This article demonstrates how to run Oracle True Cache locally with docker-compose for testing, local development, and experimentation. We'll also enable the True Cache Redis API and verify it with redis-cli. In the compose script, one container serves as the primary database, while the second is the True Cache read-only replica. Both the primary and True Cache databases use the…

When an agent finishes a task, the next session usually starts from zero. A transcript may exist somewhere, but no useful handoffs, preferences, or decisions make it into persistent, searchable memory. A transcript records what happened. Durable memory is a smaller, curated set of information selected for a purpose, with a source, an access scope, and rules for correction and deletion.

UCP metrics are database-client metrics from Oracle UCP (Universal Connection Pool), Oracle’s JDBC connection pool. Like HikariCP, UCP exposes pool-specific metrics: current and maximum connections, callers waiting for a connection, acquisition time, and pool lifecycle activity. These metrics help separate a slow query from a slow path to a query. When an application exhausts its pool, waits to acquire connections, or accumulates latency before calls reach Oracle AI Database, UCP metrics show where to investigate.

Implement row and column level RBAC with Deep Data Security: flexible, server-side security context with user grants.

Before you can finish evaluating a library or framework, an AI coding assistant can reinvent a custom implementation for you. In the moment, that can be astonishingly productive. LLMs are exceptional at reproducing prior art, which creates a tendency to favor bespoke software. Generating a new tool can feel faster than understanding, adopting, and configuring an existing one. This is a version of the…

Oracle True Cache is a mostly diskless, read-only cache and replica that stays synchronized through redo apply: Only frequently accessed blocks reside in memory. With True Cache, read-heavy queries can be offloaded to the cache replica, improving performance on the primary. The cache keeps the same client, database model, and SQL interface as your primary database, allowing you to serve cache-friendly reads without another system or translating cache-specific objects.

If you've built software for a while, you've probably seen a similar architecture diagram: database for transactions/relational data, Redis for caching, Kafka for events, OpenSearch for full-text and keyword search, object storage as a data lake, and maybe a vector database for AI integration. There's nothing wrong with these choices: each technology is good at its job. For example, you'll find countless articles, papers, and engineering blogs extolling the virtues of Apache Kafka for asynchronous, service-to-service messaging.

Agent memory is becoming one of the most important ways to differentiate an AI application. It's getting much cheaper to turn a good idea into a working app. A small, skilled team with capable models and modern coding tools can get surprisingly far in a couple days. Increasingly, features that took months and a specialized team are faster to prototype, and faster for competitors to copy.

Real-time, multi-model data processing (and many microservices workflows) often involves transactions across several systems: an event stream, relational history, location data, vector search, and more. When a failure occurs, it can be difficult to reason about: Which work committed? Which message was published? What now needs compensating? In this code example, Oracle AI Database handles relational data, transactions, event streaming, spatial queries, vector search, and AI inference from one platform.

I remember developing my first reactive application years ago, not too long out of college, using Java 8 and Spring WebFlux. When building the app, it was a seemingly simple decision to use reactive programming: our app dispatched many concurrent HTTP requests to downstream webhooks, so a non-blocking, asynchronous framework would provide the best performance. At the time, reactive programming was also…

A multi-model database is a database that handles multiple data types, like vectors, graphs, documents, relational data, and more - in a single, converged engine. Multi-model databases are most valuable when multiple data types are closely related and must be queried transactionally. When working with real-world, multi-model data, I've found this to often be the case.

Select AI uses natural-language prompts to generate, run, inspect, and explain your data. Actions such as showsql, runsql, explainsql, and narrate allow you to review a generated query before execution, understand how it was formed, or turn results into a readable response. Select AI runs in the database, close to your data, making it uniquely suited for natural language to SQL workflows.

Connection pool examples usually start with pool sizing and end with a happy-path query. If you're building a production-grade service with Oracle UCP (Universal Connection Pool), that might miss the point: Your connection pool includes a vast array of features, covering failover, database resident connection pooling, caching, and more. In this article, we'll walk through how to configure several common Oracle UCP scenarios in Spring Boot.

Sometimes we don't need the most powerful frontier model. Sometimes we just need a local model that's good enough. Depending on the agent harness, local models are also great for data privacy! In this article, we'll use Ollama to install and use gpt-oss:20b for local inference and agent workloads. Install Ollama Ollama is easy to install on most platforms. Simply visit the…

When we talk about agent memory, we look to compose memory formation, retrieval, and persistent storage. You've probably seen an agent like Claude Code or Codex re-read the same file it just read. Most agents do not have durable memory by default. They have context for the current run, but not persistent, managed experience across runs.

When repository calls fail, JPA exceptions may be ungracefully thrown, causing your app to return HTTP 500 codes, and fail to appropriately respond to the error. Your app log most likely has the real error code (like an ORA- code) buried in a stack trace, but no action was taken on that error. However, wrapping every repository call in try/catch…

Get more out of your coding agents as an Oracle AI Database developer with skills, MCP, and an understanding of best practices.

