command. It allows you to merge multiple files into a target file directly within the HDFS namespace. This is a metadata-only operation, meaning it's incredibly fast because it doesn't actually rewrite the data blocks; it just points them to a new head. However, all files must be in the same directory and have the same block size. If you are using Spark, a better "on-the-fly" approach is to use .coalesce(1) or .repartition(1) before saving your DataFrame, though be careful as this forces all data to a single executor node.