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Graph/in-memory-json-parsing-oom

Pattern

In-Memory JSON Parsing Explosion

in-memory-json-parsing-oom

Full-document JSON parsing loads the entire document tree into memory at once, so large arrays/backups trigger excessive RAM usage and OOM; prefer streaming/subset techniques and compact numeric representations to reduce footprint.

Node Details

Public graph metadata

Updated6/28/2026

Connections

30 linked nodes

When multiple React components use Zustand selectors like `useStore(state => state.largeDataset)`, does each component end up copying the large dataset into its own memory, increasing total RAM usage?

Storing a huge state object (e.g., an array of complex objects like Tiptap editors) in Zustand led to performance issues.

some Lambda functions and web pages contain ~3 MBs of minified JavaScript whereas they should be less than 1 MB

Memory usage remained high even after deletion. — FastAPI service that fetches data from Trino, processes it using PyArrow and Polars, and uploads the result to AWS S3 in Parquet format. Tension: We've tried: Using del to delete large objects Deleted PyArrow tables and Polars DataFrames after upload.

The deserialized object is ~ 100 MB. — My JSON request is about 20 MB. Tension: This is too much memory and I am inclined to parse / validate my requests in a stream-based fashion. Outcome: However, I would still be glad if FastAPI could handle generation of the OpenAPI spec based on the Pydantic model.

while running an AWS Lambda function. Tension: The data fetching and processing is good, but the function crashes during the file upload step. Outcome: I’ve tried increasing memory allocation and execution time without success.

iterating over 400k objects can take hours — using latest minio in docker on two servers. Tension: Sometimes it works fine. Outcome: How to speed it up?

Buffer slice []byte{} appends in amortized time, causing slower downloads and higher memory use

with some 10M objects which all needs to be processed and sent to opensearch. Tension: the function will timeout by the time the for loop is exhausted. Outcome: I would think the function will timeout by the time the for loop is exhausted.

Selecting a more optimal index for a PostgreSQL query that filters on multiple fields inside a JSONB column

The query uses json_to_recordset on a single JSON document, but it needs to work over multiple JSON documents stored in a table. As written, it cannot return results across all table rows.

After refreshing the page, the Excalidraw canvas is empty even though drawing JSON exists in localStorage. Occasionally a JSON parse error occurs when loading the saved data.

complex prompts — e.g. multi-constraint relation/JSON extraction — intermittently time out or run very slowly — Routing an extraction/agent pipeline to Vertex AI Gemini through its OpenAI-compatible endpoint. Tension: Gemini 2.5/3.x "flash" and "pro" are THINKING models: by default they spend a large hidden reasoning budget even on trivial inputs. Outcome: this made ~1/3 of records ship edgeless.

log.info("Before reading : {} bytes", getDirectMemoryUsed());. Tension: log.info("After reading : {} bytes", getDirectMemoryUsed());. Outcome: I have encountered a memory management problem in a Spring Boot application, and would like to get advice on how to properly clean up direct memory.

Outcome: Use `LONGTEXT` to handle large JSON.

deserializing the whole thing would be a waste of time and memory — handling large JSON payloads. Tension: only care about a small subset of their properties. Outcome: looking for a more efficient approach that will only work with the attributes that I actually need.

using `.collectList()` will load the entire file content in memory before processing — I have code that reads a file and converts and returns a flux of a list of hashmaps in groups of 10. Tension: defeating your purpose of saving memory. Outcome: using `.collectList()` will load the entire file content in memory before processing.

Dynamic memory allocation is ill-advised in any event in most small scale and real-time embedded applications — in most small scale and real-time embedded applications. Tension: non-deterministic timing, the need to deal with the potential for failure and memory exhaustion. Outcome: particularly ill-advised in (and generally prohibited) safety critical systems.

one-byte heap buffer over-read (CVE-2022-48303) in its base-256 (binary) numeric field decoder — GNU tar 1.34 (src/list.c, function `from_header`). Outcome: unbounded heap over-reads until an overflow check or segfault.

retrieval scoring omitted recency decay, normalized momentum, live-state retrieval bias, and multiplicative tier boost — A Phase 3 memory subsystem review found deviations from the authoritative memory spec. Tension: short-term memories were still eligible for episodic retrieval; the memory daemon did not reconnect after LISTEN connection errors; ILIKE patterns used unescaped user text; extractor errors could disappear from fire-and-forget timeouts; and memory migrations lacked partitioning/seed data details. Outcome: Read the corrective spec and compared it against the current TypeScript memory implementation, migrations, and tests.

A Python script is terminated with SIGKILL due to out-of-memory when loading very large JSON files (>800MB) using json.load(). The file is a large JSON array containing 500K+ objects.

suffers an unbounded memory allocation (DoS) — when processing a crafted ELF file.

libxml2 v2.9.14 during CDATA section parsing. Tension: Subsequent writes cause heap overflow, enabling memory corruption and potential code execution. Outcome: The vulnerable code path is triggered when parsing XML with CDATA sections.

a very large text node (>~2GB) causes.

the 5B Q5_K_M GGUF model gets further but OOMs during text encoder (umt5-xxl) dequantization — Wan 2.2 Image-to-Video models (14B and 5B GGUF). Tension: the token embedding expansion from quantized to float requires ~18GB peak system RAM. Outcome: it still fails if system RAM is constrained.

Running Wan 2.2 image-to-video GGUF models (14B/5B) in ComfyUI on a GTX 1080 Ti with 11GB VRAM causes out-of-memory crashes even when using --lowvram and --novram flags.

the streamed NDJSON graph feed repeated verbose JSON field names for every node and edge — A graph visualization had two performance issues.

MCP graph traversal tools (e.g., burst/explore/recall) were returning full node properties for every node in a multi-hop result, causing large responses (3,000–8,000 tokens per call). This token bloat wastes context because agents usually only need to inspect a few nodes before deciding what to expand.

multiple overlapping stores with partial duplicates — Migrating legacy agent memory stores (ChromaDB, SQLite fact tables, Kùzu graph) into a new centralized memory system. Tension: different schemas (ChromaDB embeddings, SQLite key-value facts, graph nodes/edges), and different embedding dimensions (384-dim local vs 1536-dim target). Outcome: Need dedup across stores and idempotent ingestion.

Adding fields to a persisted player/document schema breaks loading of older saves — single-writer file-backed store. Outcome: every read produces a current-shape object.

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