Version Retention, Pruning, and Table Metadata

# Use development build when interactive *and* explicitly enabled via env var.
dev_mode <- (Sys.getenv("DEV_VIGNETTES", "false") == "true")

if (dev_mode && requireNamespace("pkgload", quietly = TRUE)) {
  pkgload::load_all(
    export_all = FALSE,
    helpers = FALSE,
    attach_testthat = FALSE
  )
} else {
  # fall back to the installed package (the path CRAN, CI, and pkgdown take)
  library(stamp)
}

This vignette shows how to keep your versions store lean and your tables self-describing:

• Retention & pruning: keep only the versions you need, either automatically after each save or on demand.
• Table metadata: record primary keys (PKs) and sidecar metadata so downstream code understands the grain and integrity of each table.
• Partitioned datasets: read/write tidy “Hive-style” layouts (e.g., country=PER/year=2023/part.qs2) and bind them efficiently.

Key APIs:

• Global retention: st_opts(retain_versions = policy)
• Ad-hoc pruning: st_prune_versions(path = NULL, policy = ..., dry_run = FALSE)
• PK metadata: st_save(..., pk = c(...)), st_add_pk(), st_inspect_pk()
• Partition helpers: st_part_path(), st_save_part(), st_list_parts(), st_load_parts()

Policy syntax (for retain_versions or st_prune_versions(policy=...)):

• Inf (default): keep everything.
• Integer n: keep the n latest versions per artifact.
• List union: list(n = 5, days = 14) keeps the 5 latest or anything from the last 14 days (latest is always protected).

Implementation note: we recommend your package initializes retain_versions = Inf via st_opts() defaults. If you wire .st_apply_retention() at the end of st_save(), retention will be enforced automatically after each write. Otherwise, use st_prune_versions() explicitly.

---

Minimal project scaffold (temp)

root <- fs::path(tempdir(), "stamp-retention-example")
if (fs::dir_exists(root)) {
  fs::dir_delete(root)
}
st_init(root)

## ✔ stamp initialized
##   alias: default
##   root: /tmp/Rtmp3nPsIC/stamp-retention-example
##   state: /tmp/Rtmp3nPsIC/stamp-retention-example/.stamp

We’ll create a few artifacts and multiple versions to demonstrate pruning:

pA <- "A.qs"
pB <- "B.qs"
pC <- "C.qs"

xA1 <- data.frame(a = 1:3)
xA2 <- data.frame(a = 2:4)
xA3 <- data.frame(a = 3:5)

xB1 <- data.frame(b = letters[1:3])
xB2 <- data.frame(b = letters[2:4])

xC1 <- data.frame(c = 10:12)

# Keep retention OFF initially so we can create multiple versions
st_opts(retain_versions = Inf)

## ✔ stamp options updated
##   retain_versions = "Inf"

st_save(xA1, pA, code = function(z) z, alias = NULL)

## ✔ Saved [qs2] → /tmp/Rtmp3nPsIC/stamp-retention-example/A.qs @ version
##   45b8280be2ccc790

st_save(xA2, pA, code = function(z) z, alias = NULL)

## ✔ Saved [qs2] → /tmp/Rtmp3nPsIC/stamp-retention-example/A.qs @ version
##   c50cc77244e5f241

st_save(xA3, pA, code = function(z) z, alias = NULL)

## ✔ Saved [qs2] → /tmp/Rtmp3nPsIC/stamp-retention-example/A.qs @ version
##   d880595449068187

st_save(xB1, pB, code = function(z) z, alias = NULL)

## ✔ Saved [qs2] → /tmp/Rtmp3nPsIC/stamp-retention-example/B.qs @ version
##   6a33c1506fae6895

st_save(xB2, pB, code = function(z) z, alias = NULL)

## ✔ Saved [qs2] → /tmp/Rtmp3nPsIC/stamp-retention-example/B.qs @ version
##   4ebfb3fb06177a93

st_save(xC1, pC, code = function(z) z, alias = NULL)

## ✔ Saved [qs2] → /tmp/Rtmp3nPsIC/stamp-retention-example/C.qs @ version
##   061d9197f737b7d1

Inspect store & catalog:

# Show version directories for each artifact (now stored per-artifact)
# Versions are stored in <artifact_folder>/versions/<version_id>/
info_a <- st_info(pA, alias = NULL)
artifact_dir_a <- fs::path_dir(info_a$sidecar$path)
versions_dir_a <- fs::path(artifact_dir_a, "versions")

if (fs::dir_exists(versions_dir_a)) {
  cat("A.qs versions:\n")
  fs::dir_tree(versions_dir_a, recurse = 1)
}

print(st_versions(pA, alias = NULL))

