perf: pre-cached indent arrays for bulk newline+spaces#676
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He-Pin wants to merge 2 commits intodatabricks:masterfrom
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perf: pre-cached indent arrays for bulk newline+spaces#676He-Pin wants to merge 2 commits intodatabricks:masterfrom
He-Pin wants to merge 2 commits intodatabricks:masterfrom
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For integer values (the common case in Jsonnet), write digits directly to CharBuilder using a scratch digit buffer instead of allocating a String via RenderUtils.renderDouble/Long.toString and copying chars. Upstream: jit branch commit d63ce90 Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Pre-compute indent arrays (newline + spaces) for depths 0-15 at Renderer construction time. On flushBuffer, use bulk appendAll for the cached array instead of character-by-character space appending. Upstream: jit branch commit 4f19bde Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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Motivation
The JSON renderer writes newline+indent sequences character by character. For deeply nested output with many array/object elements, this is a significant bottleneck. Pre-caching indent byte arrays allows bulk writes.
Key Design Decision
Pre-compute indent arrays up to a maximum depth and use
System.arraycopy-style bulk writes instead of character-by-character output. This trades a small amount of memory for significant rendering throughput.Modification
Added pre-cached indent arrays in BaseCharRenderer.scala. The renderer now uses bulk array copies for indent sequences, reducing per-character overhead.
Benchmark Results
JMH Regression Suite (1 fork, 3 warmup, 1 measurement)
All other benchmarks within noise margin.
Scala Native Hyperfine (
-N -w4 -m20)Analysis
This is particularly effective for rendering-heavy benchmarks like
reverse(which generates deeply nested JSON output) andbase64DecodeBytes(large output). On native, this narrows the gap with jrsonnet from 1.49x to just 1.08x on reverse, and from 1.61x to 1.14x on base64DecodeBytes.References
Upstream jit branch exploration at he-pin/sjsonnet@jit
Result
16-29% improvement on rendering-heavy benchmarks. Dramatically narrows the gap with jrsonnet on reverse and base64DecodeBytes.