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.

ClickHouse JS RowBinary Codec Generator for Node.js 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: Decoding a RowBinary response from ClickHouse into JS values → [reader.md](reader.md) . Streaming vs whole buffer, row objects vs columnar, fixed vs runtime schema, and the per type reader reference. Encoding JS values into a RowBinary payload to send to ClickHouse → [writer.md](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/ . First: is RowBinary even the right format? 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: Wide numerics — Int128 / Int256 / UInt128 / UInt256 , Decimal128 / Decimal256 . Binary / fixed width blobs — IPv4 , IPv6 , UUID , FixedString . High volume fixed width numeric columns generally, where each value is a 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. Core guidance (both directions) These principles apply whether you are generating a reader or a writer; the side specific operational guidance is in [reader.md](reader.md) / [writer.md](writer.md). Little endian only. RowBinary is little endian; target x86/ARM. Read and write every multi byte number with DataView accessors passing a literal true for the littleEndian flag. Correct first, then optimize. First emit a correct codec built from the plain per type API. Only after it's correct (and tested) specialize it. Don't bake performance assumptions in before correctness. Monomorphize generic/composite types. Emit specialized, inlined code per type combination instead of passing functions as arguments where the type is known ahead of time. Inline the leaf ops. The per type readX / writeX functions are the correct, 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). Annotate the type per column. Inlining erases the type structure, so put a short comment above each column's encode/decode block naming the ClickHouse type it handles. Shared scratch is not reentrant. Some hot methods reuse a module level 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. TypeScript by default. Generate TypeScript code and helpers unless the user explicitly asks for plain JavaScript. Worked examples Six end to end examples with real speedup are catalogued in [EXAMPLES.md](EXAMPLES.md). Out of scope JSON / CSV / TSV / Parquet parsing → use clickhouse js node coding . Connection errors, hangs, type mismatches → use clickhouse js node troubleshooting . Browser / Web Worker / Edge → @clickhouse/client web . Still Stuck? [ClickHouse RowBinary format](https://clickhouse.com/docs/interfaces/formats rowbinary) [ClickHouse data types](https://clickhouse.com/docs/sql reference/data types) [ClickHouse JS client docs](https://clickhouse.com/docs/integrations/javascript)

clickhouse-js-node-rowbinary