Distributed Workloads
ParadeDB is designed to scale vertically on a single Postgres primary, and many production deployments comfortably operate in the 1–10TB range. Enterprise deployments can add read replicas for ParadeDB queries. The largest single ParadeDB database we’ve seen in production is 10TB. For datasets that significantly exceed this scale, ParadeDB supports partitioned tables and can be deployed in sharded Postgres configurations. ParadeDB is fully compatible with Citus for distributed search workloads — you can create ParadeDB indexes on distributed tables and run search queries across shards. See our Citus deployment guide for more details. If you’re working with very large datasets, please reach out to us. We’d be happy to provide guidance and share our roadmap for future distributed query support.Covering Index
The ParadeDB index is a covering index, which means it stores all indexed columns inside a single index per table. This decision is intentional — by colocating all the relevant data, ParadeDB optimizes for fast reads and boolean conditions. However, this means that all columns must be defined up front at index creation time. Adding or removing columns requires aREINDEX.
DDL Replication
A commonly known limitation of Postgres logical replication is that DDL (Data Definition Language) statements are not replicated. This includes operations likeCREATE TABLE or CREATE INDEX.
If ParadeDB is running as a logical replica of a primary Postgres, DDL statements from the primary must be executed manually on the replica.
We recommend version-controlling your schema changes and applying them in a coordinated, repeatable way — either through a migration tool or deployment automation — to keep source and target databases in sync. See the logical replication guide for more details.