In the era of big data analytics, fast query performance over massive datasets has become an essential requirement. Traditional relational databases often struggle with large-scale analytical workloads, especially when data volume exceeds billions of rows. This is where Apache Kylin comes into play, offering extremely fast OLAP (Online Analytical Processing) on Hadoop and cloud platforms. It bridges the gap between big data and business intelligence (BI) by allowing users to interact with large datasets in sub-second response times using familiar tools like Tableau, Power BI, and Excel. This article provides an introduction to Apache Kylin, exploring its architecture, core features, practical use cases, and setup process.
1. What is Apache Kylin?
Apache Kylin is an open-source distributed analytics engine designed to provide extremely fast OLAP queries on large-scale datasets. Developed by eBay and later contributed to the Apache Software Foundation, Kylin pre-computes multi-dimensional cubes from data stored in distributed systems like Apache Hive or HDFS and makes these cubes available for low-latency queries.
It supports standard SQL and integrates with BI tools via ODBC/JDBC, enabling business users to explore data without needing to understand complex big data technologies.
1.1 Key Features of Apache Kylin
- Pre-computed Cubes: Kylin builds and stores pre-aggregated data cubes, which significantly improves query performance.
- SQL Interface: Supports ANSI SQL on Hadoop data, making it accessible via tools like Tableau, Excel, or any SQL client.
- Massive Scalability: Capable of handling datasets with billions of rows.
- Multi-Engine Support: Compatible with various big data technologies such as Hive, Spark, and Parquet.
- Security Integration: Offers support for Kerberos, LDAP, and user access control.
- RESTful APIs: Allows easy automation and integration into existing systems.
2. How Does Apache Kylin Work?
Apache Kylin follows a unique approach that precomputes OLAP cubes offline, storing them in a way that allows ultra-fast retrieval during query time. Here’s a breakdown of its internal workflow:
Data Source Integration
Kylin typically connects to big data sources like Hive tables or Parquet files stored in HDFS. It reads the source schema and data for cube construction.
Modeling and Cube Definition
Users define data models using Kylin’s web interface or YAML configuration. A model consists of:
- Dimensions (categorical fields used for filtering and grouping)
- Measures (aggregated values like SUM, COUNT, etc.)
- Fact Table (the main data table)
- Lookup Tables (dimension tables)
Cube Building
Once the model is defined, Apache Kylin runs a cube build job. This involves:
- Running a MapReduce or Spark job on the Hadoop cluster
- Performing multi-dimensional aggregation
- Storing the result in a key-value format (usually HBase or Parquet)
Storage
The precomputed cube is stored in:
- HBase (older versions)
- Parquet (modern versions using Kylin 4+ with ClickHouse or directly in Parquet)
Query Execution
When a user submits a SQL query:
- Kylin’s query engine parses the SQL
- It identifies the relevant cube and uses dimension filters to locate data
- The engine retrieves the pre-aggregated results with sub-second latency
Client Integration
Results are returned to clients using JDBC/ODBC or RESTful APIs. Tools like Tableau and Power BI can query Kylin as if it were a regular SQL database.
3. Apache Kylin Architecture
The architecture includes the following core components:
- Web UI: Interface to manage models, cubes, and monitor jobs.
- Metadata Store: Stores configuration and cube metadata (typically in MySQL).
- Query Engine: Parses SQL and retrieves data from cubes.
- Cube Engine: Builds the cube using Spark or MapReduce.
- Storage Layer: HBase or Parquet file system for cube storage.
- REST Server: Provides APIs for cube management and queries.
Kylin also integrates with Apache Kafka for streaming ingestion (experimental) and supports cloud-native storage like AWS S3 or Azure Blob Storage.
4. Installation Overview Using Docker
Apache Kylin provides a Docker image that simplifies local testing and development. Here is how to quickly set up Apache Kylin locally using Docker. Before you begin, ensure that Docker is installed on your system.
Pull the Image
docker pull apachekylin/apache-kylin-standalone:5.0.2-GA
Run the Container
docker run -d \
--name Kylin \
--hostname localhost \
-e TZ=UTC \
-m 10G \
-p 7070:7070 \
-p 8088:8088 \
-p 9870:9870 \
-p 8032:8032 \
-p 8042:8042 \
-p 2181:2181 \
apachekylin/apache-kylin-standalone:5.0.2-GA
docker logs --follow Kylin
-d: Run the container in detached mode (in the background).--name Kylin: Assign the container a custom name:Kylin.--hostname localhost: Set the hostname inside the container tolocalhost.-e TZ=UTC: Set the timezone to UTC within the container.-m 10G: Limit the container’s memory usage to 10 GB.-p 7070:7070: Map Kylin Web UI port to the host (access athttp://localhost:7070).-p 2181:2181: Map Zookeeper Port, needed for coordination between components.apachekylin/apache-kylin-standalone:5.0.2-GA: Use the official Apache Kylin Docker image, version 5.0.2-GA.
Access the Kylin Web UI
Open your browser and go to:
http://localhost:7070/kylin
Login with default credentials:
- Username: ADMIN
- Password: KYLIN
To get started with Apache Kylin, you first need to create a new project within the Kylin Web UI, which serves as a workspace for organizing your data models and cubes. Next, define a data model that specifies the structure and relationships of your source data, followed by creating an OLAP cube that enables fast multidimensional analysis. You can then import sample data to experiment with, or connect to an external Hive data source. The Docker image provided by Apache Kylin includes a preconfigured basic Hive setup to help streamline the initial setup process.
Querying with Apache Kylin
You can query Apache Kylin through its interactive Web UI, connect to it from BI tools using JDBC or ODBC drivers, or automate data workflows by leveraging its REST API. The following is a representative SQL query that illustrates how you can interact with data modeled and indexed by Apache Kylin for fast analytical insights.
SELECT region, SUM(sales_amount) FROM kylin_sales WHERE year = 2025 GROUP BY region;
This query would be resolved instantly if the relevant cube has been built.
5. Use Cases of Apache Kylin
Apache Kylin is particularly suited for scenarios requiring fast, complex analytics over massive datasets. Common use cases include:
- Enterprise BI Dashboards
- Marketing Campaign Analytics
- Sales Performance Reporting
- Customer Behavior Analysis
- Telecommunication and Network Data Analysis
Example Use Case
Let’s say an e-commerce company wants to analyze daily sales across regions, categories, and user demographics. This involves billions of records.
Without Kylin:
- Every query triggers expensive scans and joins across massive datasets.
- Latency is high.
With Kylin:
- Data is modeled into dimensions (e.g., region, category, user group) and measures (e.g., total sales).
- Cubes are prebuilt during off-peak hours.
- BI analysts get sub-second responses to multidimensional queries.
6. Conclusion
In this article, we explored the fundamentals of Apache Kylin, an OLAP engine designed for extremely fast analytics on big data. We covered its core features, architecture, and how it bridges the gap between massive datasets and real-time business intelligence. The article also demonstrated how it can be set up quickly using Docker.
By precomputing cubes, Kylin achieves a significant boost in query speed, making it ideal for interactive dashboards and analytics on Hadoop or cloud platforms. Whether you’re building dashboards for business users or powering analytical workloads behind the scenes, Apache Kylin is a compelling option to consider in your big data stack.
This article provided an introduction to Apache Kylin.
