Querying Complex Data Introduction

Apache Drill queries do not require prior knowledge of the actual data you are trying to access, regardless of its source system or its schema and data types. The sweet spot for Apache Drill is a SQL query workload against complex data: data made up of various types of records and fields, rather than data in a recognizable relational form (discrete rows and columns). Drill is capable of discovering the form of the data when you submit the query. Nested data formats such as JSON (JavaScript Object Notation) files and Parquet files are not only accessible: Drill provides special operators and functions that you can use to drill down into these files and ask interesting analytic questions.

These operators and functions include:

  • References to nested data values
  • Access to repeating values in arrays and arrays within arrays (array indexes)

The SQL query developer needs to know the data well enough to write queries that identify values of interest in the target file. For example, the writer needs to know what a record consists of, and its data types, in order to reliably request the right "columns" in the select list. Although these data values do not manifest themselves as columns in the source file, Drill will return them in the result set as if they had the predictable form of columns in a table. Drill also optimizes queries by treating the data as "columnar" rather than reading and analyzing complete records. (Drill uses similar parallel execution and optimization capabilities to commercial columnar MPP databases.)

Given a basic knowledge of the input file, the developer needs to know how to use the SQL extensions that Drill provides and how to use them to "reach into" the nested data. The following examples show how to write both simple queries against JSON files and interesting queries that unpack the nested data. The examples show how to use the Drill extensions in the context of standard SQL SELECT statements. For the most part, the extensions use standard JavaScript notation for referencing data elements in a hierarchy.

Before You Begin

The examples in this section operate on JSON data files. In order to write your own queries, you need to be aware of the basic data types in these files:

  • string (all data inside double quotes), such as "0001" or "Cake"
  • number: integers and floats, such as 0.55 or 10
  • null values
  • boolean values: true, false

Check that you have the following configuration setting for JSON files in the Drill Web Console (dfs storage plugin configuration):

"json" : {
  "type" : "json"