Implementing client-side row and column level RBAC is a fun challenge. But what if you handle everything server-side? That's exactly the problem Deep Data Security solves for Oracle AI Database. Deep Data Security in Oracle AI Database is designed so authorization resides in the database, tied to an end user, their data roles, and runtime context. Policies automatically apply security filters, with no client-side intervention.

Still on Spring Boot 3.x, or even 2.7 in production? Spring Boot 4 is more than a framework upgrade: it’s a chance to reduce dependency weight, improve startup behavior, modernize your Java runtime, and selectively evaluate GraalVM for cold-start-sensitive services. This guide lays out a practical upgrade path across Spring Boot 4, Java 25, and native images, with a focus on measurable performance gains and migration risk.

Most bad agent output isn't too mysterious. Usually, the agent is missing the proper shape of your system. Your Oracle AI Database work may be split into live schemas, distributed apps, views, grants, SQL dialect details, generated DDL, docs, and the few sentences in your head about what the app actually does. In this article we'll look at using agents intelligently to develop with Oracle AI Database.

Many Oracle guides are written for DBAs or long-time developers, and as a newcomer it feels like you’re expected to already know the jargon. I started in the same place, learning the as I went, and…

The langchain-oracledb package makes it easy to integrate LangChain concepts with your Oracle AI Database instance. In this article, we'll explore the langchain_retrieval sample, using langchain-oracledb for content retrieval. The sample composes various langchain-oracledb classes, including to load, split, save, and retrieve content. This post is a companion article to my prior post, LangGraph persistence with Oracle AI Database…

Key Takeaways Oracle AI Database can be used as a persistence layer for LangGraph. Agent workflows need durable state to pause and resume for real decisions. OracleSaver checkpoints graph execution state so the workflow can resume using the same thread_id. A checkpoint preserves workflow state and resume history; an approved record is application data that can be queried or audited later.

Key Takeaways Database agent memory should be modeled as durable application data, not just as embeddings in a vector table. Oracle AI Database can combine vectors, relational columns, JSON metadata, Oracle Text, property graphs, and timestamps in one memory system. Hybrid retrieval is stronger than vector search alone because it can match meaning, exact identifiers, entities, relationships, recency, and importance.

Key Takeaways Oracle AI Database is worth a serious look for small and midsize teams when one application needs relational data, JSON, search, vectors, spatial queries, REST endpoints, and internal app tooling without operating a stack of separate services. In this case, it may be the best database for your needs. The free path lets you verify it: local containers, FreeSQL, SQLcl, SQL Developer, and Always Free Autonomous AI Database let you prove before paying.

Did you know you can locally test Oracle REST Data Services (ORDS) with free Oracle AI Database resources? If you rely on mock servers (or don't run automated tests at all!), you're dealing with a thin layer of comfort. Using container-driven ORDS tests can prove your app starts, connects to ORDS, and uses database APIs like the MongoDB wire-compatibility API. All with a local stack your test created.

In my own work, I found most coding agents weren't generating high-quality code for Oracle AI Database's Kafka Java API (OKafka). You can get results, but they're not idiomatic, and miss subtleties. This is why I created the okafka-java-code oracle agent skill, based off my hand-written Kafka Java API examples. Agent skills can greatly enhance code generation for Oracle AI Database apps, and this skill encapsulates solutions to the problems I kept hand-coding: how to authenticate with OKafka, how to use transactions, how to create topics, and how to use Oracle-specific serialization.

The code for this article on multi-model database patterns lives in the oracle-database-code-samples repo, under the support-ticket-intelligence sample.  When I add a graph database to an application, I'm not just adding a query feature. I need to understand how the database is set up, how its schema works, how to operate it, how to monitor it, and how to move data across service boundaries.

Can your MongoDB client be used against a document-shaped contract while the system of record stays relational? JSON Relational Duality Views expose document APIs over relational data. Those same document APIs accessed through Oracle REST Data Services' (ORDS) MongoDB-compatible API allow any MongoDB client to work with what is essentially transactional, relational data. In this article, we'll implement exactly that, walking through the…

Developers need the shortest path from claim to proof. Can I start a database locally? Can I connect from my app? Can I run my tests against it? Can I inspect the schema without guessing? Can I use it with scripts, agents, and CI? Can I easily move from a laptop to a managed cloud database? In this article, we'll look at tools that help you shorten the feedback loop for the development process you're trying to prove.

Spatial data is a database feature that sounds niche right up until you need it. When your application must answer questions like “what landmarks fall inside this map window?”, “what is the exact…

I often talk about converged, multi-model databases from the perspective of developer productivity. Owning one platform that handles many data types like relational, JSON, graph, vector yields fewer moving parts when building and operating apps. Recently, another reason is more and more compelling: security. AI-assisted vulnerability discovery is moving incredibly fast. Anthropic's Project Glasswing describes frontier models finding and exploiting software vulnerabilities at a level that…

Using Oracle AI Database Pluggable Databases (PDBs) to give each Spring Boot service its own clear data boundary. Key Takeaways Learn database-per-service design with separate studentpdb and coursepdb PDBs. Each Spring Boot service owns its own schema, tables, JDBC URL, and connection pool. The runner composes student and course data through HTTP APIs, without shared schemas or cross-PDB joins. The project can be run with Testcontainers or manually with Docker and Maven.

