Apache Drill Parquet File Format
Are you looking for a way to analyze large datasets efficiently using SQL? Apache Drill is an open-source SQL query engine that allows you to run interactive analysis on large-scale datasets. One of the key features that makes Apache Drill so powerful is its support for the Apache Drill Parquet file format.
This piece will elucidate the Parquet file structure and delve into its role in facilitating SQL on Hadoop. It will further clarify the rationale behind its design of self-descriptive data files for rapid processing. By the end, you will understand how Apache Drill and Parquet can help you work more efficiently with big data sets. This will give you a strong understanding of these tools.
What is the Apache Parquet File Format?
Parquet is an open source, column-oriented data file format designed for efficient storage and retrieval of large datasets. Created in 2013 by Cloudera and Twitter, Parquet is now a part of the Apache Hadoop ecosystem.
Some key characteristics of Parquet include:
- Columnar storage format
- Flexible compression options
- Efficient encoding schemes
- Support for complex nested data structures
Parquet’s columnar storage is particularly beneficial for queries that only need to read a subset of columns. Parquet stores data in columns, not rows. This reduces the amount of data read from disk and speeds up queries.
How Parquet Enables SQL on Hadoop
Using Parquet with Apache Drill makes it easy to analyze Hadoop data with SQL queries. Drill includes SQL engine based on SQL:2011 standard with some extensions. It can search Parquet files without loading data into a separate database or data warehouse first.
Here’s a simple example of how you might use SQL to query a Parquet file with Drill:
SELECT * FROM dfs.`/path/to/parquet/file`;
This query selects all columns from the specified Parquet file. Drill automatically infers the schema of the Parquet file and makes the data available as a SQL table.
You can also perform more complex queries, such as aggregations and joins, on Parquet data using standard SQL syntax. For instance:
SELECT product_category, SUM(total_sales) AS category_sales FROM dfs.`/path/to/sales.parquet` GROUP BY product_category ORDER BY category_sales DESC;
This query calculates the total sales for each product category from a Parquet file containing sales data. It groups the results by category and sort them in descending order.
Self-Describing Data Files
Another key feature of Parquet is that it is a self-describing file format. This means that Parquet files contain both the data itself and metadata about the schema and data types.
The self-describing nature of Parquet files has several benefits:
- Schema evolution: The schema can evolve over time without needing to update all files.
- Schema inference: Tools like Apache Drill can automatically infer the schema.
- Enforce schema when writing to ensure data consistency and improve data integrity.
Here’s an example of what the metadata in a Parquet file might look like:
creator: parquet-mr version 1.8.1 (build 4aba4dae7bb0d4edbcf7923ae1339f28fd3f7fcf)
extra: parquet.avro.schema = {"type":"record","name":"schema","namespace":"com.example","fields":[{"name":"id","type":"int"},{"name":"name","type":"string"},{"name":"address","type":{"type":"record","name":"ADDRESS","fields":[{"name":"street","type":"string"},{"name":"city","type":"string"},{"name":"state","type":"string"},{"name":"zip","type":"string"}]}}]}
file schema: schema
--------------------------------------------------------------
id: OPTIONAL INT64 R:0 D:1
name: OPTIONAL BINARY L:STRING R:0 D:1
address: OPTIONAL GROUP R:0 D:1
.street: OPTIONAL BINARY L:STRING R:0 D:1
.city: OPTIONAL BINARY L:STRING R:0 D:1
.state: OPTIONAL BINARY L:STRING R:0 D:1
.zip: OPTIONAL BINARY L:STRING R:0 D:1As you can see, the metadata includes details like the Parquet version, Avro schema, and the structure of the columns and data types. When Drill reads this file, it can use the embedded schema to automatically make the data queryable via SQL without any additional configuration.
Optimized File Processing
Parquet files optimize for fast processing in several ways.
- Column pruning: The query reads only the columns needed from disk.
- Predicate pushdown pushes filtering to the data level for efficient scans.
- Encoding and compression: Smart encoding and compression improve storage and performance.
Let’s look at an example to illustrate how column pruning works. Consider a Parquet file representing a table with columns A, B, and C.
If we run a query like:
SELECT A FROM table;
You only need to read the data for column ‘A’ from the file, not the entire table. This can greatly reduce disk I/O and improve query performance, especially for wide tables with many columns.
Apache Drill, like many other big data query engines, supports both partition pruning and column pruning when working with suitable data layouts and file formats. Column pruning in Apache Drill also works by default when you’re querying Parquet files. You don’t need to do any extra configuration or specify any special settings to take advantage of this optimization.
Another optimization, predicate pushdown, minimizes the amount of data that the system needs to process. If a query contains a WHERE clause, Parquet can push the filtering down to the file level and only return the matching rows.
For instance, if we have a query like:
SELECT * FROM users WHERE age > 30;
Parquet can skip entire row groups that don’t match the filter on age, avoiding the need to read and deserialize that data.
Finally, Parquet uses efficient compression algorithms and encoding schemes to reduce file size while still enabling fast queries. Some common compression codecs used with Parquet include Snappy, Gzip, and LZO.
Working with Parquet in Apache Drill
To get started with Parquet and Drill, you first need to install Drill, which you can download from the Apache Drill website. Drill supports reading Parquet files from a variety of sources, including:
- Local file system
- Hadoop Distributed File System (HDFS)
- Amazon S3
- Azure Blob Storage
- Google Cloud Storage
Once you have Drill installed and configured, you can use the Drill shell or Drill Web UI to run queries on your Parquet data.
Here’s an example of creating a table from a Parquet file using the Drill shell:
0: jdbc:drill:zk=local> USE dfs.tmp; CREATE TABLE users AS SELECT * FROM dfs.`/path/to/users.parquet`;
This creates a new Drill table called “users” from a Parquet file stored on the file system. You can now run queries on this table like any other SQL table.
For instance:
SELECT name, age FROM users WHERE age > 30 ORDER BY age DESC;
If you use Parquet files partitioned by date or key, Drill can avoid reading unnecessary data by using partition pruning. Partitioned Parquet is a common technique for structuring data in a way that is optimal for query performance.
Conclusion
This article explains the Apache Drill Parquet file format and how it helps with running SQL efficiently on Hadoop. We explored the benefits of Parquet’s columnar storage, self-describing data files, and query optimizations.
By using Apache Drill with Parquet, you can unlock the power of interactive SQL analytics on massive datasets. Drill easily works with standard SQL and JDBC/ODBC, making it simple to connect with current BI and data visualization tools.
Apache Drill and Parquet offer a strong and adaptable platform for big data analytics. Whether you’re analyzing data, creating reports, or using real-time dashboards, these tools can help you get the job done. To learn more, check out the official Apache Drill documentation.
DataSunrise supports Data Discovery for Parquet files. This feature helps organizations comply with data regulations and enhance data security. Data Discovery allows you to scan your Parquet files to identify sensitive data and personally identifiable information (PII). With this insight, you can take appropriate measures to protect and govern your data.
DataSunrise offers a comprehensive suite of data security tools, including data discovery, data masking, data loss prevention, and database activity monitoring. These tools integrate seamlessly with various databases, data warehouses, and big data platforms, providing a flexible and robust solution for your data security needs.
Please request an online demo to see firsthand how our tools can help you improve data security, comply with regulations, and safeguard your sensitive information.
