diff --git a/documentation/query/functions/window-functions/overview.md b/documentation/query/functions/window-functions/overview.md
index 3a533344d..8e023e213 100644
--- a/documentation/query/functions/window-functions/overview.md
+++ b/documentation/query/functions/window-functions/overview.md
@@ -2,7 +2,7 @@
 title: Window Functions Overview
 sidebar_label: Overview
 description: Introduction to window functions in QuestDB - perform calculations across related rows without collapsing results.
-keywords: [window functions, over, partition by, moving average, running total, rank, dense_rank, percent_rank, row_number, lag, lead, analytics, ema, vwema, ksum, exponential moving average, stddev, variance, covariance, correlation]
+keywords: [window functions, over, partition by, moving average, running total, rank, dense_rank, percent_rank, ntile, cume_dist, row_number, lag, lead, nth_value, analytics, ema, vwema, ksum, exponential moving average, stddev, variance, covariance, correlation]
 ---
 
 Window functions perform calculations across sets of table rows related to the current row. Unlike aggregate functions that return a single result for a group of rows, window functions return a value for **every row** while considering a "window" of related rows defined by the `OVER` clause.
@@ -44,23 +44,26 @@ Arithmetic operations on window functions (e.g., `sum(...) OVER (...) / sum(...)
 | Function | Description | Respects Frame |
 |----------|-------------|----------------|
 | [`avg()`](reference.md#avg) | Average value in window (also supports EMA and VWEMA) | Yes (standard) / No (EMA/VWEMA) |
+| [`corr()`](reference.md#corr) | Pearson correlation coefficient | Yes |
 | [`count()`](reference.md#count) | Count rows or non-null values | Yes |
-| [`sum()`](reference.md#sum) | Sum of values in window | Yes |
-| [`ksum()`](reference.md#ksum) | Sum with Kahan precision | Yes |
-| [`min()`](reference.md#min) | Minimum value in window | Yes |
-| [`max()`](reference.md#max) | Maximum value in window | Yes |
-| [`stddev_pop()` / `stddev_samp()` / `stddev()`](reference.md#stddev) | Standard deviation (population or sample) | Yes |
-| [`var_pop()` / `var_samp()` / `variance()`](reference.md#variance) | Variance (population or sample) | Yes |
 | [`covar_pop()` / `covar_samp()`](reference.md#covariance) | Covariance between two columns | Yes |
-| [`corr()`](reference.md#corr) | Pearson correlation coefficient | Yes |
-| [`first_value()`](reference.md#first_value) | First value in window | Yes |
-| [`last_value()`](reference.md#last_value) | Last value in window | Yes |
-| [`row_number()`](reference.md#row_number) | Sequential row number | No |
-| [`rank()`](reference.md#rank) | Rank with gaps for ties | No |
+| [`cume_dist()`](reference.md#cume_dist) | Cumulative distribution (0 to 1] | No |
 | [`dense_rank()`](reference.md#dense_rank) | Rank without gaps | No |
-| [`percent_rank()`](reference.md#percent_rank) | Relative rank (0 to 1) | No |
+| [`first_value()`](reference.md#first_value) | First value in window | Yes |
+| [`ksum()`](reference.md#ksum) | Sum with Kahan precision | Yes |
 | [`lag()`](reference.md#lag) | Value from previous row | No |
+| [`last_value()`](reference.md#last_value) | Last value in window | Yes |
 | [`lead()`](reference.md#lead) | Value from following row | No |
+| [`max()`](reference.md#max) | Maximum value in window | Yes |
+| [`min()`](reference.md#min) | Minimum value in window | Yes |
+| [`nth_value()`](reference.md#nth_value) | N-th value in window (`double` only) | Yes |
+| [`ntile()`](reference.md#ntile) | Bucket number from 1 to N | No |
+| [`percent_rank()`](reference.md#percent_rank) | Relative rank (0 to 1) | No |
+| [`rank()`](reference.md#rank) | Rank with gaps for ties | No |
+| [`row_number()`](reference.md#row_number) | Sequential row number | No |
+| [`stddev_pop()` / `stddev_samp()` / `stddev()`](reference.md#stddev) | Standard deviation (population or sample) | Yes |
+| [`sum()`](reference.md#sum) | Sum of values in window | Yes |
+| [`var_pop()` / `var_samp()` / `variance()`](reference.md#variance) | Variance (population or sample) | Yes |
 
