Sharding and partitioning in DBMS Theory - Step-by-Step Execution

Concept Flow - Sharding and partitioning

Start: Large Database

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Decide to split data

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Partitioning: Split data within one server

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Data divided into parts

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Each part stored in same server

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Sharding: Split data across servers

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Data divided into shards

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Each shard stored on different server

The flow shows how a large database is split either by partitioning within one server or by sharding across multiple servers.

Execution Sample

DBMS Theory

Database with 100 million records
Partition by 'Region' within one server
Shard by 'User ID' across 3 servers

This example splits data first by region inside one server (partitioning), then splits user data across three servers (sharding).

Analysis Table

Step	Action	Data Split Method	Resulting Data Location	Notes
1	Start with full database	None	Single large database	All data in one place
2	Partition by Region	Partitioning	Data divided into regions inside one server	Each region is a partition
3	Access data for Region A	Partitioning	Read from Region A partition	Faster access to smaller data
4	Shard by User ID across 3 servers	Sharding	Data split into 3 shards on different servers	Each server holds a shard
5	Access user with ID 123	Sharding	Query directed to server holding shard for ID 123	Reduces load on any one server
6	Add new server	Sharding	Rebalance shards across 4 servers	Data redistributed for balance
7	End	None	Data split efficiently	Improved performance and scalability

💡 Data splitting stops when data is distributed either by partitioning or sharding for efficient access.

State Tracker

Variable	Start	After Partitioning	After Sharding	After Rebalancing
Data Location	Single server	Multiple partitions in one server	Multiple servers with shards	Shards redistributed across servers
Query Target	Single database	Partition based on region	Shard based on user ID	Shard based on new distribution

Key Insights - 3 Insights

What is the main difference between partitioning and sharding?

Partitioning splits data within the same server into parts, while sharding splits data across multiple servers. See execution_table steps 2 and 4.

Why does sharding improve performance compared to partitioning?

Sharding spreads data and load across servers, reducing the work each server does. Partitioning only divides data inside one server, so load is not distributed. See execution_table steps 4 and 5.

What happens when a new server is added in sharding?

Data shards are rebalanced and redistributed to include the new server, improving load balance. See execution_table step 6.

Visual Quiz - 3 Questions

Look at the execution_table at step 3. Where is the data accessed from when querying Region A?

A From a partition inside the same server

B From a shard on a different server

C From the entire database without splitting

D From a backup server

💡 Hint

Check the 'Resulting Data Location' column at step 3 in execution_table.

At which step does the data start being split across multiple servers?

A Step 2

B Step 3

C Step 4

D Step 6

💡 Hint

Look for when 'Data split into 3 shards on different servers' happens in execution_table.

If the database was only partitioned and not sharded, what would be true about the data location?

A Data would be split across multiple servers

B Data would remain in one server but divided into parts

C Data would be duplicated on all servers

D Data would be stored in the cloud only

💡 Hint

Refer to variable_tracker row 'Data Location' after Partitioning.

Concept Snapshot

Sharding and partitioning split large databases for better performance.
Partitioning divides data inside one server into parts.
Sharding splits data across multiple servers (shards).
Sharding improves scalability by distributing load.
Adding servers requires rebalancing shards.
Use partitioning for simpler splits, sharding for large scale.

Full Transcript

Sharding and partitioning are methods to split large databases to improve performance and scalability. Partitioning divides data into parts within the same server, such as by region, making access faster by focusing on smaller data sets. Sharding splits data across multiple servers, each holding a shard, which spreads the load and allows the system to handle more users or data. When a new server is added, shards are rebalanced to keep the load even. This visual trace shows the steps from a single large database to partitioning by region, then sharding by user ID across servers, and finally rebalancing shards when adding servers.

Practice

1. What is the main difference between sharding and partitioning in databases?

easy

A. Sharding divides data within one database; partitioning spreads data across multiple servers.

B. Partitioning divides data within one database; sharding spreads data across multiple servers.

C. Both sharding and partitioning mean the same and are used interchangeably.

D. Partitioning is used only for backups, while sharding is for data security.

Solution

1. Step 1: Understand partitioning

   Partitioning splits data inside a single database into smaller parts for easier management and faster queries.

2. Step 2: Understand sharding

   Sharding spreads data across multiple servers or machines to handle very large datasets and improve performance.

