Druid query view: An elegant SQL interface for a more civilized age
Oct 16, 2019
Margaret Brewster
History is littered with examples of tools that open the door to new innovations. Fire allowed us to harvest more nutrients from food, the assembly line gave us cheap affordable goods and the internet gave us the ability to stay up until 4am looking at memes. While it isn’t fire and it certainly isn’t an otter eating watermelon (look at it, it’s hilarious). I believe that this summer during my internship at Imply I got an opportunity to work on a tool that in its own right has opened the door to more innovative and accessible data querying web applications.
As part of my internship, I got to work on Apache Druid, an open-source data store designed to manage large collections of data. Specifically, Apache Druid is used for low-latency near real-time data ingestion, data and metadata exploration as well as data aggregation on realtime and historical data sets. Users have three choices to leverage the functionality of druid: command line, 3rd party applications, and the included web console application. This web console is primarily what I got to work on. It is designed to provide a more streamlined, visual and user-friendly way to use Apache Druid.
The web console includes a query view where the user can query their data using DruidSQL. In the 0.14 release of Apache Druid, the only interactions available to the user were to write a query or run it. In 0.15 an auto-completer was added. Now in 0.16, we have added a new layer of SQL awareness to help move the view away from its roots as a text-only interface, to a point-and-click one. The query view now highlights aggregate columns and underlines the sort direction. Additionally, contextual modifications are suggested to people using the view that help them modify the query. Users now have the option to change sort direction, filter on current results or select an operation from a dynamically generated menu for each of the columns in their data sources. They will be offered different operations based on the shape of the query and content of the column, such as adding to the group by clause, aggregating with a relevant function or applying and removing specific filters. The auto-completer also uses the new SQL awareness to know what tables and columns it should suggest based on the current schema and data source of the query.
The motivation for implementing this feature was fairly simple: writing SQL is tedious. Most SQL environments rely on the users to be able to translate what they want to do into code. They must also interpret and refine their results based on their own knowledge of the language. This burden was placed on the user because the query view had no way of understanding peoples intentions. The parser makes the query view SQL aware and so it can now do some of that understanding and creation of the query, removing that burden. For novices, using the menus to add a filter or group by a column can help them avoid simple mistakes as well as potentially help them learn SQL syntax. For more experienced people using a point-and-click interaction is much faster and less tedious than having to type out a query in SQL. Hopefully, this feature will make using SQL in the Druid web-console simple and pleasant.
Learning SQL can be difficult and may require many resources.
How it works
Essentially, what we created is just souped up parser. The end goal is to take the input and create an abstract syntax tree, however, some transformation of the input is required to make the output meaningful. The parser works by matching the SQL against a series of rules called a grammar. When a part of the SQL matches against a rule an object of that class is generated. This object stores the information from the query as well as the formatting which will allow it to be “stringified” in the future. All of these objects are recursively assembled into an abstract syntax tree. This abstraction of the SQL is what allows the query view to understand the current query. When the you first navigate to the view, the saved query is parsed and used to generate the defaults that dictate the available menus. As the you type or click to change the query the parsed text input is stored on the state of the application and updated as it changes.
It would be impossible for the compiler to handle every possible input, because of this we made sure to create a robust fallback state. However, should the parser not be able to parse the input, we didn’t want to just revert back to the old functionality of the view. While usable, it would ruin the flow of the view and could be confusing. As an effort to preserve some of the new features, a safe failure state was implemented that allows the you to copy column names, values or certain specific pieces of SQL. Even though you still need to paste the new piece of code yourself, it is still faster than typing it out and is guaranteed to be syntax error-free.
How we built it
The process of building the parser was iterative and test-driven. This project was ideal for test-driven development, because you know what the input will be and exactly what you should be outputting. Each view in the Druid console has a query associated with it, and so I used these as my initial test cases. I needed to make sure each query could be converted to an AST but also returned to the original text while preserving its spacing. I used Jest to generate tests and check my code throughout the development process.
The tool itself was written using PegJs, TypeScript, and JavaScript. It has been published as an npm package and the code is available on Github.
