Planet Bulk Download

Planet Bulk Download

Hard-won reference for pulling PlanetScope imagery at scale with the Planet SDK v2. The SDK is thin: no retries, no batching, no awareness of cold-storage thaw, no scene grouping. You build all of that. This document is the part you will otherwise rediscover by burning a 17-hour batch job.

Architecture: three-phase pipeline

Split the job into search → activate → extract, each with its own JSONL cache, its own concurrency knob, and its own resume logic. The phases have completely different bottlenecks:

• search: cheap metadata calls, rate-limited client-side.
• activate: Planet-side cold-storage thaw queue, capped at ~10 cold scenes/min/account regardless of how many you request.
• extract: HTTP range reads via GDAL, network-bound at ~0.5s/patch on a 1.5km AOI.

Suggested layout:

Copy code
_global/
  manifest.jsonl                 # input AOIs, one per line
  search/shard_NNN.jsonl         # per-AOI search results
  activations.jsonl              # item_id -> signed URL, append-only
  extract/shard_NNN.jsonl        # per-patch extract status

Phase a re-activation pass after the first extract pass (see Reliability Gotchas below).

Scene grouping is the activation budget killer

Many patches share scenes (mean ~23 patches/scene in practice). Group all patches needing scene S, activate S once, then read every patch's window from the warm URL. Cuts activations 10-20x.

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from collections import defaultdict
scene_to_patches = defaultdict(list)
for patch in patches:
    scene_to_patches[patch.item_id].append(patch)
# Activate keys once; extract reads many windows per key.

Sharded array jobs

For search and extract, shard by patch index. Aim 1-5k entries per shard so a single shard finishes in 10-30 min (good for walltime budgeting and partial-progress visibility).

pythonCopy code
shard_id = hash(item_id) % num_shards   # for extract: keeps same scene in same task

Hash by item_id for extract so the same scene is always processed by the same task — GDAL's /vsicurl/ cache stays warm across patches.

COG range-reads beat the Orders API by ~100x

PSScene ortho_analytic_4b_sr has been a Cloud-Optimized GeoTIFF since ~2022. After :activate, the signed location URL supports HTTP range reads via GDAL /vsicurl/. For windowed access (anything less than a full strip), the Orders API queue + clip-tool path is dramatically slower and adds another queue to fail through.

Use the Orders API only when you need full-strip downloads or server-side reprojection you cannot do locally.

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import rasterio
from rasterio.windows import from_bounds

url = activation_row["location"]            # signed Planet URL with ?token=
vsicurl_url = f"/vsicurl/{url}"
with rasterio.Env(**GDAL_OPTS):
    with rasterio.open(vsicurl_url) as src:
        window = from_bounds(*patch_bounds, transform=src.transform)
        data = src.read(window=window)

GDAL config for /vsicurl/ COG reads

Apply via rasterio.Env(**opts). Pass numeric values as Python ints, not strings — GDAL_CACHEMAX and a few others go through C funcs that require int. Strings silently break with cryptic errors.

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GDAL_OPTS = dict(
    GDAL_HTTP_VERSION="2",
    GDAL_HTTP_MULTIPLEX="YES",
    GDAL_DISABLE_READDIR_ON_OPEN="EMPTY_DIR",
    CPL_VSIL_CURL_USE_HEAD="NO",
    GDAL_INGESTED_BYTES_AT_OPEN=32768,           # int
    GDAL_HTTP_MERGE_CONSECUTIVE_RANGES="YES",
    GDAL_CACHEMAX=1024,                          # int, MB
    VSI_CACHE="TRUE",
    VSI_CACHE_SIZE=67108864,                     # int
)

Critical: do NOT set CPL_VSIL_CURL_ALLOWED_EXTENSIONS. Planet's signed URLs end with ?token=..., not .tif. The extension allowlist will reject them. Half a day was lost to this.

Retry logic (the SDK has none)

Wrap every Data API call — search, list_item_assets, activate_asset, wait_asset — in a retry that handles all of:

• planet.exceptions.TooManyRequests (429). Exponential backoff starting 0.5s, cap 30s, ~8 attempts. Planet does not send Retry-After; pace yourself.
• planet.exceptions.APIError whose message contains "503", "502", "504", or "Server Error". Same backoff. The SDK does not expose a clean status code on all error paths — heuristic match on the message string is required.
• httpx.ConnectTimeout, httpx.ReadTimeout, httpx.ConnectError, httpx.RemoteProtocolError, httpx.ReadError. All happen on multi-hour runs.

pythonCopy code
import asyncio, random
import httpx
from planet.exceptions import TooManyRequests, APIError
from typing import Any, cast

