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Find Students with Study Spiral Pattern
Solve Find Students with Study Spiral Pattern by validating each student's ordered subject cycle, consistency, and repeatable rotation across sessions.
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Hard · Find Students with Study Spiral Pattern core interview pattern
Answer-first summary
Solve Find Students with Study Spiral Pattern by validating each student's ordered subject cycle, consistency, and repeatable rotation across sessions.
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Find Students with Study Spiral Pattern is a sequence-validation SQL problem, not a simple aggregation query. You need to order each student's sessions, detect the repeating subject cycle they follow, and reject students whose rotation breaks, skips, or collapses into a trivial pattern. The hard part is proving the cycle is consistent across the full timeline instead of matching a few adjacent rows.
Problem Statement
You are given a students table with each student's identity and major, and a study_sessions table that records subject, date, and hours for every study session. The goal is to return the students who follow a study spiral pattern, meaning their study history forms a repeated rotation across multiple subjects instead of random or one-off switches.
A correct solution must inspect each student's sessions in time order and verify that the subject sequence keeps cycling through the same set in the same relative order. Students should be excluded when their sequence breaks the rotation, when the pattern does not truly repeat, or when the data only shows a trivial single-subject streak that does not satisfy the intended spiral behavior.
Examples
Example 1
Input: See original problem statement.
Output: See original problem statement.
+--------------+---------+ | Column Name | Type | +--------------+---------+ | student_id | int | | student_name | varchar | | major | varchar | +--------------+---------+ student_id is the unique identifier for this table. Each row contains information about a student and their academic major.
Example 2
Input: See original problem statement.
Output: See original problem statement.
+---------------+---------+ | Column Name | Type | +---------------+---------+ | session_id | int | | student_id | int | | subject | varchar | | session_date | date | | hours_studied | decimal | +---------------+---------+ session_id is the unique identifier for this table. Each row represents a study session by a student for a specific subject.
Example 3
Input: See original problem statement.
Output: See original problem statement.
+------------+--------------+------------------+ | student_id | student_name | major | +------------+--------------+------------------+ | 1 | Alice Chen | Computer Science | | 2 | Bob Johnson | Mathematics | | 3 | Carol Davis | Physics | | 4 | David Wilson | Chemistry | | 5 | Emma Brown | Biology | +------------+--------------+------------------+
Constraints
Solution Approach
Order sessions before checking any pattern
Start by partitioning study_sessions by student_id and ordering by session_date, plus a stable tiebreaker like session_id if needed. Without a deterministic order, every downstream cycle check is unreliable, and the most common wrong answer is validating a rotation on rows that were never meant to be adjacent.
Derive the candidate rotation per student
For each student, identify the repeating subject loop rather than comparing only neighboring rows. A practical SQL path is to assign row numbers, group equivalent positions in the cycle, and confirm that each subject reappears at the same modular offset. This turns the problem into testing whether the full ordered sequence can be explained by one stable rotating cycle across multiple subjects.
Filter out broken, partial, and trivial sequences
After deriving a candidate cycle, keep only students whose entire timeline fits it. Reject sequences with out-of-order returns, duplicate subjects in the same cycle position, incomplete repetition, or a single repeated subject that never rotates. Join the passing student_ids back to students to return the final student details.
Complexity Analysis
| Metric | Value |
|---|---|
| Time | Depends on the final approach |
| Space | Depends on the final approach |
The exact cost depends on the SQL formulation, but most correct solutions spend their time sorting each student's sessions and then scanning the ordered rows to validate cycle positions. That usually makes the dominant work an ordered per-student pass after sorting, with extra space for row numbers, grouped offsets, or intermediate cycle checks.
What Interviewers Usually Probe
- They emphasize ordered sessions, which signals this is a timeline validation problem rather than a plain COUNT and GROUP BY task.
- They use rotating cycle language, which hints that modular positions or repeated offsets matter more than local pairwise comparisons.
- They call out consistency across multiple subjects, which is a warning not to accept one-subject repetition or a pattern that only appears in a short prefix.
