xml.etree vs json Nested Records: Localhost Lab

Intro — what this post promises

Serialize and parse nested telemetry-like records with json vs xml.etree.ElementTree. This lab reports ops/s and payload bytes on Linux localhost — same schema, same record count.

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Lab honesty (1 Oct 2026 IST): Python 3.13.5. Affiliates: 0. Differentiates from configparser-vs-json (lab 104) and tomllib-vs-json (lab 112) — here the peer is XML ElementTree, not INI/TOML.

Verdict up front (n=2000 records): json.dumps ~673887 ops/s; xml build+tostring ~158580; json.loads ~1520987; xml fromstring+parse ~349700. Bytes: JSON 172948, XML 248299.

Arms

Seven rounds, p50. Each record: id, host, latency_ms, ok, three tags.

Lab topology

n=2000 records · 7 rounds · p50
metric: ops/s = n / p50_s

Script: lab-evidence/124-xml-etree-vs-json/results/run_lab.py.

Lead table (p50 ops/s)

JSON wins every throughput column on this box. XML also grew the wire: 248299 bytes vs JSON 172948 (~1.44×).

Correctness check

Both paths returned 2000 / 2000 records; first id and last host matched across formats. The XML arm is a fair structural peer, not a toy one-liner.

Why XML still shows up

Vendors, SOAP leftovers, and device configs still ship XML. The cost is real: round-trip XML sat at ~106991 ops/s vs JSON ~676101. Prefer JSON for new internal envelopes; keep ElementTree when the contract is XML and you cannot change it.

Reading it for SRE work

• Internal service payloads → json (faster encode/decode, smaller blob here).
• Must speak XML → ElementTree (or lxml if you later measure it); budget CPU and bytes.
• Do not “translate to XML for logs” without a contract — you pay about 4.2× encode slowdown on this run (json ops/s ÷ xml ops/s).
• Labs 104/112 cover INI/TOML peers; this post is XML vs JSON only.

Document the chosen envelope in the runbook so on-call does not “normalize everything to XML” under incident pressure.

Encode vs decode skew

On this run, decode was the friendlier XML column (~349700 ops/s) but still trailed json.loads (~1520987 ops/s). Encode hurt more: ElementTree element allocation plus tostring sat near ~158580 ops/s against json.dumps ~673887. If your pipeline is write-heavy (agent flush), XML tax shows up first; if it is read-heavy (config ingest), budget the parse arm and the larger 248299-byte blob on the wire.

Prefer measuring your schema — attribute-heavy XML can swing these ratios. This lab keeps a fixed five-field record so ops/s stay comparable across arms.

Pitfalls

• Comparing pretty-printed JSON to minified XML (or the reverse).
• Using regex to “parse” XML instead of ElementTree when namespaces appear.
• Forgetting attribute vs text modeling differences when mapping to dicts.
• Assuming third-party XML parsers match these ElementTree numbers.

Reproduce

python3 lab-evidence/124-xml-etree-vs-json/results/run_lab.py

Evidence: summary.json, summary.txt.

Limits

One Linux box, stdlib only (no lxml). Synthetic records, not a vendor WSDL corpus.

Takeaway

On 2000 nested records, json led encode (~673887 ops/s) and decode (~1520987), with a smaller payload (172948 vs 248299 bytes). Use ElementTree when the wire format is XML; prefer JSON for new internal telemetry.