tidyr
tidyr replaced separate() with a family that says what it does.
A side-by-side editorial comparison of Apache TsFile and cmdstanpy — release velocity, themes, recent moves, and the top alternatives to consider.
TsFile is quietly rebuilding itself as an Arrow-speaking interchange format
Apache TsFile is the columnar time-series file format underlying IoTDB, maintained as three parallel implementations in Java, C++ and Python. Recent releases have concentrated on the C++ and Python ends: SIMD paths and parallel reads in 2.4.0, an Arrow-compatible result path from C++ through to Python DataFrames in 2.3.0, and conversion scripts from CSV, Parquet and Arrow into TsFile in 2.3.1. The Java side gets steadier, smaller work — serialized-size calculation, schema modification during writes, encryption configuration.
CmdStanPy is clearing deprecations ahead of a 2.0 it keeps announcing.
CmdStanPy is at v1.3.0, which added a diagnose method on CmdStanModel, sampler timing information, create_inits() across the stanfit classes, and much faster MCMC CSV parsing. Every release in this window opens with the same notice: the next non-bugfix release will be 2.0 and will remove existing deprecations. In line with that, 1.3.0 drops Python 3.8 and renames the metric argument to inv_metric.
Apache TsFile is the columnar time-series file format underlying IoTDB, maintained as three parallel implementations in Java, C++ and Python. Recent releases have concentrated on the C++ and Python ends: SIMD paths and parallel reads in 2.4.0, an Arrow-compatible result path from C++ through to Python DataFrames in 2.3.0, and conversion scripts from CSV, Parquet and Arrow into TsFile in 2.3.1. The Java side gets steadier, smaller work — serialized-size calculation, schema modification during writes, encryption configuration.
The centre of gravity has moved from format features to ecosystem reach. Arrow-backed DataFrames and format converters are not about storing time series better; they are about making TsFile readable by the Python analytics stack without a translation layer, which is the gap that keeps a specialized format confined to its own database. The C++ performance work in 2.4.0 serves the same end, since the Python bindings sit on top of it. Version numbering runs on two lines at once, with 1.1.x backports still shipping alongside the 2.x series.
Given the direction of the Arrow work, the Python interface is the most likely target for further capability rather than the Java one. The notes do not indicate when the 1.1 maintenance line ends.
CmdStanPy is at v1.3.0, which added a diagnose method on CmdStanModel, sampler timing information, create_inits() across the stanfit classes, and much faster MCMC CSV parsing. Every release in this window opens with the same notice: the next non-bugfix release will be 2.0 and will remove existing deprecations. In line with that, 1.3.0 drops Python 3.8 and renames the metric argument to inv_metric.
The package tracks upstream Stan and prepares for its own break. New inference methods arrive as Stan ships them — Laplace with Stan 2.32, Pathfinder with 2.33 — while the interface work is mostly deprecation staging and CSV input-output performance. The repeated 2.0 warning across two years of releases suggests the cut has been deferred more than once.
The notice at the top of every release points to 2.0 as the next non-bugfix version, removing the deprecations staged here including the metric argument.
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 Apache TsFile or cmdstanpy.
tidyr replaced separate() with a family that says what it does.
modeltime built conformal intervals in, then went quiet on features.
performance keeps adding ways to check a model you have already fitted.
DoWhy adds one estimation method a year and keeps its identification edge.
OpenHouse is hardening the seams where table policies and jobs quietly fail.
silx 3.0 moved its default Qt binding to PySide6 — a migration for everyone embedding it.
See all Apache TsFile alternatives → · See all cmdstanpy alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. Apache TsFile is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 editorial sparks in the last 30 days against 0. See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. Apache TsFile is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 editorial sparks in the last 30 days against 0. For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.
Top Apache TsFile alternatives in Analytics are ranked by recent ship velocity. Browse the "Apache TsFile alternatives" section above for the current picks, or visit /alternatives/apache-tsfile for the full list with editorial commentary on each.
Top cmdstanpy alternatives in Analytics are ranked by recent ship velocity. Browse the "cmdstanpy alternatives" section above for the current picks, or visit /alternatives/cmdstanpy for the full list with editorial commentary on each.