Common Pitfalls or Variants
Common pitfalls
- Checking only consecutive subject changes and missing that a later row breaks the claimed cycle.
- Building the pattern from unordered sessions, which can make a non-spiral student look valid.
- Accepting any repetition as a spiral, even when the student studies only one subject or never completes a full multi-subject loop.
Follow-up variants
- Require the spiral to cover exactly three subjects, which changes the validation from unknown cycle length to fixed modular slots.
- Allow repeated sessions on the same date and ask for deterministic tie handling, which makes stable ordering part of the core logic.
- Ask for the longest valid spiral prefix per student instead of only fully valid students, which turns the filter into segment detection.
FAQ
What makes Find Students with Study Spiral Pattern hard?
The difficulty is not joining the tables. The hard part is proving that each student's entire ordered subject history fits one repeatable multi-subject rotation, with no hidden break later in the sequence.
Why is GROUP BY alone usually not enough here?
Counts per subject do not show order. A student can have balanced subject totals and still fail the study spiral because the sessions return in the wrong sequence.
What is the core interview pattern in this problem?
It is ordered sequence validation with cycle detection. You sort each student's rows, derive the candidate rotation, and verify that every position in the timeline matches that same rotating structure.
How do I avoid false positives on partial repeats?
Do not stop after finding a small repeating window. Validate the full session timeline and reject students whose later rows break the cycle or never complete enough repetition to prove a stable spiral.
Should a single repeated subject count as a study spiral pattern?
No, not under the intended multi-subject rotating-cycle interpretation. The problem is about students rotating through multiple subjects consistently, so a one-subject streak is a trivial repeat, not a spiral.
Solution
Solution 1
#### MySQL
import pandas as pd
from datetime import timedelta
def find_study_spiral_pattern(
students: pd.DataFrame, study_sessions: pd.DataFrame
) -> pd.DataFrame:
# Convert session_date to datetime
study_sessions["session_date"] = pd.to_datetime(study_sessions["session_date"])
result = []
# Group study sessions by student
for student_id, group in study_sessions.groupby("student_id"):
# Sort sessions by date
group = group.sort_values("session_date").reset_index(drop=True)
temp = [] # Holds current contiguous segment
last_date = None
for idx, row in group.iterrows():
if not temp:
temp.append(row)
else:
delta = (row["session_date"] - last_date).days
if delta <= 2:
temp.append(row)
else:
# Check the previous contiguous segment
if len(temp) >= 6:
_check_pattern(student_id, temp, result)
temp = [row]
last_date = row["session_date"]
# Check the final segment
if len(temp) >= 6:
_check_pattern(student_id, temp, result)
# Build result DataFrame
df_result = pd.DataFrame(
result, columns=["student_id", "cycle_length", "total_study_hours"]
)
if df_result.empty:
return pd.DataFrame(
columns=[
"student_id",
"student_name",
"major",
"cycle_length",
"total_study_hours",
]
)
# Join with students table to get name and major
df_result = df_result.merge(students, on="student_id")
df_result = df_result[
["student_id", "student_name", "major", "cycle_length", "total_study_hours"]
]
return df_result.sort_values(
by=["cycle_length", "total_study_hours"], ascending=[False, False]
).reset_index(drop=True)
def _check_pattern(student_id, sessions, result):
subjects = [row["subject"] for row in sessions]
hours = sum(row["hours_studied"] for row in sessions)
n = len(subjects)
# Try possible cycle lengths from 3 up to half of the sequence
for cycle_len in range(3, n // 2 + 1):
if n % cycle_len != 0:
continue
# Extract the first cycle
first_cycle = subjects[:cycle_len]
is_pattern = True
# Compare each following cycle with the first
for i in range(1, n // cycle_len):
if subjects[i * cycle_len : (i + 1) * cycle_len] != first_cycle:
is_pattern = False
break
# If a repeated cycle is detected, store the result
if is_pattern:
result.append(
{
"student_id": student_id,
"cycle_length": cycle_len,
"total_study_hours": hours,
}
)
break # Stop at the first valid cycle found