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Data compression is a crucial component of managing databases, and in this article, we’ll venture into SQL Data Compression on Databricks and how it can improve storage efficiency. Databricks has a built-in functionality that optimizes your storage, and we can primarily manage this using SQL.
What is SQL Data Compression?
Data Compression is the process of reducing the size of data stored without losing any essential information. In SQL, data compression is available for objects within the database. The two types of data compression in SQL are ROW and PAGE compression.
SQL Data Compression in Databricks
Now, let’s dive into the SQL Data compression in Databricks. Databricks allows managing data more efficiently by, among other things, reducing the footprint of datasets on disk and speeding up query execution.
Implementing Data Compression
Let’s start with the simplest case, which requires no changes to our SQL code. Databricks has a setting called “spark.databricks.io.cache.enabled” which is used to enable the Databricks I/O cache, a crucial component for compression like ZSTD. To show how to use this, let’s use an example.
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SET spark.databricks.io.cache.enabled=true; DATAFRAME.write.format('parquet').option('compression', 'ZSTD').save('dbfs:/mnt/mydata.parquet') |
This SQL command will implement Zstandard (ZSTD), known for delivering very high compression ratios. It is done in the format method which takes as parameter the ‘parquet’ format. That is a columnar storage file format available to any project in the Hadoop ecosystem.
Using Parquet file format with SQL
Now, what if you need to use the Parquet file format with SQL? Parquet is a column-store format that is supported by many other data processing systems; it supports very efficient compression and encoding schemes. It’s as simple as calling the “PARQUET” keyword in your SQL statements, as illustrated below:
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CREATE TABLE mytable USING PARQUET OPTIONS ('compression' 'ZSTD') AS SELECT * FROM existing_table |
The SQL statement above sets up a compressed parquet table called ‘mytable’ using the Zstandard (ZSTD) compression algorithm, from an existing table ‘existing_table’. It’s worth noting that, by default, the whole Parquet file content is compressed under one algorithm. The file footer provides details about the specific algorithm used for each column.
These examples just scratch the surface of what’s possible. With SQL and Databricks, it’s possible to store data more efficiently than ever before, optimizing storage and processing times. Whether you’re working with massive datasets or simply want to get more out of your storage, data compression offers a range of benefits.