tsfile-cli — TsFile Command-Line Tool

tsfile-cli is a single, pipe-friendly C++ command-line tool for inspecting and importing Apache TsFile (.tsfile) files from the shell — the TsFile analogue of parquet-cli / pqrs. Read commands print data to stdout and diagnostics to stderr, so they compose with awk, jq, sort, and friends; the write command imports CSV/TSV into a new .tsfile. It is built on the public storage::TsFileReader and storage::TsFileTableWriter APIs and does not modify the storage engine.

Building from source

The CLI is part of the C++ module and is built by default (CMake option BUILD_TOOLS=ON). The CMake target is tsfile_cli; the produced executable is named tsfile-cli.

Prerequisites: a C++11 compiler (GCC / Clang / MSVC) and CMake ≥ 3.11. The third-party dependencies (ANTLR4, Snappy, LZ4, LZOKAY, Zlib, GoogleTest) are bundled under cpp/third_party/ and built automatically — no separate install step needed.

Choose any one of the following.

1. Build script (recommended). From cpp/:

bash build.sh -t=Debug      # -> cpp/build/Debug/bin/tsfile-cli
bash build.sh               # Release (default) -> cpp/build/Release/bin/tsfile-cli
bash build.sh install       # Release build, then run make install

2. Maven (builds the whole C++ module). From the repository root:

./mvnw clean package -P with-cpp   # -> cpp/target/build/bin/tsfile-cli

3. Plain CMake. From cpp/:

mkdir -p build/Debug && cd build/Debug
cmake ../.. -DCMAKE_BUILD_TYPE=Debug
make -j tsfile_cli                 # -> build/Debug/bin/tsfile-cli

CMake 4.x note. The bundled ANTLR4 runtime sets old CMake policies that CMake 4 rejects (Policy CMP00xx may not be set to OLD behavior). The reader and CLI do not use ANTLR4, so disable it — --disable-antlr4 for the build script, or -DENABLE_ANTLR4=OFF for plain CMake:
bash build.sh -t=Debug --disable-antlr4

Verify the binary:

./build/Debug/bin/tsfile-cli --version    # -> tsfile-cli (Apache TsFile C++) <version>
./build/Debug/bin/tsfile-cli --help

The executable links the tsfile shared library built alongside it. To run it from anywhere, either run it in place by its full path, or explicitly install it with bash build.sh install, cmake --install ., or make install. The install step places the binary under <prefix>/bin and libtsfile under <prefix>/lib. The build script does not install by default.

Usage

tsfile-cli <command> [options] <file.tsfile>
tsfile-cli --help | --version | help

Exit codes: 0 success, 1 usage/argument error, 2 file open/corrupt, 3 query/runtime error.

Reading

Command	Description
`ls`	List devices (tree model) or tables (table model), one name per line
`schema`	Per-series `target, measurement, datatype, encoding, compression`
`meta`	File summary: model, device/table/series counts, time range, file size
`stats`	Per-series `count, start_time, end_time, min, max, first, last, sum`
`count`	Per-series row counts plus a `total` row (from statistics, no page scan)
`head`	First N rows (default 10; use `-n`)
`cat`	All matching rows, streamed (`table` format buffers to align columns)
`sample`	Reproducible reservoir sample (default 10; `-n`, `--seed`)

The metadata commands (ls / schema / meta / stats / count) answer most questions without decoding data pages.

Shared options:

Option	Meaning
`-f, --format csv\|tsv\|json\|table`	Output format; defaults to `table` on a TTY, `tsv` when piped
`-d, --device <id>` / `-t, --table <name>`	Scope to one device / table (mutually exclusive)
`-m, --measurements a,b,c`	Column projection (`schema`, `stats`, `count`, `head`, `cat`, `sample`)
`-n, --limit N` / `--offset N`	Max rows / rows to skip (`head`, `cat`; `--offset` not valid for `sample`)
`--start <ms>` / `--end <ms>`	Inclusive epoch-millisecond time range (`head`, `cat`, `sample`)
`--seed N`	Reproducible sampling seed (`sample` only)
`--tag-filter C OP V` / `--tag-between C L U` / `--tag-not-between C L U`	Table TAG predicate for `head`, `cat`, `sample`; `OP` is `eq`, `neq`, `lt`, `lteq`, `gt`, `gteq`, `regexp`, or `not-regexp`
`--no-header`	Omit the header row
`--model tree\|table`	Force the model (otherwise auto-detected)

json output is NDJSON (one object per line; numbers/booleans bare, other values quoted, nulls as null; non-finite floats — NaN/Inf — become null). CSV output follows RFC 4180. Timestamps are raw epoch milliseconds. The table format buffers all rows in memory to align columns, so prefer csv/tsv/json when dumping large files.

