Data Partitioning Strategies in Distributed Systems

Andrew • Mar 18, 2025 • Distributed Systems , Data Partitioning , Sharding , Scalability

15 min read 3147 words

As data volumes grow exponentially, traditional single-server database solutions often struggle to keep up with performance and storage demands. Data partitioning—the practice of dividing a dataset across multiple storage units—has emerged as a fundamental technique for building scalable distributed systems. By strategically splitting data, organizations can overcome hardware limitations, improve query performance, and enable horizontal scaling.

This article explores various data partitioning strategies, their implementation approaches, and the trade-offs involved in choosing the right strategy for your distributed system.

Understanding Data Partitioning

Data partitioning (also known as sharding) is the process of breaking a large dataset into smaller, more manageable pieces called partitions or shards. Each partition contains a subset of the data and can be stored on a separate database server or node.

Why Partition Data?

Scalability: Overcome hardware limitations by distributing data across multiple machines
Performance: Improve query performance through parallel processing across partitions
Availability: Enhance system resilience by isolating failures to specific partitions
Manageability: Make maintenance operations like backups and index rebuilds more efficient
Data Locality: Store data close to the users or services that access it most frequently

Partitioning vs. Replication

It’s important to distinguish between partitioning and replication:

Partitioning: Divides data into distinct, non-overlapping subsets
Replication: Creates redundant copies of the same data

These techniques are often used together: data is partitioned across multiple nodes, and each partition is replicated for fault tolerance.

Partitioning:
┌───────────────┐  ┌───────────────┐  ┌───────────────┐
│ Node 1        │  │ Node 2        │  │ Node 3        │
│ User IDs 1-1M │  │ User IDs 1M-2M│  │ User IDs 2M-3M│
└───────────────┘  └───────────────┘  └───────────────┘

Replication:
┌───────────────┐  ┌───────────────┐  ┌───────────────┐
│ Primary Node  │  │ Replica 1     │  │ Replica 2     │
│ All User Data │→ │ All User Data │→ │ All User Data │
└───────────────┘  └───────────────┘  └───────────────┘

Partitioning + Replication:
┌───────────────┐  ┌───────────────┐  ┌───────────────┐
│ Node 1        │  │ Node 2        │  │ Node 3        │
│ Users 1-1M    │  │ Users 1M-2M   │  │ Users 2M-3M   │
└───────┬───────┘  └───────┬───────┘  └───────┬───────┘
        │                  │                  │
┌───────▼───────┐  ┌───────▼───────┐  ┌───────▼───────┐
│ Replica of    │  │ Replica of    │  │ Replica of    │
│ Node 1        │  │ Node 2        │  │ Node 3        │
└───────────────┘  └───────────────┘  └───────────────┘

Horizontal vs. Vertical Partitioning

There are two fundamental approaches to partitioning data: horizontal and vertical.

Horizontal Partitioning (Sharding)

Horizontal partitioning divides a table by rows, with each partition containing a subset of the rows based on a partitioning key.

Original Table (Users)
┌────┬──────────┬─────────┬─────────────────┐
│ ID │ Username │ Country │ Email           │
├────┼──────────┼─────────┼─────────────────┤
│ 1  │ alice    │ US      │ [email protected]  │
│ 2  │ bob      │ UK      │ [email protected]    │
│ 3  │ charlie  │ CA      │ [email protected]│
│ 4  │ david    │ AU      │ [email protected]  │
└────┴──────────┴─────────┴─────────────────┘

Horizontally Partitioned (by ID range)
Partition 1 (IDs 1-2)          Partition 2 (IDs 3-4)
┌────┬──────────┬─────┬─────┐  ┌────┬──────────┬─────┬─────┐
│ ID │ Username │ ... │ ... │  │ ID │ Username │ ... │ ... │
├────┼──────────┼─────┼─────┤  ├────┼──────────┼─────┼─────┤
│ 1  │ alice    │ ... │ ... │  │ 3  │ charlie  │ ... │ ... │
│ 2  │ bob      │ ... │ ... │  │ 4  │ david    │ ... │ ... │
└────┴──────────┴─────┴─────┘  └────┴──────────┴─────┴─────┘

