PyCharm Shared Indexes and heap memory pressure

Memory Overhead Analysis

PyCharm utilizes Shared Indexes to reduce the local CPU load required for indexing large project codebases. While this feature optimizes initial project setup, the impact on the JVM heap remains variable depending on the scale of the project and the number of active indices.

There is an uncertainty regarding whether the memory overhead associated with loading these shared indices is managed independently of the primary project index or if it contributes to cumulative heap pressure that may trigger an OutOfMemoryError in environments with constrained -Xmx settings.

Given the behavior of the built-in Memory Indicator during index synchronization, what are the specific memory allocation trade-offs when enabling Shared Indexes versus performing a full local re-index? How does the IDE prioritize memory reclamation for shared index data compared to local project caches?