 **Respects Frame**: Functions marked "Yes" use the frame clause (`ROWS`/`RANGE BETWEEN`). Functions marked "No" operate on the entire partition regardless of frame specification.
 
diff --git a/documentation/query/functions/window-functions/reference.md b/documentation/query/functions/window-functions/reference.md
index 39e13be6d..00e3b57f0 100644
--- a/documentation/query/functions/window-functions/reference.md
+++ b/documentation/query/functions/window-functions/reference.md
@@ -1,8 +1,8 @@
 ---
 title: Window Functions Reference
 sidebar_label: Function Reference
-description: Complete reference for all window functions in QuestDB including avg, sum, ksum, count, stddev, variance, covariance, correlation, rank, dense_rank, percent_rank, row_number, lag, lead, EMA, VWEMA, and more.
-keywords: [window functions, avg, sum, ksum, count, stddev, stddev_pop, stddev_samp, var_pop, var_samp, variance, covar_pop, covar_samp, corr, correlation, rank, dense_rank, percent_rank, row_number, lag, lead, first_value, last_value, min, max, ema, vwema, exponential moving average]
+description: Complete reference for all window functions in QuestDB including avg, sum, ksum, count, stddev, variance, covariance, correlation, rank, dense_rank, percent_rank, ntile, cume_dist, row_number, lag, lead, nth_value, EMA, VWEMA, and more.
+keywords: [window functions, avg, sum, ksum, count, stddev, stddev_pop, stddev_samp, var_pop, var_samp, variance, covar_pop, covar_samp, corr, correlation, rank, dense_rank, percent_rank, ntile, cume_dist, row_number, lag, lead, first_value, last_value, nth_value, min, max, ema, vwema, exponential moving average]
 ---
 
 This page provides detailed documentation for each window function. For an introduction to window functions and how they work, see the [Overview](overview.md). For syntax details on the `OVER` clause, see [OVER Clause Syntax](syntax.md).
@@ -29,12 +29,12 @@ avg(value, kind, param, volume) OVER (window_definition)
 
 **Arguments:**
 - `value`: Numeric column (`short`, `int`, `long`, `float`, `double`) to calculate the average of
-- `kind` (EMA/VWEMA): Smoothing mode - `'alpha'`, `'period'`, or a time unit (`'second'`, `'minute'`, `'hour'`, `'day'`, `'week'`)
+- `kind` (EMA/VWEMA): Smoothing mode. One of `'alpha'`, `'period'`, or a time unit (`'second'`, `'minute'`, `'hour'`, `'day'`, `'week'`)
 - `param` (EMA/VWEMA): Parameter for the smoothing mode (see below)
 - `volume` (VWEMA only): Numeric column representing volume weights
 
 **Return value:**
-- `double` - The average of `value` for rows in the window frame
+- `double`. The average of `value` for rows in the window frame
 
 **Description:**
 
@@ -59,7 +59,7 @@ SELECT
         ROWS BETWEEN 3 PRECEDING AND CURRENT ROW
     ) AS moving_avg
 FROM trades
-WHERE timestamp IN '[$today]';
+WHERE timestamp IN '$today';
 ```
 
 #### Exponential Moving Average (EMA)
@@ -88,7 +88,7 @@ SELECT
         ORDER BY timestamp
     ) AS ema_alpha
 FROM trades
-WHERE timestamp IN '[$today]';
+WHERE timestamp IN '$today';
 ```
 
 ```questdb-sql title="10-period EMA" demo
@@ -101,7 +101,7 @@ SELECT
         ORDER BY timestamp
     ) AS ema_10
 FROM trades
-WHERE timestamp IN '[$today]';
+WHERE timestamp IN '$today';
 ```
 
 ```questdb-sql title="Time-weighted EMA with 5-minute decay" demo
@@ -114,7 +114,7 @@ SELECT
         ORDER BY timestamp
     ) AS ema_5min
 FROM trades
-WHERE timestamp IN '[$today]';
+WHERE timestamp IN '$today';
 ```
 
