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Apache TsFile vs silx

A side-by-side editorial comparison of Apache TsFile and silx — release velocity, themes, recent moves, and the top alternatives to consider.

Apache TsFile vs silx: at a glance

FeatureApache TsFilesilx
SectorAnalyticsAnalytics
Velocity score2.52.5
Sparks · 30d00
Top themestime-series, columnar-format, apache-arrow, python-bindingssynchrotron, qt, hdf5, scientific plotting
Last editorial update1d ago2h ago
WebsiteVisit →Visit →

What is Apache TsFile?

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.

Read the full Apache TsFile trajectory →

What is silx?

silx 3.0 moved its default Qt binding to PySide6 — a migration for everyone embedding it.

silx releases a couple of times a year and reached 3.0.0 in April 2026, which raised the Python floor to 3.10 and switched the default Qt binding to PySide6. The same release reworked the viewer's data views: 3D scatter support, dedicated RGB(A) image views, the composite ImageView split into Plot2dView and ComplexImageView, and NXdata stacks displayed as images. 3.1.0 has since added asinh axis scaling, the twilight colormaps, and dark-theme icons.

Read the full silx trajectory →

Apache TsFile vs silx: editorial side-by-side

A
Apache TsFile
ANALYTICS
2.5

TsFile is quietly rebuilding itself as an Arrow-speaking interchange format

◆ Current state

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.

◆ Where it's heading

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.

◆ Prediction

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.

S
silx
ANALYTICS
2.5

silx 3.0 moved its default Qt binding to PySide6 — a migration for everyone embedding it.

◆ Current state

silx releases a couple of times a year and reached 3.0.0 in April 2026, which raised the Python floor to 3.10 and switched the default Qt binding to PySide6. The same release reworked the viewer's data views: 3D scatter support, dedicated RGB(A) image views, the composite ImageView split into Plot2dView and ComplexImageView, and NXdata stacks displayed as images. 3.1.0 has since added asinh axis scaling, the twilight colormaps, and dark-theme icons.

◆ Where it's heading

The project is doing a generational refresh of its GUI layer: modern Qt binding, modules broken out of the composite widgets that had accumulated responsibilities, and the theming work that a desktop application needs to look current. Underneath, the recurring fixes are about HDF5 behavior in real facility environments — file locking, NFS refresh, Windows display paths — which is where a synchrotron toolkit actually gets stressed. Feature growth is concentrated in silx view rather than the library API.

◆ Prediction

Expect the 3.1.x line to keep filling in plotting options and theming, with the PySide6 default flushing out binding-specific bugs from downstream applications over the next few releases.

Alternatives to Apache TsFile and silx

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 silx.

See all Apache TsFile alternatives → · See all silx alternatives →

Recent activity from Apache TsFile and silx

Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.

  1. 2d agosilx3.1.0: asinh axis scaling, twilight colormaps, dark-theme icons
  2. 20d agoApache TsFileSIMD and parallel read paths in Apache TsFile 2.4.0
  3. 2mo agoApache TsFileCSV, Parquet and Arrow conversion scripts for TsFile 2.3.1
  4. 3mo agosilx3.0.1: 2026/05/07
  5. 3mo agoApache TsFileArrow-backed DataFrames and paginated reads in TsFile 2.3.0
  6. 3mo agoApache TsFileWrite-time schema changes and read/write encryption in TsFile 2.2.1
  7. 3mo agosilx3.0.0: PySide6 becomes the default Qt binding, Python 3.10 required
  8. 3mo agosilx3.0.0rc1: release candidate for the PySide6 migration
  9. 7mo agoApache TsFilePython text types and C++ tag filtering in TsFile 2.2.0
  10. 7mo agoApache TsFileBackport maintenance on the TsFile 1.1 line
  11. 1y agosilx2.2.2: 2025/04/07
  12. 1y agosilx2.2.0b0: HSDS URL support and a multi-curve comparison window

Frequently asked questions

What is the difference between Apache TsFile and silx?

They serve adjacent needs but don't currently overlap on shipped themes. Apache TsFile and silx are shipping at a similar cadence (velocity 2.5 vs 2.5, 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 Apache TsFile better than silx?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. Apache TsFile and silx are shipping at a similar cadence (velocity 2.5 vs 2.5, 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 Apache TsFile?

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.

What are the best alternatives to silx?

Top silx alternatives in Analytics are ranked by recent ship velocity. Browse the "silx alternatives" section above for the current picks, or visit /alternatives/silx for the full list with editorial commentary on each.