Is it better to have in Spark one large parquet file vs lots of smaller parquet files? The decision to use one large parquet file or lots of smaller parquet files depends on various factors such as the size of your data, the way you access your data, and your specific use case. In this article, we shall discuss the differences in properties of Spark large vs small parquet files and an example to read both types of files.

1. Properties of Parquet files

There are some properties of Parquet files that can be compared when deciding whether to use in Spark either one large parquet file or lots of smaller parquet files:

  1. Storage Efficiency: One large parquet file can be more storage efficient compared to lots of smaller parquet files because smaller files can have more overhead due to metadata and compression. When storing many small files, the overhead of each file can add up and result in more storage usage.
  2. Processing Efficiency: One large parquet file can be more processing efficient in some cases, especially when reading or writing large amounts of data. This is because the overhead of opening and closing multiple small files can add up and affect the processing speed.
  3. Data Availability: When storing many small parquet files, it may be easier to access specific subsets of data because only the necessary files need to be read. This can improve the availability of data for specific use cases, such as data exploration or analysis.
  4. Data Management: Managing many small parquet files can be more complex than managing one large parquet file. This is because there can be more metadata to track, and it can be more difficult to organize and maintain many small files.
  5. Data Update Frequency: When updating or appending data, it can be more efficient to manage many small parquet files, as only the necessary files need to be modified. In contrast, modifying one large parquet file can be time-consuming and resource-intensive.

2. Factors to be Considered for Better Results

The decision to use one Spark large parquet file or lots of smaller parquet files depends on various factors such as the size of your data, the way you access your data, and your specific use case.

Here are some factors to consider:

  1. Size of Data: If you have a large dataset, then a single large parquet file may be difficult to manage, and it may take a long time to read or write the data. In this case, breaking the data into smaller parquet files can make it easier to handle.
  2. Access Patterns: If your access pattern involves querying a specific subset of data, then having smaller parquet files may be beneficial. When you query a small subset of data, only a subset of the parquet files needs to be read, reducing the amount of data that needs to be read from disk.
  3. Compression: Parquet files are often compressed using codecs such as Snappy or Gzip, and compression works better on larger files. If you have a small number of large files, you can take advantage of this compression efficiency. However, if you have many small files, the compression overhead can add up, and you may end up with larger storage requirements.
  4. Data Update Frequency: If your data is frequently updated or appended, then it may be easier to manage smaller parquet files rather than large ones. In the case of smaller files, you only need to update the files that have changed, reducing the amount of data that needs to be written.

3. Example of Reading Large Parquet and Small Parquet Files

Here’s a small code example in Scala to compare reading data from one large Parquet file and lots of smaller Parquet files:

3.1. Reading from one large Parquet file:


// Imports
import org.apache.spark.sql.SparkSession

// Initialize SparkSession
val spark = SparkSession.builder.appName("ReadLargeParquetFile").getOrCreate()

// Read from one large Parquet file
val df = spark.read.parquet("path/to/large/file.parquet")

// Perform some operations on the DataFrame
val result = df.groupBy("column1").sum("column2")

In this example,

  • we first create an SparkSession object, which is the entry point to Spark functionality.
  • Then, we read the data from Parquet file using the parquet() method of the DataFrameReader class.
  • The resulting DataFrame can be used for various operations such as filtering, grouping, aggregating, and so on.
  • Finally, we perform some operations on the DataFrame.

Note that you need to replace the path/to/large/file.parquet with the actual path to your Parquet file.

3.2. Reading from Multiple small Parquet files:


// Imports
import org.apache.spark.sql.SparkSession

// Initialize SparkSession
val spark = SparkSession.builder.appName("ReadSmallParquetFiles").getOrCreate()

// Read from multiple small Parquet files
val df = spark.read.option("mergeSchema", "true").parquet("path/to/directory/containing/parquet/files/*")

// Perform some operations on the DataFrame
val result = df.groupBy("column1").sum("column2")

In the above example,

  • we first create an SparkSession object, which is the entry point to Spark functionality.
  • Then, we read the data from the multiple small Parquet files using the parquet() method of the DataFrameReader class.
  • along with the option() method to specify the mergeSchema option as true. This option is used to ensure that the schema is merged correctly for all the small Parquet files.
  • The path/to/directory/containing/parquet/files/* is a wildcard pattern that matches all the Parquet files in the specified directory.
  • The resulting DataFrame can be used for various operations such as filtering, grouping, aggregating, and so on. Finally, we perform some operations on the DataFrame.

Note that you need to replace the path/to/directory/containing/parquet/files with the actual path to the directory containing your Parquet files.

From the above examples, the only difference is in how the data is read. The first example reads from one large Parquet file, while the second example reads from lots of smaller Parquet files using the mergeSchema option to ensure that the schema is merged correctly.

Note that the code for performing operations on the DataFrame and showing the result is identical in both examples. The choice of whether to use one large Parquet file or lots of smaller Parquet files depends on various factors and properties as discussed above

4. Conclusion

The choice between one Spark large Parquet file or lots of smaller Parquet files depends on various factors and there is no one-size-fits-all answer. However, here are some general conclusions to consider:

Advantages of using one large Parquet file:

  • It can be faster to read and process a single large file instead of multiple small files because there is less overhead in opening and closing files.
  • It can be easier to manage and organize a single large file, especially if you have a large amount of data.

Advantages of using lots of smaller Parquet files:

  • It can be more efficient to read only the required data, especially if you frequently access only a subset of the data.
  • It can be easier to parallelize the processing of smaller files across multiple nodes in a distributed computing environment.
  • It can be easier to update or delete a specific subset of data in smaller files without having to rewrite the entire file.

Overall, the choice between one large Parquet file or lots of smaller Parquet files depends on the specific use case, data size, and computational environment. It’s important to carefully consider the trade-offs and experiment with both options to determine the optimal approach for your use case.

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rimmalapudi

Data Engineer. I write about BigData Architecture, tools and techniques that are used to build Bigdata pipelines and other generic blogs.