clickhouse/clickhouse-js-node-rowbinary
> Generate TypeScript/JavaScript code that reads/decodes AND writes/encodes ClickHouse RowBinary streams for the ClickHouse HTTP server. Use this skill whenever a user wants to parse or produce `RowBinary`, `RowBinaryWithNames`, or `RowBinaryWithNamesAndTypes`. Node.js only, doesn't cover browsers.
npx skills add https://github.com/ClickHouse/agent-skills --skill clickhouse-js-node-rowbinary
This skill generates both directions of the wire format: readers (decode
bytes → values) and writers (encode values → bytes, the mirror). A given
task normally needs only one side. This file is the shared entry point — the
format gate plus the principles common to both directions; the per-direction
decisions, guidance, and the per-type reference tables live in two sibling files.
Pick your side — read only the one you need:
RowBinary* response from ClickHouse into JS values →reader.md. Streaming vs whole-buffer, row-objects vs columnar,
fixed vs runtime schema, and the per-type reader reference.
RowBinary payload to send to ClickHouse →writer.md. The Sink/writeX building blocks, writeRows
streaming, and the per-type writer reference.
The per-type code is real, split by direction under src/readers/ and
src/writers/.
RowBinary exists for throughput, but it is **not automatically the fastest
path** — match the format to the shape of the data before committing to a
bespoke parser.
Prefer a JSON* format (e.g. JSONEachRow) when the result is mostly
strings / JSON-like values that you consume wholesale — randomly accessing
essentially every field, running string/regexp methods on them, treating values
as text. V8's native JSON.parse is heavily optimized C++ and builds JS strings
and objects faster than a JS-level RowBinary decoder can; pair it with HTTP
response compression (gzip / zstd, which crushes JSON's repetitive keys) and
the wire cost shrinks too.
RowBinary clearly wins when the result is dominated by:
Int128/Int256/UInt128/UInt256,Decimal128/Decimal256.
IPv4, IPv6, UUID, FixedString.single DataView read.
Prefer the Native format when columnar load and client-side analytics are
the main goal (fold/scan/filter columns, feed typed arrays to a Worker or WASM).
Native is column-major, so it loads straight into one typed array per column
with no transpose.
For help choosing and consuming a JSON* format (or CSV / TSV) instead, use the
clickhouse-js-node-coding skill.
These principles apply whether you are generating a reader or a writer; the
side-specific operational guidance is in reader.md /
writer.md.
write every multi-byte number with DataView accessors passing a literal
true for the littleEndian flag.
plain per-type API. Only after it's correct (and tested) specialize it. Don't
bake performance assumptions in before correctness.
type combination instead of passing functions as arguments where the type is
known ahead of time.
readX/writeX functions are thecorrect, composable reference; the generated codec should INLINE their bodies,
not call them, so the row loop is straight-line with no per-field indirection
(and so the fixed-width coalescing can fold the offset arithmetic together).
short comment above each column's encode/decode block naming the ClickHouse
type it handles.
scratch buffer as a write-then-read pair — correct only because the access is
fully synchronous. An async/yield boundary between populating and reading
it corrupts the value.
explicitly asks for plain JavaScript.
Six end-to-end examples with real speedup are catalogued in EXAMPLES.md.
clickhouse-js-node-coding.clickhouse-js-node-troubleshooting.
@clickhouse/client-web.Take clickhouse/clickhouse-js-node-rowbinary from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.