superset2/docs/admin_docs/configuration/dashboard-performance.mdx

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# Dashboard Performance

A dashboard's perceived speed is determined by three independent things: how
many charts have to render, how many queries the backend can execute
concurrently, and how quickly the underlying data warehouse can return
results. Superset gives you levers for the first two; the third belongs to
your warehouse. This page covers the dashboard-side levers and the practical
guidance around them.

## Is there a maximum chart count per dashboard?

**No hard limit is enforced** — Superset has no configuration key that
caps the number of charts on a dashboard. In practice, dashboards behave
well up to a few dozen charts. Beyond that, you'll typically feel friction
on the initial load and during cross-filter / time-range updates, even with
the lazy-loading optimizations described below.

Rough thresholds to keep in mind:

- **Under ~25 charts**: usually no perceptible problem.
- **25–50 charts**: still fine, but you start to want tabs to break the
  page into chunks the user actually looks at.
- **Over ~50 charts**: split into multiple dashboards or use tabs
  aggressively. The bottleneck is rarely Superset itself — it's the
  warehouse executing dozens of queries in parallel and the browser
  rendering dozens of chart frames.

These are guidelines, not guarantees. A dashboard of 100 sparkline-style
charts hitting a fast cache behaves very differently from a dashboard of
20 heavy aggregations against a cold warehouse.

## Lazy rendering — `DASHBOARD_VIRTUALIZATION`

Superset's dashboard layout is virtualized at the row level. Charts that
are far below the user's current scroll position render a placeholder
instead of their visualization until the user scrolls them into view, and
go back to a placeholder if scrolled well past. The chart component itself
stays mounted throughout — only the visualization is swapped for a
placeholder — so this alone does **not** reduce backend query load; see
[Deferred data fetch](#deferred-data-fetch--dashboard_virtualization_defer_data)
below for that. This is on by default.

**Feature flag**: `DASHBOARD_VIRTUALIZATION` (default: `True`)

The flag is `stable` and marked for path-to-deprecation — meaning the
behavior will eventually be non-optional, but the flag still exists so
operators can disable it if a specific layout misbehaves.

**Behavior** (from `superset-frontend/src/dashboard/components/gridComponents/Row/Row.tsx`):

- A chart's visualization is rendered when its row scrolls within **1
  viewport height** of the visible area.
- A chart's visualization is swapped back for a placeholder when its row
  scrolls more than **4 viewport heights** away from the visible area.
- Tabs that aren't currently selected don't render their content at all
  (see below).
- The placeholder-swap-back is skipped in **embedded** mode (so an
  embedded dashboard keeps its charts rendered once they've been seen,
  which avoids re-rendering on scroll-up). Both halves are skipped for
  **headless / bot** rendering (so screenshot / report jobs load every
  chart).

## Deferred data fetch — `DASHBOARD_VIRTUALIZATION_DEFER_DATA`

By default, `DASHBOARD_VIRTUALIZATION` only controls whether a chart's
*visualization* is rendered — the chart component still mounts and issues
its data request immediately, regardless of scroll position.
`DASHBOARD_VIRTUALIZATION_DEFER_DATA` is a supplementary flag that skips
the data request itself for charts that aren't currently in view, useful
for backends where opening a connection or compiling a query is expensive
even if the result would be thrown away. It only has an effect when
`DASHBOARD_VIRTUALIZATION` is also enabled — with virtualization off,
every chart is treated as in view, so there's nothing left to defer.

**Feature flag**: `DASHBOARD_VIRTUALIZATION_DEFER_DATA` (default: `False`)

Enable this if you see warehouse load spike on dashboard *open* even
though most charts are off-screen.

## Per-tab lazy loading

**This is on by default and has no flag.** A tab's content is not rendered
until the user activates that tab, so charts inside an unselected tab do
not fetch data on dashboard open. When the user clicks the tab, that
tab's charts mount and fetch in the normal way.

Practically: tabs are the single most effective tool for a large
dashboard. Splitting 60 charts across 4 tabs effectively turns dashboard
open into "load ~15 charts," and the remaining ones lazy-load only if the
user goes looking.

## Is there a switch to cap concurrent chart queries?

**No.** Superset does not implement a frontend-side concurrent-request
limiter. Each chart issues its own data request when it mounts, and the
browser handles parallelism — typically ~6 in-flight requests per origin
under HTTP/1.1, though HTTP/2 or HTTP/3 (if your deployment terminates
TLS that way) can multiplex considerably more over a single connection.
Backend throughput is bounded by your
Gunicorn worker count for synchronous query execution, or by your Celery
worker pool when [async queries](./async-queries-celery.mdx) are enabled.

If you need to throttle warehouse load, the right place is:

1. The warehouse itself (connection pool / concurrency limits).
2. Superset's Celery configuration (smaller worker pool when async
   queries are on).
3. Splitting heavy charts across tabs or separate dashboards (each
   dashboard load only fetches what's visible).

## Splitting strategies

When a dashboard outgrows comfortable performance, the options in order
of effort:

**1. Move sections into tabs.** Same dashboard, but only the active tab's
charts fetch. This is the cheapest change and often the only one needed.

**2. Cache aggressively.** A Redis cache backend (see
[Caching](./cache.mdx)) means repeat dashboard loads serve from cache
rather than re-hitting the warehouse. This is especially impactful for
dashboards opened by many users in close succession.

**3. Enable async queries.** [Async query execution](./async-queries-celery.mdx)
via Celery decouples query duration from request lifetime, so a slow
chart doesn't block the page. The user sees other charts come in as
their queries complete.

**4. Split into multiple dashboards.** Group related charts into purpose-
specific dashboards rather than one mega-dashboard. Link them from a
landing dashboard or a navigation menu.

**5. Pre-aggregate at the warehouse level.** If the same expensive
aggregation appears across many charts, materialize it as a view or
scheduled table in the warehouse so each chart query is a cheap lookup.

## Operational notes

- The feature flags above are set in `superset_config.py`, e.g.:

  ```python
  FEATURE_FLAGS = {
      "DASHBOARD_VIRTUALIZATION": True,
      "DASHBOARD_VIRTUALIZATION_DEFER_DATA": True,
  }
  ```

- See [Feature Flags](./feature-flags.mdx) for the full list of supported
  flags and their lifecycle stages.
- Server-side screenshot jobs (alerts, scheduled reports, thumbnails)
  render the dashboard in a headless, webdriver-controlled browser, which
  intentionally bypasses row virtualization so the rendered artifact
  includes every chart, not just the ones above the fold. User-triggered
  "download as image/PDF" is different: it captures whatever's currently
  rendered in the user's own browser, so it's still subject to
  virtualization like any other page view. Metadata/YAML dashboard export
  doesn't render the frontend at all, so virtualization doesn't apply to
  it either.