Rockset vs. SingleStore

What is Rockset?

According to its website, Rockset is a “search and analytics database built for the cloud, with real-time indexing and full-featured SQL on JSON, time series, geospatial and vector data”. Rockset is built on RocksDB, and is primarily a document store with SQL being a secondary data model.

What is SingleStore?

SingleStore is the world's only real-time data platform. It can read, write and reason on petabyte-scale data in a few milliseconds. At its core is a high-performance, SQL database engine that allows you to run transactions, analytics and vector search— eliminating the need for multiple databases and unnecessary data movement. SingleStore delivers high throughput ingestion from distributed data sources while serving single-digit millisecond queries, making it an ideal platform for real-time analytics and gen AI.

highlightsHighlights

rocksetRockset

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    Fast data ingestion

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    Real-time analytics using SQL queries

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    Multimodel database with support for JSON, time series, geospatial and vector data

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    Stability at scale. Some users report that Rockset is unstable and buggy [source: publicly available employee reviews], as well as OOM issues for OLAP queries with large joins on large data sets [source: Reddit thread: Anyone using Rockset as their primary warehouse?]

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    No transaction support. Rockset is not a transactional database and is not designed to support OLTP workloads. Its architecture only supports eventual consistency; transactions lack Atomicity (the A in ACID) that are crucial for mission-critical applications.

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    Limited vector search capabilities. Indexed vector search in Rockset requires building a vector index for each partition, making it difficult to scale when dealing with changing datasets. The approach is also inefficient because it involves gathering a set of centroids close to the target vector, then applying a predicate to check if each row belongs to those nearby centroids. Indexed vector search is restricted to IVF algorithms and does not extend to more advanced algorithms like HNSW, which offer superior performance and recall.

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    Deployment. Only available on five cloud regions- All on AWS. There is no self-managed offering for users who may want to control performance, storage or security more extensively, or run on existing infrastructure.

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    Ease of getting started. No native support for Notebooks or co-pilot for writing code. No locally hosted offering.

single-storeSingleStore

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    Ingestion performance. Parallel high-throughput parallel ingestion — up to millions of events per second

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    Analytical performance. High performance and scalability with millisecond query response times; supports complex queries

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    Vector search. Support for both KNN and ANN and makes it easy for developers to do vector and full-text search with SQL.

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    CRUD. SingleStore is suitable for both OLTP and OLAP workloads. It stores data in patented row and column-based storage, making it extremely capable for both transactional and analytics use cases. SingleStore’s columnstore supports point updates, row-level locking and high-volume concurrent writes, upserts and deletes.

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    Multi-model. Support for SQL, JSON/BSON (MongoDB® API compatible), geospatial, key-value and time-series.

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    Bottomless storage and distributed architecture. Three-tier storage architecture with in-memory rowstore, on-disk columnstore and cloud object storage enabling separation of compute and storage; shared-nothing architecture that allows scaling to thousands of concurrent user sessions.

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    Deployment. Fully managed platform-as-a-service on all three hyperscaler clouds (AWS, Azure, GCP), and self-managed offering for Kubernetes/on-prem deployment

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    Data platform. Includes native data integration, compute services and Notebooks (SQL/Python/R).

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    Not ideal for pure transactional workloads (with requirements like foreign-keys, multi-master or geo-partitioning)

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    Not ideal for purely batch analytics

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    Not ideal for very small scale datasets (<1 GB)


technical-comparisonTechnical comparison

speedSpeed

Ingestion and query performance

CapabilityRocksetSingleStore
Ingestion performance
Rockset is capable of fast ingestionSingleStore Pipelines offer streaming, parallelized data ingestion with optional transforms from multiple data sources such as Amazon S3, Kafka, HDFS and Iceberg. Freshly ingested data is immediately queryable.
Query performance
Rockset claims to support p95 query latency of about 70 msSingleStore supports low-latency analytics (~10s of milliseconds) on complex queries (involving aggregates, joins, filters, etc.)
Indexes
Rockset relies on a default inverted index and does not support additional indexesSingleStore supports skiplist index, columnstore index, hash index and multiple indexes for vector search (Inverted File Index, HNSW with quantization techniques)

scaleScale

Scale applications cost-efficiently

CapabilityRocksetSingleStore
Separation of storage and compute
Rockset supports separation of storage and computeSingleStore's three-tiered "bottomless storage" includes an in-memory rowstore, a disk-based columnstore (persistent cache) and cloud object storage. This separation of storage and compute enables supporting growing data volumes using low-cost storage infrastructure.
Read Replicas
Rockset supports "compute-compute separation" to allow independent scaling of compute for multiple applications and workloadsSingleStore compute workspaces allow independently scaling compute for different workloads while utilizing a shared database.
Concurrency