The first step in the build process was really just to prove to myself that I could build a working parser. In this version, I wrote a PegJs grammar that returned the whole tree as a JSON object with each branch of the tree capturing its type as text. Each rule would match on the space immediately in front of it so it could be returned to text in one big recursive switch statement. This was a low fidelity prototype that had many opportunities for improvement. One such opportunity we found was that even though capturing the spacing in front of every rule made re-stringing the query simple it meant the parser had to go through every rule before it could match, which was not efficient.
It was at this point that we decided we needed to use classes. Making each rule create a class would allow for it to have its own instance variables and methods which could be leveraged to solve some of the problems we were seeing. Each object could now preserve the spacing inside of a rule and reapply it in a custom function. This allows the parser to work faster as every rule has a unique beginning which means quicker rejection of rules. Additionally, adding parentheses became simpler as you could call a function to add them to an object instead of capturing them before the object was created.
Once we were happy with how it was parsing the SQL, we began to add various functions to the library to help manipulate the query. We started with a few simple functions like sorting and adding a filter to the WHERE clause. Helper functions were also included to more easily create objects to pass in as arguments to these various functions. Once we had a few basic functions working, it became easier to add more to the project as ideas were suggested.
What’s next
I don’t think that we have fully exhausted functionality of this feature yet. As someone who struggles with formatting I think it would be really cool to also hard code the spacing, to allow the opportunity to fix your spacing automatically like prettier does. This wouldn’t be hard to implement as only a few lines of code would need to change and it could be a nice quality of life addition. Another feature I would be interested in adding is support for JOIN’s. Of course, more support for joins in Druid SQL would be necessary before this would be useful. From a design stand-point I think that implementing drag-and-drop could be a in interesting challenge and might feel more natural as the view would better map to the thought process of the user. Lastly, I think it could be interesting to add a highlight feature where instead of filtering on a certain value it would be highlighted in the results table. This could be useful if you are trying to understand the frequency of a result in a data set without removing the other results.
Some final thoughts
The goal when creating an interface is to create the most seamless and comfortable experience for the user. This is done by building an environment that matches the thought process of the user as closely as possible. In order to to this though, the view needs to understand the users intentions, which requires information. Most people don’t think in SQL syntax, they think about a column and what action they want to preform, the parser allows us to match this pattern of thought by giving us information about the users intentions. It is our hope that with this new tool we are pushing the boundary of what an SQL environment can be. The current implementation removes some of the burdens on the person to know exactly what they want to do, and to do it perfectly. Novices no longer need to explicitly know how to manipulate and query their data, and more experienced people can craft a query more quickly and effortlessly than ever before. Of course, there is still room for many more edge cases and features, but even so, I am confident that because of our work the Apache Druid web-console is one of the most innovative and user-friendly query environments available.
If you are interested in seeing the new features in action, check out the 0.16 release
Other blogs you might find interesting
No records found...
May 07, 2024
Imply Polaris is now on Microsoft Azure
We are thrilled to announce that Imply Polaris is now available on Microsoft Azure! Now, organizations can power their real-time analytics applications with Druid-powered Polaris clusters hosted in Microsoft...
When should you build, and when should you buy a security analytics platform? Read on about the challenges, use cases, and opportunities of doing so—and what database you’ll need.
As IoT environments become more complex, so too does data grow in volume, variety, and velocity. Learn why, when, and how to monitor your IoT environment.
How GameAnalytics Provides Flexible Data Exploration with Imply
Learn how GameAnalytics, the leading analytics provider for the gaming industry, provides insights on over 100,000 games, 1.75 billion players, and 24 billion monthly sessions.
Smart Devices, Intelligent Insights: How Rivian and Thing-it use Apache Druid for IoT Analytics
Learn how engineers and architects from electric vehicle manufacturer Rivian and smart asset management platform Thing-it use Apache Druid for their IoT analytics environments.
At Imply, we're excited to share the latest enhancements in Imply Polaris, our real-time analytics Database-as-a-Service (DBaaS) powered by Apache Druid®. Our commitment to refining your experience with Polaris...
Apache Druid® is an open-source distributed database designed for real-time analytics at scale. We are excited to announce the release of Apache Druid 29.0. This release contains over 350 commits & 67 contributors.
