Apache Druid is a game-changer for real-time analytics. Its purpose-built architecture enables fast data ingestion, storage, and retrieval for analytical queries. Druid is one of the only products in the analytics database domain that features automatic schema discovery. This offers an unparalleled ability to adapt to diverse and ever-evolving data sources, removing the necessity for tedious manual schema definition and maintenance. This feature empowers users to focus on data analysis and insights rather than spending time and effort on managing and updating schemas.
In this blog post, we will set up a schema-less ingestion that allows for changes to the data to be automatically detected. We will also create messages, publish those topics to Kafka and ingest the messages into Druid. We will cover everything from setting up your Druid environment to ingesting streaming messages from Kafka to handle real-time data.
Automatic Schema Discovery in Druid
Auto schema detection is especially helpful for analytical databases for several reasons, including the following:
Flexibility: Analytical databases often deal with diverse and evolving data sources. With auto schema detection, the database can automatically adapt to different data structures and handle schema changes without requiring manual intervention from a DBA. This is particularly helpful for frequently changing, event-driven streaming data. The additional flexibility allows for efficient and agile data exploration and analysis.
Time-saving: Manually defining and maintaining schemas for every data source can be a time-consuming and error-prone process. Auto schema detection automates this task, saving time and effort for database administrators and data engineers.
Scalability: Analytical databases typically handle large volumes of data. Auto schema detection allows the database to handle varying data sizes and structures seamlessly. As new data sources are added or existing ones evolve, the database can dynamically adjust its schema detection capabilities, ensuring scalability without compromising performance.
Ease of use: Auto schema detection simplifies the onboarding process for new data sources. Instead of requiring users to define the schema upfront, the database can automatically infer the structure and make the data available for analysis. This improves the usability of the analytical database and reduces the barrier to entry for users.
Data quality assurance: Auto schema detection can also help identify potential data quality issues. By analyzing the structure and patterns of the data, the database can highlight inconsistencies or anomalies that may require further investigation or cleansing. This ensures data accuracy and enhances the reliability of analytical insights.
Overall, auto schema detection in an analytical database streamlines the data ingestion process, accommodates diverse data sources, improves data exploration capabilities, and supports scalability in handling large volumes of data.
Prerequisites
To begin, you will need to install and configure Apache Druid on your local machine or server. Then install Kafka locally, write a Kafka producer to send messages, and set up the ingestion configuration in Druid to enable auto-schema detection.
Install Druid Locally
Download the latest Druid release from apache.org and extract the file.
From the terminal, change directories to the distribution directory, for example:
cd druid_26/distribution/target/apache-druid-27.0.0-SNAPSHOT
From the apache-druid-26.0.0 package root, run the following command to start the micro-quickstart configuration:
./bin/start-druid
This starts up instances of ZooKeeper and the Druid services
Define a Kafka producer to create and send messages to Kafka (see code sample below).
import jsonfrom datetime import datetimefrom confluent_kafka import Producerdefsimple_message(val): now = datetime.now().isoformat() msg =f'This is message number {val} from Kafka created at {now}' data ={"message": msg,"timestamp": now,#Uncomment line below to add a new field to the message#"new_field": f'message_id_{val}'}return json.dumps(data)defsend_simple_messages(i,producer,topic): msg =simple_message(i)print(msg) producer.produce(topic,value=msg) producer.flush(30)defrun_producer(): producer =Producer({'bootstrap.servers':'localhost:9092'}) topic ='test_topic'print(f'The following messages will be sent to the topic: {topic}')try:for i inrange(1,21):send_simple_messages(i, producer, topic)exceptKeyboardInterrupt:pass producer.flush(30)if __name__ =="__main__":run_producer()
The code above initializes a Kafka producer to send messages to a specified Kafka topic. The run_producer function sets up the Kafka producer with the bootstrap server configured to localhost:9092 and the topic set to test_topic, using a loop to generate the specified number of messages.
The send_simple_messages function constructs and sends individual messages. Each message is generated by the simple_message function, which creates a dictionary containing a custom message with the current timestamp and a unique identifier and the new_field, if that code line is uncommented. This dictionary is then converted to a JSON-formatted string which is sent to the Kafka topic via the producer. After sending each message, the producer is flushed prior to the termination of the script.
Creating a Streaming Data Source
Real-time data processing is critical for many applications today, making Apache Druid an indispensable tool for the smooth ingestion and analysis of streaming data due to its seamless integration with Kafka.
Let’s create a job to load data from the Druid UI. From the home page, select Load data and then Streaming (see example below).
Next, select Apache Kafka and Connect data (see example below).
Add the bootstrap server for the Kafka broker. Since we are running Kafka locally, the server is localhost:9092.
Add the topic name that we created earlier, test_topic.
Select Apply (see example below).
Ensure that Kafka is started and execute the code shown earlier to generate and send messages. The messages will be shown in the connect UI.
Here is a detailed look at a few messages (see screen print below) including the Kafka timestamp which represents the time elapsed since the Unix epoch. Defined as 00:00:00 Coordinated Universal Time (UTC) on January 1, 1970. It is a common way to represent time in many computer systems.
From this screen select Apply and Next: Parse data.
Leave the defaults and select Next: Parse time. Also, leave the defaults and select Next: Transform, Next: Filter, Next: Configure schema, and Next: Partition. On the Partition screen, select hour as the Segment granularity (see screen print below).