TRANSIENT_HTTPX = (
    httpx.ConnectTimeout, httpx.ReadTimeout, httpx.ConnectError,
    httpx.RemoteProtocolError, httpx.ReadError,
)

async def with_retry(fn, *args, max_attempts=8, base=0.5, cap=30.0, **kw):
    for attempt in range(max_attempts):
        try:
            return await fn(*args, **kw)
        except TooManyRequests:
            pass
        except APIError as e:
            msg = str(e)
            if not any(s in msg for s in ("503", "502", "504", "Server Error")):
                raise
        except TRANSIENT_HTTPX:
            pass
        delay = min(cap, base * (2 ** attempt)) * (0.5 + random.random())
        await asyncio.sleep(delay)
    raise RuntimeError(f"retry exhausted: {fn.__name__}")

sess.client("data") returns an Any-typed object that breaks static checkers. Cast to Any to silence:

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data_client = cast(Any, sess.client("data"))

Concurrency tuning (measured, paid account)

The per-account activation cap is undocumented. Ask Planet support directly if you need it lifted.

Activate SR and UDM2 in parallel via asyncio.gather. UDM2 is ~3x slower than SR for unknown reasons (worth reporting to Planet). Doing them serially halves throughput.

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sr_task   = asyncio.create_task(activate(item_id, "ortho_analytic_4b_sr"))
udm2_task = asyncio.create_task(activate(item_id, "ortho_udm2"))
sr_url, udm2_url = await asyncio.gather(sr_task, udm2_task)

Planet reliability gotchas (plan around these)

• ~24% of :activate responses return status=active with a broken URL. First range-read fails HTTP 400: "Failure writing output to destination, passed 107 returned 0". Solution: a second :activate returns a different, working URL — asset is still active, the URL itself was corrupted. Build a re-activation pass: scan extract logs for open_failed, call :activate again, append a fresh row to activations.jsonl. Use last-write-wins on item_id when loading.
• Some activations return success with location=null. Same pattern; re-activate.
• Pre-2018 archive and certain regions (India, Finland observed) have permanently dead assets. Multiple reactivations do not help. Budget 3-5% unrecoverable.
• Cold-storage thaw p99 ≥ 45 minutes for never-touched scenes. Mean ~3 min once warm. If Planet support enables it, pre-warming a list of scene IDs ahead of time collapses the long tail.

Efficient patterns

Resumable JSONL caches everywhere

Append-only, flushed every line. On rerun, build the set of done keys and skip them. Cheap and crash-safe.

pythonCopy code
def append_row(path, row):
    with open(path, "a") as f:
        f.write(json.dumps(row) + "\n")
        f.flush()

def load_done(path, key):
    done = set()
    if not os.path.exists(path):
        return done
    with open(path) as f:
        for line in f:
            try:
                done.add(json.loads(line)[key])
            except Exception:
                continue   # NFS append-races produce torn lines
    return done

Always wrap json.loads in try/except. NFS append-races produce torn lines that crash a naive loader.

Don't wipe output files before re-extract

If you wipe <patch>.tif for resample then the resample fails, you have lost the original too. Either:

• Don't wipe — let rasterio.open(path, "w") overwrite naturally on success.
• Write to <patch>.tif.tmp and os.replace on success.

UDM2 (or any small companion asset) gets its own 3-phase pass

Run a separate plan → activate → extract pass for UDM2 after the SR pipeline lands. Ships SR-first results while UDM2 is still thawing on Planet's side.

Patch-level cloud cover ≠ scene-level cloud cover

A scene with 10% scene-level cloud can have a specific 1.5km patch at 100% cover. Always probe UDM2 at the actual patch AOI post-hoc. Re-sample cloudy patches against alternative scenes from the search results.

Anti-patterns

• Do not use the Orders API for windowed access at scale.
• Do not trust the SDK to auto-retry — there are no built-in retries.
• Do not activate UDM2 inside per-patch loops — batch by unique item_id first.
• Do not set CPL_VSIL_CURL_ALLOWED_EXTENSIONS with Planet signed URLs.
• Do not pass GDAL config numeric values as strings.
• Do not run wait_asset polling without a max-attempts cap — stuck scenes will pin a worker forever.
• Do not wipe existing outputs before a re-extract attempt.
• Do not assume status=active means the URL works — verify with the first read, retry-by-reactivate on failure.

Quick pre-flight checklist

1. AOIs deduped and grouped by item_id before any activation call.
2. JSONL caches for search/activations/extract wired up and resumable.
3. Retry wrapper installed around every Data API call (429 + 5xx strings + httpx transients).
4. GDAL config dict in place, numeric values as ints, CPL_VSIL_CURL_ALLOWED_EXTENSIONS unset.
5. SR and UDM2 activations launched in parallel via asyncio.gather.
6. Concurrency capped (search ≤128, activate ≤96, extract ≤64).
7. Re-activation pass planned for open_failed extract rows.
8. Budget 3-5% permanently unrecoverable assets.
9. Shard size 1-5k entries; extract sharded by hash(item_id).
10. Post-hoc UDM2 cloud probe at patch AOI, not scene level.