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SingleStore vs Rockset

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

SingleStore Architecture vs Rockset

Separation of compute and storage
Yes - Singlestore Cloud uses cloud object storage for separation of compute and storage
Isolation of ingest and query
No - databases share ingest and queries
Yes - separate compute clusters (Virtual Instances) for ingest and query
Isolation for multiple applications
Yes - separate compute cluster (Virtual Instance) for each application
Use of storage hierarchy
• Memory - for data requiring the highest performance • High-performance block storage for persistent cache - the working dataset should fit within the persistent cache • Cloud object storage for long-term retention
• SSDs store shared hot data, accessible from any Virtual Instance cluster • Cloud storage for durability
Deployment model
Self managed and SaaS deployment options
SaaS - infrastructure, software and cluster ops managed by service provider

SingleStore is a proprietary distributed relational database that handles both transactional and analytical workloads. It relies on memory and a persistent cache to deliver low latency queries. For longer term data retention, SingleStore Cloud separates compute from cloud object storage. SingleStore Cloud pricing is based on compute and storage usage.

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.

SingleStore Ingestion vs Rockset

Data sources
Integrations to: Amazon S3, Apache Beam, GCS, HDFS, Kafka, Spark, Qlik Replicate, HVR
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
Ingests JSON as a JSON column type
• Ingests JSON and XML without a predefined schema • Ingests nested data
Transformations and rollups
SingleStore pipelines do common data shaping including normalizing and denormalizing data, adding computed columns, filtering data, mapping data, splitting records into multiple destination tables
Yes - using SQL ingest transformations

SingleStore has integrations to common data lakes and streams. With SingleStore pipelines, users can perform common data transformations during the ingestion process. SingleStore provides limited support for semi-structured data with its JSON column type. Many users structure data prior to ingestion for optimal query performance.

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.

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SingleStore Queries vs Rockset

Query language
SQL - with extensions for nested data
Developer tooling
• API for querying data via POST command • JDBC driver, Python client • Compatibility with MySQL and MariaDB to support additional drivers
• Data APIs - saved SQL queries executed via REST endpoint • Python, Java, Node.js and Go SDKs • UDFs
Visualization tools
Integrations with Cognos Analytics, Dremio, Looker, Microstrategy, Power BI, Sisense, Tableau and Tibco Spotfire
Integrations with Tableau, Looker, Grafana, Superset, Power BI, Retoolwhic

SingleStore supports SQL as its native query language and can perform SQL joins. It is designed for querying structured data with static schemas. Users can create data APIs to execute SQL statements against the database over an HTTP connection. Common SingleStore use cases include business intelligence and analytics, and the database offers a number of integrations to visualization 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.

SingleStore Performance vs Rockset

Streaming ingest
• Columnnar store/universal storage ingests on a batch basis • Data latency is typically seconds by relying on memory
• Ingests on a per-record basis • Data latency is typically 1-2 seconds
SingleStore columnar store/universal storage is immutable. Updates are fast when the data still resides in memory
Documents are mutable and can be updated at the field level
Storage format
Two table formats-either use the rowstore or columnstore/universal storage
Converged Index, comprising a rowstore, columnstore and inverted index
Indexes can be manually configured: Skiplist index, hash index, full-text index, geospatial index
• 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 when the working set is contained in memory
50-1000ms queries on 100s of TB

SingleStore has two storage formats: a rowstore and a columnar store referred to as universal storage. The columnar store is used for analytical workloads, loading data in batch and relying on memory to achieve seconds of data latency. The columnar store can also execute queries in seconds when the working set is contained in memory. SingleStore provides the ability to configure and manage additional indexes on the data for faster performance.

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.

SingleStore Scalability vs Rockset

Vertical scaling
• Cloud offering: Resize compute workspaces in the UI or using the Management API • Self-managed offering: Change cluster configuration by updating command-line arguments or to the cluster directly.
Resize compute clusters (Virtual Instances) via API or in the console
Horizontal scaling
Self-managed offering: Increase or decrease the number of nodes in the cluster. Rebalancing required
• 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

SingleStore Cloud can be sized up or down using the UI or the Management API. There is no ability to scale out by increasing or decreasing the leaf and aggregator nodes in the cloud offering. In the self-managed offering, horizontal and vertical scaling can occur by updating command-line arguments or the cluster directly. Horizontal scaling does require rebalancing

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.

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