On the Tune screen, select Use earliest offset as True and Next: Publish (see screen print below).
Leave the defaults and select Next: Edit spec.
On the Edit spec screen modify the configuration dimensijonsSpec using the JSON below:
Note that the dimensionsSpec is the section where you would typically add the data types, just has a dimensions array [] and useSchemaDiscovery set to true.
After editing the specification, select Submit (see screen print below).
Now that the ingestion job has been set and the data source has been configured, we will adjust the Kafka message by adding a field. Uncomment the highlighted line of code from the Python code shown above:
"new_field": f'message_id_{val}'
Rerun the code to create and send messages to Kafka. Then access the Query UI from the Druid console and execute this query:
SELECT*FROM test_topicORDER BY"__time"DESCLIMIT50
Notice that in the query results, the new_field that we added to the JSON message sent by the Kafka producer is included without any need to modify the ingestion spec and adding a schema (see screen print below).
Summary
In this article, we went through a comprehensive guide on installing Druid and Kafka, defining a streaming data source, and setting up an ingestion job. We also adjusted the messages published by Kafka to include an additional field. This allowed us to demonstrate Druid’s automatic schema discovery using a real-world scenario where the data set changes are seamlessly managed, eliminating the need for human intervention.
This innovative feature empowers users with unprecedented flexibility, allowing the environment to evolve with diverse data sources without the arduous task of manual schema definition or maintenance. In practical terms, this liberates users to concentrate more on tasks like interpreting data and gleaning valuable insights instead of being consumed by administrative duties. This is fantastic news for anyone interested in real-time data analysis. The ability to make sense of real-time data with agility and accuracy opens up new possibilities for transforming raw data into actionable insights.
Rick Jacobs is a Senior Technical Product Marketing Manager at Imply. His varied background includes experience at IBM, Cloudera, and Couchbase. He has over 20 years of technology experience garnered from serving in development, consulting, data science, sales engineering, and other roles. He holds several academic degrees including an MS in Computational Science from George Mason University. When not working on technology, Rick is trying to learn Spanish and pursuing his dream of becoming a beach bum.
Other blogs you might find interesting
No records found...
Sep 21, 2023
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 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',...
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...
We are in the early stages of a stream revolution, as developers build modern transactional and analytic applications that use real-time data continuously delivered.
Developers and architects must look beyond query performance to understand the operational realities of growing and managing a high performance database and if it will consume their valuable time.
Building high performance logging analytics with Polaris and Logstash
When you think of querying with Apache Druid, you probably imagine queries over massive data sets that run in less than a second. This blog is about some of the things we did as a team to discover the user...
Horizontal scaling is the key to performance at scale, which is why every database claims this. You should investigate, though, to see how much effort it takes, especially compared to Apache Druid.
When you think of querying with Apache Druid, you probably imagine queries over massive data sets that run in less than a second. This blog is about some of the things we did as a team to discover the user...
Building Analytics for External Users is a Whole Different Animal
Analytics aren’t just for internal stakeholders anymore. If you’re building an analytics application for customers, then you’re probably wondering…what’s the right database backend?
After over 30 years of working with data analytics, we’ve been witness (and sometimes participant) to three major shifts in how we find insights from data - and now we’re looking at the fourth.
Every year industry pundits predict data and analytics becoming more valuable the following year. But this doesn’t take a crystal ball to predict. There’s instead something much more interesting happening...
Today, I'm prepared to share our progress on this effort and some of our plans for the future. But before diving further into that, let's take a closer look at how Druid's core query engine executes queries,...
Product Update: SSO, Cluster level authorization, OAuth 2.0 and more security features
When you think of querying with Apache Druid, you probably imagine queries over massive data sets that run in less than a second. This blog is about some of the things we did as a team to discover the user...
When you think of querying with Apache Druid, you probably imagine queries over massive data sets that run in less than a second. This blog is about some of the things we did as a team to discover the user...
Druid Nails Cost Efficiency Challenge Against ClickHouse & Rockset
To make a long story short, we were pleased to confirm that Druid is 2 times faster than ClickHouse and 8 times faster than Rockset with fewer hardware resources!.
Unveiling Project Shapeshift Nov. 9th at Druid Summit 2021
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...
How we made long-running queries work in Apache Druid
When you think of querying with Apache Druid, you probably imagine queries over massive data sets that run in less than a second. This blog is about some of the things we did as a team to discover the user...
Uneven traffic flow in streaming pipelines is a common problem. Providing the right level of resources to keep up with spikes in demand is a requirement in order to deliver timely analytics.
Community Discoveries: multi-value dimensions in Apache Druid
Hellmar Becker is an Imply solutions engineer based in Germany, where he has been delving into the nooks-and-crannies of multi-valued dimension support in Druid. In this interview, Hellmar explains why...
Community Spotlight: Apache Pulsar and Apache Druid get close…
The community team at Imply spoke with an Apache Pulsar community member, Giannis Polyzos, about how collaboration between open source communities generates great things, and more specifically, about how...
Meet the team: Abhishek Agarwal, engineering lead in India
Abhishek is Imply’s first engineer in India. We spoke to him about setting up our operations in Bangalore and asked what kind of local talent the company is looking for.
Jihoon Son is a software engineer at Imply who works on Apache Druid®. He explains what drew him to Imply five years ago and why he’s even more inspired by the company today.