Implementation Example (PostgreSQL)

-- Create parent table
CREATE TABLE users (
    id SERIAL PRIMARY KEY,
    username VARCHAR(50) NOT NULL,
    country VARCHAR(2) NOT NULL,
    email VARCHAR(100) NOT NULL
);

-- Create partitions
CREATE TABLE users_1_1000000 PARTITION OF users
    FOR VALUES FROM (1) TO (1000000);
    
CREATE TABLE users_1000001_2000000 PARTITION OF users
    FOR VALUES FROM (1000001) TO (2000000);
    
CREATE TABLE users_2000001_3000000 PARTITION OF users
    FOR VALUES FROM (2000001) TO (3000000);

Implementation Example (MongoDB)

// Enable sharding for database
sh.enableSharding("mydb")

// Create a sharded collection with a shard key on user_id
sh.shardCollection("mydb.users", { user_id: 1 })

// Add shards
sh.addShard("shard1/server1:27017")
sh.addShard("shard2/server2:27017")
sh.addShard("shard3/server3:27017")

Vertical Partitioning

Vertical partitioning divides a table by columns, with each partition containing a subset of the columns.

Original Table (Users)
┌────┬──────────┬─────────┬─────────────────┬────────────┬───────────┐
│ ID │ Username │ Country │ Email           │ Bio        │ Settings  │
├────┼──────────┼─────────┼─────────────────┼────────────┼───────────┤
│ 1  │ alice    │ US      │ [email protected]  │ Lorem...   │ {...}     │
│ 2  │ bob      │ UK      │ [email protected]    │ Ipsum...   │ {...}     │
└────┴──────────┴─────────┴─────────────────┴────────────┴───────────┘

Vertically Partitioned
Core User Data                 User Profile Data
┌────┬──────────┬─────────┐   ┌────┬────────────┬───────────┐
│ ID │ Username │ Email   │   │ ID │ Bio        │ Settings  │
├────┼──────────┼─────────┤   ├────┼────────────┼───────────┤
│ 1  │ alice    │ [email protected] │   │ 1  │ Lorem...   │ {...}     │
│ 2  │ bob      │ [email protected] │   │ 2  │ Ipsum...   │ {...}     │
└────┴──────────┴─────────┘   └────┴────────────┴───────────┘

Implementation Example (SQL)

-- Create tables for vertical partitioning
CREATE TABLE user_core (
    id SERIAL PRIMARY KEY,
    username VARCHAR(50) NOT NULL,
    country VARCHAR(2) NOT NULL,
    email VARCHAR(100) NOT NULL
);

CREATE TABLE user_profile (
    user_id INTEGER PRIMARY KEY REFERENCES user_core(id),
    bio TEXT,
    settings JSONB
);

Partitioning Strategies

The effectiveness of data partitioning depends largely on the strategy used to distribute data across partitions. Let’s explore the most common strategies:

1. Range Partitioning

Range partitioning divides data based on ranges of a partition key value.

How It Works

Data is partitioned based on ranges of a key value, such as:

User IDs 1-1,000,000 in partition 1
User IDs 1,000,001-2,000,000 in partition 2
And so on

Implementation Example (Cassandra)

CREATE TABLE users (
    user_id UUID,
    username TEXT,
    email TEXT,
    created_at TIMESTAMP,
    PRIMARY KEY (user_id)
) WITH CLUSTERING ORDER BY (created_at DESC)
  AND PARTITIONING STRATEGY (range)
  AND PARTITION KEYS (user_id);

When to Use Range Partitioning

When data has a natural range-based organization (dates, IDs, etc.)
When you need to efficiently query ranges of data
For time-series data where recent data is accessed more frequently

Challenges

Risk of hot spots if data is not evenly distributed across ranges
Requires careful planning to ensure balanced partitions
May require rebalancing as data grows

2. Hash Partitioning

Hash partitioning applies a hash function to the partition key to determine the partition.

How It Works

A hash function is applied to the partition key
The hash value is used to determine which partition the data belongs to
Data is evenly distributed across partitions

Partition = hash(key) % number_of_partitions

Implementation Example (MySQL)

CREATE TABLE users (
    id INT NOT NULL,
    username VARCHAR(50) NOT NULL,
    email VARCHAR(100) NOT NULL,
    PRIMARY KEY (id)
) ENGINE=INNODB
PARTITION BY HASH(id)
PARTITIONS 4;

When to Use Hash Partitioning

When you need even data distribution
When most queries access data by primary key
When range queries are not a primary access pattern

Challenges

Poor performance for range queries
Difficult to add or remove partitions without rehashing
Cannot easily co-locate related data

3. List Partitioning

List partitioning assigns data to partitions based on discrete values of a column.

How It Works

Data is partitioned based on specific values of a column, such as:

All US users in partition 1
All EU users in partition 2
All APAC users in partition 3

Implementation Example (Oracle)

CREATE TABLE users (
    id NUMBER,
    username VARCHAR2(50),
    country VARCHAR2(2),
    email VARCHAR2(100)
)
PARTITION BY LIST (country) (
    PARTITION p_americas VALUES ('US', 'CA', 'MX', 'BR'),
    PARTITION p_europe VALUES ('UK', 'DE', 'FR', 'IT'),
    PARTITION p_asia VALUES ('JP', 'CN', 'IN', 'SG')
);

When to Use List Partitioning

When data naturally falls into discrete categories
When queries frequently filter by those categories
For geographic partitioning of data

Challenges

Uneven distribution if categories have vastly different sizes
Need to handle new category values that weren’t initially defined
May require frequent maintenance as categories evolve

4. Composite Partitioning

Composite partitioning combines multiple partitioning strategies, such as range-hash or list-hash.

How It Works

Data is first partitioned using one strategy, then sub-partitioned using another strategy.

Implementation Example (PostgreSQL)

-- Range-hash composite partitioning
CREATE TABLE user_events (
    user_id INT NOT NULL,
    event_time TIMESTAMP NOT NULL,
    event_type VARCHAR(50) NOT NULL,
    event_data JSONB
) PARTITION BY RANGE (event_time);

-- Create monthly partitions
CREATE TABLE user_events_2025_01 PARTITION OF user_events
    FOR VALUES FROM ('2025-01-01') TO ('2025-02-01')
    PARTITION BY HASH (user_id);

-- Create sub-partitions for January
CREATE TABLE user_events_2025_01_p1 PARTITION OF user_events_2025_01
    FOR VALUES WITH (MODULUS 4, REMAINDER 0);
CREATE TABLE user_events_2025_01_p2 PARTITION OF user_events_2025_01
    FOR VALUES WITH (MODULUS 4, REMAINDER 1);
CREATE TABLE user_events_2025_01_p3 PARTITION OF user_events_2025_01
    FOR VALUES WITH (MODULUS 4, REMAINDER 2);
CREATE TABLE user_events_2025_01_p4 PARTITION OF user_events_2025_01
    FOR VALUES WITH (MODULUS 4, REMAINDER 3);

When to Use Composite Partitioning

When you need the benefits of multiple partitioning strategies
For large tables that require both time-based organization and even distribution
When you have complex query patterns that benefit from multiple partition schemes

Challenges

Increased complexity in management and maintenance
More complex query planning and optimization
Potential for over-partitioning, leading to administrative overhead

5. Consistent Hashing

Consistent hashing is a special form of hash partitioning that minimizes data redistribution when adding or removing nodes.

How It Works

Both data items and nodes are mapped to positions on a conceptual ring using a hash function
Each data item is assigned to the first node encountered when moving clockwise from the item’s position
When a node is added or removed, only a fraction of the data needs to be redistributed

┌───────────────────────────────────────────────┐
│                                               │
│      ┌─────┐                                  │
│      │Node1│                                  │
│      └─────┘                                  │
│         │                                     │
│         ▼                                     │
│    ●────────●                                 │
│   /          \                                │
│  /            \                               │
│ ●              ●                              │
│ │              │                              │
│ │              │ ◄── Data item (assigned to   │
│ │              │      closest node clockwise) │
│ ●              ●                              │
│  \            /                               │
│   \    ●     /                                │
│    ●───┼────●                                 │
│        │                                      │
│        ▼                                      │
│     ┌─────┐                                   │
│     │Node2│                                   │
│     └─────┘                                   │
│                                               │
└───────────────────────────────────────────────┘

Implementation Example (Redis Cluster)

Redis Cluster uses a form of consistent hashing with 16384 hash slots:

# Configure Redis Cluster nodes
redis-cli --cluster create 127.0.0.1:7000 127.0.0.1:7001 127.0.0.1:7002 \
  127.0.0.1:7003 127.0.0.1:7004 127.0.0.1:7005 \
  --cluster-replicas 1

When to Use Consistent Hashing

In dynamic environments where nodes are frequently added or removed
For distributed caches and NoSQL databases
When minimizing data movement during scaling is critical

Challenges

More complex to implement than traditional hash partitioning
May still lead to uneven distribution without virtual nodes
Requires careful key design to avoid hotspots

Choosing a Partition Key

The partition key is perhaps the most critical decision in your partitioning strategy. It determines how data is distributed and directly impacts query performance.

Characteristics of a Good Partition Key

High Cardinality: Many distinct values to ensure even distribution
Immutability: Values that don’t change, avoiding the need to move data between partitions
Query Relevance: Frequently used in queries to enable partition pruning
Even Distribution: Leads to balanced partitions without hotspots

Common Partition Keys

Partition Key	Pros	Cons	Example Use Case
User ID	Even distribution, natural for user data	Poor for cross-user analytics	User profiles, preferences
Timestamp	Natural for time-series data	Potential for hotspots on recent data	Event logs, metrics
Geographic Location	Data locality, regulatory compliance	Uneven user distribution globally	User content, regional services
Tenant ID	Clean separation in multi-tenant systems	Potential for tenant size variation	SaaS applications
Product ID	Natural for product data	Uneven access patterns (popular products)	E-commerce catalogs

Implementation Example: Choosing a Partition Key in DynamoDB

// Good partition key (high cardinality, query relevant)
const params = {
    TableName: 'UserSessions',
    KeySchema: [
        { AttributeName: 'userId', KeyType: 'HASH' },  // Partition key
        { AttributeName: 'sessionId', KeyType: 'RANGE' }  // Sort key
    ],
    AttributeDefinitions: [
        { AttributeName: 'userId', AttributeType: 'S' },
        { AttributeName: 'sessionId', AttributeType: 'S' }
    ],
    ProvisionedThroughput: {
        ReadCapacityUnits: 10,
        WriteCapacityUnits: 10
    }
};

dynamodb.createTable(params, function(err, data) {
    if (err) console.log(err);
    else console.log(data);
});

Handling Cross-Partition Operations

One of the biggest challenges in partitioned systems is handling operations that span multiple partitions.

Querying Across Partitions

When a query needs data from multiple partitions, several approaches can be used:

1. Scatter-Gather

Query all partitions in parallel and combine the results.

// Pseudocode for scatter-gather query
List<Partition> partitions = getPartitions();
List<Future<Result>> futures = new ArrayList<>();

// Scatter phase: query all partitions
for (Partition partition : partitions) {
    futures.add(executorService.submit(() -> {
        return partition.executeQuery(query);
    }));
}

// Gather phase: collect and combine results
List<Result> results = new ArrayList<>();
for (Future<Result> future : futures) {
    results.add(future.get());
}

return combineResults(results);

Pros: Complete results, parallelizable Cons: Performance limited by slowest partition, resource intensive

2. Partition Pruning

Analyze the query to determine which partitions need to be accessed.

-- Example of a query that benefits from partition pruning
SELECT * FROM sales 
WHERE sale_date BETWEEN '2025-01-01' AND '2025-01-31';

-- If sales is partitioned by month, only the January partition is accessed

Pros: Improved performance by reducing partitions accessed Cons: Requires query analysis capabilities, not all queries can be pruned

3. Global Indexes

Maintain secondary indexes that span all partitions.

// DynamoDB Global Secondary Index example
CreateTableRequest createTableRequest = new CreateTableRequest()
    .withTableName("Orders")
    .withKeySchema(
        new KeySchemaElement("customerId", KeyType.HASH),
        new KeySchemaElement("orderId", KeyType.RANGE))
    .withAttributeDefinitions(
        new AttributeDefinition("customerId", ScalarAttributeType.S),
        new AttributeDefinition("orderId", ScalarAttributeType.S),
        new AttributeDefinition("orderStatus", ScalarAttributeType.S),
        new AttributeDefinition("orderDate", ScalarAttributeType.S))
    .withGlobalSecondaryIndexes(
        new GlobalSecondaryIndex()
            .withIndexName("OrderStatusIndex")
            .withKeySchema(
                new KeySchemaElement("orderStatus", KeyType.HASH),
                new KeySchemaElement("orderDate", KeyType.RANGE))
            .withProjection(new Projection().withProjectionType(ProjectionType.ALL))
            .withProvisionedThroughput(new ProvisionedThroughput(5L, 5L)));

Pros: Efficient queries on non-partition keys Cons: Index maintenance overhead, eventual consistency challenges

Transactions Across Partitions

Implementing transactions that span multiple partitions is challenging but essential for many applications.

1. Two-Phase Commit (2PC)

A protocol that ensures all partitions either commit or abort a transaction.

Coordinator                 Partition 1                Partition 2
    │                           │                           │
    ├───── Prepare ─────────────┼───── Prepare ─────────────┤
    │                           │                           │
    │                           │                           │
    │◄─── Vote (Yes/No) ────────┼◄─── Vote (Yes/No) ────────┤
    │                           │                           │
    ├───── Commit/Abort ────────┼───── Commit/Abort ────────┤
    │                           │                           │

Pros: Strong consistency guarantees Cons: Blocking protocol, performance impact, vulnerable to coordinator failures

2. Saga Pattern

A sequence of local transactions where each transaction updates data within a single partition.

// Pseudocode for Saga pattern
public void createOrderSaga(Order order) {
    try {
        // Step 1: Create order in Orders partition
        String orderId = orderService.createOrder(order);
        
        try {
            // Step 2: Reserve inventory in Inventory partition
            inventoryService.reserveItems(orderId, order.getItems());
            
            try {
                // Step 3: Process payment in Payments partition
                paymentService.processPayment(orderId, order.getPaymentDetails());
                
                // All steps succeeded
                orderService.completeOrder(orderId);
            } catch (Exception e) {
                // Compensating transaction for Step 2
                inventoryService.releaseItems(orderId);
                // Compensating transaction for Step 1
                orderService.cancelOrder(orderId);
                throw e;
            }
        } catch (Exception e) {
            // Compensating transaction for Step 1
            orderService.cancelOrder(orderId);
            throw e;
        }
    } catch (Exception e) {
        // Handle saga failure
        throw new SagaFailedException("Failed to create order", e);
    }
}

Pros: No distributed locking, better performance Cons: Eventually consistent, complex compensation logic

3. Distributed Consensus

Use consensus algorithms like Paxos or Raft to agree on transaction outcomes.

Pros: Strong consistency without blocking Cons: Complex implementation, performance overhead

Rebalancing Partitions

As data grows or access patterns change, you may need to rebalance partitions to maintain performance.

When to Rebalance

When partitions become unbalanced in size or load
When adding or removing nodes
When access patterns change significantly

Rebalancing Strategies

1. Hash-Based Rebalancing

Rehash data using a new partition count.

Before (3 partitions):
Partition = hash(key) % 3

After (5 partitions):
Partition = hash(key) % 5

Pros: Even distribution Cons: Massive data movement (up to 80% of data may move)

2. Consistent Hashing Rebalancing

Add or remove nodes with minimal data redistribution.

Pros: Minimal data movement Cons: More complex implementation

3. Range-Based Split/Merge

Split large partitions or merge small ones.

-- PostgreSQL: Split a partition
ALTER TABLE users DETACH PARTITION users_1_1000000;

CREATE TABLE users_1_500000 PARTITION OF users
    FOR VALUES FROM (1) TO (500000);
    
CREATE TABLE users_500001_1000000 PARTITION OF users
    FOR VALUES FROM (500001) TO (1000000);

Pros: Targeted rebalancing Cons: Complex management, potential downtime

Monitoring Partition Health

Implement metrics to detect when rebalancing is needed:

Partition size (bytes)
Query latency per partition
Query throughput per partition
Storage utilization per partition

Data Partitioning in Popular Databases

Different database systems implement partitioning in various ways. Here’s how some popular databases handle it:

PostgreSQL

PostgreSQL supports declarative table partitioning with range, list, and hash strategies.

-- Range partitioning in PostgreSQL
CREATE TABLE measurements (
    city_id         int not null,
    logdate         date not null,
    peaktemp        int,
    unitsales       int
) PARTITION BY RANGE (logdate);

CREATE TABLE measurements_y2025m01 PARTITION OF measurements
    FOR VALUES FROM ('2025-01-01') TO ('2025-02-01');

CREATE TABLE measurements_y2025m02 PARTITION OF measurements
    FOR VALUES FROM ('2025-02-01') TO ('2025-03-01');

MongoDB

MongoDB uses sharding to partition data across multiple servers.

// Enable sharding for a database
sh.enableSharding("mydb")

// Shard a collection using a shard key
sh.shardCollection("mydb.users", { "user_id": 1 })

// Add shards
sh.addShard("rs1/server1:27017,server2:27017,server3:27017")
sh.addShard("rs2/server4:27017,server5:27017,server6:27017")

Cassandra

Cassandra partitions data based on a partition key defined in the table schema.

-- Cassandra partitioning
CREATE TABLE users (
    user_id UUID,
    username TEXT,
    email TEXT,
    PRIMARY KEY (user_id)
);

-- Composite partition key
CREATE TABLE user_posts (
    username TEXT,
    post_id TIMEUUID,
    post_content TEXT,
    PRIMARY KEY ((username), post_id)
) WITH CLUSTERING ORDER BY (post_id DESC);

DynamoDB

Amazon DynamoDB automatically partitions data based on the partition key.

// DynamoDB table with partition key
const params = {
    TableName: 'Music',
    KeySchema: [
        { AttributeName: 'Artist', KeyType: 'HASH' },  // Partition key
        { AttributeName: 'SongTitle', KeyType: 'RANGE' }  // Sort key
    ],
    AttributeDefinitions: [
        { AttributeName: 'Artist', AttributeType: 'S' },
        { AttributeName: 'SongTitle', AttributeType: 'S' }
    ],
    ProvisionedThroughput: {
        ReadCapacityUnits: 5,
        WriteCapacityUnits: 5
    }
};

Best Practices for Data Partitioning

To get the most out of your partitioning strategy, follow these best practices:

1. Design for Your Query Patterns

Understand your application’s access patterns before choosing a partitioning strategy.

// Example: If most queries look up users by ID
// Choose user_id as the partition key

// Example: If most queries look up events by date range
// Choose date as the partition key

2. Plan for Growth

Design your partitioning scheme to accommodate future growth without major restructuring.

// Instead of hardcoding partition ranges:
users_1_1000000, users_1000001_2000000

// Use a more flexible approach:
users_2025_q1, users_2025_q2, users_2025_q3, users_2025_q4

3. Balance Partition Size and Count

Too many small partitions increase management overhead, while too few large partitions limit scalability.

4. Implement Proper Monitoring

Set up monitoring to detect partition imbalances, hot spots, and performance issues.

# Pseudocode for partition monitoring
def monitor_partitions():
    for partition in get_all_partitions():
        size = measure_partition_size(partition)
        qps = measure_queries_per_second(partition)
        latency = measure_average_latency(partition)
        
        if size > SIZE_THRESHOLD or qps > QPS_THRESHOLD or latency > LATENCY_THRESHOLD:
            alert("Partition {} needs attention: size={}, qps={}, latency={}ms"
                  .format(partition.id, size, qps, latency))

5. Test at Scale

Test your partitioning strategy with realistic data volumes and query patterns.

Conclusion

Data partitioning is a powerful technique for scaling distributed systems beyond the capabilities of a single server. By carefully choosing a partitioning strategy and partition key that align with your application’s needs, you can build systems that scale horizontally while maintaining performance and availability.

Remember that there’s no one-size-fits-all approach to data partitioning. The best strategy depends on your specific requirements, including data size, query patterns, consistency needs, and operational constraints. Start with a clear understanding of these requirements, and be prepared to evolve your partitioning strategy as your system grows and changes.

Whether you’re building a new distributed system or scaling an existing one, effective data partitioning will be a key factor in your success. By applying the principles and practices outlined in this article, you’ll be well-equipped to design and implement a partitioning strategy that meets your needs today and scales with you into the future.

Andrew

Andrew is a visionary software engineer and DevOps expert with a proven track record of delivering cutting-edge solutions that drive innovation at Ataiva.com. As a leader on numerous high-profile projects, Andrew brings his exceptional technical expertise and collaborative leadership skills to the table, fostering a culture of agility and excellence within the team. With a passion for architecting scalable systems, automating workflows, and empowering teams, Andrew is a sought-after authority in the field of software development and DevOps.

Data Partitioning Strategies in Distributed Systems

Table of Contents

Understanding Data Partitioning

Why Partition Data?

Partitioning vs. Replication

Horizontal vs. Vertical Partitioning

Horizontal Partitioning (Sharding)

Implementation Example (PostgreSQL)

Implementation Example (MongoDB)

Vertical Partitioning

Implementation Example (SQL)

Partitioning Strategies

1. Range Partitioning

How It Works

Implementation Example (Cassandra)

When to Use Range Partitioning

Challenges

2. Hash Partitioning

How It Works

Implementation Example (MySQL)

When to Use Hash Partitioning

Challenges

3. List Partitioning

How It Works

Implementation Example (Oracle)

When to Use List Partitioning

Challenges

4. Composite Partitioning

How It Works

Implementation Example (PostgreSQL)

When to Use Composite Partitioning

Challenges

5. Consistent Hashing

How It Works

Implementation Example (Redis Cluster)

When to Use Consistent Hashing

Challenges

Choosing a Partition Key

Characteristics of a Good Partition Key

Common Partition Keys

Implementation Example: Choosing a Partition Key in DynamoDB

Handling Cross-Partition Operations

Querying Across Partitions

1. Scatter-Gather

2. Partition Pruning

3. Global Indexes

Transactions Across Partitions

1. Two-Phase Commit (2PC)

2. Saga Pattern

3. Distributed Consensus

Rebalancing Partitions

When to Rebalance

Rebalancing Strategies

1. Hash-Based Rebalancing

2. Consistent Hashing Rebalancing

3. Range-Based Split/Merge

Monitoring Partition Health

Data Partitioning in Popular Databases

PostgreSQL

MongoDB

Cassandra

DynamoDB

Best Practices for Data Partitioning

1. Design for Your Query Patterns

2. Plan for Growth

3. Balance Partition Size and Count

4. Implement Proper Monitoring

5. Test at Scale

Conclusion

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