Skills are reusable, task-specific workflows for your agents: each skill is a directory centered on a SKILL.md file, with optional scripts,references, and assets packaged together. Skills bring context efficient capabilities to agents in a reliable, repeatable manner. In this article, we’ll look at what skills are, how to install them, and how the open source oracle-db-skills repo can help your agents do real work with Oracle AI Database.

One of the quickest ways to make an impressive agent demo is to prepare a clever prompt. One of the quickest ways to make that same agent fall apart in production is to give it no durable memory. In this article, we'll build a small, memory-backed assistant with LangChain4j and Oracle AI Database. The assistant can search prior incidents, runbooks, decisions, and shift handoffs to answer questions.

Did you know Oracle AI Database supports rich, full-text search and indexing, without the need to operate an external document store? In this article, we'll implement full-text search on Oracle's JSON data type, following the jdbc-json-oracle-text sample hosted on GitHub. The JSON text-search sample shows how we can: Load and bind Oracle JSON (OSON) documents with a JSON search index using Oracle Text…

Want to query graph-shaped relationships in Oracle AI Database from plain JDBC? You can do exactly that with built-in graph capabilities. In this article, we'll walk through a sample that shows how to use property graphs in Oracle AI Database, building graph queries over standard relational tables with the GRAPH_TABLE operator. If you want to skip straight to the code, start here: …

Want to run Oracle REST Data Services (ORDS) for integration tests, local experiments, or prototyping? This sample combines ORDS and Oracle AI Database Free in a disposable test environment with Testcontainers and one Maven command. If you want to skip straight to the code, the full sample is here: ords-testcontainers sample on GitHub The module exercises three useful things in one place:

Spatial data is a database feature that sounds niche right up until you need it. When your application must answer questions like "what landmarks fall inside this map window?", "what is the exact distance between these two places?", or "what objects are near this location?", you're working with spatial data. Oracle AI Database includes first-class support for spatial data. You can store geometry in tables, index it, and query it with specialized operators 

Sample applications using Oracle Database and Java 21+ - anders-swanson/oracle-database-code-samples

If your Spring Boot apps feel slow, the fix is usually not one magic JVM flag or a major database rewrite.  Most gains come from removing avoidable work, making startup leaner, and making sure the app behaves predictably under load. That is why Spring Boot performance work usually breaks into a few familiar buckets: Startup time Memory and GC behavior…

Oracle AI Database's JSON Relational Duality Views (or simply duality views) let you treat relational tables and JSON documents as two sides of the same model: build your relational schema, and use comprehensive, normalized JSON documents on top. In this article, we’ll walk through the json/jpa-duality-views sample, which combines Spring Boot, Spring Data JPA, and the Oracle JSON Duality View Spring Boot starter to generate read/write duality views from annotated JPA entities at application startup.

JSON Relational Duality Views are one of my favorite Oracle AI Database features (I’ll call them “duality views” from here on out). Duality views let you model data once, then serve it cleanly to both worlds: JSON-first application code and relational SQL workloads. In this article, we’ll get hands-on with the json/crud-duality-views code sample to demonstrate CRUD (create, read, update, delete)

Many Oracle guides are written for DBAs or long-time developers, and as a newcomer it feels like you’re expected to already know the jargon. I started in the same place, learning the as I went, and I’m still learning each day. If you're considering using Oracle AI Database, I'm here to make this easy for you. Prefer code samples over blogs?

When do you use a framework vs. write your own implementation - especially when it comes to AI? Frameworks can be the fastest path to production, only to act as a stumbling block later on. Often, the design choice isn't obvious. To better understand this question, we'll compare two different vector search implementations using Python and Oracle AI Database, looking at the benefits and drawbacks of each.

FreeSQL.com lets you use a free, hosted Oracle AI Database instance right from your browser. But, FreeSQL can also be used as a remote database for testing, POCs, and more! In this article, we'll walk though how to connect to a FreeSQL database from your local machine, no database setup required. Login to FreeSQL.com To use FreeSQL as a remote database, you'll need to login using your Oracle account.

Long-running agents often fail because they lack disciplined, accurate context. This is especially important when tasks run for hours, touch multiple systems, and must respect permissions, compliance boundaries, and other constraints. When building with agents, data is context, and context is critical. Where does this data most often live? Your database. What is an “agent harness”? An agent harness is the…

Recently, I’ve been exploring different caching database caching strategies with Oracle AI Database and came to the following conclusion: Choosing the right mix of caching and read-scaling options…