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94. Insert Interval

hardAsked at Snowflake

Insert a new interval into a sorted list of non-overlapping intervals, merging where needed. Snowflake asks this for streaming-friendly interval management — relevant to incremental micro-partition coalescing during continuous ingestion.

By Alex Chen, Founder, InterviewChamp.AI · Last verified

Source citations

Public interview reports confirming this problem appears in Snowflake loops.

  • Glassdoor (2025-Q4)— Snowflake clustering team uses this in onsites.
  • LeetCode Discuss (2025-11)— Reported at Snowflake SDE-II screens.

Problem

You are given an array of non-overlapping intervals intervals where intervals[i] = [start_i, end_i] represent the start and the end of the i-th interval and intervals is sorted in ascending order by start_i. You are also given an interval newInterval = [start, end] that represents the start and end of another interval. Insert newInterval into intervals such that intervals is still sorted in ascending order by start_i and intervals still does not have any overlapping intervals (merge overlapping intervals if necessary). Return intervals after the insertion.

Constraints

  • 0 <= intervals.length <= 10^4
  • intervals[i].length == 2
  • 0 <= start_i <= end_i <= 10^5
  • intervals is sorted by start_i in ascending order.
  • newInterval.length == 2

Examples

Example 1

Input
intervals = [[1,3],[6,9]], newInterval = [2,5]
Output
[[1,5],[6,9]]

Example 2

Input
intervals = [[1,2],[3,5],[6,7],[8,10],[12,16]], newInterval = [4,8]
Output
[[1,2],[3,10],[12,16]]

Approaches

1. Append and re-merge

Append newInterval, then re-run the LC 56 Merge Intervals algorithm.

Time
O(n log n)
Space
O(n)
// outline only — works but does a full sort.

Tradeoff: Throws away the fact that intervals is already sorted.

2. Three-phase single pass (optimal)

Phase 1: copy intervals fully before newInterval. Phase 2: merge overlapping with newInterval. Phase 3: copy remaining.

Time
O(n)
Space
O(n) output
function insert(intervals, newInterval) {
  const result = [];
  let i = 0;
  while (i < intervals.length && intervals[i][1] < newInterval[0]) {
    result.push(intervals[i++]);
  }
  while (i < intervals.length && intervals[i][0] <= newInterval[1]) {
    newInterval[0] = Math.min(newInterval[0], intervals[i][0]);
    newInterval[1] = Math.max(newInterval[1], intervals[i][1]);
    i++;
  }
  result.push(newInterval);
  while (i < intervals.length) result.push(intervals[i++]);
  return result;
}

Tradeoff: Linear, single pass. Leverages the sortedness fully.

Snowflake-specific tips

Snowflake interviewers want the three-phase pattern explicitly. Bonus signal: connect to continuous ingestion — when streaming ingestion adds new micro-partitions, the coalescer must merge them with overlapping existing partitions in a similar three-phase manner.

Common mistakes

  • Using strict < and > inconsistently — be precise about touching intervals.
  • Merging when not overlapping (newInterval still left of next interval).
  • Mutating newInterval and reusing it without copying — fine here but a gotcha in general.

Follow-up questions

An interviewer at Snowflake may pivot to one of these next:

  • Merge Intervals (LC 56) — the underlying primitive.
  • Remove Interval (LC 1272).
  • How does Snowflake coalesce streaming-ingested partitions?

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Output

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FAQ

Why three phases?

It's the natural structure: things that finish before newInterval (skip), things that overlap (merge), things that start after (skip). Each phase advances i monotonically.

How does this connect to ingestion?

Streaming ingestion adds new partitions with [min, max] ranges. The coalescer merges overlapping ranges in real time — this problem is the in-memory equivalent.

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