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https://github.com/edgedb/imdbench
IMDBench — Realistic ORM benchmarking
https://github.com/edgedb/imdbench
benchmark edgedb orm
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IMDBench — Realistic ORM benchmarking
- Host: GitHub
- URL: https://github.com/edgedb/imdbench
- Owner: edgedb
- License: apache-2.0
- Created: 2018-08-02T16:50:21.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2024-06-06T20:27:10.000Z (7 months ago)
- Last Synced: 2024-09-14T02:33:51.774Z (3 months ago)
- Topics: benchmark, edgedb, orm
- Language: Python
- Homepage: https://edgedb.github.io/imdbench
- Size: 64.5 MB
- Stars: 234
- Watchers: 15
- Forks: 24
- Open Issues: 20
-
Metadata Files:
- Readme: README.rst
- License: LICENSE
Awesome Lists containing this project
README
.. image:: banner.jpg
.. raw:: html
IMDBench
Benchmarking ORMs with realistic queries
``Rev. 1.0``
A benchmark intended to compare various Python and JavaScript
ORMs with realistic queries required for a hypothetical IMDB-style movie
database application.
Why is this needed? 🧐
---------------------
Object-relational mapping (ORM) libraries solve some important usability
issues with relational databases and continue to grow in popularity. However,
they come with important performance tradeoffs that are often poorly
understood or quantified. The question of ORM performance is more complex than
simply "they generate slow queries".
- **Query splitting ⛓**
It's common for ORMs to perform non-trivial operations (deep fetching,
nested mutation, inline aggregation, etc) by opaquely executing several
queries under the hood. This may not be obvious to the end user.
- **Aggregation (or lack thereof) 🪣**
Less mature ORMs often don't support functionality like aggregations
(counts, statistics, averages, etc), forcing users to overfetch and perform
these calculations server-side. Some ORMs provide no aggregation
functionality at all; even advanced ORMs rarely support relational
aggregations, such as ``Find the movie where id=X, returning its title and
the number of reviews about it.``
- **Transactional queries 🏦**
Since ORM users must often run several correlated queries in series to
obtain the full set of data they need, the possibility for
hard-to-reproduce data integrity bugs is introduced. Transactions can
alleviate these bugs, but this rapidly places unacceptable limits on read
capacity.
Most existing benchmarks use queries that are
`too `_
`simplistic `_
to capture these performance characteristics.
Methodology 👷
-------------
Our goal with this benchmark is to quantify the **throughput** (iterations/
second) and **latency** (milliseconds) of a set of **realistic** CRUD queries.
These queries are not arcane or complex, nor are they unreasonably simplistic
(as benchmarking queries tend to be). Queries of comparable complexity will be
necessary in any non-trivial web application.
Simulated server-database latency 🐇
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
The execution environment simulates a *1 millisecond* latency between the
server and database. This is the
`typical latency `_
between zones in a single AWS region. The vast majority of applications do not
have the resources to support per-availability-zone replication, so this
assumption is reasonable.
With serverless architectures, it's common for server code to run inside
Lambda-style functions in a different availability zone from the underlying
database, which would incur latencies far greater than 1ms.
On Linux, this latency can be simulated with ``tc`` like so:
.. code-block::
sudo tc qdisc add dev br-webapp-bench root netem delay 1ms
Dataset 🍿
^^^^^^^^^
We are simulating an `IMDB `_-style movie database
website. The sample dataset consists of 25k movies, 100k people, 100k users, and 500k reviews.
.. image:: docs/schema.png
:target: dbschema/default.esdl
The schema consists of four tables/models/types:
- ``Person`` (used to represent the cast and crew)
- ``Movie``
- ``directors -> Person`` (to many, orderable with ``list_order``)
- ``cast -> Person`` (to many, orderable with ``list_order``)
- ``User``
- ``Review``
- ``author -> User`` (to one)
- ``movie -> Movie`` (to one)
Queries
^^^^^^^
The following queries have been implemented for each target.
- ``insert_movie`` Evaluates *nested mutations* and *the ability to insert and
select in a single step*.
Insert a ``Movie``, setting its ``cast`` and ``directors``
with pre-existing ``Person`` objects. Return the new ``Movie``, including
all its properties, its ``cast``, and its ``directors``.
.. raw:: html
View query
with
new_movie := (
insert Movie {
title := <str>$title,
image := <str>$image,
description := <str>$description,
year := <int64>$year,
directors := (
select Person
filter .id = (<uuid>$d_id)
),
cast := (
select Person
filter .id in array_unpack(<array<uuid>>$cast)
),
}
)
select new_movie {
id,
title,
image,
description,
year,
directors: { id, full_name, image } order by .last_name,
cast: { id, full_name, image } order by .last_name,
};
- ``get_movie`` Evaluates *deep (3-level) fetches* and *ordered
relation fetching*.
Fetch a ``Movie`` by ID, including all its properties, its
``cast`` (in ``list_order``), its ``directors`` (in ``list_order``), and its
associated ``Reviews`` (including basic information about the review
``author``).
