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StarRocks vs Snowflake

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

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StarRocks vs Snowflake Architecture

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
Cloud object storage for shared data accessible from any virtual warehouse
Isolation of ingest and query
No, but you can limit resources for ingestion and querying separately
Yes - separate virtual warehouses for batch data loading, ELT jobs and queries
Separation of compute and storage
No, but StarRocks supports nodes that don't store data locally
Isolation for multiple applications
Yes - separate virtual warehouses for each workload

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.

Snowflake is the data warehouse built for the cloud. Snowflake is well-known for separating storage and compute for better price performance. With Snowflake, multiple virtual warehouses can be spun up or down for batch data loading, transformations and queries all on the same shared data.

StarRocks vs Snowflake Ingestion

Data sources
Streaming • Kafka • Flink Data lakes • HDFS compatible • Cloud storage
• Third party ETL tool to ingest data into Snowflake including Fivetran, Hevo or Striim • Bulk loading from S3, GCS, Azure Blob Storage • Sink Connector for Apache Kafka in Confluent Cloud
Semi structured data
Supports columns with JSON data • Does not support mixed-type columns • Support for star and snowflake schemas
Ingests JSON and XML as a VARIANT data type
Transformations and rollups
Yes, via materialized views
• Third party ELT/ETL tools like dbt • Simple COPY commands at data loading for column recording, omission and casts

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.

Snowflake is an immutable data warehouse that is built for batch ingestion and relies heavily on the modern data stack ecosystem for data connectors and transformations. Snowflake has a number of integrations to ETL and ELT solutions including Fivetran, Hevo, Striim and dbt. While Snowflake does have support for semi-structured data in the form of a VARIANT type, it is best to structure the data for optimal query performance.

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StarRocks vs Snowflake Performance

While StarRocks is mutable, the update rate is slow, which is why it is most often used for append-only workloads
Data warehouse with immutable storage. Updates rewrite and merge entire partitions
Columnar index, limited support for inverted indexes
Query latency
50-1000ms queries on 100s of TB
Seconds to minutes on petabytes of data
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
Compressed columnar format stored in cloud object storage
Streaming ingest
Data latency is typically 1-2 seconds
• Ingests on a batch basis • Snowpipe typically ingests in minutes

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

Snowflake is designed for batch analytics with analysts and data scientists infrequently accessing large-scale data for trend analysis. Snowflake, like many data warehouses, is immutable and does not support frequently changing data efficiently. Snowflake uses a columnar store to return aggregations and metrics efficiently, often with query response times in the seconds to minutes on petabytes of data.

StarRocks vs Snowflake Queries

Multi-table join support
Query language
Developer tooling
• SQL APIs - make SQL calls to Snowflake programmatically • UDFs for Javascript, Python, Java and SQL functions • Go, JDBC, .NET, Node.js, ODBC, PHP, Python drivers
Visualization tools
Compatibility with MySQL protocols enables StarRocks to work with BI tools
Integrations with QuickSight, Chartio, Domo, Looker, PowerBI, Mode, Qlik, Sigma, Sisense, Tableau, ThoughtSpot and more

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

Snowflake supports SQL as its native query language and can perform SQL joins. Snowflake for developers introduced a number of developer tools including SQL APIs, UDFs and drivers to support application development. As Snowflake was originally built for business intelligence workloads, it integrates with a number of visualization tools for trend analysis.

StarRocks vs Snowflake Scalability

Vertical scaling
• Both frontend and backend nodes can be manually resized
Resize virtual warehouses via web interface or using DDL commands for warehouses
Horizontal scaling
• Both frontend and backend nodes can be manually scaled horizontally
• Multi-cluster warehouses allocate additional clusters for higher concurrency workloads • Auto scaling policies can be set

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.

Snowflake virtual warehouses can be scaled up for faster queries or scaled out using multi-cluster warehouses to support higher concurrency workloads. Snowflake has shared blob storage that scales automatically and independently.

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