 :::note EMA behavior
@@ -148,7 +148,7 @@ SELECT
         ORDER BY timestamp
     ) AS vwema_alpha
 FROM trades
-WHERE timestamp IN '[$today]';
+WHERE timestamp IN '$today';
 ```
 
 ```questdb-sql title="10-period VWEMA" demo
@@ -161,7 +161,7 @@ SELECT
         ORDER BY timestamp
     ) AS vwema_10
 FROM trades
-WHERE timestamp IN '[$today]';
+WHERE timestamp IN '$today';
 ```
 
 ```questdb-sql title="Time-weighted VWEMA with 1-hour decay" demo
@@ -174,7 +174,7 @@ SELECT
         ORDER BY timestamp
     ) AS vwema_1h
 FROM trades
-WHERE timestamp IN '[$today]';
+WHERE timestamp IN '$today';
 ```
 
 :::note VWEMA behavior
@@ -195,13 +195,13 @@ corr(y, x) OVER (window_definition)
 ```
 
 **Arguments:**
-- `y`: Numeric column - the dependent variable
-- `x`: Numeric column - the independent variable
+- `y`: Numeric column, the dependent variable
+- `x`: Numeric column, the independent variable
 
 Rows where either `x` or `y` is `NULL` are excluded from the computation.
 
 **Return value:**
-- `double` - The Pearson correlation coefficient. Returns `NULL` when there are fewer than 2 valid pairs, or when either variable has zero variance (all values identical).
+- `double`. The Pearson correlation coefficient. Returns `NULL` when there are fewer than 2 valid pairs, or when either variable has zero variance (all values identical).
 
 **Description:**
 
@@ -254,7 +254,7 @@ count(value) OVER (window_definition)
 - `value`: Counts non-null values only
 
 **Return value:**
-- `long` - Number of rows or non-null values in the window frame
+- `long`. Number of rows or non-null values in the window frame
 
 **Description:**
 
@@ -276,7 +276,7 @@ SELECT
         RANGE BETWEEN '1' SECOND PRECEDING AND CURRENT ROW
     ) AS trades_last_second
 FROM trades
-WHERE timestamp IN '[$today]';
+WHERE timestamp IN '$today';
 ```
 
 ---
@@ -292,13 +292,13 @@ covar_samp(y, x) OVER (window_definition)
 ```
 
 **Arguments:**
-- `y`: Numeric column - the dependent variable
-- `x`: Numeric column - the independent variable
+- `y`: Numeric column, the dependent variable
+- `x`: Numeric column, the independent variable
 
 Rows where either `x` or `y` is `NULL` are excluded from the computation.
 
 **Return value:**
-- `double` - The covariance of `y` and `x` for rows in the window frame. Returns `NULL` when there are fewer than 1 (pop) or 2 (samp) valid pairs.
+- `double`. The covariance of `y` and `x` for rows in the window frame. Returns `NULL` when there are fewer than 1 (pop) or 2 (samp) valid pairs.
 
