Benchmarking is hard. Benchmarking databases, harder. Benchmarking databases that follow different approaches (relational vs document) is even harder. But the market demands these kinds of benchmarks. Despite the different data models that MongoDB and PostgreSQL expose, many organizations face the challenge of picking either technology. And performance is arguably the main deciding factor. Join this talk to discover the numbers! After $30K spent on public cloud and months of testing, there are many different scenarios to analyze. Benchmarks on three distinct categories have been performed: OLTP, OLAP and comparing MongoDB 4.0 transaction performance with PostgreSQL's. What would be faster, MongoDB or PostgreSQL?

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MONGODB vs POSTGRESQL
BENCHMARKS
Álvaro Hernández

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`whoami`
Álvaro Hernández <aht@ongres.com>
@ahachete
● Founder & CEO, OnGres
● 20+ years PostgreSQL user and DBA
● Mostly doing R&D to create new, innovative
software on Postgres
● Frequent speaker at PostgreSQL, database
conferences
● Principal Architect of ToroDB
● Founder and President of the NPO
Fundación PostgreSQL

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Introduction

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OnGres Ethics Policy
This work was sponsored by EnterpriseDB, and performed by OnGres.
It was conducted according to OnGres Ethics Policy, that observes that:
● All the work is conducted with the maximum degree of professionalism
and independence.
● No technology is favored over another.
● No results are edited or omitted.
● The sponsor of the work does not intervene in the strategy,
implementation or execution of the work.
● Results are verifiable by external, third parties.

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Benchmarking is hard
● Bench-marketing is easy --but not trustable.
● Benchmarking is hard.
● Benchmarking databases is harder.
● Benchmarking databases that follow different design models, is
even harder.
● Are MongoDB and PostgreSQL comparable?
● The market demands this: informed decision, performance is key.

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Pursuing benchmarking fairness
How to present a fair arena in which the technologies compete in an
apples-to-apples scenario?
● Transparency and reproducibility. Infrastructure-as-Code.
https://gitlab.com/ongresinc/benchplatform/
https://gitlab.com/ongresinc/txbenchmark/
http://benchplatform.ongres.com.s3.amazonaws.com/
● Multiple benchmarks
● Close-to-real workloads
● Production-grade setups

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Types of benchmarks
Three main benchmark categories:
● Transactions benchmark
● OLTP
○ In-memory dataset (4GB)
○ Larger, on-disk dataset (2TB)
● OLAP

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The contenders
MongoDB 4.0
● Community version used
● Journaling active
● Replication active
(single node)
● No further tuning required
PostgreSQL 11
● Self-managed instance
● Basic production tuning
● Tested w/ and w/o
PgBouncer

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Architecture: client-server, running on AWS
Data volume: io1, with number of reserved IOPS depending on the test.

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Benchmarks:
Transactions

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Previous discussion: isolation levels

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Benchmark description
● Custom-developed benchmark.
● Inspired by MongoDB post about MongoDB transactions.
● Simulates air reservation system: check flight schedule, plane
availability, purchase, audit log.
● Tx with query, insertions and upsert.
● Programmed in Java. Official JDBC and MongoDB drivers.
● Open source: https://gitlab.com/ongresinc/benchplatform

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Transaction performance

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Transaction performance

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Transaction retries

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PostgreSQL latency (@ SERIALIZABLE)

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MongoDB latency

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Benchmarks:
OLTP

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Benchmark description
● Industry standard Sysbench was used.
● Supports PostgreSQL and MongoDB (via sysbench-mongodb-lua).
● Resembles real-world OLTP workload.
● Different dimensions benchmarked:
○ Dataset size: fit in memory (4GB), on-disk (2TB)
○ Read/write work split: 50/50, 95/5.
○ Filesystems: XFS or ZFS.
○ Different levels of concurrency.

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Discussion about PostgreSQL Connection Pooling
● PostgreSQL best practice is to run always behind a connection pool.
● PostgreSQL proved to be highly sensitive to the number of connections,
with degraded performance when overwhelmed.
● A connection pool offers close-to-ideal performance for almost any
workload.
● MongoDB does not require connection pooling:
is included in drivers.

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Performance: dataset in memory

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PG connection pooling effect (dataset in memory)

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Performance: dataset on disk

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Benchmarks:
OLAP

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Benchmark description
● JSON dataset (GitHub Archive). Native to MongoDB, jsonb in PG.
● 2015 dataset: 212M records (340GB on PG, 206GB on MongoDB).
● Timing of OLAP-style queries that resemble BI natural questions:
○ Repositories ordered by most opened issues.
○ Most frequent git event types, ordered.
○ Top 10 most active actors.
○ Repositories that have more than two comments and a specific
event type, ordered.

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Query A in PostgreSQL and MongoDB

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Results

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QUESTIONS?
Álvaro Hernández
<aht@ongres.com>
@ahachete / www.ongres.com
Thank you