Quick Start (CLI)

The infrastore binary reads and writes an on-disk store directly — no server, no binding, no Python or Julia environment. This is the shortest path from a CSV to a store you can inspect.

Get the binary from the Releases page (the executables are statically linked, so there is nothing else to install) or with cargo install infrastore-cli. See Installation for the per-platform archives.

A Minimal Round-Trip

Two files: the values, and a descriptor saying what they mean.

# load.csv
value
100.0
101.5
103.0
104.2

{
  "owner_id": 42,
  "owner_type": "Generator",
  "owner_category": "Component",
  "name": "load",
  "type": "SingleTimeSeries",
  "element_type": "f64",
  "units": "MW",
  "csv": "load.csv",
  "initial_timestamp": "2024-01-01T00:00:00Z",
  "resolution": "PT1H"
}

Save that as load.json, then add and read it back:

infrastore --store demo.h5 add --descriptor load.json
infrastore --store demo.h5 list
infrastore --store demo.h5 get --owner-id 42 --name load

╭────┬───────┬────────────┬───────────┬──────────────────┬──────┬──────────┬──────────────┬────────────┬──────────┬────────┬───────┬──────────────╮
│ ID │ Owner │ Owner Type │ Category  │ Type             │ Name │ Features │ Element Type │ Resolution │ Interval │ Length │ Units │ Hash         │
├────┼───────┼────────────┼───────────┼──────────────────┼──────┼──────────┼──────────────┼────────────┼──────────┼────────┼───────┼──────────────┤
│ 1  │ 42    │ Generator  │ Component │ SingleTimeSeries │ load │ -        │ f64          │ PT1H       │ -        │ 4      │ MW    │ 09ec58683de3 │
╰────┴───────┴────────────┴───────────┴──────────────────┴──────┴──────────┴──────────────┴────────────┴──────────┴────────┴───────┴──────────────╯

╭───────────────────────────┬───────╮
│ timestamp                 │ value │
├───────────────────────────┼───────┤
│ 2024-01-01T00:00:00+00:00 │ 100   │
│ 2024-01-01T01:00:00+00:00 │ 101.5 │
│ 2024-01-01T02:00:00+00:00 │ 103   │
│ 2024-01-01T03:00:00+00:00 │ 104.2 │
╰───────────────────────────┴───────╯

What Just Happened

• demo.h5 and demo.h5.sqlite were both created. They are one artifact: the arrays are in the HDF5 file, the catalog row in the SQLite one. Move, copy, and delete them together.
• The values came from the CSV; everything else came from the descriptor. A flat grid of numbers fits a CSV; an owner, a resolution, and a feature map do not.
• The header row is required. add reads it to tell a hand-written value-only file from one infrastore export wrote, so a file whose first row is data is rejected rather than silently losing that row.
• The store assigned id 1. That id is how every later read and removal addresses the series — see Association IDs.
• Timestamps must name an instant. 2024-01-01T00:00:00Z does; a bare 2024-01-01 00:00:00 does not, and is rejected. Pass --assume-timezone UTC to say what a zoneless file meant.

Print a starting descriptor for any of the five writable types with infrastore template:

infrastore template NonSequentialTimeSeries > outages.json

Look Around

infrastore --store demo.h5 names                    # distinct series names
infrastore --store demo.h5 get --name load --plot   # a terminal sparkline
infrastore --store demo.h5 store-info               # format version, compression, catalog state
infrastore --store demo.h5 -f json list             # every read command honors -f

Next Steps

• The whole workflow — wide CSVs, forecasts, charts, associations, diff and merge — is in the CLI Developer Guide.
• Every flag and the descriptor schema: CLI Reference.
• Doing this from a program instead: Python · Julia.