osmapiR
osmapiR is the rare API client that tracks its server's wiki revision numbers in the changelog.
Comparison · Analytics
nanoparquet vs sftime
A side-by-side editorial comparison of nanoparquet and sftime — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:interoperability
nanoparquet vs sftime: at a glance
What is nanoparquet?
nanoparquet is chasing byte-level agreement with the Java and Rust Parquet readers, not feature count.
nanoparquet reads and writes Parquet from R with no Arrow dependency, which is its entire reason to exist. The 0.4.0 line renamed the reader API and added schema authoring plus `append_parquet()`, and the 0.5.x releases have gone after interoperability: definition and repetition level encodings the Apache Parquet Java library expects, flatbuffer alignment the Rust arrow-rs reader expects, 128-bit decimals, and Polars-written files that omit the dictionary page offset. The newest release adds `bit64::integer64` columns and writing to stdout.
What is sftime?
The spatiotemporal companion to sf, moving at the pace of the packages around it.
sftime extends sf with an active time column, giving R a data frame class for data that is both spatial and temporal. Its recent history is almost entirely integration work: 0.3.0 added conversion methods from spatstat point patterns, sftrack and sftraj movement objects and cubble data frames, plus dedicated tidyr::drop_na() and dplyr::dplyr_reconstruct() methods. The two releases since are a namespace version-check correction and a switch from the magrittr pipe to the native pipe in examples.
nanoparquet vs sftime: editorial side-by-side
nanoparquet
0.0ANALYTICS
nanoparquet is chasing byte-level agreement with the Java and Rust Parquet readers, not feature count.
◆ Current state
nanoparquet reads and writes Parquet from R with no Arrow dependency, which is its entire reason to exist. The 0.4.0 line renamed the reader API and added schema authoring plus `append_parquet()`, and the 0.5.x releases have gone after interoperability: definition and repetition level encodings the Apache Parquet Java library expects, flatbuffer alignment the Rust arrow-rs reader expects, 128-bit decimals, and Polars-written files that omit the dictionary page offset. The newest release adds `bit64::integer64` columns and writing to stdout.
◆ Where it's heading
Almost every entry since 0.4.0 names another engine — Java, arrow-rs, Polars, Arrow schema metadata — which tells you the maintainers are treating cross-reader fidelity as the product rather than R-side ergonomics. The type system is filling in from the edges: DECIMAL beyond 8 bytes, UUID, FLOAT16 and INTERVAL as raw lists, and now 64-bit integers with an explicit read-type option instead of a silent cast to double. Writing to `:stdout:` points at a second audience, shell pipelines rather than interactive R.
◆ Prediction
The remaining unmapped Parquet types the changelog has been parking in raw-vector lists — FLOAT16 and INTERVAL — are the obvious next targets, following the same pattern by which DECIMAL and UUID graduated to real R types.
sftime
0.0ANALYTICS
The spatiotemporal companion to sf, moving at the pace of the packages around it.
◆ Current state
sftime extends sf with an active time column, giving R a data frame class for data that is both spatial and temporal. Its recent history is almost entirely integration work: 0.3.0 added conversion methods from spatstat point patterns, sftrack and sftraj movement objects and cubble data frames, plus dedicated tidyr::drop_na() and dplyr::dplyr_reconstruct() methods. The two releases since are a namespace version-check correction and a switch from the magrittr pipe to the native pipe in examples.
◆ Where it's heading
The package's job is to be interoperable, so its releases follow whatever the surrounding spatial and tidyverse packages do. The dplyr_reconstruct() work is the clearest example of why that matters: inheriting sf's method caused column binding to silently return an sf object where an sftime object was expected, which is the kind of class-preservation bug that only surfaces two steps downstream. Development is sparse, roughly one release a year.
◆ Prediction
Expect further conversion methods as new spatiotemporal classes appear in the R spatial ecosystem, and continued tracking of dplyr and tidyr generics. The entries do not indicate any planned change to the sftime class itself.
Alternatives to nanoparquet and sftime
Other Analytics products tracked by Sparkpulse, ranked by recent ship velocity. Each card links to a full editorial trajectory and lets you pivot into a head-to-head comparison with either nanoparquet or sftime.
ymlthis
ymlthis retired itself, naming Quarto as the reason it no longer needs to exist.
forestly
forestly built an interactive safety review tool, then taught it to produce submission-ready RTF.
pharmaverseadam
pharmaverseadam is the pharmaverse's test-data mirror, and it now covers neurology.
pkglite
pkglite's whole job is knowing which files in an R package are text — and it keeps getting better at guessing.
gMCPLite
gMCPLite exists to be gMCP without Java, and its releases guard that boundary rather than extend it.
See all nanoparquet alternatives → · See all sftime alternatives →
Recent activity from nanoparquet and sftime
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 3mo agosftimeExamples switched to the native R pipe
- 3mo agonanoparquet64-bit integer columns and writing Parquet to stdout
- 4mo agonanoparquetFiles now readable by the Java and Rust Parquet libraries
- 11mo agosftimeFixes the cubble namespace version check
- 1y agonanoparquetReads Polars files that omit the dictionary page offset
- 1y agonanoparquetDate, FLOAT, and mixed-encoding read fixes
- 1y agonanoparquetSchema authoring and append_parquet arrive with a renamed API ⚡
- 1y agosftimeConverts from spatstat, sftrack and cubble objects
- 1y agonanoparquetFixes a write_parquet crash
Frequently asked questions
What is the difference between nanoparquet and sftime?
Both compete on the same themes — interoperability — within Analytics. nanoparquet and sftime are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.
Is nanoparquet better than sftime?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. nanoparquet and sftime are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.
What are the best alternatives to nanoparquet?
Top nanoparquet alternatives in Analytics are ranked by recent ship velocity. Browse the "nanoparquet alternatives" section above for the current picks, or visit /alternatives/nanoparquet for the full list with editorial commentary on each.
What are the best alternatives to sftime?
Top sftime alternatives in Analytics are ranked by recent ship velocity. Browse the "sftime alternatives" section above for the current picks, or visit /alternatives/sftime for the full list with editorial commentary on each.