Metrics collection in HLD - Scalability & System Analysis

Scalability Analysis - Metrics collection

Growth Table: Metrics Collection System

Scale	Users / Devices	Metrics Volume	Data Storage	Query Load	System Changes
Small (100 users)	100 devices	~10K metrics/min	Single DB instance	Low QPS	Simple ingestion, single server
Medium (10K users)	10,000 devices	~1M metrics/min	DB with read replicas	Moderate QPS	Introduce caching, load balancer
Large (1M users)	1,000,000 devices	~100M metrics/min	Sharded DB, distributed storage	High QPS	Partitioning, message queues, batch processing
Very Large (100M users)	100,000,000 devices	~10B metrics/min	Multi-region distributed storage	Very high QPS	Advanced sharding, edge processing, CDN for dashboards

First Bottleneck

At small to medium scale, the database is the first bottleneck because it must handle many writes and queries per second. The ingestion pipeline can overwhelm the DB with high write QPS. As scale grows, network bandwidth and storage I/O also become bottlenecks.

Scaling Solutions

• Horizontal scaling: Add more ingestion servers behind a load balancer to handle more incoming metrics.
• Caching: Use in-memory caches (e.g., Redis) to reduce read load on the database for frequent queries.
• Sharding: Partition data by device ID or time to distribute load across multiple database instances.
• Message queues: Use Kafka or similar to buffer and batch writes, smoothing spikes in traffic.
• Distributed storage: Use scalable time-series databases or cloud storage optimized for large write volumes.
• Edge processing: Aggregate or filter metrics near the source to reduce data volume sent to central servers.
• CDN for dashboards: Cache dashboard data closer to users to reduce backend query load.

Back-of-Envelope Cost Analysis

• At 1M users sending 100 metrics/minute: 100 million metrics/minute ≈ 1.7 million writes/second.
• Storage needed: Assuming 200 bytes per metric, 1.7M writes/sec × 200 bytes ≈ 340 MB/s write throughput.
• Network bandwidth: 340 MB/s ≈ 2.7 Gbps, requiring multiple network interfaces or distributed ingestion.
• Database QPS: Single DB handles ~5K QPS, so need ~340 DB shards or use specialized time-series DB.
• Cost scales with number of servers, storage, and network infrastructure.

Interview Tip

Start by clarifying the scale and data volume. Identify the main bottleneck (usually DB writes). Discuss incremental scaling steps: caching, sharding, message queues. Mention trade-offs like consistency vs latency. Show awareness of cost and complexity.

Self Check

Your database handles 1000 QPS. Traffic grows 10x to 10,000 QPS. What do you do first?

Answer: Introduce read replicas and caching to reduce DB load, and implement message queues to batch writes. Consider sharding data to distribute load across multiple DB instances.

Key Result

Metrics collection systems first hit database write bottlenecks as user and data volume grow; scaling requires horizontal ingestion, caching, sharding, and distributed storage.

Practice

1. What is the main purpose of using aggregate functions like COUNT, SUM, and AVG in metrics collection?

easy

A. To create new tables for storing data

B. To change the structure of the database tables

C. To calculate totals, averages, and counts from data

D. To delete unwanted data from the database

Solution

1. Step 1: Understand aggregate functions

   Aggregate functions like COUNT, SUM, and AVG are used to calculate summary values from multiple rows of data.

2. Step 2: Identify their purpose in metrics

   These functions help get totals, averages, and counts which are useful for metrics and reports.

3. Final Answer:

   To calculate totals, averages, and counts from data -> Option C

4. Quick Check:

   Aggregate functions = summary numbers [OK]

Hint: Aggregate functions summarize data into numbers fast [OK]

Common Mistakes:

• Thinking aggregate functions modify data structure
• Confusing aggregate functions with data deletion
• Believing aggregate functions create tables

2. Which of the following SQL queries correctly counts the number of orders in a table named Orders?

easy

A. SELECT COUNT(Orders) FROM *;

B. SELECT COUNT FROM Orders;

C. SELECT SUM(*) FROM Orders;

D. SELECT COUNT(*) FROM Orders;

Solution

1. Step 1: Recall correct COUNT syntax

   The correct syntax to count rows is COUNT(*) inside SELECT.

