orioledb VS yugabyte-db

hey hn, supabase ceo here

we've been fans of Oriole for a while now and have been long-time supporters

in case you're jumping straight to the comments: OrioleDB is a table storage extension for Postgres. it acts as a drop-in replacement for the default postgres storage engine using the Table Access Method APIs (pluggable storage). the storage engine changes the representation of table data on disk. its architecture is designed to take advantage of modern hardware like SSDs and NVRAM. it implements MVCC, the feature that allows allows multiple connected users to see different versions of the data depending on when their transaction started, via an UNDO log rather than tuple versioning.

one caveat: it requires several patches to the postgres core to expand on the type of features external storage engines extensions can implement. for this reason it could be a while before you see this land as a default engine on supabase. we will probably make it available as an option for customers who want to experiment - no timeline is decided yet.

finally, we have been working with the team on decoupled storage and compute [0]. this is experimental but promising, especially with some recent advances in S3 (specifically Express One Zone [1]). we have a demonstration in the blog post.

i'll message Alexander in case there are any technical questions

[0] https://github.com/orioledb/orioledb/blob/main/doc/usage.md#...

[1] https://aws.amazon.com/s3/storage-classes/express-one-zone/

When I saw "cloud native" I was expecting S3-ish the way Neon does it but they say it's experimental: https://github.com/orioledb/orioledb/blob/beta4/doc/usage.md... and for them to say "beta, don't use in production" and then a separate "experimental" label must make it really bad

There are some interesting things in development to potentially solve that problem.

Here's a recent HN submission about OrioleDB of the more promising ones: https://news.ycombinator.com/item?id=36740921

Source code: https://github.com/orioledb/orioledb

https://github.com/orioledb/orioledb/blob/main/doc/arch.md

> - PostgreSQL is very conservative (maybe extremely) conservative about data safety (mostly achieved via fsync-ing at the right times), and that propagates through the IO stack, including SSD firmware, to cause slowdowns

This is why our first goal is to become pure extension. Becoming part of PostgreSQL would require test of time.

> - MVCC is very nice for concurrent access - the Oriole doc doesn't say with what concurrency are the graphs achieved

Good catch. I've added information about VM type and concurrency to the blog post.

> - The title of the Oriole doc and its intro text center about solving VACUUM, which is of course a good goal, but I don't think they show that the "square wave" graphs they achieve for PostgreSQL are really in majority caused by VACUUM. Other benchmarks, like Percona's (https://www.percona.com/blog/evaluating-checkpointing-in-pos...) don't yield this very distinctive square wave pattern.

Yes, it's true. The square patters is because of checkpointing. The reason of improvements here is actually not VACUUM, but modification of relevant indexes only (and row-level WAL, which decreases overall IO).

I took a look at https://github.com/orioledb/orioledb which is a project attempting to remedy some of Postgres' shortcomings, including MVCC. It looks like they're doing something similar to MySQL with a redo log, as well as some other optimizations. So maybe this is the answer.

You don’t need a database written (or rewritten in Rust). we’re working to make Postgres scalable for the next decade too https://github.com/orioledb/orioledb

> I’d love to see B-Tree primary storage option. Aka store the row data inside the primary index.

It is coming: https://github.com/orioledb/orioledb

sorry to underwhelm!

if you like Neon, then I imagine you like their database branching model? On Friday we announced[0] our 500K investment into OrioleDB, who are working on branching[1], with the plan to upstream these changes into Postgres core.

It would be possible for us to run a fork of Postgres today which supports branching, but our long-term view is that developers would prefer a non-forked version of Postgre (to mitigate any risk of lock-in). So we will work on adding branching to Postgres core in the background, which will be a benefit to the entire Postgres ecosystem.

[0] Announcement:https://supabase.com/blog/supabase-series-b#where-were-going

[1] https://github.com/orioledb/orioledb/wiki/Database-branching

It is possible to apply Batched Nested Loop but with additional code that checks the range of the outer bigint and compare it only if it matches the range of integer. This has been added in YugabyteDB 2.21 with #20715 YSQL: Allow BNL on joins over different integer types to help migrations from PostgreSQL with such datatype inconsistencies.