If your project needs a real-time analytics database that provides subsecond performance at scale you should consider both Apache Druid and ClickHouse. Find out how to make an informed choice.
Enhancing Data Security with Role-Based Access Control in Druid and Imply
Managing user access to relevant data is a crucial aspect of any data platform. In a typical Role Based Access Control (RBAC) setup, users are assigned roles that determine their access to relevant data. We...
Comparing Data Formats for Analytics: Parquet, Iceberg, and Druid Segments
In this blog, I will give you a detailed overview of each choice. We will cover key features, benefits, defining characteristics, and provide a table comparing the file formats. Dive in and explore the characteristics...
This guide is your map to navigating the confluence of Airflow and Druid for smooth batch ingestion. We'll get you started by showing you how to setup Airflow and the Druid Provider and use it to ingest some...
How do OLAP databases work—and which one is right for you? Read this blog post to learn more about which OLAP solutions are best for different use cases.
Because it deals with fast-moving, real-time data, IoT analytics is uniquely challenging. Learn how to overcome these challenges and how to extract (and act on) valuable insights from IoT data.
OLTP and OLAP Databases: How They Differ and Where to Use Them
Learn about the differences between analytical and transactional databases—their strengths and weaknesses, what they’re used for, and which option to choose for your own use case.
Query from deep storage: Introducing a new performance tier in Apache Druid
Now, Druid offers a simpler, cost-effective solution with its new feature, Query from Deep Storage. This feature enables you to query Druid’s deep storage layer directly without having to preload all of your...
As a mobile-first digital platform, KakaoBank accumulates a substantial amount of data. Therefore, analysts need a solution that can effectively analyze and pre-process large quantities of data, visualize the...
Joins, Multi-Stage Queries, and More: Relive the Excitement of Druid Summit 2023
Druid Summit kicked off its fourth year as a global gathering of minds passionate about real-time analytics and the power of Apache Druid. This year’s event revealed a common theme: the growing significance...
An Introduction to Online Analytical Processing (OLAP)
Online analytical processing (OLAP) analyzes data at scale—and provides actionable insights to organizations. Learn about how OLAP works, what a data cube is, and which OLAP product to use.
Real-Time Data: What it is, Why it Matters, and More
Real-time data travels directly from the source to end users, so that it can be processed and acted on instantly. Learn all about the challenges, benefits, and best practices for real-time data.
Druid vs Pinot: Choosing the best database for Real-Time Analytics
Do you want fast analytics, with subsecond queries, high concurrency, and combination of streams and batch data? If so, you want real-time analytics, and you probably want to consider the two Apache Software...
What’s new in Imply Polaris – October and November 2023
At Imply, our commitment to continually improving your experience with Imply Polaris—our real-time analytics Database-as-a-Service (DBaaS) powered by Apache Druid®—is evident in recent developments. Over...
This blog covers the rationale, advantages, and step-by-step process for data transfer from AWS s3 to Apache Druid for faster real-time analytics and querying.
What’s new in Imply Polaris, our real-time analytics DBaaS – September 2023
Every week, we add new features and capabilities to Imply Polaris. Throughout September, we've focused on enhancing your experience as you explore trials, navigate data integration, oversee data management,...
Introducing incremental encoding for Apache Druid dictionary encoded columns
In this blog post we deep dive on a recent engineering effort: incremental encoding of STRING columns. In preliminary testing, it has shown to be quite promising at significantly reducing the size of segment...
Migrate Analytics Data from MongoDB to Apache Druid
This blog presents a concise guide on migrating data from MongoDB to Druid. It includes Python scripts to extract data from MongoDB, save it as CSV, and then ingest it into Druid. It also touches on maintaining...
How Druid Facilitates Real-Time Analytics for Mass Transit
Mass transit plays a key role in reimagining life in a warmer, more densely populated world. Learn how Apache Druid helps power data and analytics for mass transit.
Migrate Analytics Data from Snowflake to Apache Druid
This blog outlines the steps needed to migrate data from Snowflake to Apache Druid, a platform designed for high-performance analytical queries. The article covers the migration process, including Python scripts...
