Druid

According to the Apache Software Foundation, Apache Druid is a high-performance, real-time analytics database designed to deliver sub-second queries on streaming and batch data at scale and under load. It is positioned as a solution for powering real-time analytics applications that require fast queries and high uptime. Apache Druid is said to be suitable for companies of all sizes, from small businesses to large enterprises. It caters to a wide range of professions and industries, including data analysts, data engineers, business intelligence professionals, e-commerce companies, and digital advertising agencies.

Key Features

According to the vendor, Apache Druid offers an interactive query engine that utilizes scatter/gather for high-speed queries. Queries can be processed in parallel, enabling sub-second performance for most queries, even with very large data sets. The tiering and quality of service feature allows for configurable tiering, guaranteeing priority and avoiding resource contention. This feature enables fine-tuning of cluster resources for optimal performance.

Apache Druid automatically optimizes the data format by columnarizing, time-indexing, dictionary-encoding, bitmap-indexing, and type-aware compressing the ingested data. This optimization provides fast filtering and searching across multiple columns with compressed bitmap indexes. It also optimizes storage by compressing string columns using dictionary encoding and numeric columns using compressed raw values.

The elastic architecture of Apache Druid consists of loosely coupled components for ingestion, queries, and orchestration. This architecture enables easy scale-up and scale-out with a deep storage layer, providing flexibility and quick scalability to handle large aggregations and high-performance applications.

According to the vendor, Apache Druid offers true stream ingestion with connector-free integration with streaming platforms such as Apache Kafka and Amazon Kinesis. This feature enables query-on-arrival, high scalability, low latency, and guaranteed consistency. Apache Druid supports the ingestion of millions of events per second and continuous backup into deep storage.

The vendor claims that Apache Druid ensures non-stop reliability through automatic data services, including continuous backup, automated recovery, and multi-node replication. These services are designed to ensure high availability and durability of data, providing a reliable and fault-tolerant system for critical applications.

Apache Druid features schema auto-discovery, which allows for automatic detection, definition, and updating of column names and data types upon ingestion. This feature provides the ease of schemaless data ingestion with the performance of strongly typed schemas, reducing the need for manual schema management and improving data ingestion efficiency.

The flexible joins support in Apache Druid enables join operations during data ingestion and at query-time execution. This feature provides the fastest query performance when tables are pre-joined during ingestion, enabling efficient data analysis across multiple dimensions and tables.

Developers and analysts can leverage the familiar SQL API for end-to-end data operations in Apache Druid. The vendor states that Apache Druid supports SQL-based queries for ingestion, transformation, and querying of data, simplifying the adoption and integration of Druid into existing data workflows.