9 posts tagged with "postgresql"

DigitalOcean Managed Databases is a popular starting point for teams that want hosted PostgreSQL, MySQL, MongoDB, or Valkey without managing infrastructure. It does the basics well: provisioning, automated backups, and TLS. But as your data platform matures, the limitations become clear. Four engines, minimal monitoring, no AI-oriented features, no pipeline templates, and no way to export metrics to your own observability stack. FoundryDB offers seven engines, built-in AI presets, predictive autoscaling, database forking, and seven metrics export destinations, all on European infrastructure.

Heroku wound down its free tier in November 2022, and since then the managed Postgres add-on has remained a solid but limited option. If you have been hitting walls with single-engine lock-in, basic monitoring dashboards, no point-in-time recovery on lower plans, and the lack of multi-node high availability, it may be time to move. FoundryDB gives you a full-featured PostgreSQL service (plus six other engines) with automated HA, PITR, predictive autoscaling, and EU-hosted infrastructure, all manageable from a single CLI.

Most database dashboards give you a SQL editor and wish you luck. That works fine when you remember the exact name of the pg_stat_user_tables view or the difference between information_schema.TABLES in MySQL versus PostgreSQL. For everyone else, there is the AI Query Console.

FoundryDB's AI Query Console lets you type a question in plain English, translates it to a database-native SQL query using Claude, and executes it against your running service. The entire flow is read-only by design. You cannot accidentally drop a table by asking a question.

SQL Server licensing costs add up fast. Per-core pricing, Software Assurance renewals, and the looming end of extended support for older versions push teams to look for alternatives. PostgreSQL is the obvious destination, but rewriting thousands of stored procedures, converting T-SQL syntax, and updating every connection string is a project nobody wants to start.

Babelfish for PostgreSQL changes the equation. It adds a SQL Server wire-protocol layer (TDS) on top of PostgreSQL, so your existing applications connect to port 1433 and run T-SQL queries as before. No driver changes. No syntax rewrites. On FoundryDB, you get a fully managed Babelfish instance running on PostgreSQL 16 with backups, TLS, monitoring, and point-in-time recovery built in.

Every managed database service on FoundryDB gets automated backups from the moment it starts running. There is nothing to configure, no S3 bucket to provision, and no cron job to maintain. Backups run daily, are encrypted before they leave the server, and are stored in object storage across a separate availability zone.

But automated daily backups are only half the story. For PostgreSQL, MySQL, and MongoDB, FoundryDB continuously archives write-ahead logs (WAL), binary logs (binlog), or oplogs to enable point-in-time recovery (PITR). This means you can restore to any specific second within a 7-day window, not just to the last daily snapshot.

This post covers how backups work across all engines, how to trigger and restore them, and how to use PITR to recover from the kind of mistakes that daily snapshots cannot fix.

Retrieval-Augmented Generation (RAG) has become the standard approach for grounding LLMs in factual, up-to-date data. Instead of fine-tuning a model on your corpus (expensive, slow, stale within weeks), you retrieve relevant context at query time and feed it to the LLM alongside the user's question.

In 2026, RAG is no longer experimental. It powers customer support bots, internal knowledge search, legal document analysis, and code assistants at thousands of companies. The architecture has stabilized around a common pattern: ingest documents, generate embeddings, store vectors, retrieve at query time. What varies is how well you operate the infrastructure underneath.

This post walks through building a production RAG pipeline on FoundryDB using PostgreSQL with pgvector, Kafka for document ingestion, and Valkey for result caching.

Every database connection costs memory. PostgreSQL forks a backend process per connection (roughly 5-10 MB each), and MySQL allocates per-thread buffers that scale with your buffer configuration. At 200 concurrent connections, the overhead is manageable. At 2,000, you are spending gigabytes of RAM on connection state instead of query execution.

Connection pooling sits between your application and the database, multiplexing many client connections over a small number of backend connections. FoundryDB provides PgBouncer for PostgreSQL and ProxySQL for MySQL, both managed through the API with no manual SSH or config file editing required.

If you run a modern application stack, you probably use at least three different database engines. PostgreSQL for your application data. MongoDB or another document store for unstructured content. Valkey (or Redis) for caching and session storage. Kafka for event streaming. Maybe MySQL for a legacy service that nobody wants to migrate.

Each engine runs on a different managed provider. Each provider has its own dashboard, its own CLI, its own billing, its own alerting system, its own way of handling backups, its own access control model. You pay five bills, manage five sets of credentials, and context-switch between five different interfaces when something goes wrong at 2 AM.

We built FoundryDB to solve this problem: one platform for all your database engines, with a single API, a single dashboard, and a single bill.

Most PostgreSQL workloads are read-heavy. Dashboards, reporting queries, search, and API endpoints that serve cached-but-still-queried data all put pressure on a single primary. Vertical scaling helps, but at some point you are paying for CPU that only writes use while reads compete for the same connections.

Read replicas solve this by giving you additional PostgreSQL nodes that serve read queries from a continuously updated copy of your data. On FoundryDB, adding a replica takes one API call and a few minutes of provisioning time.