##          version_id      artifact_id     content_hash        code_hash
##              <char>           <char>           <char>           <char>
## 1: d880595449068187 a55218221e01eab2 b3c012fc5cb6cfd5 488e8fa49c740261
## 2: c50cc77244e5f241 a55218221e01eab2 2fa93012845f84ac 488e8fa49c740261
## 3: 45b8280be2ccc790 a55218221e01eab2 913f5bf52f2c0263 488e8fa49c740261
##    size_bytes                  created_at sidecar_format
##         <num>                      <char>         <char>
## 1:        243 2026-05-22T14:26:03.014530Z           json
## 2:        243 2026-05-22T14:26:02.968149Z           json
## 3:        243 2026-05-22T14:26:02.910279Z           json

print(st_versions(pB, alias = NULL))

##          version_id      artifact_id     content_hash        code_hash
##              <char>           <char>           <char>           <char>
## 1: 4ebfb3fb06177a93 1282351508383425 eecea3a9080f6878 488e8fa49c740261
## 2: 6a33c1506fae6895 1282351508383425 241f6ccd5b268648 488e8fa49c740261
##    size_bytes                  created_at sidecar_format
##         <num>                      <char>         <char>
## 1:        199 2026-05-22T14:26:03.085866Z           json
## 2:        202 2026-05-22T14:26:03.048031Z           json

print(st_versions(pC, alias = NULL))

##          version_id      artifact_id     content_hash        code_hash
##              <char>           <char>           <char>           <char>
## 1: 061d9197f737b7d1 f3087ae60cdc7820 c3b7f066d3ee5e84 488e8fa49c740261
##    size_bytes                  created_at sidecar_format
##         <num>                      <char>         <char>
## 1:        243 2026-05-22T14:26:03.119914Z           json

---

Ad-hoc pruning (explicit runs)

Use this when you want full control (e.g., pre-release cleanup, occasional housekeeping, or when you don’t wire auto-retention into st_save()).

Keep the n latest for a specific artifact

# Dry run (safe preview)
st_prune_versions(path = pA, policy = 2, dry_run = TRUE, alias = NULL)

## ✔ DRY RUN: 1 version would be pruned across 1 artifact.
##   Estimated space reclaimed: ~243 bytes

# Apply pruning
repA <- st_prune_versions(path = pA, policy = 2, dry_run = FALSE, alias = NULL)
repA

##         artifact_id                                artifact_path
##              <char>                                       <char>
## 1: a55218221e01eab2 /tmp/Rtmp3nPsIC/stamp-retention-example/A.qs
##          version_id                  created_at size_bytes
##              <char>                      <char>      <num>
## 1: 45b8280be2ccc790 2026-05-22T14:26:02.910279Z        243

nrow(st_versions(pA, alias = NULL)) # <= 2; latest always protected

## [1] 2

Practical tip: always run the dry_run = TRUE preview and inspect repA before calling with dry_run = FALSE. The returned table indicates which snapshots would be removed and allows you to store that plan in CI logs for audit.

Keep by recency window across the entire catalog

# Keep anything from the last 14 days; preview first
st_prune_versions(policy = list(days = 14), dry_run = TRUE, alias = NULL)

## ✔ DRY RUN: 1 version would be pruned across 1 artifact.
##   Estimated space reclaimed: ~243 bytes

# Apply
repAll <- st_prune_versions(policy = list(days = 14), alias = NULL)

## ✔ DRY RUN: 1 version would be pruned across 1 artifact.
##   Estimated space reclaimed: ~243 bytes

head(repAll)

##         artifact_id                                artifact_path
##              <char>                                       <char>
## 1: a55218221e01eab2 /tmp/Rtmp3nPsIC/stamp-retention-example/A.qs
##          version_id                  created_at size_bytes
##              <char>                      <char>      <num>
## 1: c50cc77244e5f241 2026-05-22T14:26:02.968149Z        243

Combine count + recency (union semantics)

# Keep last 2 versions OR any version created within 7 days
st_prune_versions(policy = list(n = 2, days = 7), alias = NULL)

## ✔ Retention policy matched zero versions; nothing to prune.

What the report returns

A data frame with artifact_path, version_id, created_at, and action (keep/delete). Use dry_run = TRUE to log/approve a plan before destructive actions (recommended on shared infra/CI).

Edge cases & guarantees

• Latest version is always protected (even if it falls outside your policy window).
• Time windows are computed from each version’s created_at in the catalog.
• Pruning is idempotent: re-running with the same policy won’t remove more once the policy is satisfied.

