Apache Druid vs StarRocks

Compare and contrast Apache Druid and StarRocks by architecture, ingestion, queries, performance, and scalability.

Compare Apache Druid to Rockset here

Compare StarRocks to Rockset here

Apache Druid vs StarRocks Architecture

Architecture

Apache Druid

StarRocks

Deployment model

• SaaS or self managed. • Imply’s Druid cloud offering requires customers to configure, scale, and capacity plan

PaaS or self managed

Use of storage hierarchy

• Queries are served from data on disk and an in-memory cache • Cloud storage or HDFS for deep storage

Data is stored on disk and in memory

Isolation of ingest and query

• Ingestion and queries are run on the same node by default • The ingestion and querying processes can be run on separate nodes, however not for real-time data

No, but you can limit resources for ingestion and querying separately

Separation of compute and storage

No, although Imply’s offering separates compute and storage

No, but StarRocks supports nodes that don't store data locally

Isolation for multiple applications

Druid’s architecture employs nodes called data servers that are used for both ingestion and queries. High ingestion or query load can cause CPU and memory contention compared with Druid alternatives. Breaking apart the pre-packaged ingestion and query server components involves planning ahead and additional complexity, and is not dynamic.

StarRocks is a high-performance OLAP database that can be deployed on the cloud or self managed. StarRocks does not separate compute and storage and offers limited options for resource isolation. It offers a robust set of features and high performance but requires considerable expertise to operate and scale.

Apache Druid vs StarRocks Ingestion

Ingestion

Apache Druid

StarRocks

Data sources

Data connectors to: • Events streams (e.g. Kafka, Kinesis) • Data lakes (e.g. S3, Google Cloud Storage) • RDBMS and HDFS databases CDC events from databases require manual conversion to Druid events

Streaming • Kafka • Flink Data lakes • HDFS compatible • Cloud storage

Semi structured data

Druid requires flattening nested data at ingest and maintaining a flattening spec as the schema changes over time

Supports columns with JSON data • Does not support mixed-type columns • Support for star and snowflake schemas

Transformations and rollups

Yes, using ingestion specs written in JSON support rollups and simple transformations. SQL ingest transformations available for Imply

Yes, via materialized views

Druid has built-in connectors that manage ingestion from common data sources. Unlike some Druid competitors, it doesn’t support nested data, so data must be flattened at ingest. Denormalization is also required at ingest, increasing operational burden for certain use cases.

StarRocks ingests data from a variety of sources, including both batch and streaming data. StarRocks can ingest nested JSON data, but enforces type at the column level.

Apache Druid vs StarRocks Performance

Performance

Apache Druid

StarRocks

Updates

Druid segments become immutable once committed and published, making it more appropriate for append-only use cases

While StarRocks is mutable, the update rate is slow, which is why it is most often used for append-only workloads

Indexing

• Bitmap index

Columnar index, limited support for inverted indexes

Query latency

Typically sub-second query latency for denormalized, flattened datasets up to 100s of TBs

50-1000ms queries on 100s of TB

Storage format

Columnar format partitioned by time

• StarRocks is a columnstore that organizes data into prefix indexes, per-column data blocks, and per-column indexes • All data is replicated 3 times to achieve both fault-tolerance and concurrency

Streaming ingest

• Sub 15 seconds

Data latency is typically 1-2 seconds

Druid is designed to make streaming data queryable as quickly as possible. JOINs are either impossible or incur a large performance penalty. Updates are only possible via batch jobs. Druid leverages data denormalization and write-time aggregation at ingestion to reduce query latency.

StarRocks was purpose-built for high-performance ingest, low-latency queries, and high concurrency. Optimized performance requires significant manual tuning.

Apache Druid vs StarRocks Queries

Queries

Apache Druid

StarRocks

Joins

Yes, only for datasets that fit in memory, and with a query latency penalty

Multi-table join support

Query language

Druid native queries • Druid SQL

SQL

Developer tooling

• Druid SQL API

Minimal

Visualization tools

Pivot, maintained by Imply

Compatibility with MySQL protocols enables StarRocks to work with BI tools

Druid has a native JSON-based query language and provides Druid SQL as an alternative that translates into its native queries. JOINs are not recommended.

StarRocks uses a high-performance vectorized SQL engine, a custom-built cost-based optimizer, and has support for materialized views.

Apache Druid vs StarRocks Scalability

Scalability

Apache Druid

StarRocks

Vertical scaling

Users can manually increase or decrease server sizes

• Both frontend and backend nodes can be manually resized

Horizontal scaling

• Users can manually add additional nodes to a scaled-out cluster. • Imply automates some of the horizontal scaling process.

• Both frontend and backend nodes can be manually scaled horizontally

Druid users are exposed to complex decisions about the number and size of servers as clusters are scaled.

StarRocks can scale up or out, but its tightly coupled compute and storage scale together for performance. This often results in resource contention and overprovisioning. Scaling StarRocks often requires deep expertise as there are many levels of the system that need to be managed.