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Graph/json-load-oom-memory-materialization

Pattern

JSON Load OOM From Memory

json-load-oom-memory-materialization

Large JSON payloads trigger out-of-memory because json.load() materializes the entire JSON array/tree in memory at once, so backup or batch scripts get SIGKILL; streaming parsers can process incrementally.

Node Details

Public graph metadata

Updated5/4/2026

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.

Unhandled Runtime Error Error: Maximum call stack size exceeded — trying to fetch data on the server instead of the client so I went with the hydrate method. Tension: worked fine fetching on client. Outcome: same result.

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.

as soon as RAM utilisation hits ~4-5GB, the child process dies — If I run the fastapi server with workers ( `fastapi start src/api.py --port 3000 --workers 2` ). Tension: The machine has 8 cores and 64GB of RAM. Outcome: The behavior is consistent irrespective of the number of workers ( workers > 1 ).

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.

The job is taking approximately 4 hours to complete — AWS Glue job that processes thousands of small JSON files from S3. Tension: which is unacceptable for our use case. Outcome: The job processes the files but is extremely slow.

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?

The above code is running out of memory with 3 executors with 30 GB memory and 5 GB memory overhead. Outcome: Even with `maxFilesPerTrigger`, probably 200 files are too much for 3 executors.

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.

The Pandas data frame (df) is fully loaded in memory then pushed to S3 by wrangler. — I am looking for a way to extract data from a database and push that data into a parquet dataset in S3 in a environment with limited memory. Tension: if the data frame is too big, the operation fails. Outcome: I would like to chunk the data frame and pass those chunks to a process.

Uploading a 12MB CSV to a Next.js API route fails with a 413 Payload Too Large error, meaning the request exceeds the server’s allowed body size. Attempts to increase the limit via `export const config` cause a build error due to deprecated config usage.

HTTP request keeps accepting data after the size limit is exceeded, wasting server resources

API response payload appears to be encoded as unreadable data

`load` is still available. — if I set `os` and `io` to `nil` before running the script. Tension: This will allow loading malicious bytecode. Outcome: Likewise, the `debug` library is still available as an attack vector too.

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.

Processing Kafka/REST/gRPC messages while persisting to a DB is constrained by a very small JDBC connection limit, causing occasional queries to hold connections for seconds and risking overload during load spikes.

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.

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.

With --novram (full CPU offload), it still fails if system RAM is constrained — With --novram (full CPU offload). Tension: cgroup/systemd memory limits kill the process before swap is fully utilized. Outcome: Adding 16GB swap doesn't help.

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.

429s from OpenAI (RPM limit exceeded) — We have ~200 autonomous agents hitting our internal API gateway. Tension: Cascading retries amplifying the load. Outcome: P99 latency spikes to 30+ seconds.

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.

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