StarRocks vs Rockset

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

StarRocks vs Rockset Architecture

Architecture

StarRocks

Rockset

Deployment model

PaaS or self managed

SaaS - infrastructure, software and cluster ops managed by service provider

Use of storage hierarchy

Data is stored on disk and in memory

• SSDs store shared hot data, accessible from any Virtual Instance cluster • Cloud storage for durability

Isolation of ingest and query

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

Yes - separate compute clusters (Virtual Instances) for ingest and query

Separation of compute and storage

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

Yes

Isolation for multiple applications

Yes - separate compute cluster (Virtual Instance) for each application

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.

Rockset is built to be a cloud-only database and does not have a self-managed option. It disaggregates compute from both hot storage and cloud storage, allowing multiple isolated compute clusters to run on the same shared data.

StarRocks vs Rockset Ingestion

Ingestion

StarRocks

Rockset

Data sources

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

Managed data connectors to: • Events streams (e.g. Kafka, Kinesis) • Database CDC (e.g. MongoDB, DynamoDB, MySQL, PostgreSQL) • Data lakes (e.g. S3, Google Cloud Storage)

Semi structured data

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

• Ingests JSON and XML without a predefined schema • Ingests nested data

Transformations and rollups

Yes, via materialized views

Yes - using SQL ingest transformations

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.

Rockset has built-in connectors that manage streaming ingestion from common data sources. It has native support for semi-structured data, so that nested JSON and XML can be ingested and queried as is.

StarRocks vs Rockset Performance

Performance

StarRocks

Rockset

Updates

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

Documents are mutable and can be updated at the field level

Indexing

Columnar index, limited support for inverted indexes

• Converged Index (row, columnar and inverted index) built on all data by default • All queries are resolved through the index

Query latency

50-1000ms queries on 100s of TB

Storage format

• 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

Converged Index, comprising a rowstore, columnstore and inverted index

Streaming ingest

Data latency is typically 1-2 seconds

• Ingests on a per-record basis • Data latency is typically 1-2 seconds

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

Rockset is designed to make streaming data queryable as quickly as possible by avoiding the need to batch data. It also updates documents efficiently by only reindexing fields that are part of an update request. Rockset indexes all data by default, which results in storage amplification but also enables low-latency queries that require less compute.

StarRocks vs Rockset Queries

Queries

StarRocks

Rockset

Joins

Multi-table join support

Yes

Query language

SQL

SQL - with extensions for nested data

Developer tooling

Minimal

• Data APIs - saved SQL queries executed via REST endpoint • Python, Java, Node.js and Go SDKs • UDFs

Visualization tools

Compatibility with MySQL protocols enables StarRocks to work with BI tools

Integrations with Tableau, Looker, Grafana, Superset, Power BI, Retoolwhic

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

Rockset supports SQL as its native query language and can perform SQL joins. Users can create data APIs by storing SQL queries in Rockset that are executed from dedicated REST endpoints. Rockset integrates with some common visualization tools, but BI is not Rockset’s primary use case.

StarRocks vs Rockset Scalability

Scalability

StarRocks

Rockset

Vertical scaling

• Both frontend and backend nodes can be manually resized

Resize compute clusters (Virtual Instances) via API or in the console

Horizontal scaling

• Both frontend and backend nodes can be manually scaled horizontally

• Add or remove compute clusters (Virtual Instances) via API or in the console • Scale out compute clusters for higher concurrency • Use separate compute clusters to isolate ingest from query or for multiple isolated applications • No rebalancing required

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.

Rockset Virtual Instances are distributed compute clusters that can be scaled up for faster queries or scaled out for practically unlimited concurrency or if compute isolation is needed. Rockset has shared storage that scales automatically and independently, so no rebalancing is required.