Oracle 23ai's Phantom Vector Memory: A Troubleshooting Guide
The article discusses the challenges faced while trying to allocate vector memory for AI Vector Search in Oracle Database 23ai, including cryptic errors, misleading outputs, and a locked-out database.
Why it matters
This article provides valuable troubleshooting insights for developers working with Oracle Database 23ai and its new AI Vector Search capabilities, helping them navigate the complexities of the Oracle multitenant architecture.
Key Points
- 1Setting the vector_memory_size parameter to allocate dedicated memory for AI Vector Search
- 2Navigating the Oracle multitenant architecture, including CDB, PDB, and SGA
- 3Troubleshooting the issue using ALTER SYSTEM and docker exec commands
- 4Understanding the discrepancy between parameter values and actual memory allocation
Details
The article walks through the author's experience in setting up the vector_memory_size parameter for Oracle Database 23ai's AI Vector Search capabilities. After successfully installing the Oracle 23ai Free container, the author tries to allocate 500MB of memory for vector indexes using the ALTER SYSTEM command. However, this leads to a series of issues, including cryptic errors, misleading parameter outputs, and a locked-out database. The author delves into the inner workings of Oracle's multitenant architecture, explaining the differences between the Container Database (CDB), Pluggable Database (PDB), and System Global Area (SGA). The article then describes the breakthrough solution using the docker exec command to access the startup log and verify the actual memory allocation, which differed from the parameter value. The article also covers understanding Oracle parameter and memory views, providing a recovery cheat sheet and explaining the IAM policy that led to the dev user needing additional grants.
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