It can be done like https://github.com/yugabyte/yugabyte-db/ has.

Therefore, interleaving or co-partitioning is probably not necessary, and would reduce agility and scalability more than improving the performance. Unless you have a good reason for it that you can share on Issue #79. But, first, test and tune the queries to see if you need something else.

This is a workaround, not supported. I've opened the following issue to get it solve in the YugabyteDB deployment: https://github.com/yugabyte/yugabyte-db/issues/19389

Note that there may still be a need for bitmaps, especially with disjunctions (OR) as the following is about conjunction (AND), and it can still be implemented, differently than PostgreSQL. This is tracked by #4634.

FROM almalinux:8 as build RUN dnf -y update &&\ dnf groupinstall -y 'Development Tools' # get YugabyteDB sources ARG YB_TAG=2.18 RUN git clone --branch ${YB_TAG} https://github.com/yugabyte/yugabyte-db.git WORKDIR yugabyte-db # install dependencies and compilation tools RUN dnf install -y https://dl.fedoraproject.org/pub/epel/epel-release-latest-8.noarch.rpm RUN dnf -y install epel-release libatomic rsync python3-devel cmake3 java-1.8.0-openjdk maven npm golang gcc-toolset-12 gcc-toolset-12-libatomic-devel patchelf glibc-langpack-en ccache vim wget python3.11-devel python3.11-pip clang ncurses-devel readline-devel libsqlite3x-devel RUN mkdir /opt/yb-build RUN chown "$USER" /opt/yb-build # Install Python 3 RUN alternatives --remove-all python3 RUN alternatives --remove-all python RUN alternatives --install /usr/bin/python python /usr/bin/python3.11 3 RUN alternatives --install /usr/bin/python3 python3 /usr/bin/python3.11 3 # add #include "pg_yb_utils.h" to src/postgres/src/pl/plpython/plpy_procedure.c RUN sed -e '/#include "postgres.h"/a#include "pg_yb_utils.h"' -i src/postgres/src/pl/plpython/plpy_procedure.c # if using python > 3.9 remove #include and #include from src/postgres/src/pl/plpython/plpython.h RUN sed -e '/#include /d' -e '/#include /d' -i src/postgres/src/pl/plpython/plpython.h # add '--with-python', to python/yugabyte/build_postgres.py under the configure_postgres method RUN sed -e "/'\.\/configure',/a\ '--with-python'," -i python/yugabyte/build_postgres.py # Build and package the release RUN YB_CCACHE_DIR="$HOME/.cache/yb_ccache" ./yb_build.sh -j$(nproc) --clean-all --build-yugabyted-ui --no-linuxbrew --clang15 -f release RUN chmod +x bin/get_clients.sh bin/parse_contention.py bin/yb-check-consistency.py RUN YB_USE_LINUXBREW=0 ./yb_release --force WORKDIR / RUN mv /yugabyte-db/build/yugabyte*.tar.gz /yugabyte.tgz

You have seen me using the official YugabyteDB Docker image extensively. This image is suitable for various purposes, including labs, development, testing, and even production. In the past, we used to create it internally due to its seamless integration with our build process. However, some companies prefer to construct the image on their own, which is indeed a commendable practice. After all, it's not advisable to run random images with root privileges on your servers. As a result, we have made a significant alteration by introducing a refined Dockerfile to our Github repository.

Of course, I can guess from the function names, but YugabyteDB is Open Source and I can search for them. What happens here is that I didn't declare a Primary Key for my table and then an internal one (ybctid) is generated, because secondary indexes need a key to address the table row. This ID generation calls /dev/urandom. I made this simple example to show that low-level traces can give a clue about high level data model problems.

To show that it is not difficut to understand what you run, when in a PostgreSQL-compatible database, I'll look at the HammerDB benchmark connected to YugabyteDB. HammerDB has no specific code for it but YugabyteDB is PostgreSQL-compatible (it uses PostgreSQL code on top of distributed storage and transaction).