 **Description:**
 
@@ -341,7 +341,7 @@ OVER ([PARTITION BY partition_expression]
 - `RESPECT NULLS` (default): Include null values
 
 **Return value:**
-- Same type as input - The first value in the window frame (or first non-null with `IGNORE NULLS`)
+- Same type as input. The first value in the window frame (or first non-null with `IGNORE NULLS`)
 
 **Description:**
 
@@ -367,7 +367,7 @@ SELECT
         ORDER BY timestamp
     ) AS first_non_null_price
 FROM trades
-WHERE timestamp IN '[$today]';
+WHERE timestamp IN '$today';
 ```
 
 ---
@@ -385,7 +385,7 @@ ksum(value) OVER (window_definition)
 - `value`: Numeric column (`short`, `int`, `long`, `float`, `double`) to sum
 
 **Return value:**
-- `double` - The sum of `value` for rows in the window frame with improved precision
+- `double`. The sum of `value` for rows in the window frame with improved precision
 
 **Description:**
 
@@ -410,7 +410,7 @@ SELECT
         ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
     ) AS cumulative_price
 FROM trades
-WHERE timestamp IN '[$today]';
+WHERE timestamp IN '$today';
 ```
 
 ```questdb-sql title="Sliding window sum with precision" demo
@@ -423,7 +423,7 @@ SELECT
         ROWS BETWEEN 3 PRECEDING AND CURRENT ROW
     ) AS rolling_sum
 FROM trades
-WHERE timestamp IN '[$today]';
+WHERE timestamp IN '$today';
 ```
 
 ---
@@ -446,7 +446,7 @@ OVER ([PARTITION BY partition_expression]
 - `RESPECT NULLS` (default): Include null values
 
 **Return value:**
-- Same type as input - The last value in the window frame (or last non-null with `IGNORE NULLS`)
+- Same type as input. The last value in the window frame (or last non-null with `IGNORE NULLS`)
 
 **Description:**
 
@@ -476,7 +476,7 @@ SELECT
         ORDER BY timestamp
     ) AS last_non_null_price
 FROM trades
-WHERE timestamp IN '[$today]';
+WHERE timestamp IN '$today';
 ```
 
 This example:
@@ -499,7 +499,7 @@ max(value) OVER (window_definition)
 - `value`: Numeric column (`short`, `int`, `long`, `float`, `double`)
 
 **Return value:**
-- Same type as input - The maximum value (excluding null) in the window frame
+- Same type as input. The maximum value (excluding null) in the window frame
 
 **Description:**
 
@@ -522,7 +522,7 @@ SELECT
         ROWS BETWEEN 3 PRECEDING AND CURRENT ROW
     ) AS highest_price
 FROM trades
-WHERE timestamp IN '[$today]';
+WHERE timestamp IN '$today';
 ```
 
 ---
@@ -540,7 +540,7 @@ min(value) OVER (window_definition)
 - `value`: Numeric column (`short`, `int`, `long`, `float`, `double`)
 
 **Return value:**
-- Same type as input - The minimum value (excluding null) in the window frame
+- Same type as input. The minimum value (excluding null) in the window frame
 