BIN=cpp/build/Debug/bin/tsfile-cli
$BIN ls -f tsv data.tsfile                          # list tables / devices
$BIN meta data.tsfile                               # quick file overview
$BIN count -t table1 -f tsv data.tsfile             # row counts, no page scan
$BIN cat -t table1 --tag-filter device eq dev_1 -m temp -f tsv data.tsfile
$BIN cat -m temp,humidity --start 1700000000000 -f csv data.tsfile | head
$BIN sample -m temp -n 20 --seed 42 -f json data.tsfile | jq .

Writing (import)

tsfile-cli write imports CSV/TSV rows into a new table-model .tsfile (the output is overwritten). The first input column is the timestamp (epoch milliseconds); the remaining columns are declared explicitly with --columns — there is no type inference.

Timestamps must be strictly increasing per device, where a device is identified by its tag column values (rows that share the same tags form one device's timeline). Rows for different tag combinations may freely interleave and reuse timestamps. Out-of-order input is rejected with the offending line number, and a failed import leaves no output file behind. --output must differ from the input file.

tsfile-cli write --table <name> --columns <spec> -o <out.tsfile> \
                 [-f csv|tsv] [--no-header] [--header-match] [-v] [<input> | -]

--columns is a comma-separated list of name:TYPE:category, where category (case-insensitive) is tag or field and TYPE (case-insensitive) is one of BOOLEAN, INT32, INT64, FLOAT, DOUBLE, STRING, TEXT, TIMESTAMP, DATE, BLOB — for example --columns "id1:STRING:tag,s1:INT64:field". DATE cells are written as YYYY-MM-DD; TIMESTAMP cells as epoch milliseconds. Each column is stored with the engine's default encoding and compression for its type.

Option	Meaning
`--table <name>`	Output table name (lower-cased)
`--columns <spec>`	Ordered data columns (excludes the leading timestamp column)
`-o, --output <path>`	Output `.tsfile` (required; overwritten)
`<input>` / `-`	Input file, or `-` / omitted for stdin
`-f csv\|tsv`	Input delimiter (default csv; `json` / `table` are rejected)
`--no-header`	Input has no header row (default: first line is a header and is skipped)
`--header-match`	Validate header names against `--columns`
`-v, --verbose`	Print `wrote N rows to <out>` to stderr (otherwise silent on success)

An empty cell is written as null. The command is silent on success (Unix-style); pass -v for a one-line summary.

# round-trip through a pipe
printf 'time,id1,s1\n0,dev,0\n1,dev,10\n' \
  | tsfile-cli write --table t1 --columns "id1:STRING:tag,s1:INT64:field" -o out.tsfile -
tsfile-cli count -f tsv out.tsfile          # -> t1.dev  s1  2

For tree-model writes, JSON input, or programmatic use, use the C++ SDK directly — see cpp/examples/cpp_examples/demo_write.cpp (TsFileTableWriter / TsFileWriter + Tablet).

Using the skill with an AI assistant

cpp/tools/skills/tsfile-cli/SKILL.md is a machine-readable reference that teaches AI coding assistants (e.g. Claude Code) how to drive tsfile-cli correctly. Such assistants auto-discover skills from a .claude/skills/ directory at session start, so “installing” the skill just means placing it there — either project-level or user-level:

# project-level (this repository only)
mkdir -p .claude/skills/tsfile-cli
cp cpp/tools/skills/tsfile-cli/SKILL.md .claude/skills/tsfile-cli/SKILL.md

# or user-level (available in all your projects)
mkdir -p ~/.claude/skills/tsfile-cli
cp cpp/tools/skills/tsfile-cli/SKILL.md ~/.claude/skills/tsfile-cli/SKILL.md

The installed SKILL.md must begin with its YAML front-matter (--- … ---) for the assistant to detect it. The in-repo copy carries an Apache license header comment above the front-matter; if discovery fails, delete that leading  block from the installed copy so --- is the first line.

Start a new assistant session afterward. The skill then activates automatically when you ask to inspect or import a .tsfile; you can also invoke it explicitly (e.g. “use the tsfile-cli skill”).

Source layout

cpp/tools/
├── tools_main.cc          # main(): forwards argv to run_cli
├── cli/                   # argument parsing, top-level dispatch, exit codes
├── format/                # csv/tsv/json/table output + CSV/TSV input parsing
├── commands/              # one file per command + shared row-query / statistics helpers
└── skills/tsfile-cli/     # model-facing skill reference (for AI assistants)