The per-county datasets range in size from very small (a few MB) to large (several GB) based on the size of the county and the number of attributes available. Likewise, the state datasets vary in size. The national dataset is quite large (100GB+ compressed, 250GB+ on disk), and performance challenges can arise even on modern database engines and modern hardware (as of 2026) and Cloud VMs.

Careful consideration needs to be made depending on your use case for the dataset, and what performance requirements you have.

5.1. Analytics Use Cases

For analytics-heavy use cases, BigQuery, DuckDB PostgreSQL + PostGIS are known to work well with this dataset. The more RAM you have the better: we recommend 128GB minimum.

At 155+ million rows, the dataset is on the upper end of what you’d normally expect a standard PostgreSQL installation to handle in an analytical usage mode, but it is known to work well with the right hardware and postgresql.conf settings.

No matter the use case, make sure the data is properly indexed / clustered on the geoid and geom columns.

5.2. Online serving use cases

For online map and query serving use cases, PostgreSQL is known to work well with this dataset.

If hosting on a single VM or physical server, the following hardware and database configurations are recommended:

5.2.1. Hardware Recommendations

• At least a 4th gen Xeon or 4th gen Epyc processor with many cores
• At least 64GB of the fastest RAM supported by your system
• A fast SSD. NVMe drives designed for CDN content-serving with high read rates tend to work well.

5.2.2. Cloud VMs

• Take special care to provision sufficient disk IOPS for usage of this dataset. This may require provisioning a larger-capacity drive than strictly necessary for data storage.
• SSD persistent disk is strongly recommended.
• Likewise for the hardware recommendations, at least 64GB of RAM is recommended for best performance for most use cases.

5.2.3. PostgreSQL configuration

• Version 17+
• Utilize partitioning where feasible, e.g. partition table by state or county.
• Configure hugepages if supported by your OS.

Partition by state/county:

create table lr_parcel ( … ) partition by list(geoid);