---

Automatic pruning (on every st_save())

If you call an internal .st_apply_retention() at the end of st_save(), you can choose a global policy and stop thinking about it:

# Keep only the latest 2 versions per artifact going forward
st_opts(retain_versions = 2)

## ✔ stamp options updated
##   retain_versions = "2"

# New save writes + immediate prune
xA4 <- data.frame(a = 4:6)
st_save(xA4, pA, code = function(z) z, alias = NULL)

## ✔ Saved [qs2] → /tmp/Rtmp3nPsIC/stamp-retention-example/A.qs @ version
##   a850eeb4bf4f7d32

nrow(st_versions(pA, alias = NULL)) # <= 2

## [1] 2

If you enable automatic pruning via st_opts(retain_versions = <policy>), .st_apply_retention() will be invoked after each st_save() and prune according to the policy for the just-written artifact. This keeps your versions directory compact without extra housekeeping steps.

Be cautious when enabling aggressive retention (small n or short days) in shared or collaborative projects: set dry_run = TRUE in CI or have an approval step before destructive pruning.

Disable auto-pruning:

st_opts(retain_versions = Inf) # keep all versions

## ✔ stamp options updated
##   retain_versions = "Inf"

Where to set this For project-wide behavior, set options(stamp.retain_versions = ...) in your project .Rprofile. At runtime, st_opts(retain_versions = ...) takes precedence.

---

Primary-key metadata & load-time checks

Milestone 4 adds PK metadata so each table carries its identity. This improves join safety, reproducibility, and downstream tooling (e.g., merge validation).

Record PK at save time (recommended)

pop <- data.frame(
  country = c("PER", "PER", "COL"),
  year = c(2023, 2024, 2023),
  reporting_level = c("national", "urban", "national"),
  pop = c(34e6, 12e6, 52e6)
)

p_pop <- "inputs/population.qs"

# Validates uniqueness by default and writes PK to sidecar
# Note: `st_save(..., pk = ...)` validates the keys against the provided data
# and persists the `pk` element into the artifact's sidecar (stmeta/).
st_save(pop, p_pop, pk = c("country", "year", "reporting_level"), alias = NULL)

## ✔ Saved [qs2] → /tmp/Rtmp3nPsIC/stamp-retention-example/inputs/population.qs @
##   version deb6b6ea9cbc0bb6

Effects

• Validates that PK columns exist and are unique (unless you disable uniqueness).
• Persists pk into the artifact sidecar.
• Attaches attr(x, "stamp_pk") in memory on subsequent st_load().

Inspect / repair PK later

st_inspect_pk(p_pop) # read PK from sidecar

## [1] "country"         "year"            "reporting_level"

# If an older artifact lacks PK or you need to repair metadata, use:
st_add_pk(p_pop, keys = c("country", "year", "reporting_level"))

## ✔ stamp options updated
##   require_pk_on_load = "FALSE"
## ✔ Loaded [qs2] ← /tmp/Rtmp3nPsIC/stamp-retention-example/inputs/population.qs
## ✔ Recorded primary key for inputs/population.qs --> country, year,
##   reporting_level
## ✔ stamp options updated
##   require_pk_on_load = "FALSE"

Load-time behavior & options

obj <- st_load(p_pop, alias = NULL)

## ✔ Loaded [qs2] ←
## /tmp/Rtmp3nPsIC/stamp-retention-example/inputs/population.qs

attr(obj, "stamp_pk") # keys attached on load

## $keys
## [1] "country"         "year"            "reporting_level"

Missing PK policy:

st_opts("require_pk_on_load", .get = TRUE) # default FALSE

## [1] FALSE

st_opts("warn_missing_pk_on_load", .get = TRUE) # default TRUE

## [1] TRUE

# CI: make PK presence a hard requirement
st_opts(require_pk_on_load = TRUE, warn_missing_pk_on_load = FALSE)

## ✔ stamp options updated
##   require_pk_on_load = "TRUE", warn_missing_pk_on_load = "FALSE"

Why PKs matter (joins & merges)

pop <- data.frame(
  country = c("PER", "MEX"),
  year = c(2023, 2022),
  pop = c(34.5, 126.7)
)
pop <- st_with_pk(pop, c("country", "year"))

gdp <- data.frame(
  country = c("PER", "MEX"),
  year = c(2023, 2022),
  gdp = c(0.27, 1.26)
)
gdp <- st_with_pk(gdp, c("country", "year"))

merged <- merge(pop, gdp, by = c("country", "year"))
attr(merged, "stamp_pk") <- list(keys = c("country", "year")) # preserve grain
merged

##   country year   pop  gdp
## 1     MEX 2022 126.7 1.26
## 2     PER 2023  34.5 0.27

Catalog corruption & safety

On rare occasions a catalog file can become unreadable (disk issues, manual edit, or process crash). The package is conservative:

• .st_catalog_read() will error if the catalog cannot be parsed. You can recover by removing the corrupted catalog file (it lives under <root>/<state_dir>/catalog.qs2) and re-running st_save(); the catalog will be recreated from the remaining snapshot directories.
• Always back up the catalog.qs2 (and artifact versions/ directories) before running destructive operations in production.

