Hydra is a high-performance Postgres database designed for serverless online analytical processing (OLAP) and online transaction processing (OLTP). To enable realtime, serverless analytics on Postgres’ simply create an analytics schema in seconds.

Hydra makes it easier to build realtime analytics on time series data by automatically managing the transactional rowstore and analytical columnstore together. Inserting data into an analytics table will automatically convert it into an analytics-optimized columnar format.

Use Hydra to eliminate the costs and latency of moving and transforming data between a row and external columnstore with data pipelines (ETL).

Hydra’s serverless analytical processing removes the possibility of resource contention with Postgres’ transactional rowstore. Data stored in analytics tables benefit from efficient data compression of 5-15X and is ideal for large data volumes (for example: 150GB becomes 15GB with a 10X compression rate).

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Postgres as a multi-workload data store

Hydra is a “multi-workload data store” with the following core capabilities:

  • high throughput, ACID-compliant online transactional processing (OLTP)

  • vectorized, parallel execution of online analytical processing (OLAP)

With Hydra transactional and analytical operations can be employed independently and in concert.

It removes the complexity and latency bottlenecks associated with running multiple heterogenous datastores with different data formats, schemas, syntax, and features.

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Analytics schema overview

  • Isolated Compute Tenancy

Each user is allocated their own process which is an isolated piece of compute designed this way to lessen contention between users, which is a common challenge for analytics.

  • Serverless processing

Hydra automatically parallelizes the work that you send to it. As a result, scheduling multiple queries at-a-time does not meaningfully increase throughput, as Hydra has already parallelized the workload across all available resources. Dedicating serverless processes to analytics means there is little to no resource impact on Postgres’ transactional workloads.

  • Sharing

Hydra’s analytics schema separates storage and compute, which means that one user cannot see another’s writes into a database until that database is updated to that user. Hydra can spin up read scaling replicas, which are zero-copy clones of the main database for read-only use, enabling high scalability of analytics workloads.

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