How does spark handle small file issues?

How does spark handle small file issues?

Options to resolve it

1. Reduce parallelism: This is most simple option and most effective when total amount of data to be processed is less.
2. Repartition on “partitionby” keys: In earlier example, we considered each task loading to 50 target partitions thus no of task got multiplied with no of partitions.

How do I compact small files in Hadoop?

1. select all files that are ripe for compaction (define your own criteria) and move them from new_data directory to reorg.
2. merge the content of all these reorg files, into a new file in history dir (feel free to GZip it on the fly, Hive will recognize the .
3. drop the files in reorg.

How does spark handle large numbers of small files?

Spark runs slowly when it reads data from a lot of small files in S3. You can make your Spark code run faster by creating a job that compacts small files into larger files. The “small file problem” is especially problematic for data stores that are updated incrementally.

How do I control file size in spark?

5 Answers

1. Write the DataFrame to HDFS, df.write.parquet(path)
2. Get the directory size and calculate the number of files val fs = FileSystem.get(sc.hadoopConfiguration) val dirSize = fs.getContentSummary(path).getLength val fileNum = dirSize/(512 * 1024 * 1024) // let’s say 512 MB per file.

What are the optimization techniques in spark?

8 Performance Optimization Techniques Using Spark

• Serialization. Serialization plays an important role in the performance for any distributed application.
• API selection.
• Advance Variable.
• Cache and Persist.
• ByKey Operation.
• File Format selection.
• Garbage Collection Tuning.
• Level of Parallelism.

How does spark handle data skewness?

Techniques for Handling Data Skew

1. More Partitions. Increasing the number of partitions data may result in data associated with a given key being hashed into more partitions.
2. Bump Up spark. sql.
3. Iterative (Chunked) Broadcast Join.
4. Adding Salt.

Is HDFS good for small files?

Furthermore, HDFS is not geared up to efficiently accessing small files: it is primarily designed for streaming access of large files. Reading through small files normally causes lots of seeks and lots of hopping from datanode to datanode to retrieve each small file, all of which is an inefficient data access pattern.

What if metadata on NameNode gets too huge?

This means that if the namenode metadata is large, restarts will be slower. The NameNode must also track changes in the block locations on the cluster. Too many small files can also cause the NameNode to run out of metadata space in memory before the DataNodes run out of data space on disk.

How do I process a 1TB file in spark?

I suppose the area of improvement would be to parallelize the reading of the 1TB file.

1. Convert the CSV File into a Parquet file format + using Snappy compression.
2. Copy the Parquet file on HDFS.
3. Change the Spark application to read from HDFS.

How many partitions should I have spark?

Spark can run 1 concurrent task for every partition of an RDD (up to the number of cores in the cluster). If you’re cluster has 20 cores, you should have at least 20 partitions (in practice 2–3x times more ).

What are the optimization techniques in hive?

Hive Performance – 10 Best Practices for Apache Hive

• Partitioning Tables: Hive partitioning is an effective method to improve the query performance on larger tables.
• De-normalizing data:
• Compress map/reduce output:
• Map join:
• Input Format Selection:
• Parallel execution:
• Vectorization:
• Unit Testing:

How do you optimize a spark join?

Sort-Merge join is composed of 2 steps. The first step is to sort the datasets and the second operation is to merge the sorted data in the partition by iterating over the elements and according to the join key join the rows having the same value. From spark 2.3 Merge-Sort join is the default join algorithm in spark.

Why do we need spark compaction in HDFS?

Spark Compaction. When streaming data into HDFS, small messages are written to a large number of files that if left unchecked will cause unnecessary strain on the HDFS NameNode. To handle this situation, it is good practice to run a compaction job on directories that contain many small files to help reduce the resource strain…

Which is not supported in spark compaction tool?

The other option that is not supported is Parquet + BZ2 and that will result in an execution error. To elaborate further, the following example has an input directory consisting of 9,999 files consuming 440 MB of space. Using the default block size, the resulting output files are 146 MB in size, easily fitting into a data block.

How big are the small files in spark?

Files F, G, and H are already perfectly sized, so it’ll be more performant to simply repartition Files A, B, C, D, and E (the small files). The small files contain 1.6 GB of data. We can read in the small files, write out 2 files with 0.8 GB of data each, and then delete all the small files.

Why does spark run slow on small files?

Spark runs slowly when it reads data from a lot of small files in S3. You can make your Spark code run faster by creating a job that compacts small files into larger files. The “small file problem” is especially problematic for data stores that are updated incrementally.