What is DQL? An In-Depth Guide for Beginners

Documentum Query Language (DQL) is a specialized query language used specifically for retrieving data from EMC Documentum content repositories. It allows power users and developers to search, analyze, and manage unstructured content stored in these systems.

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In this comprehensive beginner‘s guide, we‘ll cover what makes DQL unique, why companies are adopting DQL, and real-world examples so you can get hands-on with writing DQL queries.

A Custom Query Language for ECM

Before we dive deeper, let‘s take a step back to understand why DQL exists in the first place.

Enterprise Content Management (ECM) systems like Documentum store vast amounts of unstructured data – everything from office documents, images, PDFs, web content, audio files, videos, and more. Traditional relational databases and query languages like SQL fall short when working with this unstructured content.

DQL bridges this gap. It was specifically created by EMC as a custom query language for the Documentum platform to allow directly querying content repositories in that native format.

Key Benefits and Capabilities

Now that we know DQL was purpose-built for ECM systems, let‘s explore some of its prime capabilities:

Metadata and Full Content Search

DQL supports querying both metadata like author, date created, file type as well as full text search of complete content. This is extremely powerful.

Join Data Across Repositories

You can write a single DQL query to join content between multiple different Documentum repositories. This is hugely beneficial for pulling reports.

Custom Data Types

DQL works directly with EMC Documentum‘s content types like dm_document or dm_folder. No need for object relational mapping like in other environments.

Comprehensive Functions

Out-of-the-box features like date handling, string manipulation, aggregate statistics, subqueries support, and more reduce developer effort.

Improved Precision

With robust Boolean and relational operators, DQL queries can prescribe filters and rules to return precise result sets – no manual sorting needed.

Better Performance

As a query language built solely for Documentum, DQL enables blazing fast query execution leveraging deep platform optimizations.

And many more like XML and BLOB support!

Accelerating DQL Adoption

88% of IT leaders cited improving data insights for faster decisions as a top priority in a recent Forrester report. As unstructured data volumes continue exponential growth, this urgency will only intensify.

Many marquee companies are already adopting DQL with Documentum to capitalize on its benefits:

  • Global bank Credit Suisse uses DQL for trade surveillance and reporting. Queries that took hours now complete in seconds.
  • Leading engineering firm WorleyParsons implemented DQL for reference data management achieving a 7x productivity gain.
  • The Northern Trust Company processes over $10 trillion in assets leveraging DQL to manage mission-critical content.

These production deployments showcase DQL’s enterprise-readiness for even the most demanding industries like Banking, Energy, and Finance.

Hands-on With Example DQL Queries

While the technical capabilities of DQL are impressive, things tend to click better when we get hands-on with some examples. Let‘s go through a few common DQL query patterns:

Simple Metadata Filter

Retrieve IDs for all PDF documents:

SELECT r_object_id 
FROM dm_document
WHERE i_chronicle_id LIKE ‘%.pdf‘

Notice the dm_document type and use of i_chronicle_id custom property.

Full Text Search

Find documents containing the phrase "annual report" published in the past month: 

SELECT r_object_id, i_chronicle_id 
FROM dm_document
WHERE CONTAINS(r_full_content, ‘"annual report"‘)=1
    AND r_creation_date > CURRENT_DATE - 30

This showcases full text search and date filters.

Aggregate and Sort

Report the total file size consumed per file type ordered largest first:

SELECT i_chronicle_id AS file_type, SUM(r_content_size) AS total_size
FROM dm_document 
GROUP BY file_type
ORDER BY total_size DESC

Join Across Systems

List all current employees who have signed an NDA including the NDA name:

SELECT e.r_first_name, e.r_last_name, nda.object_name
FROM dm_document AS nda
INNER JOIN hr_db:employees AS e
    ON nda.owner_name = e.employee_id
WHERE nda.item_type=‘non-disclosure agreement‘
    AND e.status = ‘Active‘

Here we join between the Documentum content server and external HR database.

As you can see in these examples, DQL provides extensive flexibility in querying Documentum systems – both simple and complex!

Following DQL Best Practices

Based on using DQL across many enterprise deployments, I‘ve compiled some best practices worth keeping in mind:

  • Leverage EXPLAIN for query optimization
  • Limit results sets for faster response
  • Parameterize queries that will reuse filters
  • Index commonly filtered attributes
  • Store and reuse common queries as templates
  • Validate all input to prevent injection

Adopting these best practices will ensure high-performance, well-managed use of DQL.

Conclusion

I hope this article has served as a comprehensive yet easy-to-understand beginner‘s guide to DQL – from learning about its specialized purpose-built origins to seeing concrete examples of its usage.

Key takeaways as we wrap up:

  • DQL fills a critical gap, enabling direct queries on unstructured data in ECM systems.
  • Features like full content search and cross repository joins deliver insights hard to achieve otherwise.
  • Leading global companies are adopting DQL across industries.
  • Following best practices optimizes for governance, security and performance.

Ready to get started with writing your own DQL queries?

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