Agent Deserializes Entire Large JSON Response Into Memory — OOM Crash
Symptom
MemoryErroror OOM kill when processing large API responsesjson.loads()takes 60+ seconds for a 100MB response- Agent process memory usage spikes to 5GB for a 500MB JSON file
- Entire response must download before any processing begins
- Container is killed by OOM killer mid-task with no useful error message
Root Cause
json.loads() and response.json() parse the entire document into memory before returning. A 500MB JSON file expands to 2-5GB in Python object memory. This works for small responses but fails catastrophically for large ones. The fix is streaming JSON parsing — process records as they arrive without holding the full document in memory.
Fix
Option 1: Streaming JSON parsing with ijson
import ijson
import httpx
def stream_json_array(url: str, record_callback) -> int:
"""
Stream a JSON array from a URL, processing one record at a time.
Memory usage stays constant regardless of response size.
"""
count = 0
with httpx.stream("GET", url, timeout=300) as response:
response.raise_for_status()
# ijson parses incrementally from the byte stream
parser = ijson.items(response.iter_bytes(chunk_size=65536), "item")
for record in parser:
record_callback(record)
count += 1
return count
# Process 1M records in constant memory:
def process_record(record: dict):
insert_to_db(record)
count = stream_json_array("https://api.example.com/large-dataset", process_record)
print(f"Processed {count} records")
# Memory: ~5MB constant vs ~5GB peak for json.loads()
Option 2: ijson for large local files
import ijson
def process_large_json_file(
filepath: str,
array_prefix: str = "item",
batch_size: int = 1000
) -> int:
"""
Process a large JSON file record by record with constant memory.
array_prefix: JSON path to the array (e.g., "results.item" for {"results": [...]})
"""
total = 0
batch = []
with open(filepath, "rb") as f:
parser = ijson.items(f, array_prefix)
for record in parser:
batch.append(record)
if len(batch) >= batch_size:
process_batch(batch)
total += len(batch)
batch.clear()
print(f"Processed {total} records...")
# Final partial batch
if batch:
process_batch(batch)
total += len(batch)
return total
# For nested JSON: {"data": {"users": [...]}}
count = process_large_json_file("big_export.json", array_prefix="data.users.item")
Option 3: Request paginated results instead of one giant response
import httpx
import asyncio
async def fetch_all_paginated(
base_url: str,
page_size: int = 100,
max_pages: int = None,
params: dict = None
) -> list:
"""
Fetch all results via pagination instead of one massive response.
Each page is small — process and discard before fetching the next.
"""
all_results = []
page = 1
params = params or {}
async with httpx.AsyncClient() as client:
while True:
response = await client.get(
base_url,
params={**params, "page": page, "per_page": page_size},
timeout=30
)
data = response.json()
results = data.get("results", data.get("items", data.get("data", [])))
if not results:
break
all_results.extend(results)
print(f"Page {page}: {len(results)} records (total: {len(all_results)})")
# Check for "no more pages" signals
if len(results) < page_size:
break
if max_pages and page >= max_pages:
break
page += 1
return all_results
# Better: process each page immediately, don't accumulate all_results in memory
async def process_all_paginated(base_url: str, processor, page_size: int = 100):
page = 1
async with httpx.AsyncClient() as client:
while True:
response = await client.get(
base_url, params={"page": page, "per_page": page_size}
)
data = response.json()
items = data.get("items", [])
if not items:
break
for item in items:
await processor(item) # Process and discard
page += 1
Option 4: Stream HTTP response and parse chunks
import httpx
import json
def stream_jsonl(url: str) -> iter:
"""
Stream JSONL (one JSON object per line) — most memory-efficient format.
Process each line as it arrives.
"""
with httpx.stream("GET", url, timeout=300) as response:
response.raise_for_status()
buffer = ""
for chunk in response.iter_text():
buffer += chunk
while "\n" in buffer:
line, buffer = buffer.split("\n", 1)
line = line.strip()
if line:
try:
yield json.loads(line)
except json.JSONDecodeError as e:
print(f"Invalid JSONL line: {e}")
# Process streaming JSONL:
for record in stream_jsonl("https://api.example.com/export.jsonl"):
process(record) # Each record processed immediately, then GC'd
Option 5: Limit response size and paginate at the caller
import httpx
MAX_RESPONSE_BYTES = 50 * 1024 * 1024 # 50MB limit
async def safe_api_get(url: str, **kwargs) -> dict:
"""
Fetch JSON API response with size guard.
Raises if response exceeds memory safety threshold.
"""
async with httpx.AsyncClient() as client:
async with client.stream("GET", url, timeout=60, **kwargs) as response:
response.raise_for_status()
# Check Content-Length before downloading
content_length = int(response.headers.get("content-length", 0))
if content_length > MAX_RESPONSE_BYTES:
raise ValueError(
f"Response too large: {content_length / 1024**2:.0f}MB. "
f"Use pagination or streaming endpoint instead."
)
# Read with rolling size check
chunks = []
total = 0
async for chunk in response.aiter_bytes(chunk_size=65536):
total += len(chunk)
if total > MAX_RESPONSE_BYTES:
raise ValueError(
f"Response exceeded {MAX_RESPONSE_BYTES / 1024**2:.0f}MB limit. "
f"Switch to /export/stream or add pagination."
)
chunks.append(chunk)
return json.loads(b"".join(chunks))
Option 6: Use jq-style filtering to extract only needed fields
import subprocess
import json
def extract_fields_with_jq(json_file: str, jq_filter: str) -> list:
"""
Use jq to extract only needed fields from a large JSON file.
jq streams — doesn't load entire file into Python memory.
"""
result = subprocess.run(
["jq", "-c", jq_filter, json_file],
capture_output=True,
text=True,
timeout=120
)
if result.returncode != 0:
raise RuntimeError(f"jq failed: {result.stderr}")
# Each line is one JSON object
return [json.loads(line) for line in result.stdout.strip().split("\n") if line]
# Extract only id and email from a 1GB users array:
# jq filter: ".users[] | {id: .id, email: .email}"
users = extract_fields_with_jq(
"massive_export.json",
".users[] | {id: .id, email: .email}"
)
# → Only 2 fields per user in Python memory — 95% smaller
# Pure Python alternative with ijson field projection:
def extract_fields_streaming(filepath: str, fields: list[str]) -> iter:
with open(filepath, "rb") as f:
for item in ijson.items(f, "item"):
yield {k: item[k] for k in fields if k in item}
Memory Usage by Parsing Strategy
| Strategy | Memory for 1GB JSON | Speed | Best for |
|---|---|---|---|
json.loads() |
~5-8GB | Fast once loaded | Files < 50MB |
ijson streaming |
~10MB constant | Slower throughput | Files > 100MB |
| Pagination | Per-page only | Network overhead | API responses |
| JSONL streaming | ~1MB constant | Fastest | Export pipelines |
| jq + Python | ~100MB | Fast | Field extraction |
Expected Token Savings
OOM crash → restart → re-run from scratch: ~30,000 tokens + compute time ijson streaming processes same data in constant memory: 0 crashes
Environment
- Any agent processing large API exports, data pipeline outputs, or bulk database dumps
- Source: direct experience; JSON memory explosion is the #1 cause of agent OOM crashes in data-heavy workloads
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