.. raw:: html
View query
with m := Movie
select m {
id,
image,
title,
year,
description,
avg_rating,
directors: {
id,
full_name,
image
} order by @list_order empty last
then m.directors.last_name,
cast: {
id,
full_name,
image,
} order by @list_order empty last
then m.cast.last_name,
reviews := (
select m.<movie[is Review] {
id,
body,
rating,
author: {
id,
name,
image,
}
} order by .creation_time desc
)
}
filter .id = <uuid>$id;
- ``get_user`` Evaluates *reverse relation fetching* and *relation
aggregation*.
Fetch a ``User`` by ID, including all its properties and 10 most recently
written ``Reviews``. For each review, fetch all its properties, the
properties of the ``Movie`` it is about, and the *average rating* of that
movie (averaged across all reviews in the database).
.. raw:: html
View query
select User {
id,
name,
image,
latest_reviews := (
select .<author[is Review] {
id,
body,
rating,
movie: {
id,
image,
title,
avg_rating := math::mean(.<movie[is Review].rating)
}
}
order by .creation_time desc
limit 10
)
}
filter .id = <uuid>$id;
Results 📊
---------
The graphs below present the throughput/latency results for each target as a
geometric mean of the three queries. As such, it should be interpreted as a
holistic benchmark that represents the target library's collective performance
across a range of query functionality.
👀 For per-query results, interactive charts, and latency jitter statistics,
view the full report!
JavaScript ORMs [`Full Report `_]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
The libraries tested are:
- `Prisma `_
- `TypeORM `_
- `Sequelize `_
- `EdgeDB (TypeScript query builder) `_
.. image:: ./docs/js_thru.png
:target: https://edgedb.github.io/imdbench/js.html
:alt: JavaScript ORM throughput results
.. image:: ./docs/js_lat.png
:target: https://edgedb.github.io/imdbench/js.html
:alt: JavaScript ORM latency results
Python ORMs [`Full Report `_]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
The libraries tested are:
- `Django `_
- `SQLAlchemy `_
- `EdgeDB (Python client) `_
.. image:: ./docs/py_thru.png
:target: https://edgedb.github.io/imdbench/py.html
:alt: Python ORM throughput results
.. image:: ./docs/py_lat.png
:target: https://edgedb.github.io/imdbench/py.html
:alt: Python ORM latency results
Raw SQL [`Full Report `_]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
For comparison, below are the benchmark results for a tuned
PostgreSQL implementation of the benchmark queries, executed using popular
Postgres drivers:
- `asyncpg `_
- `psycopg2 `_
- `go-pgx `_
- `node-postgres `_
For reference the EdgeDB results (using the `Python client
`_) are also included.
.. image:: ./docs/sql_thru.png
:target: https://edgedb.github.io/imdbench/sql.html
:alt: SQL throughput results
.. image:: ./docs/sql_lat.png
:target: https://edgedb.github.io/imdbench/sql.html
:alt: SQL latency results
.. raw:: html
..
Most drivers cannot properly decode the results of queries
containing array-aggregated subqueries. This is necessary to retrieve usable
results from deep queries in a performant way; as such, certain benchmark
operations are implemented as 2+ serially-executed queries. By contrast,
``asyncpg`` (which was originally designed as the Postgres interface for
EdgeDB) can express all operations as a single highly-optimized SQL query
and represents the high-water mark for this benchmark.
Analysis
--------
Predictably, ORMs perform poorly on this benchmark relative to EdgeDB or raw
SQL, both of which can express more complex operations in a single query.
However, *the goal of this benchmark is not to pick on ORM libraries*. ORMs
provide a solution (albeit a limited one) to some of very real usability
issues with relational databases.
1. They can express deep or nested queries in a compact and intuitive way.
Queries return objects, instead of a flat list of rows that must be
manually denormalized.
2. They allow schema to be modeled in a declarative, object-oriented way.
3. They provide idiomatic, code-first data fetching APIs for different
languages. This is particularly important as statically typed languages
like Go and TypeScript gain popularity; the ability of ORMs to return
strongly-typed query results in a DRY, non-reduntant way is increasingly
desirable.
It is a valid decision to prioritize developer experience over performance,
especially when your expected traffic is low. However, the limitations of ORMs
can be crippling as an application scales in complexity and traffic.
The third way
^^^^^^^^^^^^^
Our goal in designing EdgeDB is to provide a third option that combines the
best of all worlds. Using EdgeDB feels like using an ORM: your schema is
declarative, your queries return structured objects, and you don't need to
deal with the hairiness of SQL. (If you're using our `TypeScript query builder
`_, you
even get full static typing.) But, vitally, you can reap these benefits
without the sacrificing performance or the power of a full-fledged query
language.
.. list-table::
* -
- ORMs
- SQL
- EdgeDB
* - Intuitive nested fetching
- 🟢
- 🔴
- 🟢
* - Declarative schema
- 🟢
- 🔴
- 🟢
* - Structured query results
- 🟢
- 🔴
- 🟢
* - Idiomatic APIs for different languages
- 🟢
- 🔴
- 🟢
* - Comprehensive standard library
- 🔴
- 🟢
- 🟢
* - Computed properties
- 🔴
- 🟢
- 🟢
* - Aggregates
- 🟡
- 🟢
- 🟢
* - Composable subquerying
- 🔴
- 🔴
- 🟢
Visit `edgedb.com `_ to learn more.
Running locally
---------------
Follow the instructions in the `Run Locally `_ guide to execute
these benchmarks on your local machine.
License
-------
Apache 2.0