Rockset vs StarRocks

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

Rockset vs StarRocks Architecture

Architecture

Rockset

StarRocks

Deployment model

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

PaaS or self managed

Use of storage hierarchy

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

Data is stored on disk and in memory

Isolation of ingest and query

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

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

Separation of compute and storage

Yes

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

Isolation for multiple applications

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

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 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 vs StarRocks Ingestion

Ingestion

Rockset

StarRocks

Data sources

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)

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

Semi structured data

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

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

Transformations and rollups

Yes - using SQL ingest transformations

Yes, via materialized views

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 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 vs StarRocks Performance

Performance

Rockset

StarRocks

Updates

Documents are mutable and can be updated at the field level

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

Indexing

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

Columnar index, limited support for inverted indexes

Query latency

50-1000ms queries on 100s of TB

Storage format

Converged Index, comprising a rowstore, columnstore and inverted index

• 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

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

Data latency is typically 1-2 seconds

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

Rockset vs StarRocks Queries

Queries

Rockset

StarRocks

Joins

Yes

Multi-table join support

Query language

SQL - with extensions for nested data

SQL

Developer tooling

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

Minimal

Visualization tools

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

Compatibility with MySQL protocols enables StarRocks to work with BI tools

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

Rockset vs StarRocks Scalability

Scalability

Rockset

StarRocks

Vertical scaling

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

• Both frontend and backend nodes can be manually resized

Horizontal scaling

• 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

• Both frontend and backend nodes can be manually scaled horizontally

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.

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.