What is Apache Doris?

What is Apache Doris?

Apache Doris is a remarkably fast, open-source analytical database optimized for real-time reporting and highly concurrent Online Analytical Processing (OLAP) workloads. Originally developed at Baidu to handle massive advertising analytics, Doris utilizes a Massively Parallel Processing (MPP) architecture to deliver sub-second query responses over datasets ranging from gigabytes to petabytes.

While distributed engines like Apache Spark or Trino are exceptionally powerful for massive data lake transformations and federated querying, they often struggle to provide the extreme low latency required for interactive customer-facing applications. When an organization builds a dashboard embedded inside a live SaaS application (where thousands of concurrent users expect instant chart rendering), they rely on highly optimized databases like Apache Doris or ClickHouse. Doris distinguishes itself through its absolute architectural simplicity, seamless MySQL compatibility, and profound capability to handle real-time streaming data ingestion alongside complex analytical aggregations.

The Streamlined MPP Architecture

Apache Doris was engineered to be exceptionally simple to deploy and operate. Unlike legacy Hadoop ecosystems that require maintaining complex dependencies like ZooKeeper or HDFS, Doris compiles down into a completely self-contained binary.

The architecture strictly consists of only two distinct processes:

  • Frontend (FE): The FE nodes are responsible for managing metadata, storing the cluster state, parsing SQL queries, generating distributed execution plans, and routing tasks.
  • Backend (BE): The BE nodes are the computational workhorses. They are entirely responsible for physically storing the data, executing the vectorized calculations, and executing complex data compaction routines in the background.

This incredibly streamlined design entirely eliminates the operational nightmares of managing complex external dependencies, making Doris exceptionally resilient and simple to scale horizontally on standard cloud virtual machines.

Vectorized Execution and Storage Mechanics

To achieve blazing fast query performance, Doris tightly integrates its storage layer with its computational execution engine, utilizing profound structural optimizations.

Columnar Storage and Intelligent Indexing

Doris stores data in a proprietary columnar format explicitly designed to minimize disk I/O. When data is ingested, Doris sorts it meticulously according to primary keys. It automatically generates incredibly rich metadata and sparse indexes (similar to the micro-partitions found in Snowflake). Additionally, Doris inherently supports ZoneMap indexes, Bloom Filters, and explicit Bitmap indexes. When a user queries a specific user ID, Doris utilizes these indexes to instantly bypass gigabytes of irrelevant data, reading only the absolute necessary blocks from physical storage.

Vectorized Execution Engine

Once the specific data blocks are pulled from storage into memory, Doris processes them using a completely Vectorized Execution engine. Instead of iterating through complex loops row-by-row, the engine passes massive contiguous chunks of columnar memory directly into the CPU. This allows the processor to utilize SIMD (Single Instruction, Multiple Data) instructions, mathematically executing complex aggregations (like SUM or AVG) across thousands of rows in a single clock cycle.

Real-Time Ingestion and Data Models

A critical differentiator for Apache Doris is its capability to handle immense real-time streaming ingestion while instantly exposing that data to analytical queries.

Organizations heavily utilize tools like Apache Kafka or Apache Flink to stream millions of events directly into Doris using its highly optimized Stream Load protocol. Doris provides distinct internal Data Models specifically designed to manage this data behavior at scale:

  • Duplicate Key Model: Designed for raw log data where every single event is retained identically as it arrives (e.g., raw web clickstreams).
  • Unique Key Model: Designed for Change Data Capture (CDC) replication. If a record with an existing ID arrives, Doris automatically completely overwrites the older version, providing exact replica states of operational databases natively.
  • Aggregate Key Model: Designed explicitly for extreme speed. As data streams into the system, Doris automatically aggregates the metrics mathematically in the background (e.g., continuously keeping a running sum of total sales per region). When an analyst queries the total, Doris simply returns the pre-calculated integer instantly.

The Lakehouse Integration

Recognizing the industry shift toward decoupled storage, modern versions of Apache Doris introduced the Multi-Catalog feature. Instead of strictly requiring organizations to ingest data into Doris’s internal storage nodes, Doris can now reach out externally.

An engineer can configure Doris to connect seamlessly to an Open Data Lakehouse (querying Apache Iceberg, Hudi, or Delta Lake tables directly) or to an external database (like Elasticsearch or MySQL). This capability allows Doris to serve as an exceptionally fast, unified query layer that accelerates critical dashboards using internal storage, while seamlessly federating slower, historical queries directly against the broader cloud data lake.

Summary of Technical Value

Apache Doris provides organizations with the exact low-latency infrastructure required to power modern, customer-facing analytical applications. By combining an incredibly simple operational architecture with profound hardware-level vectorized execution and native real-time streaming aggregation models, Doris ensures that highly concurrent dashboards load in milliseconds. It bridges the critical gap between heavy data lake engineering and instant business intelligence visualization.

Learn More

To learn more about the Data Lakehouse, read the book “Lakehouse for Everyone” by Alex Merced. You can find this and other books by Alex Merced at books.alexmerced.com.