 **Description:**
 
@@ -563,11 +563,77 @@ SELECT
         ROWS BETWEEN 3 PRECEDING AND CURRENT ROW
     ) AS lowest_price
 FROM trades
-WHERE timestamp IN '[$today]';
+WHERE timestamp IN '$today';
 ```
 
 ---
 
+### nth_value() {#nth_value}
+
+Returns the `n`-th value (1-based) within the current window frame.
+
+**Syntax:**
+```questdb-sql
+nth_value(value, n) OVER (window_definition)
+```
+
+**Arguments:**
+- `value`: `double` column or expression to retrieve
+- `n`: Positive integer constant, the 1-based position within the frame
+
+**Return value:**
+- `double`. The `n`-th value in the window frame, or `NULL` when the frame contains fewer than `n` rows
+
+**Description:**
+
+`nth_value()` is similar to `lag()`, but while `lag()` counts the offset relative to the current row, `nth_value()` counts from the start of the frame. For each row, it looks at the rows currently in the frame and returns the `n`-th one. When the frame is smaller than `n` (e.g. `n = 3` but only 2 rows are in scope), the result is `NULL`.
+
+Common use cases include:
+
+- **Reference value within a window**: Compare the current row to a fixed slot in the window (e.g. the third price in the last 10 trades)
+- **Anchor points**: Pick out a specific row from each partition, such as the second observation in a session
+- **Quantile-style spot checks**: Combine with frame clauses to read a specific position in a sliding range
+
+**Behavior:**
+- `n` must be a compile-time constant. A non-constant expression for `n` is rejected at parse time
+- `n = 1` returns the same value as `first_value(value)` for the same frame
+- `IGNORE NULLS` / `RESPECT NULLS` are not supported
+- Supports both `ROWS` and `RANGE` frames, bounded and unbounded
+- For `RANGE` frames, the query must be ordered by the designated timestamp
+
+**Example:**
+```questdb-sql title="3rd most recent price in 5-row window" demo
+SELECT
+    symbol,
+    price,
+    timestamp,
+    nth_value(price, 3) OVER (
+        PARTITION BY symbol
+        ORDER BY timestamp
+        ROWS 4 PRECEDING
+    ) AS third_price
+FROM trades
+WHERE timestamp IN '$today';
+```
+
+```questdb-sql title="Compare nth_value with first_value" demo
+SELECT
+    symbol,
+    price,
+    timestamp,
+    first_value(price) OVER w AS first_price,
+    nth_value(price, 1) OVER w AS nth_1,
+    nth_value(price, 2) OVER w AS nth_2,
+    nth_value(price, 3) OVER w AS nth_3
+FROM trades
+WHERE timestamp IN '$today' AND symbol = 'BTC-USDT'
+WINDOW w AS (ORDER BY timestamp ROWS 2 PRECEDING);
+```
+
+With a 3-row frame, `nth_3` always equals the current row's `price` because it is the last position in the frame. `nth_1` and `nth_2` return `NULL` until the frame has enough rows to fill those positions.
+
+---
+
 ### stddev_pop() / stddev_samp() / stddev() {#stddev}
 
 Calculates the standard deviation of values over the window frame. `stddev_pop()` computes population standard deviation (divides by N), `stddev_samp()` computes sample standard deviation (divides by N-1). `stddev()` is an alias for `stddev_samp()`.
@@ -583,7 +649,7 @@ stddev(value) OVER (window_definition)
 - `value`: Numeric column (`short`, `int`, `long`, `float`, `double`)
 
 **Return value:**
-- `double` - The standard deviation of `value` for rows in the window frame. Returns `NULL` when there are no values (or no non-null values). `stddev_samp()` and `stddev()` also return `NULL` when there is only one value (since N-1 = 0).
+- `double`. The standard deviation of `value` for rows in the window frame. Returns `NULL` when there are no values (or no non-null values). `stddev_samp()` and `stddev()` also return `NULL` when there is only one value (since N-1 = 0).
 
 **Description:**
 
@@ -612,7 +678,7 @@ SELECT
         ROWS BETWEEN 19 PRECEDING AND CURRENT ROW
     ) AS volatility_20
 FROM trades
-WHERE timestamp IN '[$today]';
+WHERE timestamp IN '$today';
 ```
 
 ```questdb-sql title="Z-score via subquery"
@@ -642,7 +708,7 @@ sum(value) OVER (window_definition)
 - `value`: Numeric column (`short`, `int`, `long`, `float`, `double`)
 
 **Return value:**
-- `double` - The sum of `value` for rows in the window frame
+- `double`. The sum of `value` for rows in the window frame
 
 **Description:**
 
@@ -665,7 +731,7 @@ SELECT
         ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
     ) AS cumulative_amount
 FROM trades
-WHERE timestamp IN '[$today]';
+WHERE timestamp IN '$today';
 ```
 
 ---
@@ -685,7 +751,7 @@ variance(value) OVER (window_definition)
 - `value`: Numeric column (`short`, `int`, `long`, `float`, `double`)
 
 **Return value:**
-- `double` - The variance of `value` for rows in the window frame. Returns `NULL` when there are no values. `var_samp()` and `variance()` also return `NULL` for a single value.
+- `double`. The variance of `value` for rows in the window frame. Returns `NULL` when there are no values. `var_samp()` and `variance()` also return `NULL` for a single value.
 
 **Description:**
 
@@ -707,14 +773,79 @@ SELECT
         ROWS BETWEEN 9 PRECEDING AND CURRENT ROW
     ) AS price_variance
 FROM trades
-WHERE timestamp IN '[$today]';
+WHERE timestamp IN '$today';
 ```
 
 ---
 
 ## Ranking functions
 
-These functions assign ranks or row numbers. They ignore the frame clause and operate on the entire partition.
+These functions assign ranks, row numbers, or partition-scoped distribution values. They ignore the frame clause and operate on the entire partition.
+
+### cume_dist() {#cume_dist}
+
+Returns the cumulative distribution: the number of rows at or before the current row (including peers) divided by the total number of rows in the partition. The result lies in the range (0, 1].
+
+**Syntax:**
+```questdb-sql
+cume_dist() OVER (window_definition)
+```
+
+**Arguments:**
+- None required
+
+**Return value:**
+- `double`. The cumulative distribution value for the current row's peer group
+
+**Description:**
+
+`cume_dist()` is closely related to `percent_rank()`. Where `percent_rank()` reports relative position using `(rank - 1) / (total_rows - 1)`, `cume_dist()` reports the fraction of rows with `ORDER BY` values *at or before* the current row's value. All peer rows (rows with identical `ORDER BY` values) receive the same `cume_dist`, equal to the position of the last peer divided by total rows.
+
+Use `cume_dist()` to express thresholds in terms of how much of the partition has been seen so far. Common use cases include:
+
+- **Top/bottom percentile filters**: Keep only rows with `cume_dist <= 0.1` to grab the bottom 10% of a distribution
+- **Histogram bucketing**: Group rows by `cume_dist` ranges to build empirical CDFs
+- **Anomaly thresholds**: Flag rows that fall outside the bulk of the partition's distribution
+
+**Behavior:**
+- Without `ORDER BY`, all rows are peers and `cume_dist` is `1.0` for every row
+- The last peer group in a partition always evaluates to `1.0`
+- `ROWS`, `RANGE`, and `EXCLUDE` are rejected. `cume_dist` is always partition-scoped
+
+**Example:**
+```questdb-sql title="Cumulative distribution by price" demo
+SELECT
+    symbol,
+    price,
+    timestamp,
+    cume_dist() OVER (
+        PARTITION BY symbol
+        ORDER BY price
+    ) AS price_cdf
+FROM trades
+WHERE timestamp IN '$today' AND symbol = 'BTC-USDT'
+ORDER BY price DESC;
+```
+
+The highest prices appear first with `price_cdf = 1.0`, meaning 100% of trades are at or below that price. As you scroll down, the value decreases, showing what fraction of the partition each price level covers.
+
+```questdb-sql title="cume_dist with peer rows"
+SELECT ts, val,
+    cume_dist() OVER (ORDER BY val) AS cd
+FROM tab;
+```
+
+| ts | val | cd |
+|----|-----|-----|
+| 2026-05-08T09:30:00.000000Z | 1 | 0.4 |
+| 2026-05-08T09:30:01.000000Z | 1 | 0.4 |
+| 2026-05-08T09:30:02.000000Z | 2 | 0.8 |
+| 2026-05-08T09:30:03.000000Z | 2 | 0.8 |
+| 2026-05-08T09:30:04.000000Z | 3 | 1.0 |
+
+The two rows with `val = 1` are peers, so they share `cume_dist = 2 / 5 = 0.4`. Likewise the rows with `val = 2` share `cume_dist = 4 / 5 = 0.8`.
+
+---
 
 ### dense_rank()
 
@@ -753,11 +884,75 @@ SELECT
         ORDER BY price DESC
     ) AS price_rank
 FROM trades
-WHERE timestamp IN '[$today]';
+WHERE timestamp IN '$today';
 ```
 
 ---
 
+### ntile() {#ntile}
+
+Distributes the rows of an ordered partition into `n` approximately equal buckets and returns the 1-based bucket number for each row.
+
+**Syntax:**
+```questdb-sql
+ntile(n) OVER (window_definition)
+```
+
+**Arguments:**
+- `n`: Positive integer constant, the number of buckets
+
+**Return value:**
+- `long`. Bucket number from `1` to `n`
+
+**Description:**
+
+When the partition row count divides evenly by `n`, every bucket has the same size. When it doesn't, the larger buckets come first: with 10 rows and `n = 3`, the buckets contain 4, 3, and 3 rows.
+
+Use `ntile()` to build distribution-based groupings. Common use cases include:
+
+- **Quartiles, deciles, percentiles**: Use `ntile(4)`, `ntile(10)`, or `ntile(100)` to bucket rows by an ordered measure
+- **Even-sized batches**: Split a partition into `n` worker batches without writing manual range logic
+- **Tiered classification**: Assign records to numbered tiers (top tier, middle tier, bottom tier) by some ranked metric
+
+**Behavior:**
+- `n` must be a compile-time constant. A non-constant expression is rejected at parse time
+- `n` must be a positive integer; `0`, negative values, or `NULL` are rejected
+- Without `ORDER BY`, rows are bucketed in table-scan order
+- When `n` exceeds the partition row count, each row gets its own bucket (numbered `1` through row count) and the higher bucket numbers are unused
+- `ROWS`, `RANGE`, and `EXCLUDE` are rejected. `ntile` is always partition-scoped
+
+**Example:**
+```questdb-sql title="Quartiles per symbol" demo
+SELECT
+    symbol,
+    price,
+    timestamp,
+    ntile(4) OVER (
+        PARTITION BY symbol
+        ORDER BY price
+    ) AS price_quartile
+FROM trades
+WHERE timestamp IN '$today';
+```
+
+```questdb-sql title="ntile with uneven distribution"
+SELECT ts, val,
+    ntile(3) OVER (ORDER BY ts) AS bucket
+FROM tab;
+```
+
+| ts | val | bucket |
+|----|-----|--------|
+| 2026-05-08T09:30:00.000000Z | 10.0 | 1 |
+| 2026-05-08T09:30:01.000000Z | 20.0 | 1 |
+| 2026-05-08T09:30:02.000000Z | 30.0 | 2 |
+| 2026-05-08T09:30:03.000000Z | 40.0 | 2 |
+| 2026-05-08T09:30:04.000000Z | 50.0 | 3 |
+
+With 5 rows and `n = 3`, the leading buckets (1 and 2) get an extra row each.
+
+---
+
 ### percent_rank()
 
 Returns the relative rank of the current row within its partition as a value between 0 and 1.
@@ -805,7 +1000,7 @@ SELECT
         ORDER BY price DESC
     ) AS price_percentile
 FROM trades
-WHERE timestamp IN '[$today]';
+WHERE timestamp IN '$today';
 ```
 
 ```questdb-sql title="Compare rank functions" demo
@@ -816,7 +1011,7 @@ SELECT
     rank() OVER (ORDER BY price DESC) AS rank,
     percent_rank() OVER (ORDER BY price DESC) AS percent_rank
 FROM trades
-WHERE timestamp IN '[$today]'
+WHERE timestamp IN '$today'
     AND symbol = 'BTC-USDT';
 ```
 
@@ -869,7 +1064,7 @@ SELECT
         ORDER BY price DESC
     ) AS price_rank
 FROM trades
-WHERE timestamp IN '[$today]';
+WHERE timestamp IN '$today';
 ```
 