Not enough is publicly known about concurrency supported by RocksetSingleStore's distributed SQL architecture can scale to thousands of concurrent user sessions
Auto scaling
Rockset's Virtual Instance Auto-Scaling is in BetaThis feature is on SingleStore's roadmap. Auto-scaling allows you to quickly and automatically adjust compute resources based on changing workload demands.
Compute service for ML
Rockset does not include a native compute serviceSingleStore's native compute service [In private preview] allows you to deploy scalable compute in a secure manner right next to your data-intensive application for AI/ML/ data prep workloads.
Multi-tenancy
Rockset supports multi-tenancy — however, it does not recommend scaling to thousands of collections or workspacesUse a single database instance to serve multiple customers while isolating data and preventing noisy neighbors.

simplicitySimplicity

Quickly build powerful applications

CapabilityRocksetSingleStore
HTAP
Rockset supports separation of storage and computeSingleStore's three-tiered "bottomless storage" includes an in-memory rowstore, a disk-based columnstore (persistent cache) and cloud object storage. This separation of storage and compute enables supporting growing data volumes using low-cost storage infrastructure.
Multi-model
Rockset supports "compute-compute separation" to allow independent scaling of compute for multiple applications and workloadsSingleStore compute workspaces allow independently scaling compute for different workloads while utilizing a shared database.
Vector search

Rockset supports KNN and ANN using FAISS-IVF alone. Indexed vector search in Rockset requires building a vector index for each partition, making it difficult to scale when dealing with changing datasets. The approach is also inefficient because it involves gathering a set of centroids close to the target vector, then applying a predicate to check if each row belongs to those nearby centroids. Indexed vector search is restricted to IVF algorithms and does not extend to more advanced algorithms like HNSW, which offer superior performance and recall. Distance metrics include DOT PRODUCT, COSINE SIMILARITY and EUCLIDEAN DISTANCE.SingleStore includes both exact K Nearest Neighbor (KNN) and Indexed Approximate Nearest Neighbor (ANN). Supported ANN algorithms include IVF and HNSW along with Product Quantization (PQ) optimizations. Distance metrics include DOT PRODUCT and EUCLIDEAN DISTANCE.
Full-text search
Rockset supports full-text and vector search.SingleStore supports full-text search. Its powerful SQL interface enables using filters, aggregations and joins for true hybrid search (vector + full-text search).
Notebooks
Rockset does not natively support SQL/Python notebooks, only a SQL editor. It does not include a co-pilot for assistance with writing code.SingleStore includes native Notebooks that allow developers and data scientists to easily write and collaborate on SQL or Python code
Ecosystem

Rockset is built on RocksDB which does not have a large ecosystem of compatible tools. Rockset does offer native connectors for Kafka, DynamoDB, MongoDB®, S3.With MySQL wire-compatibility, SingleStore benefits from an extremely large MySQL ecosystem. In addition, SingleStore has a large and growing set of native integrations for tools, connectors, frameworks, etc. Learn more.

enterprise-readyEnterprise-ready

Minimize costs, complexity and risks

CapabilityRocksetSingleStore
Availability

Rockset does not commit to availability SLAs, only support SLAs.SingleStore provides up to 99.99% uptime SLA and includes support for Point-in-Time Recovery (PITR). This allows customers to run critical applications and workloads and helps mitigate risks on business operations and reputation.
Security

Rockset's CMEKs are in private preview. Rockset offers Standard SSO and RBAC.SingleStore applies encryption to data in transit and data at rest. SingleStore supports Customer Managed Encryption Keys (AWS). SingleStore Helios has secured industry-leading security certifications including ISO 27001 and SOC 2 Type 2. It supports row-level security (RLS), Role Based Access Control (RBAC) and multiple SSO authentication mechanisms including Ping, Okta and OIDC.
Deployment optionality

Only available as a cloud service on five AWS regions.SingleStore can be deployed as a fully managed cloud service or self-managed, either on-premises or on Kubernetes on public cloud infrastructure. It is available as a cloud service in 31 regions across the leading three cloud providers (AWS, Azure, GCP).
Open-source software
Rockset is a commercial productSingleStore is wire-compatible with MySQL and MariaDB. However it is a commercial, enterprise-grade database. It offers a free tier for development use.

single-store-beat-rocksetSingleStore beat Rockset

When Adobe chose SingleStore, it evaluated Postgres, ElasticSearch and Rockset, among other products. Learn more about why Adobe chose SingleStore to power their user-facing analytical application, Workfront.

SingleStore’s latest release is exciting as it pushes what is possible when it comes to database technology, real-time analytics and building modern applications that support AI workloads. We’re looking forward to these new features as more and more of our customers are seeking ways to take full advantage of Gen Al capabilities.

Watch the SingleStore-Adobe Fireside Chat

Matt Newman
Principal Data Architect at Adobe

bringing-real-time-to-the-real-world-global-leaders-trust-single-storeBringing real-time to the real world: global leaders trust SingleStore