patroni VS pgvector

Citus doesn't provide fault tolerance. Each shard is a monolithic PostgreSQL. To reduce downtime on failures, you can protect each shard with a standby database. As this is a complex configuration, Patroni can help. For this lab I'll use the Citus+Patroni docker-compose-citus.yml from https://github.com/zalando/patroni.git:

We have been using stolon + consul for years without issue in our swarm environments. It may also be possible with patroni.

one of the solutions which made it pretty simple for us to run postgresql in a ha environment (mostly in k8s, but works standalone as well) is zalandos patroni: https://github.com/zalando/patroni it's really solid and worked for us for a few years already.

or for k8s their operator: https://github.com/zalando/postgres-operator (docker image: https://github.com/zalando/spilo) we've also tried other operators which were easier to get started, but they failed miserably (crunchyrolls operator is basically based on the zalando one)

Hello. I am currently using this docker-compose model from Zalando repository. It does not include PgBouncer in its architecture by default. I've been trying to find a containerized implementation involving Patroni, HAProxy, Etcd and PgBouncer. I didn't find anything solid so far.

General purpose: Patroni - Set up your own etcd + HAProxy + Patroni + Postgres components and it'll generally manage itself after that.

I don't have a great answer for you except that it sounds like you are trying to create your own version of patroni. Is there a good reason to not just use patroni?

git clone https://github.com/zalando/patroni.git cd patroni docker build -t patroni . docker-compose up -d

I actually used Patroni on Openshift in my company. To be update with the latest version we created our helm chart.

# Install Postgres and pgvector !apt-get update && apt install postgresql postgresql-server-dev-14 !git clone --branch v0.6.2 https://github.com/pgvector/pgvector.git !cd pgvector && make && make install # Start database !service postgresql start !sudo -u postgres psql -U postgres -c "ALTER USER postgres PASSWORD 'pass';"

When talking about Vector Databases, in the market we can find the specialized ones and multi-model, most of the major database providers like Oracle, PostgreSQL or MongoDB, for mention some of them, have integrated a specific solution to retrieve vector data.

For this example, I wanted to focus on how pgvector  – an open-source vector similarity search for Postgres – can be used to identify data similarities that exist in enterprise data.

pgvector supports dense vector search well, but it does not have plan to support sparse vector.

There's an issue in the pgvector repo about someone having several ~10-20million row tables and getting acceptable performance with the right hardware and some performance tuning: https://github.com/pgvector/pgvector/issues/455

I'm in the early stages of evaluating pgvector myself. but having used pinecone I currently am liking pgvector better because of it being open source. The indexing algorithm is clear, one can understand and modify the parameters. Furthermore the database is postgresql, not a proprietary document store. When the other data in the problem is stored relationally, it is very convenient to have the vectors stored like this as well. And postgresql has good observability and metrics. I think when it comes to flexibility for specialized applications, pgvector seems like the clear winner. But I can definitely see pinecone's appeal if vector search is not a core component of the problem/business, as it is very easy to use and scales very easily

The Spring AI project aims to streamline the development of applications that incorporate artificial intelligence functionality without unnecessary complexity. On this example we use features like: Embedding, Prompts, ETL and save all embedding on PGvector(Postgres Vector database)

Official GitHub repository of the pgvector extension

pgvector 0.6.0 was just released and will be available on Supabase projects soon. Again, a special shout out to Andrew Kane and everyone else who worked on parallel index builds.

The pgvector extension adds vector similarity search capabilities to your PostgreSQL database. To use the extension, you have to first create it in your database. You can install the extension, by connecting to your database and running the CREATE EXTENSION command from the psql command prompt: