Database Partitioning Discussion

Question:

When we talk about database partitioning - is it every table got its own partition?

Answer:

Not necessarily. In database partitioning, not every table needs to be partitioned, and the granularity of partitioning depends on the database design, performance needs, and access patterns.

Types of Partitioning:

1. Horizontal Partitioning (Sharding) – Rows of a table are divided across multiple partitions based on some criteria (e.g., customer ID, date range).

2. Vertical Partitioning – Columns of a table are split into different partitions (e.g., frequently accessed columns vs. rarely accessed columns).

3. Range Partitioning – Data is split into partitions based on a value range (e.g., partitioning by year).

4. Hash Partitioning – A hash function is applied to determine which partition a row belongs to.

5. List Partitioning – Data is partitioned based on a predefined list of values.

Question:

I mean if I am doing DB partitioning - and my DB got two tables. Do both tables get partitioned?

Answer:

No, if you are partitioning your database, not all tables need to be partitioned by default. Partitioning is typically applied to large tables where performance, scalability, and manageability benefits outweigh the complexity it introduces.

Key Considerations:

• Table-by-Table Decision – Each table is evaluated separately to determine if partitioning is beneficial.

• Partitioning Based on Size and Query Load – Large tables (e.g., billions of rows) benefit the most from partitioning, while small tables do not need it.

• Independent vs. Shared Strategy –

- You can partition only one table if that’s the bottleneck.

- If two tables are frequently joined on a partitioned column, you might want to partition both similarly (partition alignment).

• Primary vs. Lookup Tables –

- A large transaction table (e.g., Orders) may be partitioned by date.

- A small reference table (e.g., Countries) typically does not need partitioning.

Example:

• Suppose you have:

1. Orders table (millions of rows, growing fast)

2. Users table (few thousand rows, relatively static)

• Partitioning Strategy:

- Orders: Partitioned by order_date (Range partitioning).

- Users: No need for partitioning as it's small.

Question:

So if Orders get partitioned into multiple shards - will every shard have its own copy of Users table too?

Answer:

Not necessarily. When partitioning (or sharding) a large table like Orders, the handling of the Users table depends on the chosen sharding strategy:

Possible Approaches for the Users Table in a Sharded System:

1. Replicated (Denormalized) Users Table (Common for Reference Data)

• Each shard contains a full copy of the Users table.

• Works well when Users is small and frequently needed for joins.

• Ensures fast lookups without cross-shard queries.

• Downside: Needs synchronization across shards when a user updates.

2. Unsharded (Single Centralized Users Table)

• The Users table remains in a single database (separate from Orders shards).

• Each shard queries the central Users table when needed.

• Reduces duplication but adds latency and a single point of failure.

• Best for when user updates are frequent and consistency is critical.

3. Co-located (Sharded Alongside Orders by User ID)

• Both Users and Orders are sharded using the same key (user_id).

• Ensures that user data is always in the same shard as their orders.

• Improves query efficiency but requires evenly distributing users across shards.

• Best for high-traffic applications with user-based partitions.

Which Approach to Choose?

• If Users is small → Replicate across shards for fast local joins.

• If Users is large and updated often → Keep in a central database.

• If Users and Orders are tightly linked → Shard both on user_id.