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analysing-exported-session-recordings

Data & analytics

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Updated 10-16 03:44

About this skill

Analysing exported session recordings

An export bundles a recording's raw v2 storage blocks (under data/) plus its ClickHouse metadata (session-replay-events.json, events.json). Use this skill to crack those blocks open and measure what the recording is made of, so size or performance conclusions rest on decoded data — not on raw bytes.

Get the zip first via the exporting-session-recordings skill.

The one thing that will mislead you: the blocks are double-compressed

A data/ block is not readable JSON. It is two layers deep, and skipping either layer makes the bytes look like binary garbage or "corruption" that isn't there:

  1. Whole block: snappy-compressed. The exporter fetches blocks with decompress=False (export_recording/activities.py), so it writes the raw S3 object. Each block is snappy-compressed and written at its byte offset, zero-padded (so a data/ file is mostly \x00 with the real block at the end). Snappy keeps literal text runs verbatim with binary copy-tokens between them, which is why a raw block reads as "half JSON, half binary".
  2. Per field: gzip-compressed. Once snappy-decompressed, a block is JSONL of ["windowId", {event}] rows. Events tagged cv: "2024-10" have their heavy DOM fields (full-snapshot data; mutation adds/attributes/texts/removes) individually gzip-compressed and stored as a binary-in-JSON string via fflate strFromU8(gzipSync(strToU8(json)), true) — each string char is one gzip byte.

Do not measure or draw conclusions from the raw block bytes. Reading them as UTF-8 produces phantom U+FFFD runs and wildly wrong size splits (e.g. network bodies look tiny because they are still snappy-compressed). Always decode both layers first, then measure. The decoder script below does this; reach for it before hand-rolling anything.

The exporter is lossless — it round-trips the raw S3 block (base64 through Redis, binary write). Do not "fix" the exporter to decompress: export and import are a matched pair that re-upload the raw blocks, so changing the export format breaks import_recording. The decode belongs on the read side.

Decoding and the size report

scripts/decode_recording_export.py does snappy-then-gzip and reports composition. It needs a snappy codec (pip install python-snappy, or cramjam).

# size + composition report (accepts the zip directly, or an already-extracted dir)
python scripts/decode_recording_export.py export-<session_id>.zip

# also emit fully-decoded events (DOM fields un-gzipped and inlined) for deeper digging
python scripts/decode_recording_export.py export-<session_id>.zip --dump-jsonl decoded.jsonl

The report gives you:

  • Decompressed DOM payload split across full snapshots vs mutation adds/attributes/texts/removes — adds and repeated full snapshots are the usual DOM-side offenders.
  • Network bodies by response content-type — these are stored un-gzipped in the JSONL, so compare them to DOM on decoded size, never on raw bytes.
  • Top tag names and mutated attribute keys, to see what the DOM churn actually is (e.g. SVG icon trees, inline style animation churn).

Reading the numbers honestly

  • Compare like with like. DOM fields are gzip-then-snappy; network bodies are plaintext-then-snappy. The report's decompressed sizes tell you what is in the recording (and what the player must hold), which is the right lens for "what should we cut". For "what does it cost to store/ingest", remember network bodies compress far better than already-gzipped DOM, so their stored share is smaller than their decoded share.
  • A handful of blocks may fail to snappy-decompress (empty/edge blocks). The script logs and skips them; a few skips do not move the totals.
  • Blocks are sequential byte ranges and should not overlap — if you write your own pass, sanity-check for duplicate (name, timestamp, kind, len) bodies before trusting a total.

Worked example (validated)

A long single-page-app session decoded to ~70 MiB of DOM payload but ~253 MiB of network bodies, of which binary/octet-stream (136 MiB), image/webp (18.5 MiB) and image/svg+xml (3.5 MiB) were binary assets captured as text. The lesson that earned this skill: measuring those same bodies on the raw (still-snappy) bytes reported only single-digit MiB and pointed at the wrong culprit. Decoded, the conclusion flips — skipping binary network bodies (feat(replay): skip binary/asset bodies in network capture, posthog-js #3912) is the dominant lever for a session like this, not a rounding error. Decode before you conclude.