Hybrid tables

A hybrid table is a Snowflake table type that is optimized for hybrid transactional and operational workloads that require low latency and high throughput on small random point reads and writes. A hybrid table supports unique and referential integrity constraint enforcement that is critical for transactional workloads. You can use a hybrid table along with other Snowflake tables and features to power Unistore workloads that bring transactional and analytical data together in a single platform.

Use cases that may benefit from hybrid tables include:

  • Build a cohort for a targeted marketing campaign through an interactive user interface.

  • Maintain a central workflow state to coordinate large parallel data transformation pipelines.

  • Serve a precomputed promotion treatment for users who are visiting your website or mobile app.


Hybrid tables are integrated seamlessly into the existing Snowflake architecture. Customers connect to the same Snowflake database service. Queries are compiled and optimized in the cloud services layer and executed in the same query engine in virtual warehouses. This architecture has several key benefits:

  • Snowflake platform features, such as data governance, work with hybrid tables out of the box.

  • You can run hybrid workloads that mix operational and analytical queries.

  • You can join hybrid tables with other Snowflake tables, and the query executes natively and efficiently in the same query engine. No federation is required.

  • You can execute an atomic transaction across hybrid tables and other Snowflake tables. There is no need to orchestrate your own two-phase commit.

Unistore architecture

Hybrid tables leverage a row store as the primary data store to provide excellent operational query performance. When you write to a hybrid table, the data is written directly into the row store. Data is asynchronously copied into object storage in order to provide better performance and workload isolation for large scans without affecting your ongoing operational workloads. Some data may also be cached in columnar format on your warehouse in order to provide better performance on analytical queries. You simply execute SQL statements against the logical hybrid table and the Snowflake query optimizer decides where to read data from in order to provide the best performance. You get one consistent view of your data without needing to worry about the underlying infrastructure.


Because the primary storage for hybrid tables is a row store, hybrid tables typically have a larger storage footprint than standard tables. The main reason for the difference is that columnar data for standard tables often achieves higher rates of compression. For details about storage costs, see Understand cost for hybrid tables.


Hybrid tables provide some additional features that are not supported by other Snowflake table types.


Hybrid tables

Standard tables

Primary data layout

Row-oriented, with secondary columnar storage

Columnar micro-partitions


Row-level locking

Partition or table locking

PRIMARY KEY constraints

Required, enforced

Optional, not enforced

FOREIGN KEY constraints

Optional, enforced (referential integrity)

Optional, not enforced

UNIQUE constraints

Optional, enforced

Optional, not enforced

NOT NULL constraints

Optional, enforced

Optional, enforced


Supported for performance; updated synchronously on writes

The search optimization service indexes columns for better point-lookup performance; batch updated/maintained asynchronously

A constraint is enforced when it protects a column from being updated in certain ways. For example, a column that is declared NOT NULL cannot contain a NULL value. An attempt to copy or insert a NULL value into a NOT NULL column always results in an error. For hybrid tables, you cannot set the NOT ENFORCED property on PRIMARY KEY, FOREIGN KEY, and UNIQUE constraints. Setting this property results in an “invalid constraint property” error.

A constraint is required when one or more columns in a table must have such a constraint, which is only true for PRIMARY KEY constraints on hybrid tables.

Determining when to use a hybrid table

While you should expect Snowflake standard tables to offer better performance on large analytical queries, hybrid tables allow for faster results on short-running operational queries. The following types of queries are most likely to benefit from hybrid tables:

  • High concurrency random point reads versus large range reads.

  • High concurrency random writes versus large sequential writes (for example, bulk loading).

  • Retrieval of a small number of entire records (for example, customer object) versus narrow projections with analytical functions (for example, aggregations or GROUP BY queries).

If your queries fit in one of these models, hybrid tables may be the preferred choice for storing your data.