2. Step 2: Check each option

   SELECT COUNT(*) FROM Orders; uses COUNT(*) correctly. Others have syntax errors or wrong functions.

3. Final Answer:

   SELECT COUNT(*) FROM Orders; -> Option D

4. Quick Check:

   COUNT(*) counts rows correctly [OK]

Hint: Use COUNT(*) to count all rows in a table [OK]

Common Mistakes:

• Omitting parentheses after COUNT
• Using COUNT without specifying columns or *
• Confusing SUM with COUNT

3. Given the table Sales with columns amount and region, what will this query return?

SELECT AVG(amount) FROM Sales WHERE region = 'East';

medium

A. The average sales amount for the East region

B. The total sales amount for all regions

C. The count of sales records in the East region

D. An error because AVG cannot be used with WHERE

Solution

1. Step 1: Understand the WHERE clause effect

   The WHERE clause filters rows to only those where region is 'East'.

2. Step 2: Understand AVG function

   AVG calculates the average of the filtered amount values.

3. Final Answer:

   The average sales amount for the East region -> Option A

4. Quick Check:

   AVG with WHERE filters = average for filtered data [OK]

Hint: WHERE filters rows before aggregate functions run [OK]

Common Mistakes:

• Thinking AVG returns total sum
• Confusing COUNT with AVG
• Believing WHERE cannot be used with aggregates

4. Identify the error in this SQL query that tries to get the total sales for the 'West' region:

SELECT SUM(amount) FROM Sales WHERE region == 'West';

medium

A. Using double equals (==) instead of single equals (=) in WHERE clause

B. SUM cannot be used with WHERE clause

C. Missing GROUP BY clause for aggregation

D. amount column cannot be summed

Solution

1. Step 1: Check WHERE clause syntax

   SQL uses single equals (=) for comparison, not double equals (==).

2. Step 2: Confirm aggregate and WHERE usage

   SUM works with WHERE filtering; GROUP BY is not needed here.

3. Final Answer:

   Using double equals (==) instead of single equals (=) in WHERE clause -> Option A

4. Quick Check:

   Use = for comparisons in SQL WHERE [OK]

Hint: Use single = for comparisons in SQL WHERE [OK]

Common Mistakes:

• Using == instead of = in WHERE
• Thinking GROUP BY is always required with SUM
• Assuming SUM cannot be used with WHERE

5. You want to find the average order amount per customer but only for customers who placed more than 5 orders. Which SQL approach correctly achieves this?

hard

A. Use WHERE COUNT(*) > 5 and SELECT AVG(amount) without GROUP BY

B. Use GROUP BY customer_id with HAVING COUNT(*) > 5 and SELECT AVG(amount)

C. Use WHERE AVG(amount) > 5 and GROUP BY customer_id

D. Use HAVING AVG(amount) > 5 without GROUP BY

Solution

1. Step 1: Understand grouping by customer

   To get average per customer, group rows by customer_id.

2. Step 2: Filter groups with more than 5 orders

   Use HAVING with COUNT(*) > 5 to keep only customers with more than 5 orders.

3. Step 3: Select average order amount

   Use AVG(amount) in SELECT to get average per filtered customer group.

4. Final Answer:

   Use GROUP BY customer_id with HAVING COUNT(*) > 5 and SELECT AVG(amount) -> Option B

5. Quick Check:

   GROUP BY + HAVING filters groups before AVG [OK]

Hint: Use HAVING to filter groups after GROUP BY [OK]

Common Mistakes:

• Using WHERE to filter aggregated counts
• Forgetting GROUP BY when using aggregates per group
• Using HAVING without GROUP BY