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Define what “matches” means
A chart and a CSV can match in several different ways. Choose and document the contract before implementing the export button.
| Match contract | CSV contains | Typical consequence |
|---|---|---|
| Source dataset | All accepted rows, regardless of zoom or viewport | Best for analysis and reproducibility |
| Loaded dataset | Rows currently available in the browser | Requires a visible incomplete-data state |
| Visible range | Rows inside the current time or category window | Zooming changes export scope by design |
| Displayed aggregate | Bars, buckets or OHLCV groups drawn on screen | Export is a summary, not the original observations |
| Displayed fields | Only fields currently shown in the visualization | Useful for focused reports, but lossy |
Do not imply that one vendor’s behavior is universal. SAP Analytics Cloud documents exports of all chart data and says filters are considered. TradingView describes exported data as what the chart shows and indicates that data must be loaded first. Amazon Quick exports fields currently visible in its visualization and excludes filtered-out data. Google Data Studio applies filters and date-range controls, while table pagination can affect whether all or only displayed rows are exported.
Keep canonical rows separate from drawing data
Authoritative representation
Normalize each accepted candle or observation into a canonical row and retain it for hover lookup, details, filtering and CSV generation. This representation should not depend on CSS width, zoom, device-pixel ratio or the renderer’s node budget.
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Viewport representation
Build a second structure solely for drawing. For a time-series candle chart, an implementation can group observations into cached OHLCV buckets: first open, highest high, lowest low, final close and summed volume, provided all contributing values are known. The described implementation targets roughly one group per two CSS pixels and returns to individual candles when the user zooms in far enough. Those are implementation choices, not charting standards.
Cache interior groups to avoid rebuilding them while panning, but calculate clipped boundary groups from observations actually inside the visible range. Never use an aggregate bucket as the CSV source unless the product explicitly promises an aggregated export.
Make loading and completeness explicit
A preview can look complete even when the browser has only part of the year of minute candles. Keep the last accepted dataset separate from a load in progress, and expose completeness before enabling or confirming a download.
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States to represent
- Complete: the request finished and no continuation cursor remains.
- Partial: a row ceiling was reached while a continuation cursor remains.
- Invalid pagination: the service returned a repeated cursor, so continuing could loop or duplicate rows.
- Canceled: the user or application stopped the request before completion.
- Loading: new rows are being fetched; the previous accepted dataset remains identifiable.
Show the state beside the export control, for example “CSV contains 412,000 loaded rows; more data is available.” A canceled or partial request must not be labeled complete. If export is allowed, include the exact scope in the confirmation and file metadata.
Chunking can reduce some intermediate allocations, but it does not make memory constant or turn a browser operation into disk streaming. For very large exports, consider a server-side job or a streaming format while preserving the same declared scope.
Choose raw or formatted values deliberately
Decide whether the CSV contains underlying values or values formatted for display. Raw values preserve precision and machine readability; formatted values preserve what a reader saw, but may include rounding, scaling, units or currency symbols.
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SAP Analytics Cloud’s “Include Formatting” behavior can export displayed scaling, units and currencies, including rounded display values. Google Data Studio provides a keep-value-formatting option. If your product supports both modes, name them in the menu and record the choice in the export metadata. Do not call a rounded, percentage-formatted column an unqualified source value.
Apply filters, dates and fields according to the contract
Define whether dashboard filters and date controls affect the download, then keep the chart, status text and export query on the same filter model. If the contract is “all source rows,” filters may affect the preview but not the CSV; that difference must be visible. If the contract is “current view,” filtered-out observations must be excluded consistently.
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- State the time zone and whether date boundaries are inclusive.
- State whether hidden series or fields are included.
- State whether aggregation occurs before or after filtering.
- Include the row count and, where useful, the selected range in the download dialog.
Optimize the expensive stage, not just the renderer
Track four quantities independently: source rows, represented rows after aggregation or sampling, prepared rows sent to the chart, and rendered nodes or vertices. TanStack Charts’ large-data guidance uses this separation because a renderer swap cannot fix excessive preparation, scene compilation, painting or interaction work.
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Pick a representation based on the task
- Raw output: use when independently rendering every observation remains meaningful and affordable.
- Aggregation: use when the user needs extrema, trends or density across a wide range; preserve semantics such as first open, high, low, final close and volume.
- Sampling: use when representative shape matters more than exact extrema, and document the method.
- Bounded window: use for streaming data when retention and a rolling range are explicit.
Measure preparation time, scene compilation, paint, pointer interaction and memory separately. For streaming sources, also specify retention, whether old rows are discarded, and whether the viewport follows new data.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Keep image export separate from CSV semantics
Raster output has its own dimensions and density rules. TanStack’s export guidance treats scale as raster density rather than chart layout: a 1,200 × 675 chart at scale 2 produces a 2,400 × 1,350 canvas. That setting changes pixels, not the CSV’s rows, filters or value formatting.
Browser raster export also requires a document and window, nonzero dimensions, Canvas 2D support and successfully decoded SVG sources. A failed image export should not change or invalidate a CSV export.
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Plan for product-specific limits and security
Document limits next to the export action, with the product and edition that imposes them. Google Data Studio documents a 750,000-row chart-export maximum. Amazon Quick documents a default CSV limit of 500 MB or one million rows, whichever comes first; separate subscription features may have different limits. These are vendor limits, not industry standards.
CSV files can also create a security problem when imported into spreadsheet software. Amazon Quick warns that imported data containing formulas or commands can enable CSV injection. Escape or neutralize formula-leading content according to your security policy, and warn users when exporting untrusted text.
A practical export checklist
- Define the contract: source, loaded, visible range, aggregate or visible fields.
- Keep canonical normalized rows independent from viewport aggregates.
- Apply filters and date ranges in one shared state model.
- Choose raw or display-formatted values and label the choice.
- Track complete, partial, repeated-cursor, canceled and loading states.
- Show scope, row count and limits before download.
- Generate CSV from canonical rows unless an aggregate export is explicitly promised.
- Measure preparation, rendering, interaction and memory separately.
- Test zooming, panning, filtering, cancellation and resumed loading against exported rows.
- Sanitize spreadsheet-dangerous cell content and document file-size or row ceilings.
Frequently Asked Questions
Should zooming out change the CSV row count?
Not when the contract is a source- or loaded-dataset export. Zoom should change only the drawing representation; a visible-range export is a different, explicitly documented contract.
Can I export the same OHLCV buckets shown on screen?
Yes, but only if the product labels the file as an aggregated export and defines bucket boundaries and value semantics. Otherwise generate the CSV from canonical observations.
What should happen when loading stops at a row limit?
Mark the dataset partial, show that more data remains, and either block export or clearly label the file as containing loaded rows only.
Quick Recap
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