 ---
@@ -911,7 +1106,7 @@ SELECT
         ORDER BY timestamp
     ) AS trade_number
 FROM trades
-WHERE timestamp IN '[$today]';
+WHERE timestamp IN '$today';
 ```
 
 ---
@@ -958,7 +1153,7 @@ OVER ([PARTITION BY partition_expression] [ORDER BY sort_expression])
 - `RESPECT NULLS` (default): Include null values in offset counting
 
 **Return value:**
-- Same type as input - Value from the specified previous row
+- Same type as input. Value from the specified previous row
 
 **Description:**
 
@@ -988,7 +1183,7 @@ SELECT
         ORDER BY timestamp
     ) AS price_two_rows_back
 FROM trades
-WHERE timestamp IN '[$today]';
+WHERE timestamp IN '$today';
 ```
 
 This example:
@@ -1016,7 +1211,7 @@ OVER ([PARTITION BY partition_expression] [ORDER BY sort_expression])
 - `RESPECT NULLS` (default): Include null values in offset counting
 
 **Return value:**
-- Same type as input - Value from the specified following row
+- Same type as input. Value from the specified following row
 
 **Description:**
 
@@ -1046,7 +1241,7 @@ SELECT
         ORDER BY timestamp
     ) AS price_after_next
 FROM trades
-WHERE timestamp IN '[$today]';
+WHERE timestamp IN '$today';
 ```
 
 This example:
@@ -1072,7 +1267,7 @@ SELECT
         ROWS BETWEEN 3 PRECEDING AND CURRENT ROW
     ) AS bid_moving_avg
 FROM market_data
-WHERE timestamp IN '[$today]';
+WHERE timestamp IN '$today';
 ```
 
 ### Cumulative bid size
@@ -1090,7 +1285,7 @@ SELECT
         ROWS BETWEEN 5 PRECEDING AND CURRENT ROW
     ) AS bid_volume_l1_5rows
 FROM market_data
-WHERE timestamp IN '[$today]';
+WHERE timestamp IN '$today';
 ```
 
 ### Time-based rolling sum
@@ -1106,7 +1301,7 @@ SELECT
         RANGE BETWEEN '1' MINUTE PRECEDING AND CURRENT ROW
     ) AS bid_volume_1min
 FROM market_data
-WHERE timestamp IN '[$today]' AND symbol = 'GBPUSD';
+WHERE timestamp IN '$today' AND symbol = 'GBPUSD';
 ```
 
 ### Trade frequency analysis
@@ -1121,7 +1316,7 @@ SELECT
     COUNT(CASE WHEN side = 'buy' THEN 1 END) OVER w AS buys_per_minute,
     COUNT(CASE WHEN side = 'sell' THEN 1 END) OVER w AS sells_per_minute
 FROM trades
-WHERE timestamp IN '[$today]' AND symbol = 'BTC-USDT'
+WHERE timestamp IN '$today' AND symbol = 'BTC-USDT'
 WINDOW w AS (ORDER BY timestamp RANGE BETWEEN 60000000 PRECEDING AND CURRENT ROW);
 ```
 
@@ -1130,7 +1325,6 @@ WINDOW w AS (ORDER BY timestamp RANGE BETWEEN 60000000 PRECEDING AND CURRENT ROW
 ## Notes
 
 - The order of rows in the result set is not guaranteed to be consistent across query executions. Use an `ORDER BY` clause outside the `OVER` clause to ensure consistent ordering.
-- Ranking functions (`row_number`, `rank`, `dense_rank`, `percent_rank`) and offset functions (`lag`, `lead`) ignore frame specifications.
+- Ranking functions (`row_number`, `rank`, `dense_rank`, `percent_rank`, `cume_dist`, `ntile`) and offset functions (`lag`, `lead`) ignore frame specifications.
 - For time-based calculations, consider using `RANGE` frames with timestamp columns.
 - Aggregate window functions (`avg`, `sum`, `ksum`, `count`, `min`, `max`) support numeric types: `short`, `int`, `long`, `float`, `double`. The `decimal` type is not supported.
-- `ntile()` and `cume_dist()` are not currently supported.