3. Final Answer:

   Partitioning divides data within one database; sharding spreads data across multiple servers. -> Option B

4. Quick Check:

   Partitioning = single database, Sharding = multiple servers [OK]

Hint: Partitioning = one DB; Sharding = many servers [OK]

Common Mistakes:

• Confusing sharding with partitioning
• Thinking both are the same
• Assuming partitioning involves multiple servers

2. Which of the following is a correct way to describe horizontal partitioning in a database?

easy

A. Splitting a table into multiple tables with the same columns but different rows.

B. Splitting a table into multiple tables with different columns but same rows.

C. Combining multiple tables into one large table.

D. Backing up the entire database to a separate server.

Solution

1. Step 1: Define horizontal partitioning

   Horizontal partitioning means dividing a table by rows, so each partition has the same columns but different sets of rows.

2. Step 2: Check options

   Splitting a table into multiple tables with the same columns but different rows. matches this definition exactly, while others describe different concepts or unrelated actions.

3. Final Answer:

   Splitting a table into multiple tables with the same columns but different rows. -> Option A

4. Quick Check:

   Horizontal partitioning = split rows [OK]

Hint: Horizontal partitioning splits rows, not columns [OK]

Common Mistakes:

• Mixing horizontal with vertical partitioning
• Thinking partitioning means backup
• Confusing rows with columns

3. Consider a database sharded by user ID across three servers: Server 1 stores users with IDs ending in 0-3, Server 2 stores 4-6, and Server 3 stores 7-9. If a query requests data for user ID 27, which server will handle the request?

medium

A. Server 3

B. Server 2

C. Server 1

D. All servers

Solution

1. Step 1: Identify the shard key and ranges

   The sharding is based on the last digit of user ID: 0-3 on Server 1, 4-6 on Server 2, 7-9 on Server 3.

2. Step 2: Find the last digit of user ID 27

   The last digit of 27 is 7, which falls in the 7-9 range assigned to Server 3.

3. Final Answer:

   Server 3 -> Option A

4. Quick Check:

   User ID 27 ends with 7, so Server 3 [OK]

Hint: Check last digit of ID to find server [OK]

Common Mistakes:

• Ignoring the last digit and guessing server
• Choosing all servers instead of one
• Mixing up the shard ranges

4. A database administrator tries to shard a database but notices that some shards have much more data than others, causing slow queries. What is the most likely problem?

medium

A. The backup process is running during queries.

B. The database is not partitioned vertically.

C. The database server hardware is outdated.

D. The shard key is not chosen properly, causing uneven data distribution.

Solution

1. Step 1: Understand shard key role

   The shard key determines how data is split across shards. A poor choice can cause uneven data distribution.

2. Step 2: Analyze the problem

   Uneven shard sizes causing slow queries usually mean the shard key is not distributing data evenly.

3. Final Answer:

   The shard key is not chosen properly, causing uneven data distribution. -> Option D

4. Quick Check:

   Uneven shards = bad shard key choice [OK]

Hint: Uneven shards? Check shard key choice [OK]

Common Mistakes:

• Blaming hardware without checking shard key
• Confusing sharding with partitioning issues
• Ignoring data distribution patterns

5. You have a large customer database that is partitioned by region within a single server. To improve performance and handle growth, you want to shard the data across multiple servers. Which approach best combines partitioning and sharding?

hard

A. Use only partitioning by region on one server; sharding is unnecessary.

B. Partition the database by customer type across servers, and shard data by region within each server.

C. Shard the database by region across servers, and within each server, partition data by customer type.

D. Backup the database regularly instead of sharding or partitioning.

Solution

1. Step 1: Understand combining sharding and partitioning

   Sharding splits data across servers; partitioning splits data inside each server for better management.

2. Step 2: Analyze the best approach

   Sharding by region spreads data geographically, and partitioning by customer type inside each shard improves query speed and organization.

3. Final Answer:

   Shard the database by region across servers, and within each server, partition data by customer type. -> Option C

4. Quick Check:

   Shard by region, partition by type inside servers [OK]

Hint: Shard first, then partition inside shards [OK]

Common Mistakes:

• Mixing up shard and partition levels
• Ignoring partitioning after sharding
• Thinking backup replaces sharding