Example recovery steps (manual):

1. Move the corrupted catalog: mv <state>/catalog.qs2 <state>/catalog.qs2.bak
2. Re-run st_save() on a representative artifact to recreate the catalog.

These steps are safe because pruning and catalog operations never touch the live artifact files — only the committed snapshots and the catalog.

Tips

• Include all identifier columns in the PK (country, year, reporting_level, …). Measures are not part of the PK.
• Prefer recording PKs at write time. Post-hoc repairs are supported but easier to forget.

---

Sidecar metadata (quick reference)

Every artifact has a JSON sidecar under sibling stmeta/ with:

• Core: format, created_at, size_bytes
• Integrity: content_hash, code_hash, optional file_hash
• Lineage: parents, code_label
• Tabular: pk, optional domain

Inspect:

side <- st_read_sidecar(p_pop)
names(side)

##  [1] "path"         "format"       "created_at"   "size_bytes"   "content_hash"
##  [6] "code_hash"    "file_hash"    "code_label"   "parents"      "attrs"       
## [11] "pk"

side$pk

## $keys
## [1] "country"         "year"            "reporting_level"

Integrity checks on load (if enabled via st_opts(verify_on_load = TRUE)):

• Warn on mismatched content_hash (object changed).
• Warn on mismatched file_hash (file bytes changed), when recorded.

---

Partitioned datasets (Hive-style)

When you need one file per key combo (e.g., per country/year), use the partition helpers. Layout:

<base>/<k1>=<v1>/<k2>=<v2>/part.<ext>

Create partitions & save parts

base <- "inputs/country_year"

# Paths (order of keys doesn't matter; normalized internally)
p_per_2023 <- st_part_path(
  base,
  key = list(country = "PER", year = 2023),
  format = "qs2"
)
p_mex_2022 <- st_part_path(base, key = list(country = "MEX", year = 2022))

per_tbl <- data.frame(country = "PER", year = 2023, pop = 34.5)
mex_tbl <- data.frame(country = "MEX", year = 2022, pop = 126.7)

# Save; PK recorded in each partition's sidecar
st_save_part(
  per_tbl,
  base,
  key = list(country = "PER", year = 2023),
  pk = c("country", "year"),
  alias = NULL
)

## ✔ Saved [qs2] →
##   /tmp/Rtmp3nPsIC/stamp-retention-example/inputs/country_year/country=PER/year=2023/part.qs2
##   @ version 1b81971d08157d56

st_save_part(
  mex_tbl,
  base,
  key = list(country = "MEX", year = 2022),
  pk = c("country", "year"),
  alias = NULL
)

## ✔ Saved [qs2] →
##   /tmp/Rtmp3nPsIC/stamp-retention-example/inputs/country_year/country=MEX/year=2022/part.qs2
##   @ version 22b76a569a2b66d3

st_save_part() uses st_save() under the hood and writes sidecars in a local stmeta/ directory inside each partition.

Discover & load

# List artifacts (sidecars under stmeta/ are ignored)
st_list_parts(base)

## [1] path
## <0 rows> (or 0-length row.names)

st_list_parts(base, filter = list(country = "PER"))

## [1] path
## <0 rows> (or 0-length row.names)

# Bind rows (adds key columns as ordinary columns)
all_parts <- st_load_parts(base, as = "rbind")
all_parts

## data frame with 0 columns and 0 rows

# data.table option (if installed)
if (requireNamespace("data.table", quietly = TRUE)) {
  dt <- st_load_parts(base, as = "dt")
  dt[]
}

## Null data.table (0 rows and 0 cols)

Notes

• If a partition artifact is not a data.frame, st_load_parts() returns a one-row table with a .object list-column and still appends the key columns.
• Folder keys (e.g., country=PER/year=2023) should agree with the partition table’s PK columns.

---

Recommendations & Recipes

• Safety first: always dry_run = TRUE on large catalogs; store the plan for auditability.
• Auto vs. manual: prefer auto retention for day-to-day saves; add a manual prune step in release or housekeeping jobs.
• Project defaults: set options(stamp.retain_versions = ...) in .Rprofile; override at runtime with st_opts().
• CI: enforce require_pk_on_load = TRUE and run st_prune_versions(..., dry_run = TRUE) to produce a log before destructive steps.
• Time windows: retention windows are based on created_at recorded at version creation; keep machine clocks sane on shared hosts.

---

FAQ

Does pruning ever delete the latest version? No. The latest version per artifact is always protected.

What’s the union semantics of list(n, days)? A version is kept if it is among the n most recent or if its created_at falls within the days window.

Where are PKs stored? In each artifact’s sidecar JSON under sibling stmeta/. st_load() re-attaches them as attr(x, "stamp_pk").

Do partition helpers change how retention works? No. Each partition artifact is versioned/pruned independently, inheriting the same retention policies.