Data & Intelligence

8 Ways to Data Scientist’s Can Optimize Their Parquet Queries

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Some data formats are columnar. This means they store information in columns or rows. They are popular because they can be used for certain types of queries more easily than row-based ones. Parquet supports parallel query processing, meaning it can split up your data into several files in order to read in multiple processors at once. This allows you to handle very large datasets faster by splitting the data into pieces so it can be worked on simultaneously. In this article, we will discuss 8 ways to optimize your queries with Parquet.

1) Use Parquet Tables with Partitioned Columns

When generating partitioned tables, make sure to include the columns you want to be partition columns in the table’s schema definition. If used correctly, partitioning your data can significantly improve the performance of a number of operations. For example, you may use this technique to group related records based on some criteria and ensure that data is read only from relevant partitions instead of all of them, resulting in faster load times and greater efficiency for your application.

2) Use Parquet Block Size that Matches the Loading Speed of Your Data

When you store data in Parquet format, it is important to choose the number of records per block correctly. For example, if you create a large block size (for example, 1 million records), there will be more file fragmentation which can affect I/O speed.

3) Use Column-wise Storage

Make sure to include the columns you want to store in column-wise mode when saving data in Parquet format. Queries that operate on a subset of the columns will not require any other columns to be loaded and will save disk space and memory usage.

4) Don’t use unnecessary columns

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When you’re reading a Parquet file, be sure to just include the columns required by your queries. In some situations, this might imply reading every column from a file and then storing them in another location for further analysis or processing.

5) Use Parquet Encodings that Support Your Data Types

Parquet supports a wide range of data types, so make sure your data is properly represented. If you use boolean values (e.g., true or false), the Parquet format will serialize them as strings (e.g., “true” or “false”). The “bits” encoding, on the other hand, can be used to effectively handle strings made up of zeros and ones. If you understand that the boolean values are really stored as 0s and 1s, you may use the “bits” encoding. This optimization will reduce network usage since fewer bytes need to be sent over the wire.

6) Use Dictionary Encodings for Time-Series Data

Dictionary encoding is a type of compression that stores the unique values and their frequency in the compressed data. When a value is requested, the dictionary may be quickly consulted to discover its frequency, which is useful when dealing with high cardinality columns like timestamps.

7) Use Column-Swapped Encodings for Large Binary Data

Use “BLOBS” encoding or “BYTES” when you work with binary data. This is good for big binary data because it will be compressed and stored next to one another. This may help your memory usage during queries.

8) Use Variant Data Types When Possible

In a Parquet file, variant data types in a single column may hold different data types. This implies that each row has several values of various kinds rather than just one value per row. These are beneficial because they reduce the amount of space required to store the most frequent type of data stored within them while still allowing you to work with a larger variety.

Conclusion:

Parquet is a columnar data storage format used to better organize and query large data sets. You may improve your queries’ performance and use less resources by following these few simple standards.

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David Callaghan, Solutions Architect

As a solutions architect with Perficient, I bring twenty years of development experience and I'm currently hands-on with Hadoop/Spark, blockchain and cloud, coding in Java, Scala and Go. I'm certified in and work extensively with Hadoop, Cassandra, Spark, AWS, MongoDB and Pentaho. Most recently, I've been bringing integrated blockchain (particularly Hyperledger and Ethereum) and big data solutions to the cloud with an emphasis on integrating Modern Data produces such as HBase, Cassandra and Neo4J as the off-blockchain repository.

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