Apache Kafka, Flink, and Druid: Open Source Essentials for Real-Time Data Applications
Apache Kafka, Flink, and Druid, when used together, create a real-time data architecture that eliminates all these wait states. In this blog post, we’ll explore how the combination of these tools enables...
Visualizing Data in Apache Druid with the Plotly Python Library
In today's data-driven world, making sense of vast datasets can be a daunting task. Visualizing this data can transform complicated patterns into actionable insights. This blog delves into the utilization of...
Bringing Real-Time Data to Solar Power with Apache Druid
In a rapidly warming world, solar power is critical for decarbonization. Learn how Apache Druid empowers a solar equipment manufacturer to provide real-time data to users, from utility plant operators to homeowners
When to Build (Versus Buy) an Observability Application
Observability is the key to software reliability. Here’s how to decide whether to build or buy your own solution—and why Apache Druid is a popular database for real-time observability
How Innowatts Simplifies Utility Management with Apache Druid
Data is a key driver of progress and innovation in all aspects of our society and economy. By bringing digital data to physical hardware, the Internet of Things (IoT) bridges the gap between the online and...
Three Ways to Use Apache Druid for Machine Learning Workflows
An excellent addition to any machine learning environment, Apache Druid® can facilitate analytics, streamline monitoring, and add real-time data to operations and training
Apache Druid® is an open-source distributed database designed for real-time analytics at scale. Apache Druid 27.0 contains over 350 commits & 46 contributors. This release's focus is on stability and scaling...
Unleashing Real-Time Analytics in APJ: Introducing Imply Polaris on AWS AP-South-1
Imply, the company founded by the original creators of Apache Druid, has exciting news for developers in India seeking to build real-time analytics applications. Introducing Imply Polaris, a powerful database-as-a-Service...
In this guide, we will walk you through creating a very simple web app that shows a different embedded chart for each user selected from a drop-down. While this example is simple it highlights the possibilities...
Automate Streaming Data Ingestion with Kafka and Druid
In this blog post, we explore the integration of Kafka and Druid for data stream management and analysis, emphasizing automatic topic detection and ingestion. We delve into the creation of 'Ingestion Spec',...
This guide explores configuring Apache Druid to receive Kafka streaming messages. To demonstrate Druid's game-changing automatic schema discovery. Using a real-world scenario where data changes are handled...
Imply Polaris, our ever-evolving Database-as-a-Service, recently focused on global expansion, enhanced security, and improved data handling and visualization. This fully managed cloud service, based on Apache...
Introducing hands-on developer tutorials for Apache Druid
The objective of this blog is to introduce the new set of interactive tutorials focused on the Druid API fundamentals. These tutorials are available as Jupyter Notebooks and can be downloaded as a Docker container.
In this blog article I’ll unpack schema auto-discovery, a new feature now available in Druid 26.0, that enables Druid to automatically discover data fields and data types and update tables to match changing...
Druid now has a new function, Unnest. Unnest explodes an array into individual elements. This blog contains design methodology and examples for this new Unnest function both from native and SQL binding perspectives.
What’s new in Imply Polaris – Our Real-Time Analytics DBaaS
Every week we add new features and capabilities to Imply Polaris. This month, we’ve expanded security capabilities, added new query functionality, and made it easier to monitor your service with your preferred...
Apache Druid® 26.0, an open-source distributed database for real-time analytics, has seen significant improvements with 411 new commits, a 40% increase from version 25.0. The expanded contributor base of 60...
How to Build a Sentiment Analysis Application with ChatGPT and Druid
Leveraging ChatGPT for sentiment analysis, when combined with Apache Druid, offers results from large data volumes. This integration is easily achievable, revealing valuable insights and trends for businesses...
In this blog, we will compare Snowflake and Druid. It is important to note that reporting data warehouses and real-time analytics databases are different domains. Choosing the right tool for your specific requirements...
Learn how to achieve sub-second responses with Apache Druid
Learn how to achieve sub-second responses with Apache Druid. This article is an in-depth look at how Druid resolves queries and describes data modeling techniques that improve performance.
Apache Druid uses load rules to manage the ageing of segments from one historical tier to another and finally to purge old segments from the cluster. In this article, we’ll show what happens when you make...
