PlanetScale vs Amazon Aurora benchmarks

This page includes benchmarks that compare the performance of Postgres on PlanetScale with Postgres on Aurora, along with all of the resources needed to reproduce these results. We also recommend reading our Benchmarking Postgres blog post, which covers the methodology used in these benchmarks and the steps taken to maintain objectivity. We invite other vendors to provide feedback.

Benchmark configuration

All benchmarks described here were run with the following configuration:

• Configuration: All Postgres configuration options left at each platform's defaults, except for connection limits and timeouts which may be modified to facilitate benchmarking.
• Benchmark machine: Benchmarks were executed from a c6a.xlarge in us-east-1, the same region as both databases.

TPCC Benchmarks

TPCC is a widely-used benchmark to measure general-purpose OLTP workload performance. This includes, selects, inserts, updates, and deletes.

Benchmark data: A TPCC data set generated with TABLES=20 and SCALE=250 using the Percona sysbench-tpcc scripts. This produces a ~500 gigabyte Postgres database. You can replicate the data following these instructions.

Benchmark execution: Using the Percona tpcc scripts running a load with 100 simultaneous connections. We run the load on each database for 5 minutes (300 seconds).

Queries per second

Our first benchmark measures queries per second (QPS) at 32 connections and 64 connections, revealing that PlanetScale performs much better:

Click the graphs in the sidebar to toggle the number of connections. The PlanetScale database averaged ~18,000 QPS. Aurora averaged ~12,000 QPS.

p99 latency

We also measured the p99 latency for the duration of the benchmark run (lower is better):

Despite both being in us-east-1, PlanetScale shows much lower latency due to locally-attached NVMe drives with unlimited IOPS, 8th-generation AArch64 CPUs, and high-performance query path infrastructure.

OLTP benchmarks

In addition to TPCC, we run the OLTP Read-only sysbench benchmark. OLTP workloads tend to be 80%+ reads, and this benchmark allows us to isolate performance for such queries.

Benchmark data: A simple OLTP data set generated with TABLES=10 and SCALE=130000000 using standard sysbench. This produces a ~300 gigabyte Postgres database. You can find instructions for replicating this data here.

Benchmark execution: Using the standard sysbench tool using the oltp_read_only and oltp_point_selects benchmarks. You can find instructions for replicating this benchmark here.

Queries per second

This benchmark contains only SELECT queries, including ones with range scans and aggregations.

The PlanetScale database averaged ~35,000 QPS. Aurora also averaged around ~35,000 QPS. However, PlanetScale provides a much more consistent result, with less dips in performance.

p99 latency

While running this benchmark, we measured the p99 latency of queries (lower is better):

PlanetScale offers significantly better consistency, which is desirable for predictable performance.

Query-path latency

We measured pure query-path latency by running SELECT 1; 200 times on a single connection. This tests the overhead of any database query.

Results compare PlanetScale + PSBouncer, standard PlanetScale connection, direct-to-Postgres on PlanetScale, and a direct connection to Aurora. Lower is better.

Direct connections to both Postgres and Aurora are very fast. Adding in PSBouncer and other proprietary PlanetScale networking technology add latency, but add benefits of query routing, buffering, and other features. (Note: These were all same-AZ tests).

Cost

A PlanetScale M-320 with 929GB of storage costs $1,399/mo. This includes three nodes with 4 vCPUs and 32GB RAM each, one primary and two replicas. Replicas can be used for handling additional read queries and for high-availability. The benchmark results shown here only utilized the primary.

For demanding IO workloads like this, Aurora IO optimized is usually lower-cost than standard pricing, so we choose that pricing model. A single db.r8g.xlarge costs $516/mo, and 929GB of storage costs $209/mo. To match the capabilities and availability of the 3-node PlanetScale M-320, we must add two replicas. This would lead to a $516 * 3 + $209 = $1,754/mo.

PlanetScale offers better performance at a lower cost for applications that require high availability and resiliency.