Real-Time Analytics: Building Blocks and Architecture
This blog identifies the key technical considerations for real-time analytics. It answers what is the right data architecture and why. It spotlights the technologies used at Confluent, Reddit, Target and 1000s...
What’s new in Imply Polaris – Our Real-Time Analytics DBaaS
This blog explains some of the new features, functionality and connectivity added to Imply Polaris over the last two months. We've expanded ingestion capabilities, simplified operations and increased reliability...
Wow, that was easy – Up and running with Apache Druid
The objective of this blog is to provide a step-by-step guide on setting up Druid locally, including the use of SQL ingestion for importing data and executing analytical queries.
Tales at Scale Podcast Kicks off with the Apache Druid Origin Story
Tales at Scale cracks open the world of analytics projects and shares stories from developers and engineers who are building analytics applications or working within the real-time data space. One of the key...
Real-time Analytics Database uses partitioning and pruning to achieve its legendary performance
Apache Druid uses partitioning (splitting data) and pruning (selecting subset of data) to achieve its legendary performance. Learn how to use the CLUSTERED BY clause during ingestion for performance and high...
Easily embed analytics into your own apps with Imply’s DBaaS
This blog explains how developers can leverage Imply Polaris to embed robust visualization options directly into their own applications without them having to build a UI. This is super important because consuming...
Building an Event Analytics Pipeline with Confluent Cloud and Imply’s real time DBaaS, Polaris
Learn how to set up a pipeline that generates a simulated clickstream event stream and sends it to Confluent Cloud, processes the raw clickstream data using managed ksqlDB in Confluent Cloud, delivers the processed...
We are excited to announce the availability of Imply Polaris in Europe, specifically in AWS eu-central-1 region based in Frankfurt. Since its launch in March 2022, Imply Polaris, the fully managed Database-as-a-Service...
Should You Build or Buy Security Analytics for SecOps?
When should you build—or buy—a security analytics platform for your environment? Here are some common considerations—and how Apache Druid is the ideal foundation for any in-house security solution.
Combating financial fraud and money laundering at scale with Apache Druid
Learn how Apache Druid enables financial services firms and FinTech companies to get immediate insights from petabytes-plus data volumes for anti-fraud and anti-money laundering compliance.
This is a what's new to Imply in Dec 2022. We’ve added two new features to Imply Polaris to make it easier for your end users to take advantage of real-time insights.
Imply Pivot delivers the final mile for modern analytics applications
This blog is focused on how Imply Pivot delivers the final mile for building an anlaytics app. It showcases two customer examples - Twitch and ironsource.
For decades, analytics has been defined by the standard reporting and BI workflow, supported by the data warehouse. Now, 1000s of companies are realizing an expansion of analytics beyond reporting, which requires...
Apache Druid is at the heart of Imply. We’re an open source business, and that’s why we’re committed to making Druid the best open source database for modern analytics applications
When it comes to modern data analytics applications, speed is of the utmost importance. In this blog we discuss two approximation algorithms which can be used to greatly enhance speed with only a slight reduction...
The next chapter for Imply Polaris: celebrating 250+ accounts, continued innovation
Today we announced the next iteration of Imply Polaris, the fully managed Database-as-a-Service that helps you build modern analytics applications faster, cheaper, and with less effort. Since its launch in...
We obviously talk a lot about #ApacheDruid on here. But what are folks actually building with Druid? What is a modern analytics application, exactly? Let's find out
Elasticity is important, but beware the database that can only save you money when your application is not in use. The best solution will have excellent price-performance under all conditions.
Druid 0.23 – Features And Capabilities For Advanced Scenarios
Many of Druid’s improvements focus on building a solid foundation, including making the system more stable, easier to use, faster to scale, and better integrated with the rest of the data ecosystem. But for...
Apache Druid 0.23.0 contains over 450 updates, including new features, major performance enhancements, bug fixes, and major documentation improvements.
Imply Polaris is a fully managed database-as-a-service for building realtime analytics applications. John is the tech lead for the Polaris UI, known internally as the Unified App. It began with a profound question:...
There is a new category within data analytics emerging which is not centered in the world of reports and dashboards (the purview of data analysts and data scientists), but instead centered in the world of applications...